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-{"input": "Combining almost 50, 000 responses to 11 cross-sectional surveys between 2017 and 2021, displacement is neg­atively associated with perceptions of social cohesion in aggregate. But at the individual level, those who report hosting displaced populations in their communities often have higher perceptions of social cohesion. These results are strongest among respondents who self-report hosting IDPs as opposed to refugees, but important heterogeneity across indicators, local context, and gender should guide policy meant to promote social cohesion in forced displacement. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at ppham @ hsph. harvard. edu. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["cross-sectional surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In aggregate, we expect to observe a negative relationship between the percentage of the population that reports displacement, but at the individual level we expect that experience with hosting may, in certain circumstances, be positively associated with perceptions of social cohesion, regarding perceptions of relationships and solidarity. 4 Displacement and Social Cohesion: Survey Evidence To empirically evaluate the relationships between hosting displaced populations and social cohe- sion, this paper analyzes a series of surveys of civilian adults conducted in eastern DRC. 8 Each survey uses a multi-stage cluster sampling strategy capturing all territoires9 in North Kivu, South Kivu and Ituri provinces. The final sampling units are randomly selected adults above the age of 18 to avoid bias toward men and / or heads of households. Multiple attempts are made over the course of one day to contact selected respondents and if necessary, appointments are made for in- terview. Surveys are enumerated by Congolese college students or professionals and interviews are conducted by members of the same gender and ethnicity as respondents to minimize enumerator- induced response bias. Further methodological details have been published (Vinck, Pham, Bindu, Bedford & Nilles 2019) elsewhere and additional details and sample size calculation are detailed in Appendix. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["surveys of civilian adults"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These surveys are part of a long-term data collection effort by the research team (Vinck & Pham 2014) and were collected separately from the focus group discussions. The focus group discussions thus did not directly influence the design of the surveys, but rather directed the analysis strategy of the surveys that our team has collected at regular intervals in eastern DRC. The survey data are analyzed in two ways. First, 11 surveys collected between 2017 and 2021 8Eastern DRC is a site of ongoing violence, raising a number of ethical, methodological, and practical concerns about collecting data. We discuss the ethical protections we implemented when collecting this survey data in the Appendix, Section B. 9Territoires are sub-provincial administrative units. Additional details on the structure of administrative units are available in the Appendix, Section A. 1. 21 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": ". 12 10. 45 – # 20 December 2019 5752 4. 71 8. 14 – # 21 November 2020 2627 4. 19 5. 14 – # 22 February-March 2021 5847 6. 86 9. 30 – Overall July 2017-March 2021 49831 4. 64 8. 19 30. 58 Table 2: Details on Surveys and Displacement Trends Second, the paper conducts an individual-level analysis of two cross-sectional surveys of 1, 933 and 5, 951 individuals conducted in March-April 2018 and June- July 2018, respectively, to probe the relationship between hosting displacees and social cohesion in more detail. This survey wave 22 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["cross-sectional surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The relation- ships between displacement dynamics and perceptions of each manifestation of social cohesion are analyzed separately by running 12 models for each independent variable (6 regressions for each sample). The regressions are correlations and should not be interpreted causally. Hosting status and displacement flows are likely related to perceptions of social cohesion in indirect ways and the structure of the survey data limit the ability to specify the channels through which these relation- ships run. Each regression controls for characteristics that may influence respondents ’ perceptions of social cohesion outside of the presence of IDPs or refugees in the local community such as province, gender, age, marital status, level of education, employment, and exposure to violence. 11Poll numbers correspond to the number wave in our larger project, as described and shown in Table 2. 28 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Sources of information This work includes a descriptive analysis that allows us to identify how the prevalence of non- standard employment (NSE) has evolved in the last two decades in different regions of the planet, as well as the evolution of the profile of workers in those roles, in terms of their educational level, salary per hour, and type of tasks performed. This analysis was based mainly on periodic surveys of households that included information regarding the employment and educational situation of individuals. Although the denomination of this type of surveys varies from country to country, in all the cases analyzed there is usually a survey of annual or higher frequency that includes information required to identify the labor status of the individuals as well as to analyze the salary profile and education of the employed. However, it should be noted that the identification of the type of work relationship (standard or non- standard) is frequently limited in these data sources. Indeed, it is only possible to identify part-time employment and temporary employment (not in all cases) within the non-standard forms of employment mentioned in the previous section. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["periodic surveys of households"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 7 of 51 In this sense, it is necessary to draw attention to the fact that the non-standard employment statistics presented below are not homogeneous among countries. In countries where both forms of non- standard employment were identified, we define non-standard employment as those occupations that satisfy at least one of the conditions, that is, either corresponds to a part-time occupation or a temporary job. In countries where temporary employment was not identified in the data, our non- standard employment category will coincide with part-time employment. Note that in either case, as other non-standard employment modalities are not identified, the indicators presented in this paper indicate a lower level with respect to the true dimension of the phenomenon. The only aspect addressed that required the use of additional information was the analysis linked to the profile of tasks that are developed in the framework of non-standard jobs. To carry out this analysis, the information available in the O * NET (Occupational Information Network) database was used in conjunction with the Household surveys. This database provides information referring to the content of tasks of the occupations. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Since 2003, O * NET data have been collected in the United States for approximately 1, 000 occupations based on the Standard Occupational Classification (SOC), and it has been updated periodically from then until 2014. 5 Following Acemoglu and Autor (2011), Hardy et al. (2015) and Apella and Zunino (2017), five measures of content or intensity of main tasks performed by workers are constructed: non-routine cognitive analytical and interpersonal, routine cognitive and manual and non-routine manual. The definition of the type of task performed by the worker is associated with the risk of automation and therefore its implication in terms of earned wage. While routine, and especially manual, are susceptible of automation, those non-routine tasks, especially cognitive tasks (both analytical and interpersonal) not only are not exposed to the risk of automation but also could be complemented by automation, increasing the productivity of the workers. 4. NSE trends in Latin America and the Caribbean and Europe and Central Asia As was mentioned in the previous section, the available data sources limit the statistical analysis presented below to two types of NSE: temporary employment and part-time employment. At the same time, it was not possible in all cases to identify the employees whose employment relationship is temporary. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In this sense, it is necessary to take into consideration that all indicators of prevalence of NSE and its profile will be limited to a subset of this kind of workers. 4. 1 Latin America and the Caribbean This section focuses on the Latin America and the Caribbean region, where a set of 9 countries, that we consider representing the different realities of the region in an exhaustive way, was analyzed. Specifically, the analysis was conducted for Argentina, Brazil, Bolivia, Chile, El Salvador, Mexico, Peru, Dominican Republic and Uruguay. 5O * NET is the successor of DOT (Dictionary of Occupational Titles) which is no longer updated. O * NET was launched in 1998 on the basis of the BLS Occupational Employment Statistics codes. In 2003, it was changed to SOC which implies that the consistent measures of task content are calculated from 2003. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["BLS Occupational Employment Statistics"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 8 of 51 In the 9 cases mentioned, it was possible to identify part-time workers, while temporary workers were only identified in the cases of Argentina, Brazil, Chile, Mexico and El Salvador. 4. 1. 1 Variation of the NSE as a percentage of total employment The prevalence of NSE in the total employment has not shown very significant variations in the countries considered in the last two decades (Figure 1). Indeed, most of the countries analyzed show non-standard employment registers similar to those observed in the mid-1990s. The exceptions where the variation is a little more relevant are Brazil and Uruguay, where there are contractions in the incidence of the NSE of the order of 10 and 5 percentage points respectively and Mexico, where there is an increase of 5 percentage points. Figure 1: Prevalence of NSE among salaried employees. (Mid-90s / Mid-2010s) Source: Own calculations based on Household surveys Analyzing the prevalence of NSE by types of occupations, considering the ISCO classification at one digit, we find a quite similar pattern across countries. Indeed, in most of the considered countries, the “ Elementary Occupations ” are the category where the prevalence of NSE is higher. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 9 of 51 Figure 2: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) Source: Own calculations based on Household surveys When these results are analyzed by the type of NSE, we find significant differences between the trends in Part-time and Temporary employment. On the one hand, there is a stable or growing prevalence of part-time employment among the salaried employees, where Uruguay is the only exception, characterized by a decrease in the incidence of this type of employment (Figure 3). In the cases of Peru and Bolivia, we find almost the same prevalence of part-time employment as two decades ago while in the remaining countries (Argentina, Brazil, Chile, Mexico, El Salvador and the Dominican Republic) there is a greater prevalence of part-time employment. Likewise, the prevalence of temporary employment shows a downward trend in most of the analyzed countries in the last 20 years (Figure 4). In fact, three of the five countries in which temporary employment could be identified show a significant drop in the prevalence of this type of employment (Argentina, Brazil, and Chile). El Salvador presents a stable incidence of temporary employment, while Mexico is the only country in our sample for which there is an increase in this type of NSE. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 10 of 51 Source: Own calculations based on Household surveys Figure 4: Prevalence of Temporary employment among salaried employees (Mid-90s / Mid-2010s) Source: Own calculations based on Household surveys Analyzing the evolution of non-standard employment according to their age profile, we found a slight increase in the share of the older groups (Figure 5 and 6). This slight aging in the profile of non- standard workers is observed in both part-time and temporary employment. This finding is striking since, in principle, it was expected that the non-standard modalities of employment would show an increasing participation of the younger groups of the population. However, this change in the age composition of NSE is consistent with the age profile observed in total employment. In fact, the 0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % Argentina Brazil Peru Dominican Republic El Salvador Starting point 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % Argentina Brazil Chile Mexico El Salvador Starting Point Ending Point Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 5: Part-time employment by age group. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Uruguay Brazil Chile Peru Bolivia Dominican Republic Mexico El Salvador > 64 years old 50 ‐ 64 40 ‐ 49 25 ‐ 39 15 ‐ 24 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 12 of 51 Figure 6: Temporary employment by age group. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys. In the case of temporary employment, there is evidence of increasing female participation in the five countries analyzed, with large variations in several cases. Indeed, in the five countries for which we identified temporary workers, while temporary employment was predominantly male in the 1990s, today women show a participation higher than 50 % in all cases. Figure 7: Temporary employment by age group. (Mid-1990s / Mid-2010s) employment by gender (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Brazil Chile Mexico El Salvador > 64 years old 50 ‐ 64 40 ‐ 49 25 ‐ 39 15 ‐ 24 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Uruguay Brazil Chile Peru Bolivia Dominican Republic Mexico El Salvador Female Male Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 13 of 51 Figure 8: Temporary employment by gender. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys 4. 1. 2 Profile of Non-Standard Employment The next objective of this work is to investigate possible changes in the profile of non-standard employment, usually associated with lower productivity and greater vulnerability. The analysis of the employment profile was made based on access to social security benefits, education level, labor income and the task content performed by the workers. From the point of view of social security, we find that NSE shows a higher prevalence of informality compared with SE. For instance, the average prevalence of informality for our set of countries among NSE in the ending point of the study is 40 % while the prevalence among SE is 20 %. In this sense, a rise in the prevalence of NSE could be associated to a big set of workers without access to social security benefits. From a dynamic perspective, there is no a common trend across countries regarding the prevalence of informality among NSE workers (Figure 9). Indeed, several countries (Uruguay, Brazil, Chile, and Peru) registered a small decrease in the prevalence of informality among NSE but there is another set of countries for which the opposite is observed (Mexico, El Salvador, Bolivia and Argentina). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 14 of 51 Source: Own calculations based on Household surveys From the education perspective, an improvement in the profile of workers employed as NSE can be observed in the countries analyzed, even though there are some exceptions. In fact, in most of the countries of our sample, the prevalence of workers with secondary and tertiary education increases in detriment of workers with a lower educational level. However, there are some differences by type of non-standard employment. In the case of part-time employment (Figure 10), several countries show an obvious rise in the prevalence of workers with secondary and tertiary education (Argentina, Brazil, Peru, and Mexico). There is a second group of countries that presents a decrease in the prevalence of workers at the secondary level, but, this decrease is more than compensated by the greater incidence of tertiary workers, so that, taken together, workers with secondary or higher education increased their share within part-time employees (Uruguay and Chile). Therefore, we can also conclude from this group that part-time employment presents a better educational profile today than two decades ago. In the Dominican Republic and El Salvador, we find a rise in the prevalence of workers with secondary education but a decrease in the share of workers at the tertiary level. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 15 of 51 Figure 10: Education profile of Part-time employees. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys On the other hand, in the case of temporary employment, the improvement of the educational profile is more evident and generalized than in part-time employment (Figure 10). In fact, in all the cases in which temporary employment was identified, taken together, the share of workers at the secondary or higher education increased in proportion in the last two decades. The countries in which the improvements in the education profile are less deep are Chile, where the participation of workers with secondary education decreased in the last two decades, even though this decrease is more than compensated by the greater proportion of tertiary workers, and El Salvador, where the share of workers with tertiary education remains almost constant. 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % Argentina Uruguay Brazil Chile Peru Bolivia Dominican Republic Mexico El Salvador Incomplete primary (starting point) Incomplete primary (ending point) Primary (starting point) Primary (ending point) Secondary (starting point) Secondary (ending point) Tertiary (starting point) Tertiary (ending point) Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 16 of 51 Figure 11: Education profile of Temporary employees. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys However, it should be noted that the improvement of the educational profile of workers is a generalized trend in the countries considered and cannot be considered a specific characteristic of non-standard employment since it is also observed in standard wage employment and self-employed workers. Statistics regarding the educational profile of standard employees are included in Annex II of the paper. Figure 12 presents the Kernel distribution of the labor income per working hour by country and type of employment. When we analyze what has happened at the salary level and distribution in the period under analysis, two important conclusions emerge. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. This growth in wages is simply a consequence of the economic growth experienced by these economies. Note that this average wage increase is also observed in the counterpart of standard employment in all cases.. 6 The second trend identified as generalized in the countries of study is the increase in the variance of the wage distribution. In fact, in most of the countries considered, non-standard employment wages currently show a significantly greater dispersion than that registered a decade ago. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 19 of 51 Source: Own calculations based on Household surveys Finally, we analyze the trends in the task content performed by non-standard workers following the task methodology following the methodology proposed by Acemoglu and Autor (2011). Figure 13 presents the variation in the task content index by non-standard workers vis-a-vis standard workers. A first general view suggests that non-routine cognitive task content of jobs (both analytical and interpersonal) increased in both NSE and SE even though we have some exceptions. Indeed, the only countries where non-standard employment shows a less intense profile in non-routine cognitive analytical tasks are Peru and the Dominican Republic. Additionally, Chile and El Salvador show a virtually null change in the intensity of this kind of tasks. In the case of standard employment, the change in the profile towards non-routine cognitive analytical tasks is even more obvious (the Dominican Republic is the only exception). A similar scenario is recorded in the case of the intensity of non-routine cognitive interpersonal tasks, even though in this case the trend is more pronounced in both, standard and non-standard employment. Additionally, the trends in SE and NSE are more correlated for this type of tasks. The evolution of the intensity in the routine cognitive tasks in the last two decades in NSE presents a much more heterogeneous picture. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 25 of 51 higher followed by the “ Professionals ” and “ Skilled agricultural, forestry and fishery workers ”. On the contrary, “ Managers ”, “ Technicians ”, “ Craft and related trades workers ” and “ Plant and machine operators and assemblers ” are the types of occupations with a lower incidence of NSE in the region. Figure 16: Prevalence of NSE among salaried employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Figure 17: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) Source: Own calculations based on Household surveys. As in the case of Latin American countries, the observed prevalence of NSE across types of occupations suggests a strong heterogeneity across non-standard employees. Actually, as we will analyze in detail in next sections, the productivity and task profile of workers in the categories of “ Professionals ” and “ Elementary workers ” is very different, even though both types of occupations are characterized by a higher prevalence of non-standard employment arrangements. 0 % 5 % 10 % 15 % 20 % 25 % 30 % Russia Georgia Kyrgyz Republic Turkey Armenia Albania Moldova starting point ending point 0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % 40 % 45 % 50 % Managers Professionals Technicians Clerical Support Workers Services and Sales Workers Skilled Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 27 of 51 Figure 18: Prevalence of Part-time employment among salaried employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys. Figure 19: Prevalence of Temporary employment among salaried employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys The older age profile of part-time employment in most of the analyzed countries of Eastern Europe and Asia is consistent with the age profile observed for total employment. In fact, the mean age of the labor force rose 4. 5 years on average in the countries included in the study. That is, the older age profile of NSE workers reflects the aging trend of the overall labor force. 0 % 2 % 4 % 6 % 8 % 10 % 12 % 14 % 16 % Russia Georgia Kyrgyz Republic Turkey Armenia Albania Moldova Starting point Ending point 0 % 5 % 10 % 15 % 20 % 25 % Georgia Kyrgyz Republic Turkey Armenia starting point ending point Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 28 of 51 Figure 20: Part-time employment by age groups. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Figure 21: Temporary employment by age groups. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys A less clear picture is observed in the case of temporary employment. Indeed, in the four countries where temporary employees are identified, we observe different dynamics in the age profile. On the one hand, Kyrgyzstan and Turkey do not evidence significant changes in the age profile of temporary workers in the last 10 / 15 years. On the other hand, Georgia and Armenia present changes in the age profile but in opposite directions. Armenia shows today a higher share of the older groups within temporary employees while Georgia evidences a younger profile of temporary workers. Finally, analyzing the composition of non-standard employment by gender, we have a heterogeneous picture by types of non-standard employment in the levels but with a similar trend (Figure 22 and 23). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 30 of 51 Figure 23: Temporary employment by gender. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys In summary, the evidence does not suggest a clear trend across countries regarding the prevalence of non-standard employment but rather identifies a heterogeneous panorama by countries and types of non-standard employment. 4. 2. 2 Profile of Non-Standard Employment In this section, we investigate possible changes in the profile of non-standard employment, usually associated with lower productivity and greater vulnerability. Like in the case of the LAC countries, the analysis of the employment profile was made based on the education, wages and the content of tasks performed. 9 From the educational point of view, a general tendency can be observed in the countries analyzed to improve the profile of workers linked to non-standard work contracts. In fact, in most of the countries in our sample, it is observed that taken together, the prevalence of workers with secondary and tertiary educational levels increases to the detriment of workers with a lower educational level. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 32 of 51 Figure 25: Education profile of Temporary employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Note: SP indicates the starting point of the analysis and EP states de ending point. It should be noted, however, that, like in the case of Latin American countries, the improvement in the educational profile of workers is a generalized trend in the countries considered and cannot be considered a specific characteristic of non-standard employment since it is also observed in standard wage employment and self-employed workers. Statistics regarding the educational profile of standard employees are included in the Annex of the paper. When we analyze what has happened at the salary level in the period of consideration (Figure 26), two important conclusions emerge. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. This growth in wages is simply a consequence of the economic growth experienced by the economies. Note that this average wage increase is also observed in the counterpart of standard employment in all cases. 10 10As mentioned above, standard employment is calculated as the total number of employees who do not identify with any of the non-standard categories (temporary or part-time). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Therefore, although the available evidence is very limited, we do not observe a shift towards a more intense task profile in cognitive activities in the case of temporary workers. 11 Figure 27: Variation in the task content performed by NSE and SE. (Change from the late 90s) Non-Routine Cognitive Analytical 11 Evidence of a higher intensity of the cognitive tasks in the ECA countries is presented in Keister and Lewandowski (2016). ‐ 1 ‐ 0, 5 0 0, 5 1 1, 5 Russia Georgia Kyrgyzstan Armenia Albania Moldova NSE SE Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1 Introduction Company owners and managers make two decisions with important implications in the labor market: what skills are demanded and who to hire. On the demanded skills, a driver of trends in employment is the changing demand for soft skills (Heckman and Kautz (2012), Weidmann and Deming (2021)). However, we know relatively little about what kinds of soft skills employers value in modern entry- level jobs (Heller and Kessler, 2022). On the decision of who to hire, it is in the best interest of companies to hire based on workers productivity. However, several studies have documented the existence of labor market discrimination in a wide range of contexts (Bertrand and Duflo (2017), Neumark (2018)) and it remains unclear how discrimination operates throughout the hiring process and how the existent empirical evidence on discrimination is linked to economic theory (Bertrand and Duflo, 2017). We conducted a correspondence study in 2023 using a large online job platform to assess demand for soft skills in the context of hiring discrimination in Malaysia. Malaysia is a particularly interesting setting because it is an upper-middle-income economy, home to multiple ethnicities representing large shares of the population, and previously documented gender gaps in labor force participation and wages. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["large online job platform"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of dependability (Heller and Kessler, 2022). In a recent study, Karpowitz et al. (2023) show how these soft skills interact with discriminatory practices. They find that assigning leadership roles to women in a classroom setting reduces gender discrimination. Similarly, Kaas and Manger (2012) use a correspondence study to show that presenting soft information, such as reference letters with information on conscientiousness and agreeableness, seems to mitigate discrimination. Second, unlike most correspondence studies, we exploit data on firm characteristics and decisions at multiple stages of the hiring process to conduct a rich heterogeneity analysis. Most studies are only able to observe if the candidate receives an interview offer. Hangartner et al. (2021) is an interesting exception, which tracks online employers ’ actions and collects information that allows them to study hiring decisions. We instead observe five stages in the hiring process: 1) if employers reject an application, 2) if they visit an applicant ’ s profile, 3) the number of visits to each profile, 4) if employers contact the candidate, and 5) if they offer an interview. These five outcomes provide a rich preview into the hiring decision. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In addition, we observe several characteristics of the firm and job that are not typically observable to researchers, which we will use to conduct a rich heterogeneity analysis. Most importantly, we observe the number of applicants applying to a specific job, and the number of similar jobs posted on the platform. This unique data facilitates our third contribution, in which we analyze the role of labor market competition in hiring discrimination, an area which to date has not been widely explored. de Haan et al. (2017) show that discrimination against disadvantaged groups is more likely in the presence of competition of workers from a non-discriminated group than in a non-competitive scenario. Along these lines, we hypothesize that firms will discriminate less often when there is a low supply of applicants. Unlike de Haan et al. (2017), we uniquely observe quality indicators of the applicant pool, which we use to test our hypothesis that discrimination decreases when the relative quality of applicants in the pool is low. Furthermore, we exploit our unique data to test whether firms discriminate less often when there is high demand for specific job positions. We know of no studies that have previously considered competition on the demand side. The rest of the paper is organized as follows. Section 2 provides background information of Malaysia. Section 3 details the experimental design, and section 4 provides summary statistics of the data. Section 5 explores if there is discrimination in the Malaysian labor market, section 6 studies 4 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ethnicity: For Chinese name candidates is 4 days, for Malay name candidates 5 days and for Indian name candidates 7 days. We find this evidence consistent with a hypothesis of the existence of statis- tical discrimination since employers can be taking more time to collect information of discriminated groups, or sorting applications. Companies that conduct pre-scan questionnaires in the application process are less likely to discriminate against Indian-sounding name candidates in the profile visit outcome. We do not find heterogeneous effects for location or for engineering jobs. Heterogeneity results for gender discrimination are presented in tables 21 to 26. Companies located in Kuala Lumpur and small companies are less likely to visit female profiles than male profiles. There is no effect in any of the other outcomes of the hiring process. We do not find heterogeneous effects for high-paying jobs, for companies with low processing time, for companies with pre-scan questionnaires or jobs in engineering. 6 Do Soft Skills Matter? In this section we explore if soft skills are relevant in the labor market and how soft skill signals affect ethnic and gender discrimination. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 Conclusions We conducted a correspondence study using an online job platform in Malaysia. We tested for ethnic discrimination, gender discrimination and the value of signaling soft skills in the labor market. Unlike many correspondence studies, the data allow us to observe different stages in the hiring process. We observe if the employer rejects an application, visits the profile of a candidate, number of times the profile is visited, if they contact them and if they offer an interview. Uniquely, we observe competition in the labor market on both the demand and supply sides. We do not find evidence of gender discrimination in the hiring process. Malaysia ’ s observed differential wages and labor force participation rates by gender do not seem to be associated with discrimination or human capital accumulation. More research is needed to determine why women in the Malaysian labor market have lower employment rates and wages. We find that Indian and Malay sounding name profiles are discriminated against in comparison to Chinese-sounding name profiles. There is discrimination along all the hiring process variables we observe. Malay and Indian candidates are 8 and 9 percentage points less likely to receive an interview offer relative to a Chinese candidate. Discrimination for both ethnicities is also present in other outcomes. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This question is examined in Jordan using a unique dataset that links individual data on own schooling and par­ents ’ schooling for adults, from a household survey, with the supply of schools in the subdistrict of birth at the time the individual was of age to enroll, from a school census. The identification strategy exploits the variation in the supply of basic and secondary public schools across cohorts and subdistricts of birth in Jordan, controlling for year and subdistrict-of-birth fixed effects and interactions of gov­ernorate and year-of-birth fixed effects. The findings show that the local availability of basic public schools does, in fact, increase intergenerational mobility in education. For instance, a one standard deviation increase in the supply of basic public schools per 1, 000 people reduces the father- son and mother-son associations of schooling by 18 – 20 percent and the father-daughter and mother-daughter asso­ciations by 33 – 44 percent. However, an increase in the local supply of secondary public schools does not seem to have an effect on the intergenerational mobility in education. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey", "school census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 Over the past three or four decades, the Arab world has experienced a massive expansion in educational attainment. According to the Barro and Lee educational attainment dataset, seven out of the top 20 countries in terms of increase in number of years of schooling from 1980 to 2010 were Arab countries (Barro and Lee 2013). 1 Jordan, the subject of this paper, had the seventh highest increase in educational attainment in the world, with an increase of about five years in the average years of schooling over the period. This increase is widely believed to be attributable to a massive public investment in the supply of schooling in the postindependence period in the context of a state-led development model, which virtually guaranteed employment in the public sector for graduates (Assaad 2014; Saleh 2016). The rapid increase in educational attainment has continued unabated despite the fall in returns to education that accompanied the demise in the state-led model and its employment guarantee schemes (Pritchett 2001). A slew of recent literature on the drivers of the Arab Spring protests, some of which occurred in Jordan, has identified the low economic returns to this massive increase in education as the single most important cause of the uprisings (Goldstone 2011; Campante and Chor 2012a, 2012b, 2014; Sanborn and Thyne 2014). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Barro and Lee educational attainment dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Another way to view the connection between low return on education and youth frustrations is that the rapid intergenerational mobility in education has failed to yield similar mobility in either income or social status. This delinking between educational and occupational mobility has been documented for Egypt by Binzel and Carvalho (2013). While there is no similar work on Jordan, this article contributes to this agenda by documenting the first step in this process, which is the link between public investment in schooling and the educational mobility across generations. 1. This is based on version 2. 0 of the Barro-Lee dataset for educational attainment among the total population 15 and older. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Barro-Lee dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 The article employs a unique data source from Jordan, the 2010 Jordan Labor Market Panel Survey (JLMPS 2010), which includes information on parents ’ schooling for every adult in the sample, along with the 2010 School Census produced by the Jordanian Ministry of Education (Hashemite Kingdom of Jordan, 2010). The school census provides the subdistrict, type, and date of establishment of every school in Jordan, allowing us to measure the local supply of each type of schools in each subdistrict in every year (under the presumption that there were no significant school closures or changes in type over time, which is likely the case). The exposure of an individual in the JLMPS 2010 sample to the supply of public schooling is then determined by the number of sex-appropriate basic (or secondary) public schools (per 1, 000 individuals) that were available to them in their subdistrict of birth at the time they were of age to enroll in that school level (six years of age for basic and 15 years for secondary). The richness of the data set makes it the first in the Middle East to allow such a study. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["2010 School Census", "2010 Jordan Labor Market Panel Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 One could think of Jordan ’ s growth of local supply of public basic and secondary schools as a progressive public investment in human capital that increases the human capital production for children of marginal parents in terms of income and educational attainment. Those are parents who would have chosen higher investment in the human capital of their offspring, but were constrained by the limited supply of schools in their subdistricts of residence and could not afford to send their children to more distant schools outside their jurisdiction or to provide them with homeschooling. However, the increase in public schools is expected to have less of an effect on richer or more educated parents, who are expected to provide education to their children regardless of the availability of schools in their subdistricts either by sending their children to distant schools or through homeschooling. On average, however, the increase in the local supply of public schools is expected to reduce the intergenerational correlation of educational attainment or enhance intergenerational educational mobility. III. DATA Two new and unique data sources are employed in the empirical analysis. First, the Jordan Labor Market Panel Survey of 2010, carried out by the Economic Research Forum in cooperation with the Jordanian Department of Statistics, is a rich source of information on all aspects of the Jordanian labor market (JLMPS 2010). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Jordan Labor Market Panel Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Most importantly for the purpose of the analysis is the fact that the survey provides individual-level data on own schooling and parents ’ schooling for all adults in the sample, which is quite rare in household surveys from developing countries. Also, the survey provides the actual years of schooling completed and not only the highest educational degree attained, which allow observing the schooling variable with precision. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Second, each individual in the JLMPS restricted sample is matched to the 2010 Jordanian school census. The matching process determines for each individual the number of sex- appropriate public basic and secondary schools per 1, 000 individuals available in the individual ’ s subdistrict of birth when the individual was of age to accede to this educational level (six years of age for the basic level and 15 years of age for the secondary level). 7 A school is considered sex-appropriate for a female if it is a girls ’ or a mixed school and for a male if it is a boys ’ or mixed school. The empirical analysis is also performed by entering boys ’, girls ’, and mixed schools separately. 8 Measuring the local supply of public schools at the subdistrict of birth of the individual (i. e., the child) mitigates potential endogeneity originating from parents who had a higher taste for schooling moving to subdistricts where public schooling was more abundant when their child was of school age, although it is not possible to rule out that parents might have moved across subdistricts prior to the birth of their child. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["2010 Jordanian school census", "JLMPS restricted sample"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The JLMPS sample is restricted, after matching it to the 2010 school census, to individuals born in Jordan who are aged 25 to 70 in 2010 and who have nonmissing information on age, subdistrict of birth, years of schooling, father ’ s schooling, mother ’ s schooling, and local supply of schools in subdistrict of birth. 9 These exclusions resulted in a sample of 4, 139 males and 4, 131 females, which are referred to as the male and female full samples, respectively. 7. Because of the absence of annual estimates of subdistrict populations, the population used to normalize the supply of schooling at the subdistrict level is the 2004 population of the subdistrict. There are 86 subdistricts in Jordan. If subdistrict populations are growing at different rates, this could introduce some measurement error of the true supply of schooling available to different cohorts. 8. Secondary schools include both general and vocational secondary schools. Public schools include schools under the jurisdiction of: (i) Ministry of Education, (ii) Ministry of Higher Education, (iii) Ministry of Defense, (iv) Ministry of Social Development, (v) Ministry of Religious Endowments (Awqaf), and (vi) UNRWA. 9. The original sample size of all individuals who are aged 25 to 70 years in 2010 and are born in Jordan is 8, 312 observations. The sample restrictions on the missing values result in the exclusion of 34 observations (missing age), 1 observation (missing father ’ s schooling), and 7 observations (missing mother ’ s schooling). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["JLMPS", "2010 school census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "23 and females. 10 The results are shown in table S. 1 in the supplemental appendix. The baseline effects of basic schools on educational attainment and their effects on mobility are stronger and often more significant than those estimated in table 4 for both males and females. A second concern is that Jordan received a large influx of Palestinian refugees in the aftermath of the 1967 Arab-Israeli War. While refugees benefited from the UNRWA basic schools, their educational attainment and intergenerational educational mobility were perhaps subject to a different set of constraints than those facing other Jordanians. Thus, as a robustness check, individuals who are likely to be Palestinian refugees were excluded from the sample. Since the JLMPS 2010 does not allow directly identifying Palestinian refugees who are now mostly Jordanian citizens, two indirect methods were employed to identify individuals who are likely to be Palestinian refugees. Method 1 excludes individuals born in subdistricts where the percentage of individuals who were ever enrolled (or are currently enrolled) in an UNRWA school exceeds ten percent out of all individuals below 36 years of age in the sample. Method 2 excludes individuals born in subdistricts where the percentage of UNRWA schools exceeds ten percent of the total number of schools. The results for the restricted sample according to both methods are shown in tables S. 2 and S. 3 respectively. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["JLMPS 2010"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The weight given to each province in constructing the synthetic control is based on pre-treatment outcomes. We use the pre-treatment average of the outcome dimension, Y, the unemployment rate, employment rate and the import and export per capita of the province to determine the degree of similarity between control group provinces and the treated provinces, which in turn determines the weight assigned to control provinces. The unemployment and employment rates are included to control for the general economic performance while trade values are added to control for the degree of ’ openness ’ of the province. 6 The treated unit i = 1 is constructed by taking the mean of the outcome variables in the provinces hosting refugees in 2012 or 2013. 5 Data We use several data sources for the analysis. The IV estimations use data from years 2011 and 2014 while the DD estimations use data from 2009 to 2014. The numbers of refugees up to 2012 are treated as 0. The refugee data for 2012 and 2013 are obtained from UNHCR ’ s official weekly statements in December. Data on the number of refugees in 2014 is from Erdo ˘ gan (2014), who uses statements released by the Ministry of the Interior to compile his data. All refugee data we use in the analysis is provided at the level of 81 provinces. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There were in total 1, 6 million refugees by November 2014. Data on the number of new firms and their ownership characteristics and value are provided by the Turkish Chamber of Commerce. 7 Data on total sales and gross profits are obtained from the Turkish Ministry of Science, Industry and Technology. Other economic indi- cator variables, such as population and unemployment rates, are obtained from Turkish Statistics. Since Syrians usually have guest status rather than resident status during the period of analysis, they are not counted in official statistics such as province population and unemployment rates. Turkey is officially divided into 81 provinces and that is the level of our analysis and variables throughout. The Chamber of Commerce provides data on the number of new firms and the num- ber of new foreign-owned firms at the provincial level. Enterprises defined as firms do 11 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The data on total gross profits and net sales acquired from the Turkish Ministry of Science, Industry and Technology are compiled from administrative taxation data and was provided upon request by the ministry. The key difference from the Chamber of Commerce data is that the sales and profits data include all businesses including self- proprietorships. 9 Data were provided for the years between 2010 and 2014 and are re- ported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by definition and they are likely to be less accurate and complete for smaller firms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets which includes sales and profit figures. 10 We scale the variables according to province size by dividing sales and profits by the pop- ulation of the provinces. If we use sales and profits in absolute terms, we get qualitatively similar results. The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we 12 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Chamber of Commerce data"], "vague_data": ["administrative taxation data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There is a clear increase in the entry of new foreign firms which may be driven by refugees ’ enterpreneurship. Cheaper low-skilled labor may have helped all businesses cutting costs. Balkan and Tumen (2016) had also found a decline in prices and attribute their finding to lower labor costs, which may also be one of the mechanisms driving our results. Gross profits and sales also appear to have gone up, which would be consistent with an increase in demand. As noted by Maystadt and Verwimp (2014), heterogeneous effects on specific subgroups of the native population should be expected from refugee crises. In case of the Syrian refugee crisis in Turkey, the business activity in hosting region appears to have benefited. For a complete picture of the effects of the Syrian refugee crisis on local economies in Turkey, further research will be needed on market activity, health and longer term effects. More specifically for the line of research this study focused on, further analysis using micro-level firm data would be needed to understand how firms adjust their activity, 24 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 quest for imaginative and convincing instruments for migration (see the review of the migration and poverty literature by McKenzie and Sasin, 2007, and the references therein). An additional hurdle is the need for panel data to study migration and economic mobility. The costs and difficulties in re-surveying migrants mean that attrition may be relatively high for this group and may also result in the loss of some of the most relevant households to study this process (Beegle, 2000; Rosenzweig, 2003). This paper uses unique data from a region in Tanzania to address this key question: What is the impact of physical movement out of the original community on poverty and wealth? Although we do not have experimental data, the nature of our data allows us to limit the potential sources of unobserved heterogeneity considerably. Building on a detailed panel survey conducted in the early 1990s, we re- interviewed individuals in 2004, making a notable effort to track individuals who had moved. The tracking of individuals to new locations proves crucially important for assessing welfare changes among the baseline sample. The average consumption change of individuals who migrated was more than four times higher than that of individuals who did not moved. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Those who had moved out of the Kagera Region by 2004 experienced consumption growth that was 10 times higher compared to those who remained in their original community. These averages translate into very different poverty dynamics patterns for the physically mobile and immobile. For those who stayed in the community, poverty rates drop by about 4 percentage points over these 13 years. For those who moved elsewhere within the region, poverty rates drop by about 12 percentage points, and for those who moved out of the region, they drop by 23 percentage points. Had we not tracked and interviewed people who moved out of the community – a practice found in many panel surveys – we would have seriously underestimated the extent to which poverty has gone down over the past 13 years in the Kagera Region; we would have reported poverty reduction at about half of its true value. Clemens and Pritchett (2007) raise similar concerns in the context of income growth and international migration. In addition, the data would omit the part of the population with a high information content on pathways out of poverty. Still, these statistics are not evidence that moving out of the community leads to higher income growth. As noted above, we cannot observe the counterfactual: What would income growth have been for migrants had they not migrated? We exploit some unique features of these data to address concerns about unobserved heterogeneity. First, individual fixed effects regressions for movers and stayers produce a difference-in-difference estimation of the impact of physical movement, controlling for any fixed individual factors that affect consumption. Second, we can control for initial household fixed effects in the growth rate of consumption since we observe baseline households in which some individuals migrate Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 narrative around the regressions and aims to explain why not more people migrate when benefits of doing so are so high. 2. The Setting: Tanzania and Kagera, 1994-2004 In the last decade, Tanzania has experienced a period of relatively rapid growth, attributed to liberalization, a renewed trade orientation, a stable political context, and a relatively positive business climate to boost economic performance. Real GDP growth was of the order of 4. 2 % per year between 1994 and 2004, while annual population growth was around 3. 2 % in the same period (URT, 2004). There is also evidence that growth had accelerated in the last few years compared to the 1990s. However, this growth has not been sufficiently broad-based to result in rapid poverty reduction. On the basis of the available evidence, poverty rates have declined only slightly and most of the poverty reduction progress has been made in urban areas. According to the Household Budget Survey (HBS), between 1991 and 2000 / 01, poverty declined from 39 percent to 36 percent in mainland Tanzania. The decline in poverty was steep in Dar es Salaam (from 28 % to 18 %) but minimal in rural Tanzania (from 41 % to 39 %). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Household Budget Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 3. The Data The Kagera Health and Development Survey (KHDS) was originally conducted by the World Bank and Muhimbili University College of Health Sciences (MUCHS), and consisted of about 915 households interviewed up to four times from fall 1991 to January 1994 (at 6-7 month intervals) (see World Bank, 2004, and http: / / www. worldbank. org / lsms /). The KHDS 1991-1994 serves as the baseline data for this paper. Initially designed to assess the impact of the health crisis linked to the HIV-AIDS epidemic in the area, it used a stratified design to ensure relative appropriate sampling families with adult mortality. Comparisons with the 1991 HBS suggest that in terms of basic welfare and other indicators, it can be used as a representative sample for this period for Kagera (results not shown but available upon request). The objective of the KHDS 2004 survey was to re-interview all individuals who were household members in any round of the KHDS 1991-1994 and who were alive at the last interview (Beegle, De Weerdt and Dercon, 2006). This effectively meant turning the original household survey into an individual longitudinal survey. Each household in which any of the panel individuals live would be administered the full household questionnaire. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["KHDS 1991-1994", "KHDS 2004 survey", "Kagera Health and Development Survey"], "descriptive_data": [], "vague_data": ["household survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Since the set of household members at baseline have subsequently moved, and usually not as a unit, the 2004 round results in more than 2, 700 household interviews (from the baseline sample of 912 households). Although the KHDS is a panel of respondents and the concept of a ‘ household ’ after 10-13 years is a vague notion, it is common in panel surveys to consider re-contact rates in terms of households. Excluding households in which all previous members are deceased (17 households with 27 people), the field team managed to re-contact 93 % of the baseline households. This is an excellent rate of recontact compared to panel surveys in low-income countries and high-income countries. The KHDS panel has an attrition rate that is much lower than that of other well-known panel survey summarized in Alderman et al. (2001) in which the rates ranged from 17. 5 % attrition per year to the lowest rate of 1. 5 % per year. Most of these surveys in Alderman et al. (2001) covered considerably shorter time periods (two to five years). Figure 1 charts the evolution of households from baseline to 2004. One-half of all households interviewed were tracking cases, meaning they did not reside in the baseline communities. Of those households tracked, only 38 % were located nearby the baseline community. Overall, 32 % of all households were not located in or relatively nearby the baseline communities. While tracking is costly, it is an important exercise because migration and dissolution of households are often hypothesized to be important Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["KHDS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Respondents who were untraced were much more likely to be residing outside Kagera (43 %) compared to their counterparts who were re-interviewed (8 %). The consumption data come from an extensive consumption module administered in 1991 and again in 2004. The consumption aggregate includes home produced and purchased food and non-food expenditure. The non-food component includes a range of non-food purchases, as well as utilities, expenditure on clothing / personal items, transfers out and health expenditures. Funeral expenses and health expenses prior to the death of an ill person were excluded. Monetary levels were adjusted to account for spatial and temporal price differences, using price data collected in the Kagera survey in 1991 and 2004, and, for households outside Kagera, data from the National Household Budget Survey. Consumption is expressed in per capita, per annum terms. The poverty line is set at TZS 109, 663, calibrated to yield for our sample of respondents who remained in Kagera the same poverty rate as the 2000 / 1 National Household Budget Survey estimate for Kagera (29 %). 4. Growth, Poverty and Physical Mobility in Kagera In this section, we discuss changes in living standards overall, and the changes for four mutually exclusive groups based on residence in 2004: (i) still residing in the baseline community, (ii) residing in a Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["National Household Budget Survey", "Kagera survey"], "descriptive_data": [], "vague_data": ["price data", "consumption data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 neighboring community, (iii) residing elsewhere in the Kagera Region and (iv) residing outside the Kagera Region. Table 3 shows that the basic needs poverty rate declined 8 percentage points in the full sample. This figure masks significant differences in changes between subgroups based on migration. For those found residing in the baseline community, poverty rates dropped by 4 percentage points, but rates dropped by 11, 13 and 23 percentage points for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. A similar pattern is found for consumption per capita. While consumption per capita grew by $ 65 overall, it grew by only $ 30 for those found in the same community and by $ 65, $ 100 and $ 287 for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. Dividing consumption into food and non-food components gives the same result. The most basic assessment of welfare changes would have been wrong if we had focused only on individuals still residing in the community, a practice found in many panel data surveys. We would have underestimated the growth in consumption by half of its true increase. The differences in consumption changes of groups in Table 3 are statistically significant, as shown in Table 4. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Excluding respondents who have relocated would omit those with the higher rates of income growth and poverty reduction. Table 5 reports confidence intervals for the incremental samples (which are not mutually exclusive); it gives a more detailed picture of how inference on consumption growth and poverty reduction would have changed if we had not tracked movers. It is apparent that inference from a ‘ simple ’ panel survey of respondents continuing to reside within the original communities would have produced underestimates of actual consumption growth and poverty reduction in this population. These conclusions are robust across the distribution of consumption, as well as at the mean and poverty line. Panel A in Figure 2 depicts the cumulative density function for consumption per capita for those people who remained living in the same community. Panels B, C and D make the same graph for respondents found residing in neighboring communities, elswehere in Kagera Region and outside Kagera Region. As respondents were located further from their location in 1991, so the difference between the 1991 and 2004 graphs becomes more pronounced. Note how, for people who remained in the baseline community, the 1991 and 2004 distributions lie close to each other under the poverty line and diverge above it, while for other mobility categories there is more divergence at the bottom of the graph. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 International migration — the movement of people across national borders — has important economic, social, and political implications. Despite the recent emergence of a dynamic literature, empirical analysis of migration flows and their impact lags behind the policy debate and the theoretical literature. The main reason is the absence of comprehensive and reliable data on international migration patterns and migrant characteristics at either the aggregate or the household level. The objective of this article is to use data from more than one thousand national censuses and population registers to estimate a complete global origin – destination migration matrix for each decade over 1960 – 2000. These 226 * 226 matrices, comprising every country, major territory, and dependency around the world, are divided into periods corresponding to the last five completed census rounds. The gender dimension of international migration over this period is also presented. The primary source of the raw data is the United Nations Population Division ‘ s Global Migration Database, created through the collaboration of the United Nations Population Division, the United Nations Statistics Division, the World Bank, and the University of Sussex (United Nations [2008]). This unique data repository comprises 3, 500 individual census and population register records1 for more than 230 destination countries and territories over the last five decades. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Global Migration Database"], "descriptive_data": [], "vague_data": ["national censuses and population registers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The database provides information on international bilateral migrant stocks (by citizenship2 or place of birth), sex, and age. There is considerable variation, however, in how destination countries collect, record, and disseminate immigration data. Meaningful comparison of destination country records over time is thus often confounded. In constructing global bilateral migration matrices, several challenges arise. First, destination countries typically classify migrants in different ways — by place of birth, citizenship, duration of stay, or type of visa. Using different criteria for a global dataset generates discrepancies in the data. Second, many geopolitical changes occurred between 1960 and 2000, with many international borders redrawn as new countries emerged and others disappeared. In addition to creating millions of migrants overnight — as when the Soviet Union collapsed — these events complicate the tracking of migrants over time. Third, even when national censuses of destination countries include data on international migrant stocks, the data are presented along aggregate geographic categories rather than by country of origin. Data therefore need to be disaggregated to the country level. Finally, the greatest hurdle is dealing with omitted or missing census data. Very few destination countries — especially developing countries — have conducted rigorous censuses or population registers during every census round over the second half of the twentieth century. Wars, civil strife, lack of funding, and political intransigence are but a few reasons why records may be discontinuous. 1 Of the 3, 500 sources detailed in the overarching UN Global Migration Database, 1, 107 were suitable for analysis, once repeated censuses had been removed or combined. Global Migration Database should not be confused with the Trends in International Migrant Stock Database, which lists aggregate migrant stocks for each destination country in the world at five year intervals (United Nations 2006) 2 The article treats the concepts of nationality and citizenship as analogous and uses the terms interchangeably. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["UN Global Migration Database", "Trends in International Migrant Stock Database"], "descriptive_data": [], "vague_data": ["census data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The resulting migration matrices should be viewed as work in progress, but they are an important step in an ongoing global effort to improve migration data. The matrices can be readily updated as additional or superior information surfaces, and they can easily be extended to include future census rounds. Bilateral datasets of international migration are rare. Attempts to create them have focused almost exclusively on industrialized countries as destinations because these countries have more accurate and more frequently produced data. Harrison and others (2003) calculate bilateral remittances for the countries of the Organisation for Economic Co-operation and Development (OECD) together with the 27 largest nonmembers. These estimates are based on international bilateral migrant stock data that the authors also provide, although many of the data are derived from the Trends in International Migration (OECD 2002). This report, published annually since 1973, was arguably the most comprehensive guide to international migration for many years and has been the basis for many studies (see, for example, Mayda 2007). More recently, the OECD has developed a database that provides a comprehensive overview of migration to OECD countries in 2000 (OECD 2008). These data are disaggregated by a number of covariates including age, gender, educational attainment, and place of birth. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Trends in International Migration"], "descriptive_data": ["international bilateral migrant stock data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Database. Where both censuses and population registers are available, censuses receive priority. Censuses, generally conducted decennially, are retrospective tools for surveying an entire population (or in some cases, a representative sample) at a single point in time. In addition to their universal coverage, their greatest strength is the inclusion of questions on place of birth and nationality. Censuses also typically aim to enumerate the resident population, whether documented or undocumented (Bilsborrow and others 1997). So although some migrants have a strong incentive to provide false information to enumerators, many undocumented migrants will be captured in these matrices. 7 The size and scope of the census questionnaires vary enormously, both over time and in different destination countries. And there is potential variation in the quality of censuses both across countries and over time. Richer countries have many resources at their disposal to design questionnaires, train interviewers, employ statisticians, and disseminate results. Researchers have little choice but to accept the data at face value. However, where the underlying census is clearly substandard (when there are errors that are obviously not coding errors or not easily corrected), these data are omitted from the analysis. Popular in many parts of Europe, population registers are continuous reporting systems providing up-to-date demographic and socioeconomic information for everyone surveyed. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population registers", "censuses"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Typically, registers have evolved over time (from parish records, for example). They were never developed specifically to record international migration information, and they vary considerably across countries. For example, the laws under which individuals are classified as migrants and the conditions under which they are inscribed or deregistered differ greatly (Bilsborrow and others 1997). The Raw Data The Global Migration Database is a vast collection of destination country data sources detailing migrant stocks from numerous origin countries and regions (United Nations [2008]). Compiling and maintaining the underlying primary sources require herculean efforts to scour the key census collections of the world and enter the data manually. In total, the database comprises records from some 3, 500 separate censuses from more than 230 migrant destination countries and territories, by sex and age. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Global Migration Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Last, specific adjustments are made in the case of Germany and the Republic of Korea. For Germany, bilateral data are available only by nationality. However, these data fail to take adequate account of the large number of ethnic Germans who arrived from other countries between 1944 and 1950 (mainly expellees) and those who arrived after1950 (mainly resettlers). Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3). In the case of Korea, data by nationality are readily available for each census round. However, these data fail to account for the large numbers of migrants from the People ‘ s Democratic Republic of Korea living in the Republic of Korea. Since the United Nations Trends in International Migrant Stock details the total migrant stock in the Republic of Korea by the country of birth definition and because citizenship is rarely granted to people from outside, it is simply assumed that the nationality data were comparable to the foreign-born definition. The nationality total was then subtracted from the UN total and the remaining migrants were assigned to the People ‘ s Democratic Republic of Korea. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["United Nations Trends in International Migrant Stock", "German 2005 micro-census"], "descriptive_data": [], "vague_data": ["nationality data", "bilateral data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "An alternative version of the database that has been mapped to the United Nations (2006, 2009) Trends in International Migrant Stock database is available from the authors. These data are standardized over time in terms of the years to which they refer. { Table 3 here} Calculating Missing Gender Splits Although common in the underlying data, bilateral migration data disaggregated by gender are sparser than aggregate migrant totals (see table 1). An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices. Similar to the allocation from aggregated categories in the Global Migration Database to specific origins in the master list, two measures are used for calculating gender splits; they are described in appendix 5. Combining Migrant Definitions Only a single definition of a migrant (foreign born or foreign citizen) can be applied to each destination country in the final matrices. Switching definitions over time 17 The subregions used for the disaggregations are the 21 UN regions (see http: / / unstats. un. org / unsd / methods / m49 / m49regin. htm, with the countries of Oceania aggregated into a single subregion. They do not match the large World Bank regions used in the analysis in section IV. 18 While this propensity measure is clearly inappropriate, less than 1 percent of all migrants and observations are assigned on this basis. This method is included so that every migrant in the underlying data is accounted for. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock", "Global Migration Database"], "descriptive_data": [], "vague_data": ["bilateral migration data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 { Tables 4 and 5 here} Among the first six categories that are based on raw census data, three categories (raw scaled, R & R not scaled, and R & R scaled) are constructed through the summation of bilateral raw numbers and disaggregations of some aggregate categories in the original censuses. Since these categories together constitute around 45 percent of migrants in each census round, the original bilateral portion of each cell was compared with the final number assigned to them after the various calculations as a check on accuracy. For each decade, therefore, the overall percentage contribution of the raw bilateral data to the total is calculated (table 6). 23 In each census round, at least 92 percent of all those categories are derived from the raw data. { Table 6 here} Simulating Missing Data Finally, to examine the reliability of the estimated missing census data and test the methodologies, several scenarios are assumed. All bilateral observations for a single year for four countries (Australia, United States, Switzerland, and Chile) in different parts of the world are deleted and the missing cells are filled using one of five methods. 24 The first simulation assumes that all bilateral data for 2000 are missing but that the total number of migrants is available. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["raw census data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 corridor, so only aggregate numbers can be compared. For this comparison, mid-year estimates of the world migrant stock for 1990 – 2000 are taken from the 2008 edition and estimates for the earlier censuses, 1960 – 1980, are taken from the 2005 edition (table 8). The analysis subtracts the estimated number of refugees from the total mid-year estimates of the world migrant stock from the Trends in International Migrant Stock database to yield the net number of migrants in each decade. These numbers are then compared with the decadal estimates generated through this project, both the total and the net, after subtracting estimates of migrants within the Soviet Union for 1960 – 1980 (data for 1990 and 2000 should be directly comparable) and the number of ethnic German migrants added to the German censuses. { Table 8 here} The aggregate estimates are remarkably close (the two net totals), differing at most by around 1 million migrants, except in 1990. There are several possible explanations for these differences. First, the census totals from the current work may not match because censuses do not always make allowances for temporary workers. For example, Singapore ‘ s official 2000 census records 563, 430 foreign-born migrants. The United Nations, however, reports 1, 351, 806 foreign-born migrants for 2000. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Second, there are cases where the current study reports data by nationality, but the corresponding figure in the Trends in International Migrant Stock refers to the foreign born. This situation generally arises when a census does not report the number of foreign-born migrants on a bilateral basis. Examples include Austria and Côte d ‘ Ivoire. Third, differences in the years to which the data refer can generate large disparities. For example, this study uses the 1966 data for Australia, whereas Trends in International Migrant Stock reports data for 1970. Overall, however, the fact that the totals are remarkably close in every decade adds credence to the estimates here. IV. THE EVOLUTION OF GLOBAL BILATERAL MIGRATION The greatest strengths of the global migration matrices are their bilateral coverage, the number of decades covered, and the disaggregation by gender. These data are too rich for a full analysis of all movements between all pairs of countries. Instead, this section summarizes the major trends in the evolution of bilateral migrant stocks, based primarily on World Bank regions. 25 Global Trends The migration matrix for the 1960 census round reflects a realigning world in the postcolonial era. Over the 1960-2000 period, the composition of world migration 25 Appendix 1 details the World Bank regions: South Asia, East Asia and Pacific, Sub-Saharan Africa, Latin America and the Caribbean, Europe and Central Asia, and Middle East and North Africa. High-income Middle East and North Africa refers to the predominantly oil producing countries in the Persian Gulf (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) and to Israel. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "33 Ratha, D., and W. Shaw. 2007. ― South-South Migration and Remittances. ‖ World Bank Working Paper 102, World Bank, Washington, DC. United Nations Statistics Division. 1998. Recommendations on Statistics of International Migration Revision 1. New York: United Nations. United Nations, Department of Economic and Social Affairs, Population Division. [2008]. United Nations Global Migration Database. New York: United Nations. http: / / esa. un. org / unmigration — — —. 2006. Trends in Total Migrant Stock 1960 – 2000, 2005 Revision. Database. POP / DB / MIG / Rev. 2005 / Doc. New York: United Nations. — — —. 2009. Trends in International Migrant Stock: The 2008 Revision. Database. POP / DB / MIG / Stock / Rev. 2008. New York: United Nations. http: / / www. un. org / esa / population /. — — —. 2010. ― World Population Prospects: The 2009 Revision, Highlights ‖, Working Paper No. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["United Nations Global Migration Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "throughout the day prior to the survey day. We develop our own questions around self- worth rather than employing the more standard Rosenberg Self-Esteem Scale, which we found inappropriate given the Rohingya ’ s recent experiences. Specifically, we construct an index of self-worth from two questions designed to elicit respondents ’ beliefs about how they contribute to their family and community. Finally, we adapt the Cantril Self-Anchoring Striving Scale (Cantril, 1965) to measure how stable respondents feel in their present lives and in the future. We additionally examine the impacts of each treatment on physical health, cognitive function, economic decision making, time-use, and consumption. We capture respondents ’ sense of physical health by asking how many days they have fallen sick in the past thirty days and cognitive function by employing a digit-span memory test and a series of basic arithmetic problems. We explore economic decision making along two dimensions: incentivized time preferences (Andreoni and Sprenger, 2012; Gin ´ e et al., 2018) and incentivized risk preferences (Holt and Laury, 2002). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We measure time-use through the number of hours in the previous day a respondent reports spending idle, as well as the amount of time spent on a variety of other common activities one might do in the camps (including bathing, market, chores, collection of rations, eating, child-rearing, sitting at tea stalls, praying, sleeping, visiting friends / relatives, playing games, playing sport, sitting idle). Finally, we ask respondents how much they consume, borrow and save over the past week. We further consider changes in perceptions on gender and power in two ways. First, we generate a Household Power Index, composed of a set of questions on perceptions of gendered decision-making and intimate partner violence. The questions are drawn from Haushofer and Shapiro (2016), which are themselves adapted from the Demographic Health Surveys. In addition, we produce a Work Rights Index, composed of questions around whether respondents feel that women should be allowed to work inside or outside the home or the camp block. Each outcome is described in greater detail in Appendix C. The frequency with which each outcome is collected is also presented in Appendix C. Multiple hypothesis testing We utilize two approaches to address the issue of multiple hypothesis testing. First, we present our primary outcome, psychosocial well-being, as an inverse-covariance weighted index variable following Anderson (2008). We also generate index variables for other outcomes in which this is possible, such as the cognitive index, the household power index, and the work rights index. Our second strategy is to report the sharpened False Discovery Rate (FDR) q-values for all outcomes within a particular table, which control for the expected proportion of rejections that are type I errors, likewise 12 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Demographic Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "which increases significantly under both treatment arms. In other words, the psychosocial value to employment appears to be driven largely by the non-pecuniary dimensions of the employment experience. 5. 4 Impacts of employment on reported physical health, cognitive function, and economic decision-making The positive effects of employment extend to other measures beyond psychosocial health. Table 3 presents results on reported physical health, cognitive function, and incentivized measures of risk and time preference. We observe a significant increase in the days reported healthy. This effect may be due to ‘ real ’ health improvements from increased exercise (which has also been documented to translate to improved mental health (Herbert et al. (2020))) from the employment task or ‘ perceived ’ health improvements in which improved psychoso- cial well-being translates into feeling less physically ill. Should the channel be exercise, we may expect health improvements to grow over time. Our weekly data on days healthy sug- gests this is not the case: we observe the treatment effect on health from the first week of working, and the gap remains steady throughout the following two months (Appendix Figure A3). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["weekly data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The employment arm also significantly improves cognitive function as measured through an index of memory and basic arithmetic tests, a finding consistent with a large psychol- ogy literature documenting the relationship between cognitive processes and depression (Semkovska et al., 2019). As with physical health, improvements to cognitive function are unlikely to be a direct product of the employment task itself, which was specifically de- signed to require no literacy or mathematical skill. Rather, these results are suggestive of a downstream impact to reducing depression through the experience of employment. Finally, we find no change in time preferences: treated individuals are no more or less likely to discount the future relative to control counterparts, although results may have differed had we engaged participants in an effort or consumption-based time preference game rather than a financial one. However, we find a substantial increase in risk tolerance among the employed. A greater preference for risk-taking may be indicative of employment serving as a form of psychological ‘ insurance ’ that allows participants the mental bandwidth to exercise greater risk. This is consistent with the positive impacts of employment on stability as well as with a key motive underlying universal basic income (UBI) in the developing world (Banerjee, Niehaus, and Suri, 2019). Interestingly, however, we document no parallel increase in risk tolerance in the cash transfer arm. Our result on risk preference also echoes a potential consequence of depression and anxiety described in Ridley et al. (2020), although empirical 15 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Notably, while we cannot rule out that time outside due to employment may play a role (e. g., fresh air may boost one ’ s mood), our time-use data indicates that the average refugee already spends at least three hours outside per day, with no measurable difference between employed and cash arms. As we are powered to detect changes of at least twenty minutes for each activity, our results suggest that large substitutions away from unsavory activities are unlikely to be driving the improvements in psychosocial well-being, insofar as the respondent recalls. 1718 We also investigate whether those who were more idle prior to being employed benefit more from employment. We find no impact along this margin, suggesting that the elimination of boredom per se is not the driving force behind the psychosocial value of employment (Appendix Table A10). 17Most respondents do not track their day by time, making collection of reliable time use data challenging (though recent literature documents the broader unreliability of such data). We piloted a variety of strategies, and settled on asking respondents how much time they spent on a set of activities in the previous day. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["time-use data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure A4: Participation certificate to boost ‘ resume ’ CERTIFICATE THIS ACKNOWLEDGES THAT I engaged with Pulse Bangladesh to do data collection Notes: The wording of the certificate was made such that it could be applied to both arms; cash-only arms participated in weekly surveys along with all other experiment participants, so technically also engaged in data collection for our project. 60 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "C Details on outcome measures Outcome Variable Descriptions Psychological Well-being PHQ9 The standardized total score of 9 questions from the Patient Health Questionnaire-9 (PHQ9) Life Satisfaction Index A standardized average of survey responses to four questions from Di- ener ’ s standardized scale, responses made along a seven-point Likert scale. Stress Index The standardized total score from three elements of adapted from the Cohen Stress scale. “ How many of the last 7 days have you [been able to fall asleep peacefully / felt nervous / felt frustrated]? ” Sociability (Total) The total number of conversations in the past day with adults. Sociability (Positive) The total number of conversations in the past day with adults that the respondent felt were positive. Self-Worth Index The standardized total score from the responses on a scale from 1 to 10 to two questions: “ Think of a person you know who you most respect and who brings greatest value to your [family / community]. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Patient Health Questionnaire-9"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Gender Perceptions- Violence (IPV) The standardized total score of five questions regarding norms for inti- mate partner violence (IPV) from the Demographic and Health Survey (DHS) (The important decisions in the family should be made only by the men of the family. How often would you agree? The wife has the right to express her opinion even when she disagrees with what her husband is saying. How often would you agree? A wife should tolerate being beaten by her husband in order to keep the family together. How often would you agree? A husband has the right to beat his wife. How often would you agree? It is more important to send a son to school than it is to send a daughter. How often would you agree?). Financial Well-being Savings Response to the question “ How much money do you currently have in savings? ” During the collection surveys (midlines) this question instead asked “ How much money did you save in the past week? ” Borrowing Total amount of money the household has borrowed. Economic Decision Making Risk Preference Measured using incentivized responses to the multiple price list deci- sions adapted from Holt-Laury and Sprenger (2002). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 INTRODUCTION In 2015, an estimated 2. 2 million Syrians Under Temporary Protection (SUTPs) were residing in Turkey, the majority arriving in the country over the last 4 years. 2 Turkey ’ s national population is roughly 75 million; recent refugees account for approximately 3 percent of the population. For a country that has never experienced such a large-scale, sudden inflow of foreigners, demographic changes in the composition of the population and labor force will yield unprecedented implications. This paper examines, as data allows, the relationship between the size of the foreign-born population and host community poverty rates in Turkey. First, this paper finds the poverty rates of ‘ recent migrants ’ near the Syrian border (NSB) significantly increased from 2009 to 2013. Second, the number of foreign-born households being captured by the Labor Force Survey (LFS) is expanding, which suggests a growing number of foreign households that are likely to be Syrians. Third, with respect to poverty, the results show no negative impacts on the host community as a result of the increasing size of the foreign-born population. The impact of SUTPs has been both positive and negative. Overall, a significant negative impact on host communities ’ welfare is not observed in the data. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This paper ’ s scope of analysis includes the country as a whole using a nationally representative survey. While regional case studies may reveal salient stresses on public services and job displacement, nationally, there is no significant impact. Over the period of 2009 to 2013, the poverty rates of host community households have stayed relatively stable near the Syrian border; despite the high poverty rates experienced among the recent migrants. By country of origin, the displacement of Syrians is one of the largest in recent history. As a result of the civil war that began in 2011, Syrians started to leave their homes and look for safety in neighboring countries across the region. By November 2015, about 4. 3 million Syrians were seeking refuge in primarily Turkey, Lebanon, Jordan, Iraq, and the Arab Republic of Egypt. 3 The only other time in the last half century that the world experienced a larger group of refugees from a single country is the case of Afghan refugees during the 1980s to 1990s. Refugee displacements of this size are rare. Consequently, they are not well studied and their impacts are not well understood. Moreover, the case of Afghan refugees in Pakistan is different, since they were stigmatized to a larger extent, which limited their movement in Pakistan. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["nationally representative survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 if they are built to code. 2 These spillovers or externalities are absent in more sparsely populated rural areas where damages to smaller sized and dispersed dwellings will cause less or no collateral damage. Exposure The main reason why urban risk is large and increasing is the rise in exposure. Although urbanization statistics suffer from a lack of standard definitions of what should be considered ‘ urban ’, the assumption of half the world ’ s population living in cities seems realistic. Urban populations are growing in practically all developing countries. About 40-60 percent of this growth can be attributed to natural growth, i. e., fertility of urban dwellers (Montgomery 2009). The remaining growth is due to urban expansion and migration, reducing the share of rural residents except where rural fertility is vastly larger. The latest UN urban population estimates suggest that, globally, urban population exceeded rural population for the first time in 2008 (UN 2008). In less developed regions, this threshold is expected to be reached by 2019. This continuing urbanization process will lead to an increase of exposure of people and economic activity in hazard prone urban areas. Although we can only speculate about the global distribution of disaster damage in cities today and in the future, newly available geographically referenced data yield some estimates of urban exposure to natural hazards. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["geographically referenced data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A recent global hazard analysis generated a comprehensive database of hazard events during 1975- 2007 from observed data and of event probabilities from geo-physical models (ISDR 2009). We combined this hazard information with city-specific population projections 2 It should be noted that well-documented evidence for such externalities is quite scarce. Their importance is usually taken as given: “ A building collapse may create externalities in the form of economic dislocations and other social costs in addition to the economic loss suffered by the owner. The owners may not have taken these consequences into account when evaluating specific mitigation measures. Consider the following example. A building toppling off its foundation after an earthquake could break a pipeline and cause a major fire, which would damage other homes that had not been affected by the earthquake in the first place. “ Kuenreuther and Roth (1998). See also www. quakesmart. org / index. php? option = com_content & view = article & id = 92 & Itemid = 209. But some experiences have been documented: “ As shown by research on the Great Hanshin-Awaji Earthquake, including that conducted by the Architectural Institute of Japan, Architectural Institute of Japan (1997), houses with inferior earthquake-resistant quality triggered large negative externalities in the neighborhood. For example, broken fragile houses blocked transportation networks, thereby preventing effective fire fighting and, by severing lifelines, they made recovery more difficult. ” (Nakagawaa et al. 2007). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["city-specific population projections"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 Investors actively trade-off disaster risk with gains from economic density. In addition to the city or location specific analysis, we examine how investors value risk from natural disasters. Data from a recently compiled dataset of a sample of global cities provides some insights. Gomez-Ibañez and Ruiz Nuñez (2006) constructed a dataset of central business district office rents for 155 cities around the world in 2005 to identify cities where rents seem elevated or depressed by poor land use or infrastructure policies. Their dataset also includes information on many factors that determine the supply and demand for central office space such as construction wage rates, steel and cement prices, geographic constraints, metropolitan populations and incomes. We link this information to the natural disasters hotspot dataset (Dilley et al. 2005), and examine if city demand – as reflected in office rents, is sensitive to risk from natural disasters. Gomez-Ibañez and Ruiz Nuñez (2006) focus on offices in the primary business district, which they define as the district having the highest density of employment; a very large, if not the largest, concentration of offices; and the highest rents in the metropolitan area. As we are interested in the tradeoff between economic density and disaster risk, using the central business district works well for our analysis. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["natural disasters hotspot dataset", "dataset of central business district office rents for 155 cities around the world"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "19 As property prices in the worst affected areas reduced the most, low income households responded by moving into low-rent housing being offered in these locations. On the other hand, middle income households moved away to avoid risk, and the wealthy, for whom insurance and self-protection was the most affordable, did not change where they lived. Poor people “ sort ” into low rent locations – which are often at higher risk to natural hazards. The problem is particularly acute in developing countries where there is a divide between the formal and informal markets for land. While formal developments may respect land use regulations, informal settlements are often located in hazard prone locations, such as on hill slopes, close to river banks, or near open drains and sewers. In Dhaka for example, informal settlements are developing across the metropolitan area, with many residents lacking basic public services and in locations at risk from flooding. In fact, most informal settlements do not have access to a public toilet within 100 meters, and 7, 600 households in 44 slums live within 50m of the river (World Bank 2005, Dhaka Urban Poverty Assessment). For the city of Bogotá, we use the same database discussed earlier to examine if poor people are at greater risk from natural hazards – particularly earthquakes. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "23 mega-cities, annual growth rates of peripheral population tend to reach around 10-20 percent compared to central business districts. 4. Implications for public policy Hazard management is a task both for the public sector and for private households and firms. For the public sector in cities, this includes ensuring the safety of municipal buildings and public urban infrastructure, encouraging and supporting private sector hazard risk reduction, and developing first response capacity. A considerable share of hazard risk stems from relatively small but frequent events which cause localized damage and few injuries or deaths (Bull-Kamanga et al. 2003). For instance, an analysis of detailed records of 126 thousand hazard events in Latin America showed that more than 99 percent of reported events caused less than 50 deaths or 500 destroyed houses (ISDR 2009). In aggregate, these accounted for 16. 3 percent of total hazard related mortality and 51. 3 percent of housing damage. The probability of larger events may or may not be predictable. For instance, a city may be in an earthquake risk zone, but the location specific ground shaking probabilities are not known. Individual dwelling unit level mitigation is therefore necessary everywhere in the general area of high earthquake probability. For other hazard types like landslides and floods, potential risk areas can be more easily delineated. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["detailed records of 126 thousand hazard events in Latin America"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Consequently, they may fear detection by authorities when asked to respond to surveys or participate in public initiatives. 1 Furthermore, collective repre- sentative and longitudinal data on forcibly displaced migrants, a population with high mobility rates, is difficult and costly (Ib ´ a ˜ nez et al. 2024). This complexity is compounded when focusing on children and adolescents, given the need for enumerators to receive specific training to interact with such a vulnerable demographic and for migrant parents to authorize their children ’ s involvement despite prevailing distrust issues. To address this knowledge gap, we launched the Venezuelan Refugee Panel Study for Kids (VenRePs-Kids) in Medell ´ ın, Colombia. VenRePs-Kids is a longitudinal study repre- sentative of forcibly displaced Venezuelan and Colombian children and adolescents aged 5 to 17. To our knowledge, it is the first study to gather panel data specifically on forcibly 1This concern is also prevalent among undocumented migrants in the United States, as highlighted by Amuedo-Dorantes and Lopez (2015). 2 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["VenRePs-Kids", "Venezuelan Refugee Panel Study for Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "displaced children and adolescents residing in host communities, rather than in refugee camps. Particularly, VenRePs-Kids collects data on 2, 556 households including 1, 338 Colombian and 918 Venezuelan households, respectively. The study collects rich and comprehensive data on children ’ s and adolescent ’ s development including anthropometric measures, vo- cabulary ability tests, and socio-emotional and mental health assessments. It also collects data on risk behaviors, time use, social integration measures, prosocial behaviors, and parents and caregivers sociodemographics, among other dimensions. 2 The study includes Colombian children and adolescents as the comparison group to high- light the developmental differences of Venezuelan forcibly displaced children. This choice stems from the fact that comparing Venezuelan children in Colombia with their counter- parts remaining in Venezuela is impractical due to the latter ’ s exposure to a severe eco- nomic and humanitarian crisis, marked by limited access to services and food. This envi- ronment severely hampers their potential for normal human development. Additionally, many Venezuelan children and adolescents have spent more of their lives in Colombia than in Venezuela. Therefore, Colombian children and adolescents serve as the most ap- propriate benchmark for assessing the developmental gaps of their Venezuelan peers. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["VenRePs-Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This paper analyzes data from the initial wave of VenRePS-Kids, conducted from Oc- tober to December 2022, to outline key demographics and stylized facts about forcibly displaced children and adolescents. Initially, we examine the human development dis- parities of forcibly displaced Venezuelan children and adolescents in comparison to their Colombian counterparts. Our approach to human development is broad, covering physi- cal, cognitive, socio-emotional, and mental health aspects. Additionally, we complement our analysis by exploring differences in food security, social cohesion, and the economic status of parents. Although our analysis is descriptive, it represents a crucial initial step 2Venezuelan households are defined as those where both parents and their children have a Venezuelan nationality. Colombian households are composed of Colombian citizens only. 3 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["VenRePS-Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our research complements and extends the findings of Demirci, Foster and Kirdar (2022), who investigated health and nutrition disparities between native children and Syrian refugee children aged 0 to 5 years in T ¨ urkiye, using data from the Demographic and Health Survey. The authors document find no significant differences in infant or child mortality rates between refugee children born in T ¨ urkiye and their native counterparts, it did reveal that refugee infants have lower birth weights and age-adjusted weights and heights compared to native infants. Our work broadens the scope of analysis beyond anthropometric indicators to encompass a holistic assessment of child development. By incorporating measures of physical, cognitive, socio-emotional, and mental health devel- opment, along with factors such as food security, time use, risky behaviors, and social integration, we offer a more comprehensive understanding of the developmental chal- lenges faced by displaced minors. Additionally, our study includes a wider age range, 5Chiovelli et al. (2021) examine the effects of forced displacement on separated sibling in the long-term. 8 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The upcoming section provides an overview of the situation facing Venezuelan migrants in Colombia, setting the stage for understanding the context within which our study is situated. Section three offers a comprehensive description of the VenRePS-Kids study, covering aspects such as the sampling frame, the instrument used for data collection, the representativeness of the study, its implementation process, and an overview of descriptive statistics. Section four delves into the human development disparities observed among forcibly displaced chil- dren and adolescents, providing detailed insights into the nature of these gaps. In section 9 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "III THE VENREPS-KIDS STUDY In this section, we offer a detailed overview of the VenRePs-kids Study, covering its de- sign, implementation, the questionnaire utilized, and the primary outcomes that will be employed to evaluate the human development disparities between forcibly displaced children and adolescents and their peers in host communities. III. A Design Location. Our study is conducted in Medell ´ ın, Colombia ’ s second-largest city, following Bogot ´ a. Medell ´ ın was chosen for this study because it hosts the third-largest Venezue- lan migrant population in the country, trailing only Bogot ´ a and C ´ ucuta, as indicated by the 2018 population census data. Additionally, previous research has demonstrated that survey response rates among migrants in Medell ´ ın are notably high. For instance, a na- tionally representative survey of Venezuelan migrants conducted in 2018 — which was representative across Colombia — revealed that Medell ´ ın had the highest response rates among migrants, whereas Bogot ´ a recorded the lowest (Ib ´ a ˜ nez et al. 2022). This finding supports the decision to focus our study exclusively on Medell ´ ın, also considering the challenges and high costs associated with tracking a highly mobile population longitu- dinally in previous research efforts. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["2018 population census data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A map highlighting Medell ´ ın ’ s geographic position and the locations of the households interviewed for this study is provided in Figure 2, offering visual context to our research setting. Representativeness and stratification. VenRepS-Kids is designed to be representative of two groups of youth. The first group consists of Colombian children and adolescents, aged 5 to 17, born to Colombian parents. The second group encompasses Venezuelan migrant children and adolescents of the same age range, born to Venezuelan parents, who mi- grated to Colombia between 2016 and 2020. The sample was further stratified by gender and socioeconomic levels, using Colombia ’ s neighborhood income-based classification system that ranges from 1 to 6, where six indicates the wealthiest neighborhoods. Our 13 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 2. Location of Households in the VenReP-Kids Sample Notes: The figure depicts the exact geographic location of all the households in the VenRePs-Kids study. Green and blue dots depict the location of Colombian and Venezuelan households, respectively. The map in the upper right corner illustrates the location of Medell ´ ın (blue pin) with the department of Antioquia (highlighted in red). survey focuses on strata 1 through 4, intentionally omitting strata 5 and 6 to avoid bias toward higher-income groups which are less likely to include migrants in need of sup- port. 14 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In households with more than two children, one child from each age group was chosen through a random selection process to participate, ensuring a broad representation of experiences within the study ’ s scope. Sampling frame. We use the 2018 population census data as a sampling frame for the Colombian sample. It allowed the identification of residential blocks and households with children in the desired age range. With this source of information, it was possible to identify the number of households and residential blocks with children and adoles- 7Although Colombia only grants nationality to children of Colombian nationals, it follows a jus sanguini principle, the Colombian government has introduced reforms, such as the the program Primero la Ni ˜ nez to give Colombian nationality to children of Venezuelan parents born at times when diplomatic relations between Colombia and Venezuela were cut and hence, it was not possible to apply for a Venezuelan nation- ality for this minors in Colombia. 15 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["2018 population census data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "cents. The universe of blocks (“ manzanas ”) was stratified by socioeconomic strata and a representative sample of blocks was selected at random without replacement. To en- sure a sample representative of Colombian children by age group, stratum, and sex, we followed a multi-stage random sampling process. 8 As mentioned earlier, one of the biggest constraints in characterizing the role of forced migration in children ’ s human development within developing countries is the difficulty of finding a representative sample of those migrants. This is specially true in contexts where migrants are not hosted in refugee camps, but are integrated in local communi- ties, which account for 80 % of refugees worldwide (Climate Center 2022). We address these difficulties, leveraging all available information on Venezuelan settlements across the country to construct the largest possible comprehensive listing. The listing included data on Venezuelan settlements from all available sources, such as the 2018 population census, migrant organizations, and settlements identified by iMMAP, a non-profit orga- nization. iMMAP uses multiple sources, including OIM, United Nations, local migrant organizations, and satellite images, to identify Venezuelan settlements geographically. 9 Hence, to create our sampling frame, our field team verified the geographic location of all the Venezuelan settlements in-person and implemented a snowball sampling procedure in all the settlements found. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["2018 population census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5. Time Use and Child Labor: The fifth module examined how children spend their leisure time, their involvement in child labor, and interactions with peers. 6. Pro-social Preferences and Migration Outlook: The sixth module concentrated on adolescents ’ pro-social behaviors, such as altruism and trust, and explored their expectations and intentions regarding migration. 7. Socio-emotional and Mental Health: The final module involved the administration of various scales to assess socio-emotional well-being and mental health, includ- ing trauma, behavioral problems, anxiety, and depression. The scales include the Trauma Symptom Checklist for Young Children (TSCYC), Strengths and Difficulties Questionnaire (SDQ), General Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire (PHQ-9). All these scales and the corresponding outcomes that we evaluated are described in the next subsection. The survey also employed the Peabody vocabulary test to evaluate the cognitive devel- opment of all participating children and adolescents. A summary of the survey modules is depicted in Table A. 1. III. C Sample comparability While Medell ´ ın ranks as the third city with the highest migration in Colombia, it is crucial to recognize the degree to which migrants arriving in the city differ from those migrating to other regions in Colombia. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Strengths and Difficulties Questionnaire", "Patient Health Questionnaire", "General Anxiety Disorder Scale"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Ideally, we would conduct a comparative analysis of the characteristics of migrants residing in Medellin versus those in other parts of the coun- try to discern the extent of these differences. However, the lack of comprehensive data regarding the living conditions of this population makes such analysis unfeasible. To explore how this population compares with other migrant groups in the country, we turn to the only two available data sources on migrants. First, we use the Venezuelan Refugees Panel Survey (VenRePS), conducted by Ib ´ a ˜ nez et al. (2022), which captures a 19 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Venezuelan Refugees Panel Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "representative sample of undocumented migrants in Colombia ’ s major cities as of 2020. 10 This survey encompasses information on the socioeconomic status, health, well-being, access to services, and labor market outcomes of adult undocumented migrants. Second, we compare our sample with the Administrative Venezuelan Migrant Registry (RAMV), a nationwide census of undocumented Venezuelan migrants conducted by the Colom- bian government in 2018. 11 This census surveyed Venezuelan households regarding their socioeconomic conditions and the labor market characteristics of the household head. We compare the household characteristics and the labor market outcomes of the house- hold heads in our sample with those in VenRePS and RAMV surveys in Table A. 3. 12 We observe that households in the VenRePS-Kids survey are smaller on average and have a greater number of children living in the household. The latter is anticipated since one of the eligibility criteria to participate in our survey is the presence of at least one child in the household. Furthermore, the household heads in our sample are disproportionately female and more likely to be married, aligning with the family structure targeted in our sampling frame. Regarding labor outcomes, household heads in our sample are more likely to be employed and engaged in the informal sector compared to those surveyed in VenRePS and the RAMV census. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Administrative Venezuelan Migrant Registry", "VenRePS-Kids survey", "RAMV surveys", "Administrative Venezuelan Migrant Registry (RAMV)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Despite notable disparities in the characteristics of our sample compared to the VenRePS and RAMV samples, understanding the interaction of these attributes within a frame- work of self-selection among migrants into Medellin is challenging. Moreover, it is cru- cial to note two primary distinctions between our survey and the VenRePS and RAMV surveys. Firstly, the inherent differences in the sampling frames of each survey stem from their distinct measurement objectives. Second, both surveys were conducted at different times compared to our survey. The RAMV survey was undertaken in 2018 in response 10See Ib ´ a ˜ nez et al. (2022) for specific survey and sampling details. 11Refer to Ib ´ a ˜ nez et al. (2022) for further details. 12Since the VenRePS and RAMV surveys lack information regarding children and adolescents within households, our analysis concentrates only on the household and household head characteristics that are available in all three surveys. 20 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["RAMV survey", "VenRePS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "to the Venezuelan migratory crisis, whereas the VenRepPS survey was conducted during the pandemic in 2020. These temporal inconsistencies result in varying sample composi- tions across surveys, diverging from the landscape we observe in 2022. Notably, forced migrants in the VenReps Kids survey migrated during the crisis but have since remained in the country for several years, potentially leading to disparities in household integration outcomes. IV GENERAL DESCRIPTIVE STATISTICS IV. A Key characteristics of adults Table 2 provides descriptive statistics for the adults in our study, encompassing the pri- mary caregiver, mother and father (if residing with the child), and the individual finan- cially responsible for the child (should they be different from the aforementioned per- sons). Typically, the roles of primary caregiver and financial provider are fulfilled by either the mother or the father. The table is organized into three panels for clarity: Panel A details key individual characteristics, Panel B outlines adults ’ access to services, and Panel C focuses on labor market characteristics. Within the table, columns (1) and (2) present average values for adults from Colombia and Venezuela, respectively, while the final column displays the results of mean difference tests between these two groups, with standard errors noted in brackets. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["VenRepPS survey", "VenReps Kids survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 3. Wealth Index Distribution (a) Wealth Index- Total 0. 05. 1. 15. 2. 25 k-Density- 5 0 5 10 15 Wealth Index Colombian Venezuelan Venezuelan: Before migrating (b) Adequate Housing Materials (% of Total) 0 1 2 3 4 k-Density 0. 2. 4. 6. 8 1 Average- Dwelling Material Colombian Venezuelan Venezuelan: Before migrating (c) Asset Ownership (# Total) 0. 05. 1. 15 k-Density 0 10 20 30 40 Total Assets Colombian Venezuelan Venezuelan: Before migrating (d) Access to Services (% of Total) 0 1 2 3 k-Density 0. 2. 4. 6. 8 1 Average- Access to Services Colombian Venezuelan Venezuelan: Before migrating Notes: Panel (a) presents the distribution of the wealth index for Colombian and Venezuelan households in our sample in 2022 and pre-migration. Wealth Index is an index measure of the household ’ s cumula- tive living standard constructed following The Demographic and Health Surveys (DHS) methodology. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "by the migration episode. These include the minors age and sex, the parent ’ s and grand- parent ’ s education pre-migration, and a wealth index constructed with retrospective in- formation on the household conditions pre-migration. 14 Grandparent ’ s education is used a proxy for living standards that is unaffected by the migration episode from Venezuela to Colombia and the Venezuelan crisis, which intensified in 2016. ϵij depict the standard errors clustered at the household level to correct for intra-household correlation. For robustness, we will present the estimates of equation 1 with and without controls. As further robustness, we use propensity-score weights (Hirano and Imbens 2001, Hirano, Imbens and Ridder 2003). 15 V. A Physical development: Body Mass Index and health status In our study, we examine disparities in nutritional and health status among Colombian and Venezuelan children aged 5 to 10 years, focusing on standardized body mass index (BMI), instances of overweight and underweight, and overall health status. The BMI serves as an indicator of nutritional status for both adults and children, calculated as an individual ’ s weight in kilograms divided by their height in meters squared, according to World Health Organization (WHO) guidelines. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["wealth index"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of difficulty, ranging from those suitable for children as young as 2 and a half years old to more complex items for individuals over 18 years old. 19 The Peabody Vocabulary Test is calibrated with a mean standard score of 100 and a standard deviation of 15, placing scores between 85 and 115 within the average range. This test, in its Spanish edition, has been validated for use in Colombia. Additionally, to ensure relevance and accuracy for Venezuelan nationals, we conducted a preliminary validation by administering the test to a sample of Venezuelan mothers in our study. This step confirmed that the words used in the test held consistent meanings for participants from Venezuela. Figure B. 1 depicts the distribution of PVTS scores for Venezuelan and Colombian chil- dren and adolescents in our sample. This visualization indicates that Venezuelan minors consistently score lower on the PVTS compared to their Colombian peers across the en- tire score distribution. In Table 6, we present the average disparities in percentile rank on the Peabody scale, revealing that Venezuelan children and adolescents, who are forcibly displaced, score approximately 12 p. p. lower than their Colombian counterparts. The difference is meaningful and in turn translated into Venezuelan minors falling into the a higher likelihood of having extremely low, moderately low, and low score categories. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "to severe based on the score. Moreover, depression is screened with the Patient Health Questionnaire, a 9-item tool administered directly to adolescents. These instruments collectively gauge a broad spectrum of psychological and emotional states, including post-traumatic stress, emotional disturbances, behavioral issues, inat- tention, peer relationships, prosocial behavior, anxiety, and depression, offering a com- prehensive view of the mental health and socioemotional development of the minors in our study. Figures B. 3 and B. 4 depict the distribution of the raw scores for Venezuelan and Colom- bian minors for each of the four scales. Surprinsingly, we do not observe any stinking differences on the distribution of any of these scores across groups. We are also not able to distinguish statistical differences between Colombian and Venezuelan children in any of the scales, when we estimate the specification highlighted in equation (1) as illustrated in Table 7. This is an unexpected result considering that typically, forcibly displaced pop- ulations have a high prevalence of socioemotional and mental health issues, but might be related to the fact that Venezuelan migrants have not faced war (as many forced migrants have in other contexts) directly and as such, these issues are less prevalent. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Another ex- planation of these results could be the vulnerability of the Colombian population who in many cases has also a long history of internal forced displacement and violence. V. D Social Cohesion We also delve into the differences in secondary outcomes among Colombian and Venezue- lan adolescents concerning social cohesion. We focus on assessing altruism, trust, iden- tity towards specific domains, networks, and experiences of discrimination. To measure altruism and trust, we employ the questions from the Global Preference Survey, a tool developed by Falk et al. (2022) to elicit risk, time, and social preferences. Specifically, to measure altruism we ask the adolescents how much of a fictional endowment would they be willing to donate to a good cause. To measure trust, we include the 7-itme ques- 38 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Global Preference Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "tionnaire from Falk et al. (2022), along with a question on how much do the subjects in our sample trust the Colombian government. Additionally, we inquired about the ado- lescents ’ social networks by asking about their number of friends of each nationality and whether they have felt discriminated in school. While these outcomes were not the pri- mary focus of the VenReps Kids survey, they collectively provide a general assessment of social cohesion, a crucial aspect in understanding the overall well-being of children and adolescents. We measure the average differences in altruism between Venezuelan and Colombian ado- lescents following the estimation of equation 1. Table 8 presents the results for altruism, trust, discrimination and social ties in panels A, B and C respectively. As in the previous tables, the first column for each outcome reports the results of the estimates of equation 1 without controls, and the second and third columns report the results of the estimation with all controls and the propensity score matching respectively. As for altruism, we see that Venezuelan adolescents are on average willing to donate 17 % more of their imagi- nary money endowment to a good cause relative to Colombian adolescents. As for trust, the results are mixed when analyzing the trust items separately. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["VenReps Kids survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Colombian and Venezuelan migrant children and adolescents. VII Discussion In this study, we analyze novel and unique data on forcibly displaced migrants and hosts, focusing on children and adolescents, to highlight the disparities in human development between them. The structure our analysis in two parts. In the first part, we characterize the main trends in the data. We show that forcibly displaced households have a wealth distribution skewed towards lower values relative to Colombian households. This is likely explained by the assets ownership loss that forcibly displaced households expe- rienced after the migration episode. We also identify meaningful lags in human capital accumulation between Colombian and Venezuelan children and adolescents of approxi- mately 1 year. We further note that the Colombian government ’ s supportive policies for Venezuelan forced migrants are evident through high levels of service access and pro- gram participation for migrants. Nevertheless, it remains surprising that participation is not higher, suggesting significant potential for improvement in increasing sisb ´ en and health insurance enrollments. In a second part of our analysis, we document sizeable lags in physical and cognitive de- velopment of Venezuelan children and adolescents, relative to their Colombian counter- parts. However, we were not able to identify any gaps in the socioemotional and mental health between the two groups. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 household displacement and the destruction of household dwellings during the violent events. We are also able to measure conflict intensity across time – including peaks of violence at various stages of the conflict – at the district level from event data on violence intensity during the conflict in Timor Leste. We focus on primary school effects because only a small percentage of the Timorese population attended secondary school. Our results show mixed evidence for the impact of violent conflict on educational outcomes. Mirroring some of the findings of Bellows and Miguel (2006) and others, we find evidence for a rapid recovery of the education sector in Timor Leste, and of educational outcomes, particularly for girls. However, in line with emerging results in the micro-level literature, we find that the 1999 wave of violence in Timor Leste – as well as peaks of violence in the 1970s and 1980s – resulted in negative effects on primary school attendance and attainment. This effect is particularly strong for boys. We attribute the first result to a process of educational catch-up among girls in Timor Leste that started before the conflict and continued despite the conflict. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["event data on violence intensity during the conflict"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The data shows that cohorts that were of school age during the Indonesian occupation achieved higher level of education than older cohorts (i. e. those born in the 1970s compared to those born in the 1960s or before) testifying for an increasing trend as expected. The figure shows that despite the increasing trend a large fraction of individuals have low education levels. Interestingly, all curves start to drop after the 1987 cohort. This decreasing trend is observed among individuals aged 20 or younger in 2007 and provides evidence of a mismatch between the grade attended and the grade that they should have achieved at their age. 4 This is caused by a persistent sluggishness in grade achievement due to the high level of delayed entry to school and high rates of repetition. 5 The impact of the conflict in its different phases and the subsequent reconstruction efforts on schooling levels of children in Timor Leste is therefore unclear. The early years of violence coincided with an education for all policy in which quantity was preferred to quality. In addition, the 1999 violence that followed the withdrawal of Indonesian troops led to the destruction of schools and the removal of children from school. The reconstruction program implemented after 1999 tried to counteract this destruction, and achieved fast progress. However, the education sector was still in very poor shape. In the next section, we investigate in more detail the effects of the conflict on educational outcomes of boys and girls in Timor Leste. 4. Identification strategy and data description 4 Those born in 1992 are 15 in 2007. So they might have at most completed grade 9 and this justifies part of the drop in the curves as the grade completed is right censored. 5 The high levels of school delay are also confirmed by the figures on gross and net enrolment ratios calculated using the TLSS 2001 and 2007: primary gross enrolment ratio was 105 percent in 2001 and 128 percent in 2007, while net enrolment ratios were 74 and 94 percent, respectively, in 2001 and 2007. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["TLSS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Commission for Reception, Truth and Reconciliation (CAVR). 7 This information has been collected from deponents to the Commission ‘ s statement-taking process. 8 We make use of data on the number of killings that occurred during the war in order to derive patterns and variation of violence in Timor Leste over time and across space. We use this data to identify districts and years that experienced high and low violence-intensity, both at the start of the occupation and following the withdrawal of Indonesian troops in 1999. This allows us to estimate both the impact of the first years of the conflict and the impact of the last wave of violence in 1999. 4. 1. Identification strategy: The impact of violence on school attendance in 2001 We first investigate the short-term impact of the 1999 violence. The empirical questions being addressed are: (i) whether the violence in 1999 imperiled school attendance9 and school grade deficit, and (ii) whether different channels of exposure to conflict – displacement and house destruction – affected boys and girls and different age groups differently. 4. 1. 1. Primary school attendance and grade deficit rates in 2001 We make use of information in TLSS 2001 collected at the individual and household levels on displacement and house destruction to identify conflict-affected individuals. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["TLSS 2001"], "descriptive_data": ["data on the number of killings that occurred during the war"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We have constructed two different variables that try to account for the degree of severity of the conflict. 10 The first variable identifies individuals belonging to households that were displaced due to the 1999 wave of violence (all members displaced). The second variable identifies individuals in households that report having their house completely destroyed by the violent attacks in 1999. The TLSS 2001 contains also useful retrospective information on school attendance and grade attained across three different academic years: 1998 / 99, 1999 / 00 and 2000 / 01. We are 7 Commission for Reception, Truth and Reconciliation & Benetech Human Rights Data Analysis Group. ― Human Rights Violations Database. ‖ 9 February, 2006. Website: http: / / www. hrdag. org / resources / timor-leste_data. shtml. 8 There may be potential sample biases in the statement taking procedure given the voluntary nature of the process. It is possible that those living in more remote or mountainous areas, those living far away from the areas where the statements were taken, the sick, old and disabled and those with no access to the media or means of mass communication have a lower probability of being part of the sample. By contrast, those more active in local communities are more likely to have provided a testimony. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Human Rights Violations Database", "TLSS 2001"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In order to address these potential sample biases, the CAVR supplemented its documentation with reports produced by Amnesty International and Fokupers (a local NGO). The information contained in these reports was then included into the HRVD database. 9 Note that we do not analyse school completion in 2001 because most children that were of school age in 1999 were still in school in 2001. 10 The questions we used are ― Was [NAME] displaced outside E. Timor in 1999? ‖, and ― Was the [BUILDING] damaged in the violence of 1999? ‖. 14 % of the whole sample surveyed in 2001 report having been displaced, while 26 % report that their house was destroyed. Within our sample of school age children, these figures are 16 % and 25 %, respectively. We have made sure that buildings that are reported to having been destroyed were used for living purposes only. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["HRVD database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 4. 1. 2. Empirical strategy In order to make use of the retrospective information on school attendance provided in the dataset, we have constructed what we have called an ‘ ad hoc panel ’, whereby we exploit the time-variation of the variables of interests (age, attendance status and grade attained by the respondents) by reshaping the cross-sectional structure of the TLSS 2001 dataset. In this way, we are able to obtain observations for each individual over three academic years. All key education variables are time- variant, while other individuals and households characteristics are time-invariant. Within these three years, we focus our analysis on individuals that were of primary school age (between 7 and 12 years old) in each year. In practice, we keep all children aged at minimum 7 years old in 1998 and at maximum 12 years old in 2000. As a consequence, our panel data contains children aged 8-11 years in 1999, the year of the violence. 11 Since we are interested in looking at different effects across groups of individuals, we have split the sample between boys and girls and between younger children (aged 8-9 in 1999) and older children (aged 10-11 in 1999). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["TLSS 2001 dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 of intense conflict. We examine also the overall impact of the 25 years of conflict on educational outcomes. This analysis allows us to consider the full long-term impact of the conflict on educational outcomes of different generations of children in Timor Leste. 4. 2. 1. The educational impact of the 1999 wave of violence We exploit variation in the number of killings over time and across districts to identify conflict affected individuals. Our intention here is to analyze whether individuals exposed to the violence during their primary school age show different primary school completion rates eight years after the end of the war, relative to those not affected by the conflict. The outcome variable in which we are interested is whether individuals completed primary school in 2007. Figure 7 shows average primary school attainment for all individuals in our sample. The graph shows an increasing trend in primary school completion across cohorts and a progressive reduction of the gender gap. The gap among the younger cohort (those born after 1987) is almost zero. The drop in the curve for the younger cohort confirms the presence of significant delays in school attendance. For the purpose of this analysis, we use the TLSS 2007 dataset and the HRVD dataset contained in the CAVR data publication. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["TLSS 2007 dataset", "HRVD dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As explained in section 4, the HRVD dataset contains data on the number of human rights violations occurred since the start of the conflict in 1975 until its end in 1999 for each district. The types of violations recorded are killings, deaths due to deprivation and disappearances. We use only the number of killings to identify years and districts affected by the conflict. We exclude the deaths due to deprivation because the districts in which this occurred may very likely not be those where the conflict was most intense, but were simply places were the victims were hiding as a consequence of escaping from the troops, and died for starvation. In addition, since killings are less likely to affect entire families than deaths due to deprivation, there is a lower underreporting bias attached to the former measure relative to the latter one (Silva and Ball 2006). We also exclude disappearances as, according to HRVD data, they do not show enough time and geographical variation in order to identify individuals more or less exposed to the conflict. We believe that the number of killings proxies quite well the intensity of the conflict across time and space as their occurrence largely tracked the movements of the Indonesian military operations. The other two types of violations do not seem to show the same pattern (Silva and Ball 2006). For the same reason, we believe that it proxies quite well the destruction of houses and infrastructure and the displacement of people given the way in which the last wave of violence occurred (i. e. the scorch-earth technique employed by Indonesian troops as they moved towards West Timor). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["HRVD dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The countries that are the largest source of refugees include: Afghanistan, Syria, Somalia, South Sudan, Sudan, Democratic Republic of Congo, Central African Republic, Iraq, Myanmar, and Eritrea (UNHCR, 2017). These countries account for the vast majority of refugees worldwide. We will also consider three countries in Europe that are an important source of asylum seekers in Europe, and whose recognition rate is low, and account for many returnees – these are Kosovo, Serbia and Albania. Is return possible and safe, and if so, does the economic situation bode well to provide essentials, namely a job, housing, education and reliable public services? It is helpful to identify three groups of countries: ones that are still mired in war and / or high ‐ intensity civil conflicts, countries where medium ‐ intensity conflict persist and are already in the process of rebuilding, and countries which are not in conflict. According to the Armed Conflict Survey (IISS 2017), high ‐ intensity conflict is defined by frequent (daily) armed clashes between governments, government forces and insurgents, or among non ‐ state armed groups that control territory. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Armed Conflict Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Medium ‐ intensity, on the other hand refers to “ regular armed clashes between governments, government forces and insurgents. ” (IISS 2017) Within the first category, according to the Armed Conflict Survey in 2016 are Afghanistan, Syria, Somalia, South Sudan, and Iraq, while Myanmar, Central African Republic, Democratic Republic of the Congo, and Sudan are in medium ‐ intensity conflict. (Eritrea is not included in the Armed Conflict Survey in 2017, discussed in Annex). Minorities in Albania, Kosovo and Serbia are often the objects of discrimination but countries are not in conflict. A capsule summary of the security and social situation in each of the other countries included in this survey can be found in Annex 2. The survey illustrates how impractical return is today in the countries in conflict, such as Syria, Afghanistan, and Iraq, and voluntary return in large 36 Best practice and new methods in return policy, July 2017, http: / / www. bamf. de / SharedDocs / Meldungen / EN / 2017 / EMN / 20170504 ‐ emnjahrestagung ‐ rueckkehr. html Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Armed Conflict Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "50 Rankings, with particularly poor scores for starting a business, getting electricity and registering property. Agriculture slowed in 2016 and is expected to do better in 2017. Lack of rain affects the outcome of the agricultural sector. CIA ’ s Factbook73 reports that 46. 5 % of Sudan ’ s population is living below the poverty line. Only 35 % of its population has access to electricity. Social Service It is difficult for the staff of international humanitarian aid agencies to obtain travel permits in Sudan, severely hampering the relief effort. The Democratic Republic of the Congo (DRC) Security The security situation in the Democratic Republic of Congo has been poor since 2012. An attempt to integrate a Tutsi rebel group into the Congolese military failed and prompted the defection and formation of the M23 armed group. The renewed conflict led to large population displacement and human rights abuses. Furthermore, the President of the DRC, Joseph Kabila is barred from running for a third term, but the DRC government has delayed national election originally slated for November 2016. The failure to hold election fueled sporadic street protests by Kabila ’ s opponents74. Although a deal signed by representatives of Kabila ’ s ruling party said the presidential election would be held before the end of 2017, the President himself has not endorsed the deal. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Women ’ s Empowerment in Agriculture Index (WEAI) (Alkire et al. 2012) uses individual-level data, and the linked Gender Parity Index reflects inequalities across women and men ’ s deprivation scores within the same household. Alkire, Apablaza and Jung (2014) design and implement an exploratory individual-level MPI for 31 European countries over six waves of data using EU-SILC data sets, finding no cases in which are women significantly less poor than men, and in many cases, they are significantly poorer. Espinoza-Delgado and Klasen (2018) create an individual-level MPI to understand differences in poverty between women and men in Nicaragua, finding similar overall incidence, but much higher intensity of poverty among women. Bessell (2015) and Pogge and Wisor (2016) explore deeply contextual gendered poverty measures and elucidate the ways that participatory consultations can inform the design and uses of gendered measures. Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa. Alkire, Ul Haq, and Alim (2019) use individual-level data alongside MPI data to expose gendered and intrahousehold differences among MPI poor and non-poor children. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["EU-SILC data sets"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 In this study, we apply their techniques to understand gender differences among adults as well. 2. 2 Intrahousehold analyses of multidimensional poverty The literature on multidimensional poverty measurement and intrahousehold analysis is limited. Espinoza-Delgado and Klasen (2018) propose an individual-based multidimensional poverty measure for Nicaragua and estimate gender gaps in headline statistics. Klasen and Lahoti (2016) question the neglect of intrahousehold inequality in multidimensional poverty indices by comparing a standard household-level MPI and an individual-level MPI to the MPIs proposed by Alkire and Santos (2014) and UNDP (2014), finding that females recorded a far higher poverty rate when using the individual measure and that age differentials in poverty were also larger. We follow their work of investigating poverty in the indicators for which individual data is available and compare the achievements of men and women and boys and girls living together. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 3 Methodology 3. 1 The A-F method and individual deprivations The Multidimensional Poverty Index (MPI) used in this paper was first presented, with a full methodological discussion, in Admasu et al. (2021). Here we present a general overview of the measure for the individual-level and intrahousehold analyses. The MPI is constructed based on the Alkire-Foster (AF) method of multidimensional poverty measurement (Alkire and Foster 2011). Three key statistics characterize any MPI: incidence or headcount ratio (H), which is the proportion of the population who are multidimensionally poor; intensity (A), which is the average share of weighted indicators in which multidimensionally poor people are deprived; and adjusted headcount ratio (M0 or MPI), which is the product of the incidence and intensity (MPI = H × A). The AF method uses a dual-cutoff counting approach to poverty measurement. Having fixed relative weights across indicators that sum to 100 %, it first identifies who is deprived in each indicator, then sums up the weighted deprivations each person experiences into a deprivation score. A person is identified as poor if their deprivation score meets or exceeds a cross-dimensional poverty cutoff that is greater than 0 and less than or equal to 100 %. It then aggregates this information to compute society-level MPI, incidence, and intensity. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The MPI can be decomposed by any groups for which the data are representative and broken down by indicator to show the composition of multidimensional poverty, adding to the policy relevance of the analysis. To tackle individual-level and intrahousehold analyses, we build on the work of Alkire, Ul Haq and Alim (2019). The focus is on individual deprivations, and we call the persons with individual-level data in each indicator the eligible household members. For example, children aged 6-16 years might be eligible for deprivations in terms of school attendance, but not those older or younger. For individual-level indicators, we identify who and how many household members are deprived: their gender and their age, and what proportion of eligible household members are deprived. This is a powerful and potentially informative steppingstone for analysis. Consider two households, each of which has five eligible members with data on nutrition. The aggregation rule in this example is that if any household member is undernourished then the household is undernourished. So, both households are deprived in terms of Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 Bank Account No member has a bank or mobile money account. 1 / 12 Many of the indicators align with goals identified in the 2030 Agenda for Sustainable Development, such as no hunger, good health, access to quality education, clean water and sanitation, and decent work, as well as indicators that are especially relevant for displaced people, such as possession of legal identification, physical safety, and food security. The focus on gendered dynamics justifies health indicators related to pregnancy care, combining information on prenatal care, assisted delivery, and early marriage. A full discussion of the MPI ’ s indicator selection can be found in Admasu et al. (2021). We focus on six of these 15 indicators that use individual-level data – viz years of schooling, school attendance, pregnancy care, early marriage, legal identification, and unemployment. Our intrahousehold analysis drops the two health indicators due to data limitations; the question about age at marriage was only asked to the household head in Ethiopia, Nigeria, and South Sudan, whereas in Somalia and Sudan, it was applied to more members than the head. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["health indicators", "individual-level data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The pregnancy care indicator is also excluded from the intrahousehold inequalities analysis as the reference populations for the analysis did not permit rigorous statistical testing. 3. 3 Limitations With a few exceptions, gendered MPIs have been designed using indicators that are present in standard survey instruments, which themselves struggle with normative challenges (Alkire 2018). To create improved gendered MPIs, in which people ’ s poverty can be compared across gender and age or the life cycle, research must develop “ comparable ” definitions of capability deprivation that matter to people in different age cohorts or different life situations. Reliable indicators comparing men and women ’ s income, ownership of assets, and decision-making powers in the same household are difficult, as are those measuring decent work. Health indicators also differ by gender, change across the life cycle, and vary across family structures and disability status. The MPI constructed in Admasu et al. (2021) has the same weaknesses as these measurement paradoxes, but it remains a step in the right direction. We aim to mitigate the limitations of this household-level measure by unpacking the deprivations of indicators available at the individual level, disaggregating those deprivations by gender Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "24 According to Table 12, all countries but Sudan show a significant relationship for school- age children ’ s experience of intrahousehold inequality and their displacement status, although sample sizes mean that only Nigeria and Somalia ’ s results are robust. In Nigeria, most children experiencing intrahousehold inequality reside in non-displaced households – although it is crucial to note that these children constitute most of the school-age children (71. 1 %), and the levels of intrahousehold inequality are nonetheless far higher than anticipated if displacement status had no effect. In Somalia, displaced children are significantly more likely to experience intrahousehold inequality in school attendance, as they constitute 63. 5 % of school-age children experiencing intrahousehold inequality even though they only make up 35. 3 % of the school-age children population in the sample. For the years of schooling indicator, the overall lack of intrahousehold inequality among the MPI poor in years of schooling obscures meaningful or robust differences by displacement status. Gender and displacement status appear to jointly have significant impacts in school attendance in Ethiopia, Somalia, and South Sudan. In Northeast Nigeria, it appears that displacement status has larger effects than gender. In Somalia, forcibly displaced school children experience intrahousehold inequality more often than non-displaced children, to the disadvantage of girls. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These findings were also echoed in the context of other developing countries such as India (Allard et al., 2022) and Zimbabwe (Mabugu, Maisonnave, Henseler, Chitiga-Mabugu, & Makochekanwa, 2023). In the MENA economies ’ context, the evidence is considerably sparcer. Early work on the impact of the COVID-19 pandemic on labor markets in the MENA region re- lied on high-frequency phone surveys and highlight important job losses among wage workers and an uneven impact across industries (Krafft, Assaad, & Marouani, 2021, 2022). Providing evidence from Labor Force Surveys in the Islamic Republic of Iran, Dang and Salehi-Isfahani (2023) find that the pandemic exacerbated the pre-existing low participation of females in the labor force. Wahby and Assaad (2023), on the other hand, focus on the impact of the pandemic on Syrian refugees in Jordan and find a divergence in job finding and separation rates of Syrian refugees relative to their Jorda- nian hosts after the onset of the pandemic. Focusing on cross-border commuters in the West Bank and Gaza, Adnan and Etkes (2022) find that undocumented commuters benefited relative to their documented peers after the pandemic, as Israeli policies inadvertently created incentives for employers to favor the former. This sharply con- trasts the results by Borjas and Cassidy (2020) on the impact of the pandemic on immigrants in the United States. The rest of this paper is organized as follows. Section 2 provides background information on labor markets in the West Bank and Gaza, as well as background in- formation on the COVID-19 pandemic and government responses. Section 3 describes the data. Section 4 discusses our methodology. Section 5 presents the main regression results and investigates heterogeneous effects. Section 6 provides robustness checks. Finally, we provide concluding remarks in Section 7. 4 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Labor Force Surveys", "high-frequency phone surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 Background Information 2. 1 Characteristics of West Bank and Gaza ’ s labor markets The labor markets of the West Bank and Gaza exhibit features typical of the broader Middle East and North Africa (MENA) region, but also have attributes that are highly unique. Additionally, important differences exist between the West Bank and Gaza. This section provides an overview of these characteristics. We use data from the Labor Force Surveys (LFS) of the West Bank and Gaza and we focus on 20-59 years old men. In Section 3. 1, we provide more information about the data sources and sample selection. We divide each labor market into five mutually exclusive and jointly exhaustive states: public sector employment, private formal sector employment, private informal sector employment, unemployment, and out of labor force. 1 We focus our discussion exclusively on men, as women ’ s labor force participation in both the West Bank and Gaza is very low, never reaching values above 25 %. This low participation rate is common in MENA countries and makes the role of the pandemic on women ’ s labor market outcomes relatively less important than other, more relevant structural factors. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Labor Force Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, permit-holding status is neither sufficient for, nor necessary to, working in Israel. As of 2019Q4, just under a fifth of West Bank residents had the right to work in Israel and the occupied territories, but almost a quarter among them were not commuting across the border for work. The vast majority of such individuals are holders of Israeli or Jerusalem IDs. Conversely, among those who do commute to Israel and occupied territories, 17 % do not hold valid permits or IDs. Likewise, permit-holding status does not logically affect the formality status of the commuter. The frequent border crossings between the West 2This is derived from authors ’ own calculations using the 2016 Jordan Labor Market Panel Survey. 3It is worth noting that the LFS is representative of the residents of the West Bank and Gaza, whose work may not lie in the country. 6 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Bank and Israel and occupied territories strongly suggest that commuters would be differently affected during the pandemic when border closures were enacted. Another unique feature is the presence of a large refugee population in the West Bank and Gaza. However, it is important to note that refugees in this context are de- fined quite differently from other contexts. Here, not only the individuals immediately displaced are considered refugees, but also their patrilineal descendants, even if born many decades later. In particular, the LFS dataset follows the United Nations Relief and Works Agency (UNRWA) definition of refugees, which is “ persons whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948, and who lost both home and means of livelihood as a result of the 1948 conflict, ” as well as “ the descendants of Palestine refugee males, including adopted children ” (UNRWA, 2023). Consequently, most refugees are indistinguishable in socio-economic outcomes and labor market behavior from non-refugees. However, residence in refugee camps does make a significant difference. As of 2019Q4, 5 % of the West Bank ’ s residents live in refugee camps, as do 14 % in Gaza. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "After a short respite in November, Israel imposed a third lockdown from December to February the next year. Gradual easing continued throughout March. During this lockdown, vaccination was rolled out, including to commuters with valid permits. Given this context, we expect the main shock to occur in 2020Q2 in the West Bank, with negative effects of smaller magnitudes to persist over the next quarters. In Gaza, we expect another major shock in 2020Q3 that carries over to Q4. We expect the commuters ’ outcomes to instead track events in Israel and occupied territories more closely, with a major shock in 2020Q2, recovery in Q3, and another dip in Q4. 3 Data 3. 1 Data sources and definitions We employ data from the Labor Force Surveys (LFS) of the West Bank and Gaza, collected by the Palestinian Central Bureau of Statistics and prepared by the Economic Research Forum. The surveys are conducted on a quarterly basis, covering periods from 2000 onwards. The LFS is meant to represent all households whose ordinary residence is in the West Bank and Gaza, though their place of work need not be. The LFS is representative at the region level (respectively of the West Bank and Gaza), as well as at the level of locality types (urban, rural, and refugee camps) (Palestine- Labor Force Survey, LFS, 2021). Importantly for the purpose of our analysis, the data have a panel dimension, en- abling the study of labor market transitions. The sample rotation scheme is described 9 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Labor Force Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "prior to the pandemic, a period characterized by less distinct time trends in the labor market. Since the focus of the paper is on the impact of COVID on labor market stock and dynamics, we also restrict the sample of interest to include only prime-aged working adults (aged 20 to 59). The dataset contains standard variables expected of a labor force survey, including those denoting employment, unemployment, and inactivity. It also contains information on the intensive margin of the labor supply, including hours worked and full-time and part-time status. Information on employment sector, industry, contract status, health insurance coverage, and mode of work (distinguishing between employees and self-employed, for example) is also available, allowing us to construct indicators of formality and to differentiate different modes of employment. Information on occupation is also available, but only at the level of 2-digit ISCO-08 classification. This information is enough to distinguish between white- and blue-collar occupations but it is not enough to observe additional relevant pandemic-related job characteristics such as the degree of contact with the public. 3. 2 Descriptive statistics Figure (1) tracks the evolution of labor market stocks in the West Bank and Gaza respectively over time. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A significant increase in non-participation among men can be clearly seen in the West Bank in 2020Q2, mostly at the expense of the private infor- mal sector. The labor market then quickly bounces back. By 2020Q3, labor market stocks in the West Bank appear indistinguishable from pre-pandemic periods. Gaza, on the other hand, experienced three consecutive quarters of depressed employment from 2020Q2 to 2020Q4. Non-participation spiked twice, first in 2020Q2 and then in 2020Q4, corresponding respectively to the initial lockdown orders and the subsequent outbreak in Gaza. Recovery also appears to be slow and uneven. Figure (3) shows the labor market flows. We exploit the specific panel structure of the LFS dataset, described in Section 3. 1 by focusing on one cohort of the same respon- dents who were surveyed in 2019Q1, 2019Q2, 2020Q1, 2020Q2, and finally 2020Q4. This cohort of individuals allows us to observe labor market transitions into the pan- demic; to compare with a period over the same quarters in 2019; and, finally, to observe their recovery outcomes in 2020Q4. Overall, Figure (3) shows two labor markets with high levels of churning. On average, 29 % of individuals in the sample would change their labor market states after just one quarter. These churns are especially prominent between informal employment and unemployment, and in Gaza between unemployment and non-participation. The figure also illustrates the significant differences between the West Bank and Gaza in labor market dynamics, differences already observed in the labor market stocks presented in Figure (1). In addition to the significant flows between unemployment 11 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["LFS dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the West Bank and Gaza. Out of all the estimated coefficients for each period for both outcomes (job loss and job gain), we only find a negative effect on job gain in 2019Q4, which is very small in magnitude (1 percentage point). Taken altogether, the results presented in this section bolster our confidence that we are correctly identifying the effects of the pandemic shock on labor market outcomes. Figure 12: Placebo effect on labor market flows Notes: The figure shows the output of a placebo test with a set-up analogous to Figures 6 and 9. We perform the same regression as specified in Equation (2). Our sample includes data from 2018Q2 to 2020Q1 and assumes that the pandemic started in 2019Q2. Therefore, the post-pandemic period refers to the quarters between 2019Q2 to 2020Q1. The analysis is restricted to men aged 20-59. 7 Conclusion This paper examines the effect of the pandemic on labor markets in the West Bank and Gaza using quarterly labor market data provided by national labor force surveys. With a focus on men ’ s labor market outcomes, this paper sheds light on how labor markets in the West Bank and Gaza adjusted to the COVID-19 shock examining adjustments at the extensive (employment) and intensive (hours of work) margins. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["national labor force surveys", "quarterly labor market data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "One of the main contributions of this paper is the use of panel data which allows us to examine the effect of the pandemic on labor market transitions — job loss and job gain rates — in addition to the effect on labor market stocks. Studying both stocks and flows provides a comprehensive framework to analyze the impact of the pandemic on labor markets and allows for a better understanding of the underlying mechanisms 30 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1 POST-CONFLICT TRANSITIONS WORKING PAPER NO. 16 Population Size, Concentration, and Civil War. A Geographically Disaggregated Analysis * Håvard Hegre Centre for the Study of Civil War, PRIO (CSCW) Clionadh Raleigh CSCW, PRIO & University of Colorado at Boulder Abstract Why do larger countries have more armed conflict? This paper surveys three sets of hypotheses forwarded in the conflict literature regarding the relationship between the size and location of population groups: Hypotheses based on pure population mass, on distances, on population concentrations, and some residual state-level characteristics. The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events. The analysis covers 14 countries in Central Africa. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper develops a statistical method to analyze this type of data. The analysis confirms several of the hypotheses. World Bank Policy Research Working Paper 4243, June 2007 The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post- Conflict Transitions Project can be found at http: / / econ. worldbank. org / programs / conflict). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Armed Conflict Location and Events Dataset"], "descriptive_data": [], "vague_data": ["conflict event data", "geographically disaggregated data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 population density across a state, the ecological inference issue is alleviated as we directly test the propensity of any population group to experience a conflict. Through disaggregation, we may succeed in supporting explanations based on variables such as the distance from the capital and the overall size of the country's population if we know at which locations conflicts occur. If conflicts are located mainly at some distance from countries'capitals, we might infer that large countries have more conflicts because of the difficulties of projecting governmental power. If they are located in population concentrations irrespective of location relative to the capita, other explanations should be sought. The paper makes use a new dataset called ACLED (Armed Conflict Location and Events Dataset) to allow for this type of disaggregated analysis. The dataset currently codes the location of all reported conflict events in 14 countries in Central Africa in the 1960 – 2004 period. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper suggests some adaptions to a statistical method to allow for analyzing data at this level of analysis. Related to the size of populations is their distributions. The Democratic Republic of Congo, for instance, is not only characterized by being enormously large, but also shows tremendous variation in population densities. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Armed Conflict Location and Events Dataset", "ACLED"], "descriptive_data": [], "vague_data": ["conflict event data", "geographically disaggregated data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 however, since they cannot distinguish between a country where the population is concentrated in one cluster covering 10 % of the territory and one where the population is concentrated in two clusters of 5 % each, but with a considerable geographical distance between them. Population and conflict geography in the Democratic Republic of Congo (DRC) corresponds to these arguments regarding population concentration and dispersion. Concentrations of language-based minorities are evident throughout eastern DRC. Due to the limited access of the government, the close proximity to international borders, and the dense population concentrations, these concentrated minorities have a higher potential of conflict than other, more accessible, sparsely populated areas of DRC. Figure 1 shows the population concentrations in 1990 (CIESIN data) for Central Africa. Heavily populated areas are shaded in deeper tones of red / grey. Civil conflict in DRC has overwhelmingly occurred in the eastern portion of the state, which is the most densely populated area and also geographically peripheral to the capital, Kinshasa. Of the eleven Congolese rebel groups accounted for in the dataset used in this paper, all have operated either exclusively or partially in the eastern and southern areas of DRC. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["CIESIN data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Proposition 7 State-Level Effects of Population Size: The risk of civil war events at a location varies with the size of the population of the country to which the location belongs, controlling for the local effects. 3 Research Design 3. 1 Unit of Analysis To distinguish between the different theoretical statements regarding how population sizes, population concentrations and locations relate to risk of conflict, we need to investigate exactly where conflicts occur. We have created a dataset using a Geographic Information Systems (GIS) program which converted large territories into smaller portions of 8. 6 km x 8. 6 km, totaling 74 square kilometers. Each of these grid squares are our units of observation (we will refer to them as squares). This approach is similar to that of Buhaug & Rød (2006), with two important differences. First, their squares are much larger (100x100km). Second, they code the dependent variable considerably more crudely than is done in the ACLED dataset described below. Buhaug & Rød (2006) use the `scope'and `location'variables in the Uppsala / PRIO dataset. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["ACLED dataset", "Uppsala / PRIO dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Although much more suited to geographically disaggregated analysis than other datasets, this location dataset has some limititations, It does not record changes over time in the center location and extent of conflicts, and it reports the total extent of the conflict zone without distinguishing between areas that saw repeated and extensive fighting and those that only experienced scattered activities or individual events far from the center of the conflict. 3. 2 Disaggregated Dependent Variable: ACLED The ACLED dataset (Raleigh & Hegre, 2005) deals with these problems. The dataset takes the PRIO / Uppsala Armed Conflicts Dataset as its point of departure. The dataset is limited to events within conflicts that fall within the Uppsala conflict definition; conflicts involving two parties, one of which is a government, and fighting resulting in at least 25 battle deaths. 3 ACLED is designed to parse out both the temporal and spatial actions of rebels and governments within civil wars. 3See the PRIO / Uppsala Armed Conflict Data codebook for more information (Strand, Wilhelmsen & Gleditsch, 2004). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["PRIO / Uppsala Armed Conflicts Dataset", "ACLED dataset"], "descriptive_data": [], "vague_data": ["location dataset"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 The fundamental unit of observation in ACLED is the event. Figure 2 illustrates the ACLED data for Central Africa for the 1980s and the 1990s. Each location of a conflict event is represented by a symbol. In several of these locations, multiple events occured over the periods. Events always involve two actors – a rebel group and a government – and are coded to occur at a specific point location and on a specific day. Most of the events are battles, but the dataset also records other activities. The dataset includes information on and distinguishes between six types of events: battles resulting in no change of territory, battles resulting in a transfer of territory to the rebel actor, battles Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 resulting in government forces recapturing rebel held territory, establishment of a rebel base or headquarters, rebel activity that is not battle related (e. g. presence or the killing of civilians), and territorial transfers. The dataset consists of 4, 145 battle events for the 1960 – 2004 period. In the present analysis, we use 2, 530 of these. The remaining events were dropped as they either were in countries not included in the analysis, or because information was missing for one of the key variables. Each conflict event is associated with geographic coordinates and a date of occurrence. This information allows for spatial and temporal modeling of conflict events. The dataset used in this article covers 14 countries in Central Africa. 6 of them had a conflict in the 1960 – 2004 period according to the Uppsala / PRIO Armed Conflict Dataset (Gleditsch et al., 2002): Angola, Burundi, Republic of Congo (Brazzaville), Democratic Republic of Congo (Zaire), Rwanda, and Uganda. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Uppsala / PRIO Armed Conflict Dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The observations of positive events contain more information than the non-event observations We therefore sample asymmetrically: We sample all of the transition events and 1. 0 % of the non-transition events. 3. 4 Disaggregated Independent Variables Local level data on land, population, and elevation is available in the geospatial format of raster files with a resolution of 1km. Using Geographic Information systems (GIS), attributes from raster and point data are associated with the grid square in which they lie. In this way, spatial data is georeferenced to a location that is defined by the grid cell. This process results in a data structure in which each row has within it combined information on a square defined by the grid, the national level information in which is it located, and () () () ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ ∑ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ = = ∉ = t X t X t d d j j p j R i d j j p j w w t β β 1 1 exp exp at out breaks war a | square a in war Pr Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 the local data on physical geography and population from the raster data. These data can then be imported into statistical programs for analysis. We aggregate all data up to a grid of 8. 6x8. 6km squares. Each grid square is assigned attributes of the country it is in along with information from data disaggregated to the level of the individual squares. Figure 3 illustrates this grid as a fictive country somewhat smaller than the average size in our dataset (50x50 squares, or 430x430 km) with a fairly representative but stylized population distribution. The country has three major cities, one of which is the capital, and two smaller ones. A rebel group has its headquarters at the Eastern border. The ACLED data for the Central African conflicts were aggregated up to the 8. 6x8. 6km squares and merged with information on other explanatory variables aggregated to the same level. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["ACLED data"], "descriptive_data": [], "vague_data": ["raster data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Distance from Rebel Group Headquarters We coded the location of the headquarters of the rebel groups participating in the conflicts under study, and calculated the distance from each square to the most proximate rebel group headquarters (we do not know a priori which rebel group or government that will act in a particular square). As the `distance from capital'variable, it was coded as the distance in terms of squares and log-transformed. Border Square We coded squares as border squares if a national border runs through it. Such squares belong to more than one country and are not straightforward to code. We coded national- level information for border squares according to the following rule: A border square was considered to belong to the country that was most frequent among the eight neighboring squares. In tie cases, we assigned nationality randomly between the tied countries. Interaction country-square population This variable was created to test the population settlement pattern hypothesis. It is an interaction between population count at a location (square) as a portion of the country's total population. Road type Road type is a variable by ESRI that is available in the Digitial Chart of the World Data. It is a high resolution dataset at 1: 1, 000, 000 scale and consists of arcs which indicate road mass. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A number of different road indicators are available and we choose road line type to use in the analysis. Road type is defined by the following: The reference category (0) points out squares with dual lane / divided highways, other primary roads, or road connectors within urban areas (types 1 or 8 in the ESRI dataset). The second category include secondary roads (type 2), and the third combines squares with informal or tertiary roads (tracks, trails or footpaths) or no road registered at all (types 3 and 0, respectively, in the ESRI dataset). Figure 4 overlays the types of roads in the original dataset before our recategorization. The shaded area represents the portion of Africa for which we code Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["ESRI dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 7253 This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at jlendorfer @ worldbank. org and jhoogeveen @ worldbank. org. This paper analyzes the impact of the 2012 crisis in Mali on internally displaced people, refugees and returnees. It uses information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["face-to-face household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 trust in the government and its institutions and perspectives on conflict resolution. By analyzing the impact of the crisis on welfare, the consequences of returning home versus remaining in displacement and by comparing immediate with longer term impacts, this paper contributes to the literature on refugee, IDP and returnee populations. The paper combines data from a face-to-face baseline survey with information collected via mobile phone interviews from respondents identified during the baseline. This innovative approach to data collection makes it possible to collect welfare data with high frequency (monthly) – important in a volatile crisis situation �� and allows measuring changes over time. It also permits following displaced and refugee households once they return, even if they return to areas that are inaccessible to enumerators. The remainder of this paper is organized as follows. Section 2 provides a brief overview of the methodology, the sample and sample selection. Section 3 discusses the characteristics of the displaced and returnees, looking specifically at ethnic composition, place of origin, household size, education, asset ownership and employment status. Section 4 considers how the crisis affected food consumption, employment, assets and school attendance. Section 5 is devoted to the specificities of returnees who turn out to be, on aggregate, less affected by the crisis and better off than IDPs or refugees. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["face-to-face baseline survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Section 6 presents the differing views of IDPs, refugees and returnees on what happened during the crisis and prospects for peace. Section 7 concludes the paper. 2. The Listening to Displaced People Survey The Listening to Displaced People Survey (LDPS) combines a baseline face-to-face survey with mobile phone follow-up interviews. During the baseline survey respondents were identified and information on household and respondent characteristics was collected. Once the baseline interview was completed, respondents were given a mobile phone and started to receive, at monthly intervals, phone interviews from a call center in Bamako. During these phone interviews structured questions were asked about welfare of the household. Phone interviews are standard practice in developed countries and they are increasingly being used in less developed countries, as the coverage of cell phone networks expands. Not only do these kinds of surveys allow for low cost, high frequency representative data collection (Hoogeveen et Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 al. 2014), mobile phone surveys also turn out to be remarkably flexible and adaptive. New questions can be introduced on a needs basis and in-depth qualitative interviews can be carried out at a moment ’ s notice. These qualities make mobile phone surveys well suited for monitoring welfare in volatile environments: they have been used for welfare monitoring during the ebola crisis in Liberia (Himelein 2014) and for welfare monitoring in conflict-affected areas such as South Sudan (Demombynes et al. 2013). Unique about using a mobile phone survey with a displaced, mobile population is that it allows tracking welfare during displacement, and upon return. 8 Three target populations were identified for the purpose of this survey: Internally Displaced Persons (IDPs) living in Bamako, refugees in refugee camps in Mauritania and Niger, and returnees in Gao, Timbuktu and Kidal, the capitals of regions that bear their names. The sample does not include those who were not displaced by the crisis nor those who returned to places other than the three regional capitals in the North. While the sub-sample of IDPs includes exclusively IDPs in Bamako, and the refugee sub-sample only refugees in Niger and Mauritania, the returnee group includes people who were displaced elsewhere (33 %). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 implemented in six areas: Bamako, the regional capitals of Gao, Timbuktu, and Kidal as well as one refugee camp in Mauritania and one in Niger. Bamako was selected because it is home to a large number of IDPs. The refugee camps were selected to obtain a sample of refugees. Returnees were identified in the regional capitals of Timbuktu, Gao and Kidal where the phone network was (still) functional. The approach to selecting respondents differed by location and depended on the availability of pre-existing population information.  Bamako: Listing information of all households with IDPs was obtained from the International Organization for Migration (IOM). Based on this data 10 districts were selected and in each district 10 households were randomly identified.  Gao, Timbuktu and Kidal: No listing data was available and the cities were divided into different sectors. The enumerator was assigned a starting point in a sector, a direction (North, South, East, West) and based on the code of the day 9 the enumerator selected the first household. If the code of the day was 4, the enumerator would choose the 5th house to conduct the first interview. No more than 6 houses were to be interviewed from one starting point. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 All data collection is done by GISSE a research institute in Bamako. The anonymized unit record data of the baseline and the monthly surveys can be downloaded from www. gisse. org. The response rate for the phone interviews has been very high (Table 1): after 6 rounds of monthly interviews the original sample is almost entirely intact. The low level of attrition demonstrates that mobile phone samples can be maintained over prolonged periods without being unduly affected by (non-random) respondent drop-out. 3. Characteristics of the Displaced and Returnee Population According to the 2009 population census, the two most sizeable ethnic groups in northern Mali are the Songhai (45 %) and Kel Tamasheq (32 %)-- see Table 2. The crisis brought about an ethnic divide, which is reflected in the composition of the three sub-samples. The majority of IDPs and returnees are Songhai (75 % and 71 % respectively), while the majority of refugees are Kel Tamasheq. Results suggest that the decision of where to flee was determined by ethnicity: Kel Tamasheq and Arabs left the country; Songhai fled towards Bamako. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["2009 population census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 2: Ethnic composition of IDPs, refugees, returnees in the North Ethnicity IDPs in Bamako (%) Refugees Niger (%) Refugees Mauritania (%) Returnees (%) Total I + R + R (%) Ethnic composition of the North (%) Songhai 75 21- 71 43 45 Kel Tamasheq 12 56 69 12 38 32 Arab 3- 28 4 11 3 Peulh 4 21- 6 4 7 Other 6 11 3 7 4 12 Total (%) 100 100 100 100 100 100 Total (n) 100 81 100 220 501 1, 268, 009 Source: Listening to Displaced People Survey, 2014 and 2009 Population and Housing Census. The ethnic composition of IDPs and returnees is almost identical. This is a reflection of the fact that 94 % of returnees were displaced within Mali. Only 6 % returned from outside the country. The reason why few returned refugees are in the returnee sub-sample is explained by their place of residence prior to the crisis: only 5 % of the refugees in Mauritania and Niger lived in Timbuktu town before their displacement; 2 % lived in Gao town and 1 % in Kidal town. The remaining 92 % lived in 27 different towns and villages in northern Mali, locations not covered by the survey. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Displaced People Survey", "Housing Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Figure 3: Level of education age 25 +; compared with regional average (%) (*) INSTAT refers to census 2009 Source: Listening to Displaced People Survey, 2014. With regards to ownership of consumer durables, IDPs, refugees and returnees were better endowed than the average citizen of the North (see Figure 4). As was the case for education, they are more comparable to the average citizen in Bamako than to the average citizen in the regions of Gao, Timbuktu and Kidal. Figure 4: Asset ownership compared with regional average Source: Listening to Displaced People Survey, 2014 and EMOP 2011 (INSTAT). The main occupation of IDPs, refugees and returnees before the crisis was commerce (Table 5). This held for over half of the IDPs, 37 % of refugees and 34 % of returnees. 18 % of the refugees 51 85 60 47 85 89 87 15 6 18 29 11 8 8 34 9 22 25 5 3 5 IDPs Refugees Returnees Bamako (INSTAT) Gao (INSTAT) Timbuktu (INSTAT) Kidal (INSTAT) Secondary or Higher Primary None 0 100 200 300 400 500 600 IDPs Refugees Returnees Bamako (Instat) Gao (Instat) Timbuktu (Instat) Kidal (Instat) Percentage Mobile Phone Car / Motorized Vehicle Motorbike / scooter Bicycle Refridgerator TV CD Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Displaced People Survey", "Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 displacement. 55 % of the returnees reported to have been employed before the crisis and 36 % in June 2014. Over time the employment situation among the displaced has improved steadily and by December 2014 more people reported being employed than prior to the crisis. All the returnees were able to regain employment after returning. The employment situation of IDPs, returnees, and refugees in Niger is steadily improving; only for refugees in Mauritania does one notice a steady decrease, with 100 % reporting no employment during January and February. Source: Listening to Displaced People Survey, 2014 and 2015. The ownership of livestock and consumer durables was reduced significantly as a consequence of the crisis. Table 7 demonstrates this by showing the Tropical Livestock Units (TLU) 12 owned prior to the crisis and in June 2014 as well as the percentage of ‘ yes ’ responses on a question whether a given asset was owned by the household. 13 The loss on livestock has been enormous particularly amongst IDPs and refugees who lost respectively more than 90 % and 75 % of their animals. 12 TLU is a common unit to describe livestock numbers of various species as a single figure that expresses the total amount of livestock present – irrespective of the specific composition. 13 This was a ‘ yes / no ’ question meaning that if 56 % of the Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 Figure 10: Children aged 7-12 attending school (%) Source: Listening to Displaced People Survey, 2014 and 2015. 5. Challenges Faced by Returnees The results suggest that returnees were less affected by the crisis than IDPs and refugees. This is reflected in data on asset and livestock ownership, but also in the information pertaining to exposure to violence. Returnees reported fewer victims; fewer returnees reported to have lost income as a consequence of the crisis; more of their children were able to continue schooling; and relative to IDPs and refugees, fewer perceived being poorer in June 2014 than before the crisis. Returnees are also the group that feels most secure, that has high levels of trust in the Malian army and police and that has a positive attitude towards most government policies. 88 88 97 79 76 78 99 88 92 55 74 72 91 98 92 96 100 85 90 91 96 90 86 87 87 95 89 76 75 90 94 92 97 86 73 98 Bamako Gao Timbuktu Kidal Niger Mauritania IDPs Returnees Refugees 14-Aug 14-Oct 14-Nov 14-Dec 15-Jan 15-Feb Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 Figure 11: Timing of Return (%) Source: Listening to Displaced People Survey, 2014. 94 % of the returnees were displaced inside Mali and 6 % outside the country. 67 % went to Bamako, 11 % in Koulikoro, 9 % to Ségou, 5 % to Mopti and 3 % went elsewhere (Kidal, Gao and Sikasso). The majority returned between June and October 2013 a period that followed the signing of a peace deal between the interim government and rebel factions to allow presidential elections to be held in July (first round) and August (second round) 2013. In October security in the North worsened again and ever since the number of people returning has been very limited. The main challenges reported by returnees in June 2014 were (i) poverty and food insecurity; (ii) lack of infrastructure (including lack of safe drinking water) and (iii) unemployment. 11 % of the returnees stated not to be facing any challenges (Figure 12). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Results suggest that 66 % of the returnees trust the Malian police and army most when it comes to providing security in the North. Almost half believe that the Malian army is brave and well trained. The vast majority of returnees believe that the government ’ s policies regarding reconciliation, security and social cohesion are good or very good. They also support the government ’ s approach towards decentralization and providing infrastructure such as access to potable water and electricity. As the next section will illustrate this differs strongly with the opinions of refugees. 6. Prospects for Peace IDPs, refugees and returnees have comparable opinions with regard to the requirements for peace: (i) addressing the ongoing crisis, (ii) improving security and (iii) reconciliation. Although there is agreement on what needs to be done, there is little consensus on what happened during the crisis, who the culprits are and who the main victims. Figure 15: What is the most important problem the Government needs to resolve today? (%) Source: Listening to Displaced People Survey, 2014. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "23 86 % of the IDPs, 91 % of the refugees and 88 % of the returnees are confident or fully confident that a coalition like this would be capable of providing security. Source: Listening to Displaced People Survey, 2014. In an open-ended question on who can be trusted most when it comes to ensuring security in the North, survey results suggest that the majority of refugees in Mauritania (86 %) trust the armed rebel groups as opposed to the army or police. This does not hold for refugees in Niger of whom 75 % trust the army and police. Similar results hold for IDPs and returnees, who put much more confidence in state authorities when it comes to securing the North: most trust is placed in the army and police (72 % of the IDPs and 66 % of the returnees) while little to no trust is placed in armed rebel groups (3 % of IDPs, 1 % of returnees). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "25 less cohesion. The increase in solidarity is less apparent among refugees where the majority (53 %) stated that the crisis had no effect on solidarity. Figure 18: Levels of trust, by group (June) (%) Source: Listening to Displaced People Survey, 2014. Perceptions that different groups have of others are important elements of peace. When asking for the degree to which neighbors, other villagers and people from other ethnic groups can be trusted the survey finds positive outcomes. Although all groups trust people from other ethnic groups slightly less, the general level of trust is high and it remains stable over time. Finally, consider how IDPs, refugees and returnees envision the future of Mali. The majority of refugees in Mauritania vie for an independent or autonomous North, while the majority of IDPs, returnees and refugees in Niger wish to see full government control over the North. 20 20This contradicts, in part, findings of an Afrobarometer perception survey on causes and consequences of the conflict in Mali conducted in December 2013. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Displaced People Survey", "Afrobarometer perception survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "26 Source: Listening to Displaced People Survey, 2014. 95 % of the refugees in Mauritania envision an independent or autonomous North and 26 % of the refugees in Mauritania even state the independence of Azawad (= the north) as a main condition for returning home. Stark differences can also be observed with regard to the discussion around a possible federalist solution for the North that was ongoing when the monthly phone interviews were conducted in October. As illustrated in the Figure 20 below, 80 % of the refugees in Mauritania support a federalist solution, while the majority of IDPs, returnees and refugees in Niger are not in favor. Of those who do not support a federalist solution (96 % of the IDPs, 88 % of the refugees in Niger and 95 % of the returnees), the majority of IDPs (61 %) and returnees (70 %) as well as 38 % of the refugees in Niger suggest decentralization as a possible solution to resolve the conflict. 13 % of the refugees in Niger also mention war and 27 % the integration of the North. Nonetheless, 49 % of IDPs, 86 % of returnees and 89 % of refugees believe a stable and sustainable peace accord can be achieved. by 4 % of the population living Timbuktu, 2 % in Gao and nobody in Kidal. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Afrobarometer finds that the vast majority of Malians want their country to remain a single and unified nation and that the attempt by armed groups to create a breakaway state in Mali ’ s northern territories is decisively rejected. See Afrobarometer Policy Paper 10 (Dec 2013). This difference with the Afrobarometer survey can be explained by the fact that the latter survey only focused on Malians inside the country and did not take the views of refugees into account. 5 93 2 6 86 75 20 2 3 94 Independence of the North Autonomy of the North Establish full government control over the North Figure 19: How do you envision the future of Mali? IDPs Refugees Niger Refguees Mauritania Returnees Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "27 Source: Listening to Displaced People Survey, 2014. 7. Conclusion The 2012 crisis in northern Mali led to widespread displacement. The welfare of the displaced – and those who returned – is monitored by combining a baseline survey with structured monthly follow-up interviews carried out by mobile phone. This innovative approach allows tracking changes in welfare with high frequency – even for those who returned to areas that are insecure and inaccessible to enumerators. After 6 rounds of follow-up interviews attrition rates are very low (more than 99 % response rate), demonstrating that it is possible to collect robust and representative data from hard-to-reach, conflict-affected populations. The results show that those who fled were better educated, better off and less affected by violence than the average population in the North. Those who fled lost significant amounts of durable goods (20-60 %) and livestock (50-90 %); many of their children ended up being taken out of school and their welfare (measured subjectively and by the number of meals consumed) declined considerably. Over time, the impact of the crisis on welfare has lessened and by February 2015 the majority of eligible children of the displaced were going to school and levels of employment and number of meals consumed were at pre-crisis levels. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the degree to which the global response should include a development element. We find that the average stood at around 10. 3 years at the end of 2015, with a median duration of 4 years, and significant sensitivity to a few situations. Such numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete protracted situations. To produce these numbers, we rely on the Population Statistics Database compiled and main- tained by UNHCR. The database records the number of “ persons of interest ” to UNHCR in each year since 1951 and for each situation, where a situation consists of a pair host-origin countries. The calculation of duration of exile is obtained under a no-turnover assumption, whereby a de- crease in the number of refugees for any given situation is fully attributed to exits from refugee status, while increases are assumed to be fully accounted for by new cases. Although such ap- proach tends to over-estimate the true duration of exile, the lack of individual-level data on regis- tration precludes refining the estimate further. Attempts to estimate similar statistics have been limited. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Population Statistics Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In a 2004 note to its Executive Com- mittee, UNHCR established the average at 17 years at the end of 2003 (Executive Committee of the High Commissioner ’ s Programme 2004). This number has been widely quoted by media, ac- tivists, humanitarian agencies, and development institutions (Milner 2014; United Nations 2016; UNHCR 2015). The rest of the paper is organized as follows. Section 1 gives some definitions and background information on the refugee population. In section 2, we provide some summary statistics from our main source of data, the UNHCR Population Statistics Database. Section 3 describes the method followed to construct duration statistics and presents a few stylized facts. The results of our anal- ysis are presented in section 4. Section 5 concludes. 1 Background: Definitions and Data Under the terms of the 1951 Convention Relating to the Status of Refugees – henceforth the Convention – later amended by the 1967 Protocol, a refugee is a person, who “ owing to a well-founded fear of being persecuted for reasons of race, religion, nationality, membership of a particular social group or political opinion, is outside the country of his nationality, and is unable to, or owing to such fear, is unwilling to avail himself of the protection of that country. ” Data on refugees and asylum seekers are collected by individual countries, international orga- 3 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR Population Statistics Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "nizations such as UNHCR, and national and international non-governmental organizations. Data is compiled from a number of sources, including but not restricted to individual registration of refugees and asylum seekers (information typically includes name, gender, date of birth, country of origin, marital status, and place of displacement), tracking of population movement in situa- tions where the movement is fluid or continuous, standardized surveys such as Living Standards Measurement Study (LSMS) surveys, Labor Force Surveys (LFS), Demographic and Health Sur- veys (DHS), and Multiple Indicator Cluster Surveys (MICS), administrative records and registries. Yet, data collection is a difficult exercise, due to both methodological issues (UNHCR 2014) and practical challenges, especially in situations of heightened insecurity or mass refugee situations. To date, UNHCR maintains the most comprehensive statistical database under a uniform methodology. UNHCR publishes annual data on refugee flows and stocks by countries of resi- dence and origin dating back to 1951, shortly after the Office was established. UNHCR publishes annual statistical reports ranging from “ Global Trends ”, “ Mid-year trends ”, “ Asylum trends ”, to a “ Statistical Yearbook ”. There is a consensus that these data provide the most reliable source of information (Sarzin 2016). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Labor Force Surveys", "Living Standards Measurement Study", "Multiple Indicator Cluster Surveys"], "descriptive_data": ["individual registration of refugees and asylum seekers"], "vague_data": ["administrative records"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our analysis exclusively uses data from the 1951-2015 UNHCR Population Statistics Reference database (extracted September 18 2015). Data were provided for 173 countries: 77 percent of these data were based on individual refugee registration, 13 percent on estimates, 5 percent on combined estimation and registration, and 5 percent on other sources. The data are structured as follows: for each situation, the database records annual numbers of persons of concern, which comprise “ Refugees (including refugee-like situations) ”, “ Asylum seekers ”, “ Internally Displaced Persons ”, “ Returnees ”, “ Stateless ” persons, and “ Others of concern ”. A situation is a pair country of origin / country of destination. For example, Somali refugees in Kenya account for one situation, Somali refugees in Ethiopia for another, and South Sudanese refugees in Kenya for yet another. Furthermore, a situation is considered major if it involves more than 25, 000 people. It is referred to as protracted if it is major for at least 5 continuous years. The database, and therefore our analysis, is limited to refugees under UNHCR protection. It does not include asylum seekers, i. e. individuals who have sought international protection under the 1951 Convention but whose claims for refugee status have not yet been determined, and persons in “ refugee-like situations ”, i. e. individuals outside their country or territory of origin who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained (e. g., undocumented Rohingya originating from Myanmar 4 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["UNHCR Population Statistics Reference database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "in Bangladesh). The data set we use does not cover the 5. 1 million Palestinian refugees who are under the man- date of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UN- RWA). The definition of Palestinian refugees in international law is distinct from other refugees. Palestinian refugees are people “ whose normal place of residence was Mandatory Palestine be- tween June 1946 and May 1948, who lost both their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict ”. Importantly, their patrilineal descendants are also considered refugees regardless of citizenship (UNRWA 2009). For the purpose of the analysis, we do not include Internally Displaced Persons (IDPs), who are defined as “ persons who have been forced or obliged to flee or leave their home or place of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disas- ters, and who have not crossed an internationally recognized border ” (United Nations 2004). The categories “ returnees ”, “ Stateless ”, and “ Others of concern ” are also not included. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This allows us to determine the earlier possible date of arrival for each cohort of current refugees. As an illustration, we go back to the Somalia-Kenya situation. In 2015, the number of Somali refugees in Kenya amounted to 418, 844 persons. The first year such number was reached was in 2011. The no-turnover assumption implies that all current refugees have been in exile for 3 years. But in 2010, the number of refugees was 353, 208 persons. We conclude that the difference between 2010 and 2011 represents 74, 104 new refugees in 2010, who have hence been in exile for four years. In 2009, the number was at 310, 458: the difference between 2009 and 2010 (42, 750) are therefore people who have been in exile for five years, etc. We repeat the exercise for each year until the beginning of the crisis in 1991. This gives us a num- ber of new arrivals for each year since 1991. On this basis we can calculate average and median durations for this situation. Next, we aggregate all situations and consider one single “ global refugee population ”. We have for each year a number of people who arrived in a variety of situations and make up a global flow. We can hence calculate global average and median durations. For each year, we can also break down the flow across countries of arrival. We can construct similar lower-bound envelopes starting from any “ current year ” which we choose as a reference point. For example, we can apply the same protocols to evaluate average and median durations as of 1994 (the lower-bound envelope is depicted by the dash-dot line in Figure 3). This allows us to follow the variation of aggregate mean and median averages over time. 10 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The author may be contacted at asacks @ worldbank. org. and managing these resources. The author assesses these competing hypotheses using multi-level analyses of Afrobarometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows for analysis of associations between donor and non-state actor service provision and the sense of obligation to comply with the tax authorities, the police and courts. The findings yield support for the hypothesis that the provision of services by donors and non-state actors is strengthening, rather than undermining, the relationship between citizens and the state. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "reason to believe that the provision of services by donors and non-state actors could either strengthen or weaken citizens ’ deference to government. I will identify the conditions under which these two scenarios are likely to occur. On the one hand, citizens may be less likely to support the government with deference to its laws and regulations when they credit non-state actors or donors for service provision. The provision of services by donors and non- state actors is likely to prompt citizens to question why they should pay taxes to a government that is not providing them with anything in exchange. On the other hand, the provision of goods and services by donors and non-state actors might strengthen citizens ’ legitimating beliefs and their willingness to defer to governmental laws and regulations if citizens view their government as essential to leveraging and managing these external resources. I assess these competing hypotheses using multi-level analyses of Afro- barometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows us to analyze associations between donor and non- state actor service provision and the sense of obligation to comply with the tax authorities. Third, I assess the relationship between the provision of ser- vices by donors and non-state actors and citizens ’ willingness to defer to two additional authorities, the police and courts. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afro- barometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "impression of the state ’ s role in development and give credit to the state for helping to leverage external resources. Citizens are also likely to give the state credit where mechanisms to voice complaints about non-state actors exist and where bureaucrats are able to effectively respond to complaints. Under these conditions, non-state service provision is likely to strengthen the fiscal contract. 5 Data and Methods I explore the relationship between external service provision and deference to government using Afrobarometer survey data from 19 Sub-Saharan African countries (see Table 1). Africa is an especially good place to examine these issues because of the large amount of variation both within and across African countries in the extent to which non-state actors, donors and other states are active in service provision and the extent to which governments are relatively effective and fair. Government responsiveness, corruption and reliance on non-public resources vary considerably among localities with consequences for citizen understanding of and relationship to government (Gibson and Hoffman, 2005). This project relies on the fourth round of Afrobarometer data that surveys Africans ’ views towards democracy, economics, and civil society with random, stratified, nationally representative samples. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In 2008, trained enumerators conducted face-to-face interviews in local languages with 26, 513 respondents across 19 countries. 5 The sample is designed as a representative cross-section of all citizens of voting age in a given country. The dataset used for this paper has a multilevel structure; individuals are nested within primary sampling units (PSUs), which are nested within countries. The PSUs are the smallest, well-defined geographic units for which reliable population data are available and they tend to be socially homoge- nous, thereby producing highly clustered data. In most countries, these will be Census Enumeration Areas (Afrobarometer, 2005, 37-38). Although re- spondents were not sampled based on their ethnic affiliation, there is likely to be a high level of clustering in the dataset around ethnicity. In other work, I discuss the advantages of multilevel modeling (Levi and Sacks, 2009). Treating the dependent variable as a binary outcome and taking into account the multilevel nature of our data, I estimate random intercepts for 5I excluded Zimbabwe from the analysis because of missing data on key variables. 9 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "obtained from the multilevel logistic model. Thus, I am confident that I did not lose substantial statistical information by dichotomizing this variable. 6. 2 Independent Variables 6. 2. 1 Socio-Demographic Variables I control for standard socio-demographic variables that can affect citizens ’ acceptance of government ’ s right to make people pay taxes. A question prob- ing respondents on their household income was not included in the fourth round of Afrobarometer surveys. Asking respondents to quantify their in- come can be problematic in the context of developing economies, where in- dividuals are often embedded in barter or commodity exchange, rather than, market economies. There are, however, reasonably good proxies including whether respondents own a television, radio, car, and mobile phone, and use the internet. Age, education, employment, and urban or rural residence are demographic factors that also affect household resources. 6. 2. 2 Experience with Paying Taxes or Fees It is difficult to assess just how ubiquitous taxes are in ordinary Africans ’ lives. There has not been any systematic effort to take stock of the types and amount of taxes citizens pay across Africa. Similar to pre-modern European states, African states ’ revenue raising capacity is generally low. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Afrobarometer surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, I include a measure of whether citizens believe that a large portion of tax administrators is corrupt. Second, I in- clude a variable indicating whether citizens approve of how well their local government is handling the collection of license fees on bicycles, carts and barrows. 8 Third, both the size of a country and the size of the government may affect a government ’ s ability to detect and punish evaders. I include the 7I also include a country-level indicator of government performance, the World Bank Governance indicator of government effectiveness, in the model. This indicator measures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implemen- tation, and the credibility of the government ’ s commitment to such policies (Kaufmann, Kraay and Mastruzzi, 2006, 4). This variable is not significant at the p <. 05 level. 8I included two additional measures in the model neither of which were significant at the p < 0. 05 level. One is a measure of citizens ’ approval of how well their local government council provides citizens with the information about the councils budget (i. e. revenues and expenditures). The other, the World Bank governance indicator, control of corruption, measures the extent to which public power is exercised for private gain, as well as capture of the state by elites and private interest (Kaufmann, Kraay and Mastruzzi, 2006, 4). 13 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["World Bank Governance indicator"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "natural log of a country ’ s population size and government consumption as a share of GDP; the latter is the most widespread measure of government size (Adsera and Boix, 2002; Alesina and Wacziarg, 1998; Rodrik, 1998). Tax compliance is also related to the government ’ s ability to effectively detect and punish tax avoiders, tax evaders, and tax arrears. Although an imperfect measure of states ’ deterrent capacity, Afrobarometer includes two survey questions on perceptions of government enforcement and monitoring capacities. One question probes respondents on how often ordinary people who break the law go unpunished. The other probes respondents on how often officials who commit crimes go unpunished. This latter question is also a measure of perceived government fairness- the extent to which a government implements the law evenly across all social groups. 6. 2. 5 Procedural Justice I include two indicators of procedural justice. The first probes respondents on how often people are treated unequally under the law. The next taps citizens ’ perceptions of the government ’ s treatment of their ethnic group. Specifically, respondents were asked how often their ethnic group is treated unfairly by their government. 9 6. 2. 6 Donor and Non-State Actor Provision of Services I include a measure of who citizens believe is responsible for collecting income taxes. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "I expect citizens who perceive tax collection to be the responsibility of non-state actors to be less likely to be willing to pay taxes to the state ’ s tax department than citizens who perceive tax collection to be the responsibility of the state. Perceptions of the effectiveness of donor and non-state actor provision of services are assessed using the following items. Respondents were probed on how much they believe the following non-state actors and donors do to help their country: the United Nations; international donors and NGOs; international businesses and investors; China; and the United States. 9 I also include Freedom House ’ s political liberties and civil rights ratings for the 19 countries in the sample. These two variables should capture the relative equality of influence in making policy. They indicate whether citizens are able to express their voice without fear of repression and whether elections are free and fair. Neither of these variables are significant at the p < 0. 05 level. 14 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, there is reason to believe that better service delivery may affect citizens ’ willingness to defer to the tax department only through its effect on improved outcomes that matter for citizens ’ livelihoods. Unless improved services and infrastructure have a positive impact on citizens ’ welfare, indi- viduals are unlikely to credit the government for these outputs (Sacks and Levi, 2010). The Afrobarometer ’ s objective measures of service delivery only denote the presence or absence of infrastructure and services. The data do not indicate the condition of the services and infrastructure. Citizens may perceive and reward relative improvements or sanction de- teriorations in services, rather than the absolute level of service quality they receive. If services deteriorate or improve, taxpayers may alter their beliefs about governments ’ performance and should attempt to adjust their terms 10I also tested whether there is a relationship between the presence of a concrete road, health clinic, post office and electricity grid in the enumeration areas and respondents ’ willingness to pay taxes. None of these objective indicators except for the presence of an electricity grid were significant at the p < 0. 05 level. The presence of an electricity grid is negatively associated with the willingness to defer to the tax department. 17 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of trade with government. Because the dataset used for this study is cross- sectional rather than longitudinal, I am unable to examine whether citizens adjust their beliefs and behavior to relative changes in service delivery. I was only able to test whether there are associations between the absolute service quality across neighborhoods, countries and ethnic groups and deference to the tax department, police and courts. Further, citizens may not be attributing goods and services to the gov- ernment. Rather, citizens may be attributing goods and services, such as roads, electricity grids, sewage systems, health care and education to vari- ous non-state actors including the following: the private sector; NGOs and community-based groups; churches, mosques and other religious institutions; traditional leaders; and, bilateral and multilateral donors. Survey questions on the Afrobarometer only indicate the presence or absence of services and infrastructure, and the quality of these services, but these questions do not probe respondents on who they believe are providing these services. Each of the indicators of perceptions of government performance is sig- nificant at the p < 0. 05 level. Food security is positively associated with a willingness to defer to the tax department. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "appear to be a relationship between perceptions of the helpfulness of donors and non-state actors and the willingness to defer to the police and to the courts. Individuals who believe that donors and non-state actors exert too much, rather than, too little influence over one ’ s government is associated with the willingness to defer to the court and to the police. Findings also suggest that citizens who believe non-state actors are responsible for provid- ing law and order are less likely to be willing to defer to the police and to the courts than respondents who believe the state is responsible for providing law and order. 7. 4 Conclusion This paper demonstrates that the logic of the fiscal contract is relevant to a wide variety of contemporary African states. Findings from a cross-national analysis of survey data from Africa link citizens ’ legitimating beliefs — in- dicated by a willingness to defer to the tax department, the police and the courts — to a government ’ s fulfillment of a fiscal contract. Citizens who are satisfied with their government ’ s provision of services and goods are more likely to be willing to defer to the tax department, courts and police than citizens who disapprove of government service provision. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 3. Data The data used in this paper have been collected through the Listening to Displaced People Survey (LDPS). 4 The baseline face-to-face interviews were executed between June and August 2014. The following 12 monthly interviews – from August 2014 until August 2015- were conducted using mobile phones. 5 The original sample comprised 501 respondents (51 % Male, 49 % Female) and was divided between internally displaced people (IDPs) located in the capital city Bamako, 6 refugees living in refugee camps in Mauritania and Niger, as well as returnees living in the regional capitals Gao, Timbuktu and Kidal in Northern Mali. This survey did not collect information on individuals who were never displaced. The attrition rate was very low, always around 1-2 % per wave. We need to stress that the locations were not randomly selected. Bamako was selected because it hosted a large number of IDPs. Furthermore, the main cities in the north of Mali were chosen to obtain a large sample of returnees given the funds available. Finally, a refugee camp was located in Niger since bureaucratic issues did not allow the inclusion of a camp in Burkina Faso. Nevertheless, households were selected randomly within each location. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Having returned (early) to Northern Mali did not always lead to a stable condition either: the transition probabilities from returnee to IDP or to refugee are 1. 1 % and 0. 2 % respectively. As we can see from Figure 1, the majority of the sample is Songhai and Kel Tamasheq (almost everybody identified themselves as Muslim). There are clear differences in migration decisions between ethnic groups. The reaction of most of Arab and Kel Tamasheq origin was to leave the country, while most Songhai people preferred to go south, to Bamako, or, by the time of our survey, had already returned to Northern Mali. In fact, as pointed out in (Etang-Ndip et al., 2015), IDPs and returnees have a similar ethnic composition because 94 % of returnees in our sample were IDPs. Far fewer returnees in the sampled cities of Gao, Tombouctou and Kidal returned from refugee camps in the neighboring countries for the simple reason that most refugees used to live in towns and villages outside the regional capitals of Northern Mali. Displaced Refugee Returnee Total Tamasheq Arab Songhai Peulh Bella Other Analytic weights used Source: LDPS 2014-15 Figure 1: Ethnic composition Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["LDPS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 5. 3 Desire to return (Y / N) Keeping our attention on refugees and IDPs, we wanted to deepen our understanding about their migration plans. In particular, we would like to discover which characteristics are associated with the desire to return to Northern Mali. To this end, we estimated a probit model using the same regressors as in the previous sections. The dependent variable was set equal to one when the respondent was considering the possibility to eventually go back to the North, zero otherwise. The estimated marginal effects have been reported in Table 3 for all respondents (Column 1-2), as well as for only the household heads or their spouses (Column 3-4). The strongest predictor of a planned future return was refugee status: individuals living abroad in refugee camps were up to 25 percentage points more willing to go back than IDPs. Joining this result with those on unemployment presented in the descriptive statistics, we may wonder whether this desire to go back home may have resulted from a more general malaise experienced by these respondents forced to migrate and halted in a limbo not fully integrated with the local community and labor market. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["descriptive statistics"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, another regressor indicates that security may still be pivotal: those who owned a weapon were up to 30 percentage points more likely to plan to go back. In addition to this, it is quite surprising that, if the respondent thought that the Northern Mali crisis was improving, he or she was less likely to plan a return to that area. From a technical point of view, we should point out that we have used an LPM even if the dependent variable was a binary outcome. This choice has been made since in this linear model it is straightforward to add fixed-effects. Furthermore, the coefficients can be interpreted as average partial effects. A simple logit or probit model would not have allowed the inclusion of individual fixed-effects because of the incidental parameter problem. An alternative approach would have been to estimate a conditional logit model. However, since the distribution of the fixed effects is unknown, it would not have been possible to estimate the average partial effects in this model, but only the effect of the regressors on the log-odds ratio. 13 We conclude by stressing that the monthly phone interviews were relatively short, so we did not have a rich panel data set. This may have led to omitted variable biases. Indeed, there may still be time varying factors which could have affected both the probability of being employed and the respondents ’ intentions to go back. Nevertheless, we believe that our model managed to control for 13 See (Wooldridge, 2010) page 639. Conclusions from the conditional logit model are qualitatively similar. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data set"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The objective of the survey was to analyze perceptions and priorities with regard to the Peace Accord, to analyze perceptions of security, to determine access to basic infrastructure and school attendance, to understand nutrition levels, and to measure household asset ownership. In addition to this baseline survey, the World Bank was sponsoring a mobile phone survey among IDPs in Bamako, returnees in Gao, Kidal and Timbuktu town and refugees in camps in Niger and Mauritania. This survey interviewed 500 respondents on a monthly basis. In the August 2015 round of this survey, questions about perceptions and priorities with regard to the Peace Accord were included. This paper also makes use of a subset of the responses obtained from that survey, particularly those from refugees in Niger (n = 80) and Mauritania (n = 100) as these sub-populations who live outside Mali ’ s borders are important stakeholders in the peace process whose opinions risk being ignored. 15 To select a household in a village or neighborhood for the baseline survey, random selection was used: the enumerator divided the locality into two parts and selected five 15 For a more elaborate description of this mobile phone survey, see: Etang Ndip, A., J. Hoogeveen and J. Lendorfer (2016). Socioeconomic Impact of the Crisis in Mali on Displaced People. Journal of Refugee Studies. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["mobile phone survey", "baseline survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 households in each. To select a starting point the enumerator used a code of the day16 and chose every second house in rural areas and every fifth house in urban areas. The selection of individuals within the household to answer the questionnaire was conducted as follows: the head of household (male or female) was selected to answer the first part of the questionnaire dealing with general questions about the households. Using the roster of household members which was compiled during the first part of the interview, another member of the household aged 18 or above was selected randomly to answer the second part of the questionnaire in which perception questions were asked. Alternation between male and female was ensured. The survey thus generated data that are reflective of the opinions of those aged 18 and above in northern Mali. To assess the representativeness of the data, which were collected under rather challenging circumstances, the ethnic composition of the sample was compared with the ethnic composition in the North as reported by the 2009 Census. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["2009 Census"], "descriptive_data": ["roster of household members"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Similarly, regional representation of the sample was found to be in line with actual regional distribution of the population in the north. 16 The code of the day is the sum of the two figures of the date, i. e. if it ’ s the 25th of August the code of the day is 2 + 5 = 7. The enumerater will chose house number 7 as a starting point. Arab 3 % Tamashek 32 % Songhai 45 % Peulh / Foulbe 7 % Other ethnicities 13 % Figure 2: Ethnic composition of the North, 2009 Census Arab 5 % Tamashek 36 % Songhai 49 % Peulh / Foul be 7 % Other ethnicities 3 % Figure 3: Ethnic composition of sample Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["2009 Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. Introduction The most common sampling approach for cross-sectional household surveys in the developing world is a stratified two-stage design (Grosh and Munoz, 1996). Following stratification based on administrative boundaries, clusters are selected in the first stage with probability proportional to size from a national census-based frame. In the second stage, a canvassing operation is conducted in the selected clusters to compile an updated list from which households are randomly selected. While this methodology is straight forward to implement in the field and reliably produces unbiased estimates, there are several downsides. The first downside is cost. The World Bank ’ s Living Standards Measurement Study team, which provides technical assistance on large-scale household surveys around the world, estimates the field listing operation increases the overall budget for data collection by 25 percent. Due to confidentiality concerns, the data collected during a field listing operation, typically the name of the household head and address or location description of dwellings, does not have any analytical applications beyond as a component of the weight calculations. 2 At a time when typical survey costs are in the USD millions, reducing a significant cost component will increase the financial sustainability of data collection. The second drawback to the traditional design relates to timeliness. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Living Standards Measurement Study"], "descriptive_data": ["national census-based frame"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In a mortality study in Iraq, Galway et al. (2012) used GIS and Google earth imagery for household sampling. The method used gridded population data for selection of clusters. The first cluster sampling stage of their study used the ‘ Create Spatially Balanced Points ’ (CSBP) function in the ArcGIS (v10) software. Boo et al. (2020) introduces a sampling design based on gridded population estimates as their sampling frame to implement a PPS design and derive sample size estimates for the number of grid cells. Assuming the grid square method is applied to the area itself rather than a selected PSU, the weights for the grid method are similar to those for segmentation, where the cells are the PSUs, but without the additional step of selecting segments. The weights can therefore be represented as 𝑤𝑤𝑖𝑖 ′ = (𝑁𝑁𝑘𝑘) ൫𝑁𝑁𝑘𝑘𝑘𝑘൯൫𝑁𝑁𝑘𝑘𝑘𝑘𝑘𝑘൯ 𝑘𝑘𝑘𝑘𝑘𝑘. 2. 4. North Method The “ Qibla method ” described in Himelein et al. (2017), or what is called in this paper the “ North method ” method, is an attempt to assign probability weights to random point selection methods. Several random point selection methods can be found in the literature, particularly in relation to epidemiological studies. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["gridded population data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The assumption of perfect implementation, however, is quite strong as interviewers have shown a preference for selecting respondents willing to participate in the survey (Alt, 1991), and a number of other studies found that data collected with random walk designs exhibit differences from known population statistics on gender, age, education, household size, and marital status (Bien et al. 1997, Hoffmeyer ‐ Zlotnick 2003, Blohm 2006, Eckman & Koch 2016). Probabilities of selection inherently cannot be calculated in a random walk sample design as no information is collected on how many structures are in the camp, or how likely it was that a given structure was the xth structure along any path. Random walk must then assume all structures have the same selection probability, implying constant sampling weights. Therefore, the only component of the weights for the random walk is the sub-sampling of households within a selected structure: 𝑤𝑤𝑖𝑖 ′ = 𝑁𝑁𝑗𝑗𝑗𝑗 𝑖𝑖. 2. 6. Comparison of Methods As mentioned above, stratified cluster samples with the canvassing of selected clusters is the most common sample design used to collect official socioeconomic statistics in the developing world, but in Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 other disciplines it is relatively rare. A review of published public health literature by Chen et al. (2018) found most surveys use probabilistic designs in the first stage, but random walk or similar methods in the second stage. Lupu and Michelitch (2018) suggest that the combination of random walk and quota sampling is the common approach for political science-themed surveys conducted in the developing world, with 77 percent of respondents to their expert survey using a variation on this design. Diaz de Rada and Martínez (2014) compare a combination of random walk and quota sampling (based on age and gender) to probability designs and find a more accurate estimation of age and educational attainment in the combined method than in the probability methods, but that the probability methods perform better for measuring unemployment. The authors cite the replacement protocols for the probability methods as a reason for the bias and attribute the use of quota sampling for the success in estimating age and education, compared to the gold standard of a high-quality probability sample design. There are also a limited number of papers which directly compare two or three of the methods, but none that consider this wide range of alternatives. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Chew et al. (2018) use a baseline convolutional neural network model on a gridded population sampling frame to select a sample of households in Nigeria and Guatemala. The authors found this technique to be on par with human canvassing in terms of accuracy, and to outperform other machine learning models based on crowdsource or remote sensing data. Grais et al (2007) compared an unweighted random point selection methodology to a random walk in their study of vaccination rates in urban Niger. The authors do not find statistically significant differences between the methods, though the sample size was limited and both methods were non-probabilistic. 3. Design and Field Protocols 3. 1. Experiment Design This paper makes use of a dataset from the purposefully designed methodology experiment conducted in one section of the Protection of Civilians site 1 (PoC1, Figure 1), one of the largest IDP camps in Juba, South Sudan. To generate a gold standard as the basis of comparison, a household census was conducted between August and September 2017. During this exercise, 2, 655 households were interviewed using a questionnaire designed to collect demographic information, dwelling characteristics, household consumption, and perception data. At the end of each census interview, households received a unique barcode that could be used to identify them later in the experiment. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 3. 5. North Method The North Method uses RSPs to determine the selected households. RSPs are chosen from the universe of all possible points with the boundaries of the PoC camp. To implement the North Method, 322 RSPs along with replacement RSPs were chosen. These points were random geo-coordinates within camp borders (Figure 5). If the RSP lay within a structure, the corresponding structure was selected. If not, starting at the selected RSP, enumerators walked directly north, using the compass application on their tablet, until a structure was encountered. If the structure was residential, the structure was chosen to be interviewed. In the case of multiple households present in the structure, one household was randomly chosen. If the structure was not residential or if the enumerator reached the boundary of the camp, a replacement RSP was used. As it would be extremely difficult to determine the area of the shadow in the field, satellite imagery is used for these calculations. In the case of this experiment, the selection areas are calculated using Google Earth imagery taken on December 22, 2017, approximately one month after the census of households in the PoC camp. Given the dependence of the North Method on having current satellite imagery for accurate calculations, the availability of this imagery is a major consideration for this method. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 1 ‐ Number of Forcibly Displaced Persons (1951 ‐ 2015) Source: Constructed from UNHCR population data (http: / / popstats. unhcr. org / en / time_series). Note: 2015 data are mid ‐ year and lower than end of year data. 5 http: / / www. unhcr. org / en ‐ us / figures ‐ at ‐ a ‐ glance. html. 0 10000000 20000000 30000000 40000000 50000000 60000000 70000000 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR population data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Figure 4 ‐ Research on Migration, Refugees and IDPs (% of total hits) Source: Authors ’ estimations based on Econpapers, SSRN and Google Scholars searches. This phenomenon can be explained by essentially two factors. The first relates to the humanitarian ‐ development nexus. For the longest time, refugees and IDPs remained the quasi monopoly of humanitarian organizations whose mandate is essentially the humanitarian protection of refugees and IDPs. These organizations are not typically staffed by economists and analysists but by field workers and lawyers. There was, therefore, little demand for hard economics on forced displacement for a very long time. This is changing as development organizations typically staffed by economists have started to work on forced displacement situations. The second factor relates to lack of good data. As we will see in the data section, data collection of mobile populations is complex and the main organizations in charge of data collection of refugee and IDPs data are humanitarian organizations that do not necessarily have the complex skills required for issues like sampling, questionnaire design and data analysis and have a duty to protect data by mandate. This, in turn, has resulted in very few micro data that would be both of good quality and accessible to researchers. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 people may also gain from conflict in terms of income and wealth and this may explain why some people do not move. The defining attributes of the alternative choices are very different from any other model and the task of economics is to understand what these defining attributes should be. In terms of independent variables, “ push ” factors become more important than “ pull ” factors in forced displacement models. The intensity of a conflict may be more important than the income opportunities in potential destination areas. In addition to the classic socioeconomic variables, risk aversion, stress, anxiety, other traits of personality and behavioral factors in general have to be well understood and measured. Hence, one could think of four essential blocks of independent variables including individual or household socioeconomic characteristics, “ push ” factors, “ pull ” factors and behavioral factors. Also, access to and dissemination of information related to the conflict in the place of origin but also in the potential places of destination may be crucial for people to make choices. This is where social psychology, behavioral economics and neuroeconomics may offer insights into such choices. Forced displacement data are also unusual in their form. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Forced displacement data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Deciding on whether to flee or not to flee a conflict (the migration choice) can be an individual or household choice and risk coping strategies may include temporary migration, shuttling between places, migration of only selected members of the households or migration of the whole household. This implies that individuals may stay put throughout the period observed, join or leave the household during the period, or have several episodes of out and immigration. Households may decide to leave and come back several times. In econometric terms, this means that longitudinal data may be left and right censored and have spells within. They are therefore the most complex set of panel data possible and require particular treatment of data and modeling. Survival or duration models can usually accommodate many of these complexities but it is very rare to find similar data sets used in published articles. Collecting such type of data is also not obvious, particularly if conflict is intense and survey areas cannot be reached. This is an issue where empirical economics could provide a real contribution by defining the optimal data format and adapting panel models to this format. Macro models Macroeconomics has attempted to model forced migration using models borrowed from the trade and economic migration literature such as the gravitational model (Echevarria and Gardeazabal, 2016) or used other macro models to test the impact of refugees on trade (White and Tadess, 2010). A more recent body of work is adapting trade models to take into account stochastic shocks in a dynamic framework (Cameron et al., 2007; Artuc et al., 2008). These are rational expectations models that are able to model the unpredictability of shocks, and recent work has tried to adapt these models to the context of violent Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["longitudinal data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These people are then expected to return to their place of origin once the conflict is over and governments are typically over optimistic about the duration of civil conflicts and about return of IDPs. In some cases, governments also have an interest in denying the very existence of IDPs for political purposes. Therefore, little time is spent in surveying IDPs or trying to find durable solutions in the place where they migrated. Moreover, national censuses are usually conducted every ten years and statistical agencies have little incentives to revise censuses, master samples and sample survey structure for situations that are perceived as short ‐ term. In most cases, new surveys are suspended or carried out under the pre ‐ crisis frameworks and, in either case, information on IDPs is not collected or poorly collected. This leaves specialized government agencies or international organizations in charge of IDP statistics (and care). However, unlike refugees, the IDPs do not benefit from a specialized international agency such as the UNHCR. IDP assistance is currently provided by a multitude of organizations including ministries of interior, specialized government agencies, the UNHCR, the International Organization for Migration (IOM), the UN Office for Humanitarian Affairs (UN ‐ OCHA), specialized NGOs and others. Some of these organizations collect information on IDPs and make this information public while others collect information that is not published and others do not collect information and focus on providing assistance. Most data collected are for the simple purpose of counting IDPs and do not include individual or Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["national censuses"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 household socioeconomic information. In some cases, socioeconomic information is collected at the individual or household level but more often information is collected at the community level. It is extremely rare to have unit record data sets on IDPs, which explains why we found only 18 studies in the Econpaper repository as already documented. Recognizing the problem of scarcity and availability of information on IDPs, international organizations have set up in several countries coordination mechanisms to count IDPs usually coordinated by IOM, UN ‐ OCHA or the UNHCR. There are also global efforts to centralize this information on the part of organizations such as the UNHCR, UN ‐ OCHA, the international Displacement Monitoring Centre (iDMC) or the Joint IDP Profiling Services (JIPS). These efforts are making good progress on harmonizing counts of IDPs but remain short of establishing proper data collection systems that could deliver in the years to come unit data of quality for research. Hence, research on IDPs remains constrained by lack of data, lack of a blueprint on how to collect data and lack of an organization dedicated to IDP data collection. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some data challenges common to refugees and IDPs Irrespective of the specific questions related to refugee and IDP data, there are also general questions that refer to the forcibly displaced in general and that are distinct from data collection of regular populations or even migrant populations. We explore here selected issues including sampling, unit of analysis, welfare measurement, multidimensional aspects, and the measurement of risks and vulnerabilities. Sampling. As mentioned, the UNHCR is really the only statistical agency for refugees and the UNHCR registry the only population census. As for any other populations, sampling requires the preparation of a master sample that derives from the population census. With various degrees of knowledge and accuracy, this is also what happens with refugees. However, the master sample is more difficult to construct than for regular populations because refugees live in camps and outside camps and are diluted in a host population with different types of arrangements. Some households rent, others stay at relatives ’ places, other live in makeshift shacks and others stay in camps. The information available in the UNHCR registry (the census) can also be quite inaccurate, as already discussed, and the degree of accuracy changes for different groups of refugees. Stratification by urban and rural areas, a typical approach in sampling, may mean little for a population that is mostly in urban areas whether in camps or outside camps. Refugees and IDPs are also mobile and more difficult to track over time than other populations. Several statistical institutes worldwide have developed methodologies to track and measure mobile populations such as herders, nomads or homeless people. However, tracking refugees from other countries has been in the Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["UNHCR registry"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 capacity to offer these services. Many may be at risk of violence or trafficking. These are very important aspects from the perspective of welfare economists interested in measuring well ‐ being but these measurements are complex and not usually included in multidimensional indicators of deprivation or poverty. The dimensions of deprivations to consider are more numerous and more complex to measure. Again, there is very little research in welfare economics dedicated to the special needs of these populations. Risks and vulnerabilities. The analysis of risk and vulnerability is also much more complex in the context of the forcibly displaced. Welfare economics has only approached these topics recently, in the past decade or so. Essentially, the idea is to measure the risk of being poor or falling poor in the future using cross ‐ section or panel data studying spells of poverty over time. This is work that requires accurate and complex data sets that would be rarely available in a refugee or IDP context. More importantly, the nature of the problem changes. Refugees and IDPs are by definition more at risk and more vulnerable than regular populations and these vulnerabilities are not only linked to skills and efforts but to legal status, discrimination, limited mobility and other factors that are unique or much more acute with refugees and IDPs. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["cross ‐ section or panel data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Lebanon. It was the obvious step forward in overcoming these problems and the answer to the increasing demand for evidence around the Syrian crisis. The study benefitted from a unique and unprecedented set of data. The UNHCR registry data in Jordan and Lebanon were among the better quality registry data available worldwide and the UNHCR also conducted home visits in Jordan that, at the time of the study, covered over a third of all refugees. There were also sample surveys in both Jordan and Lebanon that were small in size but representative of the population present in the registry. The home visits and the surveys included questions on income and expenditure that could be used for the welfare assessment. Using these data, the study addressed ten questions defined as follows: 1) Who are the refugees?; 2) How different are refugees from “ regular ” populations?; 3) How poor are refugees?; 4) What are the main predictors of refugees ’ welfare and poverty?; 5) How vulnerable are refugees from a monetary and non ‐ monetary perspective?; 6) Do poverty and vulnerability statuses overlap?; 7) How effective are refugee assistance programs?; 8) What is the potential for alternative policies?; 9) How does welfare compare across countries and data sets?; 10) How transferable are Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR registry data"], "vague_data": ["sample surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This study aims to assess the consequences of forced migration on ethnic diversity and conflict in Sub-Saharan Africa. We combine a unique dataset on refugee camps with individual data from the Afrobarometer Surveys across 23 African countries for the 2005 — 2016 period. We construct two standard measures of ethnic diversity: indices of ethnic fractionalization (EF) and ethnic polarization (EP). Ethnic fractionalization measures the probability that two individuals drawn from the society at random will belong to two different ethnic groups and thus increases with the number of ethnic groups present. Ethnic polarization captures antagonism between individuals and is maximized when the society is divided into two equally sized and distant ethnic groups. Although these indices have been widely used, little variation over time has been found, making causal inference difficult. The innovative aspect of our analysis is that we use data on the precise locations of refugee camps, their yearly size, and — most importantly — their annual composition in terms of countries of origin. Combined with the Ethnic Power Relations- Ethnicity of Refugees 2019 dataset, we are able to predict changes in ethnic diversity induced by refugee inflows. We then assess the relationship between refugee diversity and the likelihood of conflict. In an additional analysis, we also assess how refugee-induced changes in diversity affect the incidence of theft and violence, participation in protests, and perceptions of ethnic attachment, inter-personal trust, and institutional trust. Other studies have investigated the links between displacement and social conflict or social co- 3 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Afrobarometer Surveys", "Ethnic Power Relations- Ethnicity of Refugees 2019"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the standard ethnic diversity indices to include the annual variation in refugee ethnicities. 8 We then construct a measure of proximity between the clusters in the host country and refugees in surrounding camps by defining an 80-km buffer around each cluster. 9 To control for unobserved heterogeneity and changes within a given cluster, we introduce cluster and year fixed effects, αj and δt. To minimize the risk of confounding the refugee-induced changes in diversity with the annual changes in refugee numbers, we also control for the presence of refugees based on the same buffer as the one used to construct the refugee-induced change in diversity. More specifically, the variable Refugeesjt − 1 counts the number of refugees present in cluster j at year t − 1 within the predefined buffer. The variable is also transformed into an inverse hyperbolic sine to ease interpretation. Finally, Qjt controls for yearly shocks at the cluster level, such as weather shocks. In particular, we control for rain and temperature anomalies. Standard errors are clustered at the Afrobarometer cluster level. 4. 2 Data and descriptive statistics Our analysis combines various sources of data: Afrobarometer, UNHCR refugee camp data, Armed Conflict Location and Event Data (ACLED), Uppsala Conflict Data (UCDP), and the Ethnic Power Relations- Ethnicity of Refugees (EPR-ER) 2019 dataset. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Armed Conflict Location and Event Data", "Uppsala Conflict Data", "Ethnic Power Relations- Ethnicity of Refugees (EPR-ER) 2019 dataset"], "descriptive_data": ["UNHCR refugee camp data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using Afrobarometer ’ s geocoded surveys, we focus on clusters as our unit of observation. 10 Our sample consists of 7, 547 such locations and 76, 518 individuals in 23 countries in Sub-Saharan Africa. “ The sampling universe normally includes all citizens age 18 and older. As a standard practice, they [we] exclude people living in institutionalized settings, such as students in dormitories, patients in hos- pitals, and persons in prisons or nursing homes. ” (Afrobarometer, https: / / afrobarometer. org / surveys − and − methods / sampling − principles) Since the sampling frame is based on recent censuses, with the aim of representing all citizens of voting age in a given country, the Afrobarometer samples are unlikely to include refugees. Note also that “ the sample design is a clustered, stratified, multi-stage, 8We explain the construction of theses indices in Section 4. 2. 9We test the robustness of our results with a smaller (40 km) and a larger (120 km) radius in Section 5. 3. This choice of buffer size assures us that between 75 percent and virtually all refugee camps fall within a cluster buffer. Other studies relying on Afrobarometer data construct buffers ranging from 25 km (e. g., Michaelopoulos and Papaioannou (2011), investigating ethnic-specific pre-colonial institutional structures) to 100 km (e. g., McGuirk and Burke (2020a), analyzing the impact of food-price shocks on conflict). 10Afrobarometer is a pan-African research network conducting public attitude surveys on democracy, governance, the economy, and society in African countries that are repeated on a regular basis (Afrobarometer, 2020). 10 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location. Area stratifi- cation reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analyzed. ” Afrobarometer provides geocoded data for 6 rounds, which correspond to the 1991 – 2016 period, with the information on an individual ’ s ethnicity available from round 3 (corresponding to 2005 – 2006). We therefore restrict our analysis to the 2005 – 2016 period. The selection of countries is driven by data availability. Among the 33 countries with available Afrobarometer data, we exclude Botswana, Cape Verde, Lesotho, Madagascar, Mauritius, Sao Tome and Principe, South Africa, and Swaziland, for which no data is available on refugee camps or from the EPR-ER. We also exclude Sudan since the question on individual ethnicity is not asked in this country ’ s survey. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["EPR-ER"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The countries in our sample are Benin, Burkina Faso, Burundi, Cameroon, Gabon, Ghana, Guinea, Ivory Coast, Kenya, Liberia, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, Tanzania, Togo, Uganda, Zambia, and Zimbabwe. As described in Table B. 1, we also incorporate information on the quality of our refugee data, which is determined by comparison with official UNHCR bilateral data. Below we describe how these data have been used to define our main variables of interest and present some descriptive statistics in Table B. 2. 11 Conflict. In Equation 1, we first relate variation in ethnic diversity with data on conflict from ACLED (Linke et al., 2010). Two main definitions are used: the incidence of conflict and the intensity of conflict. Incidence is captured by an indicator equal to one if conflict occurred in a particular year within a pre-defined buffer around cluster j. Intensity is measured by summing the number of conflict events occurring in a particular year within the same buffer area. A conflict event is defined as a single altercation wherein force is used by one or more groups for a political end (Linke et al., 2010). We further describe events (non-exclusively) as violent events, non-violent events, violence against civilians, and riots. In our main analysis, we focus on violent conflicts (Section 5. 1) and report results for other outcomes as robustness tests (Section 5. 3). In doing so, we follow a recent and large literature that has combined the ACLED dataset with geographically disaggregated data in Africa (Besley and Reynal-Querol, 2014; Berman and Couttenier, 2015; Michaelopoulos and 11Panel A of Table B. 2 shows descriptive statistics for the data from refugee-hosting areas specifically, whereas panel B of Table B. 2 shows descriptive statistics for our data in all covered areas. 11 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data on conflict from ACLED"], "vague_data": ["geographically disaggregated data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Papaioannou, 2016; Berman et al., 2017; Harari and Ferrara, 2018; Eberle et al., 2020; McGuirk and Burke, 2020b). As a further robustness check, we also use data on conflict incidence and intensity from the UCDP, which uses a more conservative definition of conflict. The UCDP dataset is manually curated and compiled with automated computer assistance (Sundberg and Melander, 2013). The UCDP defines an armed conflict event as “ an incident where armed force was used by an organized actor against another organized actor, or against civilians, resulting in at least one direct death at a specific location and a specific date ” (Pettersson et al., 2020). We extract daily event observations from the UCDP dataset if the location of the actual event is exactly known, the event location is within a radius of less than 25 km around a known point, or at least the administrative district where the event happened is known. As pointed out by Eberle et al. (2020), the UCDP events are more likely to capture violence between large-scale and more structured groups. Table B. 2 shows that on average, conflict events seem to occur more in refugee-hosting areas. This is of course not a causal interpretation but a simple correlation. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["UCDP dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As can be seen from both panel A and panel B, non-violent conflicts seem to occur slightly more than violent conflicts. On average, the likelihood of violent conflict stands at about 48 %, while this figure increases to 52 % in refugee-hosting areas. Conflicts among more structured and large groups, as captured by the UCDP data, appear to be less frequent. UNHCR refugee data. To exploit the variation in ethnic diversity induced by the annual variation in refugees (and also to control for the direct effect of refugees on our outcomes), we use data on refugee camps provided by the UNHCR. The dataset contains detailed time-series information on the locations and sizes of 1, 453 refugee camps across the world and 821 refugee camps in Sub-Saharan Africa over the 2000 – 2016 period. To the best of our knowledge, the UNHCR currently provides the most comprehensive information available on refugees at the subnational level, allowing us to assess the ethnic composition of camps, which is key to our research question. First, we use the country of origin of refugees recorded for each year at the camp level to approximate the ethnic composition each camp. Second, we restrict the data on refugees to those aged 18 and above in order to make it comparable to the Afrobarometer- based individual data. Third, we only use data on refugees hosted within the boundaries of the host country. Merging data on refugee camps with the Afrobarometer, we end up with information on 172 camps 12 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["UCDP data"], "descriptive_data": ["data on refugee camps"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "that are at a maximum distance of 80 km from the 7, 547 clusters. 12 Figure 1 shows the locations of these refugee camps and clusters. Clusters are represented in green, while clusters in the vicinity of a refugee camp are represented in red. Refugee camps are designated with a red + sign. There are some important limitations associated with this data. First, the data only provides information on refugees residing in camps monitored by the UNHCR. In Figure B. 7, we combine the UNHCR refugee camp data on the annual number of refugees and the UNHCR official statistics on refugees (which includes people in refugee-like situations) at the country level. 13 Although the overall trends match, our constructed dataset clearly underestimates the true refugee population in Africa, which is not surprising since our camp-specific data does not contain dispersed refugees or refugees living outside of camps. While our data seem to represent quite fairly the number of refugees in camps, there is significant heterogeneity across countries. Based on the visual inspection of Figure B. 8, the quality of the refugee data appears to be less reliable for the following countries in our sample: Gabon, Mali, Senegal, and Togo. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR official statistics on refugees"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 1: Data and Descriptive Statistics: Clusters, Refugee Camps, and Conflicts Revised refugee diversity indices. We first use Afrobarometer data to construct standard indices of diversity, namely the EF and the EP indices (Bazzi et al., 2019; Esteban and Ray, 1994). The EF index describes the probability that two randomly selected individuals from a given location belong to two different ethnic groups (Alesina et al., 2003, 2016; Gomes, 2020b). The EF index can be defined as EFjt = Njt X e = 1 get (1 − get), (2) where Nj is the number of ethnic groups in cluster j at time t and get is the population share of 14 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ethnic group e at time t. It can also be expressed as one minus the Herfindahl index (Alesina et al., 2016). The EP index gives more weight to intergroup differences at the expense of within group homo- geneity. It can be defined as (Esteban and Ray, 1994, 1999; Montalvo and Reynal-Querol, 2005) 14 EPjt = Nrt X e = 1 (g2 et) (1 − get). (3) We compute this index for each cluster at the time of each Afrobarometer survey to assess how refugee-induced changes in diversity differ from standard indices of diversity. In order to construct the revised refugee diversity indices according to ethnicity e, we first combine information about the country of origin of refugees hosted in refugee camps c in year t with the data from the EPR-ER 2019 dataset. The EPR-ER records the ethnic composition of refugee stocks originating from neighboring countries and countries in proximity to each other (maximal distance between country borders ≤ 950 km) with at least 2, 000 refugees and provides the ethnic composition of refugees (Vogt and Girardin, 2015). More specifically, the EPR-ER dataset gives us the share of refugees from ethnic group e moving from country o to country d at year t. The EPR-ER data gives us the three main ethnic groups. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["EPR-ER 2019 dataset", "Afrobarometer survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There is one limit to our approximation in Equation 4. The ethnic composition of refugees in each year t for a given origin – destination pair of countries obtained from the EPR-ER database is assumed to be homogeneous across camps of the same origin – destination pair of countries for the refugees at year t. This may seem to be a strong assumption; however, the risk of misallocating refugees is reduced as the annual variation in the EPR-ER is generated by just a few dominant groups for a given origin – destination pair and the geographical distribution of refugees by country of origin is highly influenced by the proximity to their countries of origin. 16 As can be seen from panel A of Table B. 2, in refugee-hosting areas, on average, both EF and the EP seem to increase quite significantly when they are revised by incorporating the number of refugees in an 80-km buffer: the mean value of the standard EF index is 25. 58 %, while the mean value of the revised refugee EF index is 37. 90 %. The mean value of the standard EP index is 10. 11 %, while the mean value of the revised refugee EP index is 14. 07 %. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["EPR-ER database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 3 also shows that there is considerable variation in both indexes within our sample when averaging these indexes at the regional level over the period of investigation. 16It is possible that our approximation is noisy and could potentially induce non-random measurement errors. In Section 4. 3, we propose an instrumental variable approach and estimate Shareodet from the EPR-ER data using a gravity model. Our findings concerning the number of ethnic groups across time for a given origin – destination pair are in line with the EPR-ER data. It seems that refugees of a given origin – destination pair mainly belong to two major ethnic groups. This also means that the variation in diversity in refugee-hosting areas is coming from the refugee composition at the camp level. Figure B. 5 shows the movements of refugees from origin to destination countries under scrutiny. Somalia, the Democratic Republic of Congo, Liberia, South Sudan, and Sudan are major source countries for refugees, while Kenya, Tanzania, Uganda, Zambia, and Ghana appear to be countries hosting most refugees. Representing refugees in camps per ethnic group for the top 5 asylum countries over the sample period, Figure B. 9 shows that there is considerable variation in ethnic composition across camps. 17 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["EPR-ER data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Linking Ethnic Data from Africa", "Afrobarometer", "EPR-ER dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, we can- not exclude the possibility that refugees would sort non-randomly into areas with particular ethnic characteristics. 19 In order to address this potential endogeneity, we implement an instrumental variable (IV) ap- proach. We are particularly concerned about certain ethnic groups from certain countries of origin moving to destination countries with similar ethnic characteristics. Such endogenous selection would be reflected in the EPR-ER data. To deal with the plausibly endogenous nature of the resulting refugee EF and EP indices, we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data. The predicted (and plausibly exogenous) number of refugees by ethnic group e is then used to create other (plausibly exogenous) diversity indices to be used as instrumental variables. More specifically, we estimate the following gravity model: 17We also use this method to link data from EPR-ER on the ethnicities of refugees with data from the Murdock Atlas on their historical homeland (Section 4. 3). 18As a robustness check (Section 5. 3), we use an alternative linkage based on the relations between sets of language nodes associated with two groups. 19Another source of selection may come from the fact ethnic groups are more likely to be displaced when they share territory with regime supporters in their countries of origin (Lacina et al., 2017). Since similar ethnic groups are likely to share common borders (Michaelopoulos and Papaioannou, 2016), it is not impossible to think conflict might spill over through this channel. 19 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["EPR-ER data", "Murdock Atlas", "EPR-ER"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "REFodet = αod + γe + τt + β1Conflictot − 1 + β2Conflictet − 1 + β3Distanceed + ϵodet, (5) where REFodet is the stock of refugees of ethnic group e from country o in country d at year t. As we have data on yearly refugee stocks and would like to estimate the changes in these stocks over time using a gravity model, we include origin – destination fixed effects αod so that identification is based only on changes in stock over time (Zylkin, 2019). 20 We also include time τt and ethnic group fixed effects γe. Here we obtain data on the ethnicity of refugees from Murdock ’ s Atlas, which provides a map of ethnographic regions for Africa and the historical homelands of refugees (Murdock, 1967). To match ethnic groups across datasets, we again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity from the EPR-ER dataset and, later, with data from Afrobarometer. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Afrobarometer", "EPR-ER dataset", "Murdock ’ s Atlas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We use the sum of conflict events occurring in the historic homeland of ethnic group e in the previous year t − 1, denoted as Conflictet − 1, and we use the mean distance between the historic homeland of ethnic group e and the border of country d to predict the number of refugees of a certain ethnic group e moving from country o to d at time t. 22 In order to be consistent with EPR-ER data construction, we restrict our analysis to all origin – destination country pairs that are at a maximum distance ≤ 950 km from each other. Predicted numbers of refugees are then transformed into predicted shares for the three largest groups to follow the logic used by the EPR-ER dataset. We then plug in these predicted shares in the following way: X \\PredictedRefcet = Refocdt ∗ \\Shareodet. (6) The predicted shares of refugees per camp c are then used to compute (as documented above) refugee diversity indices to be used as instrumental variables. The first-stage equations corresponding to the 2SLS-equivalent of Equation 1 can be expressed as 20We conduct a robustness check on Equation 5, replacing the dyadic origin – destination fixed effects with separate origin and destination fixed effects (Section 5. 4). 21More information on LEDA can be found in Appendix A. 1. 22The construction of the IV follows a long tradition in using the gravity model to predict bilateral migration flows (Ravenstein, 1985, 1989; Crozet, 2004; Mayda, 2010; Garcia et al., 2015; Beine et al., 2016). In our analysis, a major difference is that we have an additional dimension: the ethnic group e. 20 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["EPR-ER data", "EPR-ER dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Indeed, despite the recent literature rejecting the conflictive impact of refugees in hosting areas (Zhou and Shaver, 2021), the magnitude of our coefficients might be explained by the confounding presence of refugees. Columns (5) and (6) further introduce climatic controls. Column (6) corresponds to Equation 1 and refers to our benchmark specification. Columns (1) and (2) show that without incorporating the changes in ethnic diversity induced by refugees we would not be able to identify a relationship between diversity and violent conflicts. In column (3), the revised refugee fractionalization index has a negative and significant coefficient, while the revised refugee polarization index has a positive and significant effect on the incidence of violent conflicts. In columns (2), (4), and (6), our coefficients of interest are of the same order of magnitude when the number of refugees is controlled for. Our results are not altered by incorporating rainfall and temperature anomalies (columns (5) and (6)), but the estimates become slightly more precise. 21 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5. 5 Ethnic diversity at different levels Despite the use of sampling weights in the construction of the diversity indices, we have no guarantee that our diversity indices are representative at the local level. Although similar ethnic diversity indices have been used at the local level (Nunn and Wantchekon, 2011; Rohner et al., 2013; Robinson, 2017; Desmet et al., 2020; Gomes, 2020b, a; Hodler et al., 2020), we cannot exclude the possibility that a lack of representativeness at the local level introduces some noise into our estimates. Ideally, we would have liked to construct our local diversity indices based on census data. However, such data are not available on an annual basis and only a minority of African countries include ethnicity questions on their censuses (Robinson, 2017). Robinson (2017) highlights other benefits but also warns against the risk of using non-random samples or of the size of samples introducing significant errors. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["census data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As summarized by Robinson (2017), “ fortunately, Afrobarometer respondents comprise stratified random samples at all levels, making population estimates based on them unbiased: thus, the major concern with using Afrobarometer sample data to construct demographic measures is unbiased measurement error. ” Based on a comparison of census-based and survey-based diversity indexes across five African countries, Robinson (2017, 224) found that a “ sample-based measure tends to underestimate the overall degree of diversity compared to census data ”. In theory, this should make it more difficult to observe the true relationship between ethnic diversity and some outcomes at the local level. Diversity indices are more likely to be measured with noise in highly diverse communities at the local level. We nonetheless argue that such a concern should not be overestimated, for three reasons. First, such noise cannot easily explain the contrast between the coefficients corresponding to the pre-revised and revised indices and the opposite results found for the revised refugee fractionalization and the revised polarization. This set of results can be explained by the fact that our identification comes from the annual changes in refugees flows. Second, the IV approach is likely to deal with the measurement errors if they are correlated with our main variables of interest. Our IV estimates therefore capture a local average treatment effect coming from the plausibly exogenous increase in annual refugee flows of particular ethnic groups. The similarity of the IV results to the OLS results supports this interpretation. Third, at the cost of introducing attenuation bias30, we also aggregate the number of conflict events at the regional level. Lines B and C of Table 7 confirm the negative and positive effects found for the revised fractionalization and polarization indexes, respectively, whether or not 30Another risk highlighted by Robinson (2017) is the fact that ethnic diversity may also capture different theoretical mechanisms at aggregated levels. 34 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer sample data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of the groups and the distances between them. For instance, (Bazzi et al., 2019) shows that polariza- tion increases ethnic attachment. Others have highlighted the reduction in trust, either interpersonal trust or institutional trust Alesina and Ferrara (2002); Beugelsdijk and Klasing (2016). To assess the importance of alternative explanations, we first replicate our analysis using individual data on violence. In addition to participation in protests, we follow McGuirk and Burke (2020b) in using the Afrobarometer survey data on interpersonal crime and physical assault. We then assess the relationship between the revised refugee diversity indices and alternative individual outcomes such as ethnic vs. national identity, generalized trust, trust in neighbors, and institutional trust (trust in government). The questions from the Afrobarometer mentioned below are used as a proxy for these outcomes: 32 1 Attack: Over the past year, how often (if ever) have you or anyone in your family: Been physically attacked? 2 Crime: Over the past year, how often (if ever) have you or anyone in your family: Feared crime in your own home? 3 National identity: Let us suppose that you had to choose between being a [Ghanaian / Kenyan / etc.] and being a [respondent ’ s identity group]. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Afrobarometer survey data"], "descriptive_data": [], "vague_data": ["individual data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research. 1989-2019. 50 (4). Taylor, J., M. Filipski, and M. Alloush (2016). Economic impact of refugees. Proceedings of the National Academy of Sciences 113 (27), 7449 – 53. United Nations High Commissioner for Refugees (2020). Global Trends: Forced Displacement in 2019. Geneva. Verme, P. and K. Schuettler (2021). The impact of forced displacement on host communities a review of the empirical literature in economics. Journal of Development Economics 102606. Verwimp, P. and J. Maystadt (2015, December). Forced Displacement and Refugees in Sub-Saharan Africa. An Economic Inquiry. World Bank Policy Research Working paper 7517. Vogt, Manuel, N.- C. B. S. R. L.- E. C. P. H. and L. Girardin (2015). Integrating Data on Ethnicity, Geography, and Conflict: The Ethnic Power Relations Data Set Family. 51 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Ethnic Power Relations Data Set Family", "UCDP Georeferenced Event Dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Appendix A Data Appendix A. 1 Linking Ethnic Data from Africa (LEDA) LEDA offers an interface — a language tree — to flexibly link ethnic groups from different databases to each other and calculate the linguistic distances between them. LEDA is currently structured around lists of ethnic groups from 12 original datasets, which are the following: ˆ Afrobarometer Surveys ˆ All Minorities at Risk (AMAR) ˆ Census data from IPUMS ˆ Ethnic Power Relations (EPR) dataset ˆ Ethnologue languages ˆ Political Relevant Ethnic Groups from Posner (2004) ˆ Ethnic groups in Francois, Trebbi & Rainer (2015) ˆ Ethnic groups from Fearon (2003) ˆ GREG Data (based on the Russian Atlas Miradova) ˆ Demographic and Health Surveys ˆ Murdock Atlas ˆ Spatially Interpolated Data on Ethnicity (SIDE) These lists are structured in LEDA ’ s interface by data source, country, year, or in the case of survey data, survey rounds. In our analysis, we use Afrobarometer, EPR, and Murdock Atlas data; therefore, we can use LEDA functions to link the different ethnic groups to each other. LEDA consists of three main linkage types: binary linking based on the relations of sets of language nodes associated with two groups; binary linking based on linguistic distances; and a full computation of dyadic linguistic distances. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "LEDA, which computes the minimum linguistic distance between two ethnic groups and, therefore, provides the closest linguistic neighbor for each given ethnic group (see Figure A. 1). This function computes a variable called distance, which measures the linguistic distance between two ethnic groups. Mathematically, these distances are calculated as DL1L2 = 1 − \u0012 2d (ω (L1,..., O) ∩ ω (L2,..., O)) d (ω (L1,..., O)) + d (ω (L2,..., O)) \u0013 δ, (A. 1) where d (ω (L1,..., O) is the length of the path from the first language to the tree ’ s origin and d (ω (L1,..., O) ∩ ω (L2,..., O) is the length of the intersection of the paths from the first and second language to the origin. δ is an exponent to discount distances further away from the root of the tree; it is typically set to 0. 5. Figure A. 1: Linking Ethnic Data from Africa Source: M ¨ uller-Crepon et al., 2020. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As a robustness check, we also use the first type of linkage: binary linking based on the relations between sets of language nodes associated with two groups. This is done using the “ setlink ” function of LEDA. With this function, the two groups are linked to each other as soon as they share any 2 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure A. 2: Injective relations We also isolate many-to-one (bijective) relations. In this case, we have to aggregate the Afro- barometer ethnicities with their unique and more aggregated correspondence in the UNHCR refugee camps data (See Figure A. 3). Figure A. 3: Bijective relations The remaining correspondences are either (i) one-to-many (bijective) but opposite to Figure A. 3 (i. e., many ethnicities from the UNHCR refugee camps data correspond to one ethnicity from the Afrobarometer) or (ii) many-to-many relations. For both cases, we apply a more pragmatic approach: a. In both cases, we disregard ethnicities that do not appear either in the Afrobarometer or in the UNHCR refugee camps data. This means that for the remaining ethnicity that has no counterpart in either the Afrobarometer or the UNHCR refugee camps data, we simply keep the name of the ethnicity as such, i. e., this information is not dropped. b. Then, after ignoring ethnicities that have no occurrence in our datasets, we check whether the one-to-many or the many-to-many relation has not boiled down to a one-to-one resp. many-to- one relation again. If so, we can treat them as above. c. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For the remaining one-to-many relations, we keep these ethnicities in the Afrobarometer as such and consider them as a single ethnic group. Some manual treatment can even further improve 4 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turk­ish Labour Force Survey to assess their labor market impact. Syrian refugees are overwhelmingly employed informally, since they were not issued work permits, making their arrival a well-defined supply shock to informal labor. Consistent with economic theory our instrumental variable estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupa­tional upgrading, there are increases in formal employment for the Turkish- though only for men without completed high school education. Women and the high-skilled are not in a good position to take advantage of lower cost informal labor. The low educated and women experience net dis­placement from the labor market and, together with those in the informal sector, declining earning opportunities. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Labour Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. INTRODUCTION Refugees pose a massive moral, political and economic challenge for potential host countries. 1 The scale of the challenge is larger than ever, with 60 million people forcibly displaced by conflicts across the world (UNHCR, 2014). War in Syria has produced more refugees than any other conflict of the past two decades: around 4. 6 million have fled the country, with an additional 7. 6 million internally displaced. 2 About 2. 5 million Syrians have found refuge in Turkey, making it the largest refugee-hosting country worldwide. This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turkish Labour Force Survey to assess the impact on Turkish employment and wages. The large majority (85 percent) of Syrians have left the refugee camps and entered the Turkish labor market. 3 They are overwhelmingly employed informally, since they were not issued work permits. This makes their arrival a well-defined supply shock to informal labor, and a particularly good context in which to test the predictions of basic economic theory. We instrument for refugee flows using travel distance between 13 origin governorates in Syria and 26 Turkish subregions (338 origin-destination pairs). This allows us to also control for distance from the Syrian border, and thus any confounding factors that are correlated with proximity to Syria. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Turkish Labour Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 permits and subsequently for the right to work. In practice, this is a long and cumbersome process and by late 2015 at most several thousand had been issued. 14 The economic impact of Syrian refugees in Turkey extends beyond changes in the potential labor supply of informal workers in important ways. There has been extensive humanitarian aid provided to the refugees, overwhelmingly by the Turkish government. Reportedly, by early 2015 the Turkish state had spent $ 6 billion (with total outside contributions $ 300 million). 15 Much of these funds have been spent on food, various services, non-food items such as medicines, clothing, shelter, and housing-related goods. In particular, there are 20 accommodation centers (camps) in 10 cities in Turkey. 2. 2 Data Sources We use the Turkish Household Labor Force Survey (LFS) micro-level data sets compiled and published by the Turkish Statistical Institute. The data contains a rich set of labor market variables along with individual-level characteristics and the region of residence. We primarily rely on two years of LFS data: 2011 (just before the arrival of the refugees) and 2014 (the last year available). 16 By design the LFS does not contain any information on Syrian refugees. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Turkish Household Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Disaster and Emergency Management Presidency of Turkey (AFAD) provides information on the number of Syrian refugees. The numbers used in this paper are taken from Erdogan (2014), who draws on information from AFAD and the Ministry of Interior and reports the number of refugees by NUTS 2 subregion. To construct our instrument we use the Syrian Labor Force Survey for 2010 (the year before the beginning of the war). Finally, Google Maps was used to derive the travel distance between each governorate in Syria and the most populous city in each NUTS 2 subregion in Turkey. 14 Most recently, in January 2016, labor market access for Syrian refugees in Turkey was eased considerably. Importantly, they now can benefit from vocational training under the Turkish Employment Agency, employers will be able have to Syrians comprise up to 10 percent of their staff, and seasonal workers are exempted from the work permit, see http: / / www. resmigazete. gov. tr / eskiler / 2016 / 01 / 20160115-23. pdf. It is of course too early to evaluate the impact of these legislative changes. 15 Hurriyet Daily News (February 2015) http: / / www. hurriyetdailynews. com / turkey-urges-worlds-help-on- syrian-refugees-as-spending-reaches-6-billion. aspx? pageID = 238 & nID = 78951 & NewsCatID = 359. 16 Starting with 2014 there was a change in the design of the Household Labour Force Survey to ensure full compliance with European Union standards. This has caused some difficulty in making comparisons across years. However, our identification strategy does not use aggregate variation across years for identification and should hence be unaffected by the changes to the design of the survey. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Household Labour Force Survey", "Syrian Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 1 provides descriptive statistics for the years 2011 and 2014. 18 Labor force participation is very low in Turkey, around 54 percent of the working-age population in 2011, though it has been rising. The reason is that female labor force participation is particularly low at about one-third. The majority of employment is private sector, around one-third of the working-age population, compared to 6 percent employed in the public sector. There are a large number of unpaid workers (7 percent) and unemployment is at 5 percent of the working-age population, an unemployment rate of about 10 percent. School attendance has been rising over the period, from 12 to 16 percent of the working-age population, and the fraction retired has been steady at about 5 percent. Correspondingly, educational attainment has been rising though still 13 percent of the working-age population has no formal education, 57 percent at least completed primary education but not high school, and high school completion has risen from 30 to 34 percent. 17 Of those who have an irregular workplace 60 percent are agricultural workers, 14 percent work in construction, 7 percent in transportation and 5 percent in retail and in manufacturing each, and 3 percent as household employees. 18 Note that in 2014 new regulations for the Household LFS were carried out within the framework of European Union criteria. Consequently, statistics are not necessarily entirely comparable across years. Since we do not use aggregate time-series variation for identification this does not affect our empirical strategy, see Section 3. For those interested, the Turkish Statistical Institute provides consistent time-series on their website. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Household LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 though somewhat lower than that of the Turkish at 30- 50 percent. Child labor is also quite prevalent, though there have been extensive efforts made to ensure that refugee children attend school. 19 Publicly available information on refugees comes from an AFAD survey of 2, 700 households in June and July 2013. Figure 2, using data from AFAD (2013), provides an overview of the Syrian governorates from which the refugees to Turkey originated. The refugees primarily come from northwest Syria. The largest source regions are Aleppo (36 percent), Idleb (21 percent) al-Raqqah (11 percent), Lattakia (9 percent), and Hamah (8 percent). Consistent with travel distance being a good predictor of refugee flows to Turkey, 80 percent of respondents report that they chose to flee to Turkey, instead of another country, due to the ease of transportation. The refugees in Turkey, unlike the later 2015 refugee flows to Western Europe, are nearly 50 percent female. Slightly over 50 percent are minors (under the age of 18). These facts reflect that to large extent Syrian families fled to Turkey together. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["AFAD survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Then the absolute refugee-induced wage change is given by Δݓഥ ൌ ݓഥଵ െ ݓഥ ଴ ൌ ൫ ݁ ஓ ෝ ೢ ∗ ଶ െ 1൯ ∗ ݓഥ ଴. 3. 3 Instrument To allow for a causal interpretation of the impact of refugee flows, see equations (1) and (4), we instrument for the ratio of refugees to working-age population (ܴ ௥ ௧ሻ. 24 Our instrumenting strategy is based on the idea that travel distance, from the Syrian governorate from which the refugee is fleeing to each potential destination Turkish subregion, is a key determinant of refugee location decisions. We use Google Maps to calculate the travel distance Tsr from each Syrian governorate capital (s), to the most populous city in each Turkish NUTS 2 subregion (r). The instrument for the number of refugees at a given point of time in each Turkish subregion is given by: ܫ ܸ ௥ ௧ ൌ ෍ 1 ܶ ௦ ௥ ߨ௦ ܴ ௧ ௦, (5) where Rt is the total number of registered Syrians in Turkey in a year and ߨ௦ the fraction of the Syrian population that lived in each governorate in 2010 (pre-war). 25 Since all our 23 The education categories are at most primary school, secondary school, and higher education. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The age categories are 15 – 19, 20 – 24, 25 – 29, 30 – 34, 35 – 39, 40 – 44, 45 – 49, 50 – 54, 55 – 59, and 60 – 64 years. There are 183 groups since we exclude groups containing less than 40 observations. 24 An additional advantage of the IV approach is that it helps deal with measurement problems. Despite the improved measures of refugee numbers in Turkey by subregion starting in 2014, there is likely considerable measurement error, resulting in attenuation bias in the OLS estimates. For the IV estimates to be consistent, it is only necessary that- conditional on the fixed effects and control variables- the flows of Syrian refugees are uncorrelated with the instrument. 25 Using data from AFAD (2013) we can also weight the aggregate refugee numbers using the Syrian source governorates of refugees in 2012-13 (see Figure 2). Results are qualitatively robust to this alternative instrument and first-stage F-statistics about the same. We prefer the use of the pre-war distribution of population in Syria, Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data from AFAD"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Second, the Turkish LFS asks respondents whether they had previously lived in a different province (one of Turkey ’ s 81 NUTS 3 regions), and if so in what year they moved to their current province. We estimate the impact of refugees on the probability a native moved to a subregion in the past year. Table 9 reports OLS and IV estimates of the impact of refugee on net population growth in subregion (Columns 1 and 2) and gross population inflows (Columns 3 and 4). Net population growth is estimated at the level of NUTS 2 subregions. Population inflows to a subregion are estimated at the individual level (and standard errors clustered by subregion- year). All regressions include subregion and year fixed effects and a year-specific control for log distance from the Syrian border. The first column presents the estimates for the whole sample, subsequent columns for different sub-samples by gender, age and education. For the full sample the net population growth in a subregion is positively correlated with refugee flows, while the IV point estimate is negative (though neither estimate is statistically significant). The probability of a Turkish person migrating to a subregion is negatively correlated with refugee flows (the OLS estimate is highly statistically significant). The IV estimate is of a similar magnitude, but no longer statistically significant. This same pattern broadly holds for both women and men. The only other statistically significant IV estimates are a decrease in the population aged 15 – 24, an age group that is likely more mobile, and of those with medium educational attainment. There is also a decrease in the inflow of low Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Turkish LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "26 education Turkish to a subregion. In sum, there is some evidence that the inflow of Syrian refugees results in a decrease in the number of Turkish living in a subregion. The evidence, however, is weak and the impact unlikely to be very large. 5. PLACEBO TESTS AND ROBUSTNESS CHECKS 5. 1 Placebo Tests The key threat to the validity of our instrument is that there are subregion specific economic trends that are correlated with the instrument, and not fully controlled for by the inclusion of the log distance of a Turkish subregion from the Syrian border. A priori this seems unlikely since the instrument is also based on travel distances, but we can test for the existence of such trends in a pre-period. Specifically, we run regressions that are analogous to those reported in Tables 5, 6 and 7 using data from the LFS 2009 and 2011. As a placebo test we pretend that the Syrian refugees had arrived between 2009 and 2011, rather than between 2011 and 2014, to see if the instrument is correlated with Turkish outcomes in this pre-period. Table 10a presents the results of our placebo tests. For the overall sample there is no statistically significant trend that is correlated with subsequent (instrumented) refugee flows in formal or informal employment, or in log wages. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data from the LFS 2009 and 2011"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As expected the positive correlation between refugee flows and 2011 school attendance rates is significant at the one percent significance level (controlling for the gender, age and education composition of a subregion the point estimate is- 0. 17). Even once we instrument for refugee flows the correlation remains significant at the one percent significance level (a point estimate of- 0. 30). However, once we control for the log distance for the Syria border there is no longer a statistically significant relationship, in either the OLS or IV, between school attendance rates in 2011 and subsequent refugee flows. This suggests that, on account of the inclusion of our distance from the border control, we can rule out the 2012 education reform confounding our estimates. 5. 3 Robustness to Varying Sample of Turkish NUTS 2 Subregions Throughout this paper we use all 26 NUTS 2 subregions of Turkey for identification. However, the results are robust to varying the particular sample of subregions. We report results for two alternative samples. First, we drop the Gaziantep subregion from the estimation. Gaziantep has the highest refugee to population ratio among all regions and reportedly towns with a refugee share of over 30 percent. The inclusion of Gaziantep may skew results if there are any non-linearities in the impact of refugees. Second, we follow Ceritoglu et al. (2015) in only considering nine subregions of Turkey. These are the five Syrian border regions of southeastern Anatolia (Hatay, Gaziantep, Sanliurfa, Mardin, and Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Women experience particularly pronounced displacement in the informal sector and no formal job gains. In contrast, for men displacement in the informal sector is fully offset by employment growth in formal sector, with no net job losses. Clearly, our main findings do not depend on the particular sample of subregions we analyze, and importantly are robust to restricting the analysis to a more homogenous group of regions. 6. CONCLUSIONS This paper combines newly available data on the 2014 distribution of 1. 6 million Syrian refugees across subregions of Turkey and the Turkish LFS, to assess the impact on Turkish labor market conditions. The Syrian refugees in Turkey are overwhelmingly employed informally, since they were not issued work permits, and so their arrival was a well-defined supply shock to informal labor. Consistent with economic theory our IV estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupational upgrading, there are increases in formal employment for the Turkish. This increase though only occurs among men without completed high school education. The employment patterns of women and the high-skilled mean they are not in a good position to take advantage of lower cost 36 Results are also robust to dropping all subregions with close to no refugees. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Turkish LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Anderson, J. (2011): “ The Gravity Model, ” Annual Review in Economics, 3 (1), 133 – 160. Angrist and Pischke (2009). Mostly Harmless Econometrics, Princeton University Press. AFAD (Disaster and Emergency Management Presidency of Turkey) (2013). Syrian Refugees in Turkey, 2013: Field Survey Results. Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015a). “ The Impact of Refugee Crisis on Host Labor Markets: The Case of the Syrian Refugee Crisis in Turkey. ” IZA Discussion Paper 8841. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015b). “ The Impact of Refugee Crises on Firm Dynamics and Internal Migration: Evidence from the Syrian Refugee Crisis in Turkey, ” mimeo. Aydemir, Abdurrahman and Murat Kırdar (2013). “ Quasi-Experimental Impact Estimates of Immigrant Labor Supply Shocks: The Role of Treatment and Comparison Group Matching and Relative Skill Composition, ” IZA Discussion Paper 7161. Baez, J. (2011). “ Civil Wars Beyond their Borders: The Human Capital and Health Consequences of Hosting Refugees. ” Journal of Development Economics 96 (2) November: 391 – 408. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "33 In school: an indicator for whether the respondent currently attends regular education (schooling). This does not preclude also being employed. Retired: an indicator for a respondent who declares that they are not engaged in job search because they are retired. Full / part-time employment: an indicator for whether a person works full or part-time for all people in private sector employment (see above definition of employment). Full-time employment is defined as usual working hours of 30 or more hours per week, part-time employment as usual working hours of less than 30 hours per week. We do not use the indicator provided in the LFS data since there seems to be some confusion in which category 30 hours per week falls (with these evenly divided between full and part-time). Education: we classify people into three education categories. Low education is defined as those with no completed formal education. Medium education is defined as those with at least completed primary education but no high school completion. Higher education is defined as people who have at least completed high school. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["LFS data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Wages: the earnings measure we use is the response to the question “ how much did you earn from your main job activity during the last month? ” In the LFS 2011 there is further information on how much of that income was irregular, for example a bonus payment, but the LFS 2014 no longer provides that breakdown. There is also a measure of the “ number of hours per week worked in the main job ” (both usual and total hours), which can be used to construct hourly wages. Since the hours worked measure does not correspond exactly to the earnings measure and introduces additional measurement error, our preferred wage measure is the monthly wage. We exclude wage observations were respondents report having usual working hours of less than 14 or more than 84 hours per week. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["LFS 2014", "LFS 2011"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9826 Despite the many simultaneous deprivations faced by forcibly displaced communities, such as food insecurity, inadequate housing, or lack of access to education, there is little research on the level and composition of multi­dimensional poverty among them, and how it might differ from that of host communities. Relying on house­hold survey data from selected areas of Ethiopia, Nigeria, Somalia, South Sudan, and Sudan, this paper proposes a Multidimensional Poverty Index (MPI) that captures the overlapping deprivations experienced by poor individuals in contexts of displacement. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using the MPI, the paper presents multi-country descriptive analysis to explore the relationships between multidimensional poverty, displace­ment status, and gender of the household head. The results reveal significant differences across displaced and host com­munities in all countries except Nigeria. In Ethiopia, South Sudan, and Sudan, female-headed households have higher MPIs, while in Somalia, those living in male-headed house­holds are more likely to be identified as multidimensionally poor. Lastly, the paper examines mismatches and overlaps in the identification of the poor by the MPI and the $ 1. 90 / day poverty line, confirming the need for complementary measures when assessing deprivations among people in con­texts of displacement. This paper is a product of the Gender Global Theme. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at atybogale @ worldbank. org, sabina. alkire @ qeh. ox. ac. uk, uekhator @ worldbank. org, fanni. kovesdi @ qeh. ox. ac. uk, juliethsa @ iadb. org, and sophie. scharlin-pettee @ qeh. ox. ac. uk. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 regards to who is poor, how poor they are, and the composition of their poverty. Based on the Alkire-Foster (AF) method, the index provides a summary measure of poverty for the population that can be disaggregated by displacement status and gender of the household head to analyze the variation in deprivations. The MPI can be further broken down by indicator to show the proportion of the population who are poor and deprived in each area. These features of the MPI can inform better policy responses, with interventions and programs targeting the most deprived communities and indicators with the highest headcount ratios. The paper proceeds as follows. Section 2 of the paper reviews some of the existing literature to provide the background and motivation for the analysis, including a summary of the different country contexts covered by the data analyzed in this paper. Section 3 outlines the Alkire- Foster method and the selected dimensions and indicators used to construct the MPI, followed by Section 4, which introduces the data. Section 5 presents the findings, first for results at the national level and then results disaggregated by displacement status. Section 6 analyzes differences in multidimensional poverty by gender of the household head to improve understanding of the gendered aspects of multidimensional poverty in these contexts. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Section 7 compares the MPI results with monetary poverty, with some concluding remarks discussed in Section 8. 2. Background and literature review 2. 1 Poverty and forced displacement By 2019, there were about 51 million IDPs across the world, most of them – 46 million – displaced by conflict and violence, with around five million displaced due to natural disasters (IDMC 2020). According to estimates by UNHCR (2020), the number of refugees reached over 20 million as of the end of 2019. While in many cases, conflict and natural disasters have been temporary, resulting in fluctuations in the number of people fleeing their homes in any given country, the global number of IDPs and refugees has grown almost every year over the last two decades. UNHCR estimates that the number of refugees has doubled over the last ten years. Nearly all IDPs live in low- and middle-income countries, and many have experienced secondary displacement. Overall, about half live in urban areas, with one-fourth in major urban areas (i. e., populations exceeding 300, 000). Since almost all IDPs are in developing countries, governments are often resource-constrained in terms of providing assistance and access to services, and in some cases, government authorities may be a cause of displacement (World Bank Group 2020). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For refugees, the situation is more varied, with most staying close to their country of origin while a smaller minority fled to countries further away. UNHCR (2020) estimates that three-quarters of all refugees were hosted by neighboring countries. To reflect the increase in forced displacement over the last decade and enable sustainable and long-term solutions to refugee situations, the UN Statistical Commission approved a new indicator, SDG Indicator 10. 7. 4, in early 2020 to measure and track the “ proportion of population who are refugees, by country of origin ��� (UNHCR 2020). While the specific challenges for displaced communities depend on the country or host community context, often, in new locations, key challenges confronting IDPs and refugees include food insecurity, lack of livelihood opportunities, and tensions and competition over resources with host communities. The multiplicity of deprivations faced by displaced Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["SDG Indicator 10. 7. 4"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Children and adolescents ’ long-term exposure to households with a more equalized division of domestic labor, as a result of violent or political conflict, warrants further investigation. 2. 3 Country contexts The countries with subnational regions covered in this study, using data from 2017 or 2018, are Ethiopia, Nigeria, Somalia, South Sudan, and Sudan. All are located in Sub-Saharan Africa, have undergone or are currently involved in armed conflict, and are affected by environmental issues such as drought, famine or flooding. Despite some commonalities, each faces a unique set of social, political and economic challenges, which cannot be accurately covered in this study. However, to contextualize the findings, a brief introduction of the country context is presented alongside the poverty estimates by the $ 1. 90 / day measure and the global Multidimensional Poverty Index (MPI). 3 3 An international measure of acute multidimensional poverty, aligined with the 2030 Agenda, that captures deprivations in health, education, and living standards for more than 100 countries (Alkire and Jahan 2018; Alkire, Kanagaratnam and Suppa 2020). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Pregnancy Care A woman who gave birth in the last 2 years did not visit a clinic while pregnant or have a trained assistant during delivery 1 / 16 Physical Safety Any member feels unsafe at home or walking alone14 1 / 16 Early Marriage A member was married before age 19 1 / 16 Living Standards Garbage Disposal Main method of solid waste disposal is dumping, burying in own compound, burning, or other 1 / 24 Drinking Water Main source of drinking water is unsafe, or it takes more than 20 minutes (round-trip) to get water15 1 / 24 Electricity It does not have electricity 1 / 24 Cooking Fuel Main energy source for cooking is solid fuels 1 / 24 Housing It is an unimproved housing type 1 / 24 Sanitation Main toilet facility is unimproved, or shared with other households16 1 / 24 Financial Security Unemployment Any member 15 or older is unemployed and looking for work17 1 / 12 Legal Identification No member has a form of legal identification 1 / 12 Bank Account No member has a bank or mobile money account 1 / 12 The MPI presented here uses equal nested weights with all four dimensions considered to be equally important, and all indicators within a dimension receiving an equal share of the total weight. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The cross-dimensional poverty cut-off is defined as k = 50 %, with those deprived in half or more of the weighted indicators identified as multidimensionally poor. 4. Data Data on forcibly displaced populations are scarce, with many household surveys excluding refugees and IDPs from the sample framework. To ensure that MPI results are representative of these communities and that they can be disaggregated for comparative analysis, an initial review of possible data sets was conducted. Feasibility was determined based on the availability of sufficient sample sizes for forcibly displaced persons for quantitative analyses, as well as inclusion of many of the indicators (on health, education, living standards, etc.) 14 A household is deprived if the respondent reports feeling moderately or very unsafe when alone at home, walking alone after dark, or walking around during the day. In Sudan, the indicator on the ‘ feeling safe from crime and violence when at home ’ was not available, and the indicator only considers answers to the questions on safety when walking alone. 15 Unprotected dug well, unprotected spring, carts with tank, tanker-truck, surface water, or other are considered as unsafe water sources according to international guidelines. See https: / / washdata. org / monitoring / drinking-water. 16 Pit latrine without slab, bucket, hanging toilet, and no facility (open defecation) are considered as unimproved sanitation facilities according to international guidelines. See https: / / washdata. org / monitoring / sanitation. 17 According to the ILO definition, those who did not participate in employment in the last four weeks (and have no work to return to) are actively looking for work and are available to start, or those currently waiting to start work are classed as unemployed. See https: / / www. ilo. org / ilostat-files / Documents / description_UR_EN. pdf. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 5. 2 Composition of poverty Unpacking the headline numbers further, important patterns emerge about the composition of multidimensional poverty among forcibly displaced and host communities in these countries. Overall, the censored headcount ratios (proportion of people who are poor and deprived in a given indicator) are lower among non-displaced communities than among refugees and IDPs, but there are large differences in which indicators are the most salient in different countries. The indicators with the largest difference between the two populations are bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria. These findings reinforce the need for policies and programming that take into account the measured experiences of IDPs and refugees. In this way, the MPI can function both as tool to monitor, track, and bear witness to the lived experiences of forcibly displaced communities, as well as advise on evidence-based interventions that address the needs of the local population. Figure 1 shows the censored headcounts of each indicator in Sudan ’ s MPI, with large differences appearing by displacement. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). As the Sudanese sample surveys IDPs living in the Abu Shouk and El Salam camps, we must understand these deprivations with the background that these settlements were created as emergency and crisis responses rather than durable, long-term solutions (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019). Although Sudan does have a national electric grid that supplies electricity to the urban and peri-urban areas of the nearby city El Fasher, IDP communities living in the camps report limited connection to the city ’ s electricity supply, reflected in the high deprivations in the electricity and cooking fuel indicators. The ad-hoc construction of dwellings in the two camps explains why 71 % of the IDP households in Abu Shouk and 65 % in El Salam live in tukuls or other permanent mud or wood structures (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019: p. 50), both of which register as unimproved housing types. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Bank branches in El Fasher have limited capital for small businesses as their headquarters in Khartoum regard the area as too great a risk and IDPs themselves as riskier investments than their host community peers (UN- HABITAT 2009: p. 8). Exposure to unclean cooking fuels and inadequate housing can lead to poor health outcomes, while lack of access to electricity and a bank account further excludes individuals from labor market integration and livelihood opportunities that would empower forcibly displaced persons to overcome their multiple, overlapping deprivations. Clearly, displacement status puts individuals at a greater risk of poverty than their host community neighbors, and we can unpack those risks in greater detail using the MPI. Results can also be broken down to show the percentage contribution of each indicator to multidimensional poverty (see Figure 2). Among refugees in Ethiopia, lack of a bank account is the largest contributor to poverty, while among host communities, the largest contributor is years of schooling. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 6. Gender differences in multidimensional poverty Next we examine differences in multidimensional poverty outcomes by the gender of the household head. Existing literature points out the limitation of household level MPI analysis in masking the intrahousehold distribution of deprivations, and thus being less sensitive to gender based differences in individual outcomes within the family unit, which might lead to underestimation of inequality and gender gaps (Espinoza-Delgado and Klasen 2018; Franco 2017; Klasen and Lahoti 2020, Rodriguez, 2016). However, as the MPI identifies poverty at the household level, our initial analysis focuses on disaggregated results by the gender of the household head. 19 We acknowledge that this approach has several limitations since most women reside in male-headed households, and the composition of households can change after displacement due to separation of family members, and widowhood. Regardless, the analysis at the household level remains relevant given the high prevalence of female-headed households that emerge after displacement, with the analysis showing large differences across countries between households based on the gender of the head. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Table 6: Percentage of displaced individuals deprived in selected indicators by gender Ethiopia South Sudan Sudan Male Female Male Female Male Female Years of schooling 55 78 * * * 36 63 * * * 32 46 * * * School attendance 16 19 * * 21 29 23 23 Early marriage 3 13 * * * 8 75 * * * 6 50 * * * Unemployment 7 5 * * * 2 0 * 3 3 Legal id 45 46 48 74 * * * 10 10 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Asterisks indicate statistical significance of mean differences between male and female at 1 % * * *, 5 % * * and 10 % * levels. Returning to patterns of household headship, Figure 5 breaks down the variation in censored headcount ratios among refugee households in Ethiopia, depending on the gender of the household head. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["High Frequency Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "22 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). To uncover the main drivers of the observed gender based differences in multidimensional poverty at country level, we study the absolute contribution of the gender difference in each indicator to the overall household gender gap (see Figure 6), calculated as the difference between the censored headcount ratio for males versus females. We find that in Ethiopia, the gender gap that disadvantages female-headed households is mostly driven by the difference in financial insecurity measures (lack of legal ID and bank account) and health measures (early marriage, physical safety, and food insecurity), which is further reinforced by the differences in the living standard and education measures. Female-headed refugee households are more food insecure, live in unimproved housing, have lower access to electricity, are more likely to be married at an early age, and have lower access to legal identification and a bank account. In South Sudan, gender gap that disadvantages female-headed households is mainly explained by the differential in the financial insecurity and health measures, but cumulative gaps in the living standard and education indicators also contribute to the overall gap at the household level. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "E Nigeria Somalia Sudan Host community Refugees Host community IDP Host community IDP Host community IDP No earners 40 % 51 % 62 % 50 % 32 % 64 % 7 % 45 % Remittance recipients only 7 % 8 % 76 % 69 % 16 % 37 %-- Female single earner 12 % 52 % 23 % 37 % 35 % 70 % 24 % 55 % Male single earner 8 % 23 % 4 % 14 % 24 % 50 % 8 % 39 % Majority female earners 3 % 57 % 9 % 28 % 9 % 28 % 15 % 45 % Equal contribution 10 % 24 % 17 % 16 % 23 % 63 % 8 % 41 % Majority male earners 5 % 16 % 15 % 19 % 20 % 52 % 9 % 39 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Overall, the results show that besides gender, displacement status and the number of household contributors plays a key role in the identification and level of poverty. In comparison with female-headed non-displaced households, more female-headed displaced households are classified as multidimensionally poor. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["High Frequency Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "26 value of consumption flow of durable goods. 22 While monetary poverty can measure temporal resource holdings, multidimensional poverty, as a more comprehensive measure, includes chronic and exacerbating sources of poverty. This difference explains the existence of mismatches between individuals identified as monetary versus MPI poor, which are often more prominent in poorer countries (Evans et al 2020). This section examines these differences in the contexts of displacement. Table 9. Percentage of the sample in each poverty category: Rows sum to 100 % Non-poor by both measures Only Monetary Poor Only Multidimensional Poor Monetary and multidimensional poor Ethiopia 38 % 23 % 12 % 27 % N. E Nigeria 13 % 69 % 4 % 15 % Somalia 20 % 32 % 14 % 34 % Sudan 33 % 47 % 4 % 17 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). This table presents the distribution of households in each of the categories in the columns. Thus, each row adds up to 100 %. South Sudan is excluded from this analysis as monetary data is not available for the country. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The lack of overlap might be explained by the relatively recent start of the displacement situation in 2014, when Boko Haram appeared in the north-eastern part of the country. Pape et al (2018) identify two groups of IDPs in this situation: one group representing 74 % of the IDP population that was more engaged in wages and non-farm business before displacement, and another group representing about 26 % of the population, that had significantly more unemployed women. Most of the displaced populations from the first group live in host communities with good access to basic services such as sanitation and water, and safety nets. However, they are disproportionally more likely to be female-headed households and lack access to education, health services, and may face more stringent labor-market barriers. In other words, this group has relatively better housing conditions, but may lack short-term resources that reduce their consumption expenditure. 22 In summary, expenditure in these three categories is computed based on the quantities and prices of a selected list of items in each category. See more details about the computation of the consumption aggregate in Appendix A of the Somali Poverty Profile (Pape et al, 2017). A similar procedure was followed in the other countries of analysis. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "% 83 % 28 % 9 % 14 % Equal contribution 93 % 81 % 83 % 16 % 17 % 17 % Majority male earners 78 % 56 % 57 % 19 % 15 % 16 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 8. Conclusion This paper contributes to the literature by analyzing multidimensional poverty among refugees and internally displaced populations. We observe that forcibly displaced communities are poorer than host communities in each of the five countries ’ sub-populations covered in the surveys, with the difference in incidence between displaced and non-displaced population ranging between 15 and 19 percentage points in South Sudan and Somalia to over 30 percentage points in Ethiopia and Sudan. Displaced communities also experience greater deprivations in nearly every indicator, although there is significant variation in which indicators are the most salient, with having a bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria showing the largest differences between the two populations. The results also indicate gender differences in the experience of multidimensional poverty, with female-headed households more likely to be poor than male-headed households in most of the countries. In addition, displaced households headed by women have a higher incidence of poverty and MPI than non-displaced female-headed households. Particularly, female-headed households in camps have higher multidimensional poverty and intensity compared to their counterparts living outside camps. Dissaggregating further, we find heterogeneity among de facto and de jure female heads. This variation lends itself to further research questions about Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["High Frequency Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Further, as Jordan and the international community develop new approaches that respond holistically to the specific needs of refugee and host communities, more evidence is needed about how gender ‐ based constraints and vulnerability affect refugee women ’ s ability to take up economic opportunities and to access the services and resources they need to enable their families to move out of poverty. Our analysis applies a gender lens to a rich set of microdata on Syrian refugees in Jordan collected by UNHCR between 2011 and 2014. As these data do not capture how the changes in policies affect refugees and the constant evolution of their situation since 2014, the analysis is not intended to directly inform current policy choices and decisions. Instead, our aim is to devise an approach that can provide greater insights into gender ‐ specific barriers, based on the premise that the experiences and potential vulnerabilities of women, men, and children are significantly different in refugee settings. We use household ‐ level data to examine the relationship between poverty and gender for Syrian refugees. Our approach is informed by a body of work in the academic literature that has used household survey data to examine the relationship between the gender of the household head and household 2 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 3 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 4 https: / / data2. unhcr. org / en / documents / download / 64568, accessed August 2, 2018. 5 https: / / reliefweb. int / sites / reliefweb. int / files / resources / 64114. pdf, accessed August 2, 2018. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["microdata on Syrian refugees in Jordan"], "vague_data": ["household survey data", "household ‐ level data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The UNHCR data identify principal applicants for each household and our analysis examines differences in household poverty between households with a female rather than male principal applicant. 6 The principal applicant is the person who receives assistance from UNHCR for the family and is self ‐ selected or selected by the family. This definition of female headship has advantages over the way that household headship is commonly identified in household surveys. An often ‐ noted drawback of the headship variable is that female headship may reflect the enumerators ’ perception about who should be considered a family head rather than who has the most responsibility for the family ’ s welfare in practice. 7 Social norms can also affect whether female respondents self ‐ identify as household heads. For example, some Eritrean returnees who would in other cultural settings be regarded as de jure female headed (single mothers, widows, divorcees, separated women) reported being male ‐ headed. Other Eritrean female returnees who would be considered de facto heads reported headship by absent husbands or male relatives (Kibreab, 2003). Our approach is therefore to distinguish between different types of female and male principal applicant households, using a typology that reflects some of the indicators of vulnerability used by UNHCR. We find that distinguishing between different types of female principal applicant households is important in the setting of Syrian refugees in Jordan. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Most male principal applicants are one of a married couple with children whereas most female principal applicants are single care ‐ givers, single persons or living in non ‐ traditional family groups. While on average female principal applicant households are no more likely to be poor than male principal applicant ones, poverty rates for some types of households are higher when these households have a female principal applicant. Households that have formed because of the unpredictable dynamics of forced displacement, such as sibling households, unaccompanied children, and 6 Identification of the head of the case (as family groupings are referred to in the UNHCR ProGres database) is determined by who best represents the family for case management purposes. It is not assumed that the household will be best represented by a man; a woman or even a child can be a head of a case, depending on standard operating procedures. 7 Even when traditional household survey data are gathered at the individual level, the information is often collected from a single respondent. The respondent is usually the self ‐ identified ‘ most knowledgeable ’ household member, which overwhelmingly corresponds to the ‘ head ’ of the household. In the case of a household survey that solicits information on ‘ headship ’, this information is gathered often through the question: “ Who is the head of this household? ” Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR ProGres database"], "descriptive_data": [], "vague_data": ["household survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 single caregivers, are an extremely vulnerable group and especially so if principal applicant is a woman or girl. Moreover, poverty gaps between male and female principal applicant ’ s for these households remain after humanitarian assistance is received. To understand how gender differentiates the poverty experienced by the Syrian refugees, we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD ‐ HV). The ProGres database for Jordan includes information on refugees ’ registration since 1935. The registration process assigns refugees a unique registration number that serves as a reference for recording data at the initial registration and in all subsequent activities, including decisions on refugee status and right of return or resettlement in a third country, as applicable. UNHCR issues refugees residing in camps a ‘ proof of registration ’ document, which they hold while they remain there. For those who live outside the camp, UNHCR provides an asylum seeker certificate stating that those on the certificate are persons of concern. The asylum seeker certificate allows Syrians to access United Nations (UN) services and assistance provided outside the camps, such as monthly cash support, nonfood goods, and healthcare (NRC and IHRC 2016). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Profile Global Registration System", "Jordan Home Visits round 3"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This assistance is crucial and has been reported to support a meal a day, a better roof, and dignity for Syrians who have fled to Jordan. 8 The JD ‐ HV database has detailed information on expenditure, sources of income, and indicators of household ‐ level welfare, for example, as reflected by recourse to coping mechanisms, standard of accommodation, or access to water, sanitation, and hygiene (WASH). JD ‐ HV data collected between October 2013 and December 2014 were first analyzed in Verme et al. (2016) who produced welfare aggregates and poverty measures to help target benefits and assistance to those most in need. Verme et al. (2016) draw attention to the precarious circumstances of Syrian refugees in Jordan and Lebanon. Around 55 percent of refugees in Jordan are vulnerable to monetary poverty and more than half are vulnerable to food shocks. Family size increases the probability of being poor, with the poverty rate almost doubling if the size of the family goes from one to two members and increasing by 17 percent when the number of children increases from one to two. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["JD ‐ HV database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "UNICEF (2017) reports that for boys, child labor, school violence, and the high costs of schooling (for transportation and stationery supplies) are the main barriers to their enrollment. For girls, barriers include the distance to the nearest school, the high cost of transportation, the need to help with household chores, health problems, and families refusing to educate their daughters. According to the JD ‐ HV database, the most common reasons parents gave for their children not attending school were financial constraints (35 percent), lack of capacity in schools (29 percent), or that children were required to work to support their family (14 percent). By 2016, the enrolment rate of Syrian refugee school ‐ age children in Jordan was 83 percent, 54 percent in formal education, and 29 percent in nonformal education (World Bank 2017b). 10 Refugee Employment It has been hard for refugees to find work in Jordan. The slowdown in growth in Jordan pre ‐ dates the arrival of Syrian refugees and the economy has been increasingly unable to absorb new labor market entrants. Between 2010 and 2016, labor force inactivity increased, employment decreased, and unemployment increased in Jordan (Malaeb and Wahba, 2018). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["JD ‐ HV database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "32 Appendix 1. Variable Definitions Table A1. 1. Variable definitions Variable Definition Age of PA Age of the principal applicant (PA) Children under 5 1 if there are one or more children below the age of 5 (inclusive) in the household Disable 1 if there are one or more disabled persons in the household Education Categorical variable. We classified education of the PA in three groups: below years, 6-11 years, and more than 12 years of education Elderly 1 if there is one or more persons above the age of 65 (inclusive) in the household Entry status Categorical variable. ProGres reports 5 entry statuses of which we selected the three categories with the largest number of PAs: Informal, formal, and smuggled. Expenditure Raw addition of all expenditure categories, which include rent, bills, food, healthcare, education, and others. Family Type Categorical variable. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For a detailed definition see section 4 Household size Number of people included in the case records of each PA in Individual ProGress dataset Wage Income 1 if the household receives income from employment and / or daily or irregular work Income from remittances 1 if the household receives income from remittances Income per capita Raw sum of household income from all sources; work, pension, assets in Syria transfers, donations, other organizations'humanitarian aid, and other divided by household size Male Adults Number of males above 18 (inclusive) in the household Marital Status Categorical variable. The classification includes married PAs with spouse in the household, married PAs without spouse in the household, widowed, single or engaged, and divorced or separated. Proportion of female Number of female divided by the household size Location Categorical variable for 11 Governorates / cities. Ajloun City, Aqaba, Balqa, Irbid Jerash, Karak, Maan, Madaba, Mafraq, Tafilah, Zarqa. In Camp 1 if the household is located in a refugee camp Poverty before UNHCR and WFP assistance 1 if household expenditure before UNHCR plus WFP assistance is below the poverty line (JD50) Poverty before UNHCR assistance 1 if household expenditure after WFP assistance but before UNHCR assistanc is below the poverty line (JD50) Source: Authors ’ elaboration. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Individual ProGress dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 The Government of Chad subscribes to this logic as evidenced by the application decree of Chad ’ s Asylum Law signed in 2023, 1 and the National Response Plan to the Impact of the Sudanese Crisis which is under preparation. The Law and the Plan promote the local integration of refugees, aim to avoid settling refugees in permanent camps and promote self-sufficiency. They offer refugees the right to own land, to engage in formal employment and commercial activities, to move freely, and to access to banking services. While participation is the stated policy objective, the reality is that previous arrivals are almost exclusively living in camps, and that the new arrivals live in “ organized sites ” (as humanitarians now call them), presumably to cope with massive arrivals but with little concrete evidence for the onward movement of refugees. This note explores the size of this four-way benefit (for refugees, hosts, the Chadian state and the international community) by estimating how much could be saved on aid for basic needs consumption by enabling refugees from Sudan to realize their economic potential. For its empirical work the note draws primary on data from the ECOSIT4 survey. 2 In 2018 – 19, Chad became one of the first countries in Africa to capture refugees and host communities in its national household survey. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["ECOSIT4 survey"], "descriptive_data": [], "vague_data": ["national household survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Refugees and Host Communities Household Survey expanded the national Household Consumption and Informal Sector Survey to include a representative sample of refugees and host communities, including Sudanese and host communities located in the east of the country. The remainder of this note is organized as follows. Section 2 presents a short discussion of the literature on the economic participation of refugees. Section 3 compares the characteristics of newly arrived refugees from Sudan with previous arrivals for whom survey data is available, to find that both groups are highly comparable. Section 4 uses the existing data to explore how the basic needs refugees are covered from own-income. Sections 5 and 6 dig deeper by exploring econometrically the correlates of higher incomes of refugees. A discussion of the results and their policy implications follows in section 7, after which section 8 concludes. 2. Benefits of economic participation of refugees Whether or not the arrival of Sudanese refugees in Chad contributes to economic growth is of limited immediate relevance as concerns about the safety of fellow humans drive the response. Nor does any decision maker suggest that hosting refugees is a development strategy Chad should pursue. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Refugees and Host Communities Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 SECTION 1: CONTEXT & INTERVENTION Lebanon ’ s political development system since Independence has been heavily influenced by its confessional system. While originally established to balance the competing interests of Lebanon ’ s diverse religious communities, it is seen as an impediment to inclusive growth and effective governance (World Bank, 2016), and has been closely tied to the economic and social inclusion challenges facing Lebanese youth today. The confessional system of governance has heavily impeded the equitable and efficient distribution of investments and public services. Provision and targeting of public services tend to be guided by considerations of confessional quotas and electoral geography rather than needs- based service delivery that favors the poor. In the absence of effective state institutions, sectarian organizations have played a key role in the provision of social services such as education, health, and welfare support to the most vulnerable groups linked to their electorates, thus deepening a sense of discriminatory and inequitable system (World Bank, 2016; Kraft et al., 2008). Regional disparities are stark, with the bulk of the poor living in peripheral areas (particularly the North and the South), with visible inequality in access to and quality of social services. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "According to the 2012 Institutional Profiles report, the quality of public services and its territorial coverage, which was weak to being with, have significantly deteriorated since 2006. 1 A combination of rising poverty, rising insecurity, and deteriorating public services have further strained inter-communal relations and contributed to deteriorations in social cohesion. Many Lebanese youth do not trust their state and become disillusioned as they are not able to affect their own life or contribute productively to society at large. 2 Political and civic engagement is reported to be low (Status of Women in the Middle East and North Africa Survey Project, 2010). 3 In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and large numbers of refugees, many Lebanese 1 On the quality of public services indicator, Lebanon ’ s score declined from 2. 5 in 2006 to 0. 8 in 2012 on a 4-point scale. On the territorial coverage indicator, its score went down from 2. 7 in 2006 to 1. 5 in 2012. 2In a Gallup World Poll, Lebanese reported low confidence in (a) their national government (37 percent) and the judiciary, (b) the honesty of elections (15 percent), and (c) the honesty of government (4 percent) (World Bank, 2016). 3 According to the SWMENA survey, only 18 percent of Lebanese women are members of an organization, compared to 34 percent of men. Men are more likely to be members of a political organization than women (21 percent of men vs. 7 percent of women), whereas women are more likely to be active in religious groups and charity organizations than men. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["SWMENA survey", "Middle East and North Africa Survey Project", "Gallup World Poll"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Challenges around skill mismatch exacerbate exclusion, as many young Lebanese lack the skills and competencies demanded by private sector employers, particularly ‘ soft skills ’. To address some of these challenges, the Lebanese government (GOL) identified volunteerism as a mechanism to enable diverse youth to work together for improved community assets and service delivery as well as increased employability. In September 2012, the GOL issued a Decree (Number 8924 / 2012) that created a new extra curriculum program that requires secondary school students to complete 60 hours of civil work. In addition, the Ministry of Social Affairs (MOSA), through its Volunteering Department, launched annual action plans for the implementation of youth volunteer summer camps across Lebanon. 4 Father ’ s education and residence (region and location of school) are the two largest contributors to inequality of opportunity in students ’ math test scores, accounting for 44 and 23 percent of total inequality, respectively (World Bank, 2016). 5 According to the 2013 Gallup Poll, 90 percent of respondents in Lebanon agreed with the statement that knowing people in high positions is critical to getting a job. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Gallup Poll"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 as self-confidence, responsibility, and respect. Additionally, the curriculum includes workplace readiness skills, such as interviewing and time management. SECTION 2: STUDY TIMELINE & DATA A quasi experimental impact evaluation design was embedded into the NVSP. As mentioned before, the NVSP received 38 applications from eligible NGOs. Per well-developed selection criteria, 8 the highest 22 ranked proposals were selected to receive funding. Each of the 38 proposals included a list of 50 youth (the minimum number of youth set by the NVSP) who would benefit from the project if selected for funding. However, as mentioned before, the 22 selected projects benefited a total of 1, 296 youth, exceeding the set target of 1, 100 volunteers. Of the 50 volunteers included in each of the 38 proposals, 22 youth per proposal were randomly selected to participate in the impact evaluation study. Therefore, the initial sample size of the study comprised a total of 825 youth: 473 youth who served as the treatment group (representing the 22 selected NGOs that received NVSP funding) and 352 youth who served as the comparison group (representing the 16 non-selected NGOs). However, two NGOs refused to participate in the study once informed that their proposals had not been selected for funding. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Therefore, the final sample size consisted of 759 youth, of which 473 treatment and 286 comparison. Detailed baseline data were collected through face-to-face interviews from July to September 2015 prior to implementation. The actual implementation varied between projects and ranged between the second half of August and end of December 2015. Sampled youth from both selected and non- selected NGOs were invited to fill out a questionnaire with detailed information on volunteers ’ socio-economic backgrounds, education levels, interests and attitudes towards volunteering, employment, soft skills, as well as social cohesion values. Follow-up data were collected between November 2016 and March 2017, approximately one year following the start of implementation, through phone and face-to-face interviews. The questionnaire contained the same modules asked and collected at baseline. Despite the high 8 Proposals were ranked based on four main selection criteria: institutional appraisal (25 points), technical appraisal (40 points), project impact (25 points), and financial appraisal (10 points). There was also a fifth criterion related to sustainability of volunteering activities, which was assigned a bonus score (5 points). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The report focuses on intent-to-treat (ITT) estimates, measuring the impact of offering volunteering opportunities and soft skills training independently of actual take-up. 9 We estimate the following individual-level intent-to-treat regression: 𝑌 ௜ ௧ ൌ 𝛼 + 𝜇 ௡ ൅ 𝛽𝑇௧ + 𝛾𝐷 ௜ + 𝛿ሺ𝑇 ∗ 𝐷ሻ ௜ ௧ + 𝜀 ௜ ௧ (1) where 𝑌 ௜ ௧ is the outcome of interest for respondent i in period t, 𝑇௧ is a post-treatment year binary variable, 𝐷 ௜ is a binary variable for being assigned to the treatment, and 𝜇 ௡ is a fixed effect for NGOs. 𝛼 represents the baseline average for the outcome of interest for non-selected youth. 𝛽 is the difference in after-and- before intervention in outcomes for non-selected youth. 𝛽 ൅ 𝛿 is the difference in after-and- before intervention in outcomes for selected youth. 𝛾 is the difference in 9 Due to some procurement delays that caused a big time-lag between baseline data collection and actual NGO project implementation, many of the volunteers who belonged to selected NGOs and who were randomly selected to participate in the impact evaluation study dropped out after their baseline data were collected and were replaced by other volunteers. Project monitoring data reveal that 23 percent of volunteers assigned to treatment did not actually end up participating in the NVSP. Given the relatively high number of non-compliance, we are unable to perform Local Average Treatment Effects (LATE) analyses to understand the impact of participating in NVSP on outcomes of interest. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Project monitoring data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 to 32 percent (see Table A1 in appendix). The total number of internally displaced people (IDP) outnumbers the stock of refugees in SSA and in the world but overall has followed a similar trend compared to the number of refugees (by country of origin). 1 Major civil wars in Central Africa mainly explained the peak in 1993 and 1994 and the increase at the end of the 1990s. Figure 1. Refugee population by origin, 1990 ‐ 2013 Note: Authors ’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014. Data from 2007 to 2013 include people in refugee ‐ like situations. Persons in refugee ‐ like situations include “ groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained ” (UNHCR 2014: 39). Refugees in Africa seem to have mainly remained in Africa. Although SSA also hosts refugees from other regions, the closeness of the ‘ blue ’ and ‘ red ’ lines in Figure 2 ‐ representing the number of refugees originating from and hosted in SSA ‐ is an indication that most refugees cross borders within Africa. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 (UNHCR, 2014), developing countries hosted 10. 1 million refugees or 86 percent of the world ’ s refugees. Contrary to what has been sometimes claimed in popular media, refugees are not “ invading ” the higher ‐ income countries. Actually, SSA has been hosting more refugees than sending them since 1990. The divergence of trends occurring in 2005 is certainly related to large inflows of refugees from North Africa and the Middle East. The second peak in 2011 corresponds to the uprisings that spread across several Arab countries (Egypt, Libya, Syria, Tunisia and Yemen), and the recent one in 2013 to the large outflows of refugees from Iraq, Syria and Yemen. Figure 2. Refugees and Internally Displaced People in SSA, 1990 ‐ 2013 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). Due to changes in classification and estimation methodology in a number of countries, 2007 figures are not fully comparable with pre ‐ 2007 figures (see also footnote 1). Gathering data on internally displaced people is much more challenging since most existing data on IDPs are incomplete or unreliable. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The vulnerability of the Internally Displaced People in SSA has certainly been overlooked for too long, but the increased support provided by UNHCR is an encouraging but challenging sign in that respect. Figure 3. Refugees and Internally Displaced People in SSA, 2003 ‐ 2013 Source: Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). The annual number of IDPs is collected from the IDMC (2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2012, 2013, 2014) annual reviews. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While camps have been recognized as posing serious challenges (Jacobsen and Crisp 1998), it is quite striking to observe that this organizational feature is not as spread in other regions of the world as in SSA. At best, only 28, 25 and 15 percent refugees are hosted in planned / managed camps in Asia, Americas, and the MENA region, respectively. Such figures are based on the most recent year available (2013) and may change significantly following the large inflows of Syrian refugees into Egypt, Lebanon, Iraq, Jordan and Turkey. Nonetheless, the differences are sufficiently striking to believe that this is a distinct feature of refugee hosting in SSA. 2 UNHCR defines a protracted refugee situation as “ one in which 25, 000 or more refugees of the same nationality have been in exile for five years or longer in a given asylum country ” (2012: 23). 3 The figures are based on refugees (including those in refugee ‐ like situation). Persons in refugee ‐ like situations include “ groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained ” (UNHCR 2014: 39). The number of refugees and people in refugee ‐ like situation for which demographic data is available does not necessarily equal the total number of refugees. However, for SSA, there is little difference between the two. We also restrict the number of refugees to those whose accommodation is known by the UNHCR (approximately 19 % in the world and 8 % for SSA). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["demographic data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Figure 5. Share of refugees hosted in camps, 2013 Source: Authors ’ presentation based on UNHCR Global Trends 2013 (UNHCR 2014). In summary, investigating the recent trends in forced displacement in Sub ‐ Saharan Africa points to the regional nature of this displacement, emphasizing the unfortunate increase in refugee movements in Eastern Africa over the most recent years. Such regional emphasis also takes some distance from the widespread view that refugees are mainly moving to Europe or other developed countries. In 2013, about 3. 7 million refugees originated from SSA but about 5. 6 million were hosted there. Most refugees from SSA remain in Africa. Refugees are mainly hosted in camps in peripheral and poor areas. The next sections will explore how refugees and hosting communities are affected by such forced displacement. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 3. Refugees as Agents of Their Own Destiny 3. 1 The Composition of Africa ’ s Refugee Population and Its Consequences One of the first elements that catch the eye in Figure 6 is the difference in the composition of the refugee population in Africa compared to the rest of the world. The share of children and women among refugees is higher in Africa than elsewhere, in particular East and West Africa stand out here. This is, at least partly, a consequence of Africa ’ s younger, general population, but other forces could be at work as well, e. g. higher mortality of adult males in Africa or adult males staying behind or being separated from the rest of the household. It does mean however that, relative to other areas, more attention should be going to the needs and capacities of women and children in Africa. This means, for example, adaption of and increased supply of schooling and health services. Figure 6. The composition of refugees by age and gender, 2013 Source: Note: UNHCR statistics (UNHCR 2014). Asia excludes Australia, Japan and New Zealand. Americas exclude Canada and the United States. These percentages have been calculated by country when demographic data are available for at least 30 % of the total. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["demographic data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 causes of death. The Danish Epidemiology Science Centre (1999) found severe malnutrition and high mortality in a survey of 422 refugee children in Guinea ‐ Bissau. They report higher malnutrition and higher mortality for children living in a non ‐ camp setting, compared to children living in a camp. The Goma epidemiology group (1995) found high prevalence of child mortality as well as acute malnutrition among children in refugee camps in Eastern Zaire, especially in female headed households. The magnitude of the difference between ‘ normal ’ mortality in the country under study, in the absence of conflict and the mortality in a refugee camp, depends on several parameters: the health infrastructure in the country as well as in the camp, the food available to camp and non ‐ camp residents, the frequency of visits by nurses or doctors, the intensity of the conflict (e. g. attacks on camps), and so on. Thus, the results are highly dependent on the context. For example, Singh et al (2005) do not find a difference in under 5 mortality among refugee versus non ‐ refugee households in western Uganda and South Sudan, whereas Verwimp and Van Bavel (2005) find higher child mortality and fertility among Rwanda refugees in Congo versus Rwandan women who did not became a refugee. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of 422 refugee children in Guinea ‐ Bissau"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "When general living conditions in one ’ s residence or home area are worse compared to a camp environment, e. g. because health services are available in the latter, mortality may also be lower in the camp. The strong presence of children in Africa ’ s refugee population implies that we should also look at the potential long ‐ term effects of forced displacement on survivors. Given the composition of the refugee population, such long ‐ term effects will be more important in Africa compared to elsewhere. Few studies have followed children exposed to forced displacement over a long time to directly infer the long ‐ term effects of forced displacement, in particular on health, education and labor market participation. Most studies of the long term effects of conflict use an indicator of exposure to violent conflict, but few of them have forced displacement as one of the indicators. There is however a very well established literature (see Currie and Vogl, 2013 for an overview) on the long ‐ term consequences of deprivation in early childhood which can be applied to the situation of refugees. If young children between the ages of 0 to 3 years old are exposed to malnutrition, disease, stress and violence during episodes of forced displacement, then, this literature shows that this deprivation will have negative long ‐ term effects. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 Revealingly, in Betts et al. ’ s (2013) survey of refugees in Uganda, 96 % of all interviewed households in the capital and 70 % outside the capital said they owned and used a mobile phone. They use this mobile phone to communicate with customers and suppliers, to get market information and to transfer money. Half of the urban refugees and 11 % of rural refugees also have access to the Internet. 4. Refugees As a Burden? As pointed in Section 2, most refugees in SSA are hosted in neighboring countries. Most of these hosting countries are likely among the least developed countries. It has been argued that these refugees may constitute an additional burden in terms of economic development in hosting countries (Mabiso et al. 2014). UNHCR (2014: 17) implicitly recognizes that potential burden by suggesting that the ratio of the size of the country ’ s hosted refugee population to its average income level can provide a proxy measure of the burden of hosting refugees. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of refugees in Uganda"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 Note: Poverty is defined as the percent of the population living with less than $ 2 a day (World Development Indicators database). The annual number of refugees in each country is given by the Center for Systematic Peace (http: / / www. systemicpeace. org /). 4. 2. Lessons from Case Studies in Kenya, Tanzania, and Uganda Given the limits of cross ‐ country comparisons, we present below three short case studies on the impact of protracted refugee situations on hosting communities. These case studies were not chosen based on a systematic review but they are sufficiently close to each other to allow for comparative learning. These case studies are also those emerging from a growing literature on the quantitative assessment of the impact of refugees on hosting communities (Mabiso et al. 2014). Case Study # 1: The protracted refugee situations in Tanzania Tanzania has been known as a refugee ‐ hosting country for long due to its peaceful history and its location surrounded by conflict ‐ affected countries (Burundi, Rwanda, Uganda, Mozambique). The first president of Tanzania, Julius Nyerere, welcomed most of refugees as a sign of pan ‐ African solidarity in the post ‐ independence periods from many African nations. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["World Development Indicators database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "25 contributions to the local communities. More than other studies, this analysis points to the transfer of physical and human capital by refugees as an important source of benefits for the local economies. Interestingly, Kreibaum (2016) provides a more quantitative approach to the issue by assessing the impact of an increase in the presence of Congolese refugees on the hosting population in the Southern and Western parts of Uganda. The results indicate a positive ‐ although small in magnitude ‐ impact on the hosts ’ welfare (consumption per adult equivalent) but with distributional effects. Those depending on wage income and transfers experienced a deterioration in welfare, suggesting labor substitutability with rural landless workers. That seems to constitute a commonality with the Tanzanian case study. In addition, increase in the provision of private education services are also found, which is consistent with the move to the so ‐ called self ‐ reliance strategy in Uganda (see below). A major contribution of this paper is to contrast these results to the Ugandan households ’ perceptions in local communities. Conditional on assuming a common trend (that could not be tested with the available data), people are found to perceive their living conditions as having worsened off in areas with a higher number of refugees. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "40 Sub ‐ Saharan Africa 8604000 5895000 7055000 5406100 5068000 N. A. MENA 6230000 8000000 6675000 8592900 10892000 N. A. Asia and Pacific 4325000 2405000 3392000 2128800 5490000 N. A. (excl. Australia, Japan, New Zealand) Americas 1126000 1280000 2176000 2900000 3661000 N. A. (excl. North America) Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). As indicated in Figure A1, these data are much lower compared to those provided from 2003 by IDMC but provide a longer time series. UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "41 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Since some adjustments could take place over time, we always use the figures from the last available report. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Most papers with few exceptions use standard OLS estimators or some of its variants (Table 2). Two papers use general equilibrium models (Bodvarsson, Van den Berg, and Lewer 2008; Hercowitz and Yashiv 2002) and two papers simply compare means between treated and non-treated groups resulting in simple difference estimations (Card, 1990 and Alix-Garcia and Bartlett, 2015). [Table 2] The unit of observation varies depending on the data at hand. Most studies rely on household survey data where individuals or households are the unit of observations and most studies include some regional dimension (more frequently administrative areas). Where longitudinal or panel data are available time is also included. Other choices for unit of observations include skills or education level, various types of population groups (based on gender, age etc.), and, in a few cases, economic sectors, industry or labor market segments. The use of fixed effects varies. Some papers use the full set of parameters depicting units of observation (for example, household, region and time fixed effects in equations where the unit of observation is constructed using household, region and time). Other papers use subsets of these parameters whereas other papers introduce variables that are not used to identify the unit of observation. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Sarvimaki (2011) uses the elements of the government ’ s placement policy as instruments (i. e. the proportion of a municipality ’ s population speaking Swedish and the hectares of potential agricultural land). Other authors focus instead on the counterfactual group testing alternative designs of the control group, sometimes including placebo groups and other times recurring to matching methods. The choice of matching methods varies from ordinary methods such as nearest neighbor to more recent advances such as Synthetic Control Methods (Abadie and Gardeazabal, 2003). The inclusion of fixed effects is common to almost all papers although the choice of fixed effects can be very different, as described above. Only one paper uses Fixed Effects (FE) and Random Effects (RE) formal models in conjunction and tests for differences (Esen and Binatli 2017). Cross-section econometrics is, by far, the method of choice even if time is included into the equations but we also found three papers employing time-series models (Carrington and de Lima 1996, Makela 2017, Fakih and Ibrahim 2015). Only few papers are able to exploit panel data (Foged and Peri 2015, Depetris-Chauvin and Santos 2017) and several of them use the same data set (Maystadt and Duranton 2018, Maystadt and Verwimp 2014; Ruiz and Vargas-Silva 2015, 2016, 2017). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Not all cross-sectional studies have multiple rounds of comparable data, covering the period before and after the crisis. When comparing impacts between locations within a country, cross-sectional data also usually does not allow to capture impacts on those who moved out and to differentiate impacts between those who were already there before the shock and those who moved in afterwards. Some of the models based on administrative areas qualify as spatial econometrics models in that they use estimation methods that derive from this literature and are published in spatial econometrics journals. Studies that compare different areas within a country are not only confronted with the potential endogeneity of the size and skill composition of the inflow and the choice of destination, but also with the endogenous reactions of the host community. Local workers might respond to the labor supply shock by dropping out of the labor force, investing in education, occupational upgrading or moving to other areas and diffusing the impact of the inflow. Even if local workers do not respond to wage variations, capital flows may equalize capital / labor ratios within the country, labor-intensive industries might move towards the regions with a high refugee or IDP influx or firms might use more labor-intensive production technologies. The reactions of the host country workers, investors and firms are medium-to long-term in nature and will play less of a role in the short-term if there are large, sudden and geographically concentrated inflows. Some of the papers explicitly analyze these potential channels, notably migration of local workers, and, to a lesser extent, occupational upgrading. Outmigration of hosts is a critical complement to the labor market analysis and excluding this outcome can lead to an underestimation of the impacts of forced displacement on the labor market outcomes of natives. The papers we reviewed that looked at tasks complexities and the question of substitution vs complementarities between refugees and natives found occupational upgrading among Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Finally, the expenditure shock which we discussed in the theory section (international aid or an increase in public spending associated with the forced displacement crisis) is considered by only a handful of papers. This is a possible confounding factor of the impact of forced displacement on host communities and one that is not easily addressed with the use of fixed effects. This is clearly a shortcoming of this literature that will require increased attention in the future. 4. Meta-analysis of empirical results 4. 1 Data The literature review covers 49 papers spanning over a period of 29 years. We were not able to find published papers prior to the work by Card in 1990, which effectively started this literature, and there is a relatively low interest in this topic between 1990 and 2011 with only one or two papers published per year. With the Syrian crisis starting in 2011 and the EU crisis in 2015 the number of papers per year increased by several fold. Most of the papers and results considered in this review are therefore very recent (Figure 2). We used academic databases and search engines (EconLit, Social Science Research Network, JSTOR, Google Scholar) and searched websites of institutions with relevant working paper series (NBER, IZA, ERF and others). Relevant unpublished papers were included by searching agendas of workshops and conferences organized during the past few years. From the papers reviewed, we selected a total of 762 results summarized in Table 3. The results database was compiled as follows. For each paper we focused on the results that the authors considered the main and Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 conclusions on the role of individual characteristics. As for employment, results on wages could not be predicted by basic theory and are in clear contrast with popular beliefs. 5. Conclusion The paper reviewed 49 empirical studies that focused on estimating the impact of forced displacement on host communities. This literature covers 17 different displacement situations in high, medium and low- income countries covering the impact on the labor and consumer markets. A total of 762 results have been used for the meta-analysis. To our knowledge, this is the first comprehensive review of this literature. The empirical modeling analysis highlighted the main traits of this literature. By definition, all studies operate ex-post, after the displacement crisis has taken place. The unexpected nature of the crisis and the randomness of the allocation of displaced persons are two elements used to defend the natural experiment assumption. However, all papers address the central question of endogeneity. The instrumental variable approach is the dominant method to address endogeneity issues and instruments tend to focus on either distance from the shock or previous location of migrants. Double difference and linear elasticity models are the dominant choice of estimation models with matching and placebo counterfactuals often supporting these choices. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Looking also at the impact of IDPs in Colombia on wages, Morales (2017) uses a labor force survey, census data and registry data to study short and long-term effects as follows: 𝑆ℎ𝑜����𝑡 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ௧ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ௧ ି ଵ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ௧ ൅ 𝜆 ௠ 𝑋 ௠ ௧ ൅ 𝛾௧ ൅ 𝛿 ௠ ൅ 𝛿 ௠ 𝑇 ൅ 𝜀 ௜ ௠ ௧ 𝐿𝑜𝑛𝑔 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ൅ 𝜆 ௠ 𝑋 ௠ ൅ 𝛿 ஽ ௠ ൅ 𝜀 ௜ ௠ where y is the log of wages, i, m, and i are individuals, municipalities and time respectively, 𝑋 ௜ ௠ ௧ are individual controls, 𝑋 ௠ ௧ is the log of total population or other municipality controls, 𝛾௧ and 𝛿 ௠ are time and municipality fixed effects, 𝛿 ௠ 𝑇 are municipality time trends, 𝛿 ஽ ௠ are department fixed effects and d is the inflow of IDPs defined as 𝑑 ௠ ௧ ൌ 100 𝑝𝑜𝑝 ௠ ௧ 𝑓 ௠ ௧ where 𝑓 ௠ ௧ is the total number of IDPs arriving in municipality m at time t. The same variable without the t subscript is used for the long-run effects equation. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey", "census data", "registry data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Poverty remains widespread and social indicators are well below the average for Sub-Saharan Africa. Chad is ranked 173 among the 177 countries surveyed in the 2006 UNDP Human Development Report. The incidence of poverty (defined as the proportion of households with annual spending below what is necessary to meet minimal needs) is estimated at 55 percent according to a 2003 household survey; an estimated four-fifths of the population of about 8. 8 million is living on less than a dollar a day3. Of the population over 15 years old, more than 73 percent (and 76 percent of women) are illiterate. Access to potable water has improved over past years, but is still limited to one out of three people in 2005. Less than two percent of the population has access to electricity and only 1021 kilometers of roads has been paved on a surface area of over 1. 2 million square kilometers. As already mentioned, Chad has recently become oil producing country; however, the economy remains largely agricultural and pastoral. About 80 percent of the country ’ s population lives in rural areas and continue to make their living4 from agriculture and livestock. Cotton is the principal cash crop, employing about 300, 000 families. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["2003 household survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "29 should be entered immediately into the accounts; and (c) Payment must be recorded as soon as they are made. Budget implementation should be reviewed periodically to ensure that programs are implemented effectively and to identify any financial or policy derailment. The review of budget execution should cover financial, physical and other performance indicators. Development budgets are often beset by implementation problems because of insufficient implementation capacities and other factors such as delays in mobilizing external financing, overoptimistic implementation schedules or difficulties in importing supplies. It is thus important to have in place mechanisms for reviewing the most significant or problematic projects. These could consist of a regular monthly or quarterly review of projects within the line ministries and a midyear review involving line ministries and central agencies29. The government has taken steps to improve the tracking of budget expenditure until the intended destination, particularly investments spending, for which a tracking survey was entrusted in 2005 with the Ministry of infrastructures. In addition, the ministries took themselves certain internal initiatives, in particular in health and education sectors, but the action plans of these ministries were not updated as envisaged in 2005, and there are neither reliable benchmark, nor quantitative targets as regards improvement of the arrival of the expenditure at intended destination in these sectors. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["tracking survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A survey was carried out in the health sector (health centers) with the support of the World Bank in 2005, but has not been yet validated. In the rural development sector, where the first expenditure tracking survey between the decentralized center and services was to be carried out in 2005 on PEFA funds, the survey is yet to be carried out. It is critical that adequate management measures are gradually put in place to stop the current waste of resources. These measures include, inter alia, the accounting improvement of material, improvement of the inventory and delivery control, and especially a transparent planning of the deliveries, including, the posting of the received deliveries, their comparison with the planned deliveries and their certification by users within each service. It would also be desirable that in each ministry, an action plan is prepared for the implementation of these measures, on the basis of existing ones, and with target indicators as regards improvement of the arrival of the expenditure at intended destination. The social ministries could usefully open the way in this field, on the basis of some projection already carried out. Without a quantitative and qualitative improvement of the arrival of the expenditure at their final recipient, the increase in the budgetary appropriations to the priority sectors will hardly be translated into substantial concrete results on the ground. 29 It is difficult to make accurate forecasts for the implementation of certain programs or developments in economic parameters such as inflation or interest rate. Some immediate needs that were not foreseen during budget execution may appear during budget execution. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["expenditure tracking survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 as the Demographic and Health Surveys (DHS), though researchers have reduced under-reporting by providing special training to interviewers, placing greater emphasis on respondents ’ privacy and safety, and allowing women multiple opportunities to disclose their experiences (Ellsberg et al., 2001a; Garcia Moreno et al., 2003; Ellsberg et al., forthcoming). Estimates of the magnitude of the problem Population-based surveys have found that between 10-70 % of women report being physically assaulted by an intimate male partner at some point in their lives (Heise, Ellsberg and Gottemoeller, 1999). See Annex A for estimates from many recent population based studies (Ellsberg et al., forthcoming). Findings from a multi-country study on domestic violence and women ’ s health carried out by the World Health Organization in fifteen sites and ten countries found that between 13-62 % of women had experienced physical violence by a partner over the course of their lifetime, and between 3-29 % of women reported violence within the past year (Figure 1. 1). Figure 1. 1. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": [], "vague_data": ["Population-based surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Percentage of women who reported sexual violence by an intimate partner (ever), physical violence by an intimate partner (ever), and physical violence by an intimate partner in the past 12 months. 50 % 59 % 30 % 27 % 34 % 13 % 31 % 50 % 62 % 34 % 33 % 47 % 23 % 41 % 37 % 20 % 10 % 14 % 6 % 17 % 23 % 47 % 23 % 29 % 31 % 6 % 23 % 40 % 42 % 49 % 3 % 18 % 19 % 16 % 8 % 19 % 16 % 8 % 13 % 29 % 3 % 17 % 25 % 13 % 15 % Bangladesh (Urban) Bangladesh (Province) Brazil (Urban) Brazil (Province) Ethiopia (Province) Japan (Urban) Namibia (Urban) Peru (Urban) Peru (Province) Thailand (Urban) Thailand (Province) Tanzania (Urban) Tanzania (Province) Serbia Samoa sexual violence ever physical violence ever physical violence past 12 months Source: Unpublished data from the WHO Multi-Country Study on Women ’ s Health and Domestic Violence Against Women. The final published comparative report is forthcoming. Cited with permission. Prevalence data on sexual violence is even more limited than physical violence. However, evidence suggests that a substantial proportion of girls and women have experienced child sexual abuse, forced sex and other forms of sexual coercion in virtually every setting of the world. For example, population-based studies have asked about “ forced ” sexual debut among sexually experienced young people and found rates from 7 % (New Zealand), to 46 % (in the Caribbean) (Heise and Garcia Moreno, 2002). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["WHO Multi-Country Study on Women ’ s Health and Domestic Violence Against Women"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A growing number of low and middle-income countries have tried to improve the law enforcement response to gender-based violence by training professionals, reorganizing police and courts, and trying to provide a more comprehensive response to survivors. Evidence of effectiveness is relatively limited; most well-evaluated initiatives come from high-income countries, and the lessons learned may not be applicable to developing countries. Evaluations of law enforcement reforms in low and middle income countries have typically been limited to case study approaches drawing from police records (notorious for under-reporting), qualitative perspectives from key informant interviews, intermediate outcomes such as changes in attitudes and knowledge among police and judges, and interviews with small numbers of women who have sought legal redress. Population-based data collection, control groups, or follow-up among more than a handful of survivors are rare. Nonetheless, the following initiatives illustrate the types of efforts that have produced important lessons learned. Training personnel in the police and judiciary and other parts of the justice system Throughout the world, organizations have launched efforts to improve the knowledge, attitudes, and practices of justice sector personnel regarding gender-based violence. Some law enforcement institutions organize training internally, as did South Africa following passage of the 1998 Domestic Violence Act (Usdin et al., 2000). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["police records"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "29 evaluations have been conducted (Renton et al., 2000; Shaw, 2000; Shaw, 2002a; Shaw, 2002b; Paine et al., 2002; White, Greene and Murphy, 2003; Interagency working Group, 2003). For example, the Medical Research Council (from the United Kingdom) conducted an external evaluation of program effectiveness in the Gambia using key informant interviews, focus groups, and a KAP (knowledge, attitudes and practices) survey in four villages, including two program sites and two controls. That study found that the Gambia program improved self-reported attitudes and behaviors related to violence against women. Specifically, the program reduced the social acceptability of wife-beating at the community level and appeared to produce a corresponding drop in that behavior. Qualitative findings from other Stepping Stones sites suggest similar benefits. Program H (Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru) is being carried out by four NGOs. It aims to change gender norms and sexual behaviors in Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru (Barker, 2003; White, Green and Murphy, 2003; Guedes, 2004). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "By 2002, ReproSalud had reached over 123, 000 women and 66, 000 men. Qualitative and quantitative evaluation data suggest that the community-based PLA approach had a positive impact on attitudes and behaviors related to gender based violence (Rogow and Bruce 2000; Ferrando, Serrano, and Pure, 2002, cited in Boender et al., 2004). The quantitative evaluation (using community based surveys) was complicated by the fact that the project coincided with a period of strong investment by the Ministry of Health, which made it difficult to isolate the project ’ s impact. Gender-equitable attitudes and practices increased significantly in both intervention and control communities, though improvements in intervention sites were slightly higher. The qualitative data suggested a much greater difference in intervention and control sites and gathered evidence of dramatic changes in social relations and men's behavior. Respondents spoke at length about decreased alcohol consumption, domestic violence, and forced sex in all intervention villages studied. In the words of one 35 year-old woman,\"Before, they brutally forced sex. They hit, especially when they were drunk. Now, no more\"(Rogow and Bruce, 2000, page 20). Individual behavior change strategies Many other programs have attempted to produce individual (rather than community-level) behavior change by working with individual men and boys. White, Greene and Murphy (2003) reviewed the literature on such programs aimed at men. That review suggests that less information is available on the effectiveness of individual behavior change strategies compared to community-level approaches. Some Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["quantitative evaluation data", "community based surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "List of acronyms CAR Central African Republic DHS Demographic and Health Surveys DRC Democratic Republic of Congo DTM Displacement Tracking Matrix FCS Fragile and Conflict-affected Situations GIDD Global Internal Displacement Database GIS Geographic Information Systems IASC Inter-Agency Standing Committee ICRC International Committee of the Red Cross IDMC Internal Displacement Monitoring Centre IDPs Internally Displaced Persons ILO IOM International Labour Organization International Organization for Migration IRRS International Recommendations for Refugee Statistics JIPs Joint IDP Profiling Service LSMS Living Standards Measurement Study MICS Multiple Indicator Cluster Surveys NGOs Non-Governmental Organizations NRC Norwegian Refugee Council OCHA Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat OAU Organization of African Unity ODA Official Development Assistance OECD Organisation for Economic Co-operation and Development SDG Sustainable Development Goal SKOPE Somalia Knowledge for Operations and Political Economy SuTPs Syrians under Temporary Protection UAV Unmanned Aerial Vehicle UNDP United Nations Development Programme UNHCR United Nations High Commissioner for Refugees UNITAR United Nations Institute for Training and Research UNOSAT UNITAR ’ s Operational Satellite Applications Programme UNRWA United Nations Relief and Works Agency for Palestine Refugees in the Near East UNSD United Nations Statistical Commission WFP World Food Programme Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Global Internal Displacement Database", "Demographic and Health Surveys", "Geographic Information Systems", "Multiple Indicator Cluster Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "displaced due to armed conflict, situations of generalized violence and violations of human rights. 28 Data on IDPs monitored by IDMC are disaggregated and currently published separately for conflict-induced displacement and disaster-induced displacement. 29 At the country level the IOM ’ s Displacement Tracking Matrix (DTM) 30 provides data on IDPs in both conflict and natural disaster settings (activated in all major natural disaster contexts in recent years). Global data on conflict-induced internal displacement reflect variations in how IDPs are defined across situations. There is no consensus on how far a person must flee in order to be considered internally displaced. The definition of internal displacement for nomadic populations, which account for a significant share of IDPs in the Horn of Africa and increasingly in the Sahel, is open to controversy. 31 Moreover, while some countries register IDP children born in displacement (e. g. Azerbaijan, Cyprus and Georgia), other countries do not (IDMC 2015). The crafting of a definition for IDPs and its application in a particular context may be heavily influenced by local and national politics in conflict and post-conflict countries, as well as the direct link between estimates of displaced populations and humanitarian assistance, which can lead to both over- and under-reporting. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Displacement Tracking Matrix"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In other contexts, deregistration signifies not the achievement of a durable solution but rather the end of state or international support (IDMC 2015). 33 The absence of a clear and operational approach to defining the ‘ end ’ of internal displacement may be one of the factors behind the continued overall increase in the global numbers of IDPs. Lack of clarity around when displacement ends also leaves room for political manipulation. Governments may find it politically expedient to artificially prolong IDP status by deterring returns or local integration, for example in Azerbaijan and Georgia to promote claims over territory (Beau 2003). In other contexts, national 28 UNHCR ’ s IDP data focus only on internally displaced populations to which it extends protection or assistance. IDMC coverage of IDP data is more expansive and in 2015 included additional data on: (a) 26 countries accounting for 4. 5 million IDPs including some significant IDP hosting countries (Turkey, India, Ethiopia, Bangladesh and Kenya); and (b) IDPs in countries where UNHCR is active who are not protected or assisted by the agency. In 2015, IDMC ’ s aggregate figure for conflict-induced internal displacement was 3. 3 million higher than UNHCR ’ s aggregate figure for IDPs protected or assisted by the agency. 29 IDMC ’ s 2016 report presents both data sets alongside each other. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["IDP data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In certain contexts, there can be significant overlaps in these two groups; however data systems may be maintained separately for conflict-induced displacement and natural disasters (e. g. in Afghanistan) leading to possible gaps or double counting if these categories are combined. 30 The IOM Displacement Tracking Matrix (DTM) is a system to track and monitor displacement and population mobility. It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route. It has been active in over 40 countries since its inception in 2004. See http: / / www. globaldtm. info /. 31 This is typically defined as nomads not having access to their traditional routes, but routes can vary. 32 IDMC has recently adjusted their methodology to facilitate greater comparability across situations and improvements are reflected in IDMC ’ s end-2015 data. 33 This is not necessarily a problem if the purpose of the registration system is to delineate entitlements to assistance rather than to determine status. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["IOM Displacement Tracking Matrix"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Historical trends and patterns of forced displacement: Insights from available global data This section provides an overview of the available global data on conflict-induced forced displacement, drawing largely on UNHCR ’ s published data on asylum-seekers, refugees and IDPs. 41 Data are presented visually in a series of figures to highlight the scope and character of the current global forced displacement crisis and identify historical trends and anomalies. These data largely focus on the scale and trends in conflict-induced displacement (i. e. the numbers of forced displaced) with some coverage of other elements such as demographics, location and accommodation. Globally, there has been an unprecedented increase in the numbers of displaced people over the last decade, largely explained by the expansion in the number of reported IDPs. Historical data show a substantial increase in the numbers of forced displaced (see Figure 3), however the expanding geographical scope and quality of displacement monitoring systems are likely to account for much of the increase in forced displacement figures. The numbers of refugees under UNHCR ’ s mandate have recorded a number of variations over time, peaking in the early 1990s (at a level 10 percent over 2015 numbers) with the conflict and displacement associated with the end of the Cold War. The number of Palestinian refugees steadily has increased steadily over time, largely as a result of natural growth. IDP numbers (for which the underlying data are the least robust) have recorded the largest progression as a consequence of: (a) the expanded scope of monitoring efforts (IDPs were not counted before 1989 and methodologies were 41 UNHCR ’ s data only include IDPs protected or assisted by the agency. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["global data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 4: Refugees and Asylum-Seekers by Migratory Path 1951 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 5: Significant Historical Crises as a Share of Total Forced Displaced 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes IDPs protected or assisted by UNHCR, asylum-seekers and refugees. Excludes IDPs not protected or assisted by UNHCR and Palestinian refugees under UNRWA ’ s mandate. Figure 6: Top 15 Countries of Origin end-2015 Source: IDMC Global Report on Internal Displacement 2016, UNHCR Global Trends 2015, UNRWA A small number of countries carry the burden of hosting the majority of refugees. Historically since 1991, 15 asylum countries, overwhelmingly in the developing world, have hosted more than 50 percent of refugees and asylum-seekers (see Figure 7). 44 By the end of 2015, while almost all countries in the world were hosting refugees, the burden was unevenly shared (see Figure 8). Only seven countries hosted more 44 Major host countries are identified based on the cumulative number of refugees and asylum-seekers over the period 1991-2015. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "than 500, 000 refugees, together accounting for half of all refugees and people in refugee-like situations (excluding Palestine refugees). Major refugee hosting countries are typically the neighbors of countries of origin. For example, Syria ’ s neighbors (Turkey, Lebanon, and Jordan) together accounted for 27 percent of total refugee numbers; Afghanistan ’ s neighbors (Pakistan and the Islamic Republic of Iran) together accounted for 16 percent; and Somalia ’ s and South Sudan ’ s neighbors (Ethiopia, Kenya and Uganda) together accounted for 11 percent. Some countries (Lebanon, Jordan and Turkey) are hosting a particularly large share of refugees relative to their population (see Figure 10). 45 However, in all other countries, the number of refugees as a percentage of the population is 3 percent or lower, and most often below 1 percent. Figure 7: Top 15 Host Countries as a Share of Total Refugees and Asylum-Seekers 1991 – 2015 Source: UNHCR Statistical Online Population Database Note: Includes refugees, people in refugee-like situations and asylum-seekers. Excludes Palestinian refugees under UNRWA ’ s mandate. 45 Nauru is a special case since the Australian government funds the offshore processing center where refugees and asylum-seekers intercepted at sea are detained pending determination of their status. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the case of refugees, host countries rarely facilitate naturalization, only a minority of refugees ever gets resettled in third countries and voluntary repatriation is frequently not a realistic option for several reasons. International law provides for three possible durable solutions for refugees, including integration within the area of displacement, repatriation to their home country or resettlement in a third country; refugee status can also cease when there are no longer compelling reasons for an individual to refuse to avail themselves of the protection of their country of origin. In 2015 only 119, 265 refugees under UNHCR ’ s mandate were either resettled, naturalized53 or ceased to be refugees; and there were only 201, 415 voluntary returns, mostly Afghanistan, Sudan, Somalia and CAR (see Figure 17). These statistics highlight the significant gap between the unprecedented numbers of refugees and the capacity of the international community to provide durable solutions. For the 85 percent of refugees hosted in developing countries, there are only minute prospects for resettlement. Figure 17: Durable Solutions Relative to Refugee Stock 2015 Source: UNHCR Global Trends 2015 Global statistics that show the low rate of refugee returns masks the variation in returns over historical periods and across displacement crises — with significant voluntary returns for some countries. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 18: Significant Refugee Returns by Country of Origin 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Countries selected based on their cumulative returns of refugees over the period 1991-2015. Return does not necessarily lead to the full reintegration of a person into their home country or area of origin. In the absence of global data on the success of reintegration following return, data on returns appear to be taken as indication of sustainable return. In reality, many returnees face impediments to reintegration and continue to have specific economic and social vulnerabilities linked to their displacement. They may not be able to reclaim land, access sufficient financial resources (e. g. accumulated during their displacement) or reestablish social networks in areas of origin, which are critical factors for successful reintegration (World Bank 2015). Sustainable refugee return is therefore not a one-off event but a process that provides returnees with adequate safety, housing, livelihoods and services that address their specific vulnerabilities and reduce the likelihood of secondary displacement (World Bank 2015). Figure 19: Voluntary Returns of Refugees 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of refugees and people in refugee- like situations protected or assisted by UNHCR. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 20: Returns of IDPs Protected or Assisted by UNHCR 1993 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of IDPs and people in IDP-like situations assisted and protected by UNHCR. Consequently, the average length of protracted refugee situations has increased over the past two decades according to UNHCR estimates (see Table 2). UNHCR estimates that the average length of ongoing Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes high-income (OECD and non-OECD) countries. Excludes Palestinian refugees under UNRWA ’ s mandate. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 22: Numbers of Refugees in Ongoing Refugee Situations end-2014 Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes Palestinian refugees under UNRWA ’ s mandate. D. Data on asylum-seekers, refugees and IDPs: sources, applications and credibility In this section, a distinction is made between: (a) the collection of source data; and (b) the compilation of data across sources (within a country or across countries). In general, there is a delineation of roles between data collectors and data compilers, however there are organizations, such as UNHCR, IOM and the Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat (OCHA), 59 that are involved in both data collection and compilation activities. Data collection: Sources for refugees, asylum-seekers and IDPs60 Collection of primary data on forcibly displaced persons is generally undertaken by national governments through their national statistical offices, line ministries or immigration agencies. However, where countries lack the capacity to undertake this work, they may rely on international organizations as well as international and local NGOs to collect data or undertake estimates. 61 In general, governments tend to collect data on refugees in developed countries, while UNHCR and NGOs tend to collect data on refugees in developing countries. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "NGOs. Initiatives such as JIPS, a collaborative project of UN and NGO actors, have also been established to support governments and operational organizations to design and implement data collection processes. There are a variety of data sources for generating statistics on forced displacement, each of which has strengths and weaknesses. Despite the significant challenges, large amounts of data are collected and disseminated every year. The main data sources and methods for the generation of statistics on forcibly displaced populations include: (a) registration of refugees and asylum-seekers; (b) registration of IDPs; (c) profiling of IDPs; (d) population movement tracking systems; (e) national population censuses; (f) sample surveys; (g) border crossings; (h) administrative records and registers; (i) general population registers; and (j) a variety of estimation methods for producing statistics when adequate and reliable data on individuals are unavailable (UNSD 2014). Several of these data sources might be used together to triangulate estimates of stocks and flows for a particular displacement situation. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["general population registers", "sample surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some data sources (such as registration systems and population censuses) are more appropriate for estimating stocks of asylum-seekers, refugees and IDPs at a particular point in time, while other data sources (such as population tracking systems and border crossings) are more appropriate for estimating flows over a specific period. In general, there is a lack of comprehensive and up-to-date data available on all stocks and flows for a particular displacement situation (see Table 4). Consequently, data on flows might be used to estimate stocks, for example in the absence of government data, the stock of refugees in many industrialized countries is estimated by UNHCR based on 10 years of individual asylum-seeker recognition. And, especially in the case of IDPs, changes in the total population combined with some contextual analysis, may be used to deduce estimates of new internal displacement or returns. However, these approximations are flawed unless data on all other flows (births, deaths, repatriation etc.) are also available, which is not usually the case. Even a static figure for the stock of IDPs in a particular location might obscure substantial flows including new displacement and returns. Moreover, there are no common definitions of the various stocks and flows, and therefore the risk of double counting or gaps cannot be discounted. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population censuses", "population tracking systems"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For example, IDPs who subsequently cross international borders may be counted as both IDPs and refugees (e. g. in the case of the Syrian displacement crisis). Table 4: Stocks and Flows Stock Increases Decreases Asylum- seekers  New applications for asylum, separately identifying individuals who were previously IDPs  Positive decisions (convention status, complementary protection status)  Rejected  Otherwise closed Refugees  Spontaneous arrivals (group recognition, temporary protection, individual recognition), separately identifying individuals who were previously IDPs  Resettlement arrivals  Births  Administrative corrections  Repatriation  Resettlement  Cessation  Naturalization  Deaths  Administrative corrections IDPs  New internal displacement  Births  Administrative corrections  Cross border flight, becoming an asylum-seeker or refugee  Return  Settlement elsewhere in the country  Local integration  Administrative corrections Source: UNHCR Global Trends, IDMC Forced Displacement Data Model Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Moreover, some refugees may not register because they are unaware that they should, and others may be reluctant to do so because they are skeptical of the integrity of the registration process (e. g. fair access to entitlements or opportunities for durable solutions) or lack confidence in protection measures. Individuals in irregular migration flows may also choose not to apply for asylum due to fear of declaring themselves to the authorities. A significant challenge with refugee registers is keeping them up to date. Individual registration can provide a robust snapshot of the stock of refugees and asylum-seekers, but registers need to be updated regularly to reflect flows, i. e. increases in refugee and asylum-seeker numbers (births, new arrivals) and decreases (deaths, departures, durable solutions). In situations of sudden mass influxes, existing registration capacity may not be adequate and the scope of registration data is then rationalized. 65 Additionally, it may not be possible to capture all demographic changes in the case of highly mobile populations. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee registers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "If registration is linked to the provision of services or other entitlements, there may be strong incentives to register births and new arrivals and weak incentives to deregister, leading to the inflation of the register over time or even instances of fraud and abuse (e. g. multiple registration, “ borrowing children ” etc.) (UNHCR 2003). Consequently, data from a refugee register may overestimate the number of refugees, requiring periodic corrective action through the verification of records. For example, in 2014 a verification of registration records for Somali refugees in the Dadaab camps in Kenya led to the deactivation of tens of thousands of records for individuals that are believed to have returned spontaneously to Somalia (UNHCR 2015). Additional problems with refugee registers include security concerns or inclement weather preventing refugees from accessing registration sites (UNHCR 2003) and the application of data protection principles. Registration of IDPs Individual registration is not as common a method of estimating numbers of IDPs as it is for refugees. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee register"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In principle, individual registration of IDPs is not used to determine the ‘ status ’ of an IDP, since IDPs have the same rights and entitlements as other citizens and do not need to apply or be granted a special legal status. 66 Rather, registration of IDPs can provide a basis for: (a) establishing the number, location, and key demographic characteristics of displaced populations; (b) providing protection and assistance; (c) keeping track of family relationships; (d) preventing fraudulent access to scarce humanitarian assistance; (e) facilitating the issuance of temporary identity cards to replace lost personal documentation (Brookings 2008); and (f) providing social security benefits. 67 Full IDP registration by international organizations is not 62 By the end of 2014, individual refugee registration was the source of about 77 percent of the data on refugees; estimation accounted for 13 percent of data, combined estimation and registration for 5 percent and other sources for 5 percent (UNHCR 2016). 63 UNHCR may undertake registration activities when national governments do not have the capacity to do so. 64 Additional data can also be recorded such as education and occupation. 65 Insufficient budgetary resources, staff, training or materials. 66 Countries with national legislation that provides a legal status for IDPs are an exception to this international standard. 67 The scope of data collected depends on the objectives of the registration exercise, for example in Kenya, registration of individuals displaced by the 2007 and 2008 post-election violence excluded ‘ integrated ’ IDPs, i. e. those who had Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "When national or international actors provide assistance, there may be an incentive for people to register in camps even if they are staying elsewhere, or to register in multiple locations (Brookings 2011). 69 Furthermore, registration data provide only a snapshot of the stock of IDPs at a particular point in time and may be out of date if registers are not maintained regularly. Registration methodologies can vary across displacement situations. For example, families may be registered rather than individuals and the population estimated based on an assumption of average family size, which can differ among organizations (UNSD 2014). IDPs may be required to present documentation, meet specific criteria or re-register periodically to maintain their benefits, which affects aggregate numbers (IDMC 2015). For example, in Ukraine, pre-requisites for IDP registration (including valid documentation, arrival from a recognized conflict zone and permanent residence registration in recognized conflict zone) means that people displaced within a non-government controlled area, people displaced from a non- recognized conflict zone in a government controlled area, unaccompanied children or people without current / valid identification are not counted as IDPs (IDMC 2015). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["registration data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Furthermore, in some contexts, registration as an IDP can expire after a prescribed timeframe without regard to whether the person achieved a durable solution (e. g. after five years in Russia). The accuracy of IDP registers is greatly impacted by political considerations, particularly a government ’ s willingness to acknowledge internal displacement and to enable the humanitarian community to respond. Some countries may be reluctant to acknowledge the presence of IDPs, or may be inclined to understate numbers to demonstrate progress in military operations or limit assistance provided to IDPs. For example, in Kenya, registration of individuals displaced by the 2007 and 2008 post-election violence excluded ‘ integrated ’ IDPs, i. e. those who had sought refuge with host communities or rented accommodation in urban areas, as assistance was limited to registered IDPs. Alternatively, aggregate numbers of IDPs in particular countries may be inflated to suggest a deterioration of the situation or to maximize humanitarian assistance. Therefore, access to IDP areas and the willingness of IDPs to be counted may be largely dependent on government policies. These political considerations can lead to disagreements on the data, undermine cooperation and in some cases even lead to reduced humanitarian funding. Profiling of IDP situations Profiling of IDP situations is a collaborative process aimed at generating reliable data that can be broadly agreed upon. As a collaborative process, it can be a crucial tool for generating agreement on persistent questions such as who is recognized as internally displaced within a given context, what are the most prevalent vulnerabilities caused by displacement, and how do IDPs fare compared to host populations. In sought refuge with host communities or rented accommodation in urban areas, as assistance was limited to registered IDPs. 68 However, in many conflict-affected countries, governments lack the basic capacity to maintain Civil Registration and Vital Statistics (CRVS) systems including the registration of births and deaths in non-displacement situations, let alone the registration of IDPs displaced due to natural disasters or conflict. 69 IOM has introduced biometric registration systems in South Sudan, Sudan, DRC and Nigeria to circumvent these problems. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "this way, profiling of IDP situations aims to underpin advocacy, protection and assistance activities as well as support the achievement of durable solutions by informing joint strategies between government, humanitarian and development actors. Profiling provides an overview of displacement-affected populations through the collection and analysis of minimum core data (number of IDPs, disaggregated by location, age and sex) and where possible additional quantitative and qualitative data (causes of displacement, patterns of displacement, protection concerns, humanitarian needs, vulnerabilities, and aspirations and prospects for durable solutions). Profiling may utilize data collection techniques at individual, household and community levels, often combining population estimation methods, a review of secondary data, focus group discussions, household surveys and key informant interviews targeted specifically at forcibly displaced populations (UNSD 2014). 70 Profiling methods focus on displacement situations, rather than only on displaced populations, and therefore includes comparisons to conditions in the host population. IDMC estimates that humanitarian profiling data forms the basis for 18 of their 60 country estimates and around 63 percent of their annual estimates (IDMC 2015), with the largest volume of data on conflict-induced internal displacement provided by OCHA followed by IOM. There are several practical challenges associated with IDP profiling exercises in displacement situations. Insecurity or terrain may impede access to displaced populations in conflict-affected or hard to reach areas. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["humanitarian profiling data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For example, profiling exercises in Pakistan do not cover all IDPs or areas affected by displacement due to insecurity, and in Afghanistan profiling of IDPs by UNHCR underestimates the scale of the initial displacement as IDPs are only interviewed once displacement sites are accessible, if they are profiled at all (IDMC 2015). Additional challenges include unwillingness of IDPs to participate due to fear of persecution, and mobile populations (IDMC 2008). There may also be political pressures to inflate or reduce numbers. Population movement tracking systems In situations where the movement of displaced populations is fluid or continuous, a movement tracking system can be a useful tool for providing rough estimates of population flows, including recurrent displacements. Movement tracking systems are useful for monitoring fluid population movements (including spontaneous and organized, internal and cross-border, and returns and resettlement) in remote or inaccessible routes and locations (including displacement sites, places of origin, and places of return and resettlement). UNHCR, IOM and other organizations have developed methods for tracking and monitoring movements of IDPs in over 30 countries, particularly in cases of disaster-induced displacement, but also in some cases of conflict-induced displacement (UNSD 2014). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These systems employ a combination of data collection techniques including key respondent interviews, focus group discussions, registration, observations and physical counts, samplings and other statistical methodologies. For examples, UNHCR ’ s population tracking systems identifies and trains local NGOs to monitor key locations such as IDP settlements, bus stations and roads to report on movements. The accuracy of data from movement tracking systems is subject to several caveats. These include: limited access to locations and routes due to insecurity; vast geographical areas to monitor; mixed population flows that include refugees, IDPs, pastoral and seasonal movements and economic migrants; massive population flows that overwhelm monitoring capacity; disinclination of individuals to provide information when there is no assistance being offered; pressures from communities to inflate figures to maximize future assistance; and political pressures to suppress accurate reporting on IDP movements. Additionally, due to the fluid nature of displacement in many contexts and the likelihood of recurring displacements, it is not possible to use movement data to provide estimates of population stocks. Population censuses National population and housing censuses often provide the most comprehensive source of population data and offer the potential for estimating numbers of forcibly displaced people. To estimate the size of displaced populations a census would need to include questions on country (and / or place) or birth, year of (internal) 70 Other data collection methods may be used such as movement tracking systems, registration, big data etc. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["movement tracking systems", "population tracking systems"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "migration, and reason for movement including asylum / refugee protection (or conflict-induced internal migration) (UNHCR 2016), or a specific question to identify IDPs or refugees. However, not all censuses cover refugees and asylum-seekers (if foreigners are considered outside the scope of the census or because they are considered a special category), 71 nor is it common practice for national censuses to include questions related to forced displacement. 72 Nevertheless, there are several examples of national censuses that have included relevant questions on forced displacement. 73, 74 In the case of protracted internal displacement situations, IDPs are likely to be included in national censuses; however, census instruments may be subject to manipulation for political purposes. There are several drawbacks of population censuses including their cost, the significant training required for enumerators to ensure consistent answers to questions on forced displacement, impediments to field operations and data processing (such as weather conditions and technical problems), the relative infrequency with which they are carried out, and the long processing time before data and statistics become available, which have consequences for the timeliness of data. Moreover, often censuses are not conducted in contested territory or conflict zones where many displaced persons reside, and this limits the completeness of the data. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population censuses"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Nevertheless, censuses are the only nation-wide source of population data for potentially providing estimates of displaced populations, especially those in non-camp settings, and often provide a basis for sampling frames for survey instruments (UNHCR 2016). Capturing displacement situations in official statistics also increases their visibility. Sample surveys Sample surveys can potentially provide a rich source of data on forcibly displaced populations. Compared with censuses and registers, sample surveys allow more detailed questions to be asked about the characteristics and situations of households. If survey instruments identify displaced populations based on individuals ’ and households ’ self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status (UNSD 2014). There are opportunities to mainstream forced displacement into international survey instruments, but this has only been done in a handful of cases. Several standardized international sample surveys have been designed for special purposes including the Living Standards Measurement Study (LSMS), 75 Labor Force 71 The UN census recommendations for the 2010 World Population and Housing Census Programme stipulate that refugees and IDPs living in camps should be counted and their numbers disaggregated in population statistics, however there is no requirement to separately distinguish displaced people living outside of camps (UNHCR 2016). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["World Population and Housing Census Programme"], "descriptive_data": [], "vague_data": ["population statistics"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The United Nations Economic Commission for Europe ’ s (UNECE) guidelines include a question on reason for migration, population with a refugee-like background and IDPs as non-core topics / questions (UNHCR 2016). 72 While most countries include questions on country of birth and citizenship, only about 40 percent include a question on year of migration, less than a quarter include a question on reason for international migration, and about a fifth include a question on the reason for internal migration (UNHCR 2016). 73 E. g. Kyrgyz Republic 1999 (refugee status), West Bank and Gaza 2007 (refugee status), Zambia 2000 and 2010 (purpose of stay), Germany 1970 (federal refugee identity card), Greece 2001 (reason for settling in Greece), Sudan and South Sudan 2008 (type of household including IDP and refugee), Liberia 1990 (ever displaced by war since 1990), Uganda 2014 (refugees). 74 UNHCR is collaborating with the Statistics Norway on systematically embedding forcibly displaced peoples in national statistics exercises and collaborates with national authorities and with UNFPA in various countries on the design of census exercises that include refugees, IDPs, returnees and stateless persons. 75 LSMS is a household survey program housed in the Bank's Development Research Group that provides technical assistance to national statistical offices in the design and implementation of multi-topic household surveys covering household behavior, welfare and interactions with government policies. All data gathered through the LSMS is published online in the Bank ’ s Central Microdata Catalog. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["LSMS", "Central Microdata Catalog"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Surveys, 76 Demographic and Health Surveys (DHS), 77 and Multiple Indicator Cluster Surveys (MICS). 78 The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner (UNSD 2014). There are only a few cases where modules or questions on forced displacement have been integrated into survey instruments (most notably in Somalia, Uganda, West Bank and Gaza, Azerbaijan, Bosnia and Herzegovina, Serbia, Ghana, as well as health surveys in Albania, Ukraine and Moldova). There are several challenges associated with ‘ mainstreaming ’ forced displacement into household surveys: (a) there is huge demand for adding sector-specific or thematic modules to international surveys; (b) it is relatively difficult to convince national statistical agencies to modify their county-specific surveys; (c) disaggregating survey results by specific vulnerable groups (e. g. refugees, IDPs, migrant populations) requires these distinctions to be integrated into the sampling frame and sometimes there is insufficient information to do this or a lack of resources to expand the sample size; (d) lack of access to displacement- affected areas; and (e) difficulties associated with integrating an inherently political topic into less controversial surveys. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Multiple Indicator Cluster Surveys", "Demographic and Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Border crossings The registration of people crossing internationals borders is conducted in many countries, and in some cases these data are used to estimate migration flows. Identifying refugees among people crossing borders is a significant challenge, particularly if individuals decide not to apply for asylum or refugee status (UNSD 2014). Additional problems associated with the collection of data on border crossings include: (a) difficulties distinguishing migrants from other people crossing a border, such as tourists, commuters, traders and truck drivers; (b) lack of capacity of many border posts and officials to handle large migration flows; (c) less scrutiny and diligence of emigration flows compared with immigration flow; and (d) lack of tight controls at most borders and the high incidence of undocumented or irregular crossings (UNSD 2014). Administrative records and registers Many countries have administrative records or registers of immigrants that could generate statistics on asylum-seekers and refugees. In particular, data on residence permits issued to refugees or asylum- seekers could be used to generate statistics on both flows and stocks of refugees. 79 For example, Eurostat collects and disseminates data on residence permits granted to those with refugee status and subsidiary protection (UNSD 2014). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data on residence permits granted to those with refugee status and subsidiary protection"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "General population registers In a small but growing number of countries, information from the central population register is the main source of migration statistics. 80 While population registers may generate statistics on both internal and international migration (if they record changes of residence, and international arrivals and departures) they do not typically record reasons for movement. However, it may be possible to link data from the central population register to those from immigration or border authorities to identify refugees and asylum- 76 Using standard ILO definitions, Labor Force Surveys collect data on work-related issues and provide a basis for measuring employment and unemployment indicators. They are typically conducted monthly in developed countries and quarterly or annually in developing countries. 77 Supported by USAID and implemented by ICF International, the DHS Program has collected, analyzed and disseminated data on population, health, HIV and nutrition through more than 300 surveys in over 90 countries. 78 MICS is an initiative of UNICEF that assists countries in collecting and analyzing health and education data in order to fill data gaps for monitoring the situation of children and women. 79 Many refugee hosting countries issue a form of identification, either specific to refugees or based on national identification documents or those issued to non-national residents. In many cases where such documents are not issued, refugee identity cards are issued in collaboration with UNHCR. 80 A population register provides a mechanism for the continuous recording of selected data on the resident population including a unique identification number, date of birth, sex, marital status, place of birth, place of residence, citizenship and language and possibly also socio-economic data, such as occupation or education. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Labor Force Surveys", "DHS Program"], "descriptive_data": ["central population register"], "vague_data": ["population register"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "seekers. 81 There are several challenges associated with using central population registers to estimate refugee and asylum-seeker populations, including: consistency of the definition of refugees with the definition in the 1951 Convention and 1967 Protocol; difficulty and cost of establishing and maintaining a population register (UNSD 2014); and confidentiality safeguards. Compilation of statistics on forcibly displaced populations Several international organizations are involved in the compilation, analysis and dissemination of statistics on forced displacement including UNHCR, 82 Eurostat, IDMC, OCHA, International Committee of the Red Cross (ICRC), 83 WFP84 and IOM. Each of these actors has their own thematic focus and specific objectives, and applies their own methodologies. Asylum-seekers and refugees UNHCR is the principal organization responsible for the compilation, analysis and dissemination of data on asylum-seekers and refugees. UNHCR maintains a publicly available statistical online database85 with data for the period 1951-2014 on refugees (including people in refugee-like situations), asylum-seekers (pending cases), returned refugees, IDPs protected or assisted by UNHCR, returned IDPs previously protected or assisted by UNHCR, stateless persons and others of concern to UNHCR, disaggregated by country of origin and asylum. 86 Data are also provided on demographics, location, asylum-seekers (refugee status determination and monthly data) and resettlement. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["central population registers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "UNHCR regularly publishes statistical reports, including “ Global Trends ”, “ Mid-Year Trends ”, “ Asylum Trends ” and “ Statistical Yearbook ”. Additionally, UNHCR hosts interagency information sharing portals for significant emergencies. 87 These portals provide data on populations of concern at regional and country levels, including time series data, demographics, location and accommodation information. 88 Eurostat compiles and publishes data on asylum (applications and decisions) and managed migration in European Union member countries. Countries and national and international NGOs also publish these statistics, based on sources of various completeness, quality and timeliness (UNSD 2014). There are sometimes substantial inconsistencies between the numbers published by different organizations for the same country, including high-income countries with good statistical systems, usually due to differences in definitions, times and statistical methods, including the mixing of data on flows and stocks (UNSD 2014). There are several challenges associated with the compilation of data on asylum-seekers and refugees. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Significant among these are: (a) the lack of capacity of national statistical agencies in many developing countries to collect robust data on refugees; (b) weak or incomplete monitoring of refugees dispersed within host communities; (c) lack of capacity to maintain up to date information on refugees (reflecting new arrivals, 81 General population registers may also provide opportunities for more elaborate analysis of the integration of refugees in asylum countries, as the data could be linked to other administrative registers, for example on labor and education (UNSD 2014). 82 UNHCR collects, compiles and publishes data on asylum-seekers, refugees and IDPs protected or assisted by UNHCR, including populations in refugee-like or IDP-like situations. 83 Established in 1863, the ICRC ’ s mission is to ensure humanitarian protection and assistance for victims of armed conflict and other situations of violence. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["General population registers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ICRC ’ s work is based on the Geneva Conventions of 1949, their Additional Protocols, its Statutes — and those of the International Red Cross and Red Crescent Movement — and the resolutions of the International Conferences of the Red Cross and Red Crescent. 84 WFP is the food assistance branch of the United Nations and the world's largest humanitarian organization addressing hunger and promoting food security. 85 See: popstats. unhcr. org. 86 IDP data are only included from 1998 onwards. 87 See: http: / / data. unhcr. org. Currently the Burundi situation, Yemen (regional refugee and migrant response plan), DRC regional refugee response, Mediterranean (refugees / migrants emergency response), CAR, Côte d ’ Ivoire, Syria Emergency, Sahel Emergency, South Sudan Situation, Horn of Africa Emergency, and the Liberia Portal. 88 IOM ’ s new Global Migration Data Analysis Centre provides limited data on global migration trends such as data on asylum application in Europe and selected countries (including demographics, country of origin, and country of asylum). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["IDP data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Disaggregated data on IDPs who are not protected or assisted by UNHCR are collected by other agencies, including IOM, but data are not comprehensive and therefore not published in IDMC ’ s global reports. 93 The difficulties of collecting disaggregated data on locations of IDPs are compounded by the fluidity of IDP movements — IDPs might suffer multiple displacements or they might resort to changing locations as a coping strategy (e. g. moving between their homes and place of displacement or testing different locations before deciding where to stay) (Brookings 2011). In recent years, efforts have been made to improve data collection for IDPs living outside of camps by employing a range of techniques including: (a) profiling; (b) household surveys; (c) collecting information on IDPs who come to camps to visit family members or collect relief items; and (d) community outreach programs (Brookings 2013). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Disaggregated data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, there is no consensus or agreed best practices on the use of these methods in different contexts or stages of displacement (Brookings 2013). 92 The absence of data on new displacement may simply mean that no displacement has taken place (IDMC 2016). 93 IDMC reports that data, disaggregated by age and sex, were available for 15 of the 60 countries it monitored in 2014, however these data were not comprehensive and are not published. Additionally, in some countries there are data provided by IOM on IDP populations by location from which the urban or rural character of the population may be inferred (e. g. if the camp is located in the capital), but data are not comprehensive and not published. While the majority of humanitarian profile data does not typically cover IDPs living outside of camp or camp-like settings (the large majority of IDPs), IOM ’ s DTM in countries such as Nigeria, Iraq, Yemen and Libya do include information about those residing in host communities. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["humanitarian profile data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 5: Coverage of Published Data on Location, Accommodation and Demographics 2015 Population end- 2015 (millions) Urban or Rural Location Accommodatio n Age Sex Refugees and people in refugee-like situations 16. 1 85 % 83 % 58 % 68 % Other people of concern to UNHCR 47. 8 (including 37. 5 IDPs) 70 % 47 % 25 % 39 % UNHCR ’ s total population of concern 63. 994 73 % 56 % 33 % 46 % IDPs monitored by IDMC but not included in UNHCR's data 3. 3 0 % 0 % 0 % 0 % Source: UNHCR Global Trends 2015 Note: Other people of concern to UNHCR include asylum-seekers, IDPs and people in IDP-like situations protected or assisted by UNHCR, stateless persons, and ‘ other ’. Overall robustness of current data The robustness of data is difficult to estimate. A review of data collection and compilation methodologies shows broad variations in terms of the accuracy and reliability of the global estimates of forced displacement that are widely used. Headline figures on forced displacement are significant in shaping public opinion and are critical for sound decision making, both to inform the allocation of resources and to design effective humanitarian and development responses. However, the available estimates are potentially misleading and should not be referred to without appropriate caveats and qualifiers. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": " In Somalia, the Mogadishu Household Survey covered both residential areas and IDP camps, collecting data on expenditures, demographics and living conditions [survey completed; analysis forthcoming]. As part of the Somalia Knowledge for Operations and Political Economy (SKOPE) initiative, the Puntland Household Survey will also cover both residential and IDP populations [ongoing].  An IDP study in South Sudan [ongoing] aims to assess the economic needs of IDPs and host communities in urban areas, covering livelihoods, water and sanitation, infrastructure as well as intentions and conditions to return. Basic information about education, employment and general health variables will also be collected.  The Iraq Crisis Response Study [ongoing] will assess the impact of the Islamic State and oil price-related crises on IDPs and households left behind in IS controlled areas. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Iraq Crisis Response Study", "Mogadishu Household Survey", "Puntland Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The study will conduct an ex-ante micro- simulation using pre-crisis household data (IHSES 2012) and macroeconomic projections for 2014 to gauge the distributional impact of the crises across groups (e. g. individuals and / or households, sectors, IDPs and host communities) and space (e. g. urban / rural, governorates).  The Economic and Social Impact Assessment for Kurdistan Region of Iraq [completed in 2015] provides an analysis of the impact of displaced people on access to and quality of service delivery across several sectors.  The Lebanon Economic and Social Impact Assessment of the Syria Conflict [completed in 2013] provides an analysis of the impact of displaced people on access to and quality of service delivery across several sectors.  The Bank and UNHCR undertook a welfare assessment of Syrian refugees living in Jordan and Lebanon [completed in 2016] focusing on welfare, poverty and vulnerability. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["IHSES 2012"], "descriptive_data": ["pre-crisis household data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The study: (i) compares the socio- economic profile of refugees with that of the Syrian population before the crisis and with the hosting populations of Jordan and Lebanon; (ii) provides a welfare and vulnerability assessment of Syrian refugees including a poverty profile, the socio-economic characteristics of higher poverty and where pockets of deep poverty are located; (iii) analyzes key drivers of welfare and poverty; and (iv) models monetary and non- monetary vulnerability.  In Lebanon, Jordan and Iraq, the Bank is leading an initiative to evaluate the socio-economic impact of the regional crises on the welfare of Syrian refugees and host communities in neighboring countries [ongoing]. Data on living conditions, access to services and economic opportunities, coping strategies and economic status are to be collected via a specialized household survey and a sub-component of the survey will be carried out on a semi-annual basis to continue to monitor and adapt support as needed.  A recent Bank paper, “ Turkey ’ s Response to the Syrian Refugee Crisis and the Road Ahead ” [completed in 2015] assessed the impact of Syrian refugees on host areas in various sectors. It found that the presence of Syrian refugees is placing a strain on municipal services, housing rental markets, social relations, and education services for Turkish households. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["specialized household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(b) Disaggregation and geo-mapping of data by location (current location and location of habitual residence; urban, peri-urban or rural location), accommodation (organized camp versus non- camp), and demographics (age and sex); (c) Expanded coverage of data collection exercises to include all areas of affected countries (security permitting); (d) Improved coverage and detailed data on displaced populations living outside of organized camps; (e) Improved coverage of ‘ flows ’, i. e. new displacement, durable solutions (returns, integration, resettlement), births, deaths, and in the case of IDPs, the numbers that flee across international borders becoming refugees; (f) Systematic data collection beginning from the earliest moment following displacement, following up as populations disperse, and continuing until sustainable / durable solutions have been achieved; and (g) Better aggregation, analysis and presentation of forced displacement data currently compiled separately by UNHCR, IOM, IDMC and UNRWA. Additional efforts are required to address the gaps in the data required for development policy and planning. These data are critical for informing the design of development policies and assistance programs. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["forced displacement data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Survey instruments enable detailed questions to be asked about the characteristics and situations of households, and if they identify displaced populations based on self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status. Additionally, more innovative tools and technologies for data collection, analysis and compilation should be explored and leveraged. For example, new methodologies (such as high resolution satellite imagery and unmanned drones) may expand the coverage of data collection efforts in insecure or inaccessible areas. Additionally, new techniques could be explored to improve the collection of robust data on flows of refugees and IDPs. Organizations such as the World Bank, UNHCR, IOM and IDMC are already exploring and in some cases are beginning to use more innovative data collection tools. These techniques include: 99 Several standardized international sample surveys have been designed for special purposes including Living Standards Measurement Studies, Labor Force Surveys, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys. The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Labor Force Surveys", "Multiple Indicator Cluster Surveys", "Demographic and Health Surveys", "Living Standards Measurement Studies"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(a) High frequency sample surveys using smartphone and cellular technologies. For example, a high-frequency survey initiative in Somalia employs a dynamic questionnaire loaded onto smartphones, which enables data to be collected from household interviews in 60 minutes. This approach was developed to overcome the challenges of insecurity, limited data gathering capacity and budgetary constraints. (b) The use of mobile phones to conduct surveys or follow up interviews following face-to-face household surveys. For example, a Bank paper on the impact of the 2012 crisis in Mali on IDPs, refugees and returnees used information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones. This combination provided a mechanism to monitor the impact of conflict on hard-to-reach populations who at times live in areas inaccessible to enumerators. And in Sierra Leone and Liberia, the Bank supported the use of mobile phones to collect key socio-economic data on the effects of the Ebola virus. (c) Crowdsourcing data on displacement. Platforms such as the Kenyan Ushahidi has crowd- sourced data on displacement in Kenya and eastern DRC by encouraging IDPs and host communities to report incidents using their mobile phones or the internet, including information about living conditions. The platform references these reports geo-spatially. (d) Geo-mapping of data on displaced populations and affected host communities. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["face-to-face household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Geographic Information Systems (GIS) and geospatial analysis can be used to map, monitor and analyze data on forced displacement. Triangulation of this information with socio-economic and other indicators can provide a rich source of data and enable insights into underlying patterns and trends over time. (e) Use of big data (mobile phone data, news scraping, social media). IDMC is pursuing big data approaches to capturing displacement data in real time in order to report on displacement situations as they are happening and to provide updates on how they are evolving (IDMC 2015). These data are not necessarily representative but can be used in conjunction with other methods to triangulate trends. For example, the Swedish NGO, Flowminder, has pioneered the use of de-identified data from mobile operators to track population displacement caused by natural disasters such as earthquakes in Haiti in 2010 and Nepal in 2015, and these techniques may also have applications in conflict-induced displacement crises. 100 (f) High-resolution satellite imagery and unmanned drones. High resolutions satellite imagery can be used to map physical structures in refugee and IDP camps including changes to the number and type of these over time, support the remote detection of displaced populations in hard to reach or insecure settings; and conduct rapid assessments during or immediately after a mass displacement (Harvard Humanitarian Initiative 2014). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["de-identified data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "recently introduced the first version of their Global Internal Displacement Database (GIDD) that allows users to explore, filter and sort IDMC ’ s data to produce graphs and tables, and export underlying data. 102 Such platforms need to incorporate safeguards to protect the privacy and confidentiality of individuals ’ data. UNHCR and Statistics Norway are currently leading an initiative to improve forced displacement statistics with the participation of national statistical agencies. This process began with the presentation of the “ Report on Statistics on Refugees and IDPs ” at the 46th session of the UN Statistical Commission in March 2015, 103 followed by an international conference in Turkey in October 2015. 104 The conference set in motion a process for national statistical agencies to collaborate to develop a set of recommendations that both countries and international organizations can use to improve data collection, reporting, data disaggregation, and overall quality, including the preparation of International Recommendations for Refugee Statistics (IRRS). Progress on this agenda was discussed at the 47th session of UNSD held in New York in March 2016, where it was recommended that the expert group should also include IDPs in its scope of work (UNSD 2016). 105 The current initiative is focused on refugees, asylum-seekers and IDPs but would ideally be extended to host communities and returnees. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Global Internal Displacement Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Institutional arrangements would need to delineate data collector and data compiler roles, as well as reflect the following principles to ensure sustainability: (a) Pursue additional activities within the overall framework of existing initiatives to ensure coherence with the activities of other actors. (b) Primary responsibility for data collection rests with national statistical agencies (with adequate arrangements in the case of IDPs to mitigate political risks). (c) Definitions and methodologies should be harmonized across countries, through a process managed under the auspices of the UN Statistical Commission. (d) Agencies such as UNHCR and IDMC can play a leading role in ensuring quality, providing technical assistance as may be needed, and aggregating data for global analyses. 102 See http: / / www. internal-displacement. org / database. 103 See http: / / unstats. un. org / unsd / statcom / doc15 / 2015-9-RefugeeStats-E. pdf. 104 See conference documentation at http: / / www. efta. int / seminars / refugee. 105 See http: / / unstats. un. org / unsd / statcom / 47th-session / documents / 2016-14-Refugee-statistics-E. pdf. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Has this hap- pened during the recent growth period in the CIS-7? Explaining labor market flows The key to understanding output and employment growth in the CIS-7 is evidently the relation- ship between self-employment which is largely informal and employees in the formal sector. And the transition from non wage (informal) to wage (formal) labor can occur thanks to 1) A migra- tion of workers from self-employment to wage labor or 2) Endogenous growth of self- employment turning into SMEs and generating formal employment. We have in fact introduced one further dimension of labor market segmentation, the wage / non-wage labor divide. Labor flows between these different states may contribute to explain the employment puzzle. For this purpose, we turn to Moldova, a country that in many respects could be considered as the average scenario in our CIS-7 sample. Moldova is also the only country that disposes of a consis- tent longitudinal panel survey between 1997 and 2002 which can be used to assess labor market flows during the growth period and test some hypotheses on the evolution of the labor market. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We use the whole longitudinal sample and the restricted panel sample to compute the statistics, the transitional probabilities and the probit regressions presented below. 19 In figure 4. 1, we show the distribution of the population across working categories and between 1998 ad 2002. The employed population has marginally increased in percentage of the total popu- lation. A migration of workers has also occurred from wage labor to non wage labor and this mi- gration has taken place mostly within agriculture. The most significant change in fact occurred among rural workers with farmers growing very significantly at the expenses of agricultural em- ployees. Non agricultural labor has remained practically unchanged during the period while agri- cultural employment has increased marginally. This phenomenon occurred during the post-1998 recession (1998-1999) and during the subsequent growth period (2000-2002). In table 4. 4, we report the population structure by category20. It is visible the constant growth of private agriculture and the constant decline of employment in agricultural enterprises in both the public and private sectors. Among non-agricultural enterprises, there is a growth in the private sector and a decline in the pubic sector suggesting a migration of workers between the two sectors 19 See http: / / www. statistica. md / for details on the survey. 20 Categories are identified on the basis of the main source of income of respondents. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Following the approach used for the Global Costing of Refugee Inclusion, successful inclusion is defined as earning sufficient income to be no longer poor and to consume more than the (international) poverty line. This definition opens two tracks for investigation: first, how much aid would be needed if the policy objective were to bring refugee consumption up to the poverty line. The answer to this question is found by identifying the poverty gap for refugees. This opens the second track which explores the factors that determine, or at least that are associated with, the size of the poverty gap. The note is organized as follows. The next section discusses data and presents some key descriptive statistics on refugees and host communities in Uganda. This is followed by a methodological section discussing how own income and aid are complements and how an analysis of poverty gaps informs about the need for assistance. This is followed by two analytical sections. The first identifies refugee poverty gaps, and assistance needs for refugees with distinct characteristics. The following section estimates how much has been saved by including refugees in the economy and explores how more could be saved. Conclusions follow. 2 The poverty numbers in World Bank (2019) are based on the official poverty line adopted in Uganda in 1997. There was a need to update this line as it was too old and producing a very low poverty rate. For example, using this line produced a national poverty rate of about 21 percent in 2019 / 20 compared to more the than 40 percent international poverty rate using the USD 2. 15 2017 PPP daily poverty line. In order to address this criticism, the poverty line was revised by the Uganda Bureau of Statistics in 2021, but it is not available for the 2018 Refugee and Host Communities Household Survey used in this note. Instead, we are using the international poverty line throughout. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["2018 Refugee and Host Communities Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 Figure 10. Selected characteristics across Ugandan and refugee households, % Source: RHCS 2018, WB staff calculations. A simple comparison between refugees and Ugandan households demonstrates that refugees lag with regards to selected characteristics found to narrow the poverty gap. For example, refugees are less likely to have access to land than Ugandans. If refugees have access to land, the majority do not own it, but have user rights. The size of land also differs a lot among Ugandan and refugee households. Most Ugandans have at least 0. 05 hectare per capita, while the majority of refugees have less than 0. 05 hectare per capita. Refugee heads of household are also less likely to work and less likely to be literate compared to their Ugandan counterparts. Refugees have higher shares of children and elderly in household size compared to Ugandans. For example, among almost 60 percent of refugee households, more than half of the household members are children and elderly compared to 42 percent of households among Ugandans. Economic inclusion dividend When a development approach to hosting refugees is followed and refugees earn incomes, there are two key beneficiaries. Refugees themselves, who gain dignity, financial autonomy and pathways to self-reliance. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["RHCS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The third proxy for economic activity is given by per capita growth of night light, computed using satellite data from the National Oceanic and Atmospheric Administration (NOAA). 6 Night light data has the benefit 6Satellite data is available for a shorter time period, 1992-2013. 9 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["satellite data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of being available on a yearly basis independently of the quality of local statistical offices and data gathering. While it comes with its own problems it can shed light on local economic activity where gathering of statistical data is incomplete. 7 This makes it a great fit for measuring growth in a context of civil conflict. Conflict incidence is measured through the number of battle-related deaths from UCDP / PRIO dataset. We run the following regression for country i at time t: git = β × incidenceit + µi + ηt + ϵit (1) where git is economic performance per capita growth of country i in year t, incidenceit is conflict incidence, µi and ηt are respectively country and year fixed effects. A cross-country analysis as in equation (1) bears considerable potential for both reverse causality and omitted variable bias. Thus, a priori, a convincing causal link is hard to establish. However, here we expect the resulting bias to be small for two rea- sons. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["UCDP / PRIO dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In all specifications, conflict incidence correlates nega- tively with country-level economic performance. The estimated coefficients of conflict incidence are statistically significant and negative. We also find that the coefficients 7For a discussion see Henderson et al. (2012). In order to calculate light per capita we use popu- lation data that is provided by a World Bank dataset. 8The standard reference here is Miguel et al. (2004). Ciccone (2011) shows that high rainfall levels three years earlier seem to be best predictors of conflict in the reduced form. Miguel and Satyanath (2011) argue that lagged negative growth shocks are a predictor of conflict onset. In any case, there is no evidence from this literature that contemporaneous growth declines cause conflict. Bazzi and Blattman (2014) corroborate the view that the relationship between income shocks and conflict is not straightforward. They do not find evidence of an effect of price shocks on conflict onset and only weak evidence on incidence. 9Results from this are presented in the Appendix. 10We take the threshold from Mueller (2016) who shows that a threshold like this leads to a similar number of coded civil wars as the threshold of 1000 battle-related deaths often used in the conflict literature. In the context here, this is a conservative approach as it is not the threshold which yields the biggest difference between conflict and non-conflict countries. 10 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["World Bank dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 Internal Conflicts and Refugees A particularly serious aspect of internal conflicts is the human suffering they generate. This is not only those who are killed or injured in conflict but the large number of people who are forced to leave their homes. The issue of refugees has received particular attention in Western media in recent years as refugee flows from Northern Africa, the Middle East and Afghanistan are increasingly reaching Europe. These refugee streams are linked to a severe humanitarian crisis with considerable funding needs for international donors and heavy strains on host countries. 21 The current refugee crisis, however, is in no way unique. Civil war has always been closely linked to humanitarian crisis and refugee streams are one way to capture this. In this section we provide a cross-country analysis aimed at investigating how the stock of refugees evolves when a civil conflict hits a country. In the analysis we will focus entirely on showing changes in the stock of refugees across time to illustrate the dimensions involved. We will base our later analysis on these population movements. We exploit country-level data gathered from several sources. Data about refugees is provided by the UNHCR Population Statistics Database. The database provides in- formation about UNHCR ’ s populations of concern from the year 1951 up to 2014. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["UNHCR Population Statistics Database"], "descriptive_data": [], "vague_data": ["country-level data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This database lists seven categories: refugees, asylum-seekers, returned refugees, internally displaced persons (IDPs), returned IDPs, stateless persons and others of concern. For each group the database provides yearly information about their composition by loca- tion of residence and origin. We exploit only the data on “ refugees ”. 22 In particular, we are interested in the annual stock of refugees for each country of residence, i. e. how many people with refugees status have left their home country each year. We focus on these numbers as they appear to be the most comparable across time and countries. However, this is likely to capture only the tip of the iceberg in some cases. The number of IDPs is extremely high in some instances but cannot be captured with the same level of confidence as refugees generally. 23 Cross-country data about conflict is provided by the UCDP / PRIO. As for the index of country-level economic activity, we use again information provided by the Penn World Table and World Bank databases. As mentioned above, our aim is to explore the dynamics of refugees during conflicts. In other words, we attempt to answer several questions. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["World Bank databases", "Penn World Table"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Do people run away from their home country when a conflict breaks out? Does the seriousness of the conflict matter in this decision? In which phase of the conflict do they leave? When do refugees come back to their home country? In order to answer these questions, we look at the impact of conflict incidence on 21We will return to these issues in the policy section. 22According to the UNHCR definition, this category includes “ individuals recognized under the 1951 Convention relating to the Status of Refugees; its 1967 Protocol; the 1969 OAU Convention Governing the Specific Aspects of Refugee Problems in Africa; those recognized in accordance with the UNHCR Statute; individuals granted complementary forms of protection; or those enjoying temporary protection; and people in a refugee-like situation ”. 23The UNHCR Global Trends Report 2014 provides evidence that confirms this hypothesis. About 59. 5 million people were forcibly displaced worldwide by the end of year 2014. Among them, 19. 5 million were refugees and 38. 2 million were IDPs. 28 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In all these cases violence probably did not affect the entire economy notably. In what follows we focus on positive net flows, i. e. we subtract outflows from inflows and code negative numbers as 0s. Our results are robust to using gross inflows but as these are not provided by all sources. 40See Frome (1983) for a discussion of using the Poisson model to study rates. For a general discus- sion of count data models, see Cameron and Trivedi (2013). Our results are also robust to using year fixed effects instead of exposure. 41The reason is that the OECD data, the Dutch Central Bank data and the UN data allows us to distinguish between net flows and gross flows. 42We also distinguish two different ways of calculating the cut-offof intensity using contemporaneous and average population in a country. In total we therefore have 14 different estimates per cut-off. 43Each coefficient is also estimated quite precisely at this cut-off. 50 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Dutch Central Bank data"], "vague_data": ["OECD data", "UN data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 17: Peace and Foreign Inflow Across Cut-offs and Data Sources The basic results are in Table 8 which shows results for equation (9) using the threshold of 0. 008 battle-related deaths per 1000 population. According to this the investment from OECD countries was almost 70 percent larger in peacetime than during conflict. The flows from other data sources shows an increase of between 35 and 50 percent. The consistency of this result across very different datasets is striking. Note also that the average change in inflows implied by these rates is very large. In 2012, average inflows in the World Bank dataset were over 9. 5 billion USD and over 3 billion USD in the OECD data. Our estimates therefore imply a gain of between 2 billion and 4 billion USD in yearly inflows for countries which emerge from conflict. In order to understand the dynamics of recovery it is useful to understand the dynamics of this change around the end of conflict. For this purpose we add a set of dummies to the equation above. We construct a dummy that indicates the start of recovery and add three forward and lag dummies to trace average investment around this date. As before we always lag the explanatory variables by one year. Results for the OECD data are shown in Figure 18. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["World Bank dataset"], "descriptive_data": [], "vague_data": ["OECD data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the original variables used to predict fragility as controls without changing results. It is only in the post-war period that exclusion and refugees become a factor that influences foreign investment flows. Finally, the results we find are robust across all datasets of foreign investment we use. These results make it at least plausible that political exclusion and refugees matter because they predict a relapse to more intense violence. As final piece of evidence for this idea we use data on political short and mid-term credit risk from the Belgian insurer Delcredere Ducroire (ONDD). We collected data on political risk evaluations from ONDD who, according to their annual report, insured transactions worth about 7 billion EUR in 2011. The variable we use measures the risk of a credit default for rea- sons beyond the control of the debtor, i. e. due to political or financial macroeconomic events. We choose this variable because it provides the most consistent time-series in the ONDD data. ONDD measures both short- and mid-term risk on a scale from 1 (low risk) to 7 (high risk). Table 12, columns (1) and (4) show that risk ratings are decreasing in peacetime. Note that, as before, we control for country fixed effects which implies that we look at changes within country. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["ONDD data"], "descriptive_data": ["data on political short and mid-term credit risk"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "violent conflict. Besley and Mueller (2015a) argue that foreign investors seem to know that growth volatility changes with strong executive constraints and therefore react significantly to their adoption. In summary, the literature suggests that a lack of constraints on executive power at the country level could play a key role in building inequalities across regions and ethnic groups. In the absence of strong executive constraint, we expect regions populated by ethnic groups that have access to executive power to perform better relative to others due to ethnic favoritism. Conversely, excluded ethnic groups should experience relatively worse economic performance compared to other groups in the absence of such constraints. 46 To test these hypothesis we use data on ethnic groups ’ access to executive power and night light intensity from the GROWup Research Front-End (RFE Release 2. 0) dataset and executive constraint data from the Polity IV dataset. We use night light intensity as a proxy for economic activity at the ethnic group level. 47 Night light data has the benefit of being available on a yearly basis and of being measured at the local level where there is poor availability of statistical data. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["GROWup Research Front-End", "Polity IV dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As in previous sections we follow Henderson et al. (2012) who argue that the relationship between GDP and night light at the country level can be expressed fairly well in a constant elasticity model in which an increase of night light by 1 percent implies an increase of GDP of about 0. 25 percent. Hodler and Raschky (2014) also look at the relationship between log nighttime light intensity and log GDP at the regional level using the panel data of regional GDP per capita assembled by Gennaioli et al. (2013) 48 and they confirm that the relationship is linear and also find an elasticity of around 0. 3. Access to political power is ranked on a scale from 1 to 7 in the GROWup dataset. Ethnic groups are\"powerful\"(monopoly of power or dominant group in power), have access to central power through a formal system of power sharing (as\"Senior\"or\"Ju- nior\"partner) or are “ excluded ” from power (self excluded, powerless or discriminated). Strong executive constraint is measured as a dummy indicating whether or not we have executive parity or subordination of the executive at the country level, a value 7 for “ xconst ” variable in Polity IV dataset. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["GROWup dataset", "Polity IV dataset"], "descriptive_data": ["panel data of regional GDP per capita"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 19: Trust and Conflict in the Cross Section The problem with a lack of trust and strong group identities is that they penetrate and pervert formal institutions. The ethnic politics analyzed by Burgess et al. (2015) is just one example. Shayo and Zussman (2011), for example, use data from Israeli small claims courts to show that Arab and Jewish judges displayed significant judicial ingroup bias. Furthermore, this bias is strongly associated with terrorism intensity in the vicinity of the court in the year preceding the ruling. Confidence-building is also a crucial ingredient for the establishment of a fertile investment climate, which in turn is a trigger of economic development post conflict. This is the core message of the World Bank Report by Mills and Fan (2006). An important role of increasing trust doubtlessly goes to the media. It has been shown, for example, that hate radio in Rwanda played a critical role in the extent of ethnic violence during the genocide. 59 Other research has shown that media coverage can have strong effects on political preferences more generally. 60 Perhaps the most direct proof of the crucial role played by the media in the post-conflict situation comes from DellaVigna et al. (2014). The authors exploit variation in radio reception of na- tionalistic Serbian radio in border regions in Croatia. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data from Israeli small claims courts"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The authors argue that Croats listened to Serbian radio for its consumption value but reacted negatively to national- istic messages intended for Serbian ears. Election results and street surveys are used to elicit preference for extremist nationalist parties among Croats who are able to listen to Serbian radio and those that do not. The authors find that 3 to 4 percent of those 59See Yanagizawa-Drott (2014). 60See, for example, Enikolopov et al. (2011) and DellaVigna and Kaplan (2007) who find large effects on voting shares. 70 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["street surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The paper extends the GTAP 8 database by separating Lebanon, Jordan, Syria, Iraq, and West Bank and Gaza from the rest of the Western Asia aggregate and Algeria and Libya from the rest of North Africa. Kuwait, Qatar, Bahrain, Saudi Arabia, UAE, and Oman are aggregated into a GCC composite group. In addition, the 57 sectors in the GTAP 8 database are aggregated into 22 sectors based on their importance for the countries in the MENA region (Table 1). The resulting MENA-specific database contains 26 countries, among which are the six Levant economies of interest in this paper (Turkey, Lebanon, Syria, Iraq, Jordan, and Egypt) and the rest of the developing MENA countries (Table 1). The procedure used to construct the individual country information employs data from several sources. The UN Statistics Division data for 2007 is the source for the six components of GDP – agriculture, hunting, forestry, and fishing (ISIC A-B); mining, manufacturing, and utilities (ISIC C-E); construction (ISIC-F); transport, storage, and communication (ISIC I); wholesale, retail trade, restaurants and hotels (ISIC G-H); and other activities (ISIC J-P). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["GTAP 8 database"], "descriptive_data": ["MENA-specific database"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We sourced bilateral trade value data from WITS and bilateral tariff data from a medley of sources, presented in Appendix Table A1. As part of this procedure, all entries in the two composite regions (rest of Western Asia and rest of Northern Africa) were split and assigned the split values to the newly created economies, while all entries for the two composite regions from the GTAP database were removed from the database. Each entry was split using the most thematically relevant external source. Sectoral GDP shares were used to split consumption and production values, trade data were used to split export and import values, and tariff information was used to assign tariff values. Export shares were used to split further production and consumption information into the final set of industries presented in Table 1. For internal consistency purposes, the required accounting relationships were imposed on the split database 8 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["WITS", "GTAP database"], "descriptive_data": ["bilateral tariff data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These tariff rate modifications are essential for this analysis as suggested by the substantial differences between the tariff rates available in the GTAP 8 database, especially those implied for Jordan, Iraq, Lebanon, and Syria (Figure 1), and the updated tariff rates, presented by country, product, and source in Appendix Tables B1-B6. Since the GTAP tariffs attributed to Jordan, Iraq, Lebanon, and Syria are composite rates, they do not correspond to the actual trade profile of these countries. Therefore, the new tariff rates differ from the GTAP ones both because of differences in the tariff lines and trade composition. By contrast, the tariff information on Egypt and Turkey in the GTAP 8 database represents relatively accurately existing preferences (Figure 1). 3. Simulation design The pre-war efforts for deeper trade integration in the Levant are reflected in the pre-simulation analysis. Starting from the newly constructed database, the pre-simulation analysis implements the deep trade initiatives discussed by the Levant countries prior to the onset of the Syrian war in 2011. The context for these reforms and the shocks associated with each of these reforms are presented in section 3. 1. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["GTAP 8 database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Between outreach and baseline, one full survey round was collected due to potential survey fatigue and on the understanding that nothing of importance would likely change in such a short period. Basic demographic information, such as age, gender, marital status and employment status were collected at outreach. At baseline, additional indicators were collected, relating to the behavior, attitudes, opinions and personalities of the participants. The only exception to this is data on optimism, which were collected at both outreach and endline. This allowed us to test whether or not the intake decision had effects, even before the training began. Endline data were collected between July 2018 and November 2019 and repeated the combined outreach and baseline surveys and experiments. Variables: We collected a range of survey and experimental indicators in order to assess our key research questions and associated hypotheses: 6 Economic and life optimism: We collected two survey questions about optimism at outreach, baseline and endline. These questions ask individuals to rank their expectation that their life and economic situation will be better in one year than it is now. Answers are 4 In addition, data were collected from Palestinian Refugees in Lebanon (PRL). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["data on optimism"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 scored on a Likert scale running from 0 (significantly worse) – 10 (significantly better). The survey questions on optimism were collected at outreach, baseline and endline. Employment status: Due to slight differences in access to labor markets for refugees in Jordan and Lebanon and differences in how we were able to ask about employment status, we tabulate employment status as whether or not an individual is employed. Participants were asked at outreach about their employment status, and, in subsequent rounds, whether or not this had changed. This variable is coded 0 for not currently employed and 1 for employed. Economic scarcity: We collect survey questions on individual perceptions on: ability to meet current needs; ability to meet future needs; expectation that access to jobs is fair; expectation that salaries are fair; and belief that unfair access to labor markets fuels tensions. Ability to meet current and future needs are coded on a Likert scale running from 1 (completely unable) to 5 (fully able). The “ fairness ” indicators are coded: 0 (unfair) or 1 (fair). Whether or not competition around employment contributes to tensions is captured on a 1 (not at all) to 5 (absolutely) Likert scale. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 the region. Third, again due to cultural closeness, the nature of these variations is known across the region. 9 Additional measures: We collected usual socio-economic and demographic information, including: age, gender, marital status and education. In addition, we collected data on self- reported risk preference and a short-form personality survey to ascertain GRIT among participants. Noting that a small number of participants did not answer all survey questions, we undertake a regression-based data interpolation process to complete the dataset. 10 Table 1: Partner Assignment and Sample Sizes by Treatment and Community Status Host Refugee Ingroup Outgroup Ingroup Outgroup T C T C T C T C Outreach / Baseline 219 48 203 48 147 72 147 49 Endline 179 34 222 37 148 45 133 51 We present summary statistics of demographic data and other covariates for the baseline (Top) and endline (Bottom) for Jordan in Table 2 and for Lebanon in Table 3. [TABLES 2 AND 3 ABOUT HERE] Identification: The “ fuzzy ”, treatment intake is not random. As can be seen in Table 1, there are some elements of attrition from the sample. The sample decreases by about 10 % from baseline to endline. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We, therefore, first examine whether or not there is structure, both, to selection into the treatment group and attrition, which could undermine our econometric approach, where we rely on difference-in-difference estimators. Imbalance between treatment and control groups could undermine the key assumption of parallel trends. For example, given that men and women face different barriers in the labor market, we should not expect employment to evolve in the same way for men and women after the treatment. We would expect to observe a difference-in-differences for a treatment group where women are more common than in the reference group, even without the program. To test for imbalances, we run a simple regression of treatment and attrition indicators at baseline on the socio-economic and demographic controls, GRIT indicators, self-reported optimism, employment status and risk. Table 4 (Column 1 for the treatment analysis, Column 2 for the attrition analysis) shows some signs of structure. In particular, host status and risk preferences are significantly different between treatment and control, with 9 For example, “ hummus ” is used to refer to chickpeas in general but can also be used for the dish involving mashed chickpeas, tahini, lemon and garlic in Lebanon. In other dialects, some qualifiers are required to specify this dish (e. g. hummus ne ’ em, or smooth hummus). This is akin to identifying a British or American individual using similar variations in foodstuffs such as courgette / zucchini; coriander / cilantro; etc. 10 Specifically, we regress variables with missing observations on the list of all variables with a complete record. We then use the predicted values from this regression to populate the missing variables. Where appropriate, predicted values are rounded to the nearest integer and within answer codes of that variable. In a second round, this process is repeated on the full set of actual and predicted values from the first stage. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["GRIT indicators"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 degree of group bias. Thus, should the program reduce bias, 𝜓𝜓7 < 0. 𝑋𝑋is the same 𝑛𝑛 × 𝑘𝑘 matrix of control variables. 𝜓𝜓 is a 𝑘𝑘 × 1 vector of regression coefficients; and 𝜔𝜔 is the idiosyncratic error. We employ two small deviations from these approaches to produce the full set of results. First of all, as we do not have two sets of control variables from outreach to baseline, we run a fixed effects analysis to understand the impact of assignment to treatment status on life and economic optimism. Second, due to a data collection error in the field, indicators of economic scarcity were not collected from all of the control group at baseline. Instead, we seek to approximate the effect of treatment on these indicators by triangulating comparisons in two dimensions. First, we test whether or not these indicators improved for the treatment group from baseline to endline. Second, we test whether or not there are differences between the treatment and control groups at endline. This stops short of causality but still reveals interesting information about the dynamics at play. We produce five outputs for each analysis, with the exception of the economic scarcity indicators. First, we use uncontrolled OLS. Second, we introduce control variables. Third, we remove the controls but add inverse probability weights. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10044 Most refugee hosting communities are characterized by high levels of poverty with precarious livelihood conditions, low access to public services, and underdeveloped infra­structure. While the unexpected inflow of refugees might bring both constraints and opportunities for improving and maintaining local livelihoods in these communities, the understanding of these effects remains limited. Using a household level micro data set from a 2018 baseline survey of the Ethiopia Development Response to Displacement Impacts Project, this paper assesses the impact of refugee inflow on the livelihood strategies of host communities with respect to diversification and agricultural commer­cialization. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["household level micro data set"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The endogeneity of refugee inflow is addressed by exploiting differences in factors that influence refugee arrival in the host communities. Specifically, the analysis uses potential refugee inflow as an instrument, which is the product of population density and intensity of con­flicts (number of fatalities per event) in the closest region of the origin country to the refugee camp weighted by the distance of the refugee camp to the closest region. The paper also constructs an aggregate index to proxy house­holds ’ livelihood diversification strategies. The findings show that refugee inflow brings substantial benefits to host communities by creating significant jobs, in which people engage as secondary occupations, and triggers an increasing demand for livestock products. Specifically, while no effect was found on diversification of activities such as a primary occupation and crop product sales, a 1 percent increase in refugee inflow leads to a 2. 7 percent rise in diversifica­tion of livelihood activities as a secondary occupation and a 15. 9 percent increase in the value of livestock product sales. These effects tend to be heterogeneous across refu­gee hosting regions and the gender of the household head: negative effects were mainly observed in Gambella region, which hosts the largest refugee population in the country, and male-headed households were more likely to benefit from the refugee presence for the whole sample. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We measure livelihood diversification using two main variables: the degree of diversification of activities as a primary occupation and the degree of diversification of activities as a secondary occupation. 3 The degree of agricultural commercialization is also measured using two variables: the value from the sale of crop and livestock products. We measure refugee inflow (presence) as the number of refugees (population) in the nearest refugee camp to the household location weighted by the household's inverted distance to the camp. The impact of refugee inflow on household livelihood strategies can be causal if there are no confounding factors that affect livelihoods in host communities when refugee inflow changes. This is unlikely as refugee flow and the location of refugee camps are not random (see e. g., Baez 2011). Refugee camps are often situated close to international borders, among others, to allow for easy repatriation of the refugees when stability is restored in their countries of origin. In addition, refugees often seek shelter in the nearest refugee camp once they arrive in the host country, which is arguably true in most hosting countries as refugees often travel on foot for 2 According to UNHCR, a protracted refugee situation is a situation in which at least 25, 000 refugees from the same nationality have been in exile for at least five years in a given host country. 3 Diversification of activities is calculated using the inverse Simpson diversity index. In constructing the index, we considered both agricultural and non-agricultural livelihood activities. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Specifically, a 1 percent increase of the refugees ’ presence5 leads to a 2. 7 and 15. 9 percent increase in the diversification of livelihood activities as a secondary occupation and value of livestock product sale, respectively. It should be noted that this analysis is taking place during a period where refugees in Ethiopia were prohibited by law from seeking work outside designated camps. This has changed after 2019 because of the revised Ethiopian Refugee Law. These effects tend to be heterogeneous across regions and to a limited extent, vary depending on the gender of the household head. The negative effects tend to be concentrated in Gambella, a region that hosts most of the refugee population in Ethiopia and where the refugee population is as large as the population of the region. Overall, compared to women-headed households, households with a male head seem to benefit through increased diversification of activities as a secondary 4 Region refers to the administration level 1 from the Database of Global Administrative Areas (GADM). The nearest region to the refugee camp is identified as the one that has the shortest straight distance to the refugee camp among all neighboring regions in the major refugee source countries. 5 As explained above, refugee presence is the number of refugees (population) in the nearest refugee camp to the household location weighted by the household ’ s inverted distance to the camp. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Database of Global Administrative Areas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Figure 1: Map of regions in Ethiopia, location of refugee camps, and refugee source countries. Source: Database of the Global Administrative Areas (GADM) (https: / / gadm. org / data. html, accessed on November 20th, 2020) In Ethiopia, the Refugees and Returnees Services (RRS, former Agency for Refugees and Returnees Affairs (ARRA)) is responsible for managing refugee camps and its oversight by making sure that the commitment of the federal government is met (Nigusie and Carver 2019). Except for Eritrean refugees, most of whom are eligible for out of camp policy, arriving refugees, at the time the data was collected, were allocated to one of the 26 refugee camps spanning the five refugee hosting regions. Refugees living outside of camps represent about 10 percent of the refugees in Ethiopia (Abebe et al. 2018). The allocation tends to be based on shared identity between the refugee and the host communities and the distance of the refugee camps from the border of the source country. The South Sudanese refugees are hosted in the refugee settlements in Gambella, except the few who were relocated to the refugee camps in Benishangul-Gumuz region. Most of these refugees arrived during the civil conflict in South Sudan in 2013 (Nigusie and Carver 2019). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 inflow). We then use the composite variable as an instrument to identify the causal impact of refugee presence on livelihood diversification and commercialization in host communities. Figure 2: Household livelihood strategy framework under refugee inflow Source: Adapted from Nielsen et al. (2013) and Walelign and Jiao (2017) 4. Data sources The major data source of the current study is the World Bank ’ s Development Response to Displacement Impacts Project (DRDIP) 12 baseline survey from Ethiopia. The Ethiopia DRDIP survey was administered between September 2017 and August 2018. The survey covers 113 Kebeles (wards) in 16 Woredas (districts) from the top five refugee-hosting regions in Ethiopia. The selection of the sample households follows stratified random sampling with proportion to size (the number of households) using Woredas as a geographic stratum. The sample originally comprised a total of 3, 390 households, who were selected using systematic random sampling within each Woreda. We used data from 3, 375 households, as 15 of them were excluded due to missing location information (GPS). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Ethiopia DRDIP survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 Figure 3: Location of refugee camps in Ethiopia and the Ethiopia Development Response to Displacement Impacts Project (DRDIP) sample households Source: Authors ’ compilation using the database of the Global Administrative Areas (GADM) (https: / / gadm. org / data. html, accessed November 20, 2020) and the Humanitarian Data Exchange (HDX) database for the refugee location (https: / / data. humdata. org / dataset / ethiopia- refugee-camp-locations, accessed November 20, 2020). From the Ethiopia DRDIP data set, we derive two measures of livelihood diversification and two measures of agricultural commercialization (all at household level). The measures of diversification include: (i) the degree of labor diversification in different productive livelihood activities (e. g., farming, wage employment) as a primary activity (occupation), and (ii) the degree of labor diversification in different livelihood activities as a secondary activity (occupation). 13 These two outcomes were constructed using the inverse Simpson diversity index as 1 ∑ 𝑛𝑛 𝑖𝑖 𝑆𝑆𝑖𝑖 2, where 𝑆𝑆𝑖𝑖 is the share of the number of adult labor engages in 𝑖𝑖𝑡𝑡ℎ livelihood activity to total active adult household labor and 𝑖𝑖 ranges from 1 to the number of livelihood activities that a household engages in (Valdivia et al. 1996). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 crop products (e. g., wheat, potatoes) and the value of five livestock products (i. e., milk, egg, butter, hides, and honey) sold in the market. 14 Several other data sources were utilized. First, the Ethiopian refugee camps location data set from the Humanitarian Data Exchange (HDX) 15 and the total number of refugees by camps from the United Nations High Commissioner for Refugees (UNHCR), Addis Ababa office. We use data from 26 official UNCHR refugee camps in Ethiopia that were operational in 2018 (see Figure 1; 3). Second, we use administrative data sets for Ethiopia and refugee source countries from the database of Global Administrative Areas (GADM). 16 We also use the conflict data set from the Armed Conflict Location and Event Data Project (ACLED) 17 and the population data from the Gridded Population of the World (GPW) data set. 18 On the basis of these data sets and the location of sample households from Ethiopia DRDIP data set, we generated the following variables: i) distance of sample households to the nearest refugee camp, the nearest region (administration level 1 in GADM) to the refugee camps, ii) distance of the refugee camps to the nearest border of the refugee source country, iii) Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["database of Global Administrative Areas", "Gridded Population of the World (GPW) data set", "Humanitarian Data Exchange (HDX)", "Ethiopia DRDIP data set"], "descriptive_data": ["Ethiopian refugee camps location data set"], "vague_data": ["administrative data sets"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the intensity of conflicts, and iv) population density in refugee host countries by region. Appendix B presents the list of variables along with description and summary statistics. 5. Empirical strategy To estimate the impact of refugee inflow19 on host community ’ s livelihood strategy choice, we use the following basic econometric model: 𝑌𝑌𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑅𝑅𝑅𝑅𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖 + 𝜈𝜈 + 𝜀𝜀𝑖𝑖 (1) Where, 𝑖𝑖 indexes a household, 𝑌𝑌𝑖𝑖 is an outcome variable of interest (livelihood diversification or commercialization of agriculture), 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 is the measure of refugee inflow, i. e., the refugee population (average of 2017 and 2018) in the nearest refugee camp weighted by the inverse of distance of the household to the refugee camps, 𝑋𝑋����𝑖 is a set of household controls, 𝜈𝜈 is kebele fixed effects, and 𝜀𝜀𝑖𝑖 is the error term. Several variables, from the DRDIP data set, were used as controls in our model. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["DRDIP data set"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Religious School Enrollment in Pakistan A Look at the Data Tahir Andrabi1 Pomona College Jishnu Das The World Bank Asim Ijaz Khwaja Harvard University Tristan Zajonc Harvard University Abstract Bold assertions have been made in policy reports and popular articles on the high and increasing enrollment in Pakistani religious schools, commonly known as madrassas. Given the importance placed on the subject by policy makers in Pakistan and those internationally, it is troubling that none of the reports and articles reviewed based their analysis on publicly available data or established statistical methodologies. This paper uses published data sources and a census of schooling choice to show that existing estimates are inflated by an order of magnitude. Madrassas account for less than 1 percent of all enrollment in the country and there is no evidence of a dramatic increase in recent years. The educational landscape in Pakistan has changed substantially in the last decade, but this is due to an explosion of private schools, an important fact that has been left out of the debate on Pakistani education. Moreover, when we look at school choice, we find that no one explanation fits the data. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["census of schooling choice"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 The only publicly available sources of data to document patterns of enrollment and available educational options for Pakistani families are household-based surveys. These are the official 1998 Census of Population (Government of Pakistan) 3, the 1991, 1998, and 2001 rounds of the Pakistan Integrated Household Survey4, and a 2003 census of schooling choice conducted by our research team. The fact that three sources use different definitions of madrassa enrollment, and were collected at different times by individuals with very different institutional affiliations provides independent verification of enrollment estimates and allows us to determine the sensitivity of our results. The household data tell us whether a child is enrolled full-time in a madrassa, but not whether a child goes for an hour on any given day to study the Quran. Therefore this data does not confound full-time with part-time attendees — a child who attends a public school during the day and a madrassa in the evening is recorded as enrolled in a public school. This is an important distinction since parents might use a modicum of madrassa or mosque based education to teach their children about religion. Consequently, if we contrast these household-based numbers with numbers from establishment-based reports, discrepancies can arise. From virtually any policy perspective, including evening quran classes in enrollment figures seems misguided. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Pakistan Integrated Household Survey", "1998 Census of Population"], "descriptive_data": ["2003 census of schooling choice"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Regrettably, until now almost all enrollment numbers cited have been based on establishment surveys which do just that. These data sources show that around 200, 000 children were enrolled full-time in madrassas before 2001. Since 2001, our school census suggests that these numbers may have increased somewhat, although the experience varies across districts. To put this number in context, total primary enrollment (grades 1-5) in public and private schools stood at 17. 4 million in 2003 (Government of Pakistan, Ministry of Finance, 2003). The choice of madrassa schooling viewed as either the percentage of eligible children or the percentage of enrolled children, is statistically insignificant for the average Pakistani household. Enrollment in madrassas accounts for approximately 0. 3 percent of all children between the ages of 5 and 19. Given that the overall enrollment rate for this age group is roughly 42 percent, this represents less than 0. 7 percent of all enrolled children, an order of magnitude less than the 33 percent cited by the International Crisis Group report (2002). 3 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 to provide statistics for private versus public enrollment. 4The PIHS is the equivalent of the widely used Living Standard Measurement Surveys (LSMS) implemented in various countries. See http: / / www. worldbank. org / lsms for extensive notes on the 1991 PIHS. See also www. statpak. gov. pk for information on the census and the Federal Bureau of Statistics data. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["PIHS", "Living Standard Measurement Surveys"], "descriptive_data": ["census of private schools"], "vague_data": ["school census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 3. 1 Data Sources We use three different types of data to verify our estimates and determine how sensitive they are to changes in definition and the year of the survey. Two sources are nationally representative, but date from 2001 or before, the third is data from a census of households carried out by the authors in 2003 as part of a project on educational choice. The first source is the “ long ” form of the population census in 1998, which is a large sample-based survey with information on enrollment. This survey is representative at the level of the district and region (rural or urban) and provides comprehensive coverage of the entire country. 7 We use this data to examine enrollment patterns across districts. The second type of data, based on household surveys, are different rounds of the Pakistan Integrated Household Survey (PIHS) carried out in 1991, 1998 and 2001. While the data is not as extensive as the census, it contains detailed household information on schooling and income, and has been used extensively by researchers both in Pakistan and the United States. Finally, we use the census of schooling choice among households that our research team conducted in August 2003 (referred to as the project on “ Learning and Educational Achievement in Punjab Schools ”, or LEAPS). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Pakistan Integrated Household Survey"], "descriptive_data": ["census of schooling choice among households"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These data are only for three districts in the province of Punjab, but is very recent, was conducted by an independent team of academics, and is a complete census of all households in the selected villages. Consequently, it yields sufficient madrassa enrollment to examine correlations with household attributes in a meaningful manner (this data source provides information on four times as many children as the PIHS). Table A2 in the appendix shows how these different data sources are used in the paper. Each source asks about madrassa enrollment in a slightly different but comparable way. The population census (1998) asks about the field-of-education (“ What is name ’ s field of education? ”) with options that include (for instance) engineering, medicine, or religious education. This question is also asked of all literate adults irrespective of their current enrollment status, allowing for comparisons in the stock of religious education over time. The PIHS rounds ask, “ What type of school is name currently attending? ” with options that include government school, private school, or deeni-madrassa (religious schooling). Finally, the LEAPS census directly asks, “ Is the child enrolled in a madrassa or an Islamic education school? ” Fortunately these different questions all give rise to similar numbers. This is reassuring since it suggests that any one particular result is not driven by the specific question or definition that was used. 7 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 (www. statpak. gov. pk). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": ["census of private schools"], "vague_data": ["population census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 There may be grounds for skepticism about these estimates for madrassa enrollment. Since the data were collected prior to 2001, geopolitical changes after September 11 could have led to greater madrassa enrollment. In addition, the household-based survey faces the usual problems of accurately estimating a low-probability event — although enrollment is less than 1 percent in these surveys, the sampling error is large (see Bauman, 2001, for a description of similar problems in estimating home-schooling in the United States). Finally, while the census of populations does not face the problem of small samples, it is not that recent (1998) and some may have reservations regarding the quality of government data. 10 The LEAPS census of schooling choice conducted in 2003 provides a rough check on these numbers (see appendix for details). This census was conducted in three districts of Punjab and villages were chosen randomly based on the criterion that each village must have at least one private school. Typically, this means that the villages lies somewhere between fully urban and fully rural populations and are not representative of the districts that they are in. Estimates from the LEAPS census show that as a percentage of enrolled children, the numbers in two of the three districts are slightly higher than those of the population census. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": [], "vague_data": ["census of populations", "household-based survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the third (Rahim Yar Khan) there is a large difference, with the census reporting that 1 percent of all school-going children attended madrassas, and the LEAPS showing that the fraction is closer to 3. 7 percent (Table II). There are three potential explanations for this difference. First, the LEAPS data is not representative of the district and could be off the mark for districts with wide variation in madrassa enrollment across rural and urban samples. Second, the experience of the last five years could have varied dramatically across districts — in some, the enrollment fractions did not change and in others it increased substantially. Third, the data could point to systematic problems with the census estimates from certain districts, or the statistical problems that arise when we try to estimate low-probability events. 3. 3 Explaining the Differences A number of reasons could account for differences between the estimates presented here and those in the popular press. 1. Differences in the sampling unit. Our estimates are all based on household surveys — an interviewer goes to a household and asks about the enrollment status of every child. Some census estimates of home rather than religious schooling in the United States — the former ranges from 1 to 2 percent (Bauman 2001) while the latter is closer to 8 percent (National Center for Education Statistics, 2001). 10 In our own analysis, we find the quality of the data generated by the Federal Bureau of Statistics in Pakistan to be consistently high. We have used the FBS Census of Private Schools (PEIP, 2000) to guide our fieldwork and feasibility study for LEAPS and found it tallied with the situation on the ground quite well, even in remote villages. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["FBS Census of Private Schools", "LEAPS"], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 4. 1 Variation across Districts Pakistan is divided administratively into four provinces with 102 districts — Punjab, Balochistan, North-West Frontier Provinces (NWFP), and Sindh — plus the federal capital Islamabad, the Federally Administered Tribal Areas (FATA), the federally administered Northern Areas and Azad Jammu and Kashmir (AJK). The four provinces or Punjab, Balochistan, Sindh and NWFP, together with Islamabad, account for more than 97 percent of the population. Geographically, parts of Balochistan, the NWFP and FATA border Afghanistan. Sindh and Balochistan are sparsely populated provinces, with the exception of Karachi in Sindh, which is the single biggest metropolis in the country with a population approaching 10 million. We use data from the population census, 1998, as well as the census of private schooling, 2000, to provide estimates of madrassa, private, and government school enrollment in each district except for those in the province of FATA. The geographical dispersion of madrassa enrollment depends on how we define madrassa prevalence. There are three alternatives. We could present a geographical breakdown of the total number of children enrolled in madrassas. This number is related to the total population of the district, and may thus reflect only the size of the district relative to others. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["population census", "census of private schooling, 2000"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 between 1948 and 1965, with increases in the percentage of adults with religious education in the cohorts born after this date. There is also wide geographical dispersion in the prevalence of madrassa education in Pakistan. Although all districts report that less than 2. 5 percent of children in the relevant age group (children between the ages of 5 and 19) are going to madrassas, the Pashto speaking belt that borders Afghanistan stands out in terms of the popularity of madrassas as an educational choice. The notion that the madrassa movement coincided with resistance to the Soviet invasion of Afghanistan is supported by the 1998 data from the population census. The increase in the stock of religiously educated individuals starts with the cohort that came of age in 1979 (the year of the Soviet invasion of Afghanistan) and the largest increase is for the cohort co-terminus with the rise of the Taliban. Combined with the fact that the largest enrollment percentage in Pakistan is in the Pashtun belt bordering Afghanistan, this suggests events in neighboring Afghanistan influence madrassa enrollment. Is there something intrinsic about Pashtun sensibility or tribal culture that leads to higher madrassa enrollment? The differentiation of the Pashtun and non-Pashtun districts does not extend to Pashtun and non-Pashtun households in the LEAPS data. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["LEAPS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We find no evidence that Pashtun households are more likely to send their children to Madrassas compared to the rest of the sample, suggesting that geopolitical factors and geographical proximity to Afghanistan matter more than cultural preferences. 16 Similarly there is no evidence for religiosity or household preference-based models of madrassa enrollment. The radical religiosity argument suggests that children are more likely to be sent to madrassas when the family favors a radical brand of Islam. If true, what are we to make of the fact that more than 75 percent of all households with a child in a madrassa also send a child to a public or private school? In a multivariate context we checked whether households identified as “ radically Islamic ” were more likely to send their child to a madrassa. 17 Again, we found no 16 The data from the LEAPS census asked about ethnic and caste identity, and households that classified themselves as “ Pathan ” or “ Afghani ” were used to represent Pashtun households. In line with the usual residential patterns of individuals with Pashtun backgrounds, most of these households are in district Attock in the North of Punjab. 17 In a largely Islamic country it is difficult to find good measures of religiosity. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "No data on religiosity was collected as part of the census and a more recent and detailed household survey that includes information on time-use elicits little variation — everyone reports high mosque attendance and regular prayers. An alternative, suggested by David Evans at Harvard University, which we pursue here, is to use recent developments in the use of “ names. ” Research by Fryer and Leavitt (2004) demonstrates the increasing use of names to define race identity in the United States. We postulate that households who named (at least) one child “ Osama ” (also spelt Usamah, Usamma or Usama) are more likely to favor a radical brand of Islam. The use of the name Osama was minimal until 1998, and then peaks in 1998 and 2001, following disruptive events. Of course, the naming of the child may reflect name recognition rather Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 11055 The paper examines the early integration of Ukrainian ref­ugee students into Italy ’ s education system following the Russia ’ s invasion of Ukraine in 2022. Using administrative and survey data, the study presents enrollment trends, aca­demic performance, and barriers to educational integration. Findings from the analysis indicate that Ukrainian refugees face lower enrollment rates, higher absenteeism, and lower test scores than other students, particularly in subjects requiring language proficiency. Despite these challenges, teachers often recommend Ukrainian refugee students for advanced educational tracks, thus revealing their optimism about the potential of these students. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["administrative and survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The long-term consequences of these trends are signiϐicant, as diminished educational outcomes and social isolation can hinder successful integration into host communities. Conversely, sustained social and educational integration efforts are vital for positive outcomes. For instance, studies indicate that long-term integration can be hampered by social, economic, and institutional barriers (Chiswick and Miller, 2014), while interventions focused on language support and community engagement can lower these barriers (Ozden and Wagner, 2020). Further, speciϐic interventions aimed at removing obstacles to education for children on the move can contribute signiϐicantly to better integration outcomes (Schuettler and Caron, 2020). This paper beneϐits from key information coming from administrative data on educational records of Ukrainian refugees in Italy for the academic years 2021-2022 to 2023-2024 for grades 6 to 13. This provides a unique opportunity to examine enrollment, attendance, test performance, and other indicators of integration into the Italian educational system. Supplemented by survey data collected in 2023-2024, this study offers an overview of the challenges and opportunities faced by Ukrainian students in secondary schools and highlights areas for potential policy development. This study advances the literature by adding empirical evidence on the short- to medium-term educational impacts of displacement on young refugees within a European host country, offering insights into the role of education policy in mitigating human capital losses. It also contributes to discussions on human development by identifying factors that support or hinder integration, highlighting pathways for improving educational and social outcomes for refugee students. Results highlight that despite gradual improvements, enrollment rates remain signiϐicantly lower among refugees compared to native and other foreign students. Ukrainian refugees also demonstrate higher absenteeism and lower academic performance, particularly in subjects requiring language proϐiciency such as Italian and English. However, good performance in mathematics suggests potential strengths linked to their prior educational backgrounds. Despite these challenges, teachers seem to be more inclined to recommend Ukrainian refugees for high-track education compared to other newly arrived foreigners, indicating potential optimism about their academic capabilities. The Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 3. Data Sources and Methods 3. 1. Administrative data on educational outcomes in Italy Two administrative data sources represent the backbone of this paper. These are the administrative data obtained from the Ministry of Education (MoE) for academic years 2021-22, 2022-23 and 2023- 24, and standardized test score data from the Italian National Institute for the Evaluation of the Educational System (INVALSI) for the 2022-2023 academic year. These datasets offer valuable insights into the educational outcomes of students in Italy, including Ukrainian refugees who entered the Italian school system following the invasion in 2022. Deϐinitions. In both datasets, students are categorized into ϐive demographic groups based on their nationality and timing of entry into the Italian educational system. These groups are Italian students, Ukrainian refugee students, non-refugee Ukrainian students, newly arrived foreign students, and other foreign students. Among Ukrainian students, the distinction between refugees and non- refugees is based on their enrollment date in the Italian education system. Ukrainian refugees are deϐined as Ukrainian students who enrolled in Italian schools after February 2022. In this paper, Ukrainian refugees are labeled “ Ukr post-Feb 2022 “, while non-refugee Ukrainians are labeled “ Ukr pre-Feb 2022 “. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This distinction is also applied to foreign students, with those entering the system after February 2022 referred to as “ Migrant post-Feb 2022 ” and others referred as “ Migrant pre-Feb 2022 ”. 7 Administrative data from MoE. The Ministry data includes information for all students who enrolled at any point during the academic year. 8 This information covers academic year, grade, gender, birth date, birthplace, citizenship. They also include school-speciϐic information such as the name and identifying code of the institution where the student is enrolled. Furthermore, the dataset includes a variety of school outcome variables, including grades in English, Italian, Mathematics, overall GPA calculated as the average across all subjects, behavior scores from grade 9 to grade 12, guidance council evaluations from lower secondary school, records of absences, late entries, and early exits. Given the timing of this study, data for academic year 2022-23 are the most complete. For academic year 2022-2023, school enrollment data at the provincial level was provided for 4, 269, 348 enrolled students across the 8 years of Italian lower and upper secondary school, encompassing both public and private institutions. The dataset includes nearly all students in the country irrespective of their citizenship. 9 Table 1 shows the distribution of the different groups of students by grade. In the 7 For ease of reference, we refer to non-Italian and non-Ukrainian students as migrants. However, we acknowledge that some of these students may be refugees or displaced students. 8 At the time of writing this paper, both MoE and INVALSI data were not fully available and, as such, only the information on enrollment was used for the academic year 2023-24. 9These numbers do not include students enrolled in Provincial centers for adult education (CPIA). See footnote 5. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["INVALSI data"], "descriptive_data": [], "vague_data": ["school enrollment data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 measured by the number of days missed during the academic year, can function as an indicator of school attachment or integration for refugee students. To address potential biases from teacher subjectivity, the analysis of score disparities relies on INVALSI test results. The standardized and anonymized nature of these tests helps mitigate subjectivity in assessment. First, the results section presents some summary statistics of the main outcomes across the different categories of students. Second, we use the administrative data to analyze empirically how Ukrainian refugees and newly arrived foreigners compared to other students as regards their education performance. This estimation is based on an OLS model with the following econometric speciϐication: 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 + 𝛽𝛽1 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 + 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 + 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 + 𝑓𝑓𝑔𝑔 + 𝑓𝑓𝑠𝑠 + ���𝑓𝑙𝑙 + ϵigs (1) where 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 represents the outcome of interest (such as test scores, absenteeism, or high-track recommendation) for student i in school s, in grade g, and with language l. The variable 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 is a dummy indicating whether the student is a Ukrainian refugee, and 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 indicates if the student is a newly arrived foreigner. 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 represents the Economic, Social, and Cultural Status of the student, and 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 indicates the student ’ s gender. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The model includes ϐixed effects for grade, school, and language spoken. 14 The results are presented in Table 3. We then narrow our focus to foreign students who joined Italian schools after February 2022, speciϐically comparing Ukrainian refugees to other newly arrived foreign students. This approach allows us to examine how Ukrainian refugees compare to other foreign students who entered the education system around the same time. By restricting the sample to these two categories of students, we estimate the following regression: 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 + 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 + 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 + 𝑓𝑓𝑔𝑔 + 𝑓𝑓𝑠𝑠 + 𝑓𝑓𝑙𝑙 + 𝜖𝜖𝑖𝑖𝑖𝑖𝑖𝑖 (2), with variables as deϐined in (1), and results presented in Table 4. Our analysis also aims to explore potential mechanisms that could explain results derived from equations (1) and (2). Using the administrative data, we investigate whether being placed in a smaller class inϐluences school achievement in the sample of Ukrainian refugees. The results are presented in Table 5. We then draw on ϐindings from the survey data to unpack and analyze how Ukrainian refugees feel in Italy, the challenges they face, and their aspirations. 4. Results 4. 1. Integration challenges faced by Ukrainian refugees in Italy Low enrollment and substantial dropout rates At the end of the 2021-2022 school year, the enrollment rate of Ukrainian refugee children in Italian schools was low. In the months following Russia ’ s full-scale invasion of Ukraine in 2022, 3, 320 Ukrainian refugees were enrolled into Italian secondary schools. This ϐigure constitutes 24 % of the 14, 106 Ukrainian refugees aged between 11 and 18 years who sought temporary protection as of 14 This variable is included to account for the potentially greater ease of learning experienced by students who speak languages that are considered closer to Italian. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "19 children and 61 % of caregivers prefer to remain in Italy. Adolescents between the ages of 15 and 19 express a greater desire to continue living in Italy compared to children aged between 9 and 14. Another survey conducted across Europe from June to December 2022 shows that only 8 % of Ukrainian refugees planned to settle outside Ukraine (Adema et al., 2024). Compared to other foreign children in Italy, the aspirations of Ukrainian refugees to return to Ukraine seems signiϐicantly higher: indeed, a recent study from ISTAT on children 11 to 19 years old shows that only 11 % of foreign children wish to return to their home country (ISTAT, 2024). The relatively strong desire to return to Ukraine can have negative effects in refugee parents'educational decisions, particularly in encouraging their children to learn the language of the host country and in enrolling in school (Dryden-Peterson et al., 2019; Zengin and Atas-Akdemir, 2020). Figure 6- Aspirations and identity of refugee caregivers and students (Source: World Bank Survey on Ukrainian refugees in Italy) Many students facing uncertain futures try to stay connected to both educational systems. Findings from the World Bank survey indicate that 25 % of children are engaging in online Ukrainian schooling while being enrolled and attending Italian schools. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["World Bank Survey on Ukrainian refugees in Italy"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The lack of certainty means children are trying to prepare for further studies (e. g., in universities) in two separate systems with varying requirements. The survey results indicate that students enrolled in both systems spend as much time in Italian schools as those attending only Italian schools, averaging 31 hours per week. However, students participating in both systems spend an additional 8 hours per week on online Ukrainian classes. This puts an extra burden on these children. Connectedness to Italy is correlated with demographic characteristics and social environment of refugee children. Additionally, Table 6 shows that making new friends in the country of destination and speaking Italian are strongly associated with higher connection to Italy. The mental distress resulting from displacement is a key barrier to educational integration for many Ukrainian refugees in Italy. The link between poor mental health and low school attendance and performance is widely acknowledged in the literature (see Fiining et al., 2019 for a systematic review). In the World Bank survey data, children and caregivers reported signs of mental distress, with 16 % of children and 24 % of refugee caregivers reported experiencing psychological distress 61 % 35 % 31 % 68 % 50 % 36 % 23 % 68 % 59 % 26 % 26 % 59 % Would like to continue living in Italy Would like to move back to Ukraine Feel strongly connected to Italy Feel strongly connected to Ukraine Caregivers (N = 283) Children between 9 and 14 years old (N = 141) Children between 15 and 20 years old (N = 96) Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["World Bank survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10100 This paper explores the impact of refugee return on social cohesion using data from Burundi, a country that expe­rienced high levels of repatriation during the 2000s. It uses a nationwide survey conducted in 2015 and relies on geographic features of the communities for identification purposes. The results suggest varying impacts of refugee return on different aspects of social cohesion. The stronger effects, suggest that refugee return has a negative impact on the feeling that community members help each other, could borrow money for emergencies from non-household members and feeling that the community is peaceful. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["nationwide survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 agreements that did not fully satisfied either party (Ndayirukiye and Takeuchi 2014). This tension related to land, and who has a claim to the land, can lead to social tensions in communities with higher levels of return. Figure 3 – Refugees in Tanzania in 2005 by province of origin in Burundi Note: The number in brackets is the number of refugees in Tanzania in 2005 which was originally from the given province in Burundi. This information comes from (UNHCR 2021b). The number in parenthesis is that number as a percentage share of the population of the provinces as estimated in 1990 Burundi Census. An important question for our hypotheses is the degree to which there is evidence of migration-related societal divisions in the country. There is no direct quantitative evidence on identity issues (i. e. returnees versus stayees), but we have data on attitudes towards emigration, remittances and return that can provide insights on these identities and even be a proxy for migration-related identity in some cases. Overall, attitudes towards emigration and return are mixed and show that there is scope for the existence of migration-related divisions. In Table 1 we report the share of respondents who agreed with different statements regarding emigration, remittances and return. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["1990 Burundi Census"], "descriptive_data": ["data on attitudes towards emigration, remittances and return"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Finally, we divide the communities based on land scarcity before the 1993 conflict in order to explore the possible role of posterior rules regarding land provision to returnees. Respondents in communities that had more and less pre-war land available have broadly similar attitudes towards return. 4. Research design 4. 1 The survey We collected the data for this project during January to March 2015 as part of a nationwide survey on issues related to migration for the Labour Market Impacts of Forced Migration (LAMFOR) project. The survey had two components. First, a household survey in which 15 households were interviewed in 100 communities (i. e. sous-collines) across the 17 provinces of the country. Second, a community survey in which a local leader was interviewed in each of the 100 communities. The number of communities selected in each province was based on information from the 2008 Census. Figure 4 indicates the location of the communities surveyed. Figure 4 – Location of communities surveyed in Burundi Note: Geolocation of the 100 communities (i. e. sous-collines) sampled in the survey. Each community corresponds to a dot. Fifteen households and a local leader were interviewed in each community. The number of communities selected in each province was based on information from the 2008 Census. In the analysis below we focus on rural areas. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["2008 Census"], "descriptive_data": [], "vague_data": ["household survey", "community survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Where 𝑌𝑌𝑖𝑖 represents one of the indicators of social cohesion explained above, 𝛿𝛿𝑗𝑗 is the province indicator, 𝑅𝑅𝑐𝑐 is the share of returnees in the community, 𝐻𝐻𝑖𝑖 indicates a series of household level controls and 𝐶𝐶𝑐𝑐 are a series of community level of controls. In the main regressions we estimate the share of returnees in the community, using the information from the survey (i. e. share who are returnees), but in the robustness section we show that results are robust to the use of an alternative indicator in which the information is provided by a community leader. The Appendix (Table A2) includes the descriptive statistics for the control variables. We present results for the full sample and divided by communities with lower / higher ethnic diversity, less / more pre-1993 war land availability and better / worse attitudes towards return. In the robustness checks we also present the results if we limit the analysis to stayees only. Limiting the sample in this way does not affect the main results of the paper. 4. 4 Identification As mentioned above, Tanzania mandated the return of all Burundian refugees from the 1993 conflict. Returnees also had a very strong incentive to return to their communities of origin as this was the place in which they were entitled to land, a very scarce resource in the country. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "such as education and caste. We also construct measures of social proximity between a migrant ’ s place of birth and each possible destination, using detailed available data on ethnicity, caste, language, and religion. We also investigate a number of factors that may influence the choice of migration destination but have not received much attention in the existing literature. Fafchamps and Shilpi (2009) have shown that the subjective welfare cost of geographical isolation is high. To investigate this issue, we include regressors controlling for population density and for the average distance to various amenities. Fafchamps and Shilpi (2008) have further shown that migrants are concerned with their welfare relative to that of their birth district as well as to that in their destination location. We examine whether relative welfare considerations influence the choice of migration destination. Additional controls include distance and prices. The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal. The diverse terrain of Nepal along with geographical variation in amenities makes it ideal for our study. The mountainous nature of Nepal means that the country faces daunting challenges in the provision of transport and energy infrastructure. These challenges are unique to Nepal, however. Similar constraints are faced by many developing countries — or regions within such countries. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["2001 population Census data"], "descriptive_data": ["LSMS survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "If many people migrate to a specific location, such as the capital city, this is likely to affect wages, incomes, and access to amenities in that location. 7 This would generate a potential endogeneity bias due to the fact that incomes and amenities in that location result in part from the decision of many migrants to locate there. To eliminate this bias, we use past data to estimate the income regression. More precisely, let T be the period for which we have income information and T + t the period at which we 5The dropped observation corresponds to the location of origin M h ii which, as explained earlier, we do not include in the analysis since including M h ii would mean de facto including the decision of whether to migrate or not. 6McFadden (1974) has shown that, in multiple choice problems of the kind studied here, the application of logit estimation is justified if (1) the errors in each latent choice equation follow the extreme value distribution and (2) errors are independent across choices. See Train (2003), Chapter 3 for a detailed discussion. The estimation of models with correlated errors across choices requires either multiple integration or the use of Bayesian estimation techniques relying on Gibbs sampling. With a choice of over 70 possible destinations, multiple integration is out of the question. Gibbs sampling remains a possibility but would require extensive programming. We choose instead to keep the logit approach but to correct the standard errors for possible correlation in errors across choices. In our case the possible efficiency gain achieved by Bayesian methods does not appear to justify the programming cost. 7The effect could be negative — e. g., congestion — or positive — e. g., agglomeration externalities. 10 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["past data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To this effect, we replace yh i with yh i / yi in equation (1) and proceed as outlined above. If migration decisions are based on relative rather than absolute income, then the coefficients of eδs − eδi and (eηs − eηi) zh should be positive and significant only when they are computed using yh i / yi. In addition to relative and absolute income differences, the analysis also examines the re- spective roles of various location characteristics such as housing and food prices, availability of public services, and density of human settlement. 8An alternative strategy for the estimation of pre-migration income distribution in cross-section data is sug- gested by Bayer, Khan and Timmins (2008). 11 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In contrast, districts of origin are distributed widely across the country. This reflects the fact that much work migration is from remote rural areas to towns and cities. The main characteristics of work migrants are reported in Table 1, together with those of non- migrant adult males. We see that work migrants are on average younger and better educated. The census contains detailed information about ethnicity, language, and religion. In the Nepal census, the term ‘ ethnicity ’ is used to capture a hodgepodge of caste and tribal distinctions. The census distinguishes up to 103 ethnic categories. Most of these categories only account for a tiny proportion of the total population. In terms of the total adult population, the most common ethnic categories are Chhetri, Brahmin, and Newar who, together, account for 35 % of 13 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Nepal census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "standard fixed or random effect approach is not sufficient to ensure correct inference; clustering standard errors by individual is necessary. This is what we do. Having described how the dependent variable is constructed, we turn to regressors. We begin by describing how we construct an estimate of g E [yhs | zh], the level of income (or consumption) yh s that a migrant with characteristics zh can expect to earn in district s. To construct such estimate, we use the 1995 / 96 NLSS data. The reason for using the 1995 / 96 data instead of the 2002 / 3 NLSS survey is to avoid reverse causation, i. e., migration causing a change in income patterns. Migrants are unlikely to be able to accurately predict the evolution of incomes in each district over time. Income and consumption levels observable before migration are thus a reasonable starting point. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["1995 / 96 NLSS data", "NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using the NLSS data we begin by estimating a regression of the form: yk s = δs + α (ak s − a) + βs (Ek s − Es) + χs (Hk s − Hs) + vk s (4) where yk s is the log of income (or consumption) of household k residing in district s, coefficients δs, βs and χs vary by district, ak s stands for the age and age squared of the household head, Ek s is the education level of the head measured in years of completed education, and Hk s = 1 if the head belongs to what we have earlier classified as a high caste (i. e., Brahmin, Chhetri or Newar). Since income or consumption are expressed in logs, βs and χs can be thought of as education and high caste premia, respectively. Female headed households are excluded from the regression since the focus is on migrant males. Vector a denotes the average age and age squared of observations across the sample. Variables E and Hs denote the district-specific averages of Ek s and Hk s. By demeaning regressors, we ensure that eδs measures the unconditional, district- specific average of yk s. Marital status, household size, and other household characteristics are 15 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["NLSS data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "not included because they are possibly affected by migration. 9 In contrast, age, education, and caste status can be regarded as exogenous to the migration decisions of adult males. Equation (4) is estimated using correct sampling weights. 10 Regression estimates for equation (4) are summarized in Table 2 where we show α as well as the average and standard error of δs, βs and χs. The coefficients eδi and eηi are large and jointly significant. There is considerable variation across districts not only in average log income and consumption but also in the income or consumption premia associated with education and high caste. These results are used to construct, for each of the 16, 000 or so work migrants in the census, a measure of the income or consumption they can expect to achieve in each of the possible destination districts. Formally, this measure is calculated as: g E [yhs | zh] = eδs + eβs (Eh s − Es) + eχs (Hh s − Hs) (5) where Eh s and Hh s are the education and high caste dummy for migrant h. Age is ignored from the calculation since work migrants typically migrate around the same age, i. e., in early adulthood. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Formula (5) can be decomposed into two parts: eδs, which measures the average income level in district s, and eηszh ≡ eβs (Eh s − Es) + eχs (Hh s − Hs) which captures individual-specific variation in income. Migration models predict that, other things being equal, the choice of migration destination should depend on g E [yhs | zh]. This means that if we regress the choice of destination separately on eδs and eηszh, they should have the same coefficient. The same methodology is used to construct other variables that may affect the choice of 9The literature has often emphasized that migrations often serve an important role in household formation. For migrants, the prospect of forming a large, successful household is likely to be one of the purposes of migration. 10The 1995 / 96 NLSS survey adopted the following sampling strategy. Within each district a small number of wards were selected at random. Within each ward, 12 randomly selected households were interviewed. Because the wards differ widely in terms of population, applying sampling weights is essential in order to obtain consistent estimates of δs. 16 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "destination. Building on a growing literature documenting the relationship between subjective welfare and relative income, Fafchamps and Shilpi (2008) show that Nepalese households care about their consumption level relative to that of others in the same location. If this is the case, it is conceivable that migrants choose their destination not so much for the absolute gain in income it may provide but for the gain in relative status that would ensue. For instance, if returns to education and ability are higher in an urban setting, an educated individual may improve his relative position in society by moving from a rural to an urban setting. To investigate this possibility, we estimate equation (4) using the log of relative income (or relative consumption) as dependent variable and construct a predicted relative income measure using the same formula (5). These are shown in the second panel of Table 1. Theories of work migration predict that individuals move to increase their utility or welfare. The 1995 / 96 NLSS asked respondents a number of questions regarding their subjective satisfac- tion level with various dimensions of consumption — namely, food, clothing, housing, health care, and child schooling. They were also asked their subjective satisfaction with their level of total income. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["NLSS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "expect the prices of many manufactures to do as well. The 1995 / 96 NLSS collected information on the quantity and price paid for rice by individual households. From this we compute a unit price per Kg. The log of the district median is used as our price index proxy. To construct an index of housing costs, we take advantage of a section of the 1995 / 96 NLSS survey focusing on housing. The survey collected information on hypothetical and actual house rental values of each household together with house characteristics such as square footage, number and type of rooms, quality of materials, and the availability of various utilities. We use these data to construct an hedonistic index of housing costs for each district. Let rk s be the house rental price paid (or estimated) by household h in district s and let xh s denote a vector of house characteristics. We estimate a regression of the form: log rk s = as + bxh s + ek s to obtain estimates of eas, the housing cost premium in each district s. Regression results are shown in Table A1 in appendix. Many house characteristics are significant with the expected sign, e. g., larger, better built houses with better in-house amenities are worth more. District price differentials are large and jointly significant. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["NLSS", "NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(2009) have shown that, in Nepal, subjective welfare is negatively associated with geographical isolation. Census data on total population and population density in each district are used as proxies for urbanization and geographical proximity: the denser the population, the less geographically isolated individuals are likely to be. We also include data on the average elevation in each district. Nepal being a mountainous country, the higher the average elevation of a district, the more costly it is to build roads, raising transport and delivery costs to the district. Ceteris paribus, we expect migrants to seek out districts with a higher population density and a lower elevation. 4 Econometric results 4. 1 Univariate analysis We now investigate the choice of migration destination. We begin with simple univariate analysis. Variables are of the form ∆ h is = xh s − xh i where i is the district of origin of migrant h and s is each of 74 possible districts of destination. We examine the average value of ∆ h is for the destination district and compare it to the value of ∆ h is for alternative destinations. For instance, let xh s be population density in district s. The average value of ∆ h is for the actual destination of the migrant tells us whether the destination district is more densely populated than the district of origin. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Census data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The comparison between ∆ h is for actual and hypothetical destinations tells us whether the actual district of destination is more densely populated than alternative destinations. Results are presented in Table 3 for all variables used in the analysis. We begin with district log income eδs. We have two estimates of eδs, one obtained using reported income data, and the other based on reported consumption data. Given that most respondents to the NLSS survey are self-employed, measurement error is typically larger for income than for consumption. We see that our estimates of log income and consumption eδs are on average 20 % and 8 % higher in 20 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["NLSS survey"], "descriptive_data": [], "vague_data": ["reported consumption data", "reported income data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The univariate analysis showed that migrants on average move to destinations where they are on average less likely to find people like them. The results presented in Table 4 present a different picture. Conditional on the other regressors, the ethnicity and language proximity indices are significant with the anticipated sign: social proximity between the migrant and the population of the destination district is higher than in alternative destinations. The religion proximity index is not significant. Taken together, these results suggest that, conditional on material benefits from migration, migrants prefer to move to a destination where they integrate more easily — and possibly enjoy network benefits in terms of access to jobs and housing (Munshi 2003, Beaman 2006). 24 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "include the rice price — which appears with the wrong sign but is only marginally significant — and elevation and population density — which are no longer significant. Comparing Tables 7 and 5, we find that in the smaller NLSS 2002 / 3 dataset none of the anticipated consumption variables is statistically significant. Other results are as before. 4. 4 Magnitude To assess the relative magnitude of our results, we multiply coefficients estimated in Tables 4 and 5 by the standard deviation of their respective regressors. We then average over the various regressions reported in Tables 4 and 5. Calculations are summarized in Table 8. The larger the value, the more influence the regressor has on the choice of a destination district. We see that the most important regressors in terms of magnitude are travel time to the near- est road, elevation, language similarity, and the price of rice. Consumption variables have an effect on migration destination that is smaller in magnitude: a one standard deviation increase in anticipated relative consumption, for instance, has an effect on destination that corresponds to a third of the effect of a one standard deviation in elevation — and one-sixth of a one stan- dard deviation in distance from the nearest road. Income variables have a negligible effect on migration decisions. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["NLSS 2002 / 3 dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These calculations confirm our earlier assessment. 5 Conclusion Combining data from a household survey and an 11 % census of the population, we have estimated destination choice regressions for Nepalese internal migrants. Results show that population density, social proximity, and access to amenities exert a strong influence on migrants ’ choice of destination. These results confirm earlier work on the factors affecting the subjective welfare cost of isolation (Fafchamps and Shilpi, 2008). 29 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey", "11 % census of the population"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It is difficult to draw causal inference from observational data. This study is no exception. The results presented here are nevertheless sufficiently suggestive to cast doubt on the theory that the choice of migration destination is driven primarily by income differentials. Other factors seem to play a strong — and probably more important — role. References 1. Adams, Richard, Remittances, Investment, and Rural Asset Accumulation in Pakistan, 30 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["observational data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 6832 This paper presents findings from the impact evaluation of the Economic Empowerment of Adolescent Girls and Young Women (EPAG) project in Liberia. The EPAG project was launched by the Liberian Ministry of Gender and Development in 2009 with the goal of increasing the employment and income of 2, 500 young Liberian women by providing livelihood and life skills training and facilitating their transition to productive work. The analysis in this paper is based on data collected during two rounds of quantitative surveys in 2010 and 2011, the second of which was conducted six months after the classroom-based phase of the training program ended. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["quantitative surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1. Introduction Sub-Saharan Africa is the youngest region in the world, and the world ’ s fastest-growing. Between 2010 and 2025, the number of people between 15 and 24 will grow to 250 million – a net increase of nearly 50 percent. Over the next decade, roughly one million young people will enter the labor market each month in Sub-Saharan Africa. However, these young people often enter the labor market too early and unprepared. Although access to education is growing, illiteracy remains high and schooling low: among the 32 Sub-Saharan African countries in the Barro-Lee (2010) data set, nearly 40 percent of women aged 15 and above have received no education at all; and the most recent (2007-2011) statistics in the World Bank ’ s Edstats data reveal that female literacy is less than 60 percent, on average. 1 This lack of preparedness contributes to a growing problem of youth unemployment. Quantifying the level of unemployment is bedeviled by lack of data and measurement issues. Household and labor force surveys throughout Africa usually record unemployment rates of less than 10 percent, 2 but those figures belie the extent of underemployment and vulnerability. Those same surveys indicate that the vast majority of working adults have insecure work in the informal sector, on the family farm, or in less- productive or unremunerated labor. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Barro-Lee", "Edstats data"], "descriptive_data": [], "vague_data": ["Household and labor force surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The most recent labor force survey from Liberia mirrors these statistics: there are roughly 1. 1 million people in the workforce, of whom 195, 000 (about 18 percent) are engaged in wage employment; the remaining 900, 000-plus workers (82 percent) are considered in vulnerable employment, working for themselves or working unpaid for their own households (LISGIS 2010). Among young women (15-24) in Liberia, the unemployment rate is 8 percent, double the rate among young men (LISGIS 2010). Most of these gaps can be explained by differences across individuals, especially in educational attainment, skills training, and years of experience. But segregation, market segmentation, and discrimination do play a role in determining these individual characteristics. Women have fewer opportunities for education or training, less access to credit, a larger share of domestic responsibilities, and less independence and control over their own lives. In Liberia, women comprise half of the employed, but only about one-quarter of paid employment (LISGIS 2011). Fourteen years of civil war in Liberia devastated the country ’ s infrastructure and institutions, and left a generation of young people with very low levels of education and training. Girls were particularly disadvantaged. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In 2003, almost 60 % of young girls and 40 % of young boys had no formal schooling (ILO / UNICEF 2005). Data from the Demographic and Health survey shows that more than 40 percent of adult women have no education, compared to fewer than 20 percent of men, while 23 percent of women and 44 percent of men have some secondary schooling (DHS 2007). Happily, access to education is rising rapidly, especially for girls: according to the Liberian labor force survey from 2010, the ratio of girls to boys enrolled in primary school has risen from 72 in 2000 to 90 in 2009. Enrollment levels and sex ratios are lower among older children and youth, as they become increasingly engaged in 1 Both are unweighted averages; Barro-Lee comprises 32 countries with data from 2010; Edstats comprises 43 countries with data from 2007-2011. 2 Defined as without work, available for work, and actively looking for work (LISGIS 2010). 2 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Barro-Lee", "Demographic and Health survey", "Liberian labor force survey"], "descriptive_data": ["Edstats comprises 43 countries with data from 2007-2011"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Programs designed to enhance employment among young people can focus on the supply side of the labor market, including skills training programs for both wage employment and entrepreneurship training; or programs to augment demand, such as wage subsidies, public works, and community service programs; or programs to help the labor market clear, such as job search assistance and placement services. In addition, it may be that the constraints facing young people are not in the labor market itself, but in other markets, such as for credit. The vast majority of jobs programs have focused on the supply side: training programs make up about 79 percent of over 600 cases included in the World Bank ’ s Youth Employment Inventory database. 3 Although rigorous and general evidence of success is limited, it seems that successful skills training programs share a few key features: they are responsive to local market conditions, they provide more than just technical skills in a specific area (including, for example, “ life skills ”), and they include ancillary services that alleviate other constraints preventing successful labor market integration (e. g., access to credit). Among the most celebrated are the Jovenes programs that provide demand-driven technical training, plus social skills that help in the labor market, plus internships. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Youth Employment Inventory database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "month placement and support phase in which the trainees were supported in their transition to self or wage employment. Upon recruitment, the participants are assigned to a\"Job Skills (JS)\"track or a\"Business Development Services (BDS)\"track. When possible, the participant's track preference was honored; however, the demand for the Job Skills track greatly exceeded the supply, so the remaining trainees were placed into the BDS track. In the first round of training, the proportion of Job Skills track places was limited to 35 % of the total training places available given the expectation that few wage jobs will be available in the Liberian job market. The Job Skills track provided training in six areas: 1) hospitality, 2) professional cleaning / waste management, 3) office / computer skills, 4) professional house / office painting, 5) security guard services, and 6) professional driving. These areas were determined based on independent labor market assessments, a review of the available market data, and input from EPAG ’ s private sector partners. All Job Skills trainees received training in entrepreneurship skills as well. The BDS training taught young women how to identify micro-enterprise opportunities based on an assessment of market needs, and how to grow and manage any existing businesses they already had. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["available market data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The literacy requirement in particular, although basic, made the EPAG program out of reach for many of the most vulnerable young Liberian women; the requirement reflected a deliberate choice on the part of program designers in the face of a tradeoff between serving the most vulnerable and serving those who could most readily make use of this relatively short training program. 7 The team recognized that it was difficult, if not impossible, to require documentation from the applicants to verify each of the eligibility criteria (especially age, since many Liberians do not have any official form of identification). Hence the application process relied primarily on self-reported data. To counter the likelihood that applicants would give false information in order to gain entry into the program, the eligibility criteria were not made public; the mobilization and outreach campaigns did not specify the precise age or education requirements for the program. During the recruitment events, each applicant had to physically present herself, fill out an application form specifying her age, education history, and residence. A simple literacy and numeracy assessment was also administered at the time of application. Beyond these basic eligibility criteria, no further selection criteria were applied, and program managers did not choose whom to train. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "stipend money, and they were formed into small groups or\"EPAG teams\", each with a coach or mentor, to foster support networks and boost attendance. 3. Methodology 3. 1. Research design The impact evaluation of the EPAG project uses a randomized controlled trial, in which eligible applicants to the program were randomly assigned to participate in one of two cohorts (or “ rounds ”) of training. The treatment group is defined as those who were offered a space in the first round of training and the control group comprises those assigned to the second round. Selection into the training rounds was performed on a computer (using Excel) and was stratified by the track choice of the applicant (job skills versus business development skills), community, and service provider. Data were collected using three quantitative household surveys (baseline, midline, and endline) and two sets of qualitative focus group discussions (one after each round of training). A timeline of the impact evaluation is depicted in Figure 1. During both the baseline and midline surveys, the head of the household in which the EPAG participant was residing was also interviewed, in order to examine potential spillover effects of the program on non-treated household members. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["quantitative household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The household head interviews also provided an opportunity to gather useful household-level information (including assets, housing characteristics, and household head characteristics) to be used as control variables in our analysis. The baseline survey was conducted before participants were informed of the outcome of the randomization, and the midline survey was conducted one year after the baseline survey, one month after completion of the first round of training and before the start of the second round. The results in this paper are based on a panel data set that includes data from the baseline and midline surveys as well as administrative and monitoring data from the program implementation team. Note that although the midline data collection occurred just one month after the completion of the intervention, the intervention itself includes 6 months of post-training follow-up; hence the midline survey captures outcomes of the first cohort 7 months after they exited the classroom-based training. This timing was necessary to prevent too long of a delay between cohorts; the second cohort of trainees started immediately after the midline survey was completed. Future analysis using endline data will examine the trends in outcomes of both groups after Round 1 completion, but will not involve comparison against a pure control group of non-participants. 3. 2. Sample and attrition Following the communications and outreach campaigns in each of the nine target communities, during which 2, 106 young women were originally recruited to be a part of the EPAG program, 8 a randomized 8 The original recruitment effort fell short of the target of enrolling 2500 participants. Hence, before the second round of training, another recruitment effort was launched and an additional 617 girls were enrolled. These girls are not included in the impact evaluation since they were not subject to the initial random assignment. 7 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data set"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Thirty-eight percent of the sample was already engaged in at least one income-generating activity (IGA) at baseline. This is consistent with the national figures from the 2007 DHS survey, which found labor force participation rates of 34 % for women aged 15-19 and 49 % for women ages 20-24. It is also consistent with the Liberian 2010 Labor Force Survey, which found labor force participation rates of 25 % for women aged 15-19 and 47 % for women aged 20-24. For the purposes of this study, to be consistent with program objectives and the Liberian context, our definition of income-generating activity encompasses the full range of activities through which people earn money, including paid employment, either formal or informal, and self-employment in small business or through petty trade. The most common types of IGAs reported at baseline were petty trade, including 15 The balance tests are run on the same sample as will be used in the impact analysis in Section 4, that is, the subset of individuals for whom we have a panel. Balance tests run on the full sample of baseline survey respondents, regardless of whether they also participated in the midline survey, confirm the same findings. A report summarizing the balance tests on the full sample, including comparisons to nationally representative data, is available upon request from the authors. 9 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["2007 DHS survey", "Liberian 2010 Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "street vending (48 % of those with at least 1 IGA), food processing for sale, including baking, cooking, and drying (16 %), and home production of crops, livestock, and fish (11 %). It is important to note that the EPAG program was not targeted toward the most vulnerable segments of Liberian society, but rather toward young women with enough education to be able to benefit from a training program of this nature. Based on comparisons from the 2007 CWIQ (Core Welfare Indicators Questionnaire) survey, the average EPAG participant is more educated, more literate, more likely to be engaged in an income-generating activity, and owns more assets than the average Liberian women of the same age group: mobile phone ownership was high (63 %), as was the proportion reporting that they had some money of their own (79 %). Even compared to other similar residents of Monrovia, the EPAG participants are better educated and have higher income. A strong sense of female empowerment at baseline emerges from the sections of the survey instrument having to do with self-confidence and agency. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Core Welfare Indicators Questionnaire"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "project had to meet a basic minimum literacy level in order to qualify for admission to the program; whereas only half of the 15-29-year-old female respondents to the nationally-representative Core Welfare Indicators Questionnaire (CWIQ) survey report that they can read and write (LISGIS 2007). Fewer than two percent of the girls in the EPAG study responded that they had no education, which is much lower than in the CWIQ survey. Second, the majority of adolescent girls and young women in Liberia reside in rural areas, whereas the survey participants were residing in urban and peri-urban areas, where access to basic social services may be much more improved. Consequently, the results are not representative of adolescent girls and young women in Liberia overall. The results are neither indicative of the average Liberian girl and young woman; nor are they indicative of the average Liberian girl or young woman in the project communities. They are only indicative of the average girl and young woman who are part of the EPAG project. Finally, many of the variables that we examine in this study are measures of self-assessed levels of satisfaction or belief. These are entirely subjective variables, and are subject to significant measurement error. There is considerable evidence that the wording of these questions can affect the answers given, as can the order in which the questions are asked. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Core Welfare Indicators Questionnaire", "CWIQ survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Surveys of employers consistently find that more than hard, technical skills, employers value these harder-to-quantify skills of honesty and integrity, problem-solving ability, work ethic, communication skills, the ability to work productively with others, responsibility and dependability (Blom and Hobbs 2007). The AGI program has focused on the development and measurement of these softer attributes that matter for employment as well as those that matter more to the individual, such as self-confidence and empowerment. Despite the challenges of measuring such subjective outcomes, the survey instruments included panels of questions designed to elucidate a nuanced picture of the personality and psychosocial characteristics that are most relevant for labor market success. Table 6A presents results on empowerment and decision-making. The first series of questions have to do with control over resources, spending decisions and earnings. Respondents were asked how much control they had over how to spend their own earnings; also, whether they had money of their own for basic uses that they alone could decide how to use, without having to ask for permission. The EPAG baseline survey found that respondents reported a high degree of control over resources even before the program started. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "their earnings on household expenses than men. Most of these studies have focused on adult women, specifically married women with children. It is not known whether the same holds true for young women, who may have other spending priorities, have less experience in managing households, and have younger children. Given the large increases in employment and earnings documented above, the EPAG program serves as a good setting to examine these types of spillovers. The evaluation included detailed interviews with the heads of the household in which EPAG participants were residing. The purpose of the household questionnaires was precisely to examine the hypothesis that investing in young girls would benefit her household. A secondary hypothesis was that EPAG participation may change gender-related attitudes in the participants ’ households. Household data was collected for 1601 out of the 1622 individuals who were interviewed at both baseline and midline; this same sample of 1601 individuals serves as the basis for both the individual and household level analysis in this paper. The estimated impact of the program on a broad range of household outcomes is summarized in Tables 8 and 9. Panel A of Table 8 examines the household size. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Household food security was measured using two methods: dietary frequency of high-value protein-rich foods, and two subjective questions on food shortages adapted from USAID ’ s FANTA questions (Coates 2007). On two of the four of the dietary frequency questions and on both of the food shortage questions, the treatment groups ’ dietary situation improved as a result of the EPAG program. Weekly consumption of fish and meat rose significantly by four percentage points in treatment households (from a high baseline value of 84 % for meat / chicken and 90 % for fish), and weekly consumption of dairy and eggs did not change significantly. Household heads report worrying less about insufficiency of household food supplies, and the reported incidence of household members going to bed hungry also decreased in treatment households relative to control. Combined, the impacts across these indicators portray a situation of improved food security and dietary composition, consistent with the hypothesis that the increased earnings of the EPAG participants were spent in part on food. 20 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, an endline survey was conducted after the second round of the EPAG program as per the timeline depicted in Figure 1. Examination of this endline survey data will permit a descriptive analysis of the outcomes of the first group of trainees 12 months after they completed the EPAG program, as well as examination of the outcomes of the second batch of trainees. The second round included not only the control group from this impact evaluation but also newly recruited participants who were offered brief basic literacy and numeracy training program prior to program entry. Work is already underway to design and implement the third round of EPAG, with a substantial redesign of the Job Skills track, an emphasis on reaching younger girls with lower literacy, and expansion to communities outside of Monrovia. If the high success rates found in this study are replicated for these future cohorts, the EPAG program should serve as a model for policy makers in Africa and the world seeking to improve lives and livelihoods of all youth, male and female. 25 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["endline survey data", "endline survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The notion that volunteering may affect positively youth ’ s sense of social cohesion has gained policy relevance since the publication of the World Development Report 2013: On Jobs, which stresses that in countries affected by conflict situations, creating the types of productive opportunities that strengthen social cohesion can help reduce the volatility of economic growth and achieve international development goals by defusing tensions and building trust among the different communities involved. This paper provides novel empirical evidence on the impact of volunteering on enhancing social cohesion values in Lebanon, a country with a fragile and highly complex political, religious and social landscape, as well as high degrees of social and economic exclusion among its young population. To our knowledge, this is the first impact evaluation that rigorously addresses this research question in Lebanon and in the Middle East and North Africa (MENA) region. The main results show that youth who were selected to participate in a volunteering program that consisted of 80 hours of inter-community volunteering activities and 20 hours of soft skills training were more likely to report higher and improved values of social cohesion in the short term. In specific, they were more likely to report higher tolerance values as well as a stronger sense of belonging to", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 SECTION 1: CONTEXT & INTERVENTION Lebanon ’ s political development system since Independence has been heavily influenced by its confessional system. While originally established to balance the competing interests of Lebanon ’ s diverse religious communities, it is seen as an impediment to inclusive growth and effective governance (World Bank, 2016), and has been closely tied to the economic and social inclusion challenges facing Lebanese youth today. The confessional system of governance has heavily impeded the equitable and efficient distribution of investments and public services. Provision and targeting of public services tend to be guided by considerations of confessional quotas and electoral geography rather than needs- based service delivery that favors the poor. In the absence of effective state institutions, sectarian organizations have played a key role in the provision of social services such as education, health, and welfare support to the most vulnerable groups linked to their electorates, thus deepening a sense of discriminatory and inequitable system (World Bank, 2016; Kraft et al., 2008). Regional disparities are stark, with the bulk of the poor living in peripheral areas (particularly the North and the South), with visible inequality in access to and quality of social services.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "According to the 2012 Institutional Profiles report, the quality of public services and its territorial coverage, which was weak to being with, have significantly deteriorated since 2006. 1 A combination of rising poverty, rising insecurity, and deteriorating public services have further strained inter-communal relations and contributed to deteriorations in social cohesion. Many Lebanese youth do not trust their state and become disillusioned as they are not able to affect their own life or contribute productively to society at large. 2 Political and civic engagement is reported to be low (Status of Women in the Middle East and North Africa Survey Project, 2010). 3 In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and large numbers of refugees, many Lebanese 1 On the quality of public services indicator, Lebanon ’ s score declined from 2. 5 in 2006 to 0. 8 in 2012 on a 4-point scale. On the territorial coverage indicator, its score went down from 2. 7 in 2006 to 1. 5 in 2012. 2In a Gallup World Poll, Lebanese reported low confidence in (a) their national government (37 percent) and the judiciary, (b) the honesty of elections (15 percent), and (c) the honesty of government (4 percent) (World Bank, 2016). 3 According to the SWMENA survey, only 18 percent of Lebanese women are members of an organization, compared to 34 percent of men. Men are more likely to be members of a political organization than women (21 percent of men vs. 7 percent of women), whereas women are more likely to be active in religious groups and charity organizations than men.", "output": {"entities": {"named_data": ["SWMENA survey", "Middle East and North Africa Survey Project", "Gallup World Poll"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Challenges around skill mismatch exacerbate exclusion, as many young Lebanese lack the skills and competencies demanded by private sector employers, particularly ‘ soft skills ’. To address some of these challenges, the Lebanese government (GOL) identified volunteerism as a mechanism to enable diverse youth to work together for improved community assets and service delivery as well as increased employability. In September 2012, the GOL issued a Decree (Number 8924 / 2012) that created a new extra curriculum program that requires secondary school students to complete 60 hours of civil work. In addition, the Ministry of Social Affairs (MOSA), through its Volunteering Department, launched annual action plans for the implementation of youth volunteer summer camps across Lebanon. 4 Father ’ s education and residence (region and location of school) are the two largest contributors to inequality of opportunity in students ’ math test scores, accounting for 44 and 23 percent of total inequality, respectively (World Bank, 2016). 5 According to the 2013 Gallup Poll, 90 percent of respondents in Lebanon agreed with the statement that knowing people in high positions is critical to getting a job.", "output": {"entities": {"named_data": ["Gallup Poll"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 as self-confidence, responsibility, and respect. Additionally, the curriculum includes workplace readiness skills, such as interviewing and time management. SECTION 2: STUDY TIMELINE & DATA A quasi experimental impact evaluation design was embedded into the NVSP. As mentioned before, the NVSP received 38 applications from eligible NGOs. Per well-developed selection criteria, 8 the highest 22 ranked proposals were selected to receive funding. Each of the 38 proposals included a list of 50 youth (the minimum number of youth set by the NVSP) who would benefit from the project if selected for funding. However, as mentioned before, the 22 selected projects benefited a total of 1, 296 youth, exceeding the set target of 1, 100 volunteers. Of the 50 volunteers included in each of the 38 proposals, 22 youth per proposal were randomly selected to participate in the impact evaluation study. Therefore, the initial sample size of the study comprised a total of 825 youth: 473 youth who served as the treatment group (representing the 22 selected NGOs that received NVSP funding) and 352 youth who served as the comparison group (representing the 16 non-selected NGOs). However, two NGOs refused to participate in the study once informed that their proposals had not been selected for funding.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Therefore, the final sample size consisted of 759 youth, of which 473 treatment and 286 comparison. Detailed baseline data were collected through face-to-face interviews from July to September 2015 prior to implementation. The actual implementation varied between projects and ranged between the second half of August and end of December 2015. Sampled youth from both selected and non- selected NGOs were invited to fill out a questionnaire with detailed information on volunteers ’ socio-economic backgrounds, education levels, interests and attitudes towards volunteering, employment, soft skills, as well as social cohesion values. Follow-up data were collected between November 2016 and March 2017, approximately one year following the start of implementation, through phone and face-to-face interviews. The questionnaire contained the same modules asked and collected at baseline. Despite the high 8 Proposals were ranked based on four main selection criteria: institutional appraisal (25 points), technical appraisal (40 points), project impact (25 points), and financial appraisal (10 points). There was also a fifth criterion related to sustainability of volunteering activities, which was assigned a bonus score (5 points).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The report focuses on intent-to-treat (ITT) estimates, measuring the impact of offering volunteering opportunities and soft skills training independently of actual take-up. 9 We estimate the following individual-level intent-to-treat regression: 𝑌 ௜ ௧ ൌ 𝛼 + 𝜇 ௡ ൅ 𝛽𝑇௧ + 𝛾𝐷 ௜ + 𝛿ሺ𝑇 ∗ 𝐷ሻ ௜ ௧ + 𝜀 ௜ ௧ (1) where 𝑌 ௜ ௧ is the outcome of interest for respondent i in period t, 𝑇௧ is a post-treatment year binary variable, 𝐷 ௜ is a binary variable for being assigned to the treatment, and 𝜇 ௡ is a fixed effect for NGOs. 𝛼 represents the baseline average for the outcome of interest for non-selected youth. 𝛽 is the difference in after-and- before intervention in outcomes for non-selected youth. 𝛽 ൅ 𝛿 is the difference in after-and- before intervention in outcomes for selected youth. 𝛾 is the difference in 9 Due to some procurement delays that caused a big time-lag between baseline data collection and actual NGO project implementation, many of the volunteers who belonged to selected NGOs and who were randomly selected to participate in the impact evaluation study dropped out after their baseline data were collected and were replaced by other volunteers. Project monitoring data reveal that 23 percent of volunteers assigned to treatment did not actually end up participating in the NVSP. Given the relatively high number of non-compliance, we are unable to perform Local Average Treatment Effects (LATE) analyses to understand the impact of participating in NVSP on outcomes of interest.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Project monitoring data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 outcomes between selected and non-selected youth at baseline. 𝛿 is the DiD estimator. 𝜀 ௜ ௧ is a mean-zero error term. Standard errors are robust and allow for intra-cluster correlation at the NGO level. 10 The DiD estimator can be derived from the above regression as follows: E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 1ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽. 1 + 𝛾. 1 + 𝛿 (1. 1) + E (𝜀 ௜ ଵ | 𝐷 ௜ ൌ 1ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽 + 𝛾 ൅ 𝛿 E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 1ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽. 0 + 𝛾. 1 + 𝛿 (0. 1) + E (𝜀 ௜ ଴ | 𝐷 ௜ ൌ 1ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛾 E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 0ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽. 1 + 𝛾. 0 + 𝛿 (1. 0) + E (𝜀 ௜ ଵ | 𝐷 ௜ ൌ 0ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽 E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 0ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽. 0 + 𝛾. 0 + 𝛿 (0. 0) + E (𝜀 ௜ ଴ | 𝐷 ௜ ൌ 0ሻ = 𝛼 ൅ 𝜇 ௡ Hence, the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "DiD estimate is (E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 1ሻ- E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 1ሻ) – (E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 0ሻ- E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 0ሻሻ ൌ ሺ 𝛽 ൅ 𝛿ሻ െ 𝛽 ൌ 𝛿. The DiD estimator relies on the “ Equal Trend Assumption ” that does not require both selected and non-selected youth to be on average balanced at baseline on key observable & unobservable characteristics. Table 1 shows that both groups differ on some key characteristics. Non-selected youth are more likely to be older, more educated (hold more academic degrees), come from Beqaa and Nabatiye, and have parents with intermediate education (grade 7 to 9). Selected youth are more likely to be males, younger, students, come from Mount Lebanon and the North, and have mothers with university education. Both groups appear balanced on key outcomes related to soft skills, tolerance values, and labor market outcomes. The exception is that non-selected youth exhibited a better sense of belonging to the Lebanese community and selected youth were more likely to have been unpaid employees (interns) at the time of baseline data collection. In addition to comparing means of observable characteristics, the study also tested for the differences in the statistical distributions of key outcomes using two sample Kolmogorov-Smirnov tests of the equality of distributions. Results indicate that the only key outcome for which there is a statistically significant difference in its distribution between the treatment and comparison groups at baseline is the sense of belonging to the Lebanese community. The largest difference between the distribution functions in the direction that the comparison group contains larger values 10 Standard errors are clustered at the NGO level because that was the unit of allocation into treatment and comparison groups. Abadie et al. 2017 argue that clustering is generally needed even if NGO fixed effects are included in the regression.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "FE remove the effect of individual-specific time-invariant characteristics so as the net effect of the predictor variable on outcome variables can be assessed, per the following equation: 𝑌 ௜ ௧ ൌ 𝛼 + 𝜇 ௡ + 𝛽𝑇௧ + 𝛾𝐷 ௜ + 𝛿ሺ𝑇 ∗ 𝐷ሻ ௜ ௧ + 𝜃ଶ𝐸ଶ ൅.. ൅ 𝜃 ௡ 𝐸 ௡ ൅ 𝜀 ௜ ௧ (3) where 𝐸 ௡ is entity n (i. e. the individual volunteer). Since they are binary (dummies), there are n-1 included in the model (i. e. 758 individual volunteers). 𝜃ଶ is the coefficient for the binary regressors (the 758 volunteers). Additionally, we propose dealing with attrition in two ways. First, we utilize the standard “ Manski Bounds ” approach (Horowitz and Manski, 2000) by imputing upper and lower bound estimates for missing data on estimated outcomes of interest at follow-up, where lower bound estimates take the lowest possible value and upper bound estimates take the highest possible value for individuals who could not be tracked over time. This allows us to provide the two extreme possible scenarios for estimated impacts had data been successfully collected for attritors. Second, we use the Inverse Probability Weighting (IPW) procedure to establish narrower bounds that might provide a better sense of whether there is a robust treatment effect. This entails first estimating a probit model that predicts the probability of data being observed (i. e. not attrition) using a set of covariates at", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 baseline that were found to be uncorrelated with the treatment in Table 1. 12 Observations are then weighted by the inverse of their probability of having data observed. Therefore, those who had a small chance of being observed are given increased weight, to compensate for those similar observations who are missing. The pseudo R-squared from the probit model suggests that those baseline covariates explain about 8 percent of the probability of data being observed. A Wald test confirmed that those variables are jointly statistically different from zero (the P-value is 0. 000). However, this still leaves a large percentage of attrition (around 92 percent) unexplained. 13 Therefore, we note that the results in the following section should be interpreted with caution. We present results in the next section for four specifications. Specification 1 presents OLS estimates from equation 1. Specification 2 presents results that control for individual fixed effects from equation 3. Specification 3 presents OLS estimates for the full sample by imputing missing observations for attritors at follow-up using lower and upper bound estimates. Specification 4 presents OLS estimates with the estimated constructed weights.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 do selected volunteers increase their teamwork / leadership and their communication skills? Do they increase their self-esteem / self-satisfaction? iii) As a result of their assignment to NVSP volunteering experience, are selected youth more likely to find a job than non-selected ones? (a) IMPACTS ON SOCIAL COHESION VALUES The two main indicators that measure improvements in social cohesion values are tolerance and a sense of belonging to the Lebanese community. Measuring social cohesion values in large-scale surveys is challenging. We are unable to use extensive measures, but rely instead on brief measures adapted from Harb (2010). The tolerance measure relies on a series of 12 questions, each of which is ranked on a four-point scale, which makes the total possible score range between 12 and 48 points. The sense of belonging to the Lebanese community measure consists of 18 questions, each of which is ranked on a seven-point scale, which makes the total possible score range between 18 and 126. Thus, higher scale values indicate higher tolerance values and a stronger sense of belonging to the Lebanese community. Both values are internally standardized so that they have a mean of 0 and a standard deviation (S. D.) of 1 in the comparison group at baseline.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "They have also been piloted ahead of data collection to test their validity and reliability. Results in table 4 indicate that assignment to NVSP has no impact on the three indicators for soft skills among selected youth for specifications 1 & 2 (see the 𝛿 estimate for columns 1, 2, & 3). The leadership skills measure appears to have worsened for both selected and non-selected youth over time, which is somewhat puzzling given that both groups are active volunteers and members in their communities (see the 𝛽 ൅ 𝛿 estimate and the 𝛽 estimate for column 1). Any changes for the communication and confidence scores one year following NVSP were not statistically 15 The selection of the indicator for this study was based on its extensive utilization (to maximize the chance for the scale to be reliable when calculating Cronbach ’ s Alpha with the data of the pilot), on the availability of detailed information regarding how the indicator was designed, and of how the scales should be interpreted once data have been collected. 16 This scale had been tested with youth aged 12-18 showing high levels of internal consistency. Additionally, it was a relatively simple scale with no need for special training to administer it or to analyze the results of the scale. 17 These skills include: awareness of one ’ s own styles of communication; understanding and valuing different styles of communication; practicing empathy; adjusting one ’ s own styles of communication to match others'styles. (communicative adaptability); and communication of essential information; Interaction management. 18 This scale has been used extensively in the psycho-social / soft skills literature, ensuring possible comparability with other studies of the soft skills literature.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 significant for both selected and non-selected youth (see the 𝛽 ൅ 𝛿 estimate and the 𝛽 estimate for columns 2 & 3). Lack of results also holds for specification 3, where imputing missing data with upper and lower bound estimates to account for the potential bias introduced by attrition did not alter the lack of impact of the program, as well as for specification 4 (see the 𝛿 estimate). Figures 1, 2, and 3 plot the distribution of soft skills scores at baseline for both selected and non- selected youth. The figures indicate that scores across the three skills are concentrated towards the end of the scale, suggesting that soft skills training offered by NVSP might have been ineffective or too basic for this pool of volunteers. 19 Indeed, as table 1 shows, a high percentage of selected youth (71 percent) and non-selected youth (63 percent) had taken previous training in soft skills prior to NVSP. Results from a process evaluation conducted separately support this explanation. The majority of NVSP volunteers in focus group discussions and interviews mentioned that they would have welcomed more advanced trainings on soft skills, as well as on technical topics and job-relevant skills that can support their employability.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These may include skills on how to write a CV, prepare for job interviews, business and entrepreneurial skills to start a business, etc. In this regard, the mechanism for improving social cohesion values appears to have come from inter-community volunteering activities, rather than improvements in soft skills. (c) IMPACTS ON LABOR MARKET OUTCOMES While the NVSP was designed primarily to improve social cohesion values among participating Lebanese youth, it was also hoped that engaging them in volunteering activities, coupled with soft skills training, would enhance their employability and thus increase their chances of employment. At baseline, half of the selected and non-selected volunteers were active and searching for a job. Among them, 49 percent reported being unemployed, 31 percent wage employed, 13 percent employed in unpaid jobs, and 7 percent self-employed (see table 1). Those active volunteers were older in age than the rest of volunteers who reported being inactive in the study ’ s sample (with an average age of 21 and closer to labor market insertion). One year later, it appears that many of 19 Our interpretation that offered soft skills are likely too basic for this pool of volunteers is provided given the scale that we used in the questionnaire to test their knowledge on soft skills. We cannot rule out the possibility that had we used a different scale, we might have found an impact, either negative or positive.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Following the approach used for the Global Costing of Refugee Inclusion, successful inclusion is defined as earning sufficient income to be no longer poor and to consume more than the (international) poverty line. This definition opens two tracks for investigation: first, how much aid would be needed if the policy objective were to bring refugee consumption up to the poverty line. The answer to this question is found by identifying the poverty gap for refugees. This opens the second track which explores the factors that determine, or at least that are associated with, the size of the poverty gap. The note is organized as follows. The next section discusses data and presents some key descriptive statistics on refugees and host communities in Uganda. This is followed by a methodological section discussing how own income and aid are complements and how an analysis of poverty gaps informs about the need for assistance. This is followed by two analytical sections. The first identifies refugee poverty gaps, and assistance needs for refugees with distinct characteristics. The following section estimates how much has been saved by including refugees in the economy and explores how more could be saved. Conclusions follow. 2 The poverty numbers in World Bank (2019) are based on the official poverty line adopted in Uganda in 1997. There was a need to update this line as it was too old and producing a very low poverty rate. For example, using this line produced a national poverty rate of about 21 percent in 2019 / 20 compared to more the than 40 percent international poverty rate using the USD 2. 15 2017 PPP daily poverty line. In order to address this criticism, the poverty line was revised by the Uganda Bureau of Statistics in 2021, but it is not available for the 2018 Refugee and Host Communities Household Survey used in this note. Instead, we are using the international poverty line throughout.", "output": {"entities": {"named_data": ["2018 Refugee and Host Communities Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 Figure 10. Selected characteristics across Ugandan and refugee households, % Source: RHCS 2018, WB staff calculations. A simple comparison between refugees and Ugandan households demonstrates that refugees lag with regards to selected characteristics found to narrow the poverty gap. For example, refugees are less likely to have access to land than Ugandans. If refugees have access to land, the majority do not own it, but have user rights. The size of land also differs a lot among Ugandan and refugee households. Most Ugandans have at least 0. 05 hectare per capita, while the majority of refugees have less than 0. 05 hectare per capita. Refugee heads of household are also less likely to work and less likely to be literate compared to their Ugandan counterparts. Refugees have higher shares of children and elderly in household size compared to Ugandans. For example, among almost 60 percent of refugee households, more than half of the household members are children and elderly compared to 42 percent of households among Ugandans. Economic inclusion dividend When a development approach to hosting refugees is followed and refugees earn incomes, there are two key beneficiaries. Refugees themselves, who gain dignity, financial autonomy and pathways to self-reliance.", "output": {"entities": {"named_data": ["RHCS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "And the international community, which can provide less humanitarian aid. 9 It is possible to estimate how much money is saved in humanitarian assistance thanks to the inclusion of refugees in Uganda ’ s economy. For this, return to Figure 8 to observe that the savings in assistance between the no income and current scenarios are US $ 150 per refugee per year (US $ 343 – US $ 193). Multiplied by 1. 5 million refugees that reside in the country, the annual savings are US $ 225 million. If the aim is to bring the consumption of all refugees to at least the international poverty line, rather than spending US $ 515 million on basic humanitarian needs assistance every year, US $ 290 million would be needed to ensure all refugees have a dignified life. Unfortunately, refugees receive less than US $ 290 million in assistance, because despite working and after receiving assistance many remain poor with levels of consumption that fall below the poverty line. Humanitarian aid is falling short in Uganda, and refugees bear the burden for it. This burden is well-documented in a 2023 document by the Uganda Refugee Operation which explores the impact of underfunding by humanitarian agencies: it points to high 9 There is potentially a third beneficiary: the Government of Uganda which might be rewarded by the international community with additional financing in return for its inclusive refugee policies. Assessing this, and how these potential rewards compare to the costs associated with economic inclusion, falls outside the scope of this paper.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 if they are built to code. 2 These spillovers or externalities are absent in more sparsely populated rural areas where damages to smaller sized and dispersed dwellings will cause less or no collateral damage. Exposure The main reason why urban risk is large and increasing is the rise in exposure. Although urbanization statistics suffer from a lack of standard definitions of what should be considered ‘ urban ’, the assumption of half the world ’ s population living in cities seems realistic. Urban populations are growing in practically all developing countries. About 40-60 percent of this growth can be attributed to natural growth, i. e., fertility of urban dwellers (Montgomery 2009). The remaining growth is due to urban expansion and migration, reducing the share of rural residents except where rural fertility is vastly larger. The latest UN urban population estimates suggest that, globally, urban population exceeded rural population for the first time in 2008 (UN 2008). In less developed regions, this threshold is expected to be reached by 2019. This continuing urbanization process will lead to an increase of exposure of people and economic activity in hazard prone urban areas. Although we can only speculate about the global distribution of disaster damage in cities today and in the future, newly available geographically referenced data yield some estimates of urban exposure to natural hazards.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["geographically referenced data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A recent global hazard analysis generated a comprehensive database of hazard events during 1975- 2007 from observed data and of event probabilities from geo-physical models (ISDR 2009). We combined this hazard information with city-specific population projections 2 It should be noted that well-documented evidence for such externalities is quite scarce. Their importance is usually taken as given: “ A building collapse may create externalities in the form of economic dislocations and other social costs in addition to the economic loss suffered by the owner. The owners may not have taken these consequences into account when evaluating specific mitigation measures. Consider the following example. A building toppling off its foundation after an earthquake could break a pipeline and cause a major fire, which would damage other homes that had not been affected by the earthquake in the first place. “ Kuenreuther and Roth (1998). See also www. quakesmart. org / index. php? option = com_content & view = article & id = 92 & Itemid = 209. But some experiences have been documented: “ As shown by research on the Great Hanshin-Awaji Earthquake, including that conducted by the Architectural Institute of Japan, Architectural Institute of Japan (1997), houses with inferior earthquake-resistant quality triggered large negative externalities in the neighborhood. For example, broken fragile houses blocked transportation networks, thereby preventing effective fire fighting and, by severing lifelines, they made recovery more difficult. ” (Nakagawaa et al. 2007).", "output": {"entities": {"named_data": [], "descriptive_data": ["city-specific population projections"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 Investors actively trade-off disaster risk with gains from economic density. In addition to the city or location specific analysis, we examine how investors value risk from natural disasters. Data from a recently compiled dataset of a sample of global cities provides some insights. Gomez-Ibañez and Ruiz Nuñez (2006) constructed a dataset of central business district office rents for 155 cities around the world in 2005 to identify cities where rents seem elevated or depressed by poor land use or infrastructure policies. Their dataset also includes information on many factors that determine the supply and demand for central office space such as construction wage rates, steel and cement prices, geographic constraints, metropolitan populations and incomes. We link this information to the natural disasters hotspot dataset (Dilley et al. 2005), and examine if city demand – as reflected in office rents, is sensitive to risk from natural disasters. Gomez-Ibañez and Ruiz Nuñez (2006) focus on offices in the primary business district, which they define as the district having the highest density of employment; a very large, if not the largest, concentration of offices; and the highest rents in the metropolitan area. As we are interested in the tradeoff between economic density and disaster risk, using the central business district works well for our analysis.", "output": {"entities": {"named_data": [], "descriptive_data": ["dataset of central business district office rents for 155 cities around the world", "natural disasters hotspot dataset"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "19 As property prices in the worst affected areas reduced the most, low income households responded by moving into low-rent housing being offered in these locations. On the other hand, middle income households moved away to avoid risk, and the wealthy, for whom insurance and self-protection was the most affordable, did not change where they lived. Poor people “ sort ” into low rent locations – which are often at higher risk to natural hazards. The problem is particularly acute in developing countries where there is a divide between the formal and informal markets for land. While formal developments may respect land use regulations, informal settlements are often located in hazard prone locations, such as on hill slopes, close to river banks, or near open drains and sewers. In Dhaka for example, informal settlements are developing across the metropolitan area, with many residents lacking basic public services and in locations at risk from flooding. In fact, most informal settlements do not have access to a public toilet within 100 meters, and 7, 600 households in 44 slums live within 50m of the river (World Bank 2005, Dhaka Urban Poverty Assessment). For the city of Bogotá, we use the same database discussed earlier to examine if poor people are at greater risk from natural hazards – particularly earthquakes.", "output": {"entities": {"named_data": ["Dhaka Urban Poverty Assessment"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "23 mega-cities, annual growth rates of peripheral population tend to reach around 10-20 percent compared to central business districts. 4. Implications for public policy Hazard management is a task both for the public sector and for private households and firms. For the public sector in cities, this includes ensuring the safety of municipal buildings and public urban infrastructure, encouraging and supporting private sector hazard risk reduction, and developing first response capacity. A considerable share of hazard risk stems from relatively small but frequent events which cause localized damage and few injuries or deaths (Bull-Kamanga et al. 2003). For instance, an analysis of detailed records of 126 thousand hazard events in Latin America showed that more than 99 percent of reported events caused less than 50 deaths or 500 destroyed houses (ISDR 2009). In aggregate, these accounted for 16. 3 percent of total hazard related mortality and 51. 3 percent of housing damage. The probability of larger events may or may not be predictable. For instance, a city may be in an earthquake risk zone, but the location specific ground shaking probabilities are not known. Individual dwelling unit level mitigation is therefore necessary everywhere in the general area of high earthquake probability. For other hazard types like landslides and floods, potential risk areas can be more easily delineated.", "output": {"entities": {"named_data": [], "descriptive_data": ["detailed records of 126 thousand hazard events in Latin America"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In our employment arm, we offer gainful employment in the form of a surveying assignment for an average of three days per week for two months. 1 The surveying task requires workers to walk through their blocks four times per day tallying the various activities their neighbors are engaged with and consumes approximately 2. 5 hours per workday, resulting in a form of part-time employment. The job is designed to embody the key features inherent to ‘ work ’. Drawing from the economics literature, workers must exert real effort and their task occupies a meaningful portion of their work day. Drawing from the sociology literature, the work involves some degree of sociability and purpose in the completion of a productive task. Employment lasts for eight weeks, a long duration given the scarce daily labor opportunities that arise in our setting. Relative to this employment arm, our control arm receives no work and a small fee for weekly survey participation. A comparison of the control to the employment arm therefore yields the psychosocial benefits of the employment intervention. In order to estimate the non-pecuniary psychosocial value of employment, we include a cash treatment arm, in which no work is offered, but a large fee (equivalent to that received by those in the employment arm) for weekly survey participation is provided. We work in the Rohingya refugee camps, situated upon the southern tip of Bangladesh. 1We obtained formal permissions from camp administration to engage our study participants in this manner through our NGO partner, Pulse Bangladesh. 1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "An additional 33 blocks were assigned to the cash group, where participants earned 450 taka (USD $ 5. 30) per week as compensation for survey participation. Finally, 83 blocks were assigned to a work group, where we offered participants gainful employment. We compensated participants in this treatment arm with 150 taka (USD $ 1. 77) per day of work. Households were assigned an average of three days of work per week, resulting in 450 taka per week on average over the course of the eight weeks and thereby equivalent to that received by the cash group. All participants were aware of the randomization process: enumerators described the three arms and displayed the random number to the participant as it appeared on their tablet, assigning the participant to his or her treatment group. Employment intervention details We now turn to the nature of the employment we offer. Employees were asked to engage in a data collection exercise in which they completed time-use sheets describing the activities of fifteen unnamed, same-sex neighbors of their choosing four times per day.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We asked that households complete their work on specific days to which they were as- signed: work schedules varied week to week, averaging three days weekly. To ensure com- pliance with the work schedule, we stationed a tamper-proof box in a preselected household within each block (the facilitator household) and informed participants that they should submit their tasks into the box at the end of each assigned workday. The facilitator would slip an additional piece of paper into the box at the end of the day to bookend that day ’ s set of submissions, and the respondent ’ s submission was marked late if it was inserted after the bookend. Facilitators were compensated with an additional 50 taka per week for their services, and had no access to the materials inside the box. Along with dropping offtheir submissions at the end of each workday, participants were instructed to visit the facilitator ’ s home on their designated ‘ collection day ’ each week. The facilitator made their home available for a few hours on this day so the enumerator could complete the check-ins with the block ’ s five respondents and pay the participants their respective amounts in a relatively private setting.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "required to purchase other basic staple foods such as salt and vegetables. Given that the WFP provisions are the only reliable rations that refugees receive, we approximate a cash transfer of 450 taka per week to at least double potential weekly consumption. Relative to the wealth refugees possess, 450 taka per week is likewise sizeable: average baseline savings is 195 taka, with the median refugee reporting zero taka in savings. Average baseline borrowing (typically in the form of store credit) is 1, 600 taka, with a median of 600 taka. Refugees have no economically meaningful assets that may be more common among the rural poor, such as land or cattle, given the unanticipated displacement which forced them from their homes. Relative to other employment opportunities, average reported pay is 300 taka per day for less than three days. The monthly cash transfer is therefore more than double what a refugee might expect from alternative employment if he or she is fortunate enough to secure a job. 4 Data Collection and Survey Instruments Timeline and survey instruments We collected data via a baseline, commencing in November 2019, and endline survey, commencing in February 2020, as well as seven midline surveys conducted prior to payment disbursal each week. These weekly surveys collected a small subset of well-being outcomes.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In an effort to ensure that our temporary interventions had no unintended negative mental health consequences on our participants, we also con- ducted a final short followup survey six weeks after the interventions concluded (Appendix Figure A1. We had 2 % attrition at endline and followup, neither differential by treatment arm (Appendix Table A1). Main outcome variables Our primary outcome of interest is psychosocial well-being, which we assess through an index of seven mental and social health measures, henceforth re- ferred to as the psychosocial (PS) index: depression, stress, life satisfaction, locus of control, sociability, self-worth, and stability. Our measures of depression, stress, life satisfaction, and locus of control are drawn from standard screening tools (PHQ-9, Cohen ’ s Perceived Stress Scale, Diener ’ s Satisfaction With Life Scale, and the Levenson Multidimensional Internal Locus of Control Scales, respectively) adapted for sensitivity to the Rohingya camp context. The PHQ, our depression screening tool, has been validated against antidepressant medica- tion (L ¨ owe et al., 2006) and employed in the cross-section among refugee populations (Poole et al., 2018) as well as in experimental evaluations of psychotherapy programs in South Asia (Patel et al., 2017; Bhat et al., 2021). For sociability, we inquire about the number of interactions that participants have had 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "throughout the day prior to the survey day. We develop our own questions around self- worth rather than employing the more standard Rosenberg Self-Esteem Scale, which we found inappropriate given the Rohingya ’ s recent experiences. Specifically, we construct an index of self-worth from two questions designed to elicit respondents ’ beliefs about how they contribute to their family and community. Finally, we adapt the Cantril Self-Anchoring Striving Scale (Cantril, 1965) to measure how stable respondents feel in their present lives and in the future. We additionally examine the impacts of each treatment on physical health, cognitive function, economic decision making, time-use, and consumption. We capture respondents ’ sense of physical health by asking how many days they have fallen sick in the past thirty days and cognitive function by employing a digit-span memory test and a series of basic arithmetic problems. We explore economic decision making along two dimensions: incentivized time preferences (Andreoni and Sprenger, 2012; Gin ´ e et al., 2018) and incentivized risk preferences (Holt and Laury, 2002).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We measure time-use through the number of hours in the previous day a respondent reports spending idle, as well as the amount of time spent on a variety of other common activities one might do in the camps (including bathing, market, chores, collection of rations, eating, child-rearing, sitting at tea stalls, praying, sleeping, visiting friends / relatives, playing games, playing sport, sitting idle). Finally, we ask respondents how much they consume, borrow and save over the past week. We further consider changes in perceptions on gender and power in two ways. First, we generate a Household Power Index, composed of a set of questions on perceptions of gendered decision-making and intimate partner violence. The questions are drawn from Haushofer and Shapiro (2016), which are themselves adapted from the Demographic Health Surveys. In addition, we produce a Work Rights Index, composed of questions around whether respondents feel that women should be allowed to work inside or outside the home or the camp block. Each outcome is described in greater detail in Appendix C. The frequency with which each outcome is collected is also presented in Appendix C. Multiple hypothesis testing We utilize two approaches to address the issue of multiple hypothesis testing. First, we present our primary outcome, psychosocial well-being, as an inverse-covariance weighted index variable following Anderson (2008). We also generate index variables for other outcomes in which this is possible, such as the cognitive index, the household power index, and the work rights index. Our second strategy is to report the sharpened False Discovery Rate (FDR) q-values for all outcomes within a particular table, which control for the expected proportion of rejections that are type I errors, likewise 12", "output": {"entities": {"named_data": ["Demographic Health Surveys", "Work Rights Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "which increases significantly under both treatment arms. In other words, the psychosocial value to employment appears to be driven largely by the non-pecuniary dimensions of the employment experience. 5. 4 Impacts of employment on reported physical health, cognitive function, and economic decision-making The positive effects of employment extend to other measures beyond psychosocial health. Table 3 presents results on reported physical health, cognitive function, and incentivized measures of risk and time preference. We observe a significant increase in the days reported healthy. This effect may be due to ‘ real ’ health improvements from increased exercise (which has also been documented to translate to improved mental health (Herbert et al. (2020))) from the employment task or ‘ perceived ’ health improvements in which improved psychoso- cial well-being translates into feeling less physically ill. Should the channel be exercise, we may expect health improvements to grow over time. Our weekly data on days healthy sug- gests this is not the case: we observe the treatment effect on health from the first week of working, and the gap remains steady throughout the following two months (Appendix Figure A3).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["weekly data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The employment arm also significantly improves cognitive function as measured through an index of memory and basic arithmetic tests, a finding consistent with a large psychol- ogy literature documenting the relationship between cognitive processes and depression (Semkovska et al., 2019). As with physical health, improvements to cognitive function are unlikely to be a direct product of the employment task itself, which was specifically de- signed to require no literacy or mathematical skill. Rather, these results are suggestive of a downstream impact to reducing depression through the experience of employment. Finally, we find no change in time preferences: treated individuals are no more or less likely to discount the future relative to control counterparts, although results may have differed had we engaged participants in an effort or consumption-based time preference game rather than a financial one. However, we find a substantial increase in risk tolerance among the employed. A greater preference for risk-taking may be indicative of employment serving as a form of psychological ‘ insurance ’ that allows participants the mental bandwidth to exercise greater risk. This is consistent with the positive impacts of employment on stability as well as with a key motive underlying universal basic income (UBI) in the developing world (Banerjee, Niehaus, and Suri, 2019). Interestingly, however, we document no parallel increase in risk tolerance in the cash transfer arm. Our result on risk preference also echoes a potential consequence of depression and anxiety described in Ridley et al. (2020), although empirical 15", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Having experienced the work task and therefore able to realistically value the work, we offer individuals in the employment arm an additional [surprise] week of work at a series of wages following the incentivized Becker-DeGroot-Marschak (BDM) method. We inform participants that we have a limited amount of funds remaining and are therefore unable to pay everyone their previous wage. This strategy realistically motivates the reservation wage elicitation exercise and makes clear that there will be no further opportunities for work. We piloted this exercise extensively. To maximize comprehension, we employ a multiple price list strategy, embed repeated confirmations, and conduct a trial run of the exercise for each respondent before the real exercise; this mimics the procedure employed in Burchardi et al. (2021) for which participants in another low-income country field context exhibited high comprehension. For those individuals who express willingness to work at a wage of zero, we offer an alternative option of answering a brief survey at the end of the week for a small, randomized fee; we then use the fraction of respondents who are willing to forego this paid option and instead work for free as an estimate of the proportion of respondents who have a negative reservation wage of at least the foregone magnitude. 16", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Second, we complement our psychosocial index with measures that are not vulnerable to experimenter demand. Demand effects are unlikely to alter one ’ s cognitive ability as measured through the arithmetic questions and memory tests of our cognitive index. Our risk and time preference games are incentivized with meaningful stakes (respondents gamble with a minimum of 1. 20 USD in the risk preference game and trade off3. 50 USD today with higher amounts tomorrow in the time preference game), stake sizes that de Quidt, Haushofer, and Roth (2018) have found effectively eliminate demand effects. Perhaps employed individuals feel a need to impress the enumerator, as their proximate employer, in a way cash recipients do not. This may lead to reporting better mental and physical health and investing greater effort in the cognitive tasks. However, we find that life satisfaction increases substantially for both groups, inconsistent with a differential desire to impress among the employed. We also observe patterns of treatment effects within our validated PHQ-9 module that are inconsistent with experimenter demand (Appendix Table 12The signaling value of the certificate may have been diminished if other employers learned about the nature of the certificate distribution. Our time in the field suggests this is unlikely: we randomized certificate distribution at the block level to limit spillovers, only five people in each block of ˜ 200 adults was involved in the experiment, and job opportunities were scarce. 13The certificate read “ I engaged with Pulse Bangladesh to do data collection ”. It was written this way in order to be generic enough to apply to all the individuals in the experiment, all of whom were providing us data from the weekly surveys. 18", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Such a channel would be consistent with psychology literature on behavioral activation, or the act of scheduling structured activities as a means of combatting depression (Cuijpers, van Straten, and Warmerdam (2007)). To explore this question, we supply a random subset of the employed with a calendar marking every date of work (Appendix Figure A5). The re- mainder receive a blank calendar and are instead informed weekly about their schedule. We find no impact of a schedule on respondent well-being or decision-making (Appendix Table A7). Despite this exercise, we cannot causally estimate the role of the structure alone on well-being, as the structure imposed by regular employment is coextensive with employment itself. Indeed, our measure of stability, which asks respondents how secure they feel at the moment and expect to feel in the future, increases substantially among the employed relative to both control and cash arms. Time use Does employment improve well-being by allowing participants to substitute time away from unsavory or psychosocially costly activities? Appendix Table A8 presents how cash and work arms use their time. We document no significant difference between the two arms in the number of hours that respondents report spending across a variety of activities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Notably, while we cannot rule out that time outside due to employment may play a role (e. g., fresh air may boost one ’ s mood), our time-use data indicates that the average refugee already spends at least three hours outside per day, with no measurable difference between employed and cash arms. As we are powered to detect changes of at least twenty minutes for each activity, our results suggest that large substitutions away from unsavory activities are unlikely to be driving the improvements in psychosocial well-being, insofar as the respondent recalls. 1718 We also investigate whether those who were more idle prior to being employed benefit more from employment. We find no impact along this margin, suggesting that the elimination of boredom per se is not the driving force behind the psychosocial value of employment (Appendix Table A10). 17Most respondents do not track their day by time, making collection of reliable time use data challenging (though recent literature documents the broader unreliability of such data). We piloted a variety of strategies, and settled on asking respondents how much time they spent on a set of activities in the previous day.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["time-use data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure A4: Participation certificate to boost ‘ resume ’ CERTIFICATE THIS ACKNOWLEDGES THAT I engaged with Pulse Bangladesh to do data collection Notes: The wording of the certificate was made such that it could be applied to both arms; cash-only arms participated in weekly surveys along with all other experiment participants, so technically also engaged in data collection for our project. 60", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "C Details on outcome measures Outcome Variable Descriptions Psychological Well-being PHQ9 The standardized total score of 9 questions from the Patient Health Questionnaire-9 (PHQ9) Life Satisfaction Index A standardized average of survey responses to four questions from Di- ener ’ s standardized scale, responses made along a seven-point Likert scale. Stress Index The standardized total score from three elements of adapted from the Cohen Stress scale. “ How many of the last 7 days have you [been able to fall asleep peacefully / felt nervous / felt frustrated]? ” Sociability (Total) The total number of conversations in the past day with adults. Sociability (Positive) The total number of conversations in the past day with adults that the respondent felt were positive. Self-Worth Index The standardized total score from the responses on a scale from 1 to 10 to two questions: “ Think of a person you know who you most respect and who brings greatest value to your [family / community].", "output": {"entities": {"named_data": ["Patient Health Questionnaire-9"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "If that person is a 10, where would you put yourself? ” Locus of Control The standardized total score from responses to four locus of control questions. “ In the last 7 days, how many days did you feel that to a great extent your life is controlled by accidental / chance happenings... ” Allocation Decision Game Indicator (yes / no) for response to an offer to participate an allocation committee to decide how money is spent. Participants are offered the opportunity to make a resource allocation decision for their community or have another individual (an NGO worker, an “ expert ”, or another refugee) make the decision. Stability Index The standardized total score from responses to two stability questions using a Cantril ladder. “ How secure [do you feel / think you will feel] [at present / five years from now] ” Physiological Index An inverse-covariance weighted average of PHQ, Stress, Life Satisfac- tion, Sociability (Total), Self-Worth, Locus of Control, and Stability indices. Gender Dynamics Gender Perceptions- Work The standardized total score of two questions regarding women ’ s work, “ How often would you agree that women should be allowed to work for a living [inside / outside] the block? ” 64", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Gender Perceptions- Violence (IPV) The standardized total score of five questions regarding norms for inti- mate partner violence (IPV) from the Demographic and Health Survey (DHS) (The important decisions in the family should be made only by the men of the family. How often would you agree? The wife has the right to express her opinion even when she disagrees with what her husband is saying. How often would you agree? A wife should tolerate being beaten by her husband in order to keep the family together. How often would you agree? A husband has the right to beat his wife. How often would you agree? It is more important to send a son to school than it is to send a daughter. How often would you agree?). Financial Well-being Savings Response to the question “ How much money do you currently have in savings? ” During the collection surveys (midlines) this question instead asked “ How much money did you save in the past week? ” Borrowing Total amount of money the household has borrowed. Economic Decision Making Risk Preference Measured using incentivized responses to the multiple price list deci- sions adapted from Holt-Laury and Sprenger (2002).", "output": {"entities": {"named_data": ["Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Outcome Variable Collection Periods Basline Midline Weekly Endline Psychological Well-being PHQ9 X X Life Satisfaction Index X X Stress Index X X X Sociability (Total) X X X Sociability (Positive) X X X Self-Worth Index X X Locus of Control X X Allocation Decision Game X X Stability Index X X Physiological Well-being Index X X Gender Dynamics Gender Perceptions- Work X X Gender Perceptions- Violence (IPV) X X Financial Well-being Savings X X ∗ X Borrowing X X Economic Decision Making Risk Preference X X Time Preference X X Other Outcomes Cognitive Ability X X ∗ X Physical Health X X ∗ X Notes: The “ Baseline ” survey was conducted with respondents before treatment assignment was revealed. The “ Midline ” survey were questions asked immediately after treatment assignments were disclosed after the baseline survey, but before the work task had begun. “ Weekly ” surveys were conducted after each week of work (if any).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "28 In section three of this study, we could not find taxes and regulations among the potential barriers to sales and employment growth that clearly separated the ups from the downs in the formal sec- tor. We also showed that the large sector has become a smaller share of the economy rather than a bigger share due to self-employment growth and due to the process of de-industrialisation. In substance, the CIS-7 may fit the developing countries scenario better than the transitional countries scenario in the Schneider and Klinglmair (2004) regressions. If this is the case, we should expect that the growth of the informal sector negatively contributes to growth. The data we have do not contradict this hypothesis given that the shadow economy has been on the rise during the recession period and has stabilised during the growth period. This digression on informality suggests that self-employment may partially act as an host to in- formal and illegal activities especially during recessions where self-employment may constitute a refuge for small informal and illegal businesses. Self-employment is also evidently a sector of ne- cessity for those who wish to keep health and pension records alive and do not want to formally register anywhere else. In times of growth this sector may instead function as a first step to for- mality, an entry gate to the formal sector given its lower entry barriers and taxes.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Has this hap- pened during the recent growth period in the CIS-7? Explaining labor market flows The key to understanding output and employment growth in the CIS-7 is evidently the relation- ship between self-employment which is largely informal and employees in the formal sector. And the transition from non wage (informal) to wage (formal) labor can occur thanks to 1) A migra- tion of workers from self-employment to wage labor or 2) Endogenous growth of self- employment turning into SMEs and generating formal employment. We have in fact introduced one further dimension of labor market segmentation, the wage / non-wage labor divide. Labor flows between these different states may contribute to explain the employment puzzle. For this purpose, we turn to Moldova, a country that in many respects could be considered as the average scenario in our CIS-7 sample. Moldova is also the only country that disposes of a consis- tent longitudinal panel survey between 1997 and 2002 which can be used to assess labor market flows during the growth period and test some hypotheses on the evolution of the labor market.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["consis- tent longitudinal panel survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We use the whole longitudinal sample and the restricted panel sample to compute the statistics, the transitional probabilities and the probit regressions presented below. 19 In figure 4. 1, we show the distribution of the population across working categories and between 1998 ad 2002. The employed population has marginally increased in percentage of the total popu- lation. A migration of workers has also occurred from wage labor to non wage labor and this mi- gration has taken place mostly within agriculture. The most significant change in fact occurred among rural workers with farmers growing very significantly at the expenses of agricultural em- ployees. Non agricultural labor has remained practically unchanged during the period while agri- cultural employment has increased marginally. This phenomenon occurred during the post-1998 recession (1998-1999) and during the subsequent growth period (2000-2002). In table 4. 4, we report the population structure by category20. It is visible the constant growth of private agriculture and the constant decline of employment in agricultural enterprises in both the public and private sectors. Among non-agricultural enterprises, there is a growth in the private sector and a decline in the pubic sector suggesting a migration of workers between the two sectors 19 See http: / / www. statistica. md / for details on the survey. 20 Categories are identified on the basis of the main source of income of respondents.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 as the Demographic and Health Surveys (DHS), though researchers have reduced under-reporting by providing special training to interviewers, placing greater emphasis on respondents ’ privacy and safety, and allowing women multiple opportunities to disclose their experiences (Ellsberg et al., 2001a; Garcia Moreno et al., 2003; Ellsberg et al., forthcoming). Estimates of the magnitude of the problem Population-based surveys have found that between 10-70 % of women report being physically assaulted by an intimate male partner at some point in their lives (Heise, Ellsberg and Gottemoeller, 1999). See Annex A for estimates from many recent population based studies (Ellsberg et al., forthcoming). Findings from a multi-country study on domestic violence and women ’ s health carried out by the World Health Organization in fifteen sites and ten countries found that between 13-62 % of women had experienced physical violence by a partner over the course of their lifetime, and between 3-29 % of women reported violence within the past year (Figure 1. 1). Figure 1. 1.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": [], "vague_data": ["Population-based surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Percentage of women who reported sexual violence by an intimate partner (ever), physical violence by an intimate partner (ever), and physical violence by an intimate partner in the past 12 months. 50 % 59 % 30 % 27 % 34 % 13 % 31 % 50 % 62 % 34 % 33 % 47 % 23 % 41 % 37 % 20 % 10 % 14 % 6 % 17 % 23 % 47 % 23 % 29 % 31 % 6 % 23 % 40 % 42 % 49 % 3 % 18 % 19 % 16 % 8 % 19 % 16 % 8 % 13 % 29 % 3 % 17 % 25 % 13 % 15 % Bangladesh (Urban) Bangladesh (Province) Brazil (Urban) Brazil (Province) Ethiopia (Province) Japan (Urban) Namibia (Urban) Peru (Urban) Peru (Province) Thailand (Urban) Thailand (Province) Tanzania (Urban) Tanzania (Province) Serbia Samoa sexual violence ever physical violence ever physical violence past 12 months Source: Unpublished data from the WHO Multi-Country Study on Women ’ s Health and Domestic Violence Against Women. The final published comparative report is forthcoming. Cited with permission. Prevalence data on sexual violence is even more limited than physical violence. However, evidence suggests that a substantial proportion of girls and women have experienced child sexual abuse, forced sex and other forms of sexual coercion in virtually every setting of the world. For example, population-based studies have asked about “ forced ” sexual debut among sexually experienced young people and found rates from 7 % (New Zealand), to 46 % (in the Caribbean) (Heise and Garcia Moreno, 2002).", "output": {"entities": {"named_data": ["WHO Multi-Country Study on Women ’ s Health and Domestic Violence Against Women"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A growing number of low and middle-income countries have tried to improve the law enforcement response to gender-based violence by training professionals, reorganizing police and courts, and trying to provide a more comprehensive response to survivors. Evidence of effectiveness is relatively limited; most well-evaluated initiatives come from high-income countries, and the lessons learned may not be applicable to developing countries. Evaluations of law enforcement reforms in low and middle income countries have typically been limited to case study approaches drawing from police records (notorious for under-reporting), qualitative perspectives from key informant interviews, intermediate outcomes such as changes in attitudes and knowledge among police and judges, and interviews with small numbers of women who have sought legal redress. Population-based data collection, control groups, or follow-up among more than a handful of survivors are rare. Nonetheless, the following initiatives illustrate the types of efforts that have produced important lessons learned. Training personnel in the police and judiciary and other parts of the justice system Throughout the world, organizations have launched efforts to improve the knowledge, attitudes, and practices of justice sector personnel regarding gender-based violence. Some law enforcement institutions organize training internally, as did South Africa following passage of the 1998 Domestic Violence Act (Usdin et al., 2000).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["police records"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In other settings, NGOs such as Rozan in Pakistan (Rashid, 2001), Profamilia in the Dominican Republic (Guedes et al., 2002), and the Musasa Project in Zimbabwe have trained law enforcement personnel on issues related to gender-based violence. Elsewhere, governments have collaborated with the United Nations to provide training and support for the police and judiciary. For example, ILANUD is a joint institute of the government of Costa Rica and the United Nations that works with governmental agencies throughout Latin America to improve the work of prosecutors, judges, lawyers, police and other professionals in criminal justice generally, and gender-based violence specifically (Villanueva, 1999; ILANUD, n. d.). Most of these initiatives have been evaluated using key informant interviews and pre and post questionnaires before and after training-if they have been evaluated at all. Nonetheless, training appears to be both constructive and urgently needed (Rashid, 2001; Villanueva, 1999). Other lessons learned include the finding that changing attitudes of law enforcement is a challenging, long-term process. The quality of the trainings ’ content and the skills of the trainer are essential. Training appears to be most effective when all levels of personnel (especially high-level officials) participate, and when training is backed up with changes throughout the institution, such as policies, procedures, adequate resources, and continual monitoring and evaluation. Special police stations or cells for crimes against women All-women police stations began in Brazil and were later tried in other countries in Latin America and Asia. As of 2003, for example, Nicaragua had 17 police stations for women and children (called", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Unfortunately, evidence suggests that without system-wide reforms and support, single training sessions or even routine screening policies rarely produce long-term changes in the quality of care for survivors (McLeer et al., 1989; Heise, Ellsberg, and Gottemoeller, 1999). Instead, Heise and colleagues argue that the most effective way to improve the health care response is to use a “ systems approach ” involving reforms throughout the organization. Typically, these initiatives include changes in norms, policies and protocols, infrastructure upgrades to ensure private consultations, training all staff (including managers), ensuring that providers have adequate resources such as referral networks and directories, and strengthening the ability of staff to provide emergency services such as danger assessment, safety planning, emotional support, STI prophylaxis, and emergency contraception. In settings where adequate referral services do not exist, health programs sometimes offer specialized services such as counseling, legal aid and women ’ s support groups. The International Planned Parenthood Federation, Western Hemisphere Region (IPPF / WHR) carried out an initiative illustrating the “ systems approach ” in four member associations in Latin America, namely: Profamilia (the Dominican Republic), INPPARES (Peru), and PLAFAM (Venezuela), with some participation from BEMFAM (Brazil).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Quantitative and qualitative baseline, midterm and follow-up studies concluded that the initiative improved provider attitudes and practices; strengthened patient privacy and confidentiality; increased detection of women who experienced physical and sexual abuse; improved the overall quality of women ’ s health care; and benefited survivors through the provision of specialized services such as legal aid, counseling and support groups (Guedes, Bott, and Cuca, 2002; Guedes et al., 2002; Bott, Guedes, and Guezmes, forthcoming). This initiative benefited from generous funding from international donors, and it might be difficult for other organizations to replicate the project in its entirety; however, IPPF / WHR has disseminated a large body of recommendations and tools designed to help organizations in low-income settings build on their experiences. Routine screening (also called routine enquiry) Research indicates that without routine screening, providers typically identify only a fraction of women requiring assistance with physical or sexual abuse. Routine screening for violence has increasingly been considered the standard of care within women ’ s health services in the United States and other industrialized countries (American Medical Association, 1992; Buel, 2001). However, a vigorous debate has erupted over the benefits and risks of routine screening, particularly in resource-poor settings (Ramsay et al., 2002; Garcia Moreno, 2002). Some argue that routine screening may harm women in settings where providers are unprepared to respond appropriately, where privacy and confidentiality cannot be ensured, and where adequate referral services do not exist. In many settings, providers blame victims of gender-based violence-without an appreciation of gender issues or human rights-and may", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "29 evaluations have been conducted (Renton et al., 2000; Shaw, 2000; Shaw, 2002a; Shaw, 2002b; Paine et al., 2002; White, Greene and Murphy, 2003; Interagency working Group, 2003). For example, the Medical Research Council (from the United Kingdom) conducted an external evaluation of program effectiveness in the Gambia using key informant interviews, focus groups, and a KAP (knowledge, attitudes and practices) survey in four villages, including two program sites and two controls. That study found that the Gambia program improved self-reported attitudes and behaviors related to violence against women. Specifically, the program reduced the social acceptability of wife-beating at the community level and appeared to produce a corresponding drop in that behavior. Qualitative findings from other Stepping Stones sites suggest similar benefits. Program H (Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru) is being carried out by four NGOs. It aims to change gender norms and sexual behaviors in Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru (Barker, 2003; White, Green and Murphy, 2003; Guedes, 2004).", "output": {"entities": {"named_data": ["KAP (knowledge, attitudes and practices) survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The initiative includes four components, namely: a) training professionals to work with young men in the area of health and gender-equity using a set of manuals and videos; b) social marketing of condoms; c) promoting health services; and d) evaluating changes in gender norms. In 2002, PROMUNDO and Horizons began a 2-year evaluation to measure the effectiveness of two different approaches, compared to a control site. Researchers have developed a\"Gender-Equitable Men\"(Leichert) scale with 24 items for measuring attitudes. Methods include pre and post-tests as well as a six-month follow-up community-based survey. In addition, they are gathering qualitative information among men and their female partners. Preliminary results suggest that the program has been successful at increasing gender equitable norms and reducing behavior that puts men at increased risk of HIV / AIDS. ReproSalud (Peru): Manuela Ramos launched ReproSalud in 1995 as a USAID-funded rural reproductive health program. ReproSalud used participatory rural appraisal (PLA) to help women's groups identify women's reproductive health needs and to organize community meetings to design strategies to address those needs. Domestic violence and forced sex within marriage emerged as important problems in those communities. In response, ReproSalud organized workshops for women and men on gender issues, carried out community awareness campaigns and established a microcredit program for women.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["community-based survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "By 2002, ReproSalud had reached over 123, 000 women and 66, 000 men. Qualitative and quantitative evaluation data suggest that the community-based PLA approach had a positive impact on attitudes and behaviors related to gender based violence (Rogow and Bruce 2000; Ferrando, Serrano, and Pure, 2002, cited in Boender et al., 2004). The quantitative evaluation (using community based surveys) was complicated by the fact that the project coincided with a period of strong investment by the Ministry of Health, which made it difficult to isolate the project ’ s impact. Gender-equitable attitudes and practices increased significantly in both intervention and control communities, though improvements in intervention sites were slightly higher. The qualitative data suggested a much greater difference in intervention and control sites and gathered evidence of dramatic changes in social relations and men's behavior. Respondents spoke at length about decreased alcohol consumption, domestic violence, and forced sex in all intervention villages studied. In the words of one 35 year-old woman,\"Before, they brutally forced sex. They hit, especially when they were drunk. Now, no more\"(Rogow and Bruce, 2000, page 20). Individual behavior change strategies Many other programs have attempted to produce individual (rather than community-level) behavior change by working with individual men and boys. White, Greene and Murphy (2003) reviewed the literature on such programs aimed at men. That review suggests that less information is available on the effectiveness of individual behavior change strategies compared to community-level approaches. Some", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["quantitative evaluation data", "community based surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "33 recommendations (Jewkes, 2000). Their efforts contributed to the Employment of Educators Act and new Department of Education guidelines, both of which were introduced in 2000. These regulations mandate dismissal of educators found guilty of sexual or physical assault, or of having a sexual relationship with a student. They also define penalties for failing to report abuse. It remains to be seen whether these measures will have the intended impact. After the act was passed, Human Rights Watch (2001) suggested that the South African government needed to do more to increase awareness of the law among school principals and to strengthen enforcement. Institutional reform Efforts to improve the institutional response to gender-based violence range from sensitization and training of staff, sexual harassment policies, curriculum reform, school-wide anti-violence awareness campaigns, counseling and referrals, and broader efforts to reduce discrimination against girls and improve school safety. Initiatives to increase female enrolment by improving girls ’ safety at and on the way to school As mentioned earlier, parental concerns about girls ’ safety in school appears to lower female school enrolment in settings such as South Asia, Africa and the Middle East.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some initiatives have addressed these concerns by establishing single sex schools, hiring more female teachers, building separate latrines or canteens for girls, reducing the distance that girls must travel in order to receive an education, and / or providing in-service gender sensitivity training to teachers, principals and inspectors (UNICEF, 2004). For example, the UNICEF African Girls Education Initiative (AGEI) used a combination of these approaches, (along with other strategies) to boost girls ’ enrolment in 34 African countries (UNICEF, 2003a). Evidence of this project ’ s effectiveness was limited in many sites, largely due to limitations in the evaluation design. While some demonstrated significant enrolment increases (for example, 15 % in Guinea, 12 % in Senegal, and 9 % in Benin) in relatively short periods of time, the extent to which this was due to the project impact was not clear. Overall, however, the experience of this project suggests that addressing concerns about girls ’ safety and reducing the risk of sexual harassment and violence in schools is not only a high priority for parents, but also a potentially promising way to improve girls ’ access to education in selected settings (UNICEF, 2003b).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Improving attitudes, knowledge, skills and practices of educators Many initiatives have aimed to improve educators ’ attitudes, knowledge and practices in regards to gender discrimination, sexual violence and sexual harassment. Few have been well documented or evaluated. A comparative study of HIV / AIDS education in three African countries found evidence that awareness and responses to sexual harassment in the Uganda sites was markedly better than those in Botswana and Malawi; researchers attributed this to the Ugandan government ’ s efforts to curb sexual harassment in schools (Bennell, Hyde, and Swainson, 2002). The South African National Department of Education (in collaboration with international organizations) has developed a training module for educators (South African National Department of Education, 2001). Composed of eight interactive workshops and other materials, the module aims to increase educators ’ awareness of sexual harassment and gender violence, highlight the links between violence and HIV / AIDS and increase the safety of the school environment. The module is a professional development tool, rather than a part of the national curriculum. It has been field tested in some sites, and according to some reports is being rolled out nationwide. In other settings, schools have trained educators to teach courses promoting gender-equitable norms and nonviolence among students. For example, a consortium of researchers and advocates field-tested the\"Gender and conflict\"Model Curriculum in South Africa (Dreyer et al., 2001; Guedes, 2004) to compare a\"whole school\"approach (which trained the entire primary school staff, including principals and auxiliary staff) with a “ trainer of trainers ” approach (which trained two teachers from each school and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "38 Expanding social services for women and children In many countries, public and private institutions have worked to improve social services for women and children who experience violence. In some settings, NGOs and coalitions such as the Nicaraguan Network of Women against Violence have spearheaded these initiatives (Velzeboer et al., 2003). In other settings, governments have promoted institutional reforms by establishing ministries, departments or agencies devoted to the advancement of women, including Mexico, Jamaica, Guatemala, Bolivia, Peru (Center for Reproductive Laws and Policy, 2000); these agencies often work to strengthen comprehensive services for survivors of gender-based violence. For example, in El Salvador, the Salvadoran Institute for the Development of Women is a government agency that coordinates the “ Program to Strengthen the Family ” (Programa de Saneamiento de la Relación Familiar), a multi-sectoral effort among public and private institutions (Valdez, 1999). In some settings, such as Nicaragua and Costa Rica, coalitions of government agencies and NGOs develop National Plans to improve the network of services for women and children affected by violence (Velzeboer et al., 2003).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "At a community level, social service initiatives accompanied by substantial outreach efforts have sometimes increased the proportion of women who know what services exist and where, as well as the numbers of women who seek help, but little scientific research has explored the impact of expanded social services on violence prevention. Batterer programs Increasingly, NGOs and governments have attempted to reduce violence against women by organizing treatment programs for batterers, aimed at changing their attitudes and behaviors. Most are run by NGOs, but they often depend on court- mandated attendance as an alternative to criminal sanctions. Most batterer programs have been carried out in high-income countries, but increasingly they have been implemented by developing country NGOs, such as the Instituto Noos in Brazil (White, Greene, and Murphy, 2003) and CORIAC in Mexico (Morrison and Biehl, 1999). Many studies have evaluated these programs ’ effectiveness in high-income countries, but most evaluations have been methodologically flawed. The only randomized controlled trial to date was carried out by the United States Navy, which found no reduction in abuse compared with controls (Dunford, 2000). Battered women often identify treatment or counseling for their husbands as a high priority (e. g. Ellsberg, 2001b), but it remains to be seen whether cost-effective strategies for changing batterer behavior can be found. Shelters Many researchers and advocates have called on governments and donors to invest in shelters for women who experience gender-based violence. Typically, these facilities offer emergency refuge as well as counseling, medical and legal assistance, job training, telephone hotlines, and other services. Most rigorous evaluation studies on the effectiveness and quality of shelters come from settings such as the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, subsequent multivariate analysis and qualitative research found that the link between micro-credit and violence was more complex (Hashemi, Schuler and Riley, 1996; Schuler, Hashemi and Badal, 1998). While participation in microcredit programs appeared to increase women ’ s empowerment over time, levels of violence did not decline, and in some cases even rose. Ultimately, researchers concluded that-- similar to other types of empowerment initiatives-- micro-credit programs appear to work in two directions at once. On the one hand, they reduce women's vulnerability to violence by strengthening their access to resources and making women's lives more public; on the other hand, they may increase the risk of violence by challenging patriarchal norms and escalating conflict in the household. Some micro-credit programs are trying to reduce the potential risks of exacerbating violence associated with micro-credit. For example, RADAR (South Africa) has integrated HIV / AIDS and gender-based violence prevention into an existing microcredit program for women in poor rural communities. Since RADAR is designed as a prospective, randomized community intervention trial, it may-- in the future-- contribute to a richer understanding of how to provide the benefits of micro-credit while mitigating the risks (RADAR, n. d.).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "presented by equation 3 where Rit is instrumented by the instrument defined in equation 2. Since we use all provinces in the main IV estimations, i is defined as i = (1,..., 81) while t is defined as t = (2011, 2014). Following Del Carpio and Wagner (2015), we use the year-specific natural logarithm of the distance to the closest border crossing, LDit as a control variable. The year-specific distance control is defined as LDi2011 = 0 and LDi2014 = LDi. Yit = a + ρRit + Pi + Tt + βLDit + eit (3) Our second strategy is to estimate a linear difference-in-differences model with province level fixed effects for all outcomes, which is the method used by Ceritoglu et al. (2017). Their approach defines the years 2012 and 2013 as treatment years and the previous years as pre-treatment. We do exclude 2014 in the DD model since Syrian refugees have been spreading across Turkey from 2014 onwards, while they were more concentrated near the border areas that we define as the treatment region in 2012 and 2013. 4 In effect, the DD estimates use treatment years that are completely excluded from the IV model: 2012 and 2013. A second issue in the DD specification is the definition of the control area.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The weight given to each province in constructing the synthetic control is based on pre-treatment outcomes. We use the pre-treatment average of the outcome dimension, Y, the unemployment rate, employment rate and the import and export per capita of the province to determine the degree of similarity between control group provinces and the treated provinces, which in turn determines the weight assigned to control provinces. The unemployment and employment rates are included to control for the general economic performance while trade values are added to control for the degree of ’ openness ’ of the province. 6 The treated unit i = 1 is constructed by taking the mean of the outcome variables in the provinces hosting refugees in 2012 or 2013. 5 Data We use several data sources for the analysis. The IV estimations use data from years 2011 and 2014 while the DD estimations use data from 2009 to 2014. The numbers of refugees up to 2012 are treated as 0. The refugee data for 2012 and 2013 are obtained from UNHCR ’ s official weekly statements in December. Data on the number of refugees in 2014 is from Erdo ˘ gan (2014), who uses statements released by the Ministry of the Interior to compile his data. All refugee data we use in the analysis is provided at the level of 81 provinces.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There were in total 1, 6 million refugees by November 2014. Data on the number of new firms and their ownership characteristics and value are provided by the Turkish Chamber of Commerce. 7 Data on total sales and gross profits are obtained from the Turkish Ministry of Science, Industry and Technology. Other economic indi- cator variables, such as population and unemployment rates, are obtained from Turkish Statistics. Since Syrians usually have guest status rather than resident status during the period of analysis, they are not counted in official statistics such as province population and unemployment rates. Turkey is officially divided into 81 provinces and that is the level of our analysis and variables throughout. The Chamber of Commerce provides data on the number of new firms and the num- ber of new foreign-owned firms at the provincial level. Enterprises defined as firms do 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The data on total gross profits and net sales acquired from the Turkish Ministry of Science, Industry and Technology are compiled from administrative taxation data and was provided upon request by the ministry. The key difference from the Chamber of Commerce data is that the sales and profits data include all businesses including self- proprietorships. 9 Data were provided for the years between 2010 and 2014 and are re- ported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by definition and they are likely to be less accurate and complete for smaller firms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets which includes sales and profit figures. 10 We scale the variables according to province size by dividing sales and profits by the pop- ulation of the provinces. If we use sales and profits in absolute terms, we get qualitatively similar results. The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we 12", "output": {"entities": {"named_data": [], "descriptive_data": ["Chamber of Commerce data"], "vague_data": ["administrative taxation data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There is a clear increase in the entry of new foreign firms which may be driven by refugees ’ enterpreneurship. Cheaper low-skilled labor may have helped all businesses cutting costs. Balkan and Tumen (2016) had also found a decline in prices and attribute their finding to lower labor costs, which may also be one of the mechanisms driving our results. Gross profits and sales also appear to have gone up, which would be consistent with an increase in demand. As noted by Maystadt and Verwimp (2014), heterogeneous effects on specific subgroups of the native population should be expected from refugee crises. In case of the Syrian refugee crisis in Turkey, the business activity in hosting region appears to have benefited. For a complete picture of the effects of the Syrian refugee crisis on local economies in Turkey, further research will be needed on market activity, health and longer term effects. More specifically for the line of research this study focused on, further analysis using micro-level firm data would be needed to understand how firms adjust their activity, 24", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The instrument becomes even weaker if we use refugee to population ratios rather than the number of refugees, therefore we use the absolute number of refugees throughout. 4Including 2014 data in the DD estimations generally results in more statistically significant and larger coefficients but does not change the direction of the results. 5The weights are calculated using the Stata package synth provided by the authors. We use the option nested to calculate the weights through the nested optimization procedure described in Abadie et al. (2011). 6Tunceli ’ s 2011 export and import values are missing for 2011, therefore only 2009 and 2010 values could be used in calculating Tunceli ’ s average. 7The Chamber of Commerce also provides information on the number of firms that shut down. However, reporting exits is not mandatory and the indicator is therefore less reliable. We found no significant effects in both the IV and DD estimates on the number of firms that shut down. 8Since the number of new foreign firms is 0 in several observations, we add 1 to the value. As an alternative, we used the hyperbolic inverse sine transformation which does not have the same problem with 0s as log transformation and found similar results (Burbidge et al., 1988). 9Publicly available data from the Ministry of Science, Industry and Technology indicate that less than 10 % of total revenue is from micro-establishments. Most of the net sales and gross profits reported stem from larger firms that should be included in the Chamber of Commerce data. 10All firms exceeding 200, 000 Turkish Liras (ca. $ 85, 000) in sales are obligated to report detailed balance sheets. Smaller firms may still report their balance sheets but would be doing so on a voluntary 25", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "such as education and caste. We also construct measures of social proximity between a migrant ’ s place of birth and each possible destination, using detailed available data on ethnicity, caste, language, and religion. We also investigate a number of factors that may influence the choice of migration destination but have not received much attention in the existing literature. Fafchamps and Shilpi (2009) have shown that the subjective welfare cost of geographical isolation is high. To investigate this issue, we include regressors controlling for population density and for the average distance to various amenities. Fafchamps and Shilpi (2008) have further shown that migrants are concerned with their welfare relative to that of their birth district as well as to that in their destination location. We examine whether relative welfare considerations influence the choice of migration destination. Additional controls include distance and prices. The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal. The diverse terrain of Nepal along with geographical variation in amenities makes it ideal for our study. The mountainous nature of Nepal means that the country faces daunting challenges in the provision of transport and energy infrastructure. These challenges are unique to Nepal, however. Similar constraints are faced by many developing countries — or regions within such countries.", "output": {"entities": {"named_data": ["2001 population Census data"], "descriptive_data": ["LSMS survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "If many people migrate to a specific location, such as the capital city, this is likely to affect wages, incomes, and access to amenities in that location. 7 This would generate a potential endogeneity bias due to the fact that incomes and amenities in that location result in part from the decision of many migrants to locate there. To eliminate this bias, we use past data to estimate the income regression. More precisely, let T be the period for which we have income information and T + t the period at which we 5The dropped observation corresponds to the location of origin M h ii which, as explained earlier, we do not include in the analysis since including M h ii would mean de facto including the decision of whether to migrate or not. 6McFadden (1974) has shown that, in multiple choice problems of the kind studied here, the application of logit estimation is justified if (1) the errors in each latent choice equation follow the extreme value distribution and (2) errors are independent across choices. See Train (2003), Chapter 3 for a detailed discussion. The estimation of models with correlated errors across choices requires either multiple integration or the use of Bayesian estimation techniques relying on Gibbs sampling. With a choice of over 70 possible destinations, multiple integration is out of the question. Gibbs sampling remains a possibility but would require extensive programming. We choose instead to keep the logit approach but to correct the standard errors for possible correlation in errors across choices. In our case the possible efficiency gain achieved by Bayesian methods does not appear to justify the programming cost. 7The effect could be negative — e. g., congestion — or positive — e. g., agglomeration externalities. 10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["past data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To this effect, we replace yh i with yh i / yi in equation (1) and proceed as outlined above. If migration decisions are based on relative rather than absolute income, then the coefficients of eδs − eδi and (eηs − eηi) zh should be positive and significant only when they are computed using yh i / yi. In addition to relative and absolute income differences, the analysis also examines the re- spective roles of various location characteristics such as housing and food prices, availability of public services, and density of human settlement. 8An alternative strategy for the estimation of pre-migration income distribution in cross-section data is sug- gested by Bayer, Khan and Timmins (2008). 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In contrast, districts of origin are distributed widely across the country. This reflects the fact that much work migration is from remote rural areas to towns and cities. The main characteristics of work migrants are reported in Table 1, together with those of non- migrant adult males. We see that work migrants are on average younger and better educated. The census contains detailed information about ethnicity, language, and religion. In the Nepal census, the term ‘ ethnicity ’ is used to capture a hodgepodge of caste and tribal distinctions. The census distinguishes up to 103 ethnic categories. Most of these categories only account for a tiny proportion of the total population. In terms of the total adult population, the most common ethnic categories are Chhetri, Brahmin, and Newar who, together, account for 35 % of 13", "output": {"entities": {"named_data": ["Nepal census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "standard fixed or random effect approach is not sufficient to ensure correct inference; clustering standard errors by individual is necessary. This is what we do. Having described how the dependent variable is constructed, we turn to regressors. We begin by describing how we construct an estimate of g E [yhs | zh], the level of income (or consumption) yh s that a migrant with characteristics zh can expect to earn in district s. To construct such estimate, we use the 1995 / 96 NLSS data. The reason for using the 1995 / 96 data instead of the 2002 / 3 NLSS survey is to avoid reverse causation, i. e., migration causing a change in income patterns. Migrants are unlikely to be able to accurately predict the evolution of incomes in each district over time. Income and consumption levels observable before migration are thus a reasonable starting point.", "output": {"entities": {"named_data": ["1995 / 96 NLSS data", "NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using the NLSS data we begin by estimating a regression of the form: yk s = δs + α (ak s − a) + βs (Ek s − Es) + χs (Hk s − Hs) + vk s (4) where yk s is the log of income (or consumption) of household k residing in district s, coefficients δs, βs and χs vary by district, ak s stands for the age and age squared of the household head, Ek s is the education level of the head measured in years of completed education, and Hk s = 1 if the head belongs to what we have earlier classified as a high caste (i. e., Brahmin, Chhetri or Newar). Since income or consumption are expressed in logs, βs and χs can be thought of as education and high caste premia, respectively. Female headed households are excluded from the regression since the focus is on migrant males. Vector a denotes the average age and age squared of observations across the sample. Variables E and Hs denote the district-specific averages of Ek s and Hk s. By demeaning regressors, we ensure that eδs measures the unconditional, district- specific average of yk s. Marital status, household size, and other household characteristics are 15", "output": {"entities": {"named_data": ["NLSS data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "not included because they are possibly affected by migration. 9 In contrast, age, education, and caste status can be regarded as exogenous to the migration decisions of adult males. Equation (4) is estimated using correct sampling weights. 10 Regression estimates for equation (4) are summarized in Table 2 where we show α as well as the average and standard error of δs, βs and χs. The coefficients eδi and eηi are large and jointly significant. There is considerable variation across districts not only in average log income and consumption but also in the income or consumption premia associated with education and high caste. These results are used to construct, for each of the 16, 000 or so work migrants in the census, a measure of the income or consumption they can expect to achieve in each of the possible destination districts. Formally, this measure is calculated as: g E [yhs | zh] = eδs + eβs (Eh s − Es) + eχs (Hh s − Hs) (5) where Eh s and Hh s are the education and high caste dummy for migrant h. Age is ignored from the calculation since work migrants typically migrate around the same age, i. e., in early adulthood.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Formula (5) can be decomposed into two parts: eδs, which measures the average income level in district s, and eηszh ≡ eβs (Eh s − Es) + eχs (Hh s − Hs) which captures individual-specific variation in income. Migration models predict that, other things being equal, the choice of migration destination should depend on g E [yhs | zh]. This means that if we regress the choice of destination separately on eδs and eηszh, they should have the same coefficient. The same methodology is used to construct other variables that may affect the choice of 9The literature has often emphasized that migrations often serve an important role in household formation. For migrants, the prospect of forming a large, successful household is likely to be one of the purposes of migration. 10The 1995 / 96 NLSS survey adopted the following sampling strategy. Within each district a small number of wards were selected at random. Within each ward, 12 randomly selected households were interviewed. Because the wards differ widely in terms of population, applying sampling weights is essential in order to obtain consistent estimates of δs. 16", "output": {"entities": {"named_data": ["NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "destination. Building on a growing literature documenting the relationship between subjective welfare and relative income, Fafchamps and Shilpi (2008) show that Nepalese households care about their consumption level relative to that of others in the same location. If this is the case, it is conceivable that migrants choose their destination not so much for the absolute gain in income it may provide but for the gain in relative status that would ensue. For instance, if returns to education and ability are higher in an urban setting, an educated individual may improve his relative position in society by moving from a rural to an urban setting. To investigate this possibility, we estimate equation (4) using the log of relative income (or relative consumption) as dependent variable and construct a predicted relative income measure using the same formula (5). These are shown in the second panel of Table 1. Theories of work migration predict that individuals move to increase their utility or welfare. The 1995 / 96 NLSS asked respondents a number of questions regarding their subjective satisfac- tion level with various dimensions of consumption — namely, food, clothing, housing, health care, and child schooling. They were also asked their subjective satisfaction with their level of total income.", "output": {"entities": {"named_data": ["NLSS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "expect the prices of many manufactures to do as well. The 1995 / 96 NLSS collected information on the quantity and price paid for rice by individual households. From this we compute a unit price per Kg. The log of the district median is used as our price index proxy. To construct an index of housing costs, we take advantage of a section of the 1995 / 96 NLSS survey focusing on housing. The survey collected information on hypothetical and actual house rental values of each household together with house characteristics such as square footage, number and type of rooms, quality of materials, and the availability of various utilities. We use these data to construct an hedonistic index of housing costs for each district. Let rk s be the house rental price paid (or estimated) by household h in district s and let xh s denote a vector of house characteristics. We estimate a regression of the form: log rk s = as + bxh s + ek s to obtain estimates of eas, the housing cost premium in each district s. Regression results are shown in Table A1 in appendix. Many house characteristics are significant with the expected sign, e. g., larger, better built houses with better in-house amenities are worth more. District price differentials are large and jointly significant.", "output": {"entities": {"named_data": ["NLSS", "NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(2009) have shown that, in Nepal, subjective welfare is negatively associated with geographical isolation. Census data on total population and population density in each district are used as proxies for urbanization and geographical proximity: the denser the population, the less geographically isolated individuals are likely to be. We also include data on the average elevation in each district. Nepal being a mountainous country, the higher the average elevation of a district, the more costly it is to build roads, raising transport and delivery costs to the district. Ceteris paribus, we expect migrants to seek out districts with a higher population density and a lower elevation. 4 Econometric results 4. 1 Univariate analysis We now investigate the choice of migration destination. We begin with simple univariate analysis. Variables are of the form ∆ h is = xh s − xh i where i is the district of origin of migrant h and s is each of 74 possible districts of destination. We examine the average value of ∆ h is for the destination district and compare it to the value of ∆ h is for alternative destinations. For instance, let xh s be population density in district s. The average value of ∆ h is for the actual destination of the migrant tells us whether the destination district is more densely populated than the district of origin.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Census data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The comparison between ∆ h is for actual and hypothetical destinations tells us whether the actual district of destination is more densely populated than alternative destinations. Results are presented in Table 3 for all variables used in the analysis. We begin with district log income eδs. We have two estimates of eδs, one obtained using reported income data, and the other based on reported consumption data. Given that most respondents to the NLSS survey are self-employed, measurement error is typically larger for income than for consumption. We see that our estimates of log income and consumption eδs are on average 20 % and 8 % higher in 20", "output": {"entities": {"named_data": ["NLSS survey"], "descriptive_data": [], "vague_data": ["reported consumption data", "reported income data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The univariate analysis showed that migrants on average move to destinations where they are on average less likely to find people like them. The results presented in Table 4 present a different picture. Conditional on the other regressors, the ethnicity and language proximity indices are significant with the anticipated sign: social proximity between the migrant and the population of the destination district is higher than in alternative destinations. The religion proximity index is not significant. Taken together, these results suggest that, conditional on material benefits from migration, migrants prefer to move to a destination where they integrate more easily — and possibly enjoy network benefits in terms of access to jobs and housing (Munshi 2003, Beaman 2006). 24", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "include the rice price — which appears with the wrong sign but is only marginally significant — and elevation and population density — which are no longer significant. Comparing Tables 7 and 5, we find that in the smaller NLSS 2002 / 3 dataset none of the anticipated consumption variables is statistically significant. Other results are as before. 4. 4 Magnitude To assess the relative magnitude of our results, we multiply coefficients estimated in Tables 4 and 5 by the standard deviation of their respective regressors. We then average over the various regressions reported in Tables 4 and 5. Calculations are summarized in Table 8. The larger the value, the more influence the regressor has on the choice of a destination district. We see that the most important regressors in terms of magnitude are travel time to the near- est road, elevation, language similarity, and the price of rice. Consumption variables have an effect on migration destination that is smaller in magnitude: a one standard deviation increase in anticipated relative consumption, for instance, has an effect on destination that corresponds to a third of the effect of a one standard deviation in elevation — and one-sixth of a one stan- dard deviation in distance from the nearest road. Income variables have a negligible effect on migration decisions.", "output": {"entities": {"named_data": ["NLSS 2002 / 3 dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These calculations confirm our earlier assessment. 5 Conclusion Combining data from a household survey and an 11 % census of the population, we have estimated destination choice regressions for Nepalese internal migrants. Results show that population density, social proximity, and access to amenities exert a strong influence on migrants ’ choice of destination. These results confirm earlier work on the factors affecting the subjective welfare cost of isolation (Fafchamps and Shilpi, 2008). 29", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey", "11 % census of the population"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It is difficult to draw causal inference from observational data. This study is no exception. The results presented here are nevertheless sufficiently suggestive to cast doubt on the theory that the choice of migration destination is driven primarily by income differentials. Other factors seem to play a strong — and probably more important — role. References 1. Adams, Richard, Remittances, Investment, and Rural Asset Accumulation in Pakistan, 30", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["observational data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 11055 The paper examines the early integration of Ukrainian ref­ugee students into Italy ’ s education system following the Russia ’ s invasion of Ukraine in 2022. Using administrative and survey data, the study presents enrollment trends, aca­demic performance, and barriers to educational integration. Findings from the analysis indicate that Ukrainian refugees face lower enrollment rates, higher absenteeism, and lower test scores than other students, particularly in subjects requiring language proficiency. Despite these challenges, teachers often recommend Ukrainian refugee students for advanced educational tracks, thus revealing their optimism about the potential of these students.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["administrative and survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Language barriers, mental health challenges, and uncertain futures are identi­fied as major obstacles to integration. The study highlights the importance of tailored interventions, such as psycho­logical support and more dedicated teaching time, to foster refugee students ’ academic and social inclusion. This paper is a product of the Development Data Group, Development Economics and the Social Protection and Labor Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at michela_carlana @ hks. harvard. edu; pcastaing @ worldbank. org; mtestaverde @ worldbank. org; and mtiberti @ worldbank. org.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The long-term consequences of these trends are signiϐicant, as diminished educational outcomes and social isolation can hinder successful integration into host communities. Conversely, sustained social and educational integration efforts are vital for positive outcomes. For instance, studies indicate that long-term integration can be hampered by social, economic, and institutional barriers (Chiswick and Miller, 2014), while interventions focused on language support and community engagement can lower these barriers (Ozden and Wagner, 2020). Further, speciϐic interventions aimed at removing obstacles to education for children on the move can contribute signiϐicantly to better integration outcomes (Schuettler and Caron, 2020). This paper beneϐits from key information coming from administrative data on educational records of Ukrainian refugees in Italy for the academic years 2021-2022 to 2023-2024 for grades 6 to 13. This provides a unique opportunity to examine enrollment, attendance, test performance, and other indicators of integration into the Italian educational system. Supplemented by survey data collected in 2023-2024, this study offers an overview of the challenges and opportunities faced by Ukrainian students in secondary schools and highlights areas for potential policy development. This study advances the literature by adding empirical evidence on the short- to medium-term educational impacts of displacement on young refugees within a European host country, offering insights into the role of education policy in mitigating human capital losses. It also contributes to discussions on human development by identifying factors that support or hinder integration, highlighting pathways for improving educational and social outcomes for refugee students. Results highlight that despite gradual improvements, enrollment rates remain signiϐicantly lower among refugees compared to native and other foreign students. Ukrainian refugees also demonstrate higher absenteeism and lower academic performance, particularly in subjects requiring language proϐiciency such as Italian and English. However, good performance in mathematics suggests potential strengths linked to their prior educational backgrounds. Despite these challenges, teachers seem to be more inclined to recommend Ukrainian refugees for high-track education compared to other newly arrived foreigners, indicating potential optimism about their academic capabilities. The", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 3. Data Sources and Methods 3. 1. Administrative data on educational outcomes in Italy Two administrative data sources represent the backbone of this paper. These are the administrative data obtained from the Ministry of Education (MoE) for academic years 2021-22, 2022-23 and 2023- 24, and standardized test score data from the Italian National Institute for the Evaluation of the Educational System (INVALSI) for the 2022-2023 academic year. These datasets offer valuable insights into the educational outcomes of students in Italy, including Ukrainian refugees who entered the Italian school system following the invasion in 2022. Deϐinitions. In both datasets, students are categorized into ϐive demographic groups based on their nationality and timing of entry into the Italian educational system. These groups are Italian students, Ukrainian refugee students, non-refugee Ukrainian students, newly arrived foreign students, and other foreign students. Among Ukrainian students, the distinction between refugees and non- refugees is based on their enrollment date in the Italian education system. Ukrainian refugees are deϐined as Ukrainian students who enrolled in Italian schools after February 2022. In this paper, Ukrainian refugees are labeled “ Ukr post-Feb 2022 “, while non-refugee Ukrainians are labeled “ Ukr pre-Feb 2022 “.", "output": {"entities": {"named_data": [], "descriptive_data": ["standardized test score data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This distinction is also applied to foreign students, with those entering the system after February 2022 referred to as “ Migrant post-Feb 2022 ” and others referred as “ Migrant pre-Feb 2022 ”. 7 Administrative data from MoE. The Ministry data includes information for all students who enrolled at any point during the academic year. 8 This information covers academic year, grade, gender, birth date, birthplace, citizenship. They also include school-speciϐic information such as the name and identifying code of the institution where the student is enrolled. Furthermore, the dataset includes a variety of school outcome variables, including grades in English, Italian, Mathematics, overall GPA calculated as the average across all subjects, behavior scores from grade 9 to grade 12, guidance council evaluations from lower secondary school, records of absences, late entries, and early exits. Given the timing of this study, data for academic year 2022-23 are the most complete. For academic year 2022-2023, school enrollment data at the provincial level was provided for 4, 269, 348 enrolled students across the 8 years of Italian lower and upper secondary school, encompassing both public and private institutions. The dataset includes nearly all students in the country irrespective of their citizenship. 9 Table 1 shows the distribution of the different groups of students by grade. In the 7 For ease of reference, we refer to non-Italian and non-Ukrainian students as migrants. However, we acknowledge that some of these students may be refugees or displaced students. 8 At the time of writing this paper, both MoE and INVALSI data were not fully available and, as such, only the information on enrollment was used for the academic year 2023-24. 9These numbers do not include students enrolled in Provincial centers for adult education (CPIA). See footnote 5.", "output": {"entities": {"named_data": ["INVALSI data"], "descriptive_data": [], "vague_data": ["school enrollment data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 measured by the number of days missed during the academic year, can function as an indicator of school attachment or integration for refugee students. To address potential biases from teacher subjectivity, the analysis of score disparities relies on INVALSI test results. The standardized and anonymized nature of these tests helps mitigate subjectivity in assessment. First, the results section presents some summary statistics of the main outcomes across the different categories of students. Second, we use the administrative data to analyze empirically how Ukrainian refugees and newly arrived foreigners compared to other students as regards their education performance. This estimation is based on an OLS model with the following econometric speciϐication: 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 + 𝛽𝛽1 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 + 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 + 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 + 𝑓𝑓𝑔𝑔 + 𝑓𝑓𝑠𝑠 + 𝑓𝑓𝑙𝑙 + ϵigs (1) where 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 represents the outcome of interest (such as test scores, absenteeism, or high-track recommendation) for student i in school s, in grade g, and with language l. The variable 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 is a dummy indicating whether the student is a Ukrainian refugee, and 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 indicates if the student is a newly arrived foreigner. 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 represents the Economic, Social, and Cultural Status of the student, and 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 indicates the student ’ s gender.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The model includes ϐixed effects for grade, school, and language spoken. 14 The results are presented in Table 3. We then narrow our focus to foreign students who joined Italian schools after February 2022, speciϐically comparing Ukrainian refugees to other newly arrived foreign students. This approach allows us to examine how Ukrainian refugees compare to other foreign students who entered the education system around the same time. By restricting the sample to these two categories of students, we estimate the following regression: 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 + 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 + 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 + 𝑓𝑓𝑔𝑔 + 𝑓𝑓𝑠𝑠 + 𝑓𝑓𝑙𝑙 + 𝜖𝜖𝑖𝑖𝑖𝑖𝑖𝑖 (2), with variables as deϐined in (1), and results presented in Table 4. Our analysis also aims to explore potential mechanisms that could explain results derived from equations (1) and (2). Using the administrative data, we investigate whether being placed in a smaller class inϐluences school achievement in the sample of Ukrainian refugees. The results are presented in Table 5. We then draw on ϐindings from the survey data to unpack and analyze how Ukrainian refugees feel in Italy, the challenges they face, and their aspirations. 4. Results 4. 1. Integration challenges faced by Ukrainian refugees in Italy Low enrollment and substantial dropout rates At the end of the 2021-2022 school year, the enrollment rate of Ukrainian refugee children in Italian schools was low. In the months following Russia ’ s full-scale invasion of Ukraine in 2022, 3, 320 Ukrainian refugees were enrolled into Italian secondary schools. This ϐigure constitutes 24 % of the 14, 106 Ukrainian refugees aged between 11 and 18 years who sought temporary protection as of 14 This variable is included to account for the potentially greater ease of learning experienced by students who speak languages that are considered closer to Italian.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 coefϐicients from this estimation. The results indicate that Ukrainian refugees are consistently more absent than other foreign students who entered the Italian education system around the same time. Lower test score performance The evidence suggests that Ukrainian refugees in Italy face important learning gaps across all subjects. Figure 4 reports the INVALSI test scores by topic and category of students. 23 The educational disparity is particularly pronounced between Ukrainian refugees and Italian students, but signiϐicant gaps also exist between refugees and both Ukrainian nationals and foreign students who were enrolled in Italian schools before February 2022. However, Ukrainian refugees tend to have INVALSI scores comparable to migrant students who joined the educational system after February 2022. Notably, Figure 4 shows that Ukrainian refugees perform better in mathematics than recent migrants but score lower in Italian. Figure 4-INVALSI scores in Grades 8, 10, and 13 (Source: INVALSI, a. y. 2022-23) Table 3 presents the regression estimates that control for various potential confounding factors. The results indicate that both Ukrainian refugees and recent migrants score lower across all subjects. In mathematics, both groups score 16 points less than the rest of the sample. As expected, given their relatively short time in Italy, their performance in Italian is notably weaker.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "19 children and 61 % of caregivers prefer to remain in Italy. Adolescents between the ages of 15 and 19 express a greater desire to continue living in Italy compared to children aged between 9 and 14. Another survey conducted across Europe from June to December 2022 shows that only 8 % of Ukrainian refugees planned to settle outside Ukraine (Adema et al., 2024). Compared to other foreign children in Italy, the aspirations of Ukrainian refugees to return to Ukraine seems signiϐicantly higher: indeed, a recent study from ISTAT on children 11 to 19 years old shows that only 11 % of foreign children wish to return to their home country (ISTAT, 2024). The relatively strong desire to return to Ukraine can have negative effects in refugee parents'educational decisions, particularly in encouraging their children to learn the language of the host country and in enrolling in school (Dryden-Peterson et al., 2019; Zengin and Atas-Akdemir, 2020). Figure 6- Aspirations and identity of refugee caregivers and students (Source: World Bank Survey on Ukrainian refugees in Italy) Many students facing uncertain futures try to stay connected to both educational systems. Findings from the World Bank survey indicate that 25 % of children are engaging in online Ukrainian schooling while being enrolled and attending Italian schools.", "output": {"entities": {"named_data": [], "descriptive_data": ["World Bank Survey on Ukrainian refugees in Italy"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The lack of certainty means children are trying to prepare for further studies (e. g., in universities) in two separate systems with varying requirements. The survey results indicate that students enrolled in both systems spend as much time in Italian schools as those attending only Italian schools, averaging 31 hours per week. However, students participating in both systems spend an additional 8 hours per week on online Ukrainian classes. This puts an extra burden on these children. Connectedness to Italy is correlated with demographic characteristics and social environment of refugee children. Additionally, Table 6 shows that making new friends in the country of destination and speaking Italian are strongly associated with higher connection to Italy. The mental distress resulting from displacement is a key barrier to educational integration for many Ukrainian refugees in Italy. The link between poor mental health and low school attendance and performance is widely acknowledged in the literature (see Fiining et al., 2019 for a systematic review). In the World Bank survey data, children and caregivers reported signs of mental distress, with 16 % of children and 24 % of refugee caregivers reported experiencing psychological distress 61 % 35 % 31 % 68 % 50 % 36 % 23 % 68 % 59 % 26 % 26 % 59 % Would like to continue living in Italy Would like to move back to Ukraine Feel strongly connected to Italy Feel strongly connected to Ukraine Caregivers (N = 283) Children between 9 and 14 years old (N = 141) Children between 15 and 20 years old (N = 96)", "output": {"entities": {"named_data": [], "descriptive_data": ["World Bank survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Consequently, they may fear detection by authorities when asked to respond to surveys or participate in public initiatives. 1 Furthermore, collective repre- sentative and longitudinal data on forcibly displaced migrants, a population with high mobility rates, is difficult and costly (Ib ´ a ˜ nez et al. 2024). This complexity is compounded when focusing on children and adolescents, given the need for enumerators to receive specific training to interact with such a vulnerable demographic and for migrant parents to authorize their children ’ s involvement despite prevailing distrust issues. To address this knowledge gap, we launched the Venezuelan Refugee Panel Study for Kids (VenRePs-Kids) in Medell ´ ın, Colombia. VenRePs-Kids is a longitudinal study repre- sentative of forcibly displaced Venezuelan and Colombian children and adolescents aged 5 to 17. To our knowledge, it is the first study to gather panel data specifically on forcibly 1This concern is also prevalent among undocumented migrants in the United States, as highlighted by Amuedo-Dorantes and Lopez (2015). 2", "output": {"entities": {"named_data": ["VenRePs-Kids", "Venezuelan Refugee Panel Study for Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "displaced children and adolescents residing in host communities, rather than in refugee camps. Particularly, VenRePs-Kids collects data on 2, 556 households including 1, 338 Colombian and 918 Venezuelan households, respectively. The study collects rich and comprehensive data on children ’ s and adolescent ’ s development including anthropometric measures, vo- cabulary ability tests, and socio-emotional and mental health assessments. It also collects data on risk behaviors, time use, social integration measures, prosocial behaviors, and parents and caregivers sociodemographics, among other dimensions. 2 The study includes Colombian children and adolescents as the comparison group to high- light the developmental differences of Venezuelan forcibly displaced children. This choice stems from the fact that comparing Venezuelan children in Colombia with their counter- parts remaining in Venezuela is impractical due to the latter ’ s exposure to a severe eco- nomic and humanitarian crisis, marked by limited access to services and food. This envi- ronment severely hampers their potential for normal human development. Additionally, many Venezuelan children and adolescents have spent more of their lives in Colombia than in Venezuela. Therefore, Colombian children and adolescents serve as the most ap- propriate benchmark for assessing the developmental gaps of their Venezuelan peers.", "output": {"entities": {"named_data": ["VenRePs-Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This paper analyzes data from the initial wave of VenRePS-Kids, conducted from Oc- tober to December 2022, to outline key demographics and stylized facts about forcibly displaced children and adolescents. Initially, we examine the human development dis- parities of forcibly displaced Venezuelan children and adolescents in comparison to their Colombian counterparts. Our approach to human development is broad, covering physi- cal, cognitive, socio-emotional, and mental health aspects. Additionally, we complement our analysis by exploring differences in food security, social cohesion, and the economic status of parents. Although our analysis is descriptive, it represents a crucial initial step 2Venezuelan households are defined as those where both parents and their children have a Venezuelan nationality. Colombian households are composed of Colombian citizens only. 3", "output": {"entities": {"named_data": ["VenRePS-Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "vices. Our findings reveal significant delays in the physical and cognitive development of Venezuelan minors compared to their Colombian peers. Specifically, we observe a 0. 3 standard deviation difference in Body Mass Index (BMI), indicative of nutritional status, and a 12-percentage point difference in the Peabody Vocabulary Test scores, which as- sesses receptive vocabulary and verbal ability. Surprisingly, our analysis does not identify any disparities in socio-emotional and mental health between the two groups. This out- come is unexpected, given the high incidence of socio-emotional and mental health chal- lenges among forcibly displaced populations. The absence of discernible gaps in these areas could be attributed to the non-exposure of Venezuelan migrants to warfare, or it may reflect the vulnerabilities of the Colombian population, which has its own extensive history of internal forced displacement and violence. When examining the role of time of settlement, regularization status, and service access on the developmental disparities between Venezuelan and Colombian minors, we un- cover two significant facts. On the one hand, the gaps in both cognitive and physical development are diminishing over time.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our research complements and extends the findings of Demirci, Foster and Kirdar (2022), who investigated health and nutrition disparities between native children and Syrian refugee children aged 0 to 5 years in T ¨ urkiye, using data from the Demographic and Health Survey. The authors document find no significant differences in infant or child mortality rates between refugee children born in T ¨ urkiye and their native counterparts, it did reveal that refugee infants have lower birth weights and age-adjusted weights and heights compared to native infants. Our work broadens the scope of analysis beyond anthropometric indicators to encompass a holistic assessment of child development. By incorporating measures of physical, cognitive, socio-emotional, and mental health devel- opment, along with factors such as food security, time use, risky behaviors, and social integration, we offer a more comprehensive understanding of the developmental chal- lenges faced by displaced minors. Additionally, our study includes a wider age range, 5Chiovelli et al. (2021) examine the effects of forced displacement on separated sibling in the long-term. 8", "output": {"entities": {"named_data": ["Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The upcoming section provides an overview of the situation facing Venezuelan migrants in Colombia, setting the stage for understanding the context within which our study is situated. Section three offers a comprehensive description of the VenRePS-Kids study, covering aspects such as the sampling frame, the instrument used for data collection, the representativeness of the study, its implementation process, and an overview of descriptive statistics. Section four delves into the human development disparities observed among forcibly displaced chil- dren and adolescents, providing detailed insights into the nature of these gaps. In section 9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "III THE VENREPS-KIDS STUDY In this section, we offer a detailed overview of the VenRePs-kids Study, covering its de- sign, implementation, the questionnaire utilized, and the primary outcomes that will be employed to evaluate the human development disparities between forcibly displaced children and adolescents and their peers in host communities. III. A Design Location. Our study is conducted in Medell ´ ın, Colombia ’ s second-largest city, following Bogot ´ a. Medell ´ ın was chosen for this study because it hosts the third-largest Venezue- lan migrant population in the country, trailing only Bogot ´ a and C ´ ucuta, as indicated by the 2018 population census data. Additionally, previous research has demonstrated that survey response rates among migrants in Medell ´ ın are notably high. For instance, a na- tionally representative survey of Venezuelan migrants conducted in 2018 — which was representative across Colombia — revealed that Medell ´ ın had the highest response rates among migrants, whereas Bogot ´ a recorded the lowest (Ib ´ a ˜ nez et al. 2022). This finding supports the decision to focus our study exclusively on Medell ´ ın, also considering the challenges and high costs associated with tracking a highly mobile population longitu- dinally in previous research efforts.", "output": {"entities": {"named_data": [], "descriptive_data": ["2018 population census data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A map highlighting Medell ´ ın ’ s geographic position and the locations of the households interviewed for this study is provided in Figure 2, offering visual context to our research setting. Representativeness and stratification. VenRepS-Kids is designed to be representative of two groups of youth. The first group consists of Colombian children and adolescents, aged 5 to 17, born to Colombian parents. The second group encompasses Venezuelan migrant children and adolescents of the same age range, born to Venezuelan parents, who mi- grated to Colombia between 2016 and 2020. The sample was further stratified by gender and socioeconomic levels, using Colombia ’ s neighborhood income-based classification system that ranges from 1 to 6, where six indicates the wealthiest neighborhoods. Our 13", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 2. Location of Households in the VenReP-Kids Sample Notes: The figure depicts the exact geographic location of all the households in the VenRePs-Kids study. Green and blue dots depict the location of Colombian and Venezuelan households, respectively. The map in the upper right corner illustrates the location of Medell ´ ın (blue pin) with the department of Antioquia (highlighted in red). survey focuses on strata 1 through 4, intentionally omitting strata 5 and 6 to avoid bias toward higher-income groups which are less likely to include migrants in need of sup- port. 14", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In households with more than two children, one child from each age group was chosen through a random selection process to participate, ensuring a broad representation of experiences within the study ’ s scope. Sampling frame. We use the 2018 population census data as a sampling frame for the Colombian sample. It allowed the identification of residential blocks and households with children in the desired age range. With this source of information, it was possible to identify the number of households and residential blocks with children and adoles- 7Although Colombia only grants nationality to children of Colombian nationals, it follows a jus sanguini principle, the Colombian government has introduced reforms, such as the the program Primero la Ni ˜ nez to give Colombian nationality to children of Venezuelan parents born at times when diplomatic relations between Colombia and Venezuela were cut and hence, it was not possible to apply for a Venezuelan nation- ality for this minors in Colombia. 15", "output": {"entities": {"named_data": ["2018 population census data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "cents. The universe of blocks (“ manzanas ”) was stratified by socioeconomic strata and a representative sample of blocks was selected at random without replacement. To en- sure a sample representative of Colombian children by age group, stratum, and sex, we followed a multi-stage random sampling process. 8 As mentioned earlier, one of the biggest constraints in characterizing the role of forced migration in children ’ s human development within developing countries is the difficulty of finding a representative sample of those migrants. This is specially true in contexts where migrants are not hosted in refugee camps, but are integrated in local communi- ties, which account for 80 % of refugees worldwide (Climate Center 2022). We address these difficulties, leveraging all available information on Venezuelan settlements across the country to construct the largest possible comprehensive listing. The listing included data on Venezuelan settlements from all available sources, such as the 2018 population census, migrant organizations, and settlements identified by iMMAP, a non-profit orga- nization. iMMAP uses multiple sources, including OIM, United Nations, local migrant organizations, and satellite images, to identify Venezuelan settlements geographically. 9 Hence, to create our sampling frame, our field team verified the geographic location of all the Venezuelan settlements in-person and implemented a snowball sampling procedure in all the settlements found.", "output": {"entities": {"named_data": ["2018 population census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5. Time Use and Child Labor: The fifth module examined how children spend their leisure time, their involvement in child labor, and interactions with peers. 6. Pro-social Preferences and Migration Outlook: The sixth module concentrated on adolescents ’ pro-social behaviors, such as altruism and trust, and explored their expectations and intentions regarding migration. 7. Socio-emotional and Mental Health: The final module involved the administration of various scales to assess socio-emotional well-being and mental health, includ- ing trauma, behavioral problems, anxiety, and depression. The scales include the Trauma Symptom Checklist for Young Children (TSCYC), Strengths and Difficulties Questionnaire (SDQ), General Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire (PHQ-9). All these scales and the corresponding outcomes that we evaluated are described in the next subsection. The survey also employed the Peabody vocabulary test to evaluate the cognitive devel- opment of all participating children and adolescents. A summary of the survey modules is depicted in Table A. 1. III. C Sample comparability While Medell ´ ın ranks as the third city with the highest migration in Colombia, it is crucial to recognize the degree to which migrants arriving in the city differ from those migrating to other regions in Colombia.", "output": {"entities": {"named_data": ["Trauma Symptom Checklist for Young Children", "Strengths and Difficulties Questionnaire", "Patient Health Questionnaire", "General Anxiety Disorder Scale"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Ideally, we would conduct a comparative analysis of the characteristics of migrants residing in Medellin versus those in other parts of the coun- try to discern the extent of these differences. However, the lack of comprehensive data regarding the living conditions of this population makes such analysis unfeasible. To explore how this population compares with other migrant groups in the country, we turn to the only two available data sources on migrants. First, we use the Venezuelan Refugees Panel Survey (VenRePS), conducted by Ib ´ a ˜ nez et al. (2022), which captures a 19", "output": {"entities": {"named_data": ["Venezuelan Refugees Panel Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "representative sample of undocumented migrants in Colombia ’ s major cities as of 2020. 10 This survey encompasses information on the socioeconomic status, health, well-being, access to services, and labor market outcomes of adult undocumented migrants. Second, we compare our sample with the Administrative Venezuelan Migrant Registry (RAMV), a nationwide census of undocumented Venezuelan migrants conducted by the Colom- bian government in 2018. 11 This census surveyed Venezuelan households regarding their socioeconomic conditions and the labor market characteristics of the household head. We compare the household characteristics and the labor market outcomes of the house- hold heads in our sample with those in VenRePS and RAMV surveys in Table A. 3. 12 We observe that households in the VenRePS-Kids survey are smaller on average and have a greater number of children living in the household. The latter is anticipated since one of the eligibility criteria to participate in our survey is the presence of at least one child in the household. Furthermore, the household heads in our sample are disproportionately female and more likely to be married, aligning with the family structure targeted in our sampling frame. Regarding labor outcomes, household heads in our sample are more likely to be employed and engaged in the informal sector compared to those surveyed in VenRePS and the RAMV census.", "output": {"entities": {"named_data": ["VenRePS", "Administrative Venezuelan Migrant Registry", "VenRePS-Kids survey", "RAMV surveys", "Administrative Venezuelan Migrant Registry (RAMV)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Despite notable disparities in the characteristics of our sample compared to the VenRePS and RAMV samples, understanding the interaction of these attributes within a frame- work of self-selection among migrants into Medellin is challenging. Moreover, it is cru- cial to note two primary distinctions between our survey and the VenRePS and RAMV surveys. Firstly, the inherent differences in the sampling frames of each survey stem from their distinct measurement objectives. Second, both surveys were conducted at different times compared to our survey. The RAMV survey was undertaken in 2018 in response 10See Ib ´ a ˜ nez et al. (2022) for specific survey and sampling details. 11Refer to Ib ´ a ˜ nez et al. (2022) for further details. 12Since the VenRePS and RAMV surveys lack information regarding children and adolescents within households, our analysis concentrates only on the household and household head characteristics that are available in all three surveys. 20", "output": {"entities": {"named_data": ["RAMV survey", "VenRePS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "to the Venezuelan migratory crisis, whereas the VenRepPS survey was conducted during the pandemic in 2020. These temporal inconsistencies result in varying sample composi- tions across surveys, diverging from the landscape we observe in 2022. Notably, forced migrants in the VenReps Kids survey migrated during the crisis but have since remained in the country for several years, potentially leading to disparities in household integration outcomes. IV GENERAL DESCRIPTIVE STATISTICS IV. A Key characteristics of adults Table 2 provides descriptive statistics for the adults in our study, encompassing the pri- mary caregiver, mother and father (if residing with the child), and the individual finan- cially responsible for the child (should they be different from the aforementioned per- sons). Typically, the roles of primary caregiver and financial provider are fulfilled by either the mother or the father. The table is organized into three panels for clarity: Panel A details key individual characteristics, Panel B outlines adults ’ access to services, and Panel C focuses on labor market characteristics. Within the table, columns (1) and (2) present average values for adults from Colombia and Venezuela, respectively, while the final column displays the results of mean difference tests between these two groups, with standard errors noted in brackets.", "output": {"entities": {"named_data": ["VenRepPS survey", "VenReps Kids survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 3. Wealth Index Distribution (a) Wealth Index- Total 0. 05. 1. 15. 2. 25 k-Density- 5 0 5 10 15 Wealth Index Colombian Venezuelan Venezuelan: Before migrating (b) Adequate Housing Materials (% of Total) 0 1 2 3 4 k-Density 0. 2. 4. 6. 8 1 Average- Dwelling Material Colombian Venezuelan Venezuelan: Before migrating (c) Asset Ownership (# Total) 0. 05. 1. 15 k-Density 0 10 20 30 40 Total Assets Colombian Venezuelan Venezuelan: Before migrating (d) Access to Services (% of Total) 0 1 2 3 k-Density 0. 2. 4. 6. 8 1 Average- Access to Services Colombian Venezuelan Venezuelan: Before migrating Notes: Panel (a) presents the distribution of the wealth index for Colombian and Venezuelan households in our sample in 2022 and pre-migration. Wealth Index is an index measure of the household ’ s cumula- tive living standard constructed following The Demographic and Health Surveys (DHS) methodology.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "by the migration episode. These include the minors age and sex, the parent ’ s and grand- parent ’ s education pre-migration, and a wealth index constructed with retrospective in- formation on the household conditions pre-migration. 14 Grandparent ’ s education is used a proxy for living standards that is unaffected by the migration episode from Venezuela to Colombia and the Venezuelan crisis, which intensified in 2016. ϵij depict the standard errors clustered at the household level to correct for intra-household correlation. For robustness, we will present the estimates of equation 1 with and without controls. As further robustness, we use propensity-score weights (Hirano and Imbens 2001, Hirano, Imbens and Ridder 2003). 15 V. A Physical development: Body Mass Index and health status In our study, we examine disparities in nutritional and health status among Colombian and Venezuelan children aged 5 to 10 years, focusing on standardized body mass index (BMI), instances of overweight and underweight, and overall health status. The BMI serves as an indicator of nutritional status for both adults and children, calculated as an individual ’ s weight in kilograms divided by their height in meters squared, according to World Health Organization (WHO) guidelines.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["wealth index"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Due to the variations in weight and height ratios among children and adolescents according to gender and age, the benchmarks for determining nutritional status are specifically tailored to these factors. We adhere to WHO guidelines to calculate the standardized BMI values for minors. A BMI exceeding one standard deviation (SD) from the mean suggests overweight, while a BMI less than minus one SD indicates underweight. 16 Health status is assessed through a binary variable, assigned a value of one if the caregiver has reported any health issues such as disease or chronic pain, accidents, dental pain, surgical interventions, or preg- 14For the Colombian households the wealth index is measured with contemporaneous data. 15This procedure restricts the sample to the common support of the propensity score for being a forced migrant and weights observations for Colombian kids by a non-parametric function of the propensity score. This procedure has been shown to increase the estimate ’ s efficiency. 16Furthermore, a BMI greater than 2SD is indicative of obesity risk, and less than- 2SD signals a risk of severe thinness. 33", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of difficulty, ranging from those suitable for children as young as 2 and a half years old to more complex items for individuals over 18 years old. 19 The Peabody Vocabulary Test is calibrated with a mean standard score of 100 and a standard deviation of 15, placing scores between 85 and 115 within the average range. This test, in its Spanish edition, has been validated for use in Colombia. Additionally, to ensure relevance and accuracy for Venezuelan nationals, we conducted a preliminary validation by administering the test to a sample of Venezuelan mothers in our study. This step confirmed that the words used in the test held consistent meanings for participants from Venezuela. Figure B. 1 depicts the distribution of PVTS scores for Venezuelan and Colombian chil- dren and adolescents in our sample. This visualization indicates that Venezuelan minors consistently score lower on the PVTS compared to their Colombian peers across the en- tire score distribution. In Table 6, we present the average disparities in percentile rank on the Peabody scale, revealing that Venezuelan children and adolescents, who are forcibly displaced, score approximately 12 p. p. lower than their Colombian counterparts. The difference is meaningful and in turn translated into Venezuelan minors falling into the a higher likelihood of having extremely low, moderately low, and low score categories.", "output": {"entities": {"named_data": ["Peabody Vocabulary Test"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Additionally, these findings remain consistent even when adjusting for a range of control variables pertaining to the individual, their parents, and grandparents, underlining the robustness of these observations. V. C Socioemotional and mental health development To explore the differences in mental health and socioemotional development across Colom- bian and Venezuelan minors we use multiple scales. For children aged 5-10 years, the Trauma Symptoms Checklist for Young Children is employed. This 90-item question- 19Children only respond to items within their “ critical range ”, determined by a lower limit called the “ base item ” and an upper limit called the “ ceiling item ”. The base item, marking the starting point, is determined by the individual ’ s chronological age in years (date of test administration- date of birth). Once the child correctly answers 8 consecutive questions, they reach the ” Base ”. Subsequently, upon making 6 mistakes within 8 consecutive questions, the ceiling is established. The direct score is calculated as the item number where the test ends (ceiling item) minus the number of errors from the highest base to the end of the ceiling. 36", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "to severe based on the score. Moreover, depression is screened with the Patient Health Questionnaire, a 9-item tool administered directly to adolescents. These instruments collectively gauge a broad spectrum of psychological and emotional states, including post-traumatic stress, emotional disturbances, behavioral issues, inat- tention, peer relationships, prosocial behavior, anxiety, and depression, offering a com- prehensive view of the mental health and socioemotional development of the minors in our study. Figures B. 3 and B. 4 depict the distribution of the raw scores for Venezuelan and Colom- bian minors for each of the four scales. Surprinsingly, we do not observe any stinking differences on the distribution of any of these scores across groups. We are also not able to distinguish statistical differences between Colombian and Venezuelan children in any of the scales, when we estimate the specification highlighted in equation (1) as illustrated in Table 7. This is an unexpected result considering that typically, forcibly displaced pop- ulations have a high prevalence of socioemotional and mental health issues, but might be related to the fact that Venezuelan migrants have not faced war (as many forced migrants have in other contexts) directly and as such, these issues are less prevalent.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Another ex- planation of these results could be the vulnerability of the Colombian population who in many cases has also a long history of internal forced displacement and violence. V. D Social Cohesion We also delve into the differences in secondary outcomes among Colombian and Venezue- lan adolescents concerning social cohesion. We focus on assessing altruism, trust, iden- tity towards specific domains, networks, and experiences of discrimination. To measure altruism and trust, we employ the questions from the Global Preference Survey, a tool developed by Falk et al. (2022) to elicit risk, time, and social preferences. Specifically, to measure altruism we ask the adolescents how much of a fictional endowment would they be willing to donate to a good cause. To measure trust, we include the 7-itme ques- 38", "output": {"entities": {"named_data": ["Global Preference Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "tionnaire from Falk et al. (2022), along with a question on how much do the subjects in our sample trust the Colombian government. Additionally, we inquired about the ado- lescents ’ social networks by asking about their number of friends of each nationality and whether they have felt discriminated in school. While these outcomes were not the pri- mary focus of the VenReps Kids survey, they collectively provide a general assessment of social cohesion, a crucial aspect in understanding the overall well-being of children and adolescents. We measure the average differences in altruism between Venezuelan and Colombian ado- lescents following the estimation of equation 1. Table 8 presents the results for altruism, trust, discrimination and social ties in panels A, B and C respectively. As in the previous tables, the first column for each outcome reports the results of the estimates of equation 1 without controls, and the second and third columns report the results of the estimation with all controls and the propensity score matching respectively. As for altruism, we see that Venezuelan adolescents are on average willing to donate 17 % more of their imagi- nary money endowment to a good cause relative to Colombian adolescents. As for trust, the results are mixed when analyzing the trust items separately.", "output": {"entities": {"named_data": ["VenReps Kids survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Colombian and Venezuelan migrant children and adolescents. VII Discussion In this study, we analyze novel and unique data on forcibly displaced migrants and hosts, focusing on children and adolescents, to highlight the disparities in human development between them. The structure our analysis in two parts. In the first part, we characterize the main trends in the data. We show that forcibly displaced households have a wealth distribution skewed towards lower values relative to Colombian households. This is likely explained by the assets ownership loss that forcibly displaced households expe- rienced after the migration episode. We also identify meaningful lags in human capital accumulation between Colombian and Venezuelan children and adolescents of approxi- mately 1 year. We further note that the Colombian government ’ s supportive policies for Venezuelan forced migrants are evident through high levels of service access and pro- gram participation for migrants. Nevertheless, it remains surprising that participation is not higher, suggesting significant potential for improvement in increasing sisb ´ en and health insurance enrollments. In a second part of our analysis, we document sizeable lags in physical and cognitive de- velopment of Venezuelan children and adolescents, relative to their Colombian counter- parts. However, we were not able to identify any gaps in the socioemotional and mental health between the two groups.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 As displacement crises are largely unpredictable, all the studies surveyed in this paper are evaluations conducted ex-post. In theory, a few of the crises studied could have been predicted but it would not be possible to allocate individuals to treated and non-treated groups randomly given that, by the definition of forced displacement we provided, people are fleeing violence, persecution or high levels of insecurity or uncertainty. Consequently, none of the papers reviewed is based on a Randomized Controlled Trial (RCT). Due to the randomness of the decision to leave (because of conflict, violence, insecurity or major political events) and / or the random allocation of displaced people in the country of destination (by policy or by default), some authors argue that they are in the presence of natural experiments. All authors do, however, address the question of endogeneity and, if one searches for a common thread, these evaluations would be better described as quasi-natural experiments. The basic model used by the literature is a model of the following form: 𝑦 ௜ ൌ 𝛼 ൅ 𝛽𝐹𝐷 ௜ ൅ 𝛾𝐹𝐸 ௜ ൅ 𝜀 ௜ Where i is the unit of observation, y is one of the four outcomes described, FD is the forced displacement shock and FE are fixed effects.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Most papers with few exceptions use standard OLS estimators or some of its variants (Table 2). Two papers use general equilibrium models (Bodvarsson, Van den Berg, and Lewer 2008; Hercowitz and Yashiv 2002) and two papers simply compare means between treated and non-treated groups resulting in simple difference estimations (Card, 1990 and Alix-Garcia and Bartlett, 2015). [Table 2] The unit of observation varies depending on the data at hand. Most studies rely on household survey data where individuals or households are the unit of observations and most studies include some regional dimension (more frequently administrative areas). Where longitudinal or panel data are available time is also included. Other choices for unit of observations include skills or education level, various types of population groups (based on gender, age etc.), and, in a few cases, economic sectors, industry or labor market segments. The use of fixed effects varies. Some papers use the full set of parameters depicting units of observation (for example, household, region and time fixed effects in equations where the unit of observation is constructed using household, region and time). Other papers use subsets of these parameters whereas other papers introduce variables that are not used to identify the unit of observation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Very few papers provide explanations for these choices and there is no clear common approach to this choice. There are also only a handful of papers that discuss estimations of the error term and choices made in this regard. The two prevalent evaluation methods used by these studies are Differences-in Difference (DD) methods and linear elasticities models. In the first case, the variable of interest (FD) is a discrete status variable (generally a pre / post- treated / non-treated interaction term) and the coefficient of interest measures the impact on outcomes in the presence or absence of displaced people. In the second case, the model is typically in log form and is based on a shock variable that measures the intensity of the shock such as the number or share of refugees per geographical unit. In this case, the coefficient measures the elasticity of outcomes to the intensity of displacement. A few papers conduct simple differences illustrating results graphically or in tabular form. A few papers use ordinary matching methods (Alix-Garcia and Bartlett 2015, Aydemir and Kirdar 2018, Murard and Sakalli 2017; Mayda et al. 2017) and three papers use Synthetic Matching Methods (Peri and Yasenov 2017; Borjas 2017; Makela 2017). We could not find any paper using a discontinuity design. 12 The essential ingredients used to measure the population shock are the number or presence of forcibly displaced persons, the size of the host population and the distance of the displaced from host communities if the displaced are clustered in camps or other forms of independent settlements. The literature covering high-income countries tends to focus on labor markets and the host population is often defined in terms of 12 Schumann (2014) is an exception, but only looks at the impacts on municipality size.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Sarvimaki (2011) uses the elements of the government ’ s placement policy as instruments (i. e. the proportion of a municipality ’ s population speaking Swedish and the hectares of potential agricultural land). Other authors focus instead on the counterfactual group testing alternative designs of the control group, sometimes including placebo groups and other times recurring to matching methods. The choice of matching methods varies from ordinary methods such as nearest neighbor to more recent advances such as Synthetic Control Methods (Abadie and Gardeazabal, 2003). The inclusion of fixed effects is common to almost all papers although the choice of fixed effects can be very different, as described above. Only one paper uses Fixed Effects (FE) and Random Effects (RE) formal models in conjunction and tests for differences (Esen and Binatli 2017). Cross-section econometrics is, by far, the method of choice even if time is included into the equations but we also found three papers employing time-series models (Carrington and de Lima 1996, Makela 2017, Fakih and Ibrahim 2015). Only few papers are able to exploit panel data (Foged and Peri 2015, Depetris-Chauvin and Santos 2017) and several of them use the same data set (Maystadt and Duranton 2018, Maystadt and Verwimp 2014; Ruiz and Vargas-Silva 2015, 2016, 2017).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Not all cross-sectional studies have multiple rounds of comparable data, covering the period before and after the crisis. When comparing impacts between locations within a country, cross-sectional data also usually does not allow to capture impacts on those who moved out and to differentiate impacts between those who were already there before the shock and those who moved in afterwards. Some of the models based on administrative areas qualify as spatial econometrics models in that they use estimation methods that derive from this literature and are published in spatial econometrics journals. Studies that compare different areas within a country are not only confronted with the potential endogeneity of the size and skill composition of the inflow and the choice of destination, but also with the endogenous reactions of the host community. Local workers might respond to the labor supply shock by dropping out of the labor force, investing in education, occupational upgrading or moving to other areas and diffusing the impact of the inflow. Even if local workers do not respond to wage variations, capital flows may equalize capital / labor ratios within the country, labor-intensive industries might move towards the regions with a high refugee or IDP influx or firms might use more labor-intensive production technologies. The reactions of the host country workers, investors and firms are medium-to long-term in nature and will play less of a role in the short-term if there are large, sudden and geographically concentrated inflows. Some of the papers explicitly analyze these potential channels, notably migration of local workers, and, to a lesser extent, occupational upgrading. Outmigration of hosts is a critical complement to the labor market analysis and excluding this outcome can lead to an underestimation of the impacts of forced displacement on the labor market outcomes of natives. The papers we reviewed that looked at tasks complexities and the question of substitution vs complementarities between refugees and natives found occupational upgrading among", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In order to address this issue, one has to consider a labor supply model that is able to measure both effects separately whereas most papers confound these two effects into one. Foged and Peri (2015) is one of the exceptions, as their paper looks at the intensive margin (fraction of year worked). Rozo and Sviastchi (2018) include the number of hours worked, and Ruiz and Vargas-Silva (2017) look at the changes in number of hours dedicated to a task (including employment outside the household). The second question relates to possible spurious correlations generated by how variables are combined in models. Linear models that use ratios of two variables as dependent variable (think of average prices or wages, employment rates or consumption per capita) and the denominator of this ratio as independent variables (think of the share of refugees on host communities or household size) can produce spurious correlations (Kronmal 1993). This is noted and addressed in Clemens and Hunt (2017) who show how addressing this issue change results for several studies in the literature covered here. Indeed, almost all models reviewed use the same population or household size on both sides of the equations.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Finally, the expenditure shock which we discussed in the theory section (international aid or an increase in public spending associated with the forced displacement crisis) is considered by only a handful of papers. This is a possible confounding factor of the impact of forced displacement on host communities and one that is not easily addressed with the use of fixed effects. This is clearly a shortcoming of this literature that will require increased attention in the future. 4. Meta-analysis of empirical results 4. 1 Data The literature review covers 49 papers spanning over a period of 29 years. We were not able to find published papers prior to the work by Card in 1990, which effectively started this literature, and there is a relatively low interest in this topic between 1990 and 2011 with only one or two papers published per year. With the Syrian crisis starting in 2011 and the EU crisis in 2015 the number of papers per year increased by several fold. Most of the papers and results considered in this review are therefore very recent (Figure 2). We used academic databases and search engines (EconLit, Social Science Research Network, JSTOR, Google Scholar) and searched websites of institutions with relevant working paper series (NBER, IZA, ERF and others). Relevant unpublished papers were included by searching agendas of workshops and conferences organized during the past few years. From the papers reviewed, we selected a total of 762 results summarized in Table 3. The results database was compiled as follows. For each paper we focused on the results that the authors considered the main and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 conclusions on the role of individual characteristics. As for employment, results on wages could not be predicted by basic theory and are in clear contrast with popular beliefs. 5. Conclusion The paper reviewed 49 empirical studies that focused on estimating the impact of forced displacement on host communities. This literature covers 17 different displacement situations in high, medium and low- income countries covering the impact on the labor and consumer markets. A total of 762 results have been used for the meta-analysis. To our knowledge, this is the first comprehensive review of this literature. The empirical modeling analysis highlighted the main traits of this literature. By definition, all studies operate ex-post, after the displacement crisis has taken place. The unexpected nature of the crisis and the randomness of the allocation of displaced persons are two elements used to defend the natural experiment assumption. However, all papers address the central question of endogeneity. The instrumental variable approach is the dominant method to address endogeneity issues and instruments tend to focus on either distance from the shock or previous location of migrants. Double difference and linear elasticity models are the dominant choice of estimation models with matching and placebo counterfactuals often supporting these choices.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Looking also at the impact of IDPs in Colombia on wages, Morales (2017) uses a labor force survey, census data and registry data to study short and long-term effects as follows: 𝑆ℎ𝑜𝑟𝑡 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ௧ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ௧ ି ଵ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ௧ ൅ 𝜆 ௠ 𝑋 ௠ ௧ ൅ 𝛾௧ ൅ 𝛿 ௠ ൅ 𝛿 ௠ 𝑇 ൅ 𝜀 ௜ ௠ ௧ 𝐿𝑜𝑛𝑔 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ൅ 𝜆 ௠ 𝑋 ௠ ൅ 𝛿 ஽ ௠ ൅ 𝜀 ௜ ௠ where y is the log of wages, i, m, and i are individuals, municipalities and time respectively, 𝑋 ௜ ௠ ௧ are individual controls, 𝑋 ௠ ௧ is the log of total population or other municipality controls, 𝛾௧ and 𝛿 ௠ are time and municipality fixed effects, 𝛿 ௠ 𝑇 are municipality time trends, 𝛿 ஽ ௠ are department fixed effects and d is the inflow of IDPs defined as 𝑑 ௠ ௧ ൌ 100 𝑝𝑜𝑝 ௠ ௧ 𝑓 ௠ ௧ where 𝑓 ௠ ௧ is the total number of IDPs arriving in municipality m at time t. The same variable without the t subscript is used for the long-run effects equation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey", "census data", "registry data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The third proxy for economic activity is given by per capita growth of night light, computed using satellite data from the National Oceanic and Atmospheric Administration (NOAA). 6 Night light data has the benefit 6Satellite data is available for a shorter time period, 1992-2013. 9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["satellite data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of being available on a yearly basis independently of the quality of local statistical offices and data gathering. While it comes with its own problems it can shed light on local economic activity where gathering of statistical data is incomplete. 7 This makes it a great fit for measuring growth in a context of civil conflict. Conflict incidence is measured through the number of battle-related deaths from UCDP / PRIO dataset. We run the following regression for country i at time t: git = β × incidenceit + µi + ηt + ϵit (1) where git is economic performance per capita growth of country i in year t, incidenceit is conflict incidence, µi and ηt are respectively country and year fixed effects. A cross-country analysis as in equation (1) bears considerable potential for both reverse causality and omitted variable bias. Thus, a priori, a convincing causal link is hard to establish. However, here we expect the resulting bias to be small for two rea- sons.", "output": {"entities": {"named_data": ["PRIO dataset", "UCDP / PRIO dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, the cross-country literature has not found that negative, contemporaneous shocks to growth systematically lead to violence. 8 Second, we have run a large number of robustness checks by adding time trends or lagged growth to our specification and controlling for rainfall shocks directly. 9 The upshot from this is not only that results remain significant but also that the estimated coefficients barely change. This does not mean that a causal link from falling growth to conflict can be ruled out. But it is unlikely to drive the macro relationship we see in the data. In order to further explore the relationship between violence and country-level out- put we run two specifications of the model described above. In the first model conflict in country i at time t is defined by any violence, i. e. if at least one battle related deaths occurs. In the second specification, conflict is defined by a higher threshold, by 0. 008 deaths per 1000 population. 10 We expect to get different results from the two specifications. From the analysis of Figure 1 we know that economic damage of civil war increases with the severity of conflict. The estimated impact from the second model should therefore be more acute. Table 1, panel A and B, reports the results. Each column contains one of our measures for economic growth.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In all specifications, conflict incidence correlates nega- tively with country-level economic performance. The estimated coefficients of conflict incidence are statistically significant and negative. We also find that the coefficients 7For a discussion see Henderson et al. (2012). In order to calculate light per capita we use popu- lation data that is provided by a World Bank dataset. 8The standard reference here is Miguel et al. (2004). Ciccone (2011) shows that high rainfall levels three years earlier seem to be best predictors of conflict in the reduced form. Miguel and Satyanath (2011) argue that lagged negative growth shocks are a predictor of conflict onset. In any case, there is no evidence from this literature that contemporaneous growth declines cause conflict. Bazzi and Blattman (2014) corroborate the view that the relationship between income shocks and conflict is not straightforward. They do not find evidence of an effect of price shocks on conflict onset and only weak evidence on incidence. 9Results from this are presented in the Appendix. 10We take the threshold from Mueller (2016) who shows that a threshold like this leads to a similar number of coded civil wars as the threshold of 1000 battle-related deaths often used in the conflict literature. In the context here, this is a conservative approach as it is not the threshold which yields the biggest difference between conflict and non-conflict countries. 10", "output": {"entities": {"named_data": ["World Bank dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 Internal Conflicts and Refugees A particularly serious aspect of internal conflicts is the human suffering they generate. This is not only those who are killed or injured in conflict but the large number of people who are forced to leave their homes. The issue of refugees has received particular attention in Western media in recent years as refugee flows from Northern Africa, the Middle East and Afghanistan are increasingly reaching Europe. These refugee streams are linked to a severe humanitarian crisis with considerable funding needs for international donors and heavy strains on host countries. 21 The current refugee crisis, however, is in no way unique. Civil war has always been closely linked to humanitarian crisis and refugee streams are one way to capture this. In this section we provide a cross-country analysis aimed at investigating how the stock of refugees evolves when a civil conflict hits a country. In the analysis we will focus entirely on showing changes in the stock of refugees across time to illustrate the dimensions involved. We will base our later analysis on these population movements. We exploit country-level data gathered from several sources. Data about refugees is provided by the UNHCR Population Statistics Database. The database provides in- formation about UNHCR ’ s populations of concern from the year 1951 up to 2014.", "output": {"entities": {"named_data": ["UNHCR Population Statistics Database"], "descriptive_data": [], "vague_data": ["country-level data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This database lists seven categories: refugees, asylum-seekers, returned refugees, internally displaced persons (IDPs), returned IDPs, stateless persons and others of concern. For each group the database provides yearly information about their composition by loca- tion of residence and origin. We exploit only the data on “ refugees ”. 22 In particular, we are interested in the annual stock of refugees for each country of residence, i. e. how many people with refugees status have left their home country each year. We focus on these numbers as they appear to be the most comparable across time and countries. However, this is likely to capture only the tip of the iceberg in some cases. The number of IDPs is extremely high in some instances but cannot be captured with the same level of confidence as refugees generally. 23 Cross-country data about conflict is provided by the UCDP / PRIO. As for the index of country-level economic activity, we use again information provided by the Penn World Table and World Bank databases. As mentioned above, our aim is to explore the dynamics of refugees during conflicts. In other words, we attempt to answer several questions.", "output": {"entities": {"named_data": ["Penn World Table", "World Bank databases"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Do people run away from their home country when a conflict breaks out? Does the seriousness of the conflict matter in this decision? In which phase of the conflict do they leave? When do refugees come back to their home country? In order to answer these questions, we look at the impact of conflict incidence on 21We will return to these issues in the policy section. 22According to the UNHCR definition, this category includes “ individuals recognized under the 1951 Convention relating to the Status of Refugees; its 1967 Protocol; the 1969 OAU Convention Governing the Specific Aspects of Refugee Problems in Africa; those recognized in accordance with the UNHCR Statute; individuals granted complementary forms of protection; or those enjoying temporary protection; and people in a refugee-like situation ”. 23The UNHCR Global Trends Report 2014 provides evidence that confirms this hypothesis. About 59. 5 million people were forcibly displaced worldwide by the end of year 2014. Among them, 19. 5 million were refugees and 38. 2 million were IDPs. 28", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The third graph represents the kernel density of our productivity loss measure for observations with positive loss, which represents around 11 percent of country-year observations in our dataset. It gives an idea of the distribution of this variable across country-years. The distribution shows a long and thin upper tail driven by countries with repetitive and highly intense conflict history like Afghanistan or the Lebanon. The average productivity loss is 15 percent in this sample and one fourth of all country-year observations are associated with losses of more than 20 percent of productivity. Even if these estimates were drastically overestimated they indicate that the long run impact of mass violence through this channel could be substantial. 5. 2 Macro Evidence In this subsection, we investigate the correlation between the aggregate loss measure and output. For this purpose, we use the height loss measure from the previous sub- section to estimate the marginal effect of an extra cm loss on log GDP. This serves two objectives. First, we explore whether the micro evidence can be used as a conduit for understanding the long-term damage to output from conflict. Second, we check whether the aggregate loss in output that we get is consistent with the micro estimates of marginal economic return to health.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We follow Besley and Mueller (2015a) and analyze international investment flows in a fixed effects Pseudo Poisson framework. The investment flow at the country level is given by E { xit} = exp (αc + γ0 ∗ peaceit + log Xt) (9) where xit is the inflow of investment in country i in year t. We use the exposure model which controls for global investment flows through Xt. The regression with total inflows as an exposure variable can be thought of as modeling the annual rate of investment inflows into a country in each year. 40 The variable peaceit is a dummy that takes a value of 1 in all years with peace. We lag this variable by one year to allow for the fact that investment needs some planning and will not react immediately to changes in the host country. We expect γ0 > 0 if inflows increase after the end of conflict. It is likely that effects of violence will be most visible if the conflict has been intense in terms of battle related deaths per capita. Yet, the right cut-offfor the peace dummy is a priori not clear. India, for example, is coded as in conflict throughout the period if we choose a very low threshold. Choosing a higher cut-offmeans we treat low intensities as experiencing no conflict.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In all these cases violence probably did not affect the entire economy notably. In what follows we focus on positive net flows, i. e. we subtract outflows from inflows and code negative numbers as 0s. Our results are robust to using gross inflows but as these are not provided by all sources. 40See Frome (1983) for a discussion of using the Poisson model to study rates. For a general discus- sion of count data models, see Cameron and Trivedi (2013). Our results are also robust to using year fixed effects instead of exposure. 41The reason is that the OECD data, the Dutch Central Bank data and the UN data allows us to distinguish between net flows and gross flows. 42We also distinguish two different ways of calculating the cut-offof intensity using contemporaneous and average population in a country. In total we therefore have 14 different estimates per cut-off. 43Each coefficient is also estimated quite precisely at this cut-off. 50", "output": {"entities": {"named_data": [], "descriptive_data": ["Dutch Central Bank data"], "vague_data": ["OECD data", "UN data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 17: Peace and Foreign Inflow Across Cut-offs and Data Sources The basic results are in Table 8 which shows results for equation (9) using the threshold of 0. 008 battle-related deaths per 1000 population. According to this the investment from OECD countries was almost 70 percent larger in peacetime than during conflict. The flows from other data sources shows an increase of between 35 and 50 percent. The consistency of this result across very different datasets is striking. Note also that the average change in inflows implied by these rates is very large. In 2012, average inflows in the World Bank dataset were over 9. 5 billion USD and over 3 billion USD in the OECD data. Our estimates therefore imply a gain of between 2 billion and 4 billion USD in yearly inflows for countries which emerge from conflict. In order to understand the dynamics of recovery it is useful to understand the dynamics of this change around the end of conflict. For this purpose we add a set of dummies to the equation above. We construct a dummy that indicates the start of recovery and add three forward and lag dummies to trace average investment around this date. As before we always lag the explanatory variables by one year. Results for the OECD data are shown in Figure 18.", "output": {"entities": {"named_data": ["World Bank dataset"], "descriptive_data": [], "vague_data": ["OECD data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the original variables used to predict fragility as controls without changing results. It is only in the post-war period that exclusion and refugees become a factor that influences foreign investment flows. Finally, the results we find are robust across all datasets of foreign investment we use. These results make it at least plausible that political exclusion and refugees matter because they predict a relapse to more intense violence. As final piece of evidence for this idea we use data on political short and mid-term credit risk from the Belgian insurer Delcredere Ducroire (ONDD). We collected data on political risk evaluations from ONDD who, according to their annual report, insured transactions worth about 7 billion EUR in 2011. The variable we use measures the risk of a credit default for rea- sons beyond the control of the debtor, i. e. due to political or financial macroeconomic events. We choose this variable because it provides the most consistent time-series in the ONDD data. ONDD measures both short- and mid-term risk on a scale from 1 (low risk) to 7 (high risk). Table 12, columns (1) and (4) show that risk ratings are decreasing in peacetime. Note that, as before, we control for country fixed effects which implies that we look at changes within country.", "output": {"entities": {"named_data": ["ONDD data"], "descriptive_data": ["data on political short and mid-term credit risk"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Within-country risk falls signif- icantly in peacetime. The effect is also economically meaningful- about one quarter of a standard deviation in the case of short term risks. In columns (2) and (5) we show the specification in which we add a dummy for the first five years of recovery and fragile peace. The coefficient on fragile peace is positive and of similar size in both cases. Mid-term risk is evaluated significantly higher in periods that are followed by conflict. In columns (3) and (6) we include the fitted values gained from a regression of fragility on refugees and political exclusion. Again the fitted values predict higher risk evaluations by ONDD. The estimate is not very precise but quantitatively large both for short- and mid-term evaluations. A rise in the fitted risk by 10 percentage points coincides with an increase in risk evaluations by 0. 08 to 0. 16. Evaluations like this have real-life repercussions as they are used to decide on insurance premiums. Our results signal a clear margin for policy. Attracting foreign investment appears to be a lot harder if a government excludes or even discriminates against parts of the population and refugees have not returned to their homes. We argue that this is true even if investors only care about stability. In this view, investment can be attracted to a country through policies that de-escalate conflict and commit the warring parties to peace in the period right after violence stops. 56", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "violent conflict. Besley and Mueller (2015a) argue that foreign investors seem to know that growth volatility changes with strong executive constraints and therefore react significantly to their adoption. In summary, the literature suggests that a lack of constraints on executive power at the country level could play a key role in building inequalities across regions and ethnic groups. In the absence of strong executive constraint, we expect regions populated by ethnic groups that have access to executive power to perform better relative to others due to ethnic favoritism. Conversely, excluded ethnic groups should experience relatively worse economic performance compared to other groups in the absence of such constraints. 46 To test these hypothesis we use data on ethnic groups ’ access to executive power and night light intensity from the GROWup Research Front-End (RFE Release 2. 0) dataset and executive constraint data from the Polity IV dataset. We use night light intensity as a proxy for economic activity at the ethnic group level. 47 Night light data has the benefit of being available on a yearly basis and of being measured at the local level where there is poor availability of statistical data.", "output": {"entities": {"named_data": ["GROWup Research Front-End", "Polity IV dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As in previous sections we follow Henderson et al. (2012) who argue that the relationship between GDP and night light at the country level can be expressed fairly well in a constant elasticity model in which an increase of night light by 1 percent implies an increase of GDP of about 0. 25 percent. Hodler and Raschky (2014) also look at the relationship between log nighttime light intensity and log GDP at the regional level using the panel data of regional GDP per capita assembled by Gennaioli et al. (2013) 48 and they confirm that the relationship is linear and also find an elasticity of around 0. 3. Access to political power is ranked on a scale from 1 to 7 in the GROWup dataset. Ethnic groups are\"powerful\"(monopoly of power or dominant group in power), have access to central power through a formal system of power sharing (as\"Senior\"or\"Ju- nior\"partner) or are “ excluded ” from power (self excluded, powerless or discriminated). Strong executive constraint is measured as a dummy indicating whether or not we have executive parity or subordination of the executive at the country level, a value 7 for “ xconst ” variable in Polity IV dataset.", "output": {"entities": {"named_data": ["GROWup dataset", "Polity IV dataset"], "descriptive_data": ["panel data of regional GDP per capita"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 19: Trust and Conflict in the Cross Section The problem with a lack of trust and strong group identities is that they penetrate and pervert formal institutions. The ethnic politics analyzed by Burgess et al. (2015) is just one example. Shayo and Zussman (2011), for example, use data from Israeli small claims courts to show that Arab and Jewish judges displayed significant judicial ingroup bias. Furthermore, this bias is strongly associated with terrorism intensity in the vicinity of the court in the year preceding the ruling. Confidence-building is also a crucial ingredient for the establishment of a fertile investment climate, which in turn is a trigger of economic development post conflict. This is the core message of the World Bank Report by Mills and Fan (2006). An important role of increasing trust doubtlessly goes to the media. It has been shown, for example, that hate radio in Rwanda played a critical role in the extent of ethnic violence during the genocide. 59 Other research has shown that media coverage can have strong effects on political preferences more generally. 60 Perhaps the most direct proof of the crucial role played by the media in the post-conflict situation comes from DellaVigna et al. (2014). The authors exploit variation in radio reception of na- tionalistic Serbian radio in border regions in Croatia.", "output": {"entities": {"named_data": [], "descriptive_data": ["data from Israeli small claims courts"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The authors argue that Croats listened to Serbian radio for its consumption value but reacted negatively to national- istic messages intended for Serbian ears. Election results and street surveys are used to elicit preference for extremist nationalist parties among Croats who are able to listen to Serbian radio and those that do not. The authors find that 3 to 4 percent of those 59See Yanagizawa-Drott (2014). 60See, for example, Enikolopov et al. (2011) and DellaVigna and Kaplan (2007) who find large effects on voting shares. 70", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["street surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Appel and Loyle (2012) analyze the role of Post Conflict Justice (PCJ) institutions in attracting FDI in post-conflict countries. They show that post- conflict states that adopt PCJ are more likely to receive higher levels of FDI compared with post-conflict states that refrain from implementing these institutions. 71", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 7253 This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at jlendorfer @ worldbank. org and jhoogeveen @ worldbank. org. This paper analyzes the impact of the 2012 crisis in Mali on internally displaced people, refugees and returnees. It uses information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones.", "output": {"entities": {"named_data": [], "descriptive_data": ["face-to-face household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 trust in the government and its institutions and perspectives on conflict resolution. By analyzing the impact of the crisis on welfare, the consequences of returning home versus remaining in displacement and by comparing immediate with longer term impacts, this paper contributes to the literature on refugee, IDP and returnee populations. The paper combines data from a face-to-face baseline survey with information collected via mobile phone interviews from respondents identified during the baseline. This innovative approach to data collection makes it possible to collect welfare data with high frequency (monthly) – important in a volatile crisis situation – and allows measuring changes over time. It also permits following displaced and refugee households once they return, even if they return to areas that are inaccessible to enumerators. The remainder of this paper is organized as follows. Section 2 provides a brief overview of the methodology, the sample and sample selection. Section 3 discusses the characteristics of the displaced and returnees, looking specifically at ethnic composition, place of origin, household size, education, asset ownership and employment status. Section 4 considers how the crisis affected food consumption, employment, assets and school attendance. Section 5 is devoted to the specificities of returnees who turn out to be, on aggregate, less affected by the crisis and better off than IDPs or refugees.", "output": {"entities": {"named_data": [], "descriptive_data": ["face-to-face baseline survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Section 6 presents the differing views of IDPs, refugees and returnees on what happened during the crisis and prospects for peace. Section 7 concludes the paper. 2. The Listening to Displaced People Survey The Listening to Displaced People Survey (LDPS) combines a baseline face-to-face survey with mobile phone follow-up interviews. During the baseline survey respondents were identified and information on household and respondent characteristics was collected. Once the baseline interview was completed, respondents were given a mobile phone and started to receive, at monthly intervals, phone interviews from a call center in Bamako. During these phone interviews structured questions were asked about welfare of the household. Phone interviews are standard practice in developed countries and they are increasingly being used in less developed countries, as the coverage of cell phone networks expands. Not only do these kinds of surveys allow for low cost, high frequency representative data collection (Hoogeveen et", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 al. 2014), mobile phone surveys also turn out to be remarkably flexible and adaptive. New questions can be introduced on a needs basis and in-depth qualitative interviews can be carried out at a moment ’ s notice. These qualities make mobile phone surveys well suited for monitoring welfare in volatile environments: they have been used for welfare monitoring during the ebola crisis in Liberia (Himelein 2014) and for welfare monitoring in conflict-affected areas such as South Sudan (Demombynes et al. 2013). Unique about using a mobile phone survey with a displaced, mobile population is that it allows tracking welfare during displacement, and upon return. 8 Three target populations were identified for the purpose of this survey: Internally Displaced Persons (IDPs) living in Bamako, refugees in refugee camps in Mauritania and Niger, and returnees in Gao, Timbuktu and Kidal, the capitals of regions that bear their names. The sample does not include those who were not displaced by the crisis nor those who returned to places other than the three regional capitals in the North. While the sub-sample of IDPs includes exclusively IDPs in Bamako, and the refugee sub-sample only refugees in Niger and Mauritania, the returnee group includes people who were displaced elsewhere (33 %).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 implemented in six areas: Bamako, the regional capitals of Gao, Timbuktu, and Kidal as well as one refugee camp in Mauritania and one in Niger. Bamako was selected because it is home to a large number of IDPs. The refugee camps were selected to obtain a sample of refugees. Returnees were identified in the regional capitals of Timbuktu, Gao and Kidal where the phone network was (still) functional. The approach to selecting respondents differed by location and depended on the availability of pre-existing population information.  Bamako: Listing information of all households with IDPs was obtained from the International Organization for Migration (IOM). Based on this data 10 districts were selected and in each district 10 households were randomly identified.  Gao, Timbuktu and Kidal: No listing data was available and the cities were divided into different sectors. The enumerator was assigned a starting point in a sector, a direction (North, South, East, West) and based on the code of the day 9 the enumerator selected the first household. If the code of the day was 4, the enumerator would choose the 5th house to conduct the first interview. No more than 6 houses were to be interviewed from one starting point.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 All data collection is done by GISSE a research institute in Bamako. The anonymized unit record data of the baseline and the monthly surveys can be downloaded from www. gisse. org. The response rate for the phone interviews has been very high (Table 1): after 6 rounds of monthly interviews the original sample is almost entirely intact. The low level of attrition demonstrates that mobile phone samples can be maintained over prolonged periods without being unduly affected by (non-random) respondent drop-out. 3. Characteristics of the Displaced and Returnee Population According to the 2009 population census, the two most sizeable ethnic groups in northern Mali are the Songhai (45 %) and Kel Tamasheq (32 %)-- see Table 2. The crisis brought about an ethnic divide, which is reflected in the composition of the three sub-samples. The majority of IDPs and returnees are Songhai (75 % and 71 % respectively), while the majority of refugees are Kel Tamasheq. Results suggest that the decision of where to flee was determined by ethnicity: Kel Tamasheq and Arabs left the country; Songhai fled towards Bamako.", "output": {"entities": {"named_data": ["2009 population census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 2: Ethnic composition of IDPs, refugees, returnees in the North Ethnicity IDPs in Bamako (%) Refugees Niger (%) Refugees Mauritania (%) Returnees (%) Total I + R + R (%) Ethnic composition of the North (%) Songhai 75 21- 71 43 45 Kel Tamasheq 12 56 69 12 38 32 Arab 3- 28 4 11 3 Peulh 4 21- 6 4 7 Other 6 11 3 7 4 12 Total (%) 100 100 100 100 100 100 Total (n) 100 81 100 220 501 1, 268, 009 Source: Listening to Displaced People Survey, 2014 and 2009 Population and Housing Census. The ethnic composition of IDPs and returnees is almost identical. This is a reflection of the fact that 94 % of returnees were displaced within Mali. Only 6 % returned from outside the country. The reason why few returned refugees are in the returnee sub-sample is explained by their place of residence prior to the crisis: only 5 % of the refugees in Mauritania and Niger lived in Timbuktu town before their displacement; 2 % lived in Gao town and 1 % in Kidal town. The remaining 92 % lived in 27 different towns and villages in northern Mali, locations not covered by the survey.", "output": {"entities": {"named_data": ["Displaced People Survey", "Housing Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Figure 3: Level of education age 25 +; compared with regional average (%) (*) INSTAT refers to census 2009 Source: Listening to Displaced People Survey, 2014. With regards to ownership of consumer durables, IDPs, refugees and returnees were better endowed than the average citizen of the North (see Figure 4). As was the case for education, they are more comparable to the average citizen in Bamako than to the average citizen in the regions of Gao, Timbuktu and Kidal. Figure 4: Asset ownership compared with regional average Source: Listening to Displaced People Survey, 2014 and EMOP 2011 (INSTAT). The main occupation of IDPs, refugees and returnees before the crisis was commerce (Table 5). This held for over half of the IDPs, 37 % of refugees and 34 % of returnees. 18 % of the refugees 51 85 60 47 85 89 87 15 6 18 29 11 8 8 34 9 22 25 5 3 5 IDPs Refugees Returnees Bamako (INSTAT) Gao (INSTAT) Timbuktu (INSTAT) Kidal (INSTAT) Secondary or Higher Primary None 0 100 200 300 400 500 600 IDPs Refugees Returnees Bamako (Instat) Gao (Instat) Timbuktu (Instat) Kidal (Instat) Percentage Mobile Phone Car / Motorized Vehicle Motorbike / scooter Bicycle Refridgerator TV CD", "output": {"entities": {"named_data": ["Displaced People Survey", "Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 displacement. 55 % of the returnees reported to have been employed before the crisis and 36 % in June 2014. Over time the employment situation among the displaced has improved steadily and by December 2014 more people reported being employed than prior to the crisis. All the returnees were able to regain employment after returning. The employment situation of IDPs, returnees, and refugees in Niger is steadily improving; only for refugees in Mauritania does one notice a steady decrease, with 100 % reporting no employment during January and February. Source: Listening to Displaced People Survey, 2014 and 2015. The ownership of livestock and consumer durables was reduced significantly as a consequence of the crisis. Table 7 demonstrates this by showing the Tropical Livestock Units (TLU) 12 owned prior to the crisis and in June 2014 as well as the percentage of ‘ yes ’ responses on a question whether a given asset was owned by the household. 13 The loss on livestock has been enormous particularly amongst IDPs and refugees who lost respectively more than 90 % and 75 % of their animals. 12 TLU is a common unit to describe livestock numbers of various species as a single figure that expresses the total amount of livestock present – irrespective of the specific composition. 13 This was a ‘ yes / no ’ question meaning that if 56 % of the", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 Figure 10: Children aged 7-12 attending school (%) Source: Listening to Displaced People Survey, 2014 and 2015. 5. Challenges Faced by Returnees The results suggest that returnees were less affected by the crisis than IDPs and refugees. This is reflected in data on asset and livestock ownership, but also in the information pertaining to exposure to violence. Returnees reported fewer victims; fewer returnees reported to have lost income as a consequence of the crisis; more of their children were able to continue schooling; and relative to IDPs and refugees, fewer perceived being poorer in June 2014 than before the crisis. Returnees are also the group that feels most secure, that has high levels of trust in the Malian army and police and that has a positive attitude towards most government policies. 88 88 97 79 76 78 99 88 92 55 74 72 91 98 92 96 100 85 90 91 96 90 86 87 87 95 89 76 75 90 94 92 97 86 73 98 Bamako Gao Timbuktu Kidal Niger Mauritania IDPs Returnees Refugees 14-Aug 14-Oct 14-Nov 14-Dec 15-Jan 15-Feb", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 Figure 11: Timing of Return (%) Source: Listening to Displaced People Survey, 2014. 94 % of the returnees were displaced inside Mali and 6 % outside the country. 67 % went to Bamako, 11 % in Koulikoro, 9 % to Ségou, 5 % to Mopti and 3 % went elsewhere (Kidal, Gao and Sikasso). The majority returned between June and October 2013 a period that followed the signing of a peace deal between the interim government and rebel factions to allow presidential elections to be held in July (first round) and August (second round) 2013. In October security in the North worsened again and ever since the number of people returning has been very limited. The main challenges reported by returnees in June 2014 were (i) poverty and food insecurity; (ii) lack of infrastructure (including lack of safe drinking water) and (iii) unemployment. 11 % of the returnees stated not to be facing any challenges (Figure 12).", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Results suggest that 66 % of the returnees trust the Malian police and army most when it comes to providing security in the North. Almost half believe that the Malian army is brave and well trained. The vast majority of returnees believe that the government ’ s policies regarding reconciliation, security and social cohesion are good or very good. They also support the government ’ s approach towards decentralization and providing infrastructure such as access to potable water and electricity. As the next section will illustrate this differs strongly with the opinions of refugees. 6. Prospects for Peace IDPs, refugees and returnees have comparable opinions with regard to the requirements for peace: (i) addressing the ongoing crisis, (ii) improving security and (iii) reconciliation. Although there is agreement on what needs to be done, there is little consensus on what happened during the crisis, who the culprits are and who the main victims. Figure 15: What is the most important problem the Government needs to resolve today? (%) Source: Listening to Displaced People Survey, 2014.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "23 86 % of the IDPs, 91 % of the refugees and 88 % of the returnees are confident or fully confident that a coalition like this would be capable of providing security. Source: Listening to Displaced People Survey, 2014. In an open-ended question on who can be trusted most when it comes to ensuring security in the North, survey results suggest that the majority of refugees in Mauritania (86 %) trust the armed rebel groups as opposed to the army or police. This does not hold for refugees in Niger of whom 75 % trust the army and police. Similar results hold for IDPs and returnees, who put much more confidence in state authorities when it comes to securing the North: most trust is placed in the army and police (72 % of the IDPs and 66 % of the returnees) while little to no trust is placed in armed rebel groups (3 % of IDPs, 1 % of returnees).", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "25 less cohesion. The increase in solidarity is less apparent among refugees where the majority (53 %) stated that the crisis had no effect on solidarity. Figure 18: Levels of trust, by group (June) (%) Source: Listening to Displaced People Survey, 2014. Perceptions that different groups have of others are important elements of peace. When asking for the degree to which neighbors, other villagers and people from other ethnic groups can be trusted the survey finds positive outcomes. Although all groups trust people from other ethnic groups slightly less, the general level of trust is high and it remains stable over time. Finally, consider how IDPs, refugees and returnees envision the future of Mali. The majority of refugees in Mauritania vie for an independent or autonomous North, while the majority of IDPs, returnees and refugees in Niger wish to see full government control over the North. 20 20This contradicts, in part, findings of an Afrobarometer perception survey on causes and consequences of the conflict in Mali conducted in December 2013.", "output": {"entities": {"named_data": ["Displaced People Survey", "Afrobarometer perception survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "26 Source: Listening to Displaced People Survey, 2014. 95 % of the refugees in Mauritania envision an independent or autonomous North and 26 % of the refugees in Mauritania even state the independence of Azawad (= the north) as a main condition for returning home. Stark differences can also be observed with regard to the discussion around a possible federalist solution for the North that was ongoing when the monthly phone interviews were conducted in October. As illustrated in the Figure 20 below, 80 % of the refugees in Mauritania support a federalist solution, while the majority of IDPs, returnees and refugees in Niger are not in favor. Of those who do not support a federalist solution (96 % of the IDPs, 88 % of the refugees in Niger and 95 % of the returnees), the majority of IDPs (61 %) and returnees (70 %) as well as 38 % of the refugees in Niger suggest decentralization as a possible solution to resolve the conflict. 13 % of the refugees in Niger also mention war and 27 % the integration of the North. Nonetheless, 49 % of IDPs, 86 % of returnees and 89 % of refugees believe a stable and sustainable peace accord can be achieved. by 4 % of the population living Timbuktu, 2 % in Gao and nobody in Kidal.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Afrobarometer finds that the vast majority of Malians want their country to remain a single and unified nation and that the attempt by armed groups to create a breakaway state in Mali ’ s northern territories is decisively rejected. See Afrobarometer Policy Paper 10 (Dec 2013). This difference with the Afrobarometer survey can be explained by the fact that the latter survey only focused on Malians inside the country and did not take the views of refugees into account. 5 93 2 6 86 75 20 2 3 94 Independence of the North Autonomy of the North Establish full government control over the North Figure 19: How do you envision the future of Mali? IDPs Refugees Niger Refguees Mauritania Returnees", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "27 Source: Listening to Displaced People Survey, 2014. 7. Conclusion The 2012 crisis in northern Mali led to widespread displacement. The welfare of the displaced – and those who returned – is monitored by combining a baseline survey with structured monthly follow-up interviews carried out by mobile phone. This innovative approach allows tracking changes in welfare with high frequency – even for those who returned to areas that are insecure and inaccessible to enumerators. After 6 rounds of follow-up interviews attrition rates are very low (more than 99 % response rate), demonstrating that it is possible to collect robust and representative data from hard-to-reach, conflict-affected populations. The results show that those who fled were better educated, better off and less affected by violence than the average population in the North. Those who fled lost significant amounts of durable goods (20-60 %) and livestock (50-90 %); many of their children ended up being taken out of school and their welfare (measured subjectively and by the number of meals consumed) declined considerably. Over time, the impact of the crisis on welfare has lessened and by February 2015 the majority of eligible children of the displaced were going to school and levels of employment and number of meals consumed were at pre-crisis levels.", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 International migration — the movement of people across national borders — has important economic, social, and political implications. Despite the recent emergence of a dynamic literature, empirical analysis of migration flows and their impact lags behind the policy debate and the theoretical literature. The main reason is the absence of comprehensive and reliable data on international migration patterns and migrant characteristics at either the aggregate or the household level. The objective of this article is to use data from more than one thousand national censuses and population registers to estimate a complete global origin – destination migration matrix for each decade over 1960 – 2000. These 226 * 226 matrices, comprising every country, major territory, and dependency around the world, are divided into periods corresponding to the last five completed census rounds. The gender dimension of international migration over this period is also presented. The primary source of the raw data is the United Nations Population Division ‘ s Global Migration Database, created through the collaboration of the United Nations Population Division, the United Nations Statistics Division, the World Bank, and the University of Sussex (United Nations [2008]). This unique data repository comprises 3, 500 individual census and population register records1 for more than 230 destination countries and territories over the last five decades.", "output": {"entities": {"named_data": ["Global Migration Database"], "descriptive_data": [], "vague_data": ["national censuses and population registers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The database provides information on international bilateral migrant stocks (by citizenship2 or place of birth), sex, and age. There is considerable variation, however, in how destination countries collect, record, and disseminate immigration data. Meaningful comparison of destination country records over time is thus often confounded. In constructing global bilateral migration matrices, several challenges arise. First, destination countries typically classify migrants in different ways — by place of birth, citizenship, duration of stay, or type of visa. Using different criteria for a global dataset generates discrepancies in the data. Second, many geopolitical changes occurred between 1960 and 2000, with many international borders redrawn as new countries emerged and others disappeared. In addition to creating millions of migrants overnight — as when the Soviet Union collapsed — these events complicate the tracking of migrants over time. Third, even when national censuses of destination countries include data on international migrant stocks, the data are presented along aggregate geographic categories rather than by country of origin. Data therefore need to be disaggregated to the country level. Finally, the greatest hurdle is dealing with omitted or missing census data. Very few destination countries — especially developing countries — have conducted rigorous censuses or population registers during every census round over the second half of the twentieth century. Wars, civil strife, lack of funding, and political intransigence are but a few reasons why records may be discontinuous. 1 Of the 3, 500 sources detailed in the overarching UN Global Migration Database, 1, 107 were suitable for analysis, once repeated censuses had been removed or combined. Global Migration Database should not be confused with the Trends in International Migrant Stock Database, which lists aggregate migrant stocks for each destination country in the world at five year intervals (United Nations 2006) 2 The article treats the concepts of nationality and citizenship as analogous and uses the terms interchangeably.", "output": {"entities": {"named_data": ["UN Global Migration Database", "Trends in International Migrant Stock Database"], "descriptive_data": [], "vague_data": ["census data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 The main contributions of this article lie in identifying and overcoming these challenges in order to construct a consistent and complete set of origin – destination matrices of international migrant stocks for 1960 – 2000, disaggregated by gender. The starting point is a master set of 226 origin or destination countries and regions. Despite border changes, all migrants are assigned to this master set so that migrations can be meaningfully tracked over time. These assignments, especially in cases where only aggregate data are available, are made using several alternative propensity measures based either on a destination country ‘ s propensity to accept international migrants or on an origin country ‘ s propensity to send migrants abroad. Cases of omitted data occur when destination countries do not collect or publicly disseminate the information on migrants. When data from census rounds are missing altogether, the approach taken depends on the extent of the omission (see appendices 3 and 4). When sufficient data are available for other decades, interpolation is used. When not enough data are available, propensity measures are used to generate bilateral data. When a gender breakdown is missing, gender splits are calculated based on supplementary statistics or other data in the matrices (see appendix 5).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The resulting migration matrices should be viewed as work in progress, but they are an important step in an ongoing global effort to improve migration data. The matrices can be readily updated as additional or superior information surfaces, and they can easily be extended to include future census rounds. Bilateral datasets of international migration are rare. Attempts to create them have focused almost exclusively on industrialized countries as destinations because these countries have more accurate and more frequently produced data. Harrison and others (2003) calculate bilateral remittances for the countries of the Organisation for Economic Co-operation and Development (OECD) together with the 27 largest nonmembers. These estimates are based on international bilateral migrant stock data that the authors also provide, although many of the data are derived from the Trends in International Migration (OECD 2002). This report, published annually since 1973, was arguably the most comprehensive guide to international migration for many years and has been the basis for many studies (see, for example, Mayda 2007). More recently, the OECD has developed a database that provides a comprehensive overview of migration to OECD countries in 2000 (OECD 2008). These data are disaggregated by a number of covariates including age, gender, educational attainment, and place of birth.", "output": {"entities": {"named_data": ["Trends in International Migration"], "descriptive_data": ["international bilateral migrant stock data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Another series of papers, again concentrating on the OECD, examines the brain drain in 1990 and 2000 (see, for example, Docquier and Marfouk 2006); migrants ‘ gender (Docquier, Lowell, and Marfouk 2009); age of entry (Beine, Docquier, and Rapoport 2007); and the medical brain drain (Bhargava and Docquier 2007). Parsons and others (2007) construct a matrix encompassing the entire world for the 2000 census round. Until now, this was the most comprehensive global overview of bilateral migrant movements. Ratha and Shaw (2007) use an earlier version of the dataset in a paper focusing on migration between developing countries (generally referred to as South – South migration in the literature) and bilateral remittance flows. The data in the current article reveal several important patterns. Between 1960 and 2000, the global migrant stock rose from 92 million to 165 million, but fell as a share of world population, from 3. 05 percent to 2. 71 percent. A large share of the stock in 1960", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Database. Where both censuses and population registers are available, censuses receive priority. Censuses, generally conducted decennially, are retrospective tools for surveying an entire population (or in some cases, a representative sample) at a single point in time. In addition to their universal coverage, their greatest strength is the inclusion of questions on place of birth and nationality. Censuses also typically aim to enumerate the resident population, whether documented or undocumented (Bilsborrow and others 1997). So although some migrants have a strong incentive to provide false information to enumerators, many undocumented migrants will be captured in these matrices. 7 The size and scope of the census questionnaires vary enormously, both over time and in different destination countries. And there is potential variation in the quality of censuses both across countries and over time. Richer countries have many resources at their disposal to design questionnaires, train interviewers, employ statisticians, and disseminate results. Researchers have little choice but to accept the data at face value. However, where the underlying census is clearly substandard (when there are errors that are obviously not coding errors or not easily corrected), these data are omitted from the analysis. Popular in many parts of Europe, population registers are continuous reporting systems providing up-to-date demographic and socioeconomic information for everyone surveyed.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["censuses", "population registers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Typically, registers have evolved over time (from parish records, for example). They were never developed specifically to record international migration information, and they vary considerably across countries. For example, the laws under which individuals are classified as migrants and the conditions under which they are inscribed or deregistered differ greatly (Bilsborrow and others 1997). The Raw Data The Global Migration Database is a vast collection of destination country data sources detailing migrant stocks from numerous origin countries and regions (United Nations [2008]). Compiling and maintaining the underlying primary sources require herculean efforts to scour the key census collections of the world and enter the data manually. In total, the database comprises records from some 3, 500 separate censuses from more than 230 migrant destination countries and territories, by sex and age.", "output": {"entities": {"named_data": ["Global Migration Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Last, specific adjustments are made in the case of Germany and the Republic of Korea. For Germany, bilateral data are available only by nationality. However, these data fail to take adequate account of the large number of ethnic Germans who arrived from other countries between 1944 and 1950 (mainly expellees) and those who arrived after1950 (mainly resettlers). Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3). In the case of Korea, data by nationality are readily available for each census round. However, these data fail to account for the large numbers of migrants from the People ‘ s Democratic Republic of Korea living in the Republic of Korea. Since the United Nations Trends in International Migrant Stock details the total migrant stock in the Republic of Korea by the country of birth definition and because citizenship is rarely granted to people from outside, it is simply assumed that the nationality data were comparable to the foreign-born definition. The nationality total was then subtracted from the UN total and the remaining migrants were assigned to the People ‘ s Democratic Republic of Korea.", "output": {"entities": {"named_data": ["United Nations Trends in International Migrant Stock", "German 2005 micro-census"], "descriptive_data": [], "vague_data": ["bilateral data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Recording and Recoding There is little standardization in the recording and dissemination practices for censuses across destination countries. 14 The level of detail with which destination countries record and disseminate migration data depends on the design of the original questionnaire. Some census questionnaires ask for a specific country of birth and others simply ask for a general geographic region, such as Africa. Even if the original questionnaire asked detailed questions, some countries disseminate data only on how many residents were born abroad or have foreign citizenship. In general, three types of migrant origin are observed in the disseminated census data:  Specific geographic regions: Some of these correspond to exactly one of the 226 countries and territories in the master list. Others pertain to localities that tend to be obscure territories, islands, or regions, such as the Isle of Man or Ceuta.  Aggregate geographic regions: These correspond to two or more countries or territories in the master list. They can be continents (such as Africa), parts of continents (such as South Asia), political alliances (European Union), or other classifications (such as Other Ex-French Africa; Algeria, Tunisia, and Morocco; and Melanesia). The data for these aggregate regions need to be allocated to the 226 countries in the master list. The details of the procedures are discussed below.  Miscellaneous categories: These include refugees, stateless, and born at sea. There are generally no geographic correspondences for these. Thousands of geographic regions and categories emerged from the more than one thousand individual destination country sources chosen for the analysis. The vast majority of these are repetitions that refer to identical geographic locations using different 14 The United Nations (1998) has developed recommendations aimed at promoting standardized recording practices across countries. Until such practices are followed uniformly, harmonization will remain a key issue in understanding and comparing migration statistics.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "An alternative version of the database that has been mapped to the United Nations (2006, 2009) Trends in International Migrant Stock database is available from the authors. These data are standardized over time in terms of the years to which they refer. { Table 3 here} Calculating Missing Gender Splits Although common in the underlying data, bilateral migration data disaggregated by gender are sparser than aggregate migrant totals (see table 1). An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices. Similar to the allocation from aggregated categories in the Global Migration Database to specific origins in the master list, two measures are used for calculating gender splits; they are described in appendix 5. Combining Migrant Definitions Only a single definition of a migrant (foreign born or foreign citizen) can be applied to each destination country in the final matrices. Switching definitions over time 17 The subregions used for the disaggregations are the 21 UN regions (see http: / / unstats. un. org / unsd / methods / m49 / m49regin. htm, with the countries of Oceania aggregated into a single subregion. They do not match the large World Bank regions used in the analysis in section IV. 18 While this propensity measure is clearly inappropriate, less than 1 percent of all migrants and observations are assigned on this basis. This method is included so that every migrant in the underlying data is accounted for.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock", "Global Migration Database"], "descriptive_data": [], "vague_data": ["bilateral migration data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 { Tables 4 and 5 here} Among the first six categories that are based on raw census data, three categories (raw scaled, R & R not scaled, and R & R scaled) are constructed through the summation of bilateral raw numbers and disaggregations of some aggregate categories in the original censuses. Since these categories together constitute around 45 percent of migrants in each census round, the original bilateral portion of each cell was compared with the final number assigned to them after the various calculations as a check on accuracy. For each decade, therefore, the overall percentage contribution of the raw bilateral data to the total is calculated (table 6). 23 In each census round, at least 92 percent of all those categories are derived from the raw data. { Table 6 here} Simulating Missing Data Finally, to examine the reliability of the estimated missing census data and test the methodologies, several scenarios are assumed. All bilateral observations for a single year for four countries (Australia, United States, Switzerland, and Chile) in different parts of the world are deleted and the missing cells are filled using one of five methods. 24 The first simulation assumes that all bilateral data for 2000 are missing but that the total number of migrants is available.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["raw census data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 corridor, so only aggregate numbers can be compared. For this comparison, mid-year estimates of the world migrant stock for 1990 – 2000 are taken from the 2008 edition and estimates for the earlier censuses, 1960 – 1980, are taken from the 2005 edition (table 8). The analysis subtracts the estimated number of refugees from the total mid-year estimates of the world migrant stock from the Trends in International Migrant Stock database to yield the net number of migrants in each decade. These numbers are then compared with the decadal estimates generated through this project, both the total and the net, after subtracting estimates of migrants within the Soviet Union for 1960 – 1980 (data for 1990 and 2000 should be directly comparable) and the number of ethnic German migrants added to the German censuses. { Table 8 here} The aggregate estimates are remarkably close (the two net totals), differing at most by around 1 million migrants, except in 1990. There are several possible explanations for these differences. First, the census totals from the current work may not match because censuses do not always make allowances for temporary workers. For example, Singapore ‘ s official 2000 census records 563, 430 foreign-born migrants. The United Nations, however, reports 1, 351, 806 foreign-born migrants for 2000.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock database"], "descriptive_data": [], "vague_data": ["decadal estimates"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Second, there are cases where the current study reports data by nationality, but the corresponding figure in the Trends in International Migrant Stock refers to the foreign born. This situation generally arises when a census does not report the number of foreign-born migrants on a bilateral basis. Examples include Austria and Côte d ‘ Ivoire. Third, differences in the years to which the data refer can generate large disparities. For example, this study uses the 1966 data for Australia, whereas Trends in International Migrant Stock reports data for 1970. Overall, however, the fact that the totals are remarkably close in every decade adds credence to the estimates here. IV. THE EVOLUTION OF GLOBAL BILATERAL MIGRATION The greatest strengths of the global migration matrices are their bilateral coverage, the number of decades covered, and the disaggregation by gender. These data are too rich for a full analysis of all movements between all pairs of countries. Instead, this section summarizes the major trends in the evolution of bilateral migrant stocks, based primarily on World Bank regions. 25 Global Trends The migration matrix for the 1960 census round reflects a realigning world in the postcolonial era. Over the 1960-2000 period, the composition of world migration 25 Appendix 1 details the World Bank regions: South Asia, East Asia and Pacific, Sub-Saharan Africa, Latin America and the Caribbean, Europe and Central Asia, and Middle East and North Africa. High-income Middle East and North Africa refers to the predominantly oil producing countries in the Persian Gulf (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) and to Israel.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "33 Ratha, D., and W. Shaw. 2007. ― South-South Migration and Remittances. ‖ World Bank Working Paper 102, World Bank, Washington, DC. United Nations Statistics Division. 1998. Recommendations on Statistics of International Migration Revision 1. New York: United Nations. United Nations, Department of Economic and Social Affairs, Population Division. [2008]. United Nations Global Migration Database. New York: United Nations. http: / / esa. un. org / unmigration — — —. 2006. Trends in Total Migrant Stock 1960 – 2000, 2005 Revision. Database. POP / DB / MIG / Rev. 2005 / Doc. New York: United Nations. — — —. 2009. Trends in International Migrant Stock: The 2008 Revision. Database. POP / DB / MIG / Stock / Rev. 2008. New York: United Nations. http: / / www. un. org / esa / population /. — — —. 2010. ― World Population Prospects: The 2009 Revision, Highlights ‖, Working Paper No.", "output": {"entities": {"named_data": ["United Nations Global Migration Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This study aims to assess the consequences of forced migration on ethnic diversity and conflict in Sub-Saharan Africa. We combine a unique dataset on refugee camps with individual data from the Afrobarometer Surveys across 23 African countries for the 2005 — 2016 period. We construct two standard measures of ethnic diversity: indices of ethnic fractionalization (EF) and ethnic polarization (EP). Ethnic fractionalization measures the probability that two individuals drawn from the society at random will belong to two different ethnic groups and thus increases with the number of ethnic groups present. Ethnic polarization captures antagonism between individuals and is maximized when the society is divided into two equally sized and distant ethnic groups. Although these indices have been widely used, little variation over time has been found, making causal inference difficult. The innovative aspect of our analysis is that we use data on the precise locations of refugee camps, their yearly size, and — most importantly — their annual composition in terms of countries of origin. Combined with the Ethnic Power Relations- Ethnicity of Refugees 2019 dataset, we are able to predict changes in ethnic diversity induced by refugee inflows. We then assess the relationship between refugee diversity and the likelihood of conflict. In an additional analysis, we also assess how refugee-induced changes in diversity affect the incidence of theft and violence, participation in protests, and perceptions of ethnic attachment, inter-personal trust, and institutional trust. Other studies have investigated the links between displacement and social conflict or social co- 3", "output": {"entities": {"named_data": ["Afrobarometer Surveys", "Ethnic Power Relations- Ethnicity of Refugees 2019"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the standard ethnic diversity indices to include the annual variation in refugee ethnicities. 8 We then construct a measure of proximity between the clusters in the host country and refugees in surrounding camps by defining an 80-km buffer around each cluster. 9 To control for unobserved heterogeneity and changes within a given cluster, we introduce cluster and year fixed effects, αj and δt. To minimize the risk of confounding the refugee-induced changes in diversity with the annual changes in refugee numbers, we also control for the presence of refugees based on the same buffer as the one used to construct the refugee-induced change in diversity. More specifically, the variable Refugeesjt − 1 counts the number of refugees present in cluster j at year t − 1 within the predefined buffer. The variable is also transformed into an inverse hyperbolic sine to ease interpretation. Finally, Qjt controls for yearly shocks at the cluster level, such as weather shocks. In particular, we control for rain and temperature anomalies. Standard errors are clustered at the Afrobarometer cluster level. 4. 2 Data and descriptive statistics Our analysis combines various sources of data: Afrobarometer, UNHCR refugee camp data, Armed Conflict Location and Event Data (ACLED), Uppsala Conflict Data (UCDP), and the Ethnic Power Relations- Ethnicity of Refugees (EPR-ER) 2019 dataset.", "output": {"entities": {"named_data": ["Armed Conflict Location and Event Data", "Uppsala Conflict Data"], "descriptive_data": ["UNHCR refugee camp data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using Afrobarometer ’ s geocoded surveys, we focus on clusters as our unit of observation. 10 Our sample consists of 7, 547 such locations and 76, 518 individuals in 23 countries in Sub-Saharan Africa. “ The sampling universe normally includes all citizens age 18 and older. As a standard practice, they [we] exclude people living in institutionalized settings, such as students in dormitories, patients in hos- pitals, and persons in prisons or nursing homes. ” (Afrobarometer, https: / / afrobarometer. org / surveys − and − methods / sampling − principles) Since the sampling frame is based on recent censuses, with the aim of representing all citizens of voting age in a given country, the Afrobarometer samples are unlikely to include refugees. Note also that “ the sample design is a clustered, stratified, multi-stage, 8We explain the construction of theses indices in Section 4. 2. 9We test the robustness of our results with a smaller (40 km) and a larger (120 km) radius in Section 5. 3. This choice of buffer size assures us that between 75 percent and virtually all refugee camps fall within a cluster buffer. Other studies relying on Afrobarometer data construct buffers ranging from 25 km (e. g., Michaelopoulos and Papaioannou (2011), investigating ethnic-specific pre-colonial institutional structures) to 100 km (e. g., McGuirk and Burke (2020a), analyzing the impact of food-price shocks on conflict). 10Afrobarometer is a pan-African research network conducting public attitude surveys on democracy, governance, the economy, and society in African countries that are repeated on a regular basis (Afrobarometer, 2020). 10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location. Area stratifi- cation reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analyzed. ” Afrobarometer provides geocoded data for 6 rounds, which correspond to the 1991 – 2016 period, with the information on an individual ’ s ethnicity available from round 3 (corresponding to 2005 – 2006). We therefore restrict our analysis to the 2005 – 2016 period. The selection of countries is driven by data availability. Among the 33 countries with available Afrobarometer data, we exclude Botswana, Cape Verde, Lesotho, Madagascar, Mauritius, Sao Tome and Principe, South Africa, and Swaziland, for which no data is available on refugee camps or from the EPR-ER. We also exclude Sudan since the question on individual ethnicity is not asked in this country ’ s survey.", "output": {"entities": {"named_data": ["EPR-ER"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The countries in our sample are Benin, Burkina Faso, Burundi, Cameroon, Gabon, Ghana, Guinea, Ivory Coast, Kenya, Liberia, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, Tanzania, Togo, Uganda, Zambia, and Zimbabwe. As described in Table B. 1, we also incorporate information on the quality of our refugee data, which is determined by comparison with official UNHCR bilateral data. Below we describe how these data have been used to define our main variables of interest and present some descriptive statistics in Table B. 2. 11 Conflict. In Equation 1, we first relate variation in ethnic diversity with data on conflict from ACLED (Linke et al., 2010). Two main definitions are used: the incidence of conflict and the intensity of conflict. Incidence is captured by an indicator equal to one if conflict occurred in a particular year within a pre-defined buffer around cluster j. Intensity is measured by summing the number of conflict events occurring in a particular year within the same buffer area. A conflict event is defined as a single altercation wherein force is used by one or more groups for a political end (Linke et al., 2010). We further describe events (non-exclusively) as violent events, non-violent events, violence against civilians, and riots. In our main analysis, we focus on violent conflicts (Section 5. 1) and report results for other outcomes as robustness tests (Section 5. 3). In doing so, we follow a recent and large literature that has combined the ACLED dataset with geographically disaggregated data in Africa (Besley and Reynal-Querol, 2014; Berman and Couttenier, 2015; Michaelopoulos and 11Panel A of Table B. 2 shows descriptive statistics for the data from refugee-hosting areas specifically, whereas panel B of Table B. 2 shows descriptive statistics for our data in all covered areas. 11", "output": {"entities": {"named_data": [], "descriptive_data": ["data on conflict from ACLED"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Papaioannou, 2016; Berman et al., 2017; Harari and Ferrara, 2018; Eberle et al., 2020; McGuirk and Burke, 2020b). As a further robustness check, we also use data on conflict incidence and intensity from the UCDP, which uses a more conservative definition of conflict. The UCDP dataset is manually curated and compiled with automated computer assistance (Sundberg and Melander, 2013). The UCDP defines an armed conflict event as “ an incident where armed force was used by an organized actor against another organized actor, or against civilians, resulting in at least one direct death at a specific location and a specific date ” (Pettersson et al., 2020). We extract daily event observations from the UCDP dataset if the location of the actual event is exactly known, the event location is within a radius of less than 25 km around a known point, or at least the administrative district where the event happened is known. As pointed out by Eberle et al. (2020), the UCDP events are more likely to capture violence between large-scale and more structured groups. Table B. 2 shows that on average, conflict events seem to occur more in refugee-hosting areas. This is of course not a causal interpretation but a simple correlation.", "output": {"entities": {"named_data": ["UCDP dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As can be seen from both panel A and panel B, non-violent conflicts seem to occur slightly more than violent conflicts. On average, the likelihood of violent conflict stands at about 48 %, while this figure increases to 52 % in refugee-hosting areas. Conflicts among more structured and large groups, as captured by the UCDP data, appear to be less frequent. UNHCR refugee data. To exploit the variation in ethnic diversity induced by the annual variation in refugees (and also to control for the direct effect of refugees on our outcomes), we use data on refugee camps provided by the UNHCR. The dataset contains detailed time-series information on the locations and sizes of 1, 453 refugee camps across the world and 821 refugee camps in Sub-Saharan Africa over the 2000 – 2016 period. To the best of our knowledge, the UNHCR currently provides the most comprehensive information available on refugees at the subnational level, allowing us to assess the ethnic composition of camps, which is key to our research question. First, we use the country of origin of refugees recorded for each year at the camp level to approximate the ethnic composition each camp. Second, we restrict the data on refugees to those aged 18 and above in order to make it comparable to the Afrobarometer- based individual data. Third, we only use data on refugees hosted within the boundaries of the host country. Merging data on refugee camps with the Afrobarometer, we end up with information on 172 camps 12", "output": {"entities": {"named_data": ["UCDP data"], "descriptive_data": ["data on refugee camps"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "that are at a maximum distance of 80 km from the 7, 547 clusters. 12 Figure 1 shows the locations of these refugee camps and clusters. Clusters are represented in green, while clusters in the vicinity of a refugee camp are represented in red. Refugee camps are designated with a red + sign. There are some important limitations associated with this data. First, the data only provides information on refugees residing in camps monitored by the UNHCR. In Figure B. 7, we combine the UNHCR refugee camp data on the annual number of refugees and the UNHCR official statistics on refugees (which includes people in refugee-like situations) at the country level. 13 Although the overall trends match, our constructed dataset clearly underestimates the true refugee population in Africa, which is not surprising since our camp-specific data does not contain dispersed refugees or refugees living outside of camps. While our data seem to represent quite fairly the number of refugees in camps, there is significant heterogeneity across countries. Based on the visual inspection of Figure B. 8, the quality of the refugee data appears to be less reliable for the following countries in our sample: Gabon, Mali, Senegal, and Togo.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR official statistics on refugees"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 1: Data and Descriptive Statistics: Clusters, Refugee Camps, and Conflicts Revised refugee diversity indices. We first use Afrobarometer data to construct standard indices of diversity, namely the EF and the EP indices (Bazzi et al., 2019; Esteban and Ray, 1994). The EF index describes the probability that two randomly selected individuals from a given location belong to two different ethnic groups (Alesina et al., 2003, 2016; Gomes, 2020b). The EF index can be defined as EFjt = Njt X e = 1 get (1 − get), (2) where Nj is the number of ethnic groups in cluster j at time t and get is the population share of 14", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ethnic group e at time t. It can also be expressed as one minus the Herfindahl index (Alesina et al., 2016). The EP index gives more weight to intergroup differences at the expense of within group homo- geneity. It can be defined as (Esteban and Ray, 1994, 1999; Montalvo and Reynal-Querol, 2005) 14 EPjt = Nrt X e = 1 (g2 et) (1 − get). (3) We compute this index for each cluster at the time of each Afrobarometer survey to assess how refugee-induced changes in diversity differ from standard indices of diversity. In order to construct the revised refugee diversity indices according to ethnicity e, we first combine information about the country of origin of refugees hosted in refugee camps c in year t with the data from the EPR-ER 2019 dataset. The EPR-ER records the ethnic composition of refugee stocks originating from neighboring countries and countries in proximity to each other (maximal distance between country borders ≤ 950 km) with at least 2, 000 refugees and provides the ethnic composition of refugees (Vogt and Girardin, 2015). More specifically, the EPR-ER dataset gives us the share of refugees from ethnic group e moving from country o to country d at year t. The EPR-ER data gives us the three main ethnic groups.", "output": {"entities": {"named_data": ["EPR-ER 2019 dataset", "Afrobarometer survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There is one limit to our approximation in Equation 4. The ethnic composition of refugees in each year t for a given origin – destination pair of countries obtained from the EPR-ER database is assumed to be homogeneous across camps of the same origin – destination pair of countries for the refugees at year t. This may seem to be a strong assumption; however, the risk of misallocating refugees is reduced as the annual variation in the EPR-ER is generated by just a few dominant groups for a given origin – destination pair and the geographical distribution of refugees by country of origin is highly influenced by the proximity to their countries of origin. 16 As can be seen from panel A of Table B. 2, in refugee-hosting areas, on average, both EF and the EP seem to increase quite significantly when they are revised by incorporating the number of refugees in an 80-km buffer: the mean value of the standard EF index is 25. 58 %, while the mean value of the revised refugee EF index is 37. 90 %. The mean value of the standard EP index is 10. 11 %, while the mean value of the revised refugee EP index is 14. 07 %.", "output": {"entities": {"named_data": ["EPR-ER database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 3 also shows that there is considerable variation in both indexes within our sample when averaging these indexes at the regional level over the period of investigation. 16It is possible that our approximation is noisy and could potentially induce non-random measurement errors. In Section 4. 3, we propose an instrumental variable approach and estimate Shareodet from the EPR-ER data using a gravity model. Our findings concerning the number of ethnic groups across time for a given origin – destination pair are in line with the EPR-ER data. It seems that refugees of a given origin – destination pair mainly belong to two major ethnic groups. This also means that the variation in diversity in refugee-hosting areas is coming from the refugee composition at the camp level. Figure B. 5 shows the movements of refugees from origin to destination countries under scrutiny. Somalia, the Democratic Republic of Congo, Liberia, South Sudan, and Sudan are major source countries for refugees, while Kenya, Tanzania, Uganda, Zambia, and Ghana appear to be countries hosting most refugees. Representing refugees in camps per ethnic group for the top 5 asylum countries over the sample period, Figure B. 9 shows that there is considerable variation in ethnic composition across camps. 17", "output": {"entities": {"named_data": ["EPR-ER data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer.", "output": {"entities": {"named_data": ["Linking Ethnic Data from Africa", "Afrobarometer", "EPR-ER dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "at the same level with a similar categorization process; instead, the information can relate to an individual ’ s linguistic ethnicity, dialect, or an ethnic group encompassing several languages. In our main analysis, we use LEDA ’ s binary linking at the “ dialect ” level, based on the minimum linguistic distance to link these ethnic groups. 17 This involves computing a value corresponding to the shortest path (see Equation A. 1) between ethnic groups using a language tree. In our case, “ dialect ” is the level defined to match the two groups (see Figure A. 1 from M ¨ uller-Crepon et al. (2020) for a Ghanaian case). 18 We further describe the use of the LEDA software package in Section Appendix A. 1. 4. 3 An instrumental variable approach In Section 4. 1, we acknowledged that non-random measurement errors might be a concern. Another major identification challenge is the risk that our revised measures of diversity are biased due to the selection of hosting areas by refugees. We should first acknowledge that the ability of refugees to select their places of residence is much more limited than economic migrants.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, we can- not exclude the possibility that refugees would sort non-randomly into areas with particular ethnic characteristics. 19 In order to address this potential endogeneity, we implement an instrumental variable (IV) ap- proach. We are particularly concerned about certain ethnic groups from certain countries of origin moving to destination countries with similar ethnic characteristics. Such endogenous selection would be reflected in the EPR-ER data. To deal with the plausibly endogenous nature of the resulting refugee EF and EP indices, we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data. The predicted (and plausibly exogenous) number of refugees by ethnic group e is then used to create other (plausibly exogenous) diversity indices to be used as instrumental variables. More specifically, we estimate the following gravity model: 17We also use this method to link data from EPR-ER on the ethnicities of refugees with data from the Murdock Atlas on their historical homeland (Section 4. 3). 18As a robustness check (Section 5. 3), we use an alternative linkage based on the relations between sets of language nodes associated with two groups. 19Another source of selection may come from the fact ethnic groups are more likely to be displaced when they share territory with regime supporters in their countries of origin (Lacina et al., 2017). Since similar ethnic groups are likely to share common borders (Michaelopoulos and Papaioannou, 2016), it is not impossible to think conflict might spill over through this channel. 19", "output": {"entities": {"named_data": ["EPR-ER data", "Murdock Atlas", "EPR-ER"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "REFodet = αod + γe + τt + β1Conflictot − 1 + β2Conflictet − 1 + β3Distanceed + ϵodet, (5) where REFodet is the stock of refugees of ethnic group e from country o in country d at year t. As we have data on yearly refugee stocks and would like to estimate the changes in these stocks over time using a gravity model, we include origin – destination fixed effects αod so that identification is based only on changes in stock over time (Zylkin, 2019). 20 We also include time τt and ethnic group fixed effects γe. Here we obtain data on the ethnicity of refugees from Murdock ’ s Atlas, which provides a map of ethnographic regions for Africa and the historical homelands of refugees (Murdock, 1967). To match ethnic groups across datasets, we again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity from the EPR-ER dataset and, later, with data from Afrobarometer.", "output": {"entities": {"named_data": ["Afrobarometer", "LEDA21", "EPR-ER dataset", "Murdock ’ s Atlas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We use the sum of conflict events occurring in the historic homeland of ethnic group e in the previous year t − 1, denoted as Conflictet − 1, and we use the mean distance between the historic homeland of ethnic group e and the border of country d to predict the number of refugees of a certain ethnic group e moving from country o to d at time t. 22 In order to be consistent with EPR-ER data construction, we restrict our analysis to all origin – destination country pairs that are at a maximum distance ≤ 950 km from each other. Predicted numbers of refugees are then transformed into predicted shares for the three largest groups to follow the logic used by the EPR-ER dataset. We then plug in these predicted shares in the following way: X \\PredictedRefcet = Refocdt ∗ \\Shareodet. (6) The predicted shares of refugees per camp c are then used to compute (as documented above) refugee diversity indices to be used as instrumental variables. The first-stage equations corresponding to the 2SLS-equivalent of Equation 1 can be expressed as 20We conduct a robustness check on Equation 5, replacing the dyadic origin – destination fixed effects with separate origin and destination fixed effects (Section 5. 4). 21More information on LEDA can be found in Appendix A. 1. 22The construction of the IV follows a long tradition in using the gravity model to predict bilateral migration flows (Ravenstein, 1985, 1989; Crozet, 2004; Mayda, 2010; Garcia et al., 2015; Beine et al., 2016). In our analysis, a major difference is that we have an additional dimension: the ethnic group e. 20", "output": {"entities": {"named_data": ["EPR-ER data", "EPR-ER dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Indeed, despite the recent literature rejecting the conflictive impact of refugees in hosting areas (Zhou and Shaver, 2021), the magnitude of our coefficients might be explained by the confounding presence of refugees. Columns (5) and (6) further introduce climatic controls. Column (6) corresponds to Equation 1 and refers to our benchmark specification. Columns (1) and (2) show that without incorporating the changes in ethnic diversity induced by refugees we would not be able to identify a relationship between diversity and violent conflicts. In column (3), the revised refugee fractionalization index has a negative and significant coefficient, while the revised refugee polarization index has a positive and significant effect on the incidence of violent conflicts. In columns (2), (4), and (6), our coefficients of interest are of the same order of magnitude when the number of refugees is controlled for. Our results are not altered by incorporating rainfall and temperature anomalies (columns (5) and (6)), but the estimates become slightly more precise. 21", "output": {"entities": {"named_data": ["refugee fractionalization index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5. 5 Ethnic diversity at different levels Despite the use of sampling weights in the construction of the diversity indices, we have no guarantee that our diversity indices are representative at the local level. Although similar ethnic diversity indices have been used at the local level (Nunn and Wantchekon, 2011; Rohner et al., 2013; Robinson, 2017; Desmet et al., 2020; Gomes, 2020b, a; Hodler et al., 2020), we cannot exclude the possibility that a lack of representativeness at the local level introduces some noise into our estimates. Ideally, we would have liked to construct our local diversity indices based on census data. However, such data are not available on an annual basis and only a minority of African countries include ethnicity questions on their censuses (Robinson, 2017). Robinson (2017) highlights other benefits but also warns against the risk of using non-random samples or of the size of samples introducing significant errors.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["census data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As summarized by Robinson (2017), “ fortunately, Afrobarometer respondents comprise stratified random samples at all levels, making population estimates based on them unbiased: thus, the major concern with using Afrobarometer sample data to construct demographic measures is unbiased measurement error. ” Based on a comparison of census-based and survey-based diversity indexes across five African countries, Robinson (2017, 224) found that a “ sample-based measure tends to underestimate the overall degree of diversity compared to census data ”. In theory, this should make it more difficult to observe the true relationship between ethnic diversity and some outcomes at the local level. Diversity indices are more likely to be measured with noise in highly diverse communities at the local level. We nonetheless argue that such a concern should not be overestimated, for three reasons. First, such noise cannot easily explain the contrast between the coefficients corresponding to the pre-revised and revised indices and the opposite results found for the revised refugee fractionalization and the revised polarization. This set of results can be explained by the fact that our identification comes from the annual changes in refugees flows. Second, the IV approach is likely to deal with the measurement errors if they are correlated with our main variables of interest. Our IV estimates therefore capture a local average treatment effect coming from the plausibly exogenous increase in annual refugee flows of particular ethnic groups. The similarity of the IV results to the OLS results supports this interpretation. Third, at the cost of introducing attenuation bias30, we also aggregate the number of conflict events at the regional level. Lines B and C of Table 7 confirm the negative and positive effects found for the revised fractionalization and polarization indexes, respectively, whether or not 30Another risk highlighted by Robinson (2017) is the fact that ethnic diversity may also capture different theoretical mechanisms at aggregated levels. 34", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer sample data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of the groups and the distances between them. For instance, (Bazzi et al., 2019) shows that polariza- tion increases ethnic attachment. Others have highlighted the reduction in trust, either interpersonal trust or institutional trust Alesina and Ferrara (2002); Beugelsdijk and Klasing (2016). To assess the importance of alternative explanations, we first replicate our analysis using individual data on violence. In addition to participation in protests, we follow McGuirk and Burke (2020b) in using the Afrobarometer survey data on interpersonal crime and physical assault. We then assess the relationship between the revised refugee diversity indices and alternative individual outcomes such as ethnic vs. national identity, generalized trust, trust in neighbors, and institutional trust (trust in government). The questions from the Afrobarometer mentioned below are used as a proxy for these outcomes: 32 1 Attack: Over the past year, how often (if ever) have you or anyone in your family: Been physically attacked? 2 Crime: Over the past year, how often (if ever) have you or anyone in your family: Feared crime in your own home? 3 National identity: Let us suppose that you had to choose between being a [Ghanaian / Kenyan / etc.] and being a [respondent ’ s identity group].", "output": {"entities": {"named_data": ["Afrobarometer survey data"], "descriptive_data": [], "vague_data": ["individual data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research. 1989-2019. 50 (4). Taylor, J., M. Filipski, and M. Alloush (2016). Economic impact of refugees. Proceedings of the National Academy of Sciences 113 (27), 7449 – 53. United Nations High Commissioner for Refugees (2020). Global Trends: Forced Displacement in 2019. Geneva. Verme, P. and K. Schuettler (2021). The impact of forced displacement on host communities a review of the empirical literature in economics. Journal of Development Economics 102606. Verwimp, P. and J. Maystadt (2015, December). Forced Displacement and Refugees in Sub-Saharan Africa. An Economic Inquiry. World Bank Policy Research Working paper 7517. Vogt, Manuel, N.- C. B. S. R. L.- E. C. P. H. and L. Girardin (2015). Integrating Data on Ethnicity, Geography, and Conflict: The Ethnic Power Relations Data Set Family. 51", "output": {"entities": {"named_data": ["Ethnic Power Relations Data Set Family", "UCDP Georeferenced Event Dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Appendix A Data Appendix A. 1 Linking Ethnic Data from Africa (LEDA) LEDA offers an interface — a language tree — to flexibly link ethnic groups from different databases to each other and calculate the linguistic distances between them. LEDA is currently structured around lists of ethnic groups from 12 original datasets, which are the following: ˆ Afrobarometer Surveys ˆ All Minorities at Risk (AMAR) ˆ Census data from IPUMS ˆ Ethnic Power Relations (EPR) dataset ˆ Ethnologue languages ˆ Political Relevant Ethnic Groups from Posner (2004) ˆ Ethnic groups in Francois, Trebbi & Rainer (2015) ˆ Ethnic groups from Fearon (2003) ˆ GREG Data (based on the Russian Atlas Miradova) ˆ Demographic and Health Surveys ˆ Murdock Atlas ˆ Spatially Interpolated Data on Ethnicity (SIDE) These lists are structured in LEDA ’ s interface by data source, country, year, or in the case of survey data, survey rounds. In our analysis, we use Afrobarometer, EPR, and Murdock Atlas data; therefore, we can use LEDA functions to link the different ethnic groups to each other. LEDA consists of three main linkage types: binary linking based on the relations of sets of language nodes associated with two groups; binary linking based on linguistic distances; and a full computation of dyadic linguistic distances.", "output": {"entities": {"named_data": ["Demographic and Health Surveys", "Ethnic Power Relations", "GREG Data", "Linking Ethnic Data from Africa", "Afrobarometer Surveys", "Afrobarometer", "Murdock Atlas", "Spatially Interpolated Data on Ethnicity"], "descriptive_data": [], "vague_data": ["Census data", "survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "LEDA, which computes the minimum linguistic distance between two ethnic groups and, therefore, provides the closest linguistic neighbor for each given ethnic group (see Figure A. 1). This function computes a variable called distance, which measures the linguistic distance between two ethnic groups. Mathematically, these distances are calculated as DL1L2 = 1 − \u0012 2d (ω (L1,..., O) ∩ ω (L2,..., O)) d (ω (L1,..., O)) + d (ω (L2,..., O)) \u0013 δ, (A. 1) where d (ω (L1,..., O) is the length of the path from the first language to the tree ’ s origin and d (ω (L1,..., O) ∩ ω (L2,..., O) is the length of the intersection of the paths from the first and second language to the origin. δ is an exponent to discount distances further away from the root of the tree; it is typically set to 0. 5. Figure A. 1: Linking Ethnic Data from Africa Source: M ¨ uller-Crepon et al., 2020.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As a robustness check, we also use the first type of linkage: binary linking based on the relations between sets of language nodes associated with two groups. This is done using the “ setlink ” function of LEDA. With this function, the two groups are linked to each other as soon as they share any 2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure A. 2: Injective relations We also isolate many-to-one (bijective) relations. In this case, we have to aggregate the Afro- barometer ethnicities with their unique and more aggregated correspondence in the UNHCR refugee camps data (See Figure A. 3). Figure A. 3: Bijective relations The remaining correspondences are either (i) one-to-many (bijective) but opposite to Figure A. 3 (i. e., many ethnicities from the UNHCR refugee camps data correspond to one ethnicity from the Afrobarometer) or (ii) many-to-many relations. For both cases, we apply a more pragmatic approach: a. In both cases, we disregard ethnicities that do not appear either in the Afrobarometer or in the UNHCR refugee camps data. This means that for the remaining ethnicity that has no counterpart in either the Afrobarometer or the UNHCR refugee camps data, we simply keep the name of the ethnicity as such, i. e., this information is not dropped. b. Then, after ignoring ethnicities that have no occurrence in our datasets, we check whether the one-to-many or the many-to-many relation has not boiled down to a one-to-one resp. many-to- one relation again. If so, we can treat them as above. c.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": ["UNHCR refugee camps data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For the remaining one-to-many relations, we keep these ethnicities in the Afrobarometer as such and consider them as a single ethnic group. Some manual treatment can even further improve 4", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 INTRODUCTION In 2015, an estimated 2. 2 million Syrians Under Temporary Protection (SUTPs) were residing in Turkey, the majority arriving in the country over the last 4 years. 2 Turkey ’ s national population is roughly 75 million; recent refugees account for approximately 3 percent of the population. For a country that has never experienced such a large-scale, sudden inflow of foreigners, demographic changes in the composition of the population and labor force will yield unprecedented implications. This paper examines, as data allows, the relationship between the size of the foreign-born population and host community poverty rates in Turkey. First, this paper finds the poverty rates of ‘ recent migrants ’ near the Syrian border (NSB) significantly increased from 2009 to 2013. Second, the number of foreign-born households being captured by the Labor Force Survey (LFS) is expanding, which suggests a growing number of foreign households that are likely to be Syrians. Third, with respect to poverty, the results show no negative impacts on the host community as a result of the increasing size of the foreign-born population. The impact of SUTPs has been both positive and negative. Overall, a significant negative impact on host communities ’ welfare is not observed in the data.", "output": {"entities": {"named_data": ["Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This paper ’ s scope of analysis includes the country as a whole using a nationally representative survey. While regional case studies may reveal salient stresses on public services and job displacement, nationally, there is no significant impact. Over the period of 2009 to 2013, the poverty rates of host community households have stayed relatively stable near the Syrian border; despite the high poverty rates experienced among the recent migrants. By country of origin, the displacement of Syrians is one of the largest in recent history. As a result of the civil war that began in 2011, Syrians started to leave their homes and look for safety in neighboring countries across the region. By November 2015, about 4. 3 million Syrians were seeking refuge in primarily Turkey, Lebanon, Jordan, Iraq, and the Arab Republic of Egypt. 3 The only other time in the last half century that the world experienced a larger group of refugees from a single country is the case of Afghan refugees during the 1980s to 1990s. Refugee displacements of this size are rare. Consequently, they are not well studied and their impacts are not well understood. Moreover, the case of Afghan refugees in Pakistan is different, since they were stigmatized to a larger extent, which limited their movement in Pakistan.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["nationally representative survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While there is a large literature on the role of immigrants on the native-born population in terms of labor market competition, there is a limited amount of studies that examine the effect from displaced populations. Many conclusions from the traditional literature on the study of immigrants ’ impact on natives cannot be applied to the case of Syrians in Turkey. There are many differences between the inflow of Syrians and other flows of extended family and economic immigrants. First, the sheer volume of Syrian refugees and the short time- frame in which they entered Turkey is unprecedented. For the case of Syrians in Turkey, or displaced populations in general, large movements of refugees are not restricted due to humanitarian reasons. Second, formal immigration processes are controlled, limited, and regulated by destination countries. Therefore, results from literature on “ immigrants ” are very different than a focus on displaced or refugee populations. Recent literature on the labor market effects of SUTPs estimates negative impacts on host community employment rates. The negative displacement results are largest for the young, women, informal workers, 2 United Nations High Commissioner for Refugees (UNHCR) – Syrian Regional Refugee Response, Inter-agency Information Sharing Portal 3 (UNHCR) – Syrian Regional Refugee Response, Inter-agency Information Sharing Portal", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 and the less educated (Ceritoglu, Tunculer, Torun, and Tumen, 2015; Del Carpio and Wagner, 2015). The economic effects of SUTPs not only vary across different segments of the labor market, there are also strong regional differences in their economic effects. Using synthetic modelling methods, Ozturkler and Goksel (2015) estimate the impact of Syrian refugees on local prices, wages, inflation, and services in 10 cities with large refugee populations. Some of the salient negative effects have been increases in rental prices, increases in inflation at border cities, illegal hiring by small business, and decreases in wages. However, in some cities (Gaziantep, Adana, Kahramanmaras, and Mardin), the presence of refugees has improved the trade balance, and economic activity in these areas are projected to increase as economic integration with MENA deepens. Orhan and Gundogar (2015) also note both positive and negative aspects of SUTPs. A primary contribution of this paper is the estimation of poverty at the sub-national level and among population groups of interest. Since migration, geographic, and welfare variables do not exist in a single data set, imputation techniques are required to overcome these limitations and to compute household level poverty.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The imputation of income poverty is done using the Turkish Labor Force Survey (LFS), and with information and modeling parameters determined from the Survey of Income and Living Conditions (SILC). More details and validation of this methodology is discussed throughout this paper. While explicit identification of Syrians in available surveys is not feasible, there is evidence of an increase in the amount of foreign-born individuals that is being captured in the LFS. The arrival year of foreign-born migrants is available in the data which allows for identification of “ Settled Migrants ” and “ Recent Migrants ”. The latter is used as a proxy for Syrian refugees for the purposes of this paper. National official surveys that are conducted under-report the refugee population. Yet, since about 10 percent of Syrian refugees are in camps and the remaining are residing throughout the country, it is not surprising that they are accessible to interviews by the LFS. Despite data limitations, there are strong and significant trends in the poverty rates for the recent foreign- born, especially for those near the Syrian border. In 2013, recent migrants near the Syrian border were the poorest group4 in Turkey. While this statistic in itself is not initially surprising, fluctuating welfare trends of recent migrants over time is noteworthy.", "output": {"entities": {"named_data": ["Survey of Income and Living Conditions", "Turkish Labor Force Survey"], "descriptive_data": [], "vague_data": ["National official surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In previous years, migrant households in Turkey tend to have much lower poverty on average than even the host community. Across comparison groups and time, the poverty rates of recent migrants is higher than among the host community in only one instance: in 2013 near the Syrian border. This sudden change in the historically stable pattern implies that the LFS is able to capture at least a part of the incoming SUTPs who have significantly different socioeconomic profiles in comparison the previous economic migrants. Throughout history, immigration to Turkey has been relatively limited and consisted mostly of those of Turkish heritage. In the early 20th century, immigration was encouraged by the government as a method to increase the population. Since 1970, immigration has slowed down and has been even discouraged at times. Many immigrants to Turkey are of Muslim Turkish background, since the government prioritized preserving a national identity. This is likely why “ migrants ” had very similar or even lower poverty rates than the host community. The sharp degradation of welfare among recent migrants in 2013 illustrates the severity of poverty that is arising very likely from a growing population of Syrian refugees. The Syrian refugee inflow to Turkey 4 Based on grouping by host community, established migrant, recent migrant.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 began in April 2011 and has been continuing at an increasing pace as the conflict in Syria expands. 5 The Turkish government has provided a tremendous amount of support in the form of shelter and essential items to help sustain the livelihood of large numbers of refugees. However, aid funds are not limitless and refugees face hardships that will persist over the long-term. The refugee camp population in Turkey has been stable since March of 2013 as the physical capacity of the camps have been exhausted. 6 This saturation has resulted in a steep increase in the number of Syrians living outside camps across Turkey. The proportion of Syrian refugees living outside camps increased from 53 percent to 87 percent between March 2013 and November 2014. 7 In addition, even though refugees living outside camps continue to be concentrated near the Syrian border (64 percent), the dispersion of Syrians across the country has expanded, especially in major urban centers such as Istanbul and Ankara. The results in this paper are limited to 2013 due to changes in the 2014 LFS that make poverty estimations incomparable to previous years. 8 Therefore our results may provide only a partial insight into the impact of SUTPs, since the dispersion of Syrians across Turkey has increased greatly in 2014 and 2015.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The descriptive characteristics of foreign-born and host community households are still comparable, and therefore allow for comparisons between 2013 and 2014. It is possible to make some inferences on welfare changes by looking at changes in host community employment rates and labor market characteristics. Overall, this paper finds no negative effects on host community welfare from an increasing population of SUTPs. As other authors have stated, the influx of SUTPs has had both positive and negative impacts. It seems on average, the host community has been strong and adaptive, and not negatively impacted. This is not to disregard that real strains do exist in some regions where the SUTP population is very large. Nor do these results undermine findings of displacement effects in the labor market that certain types of workers are experiencing. However, on average nationally, we do not see a systematic decline in the welfare of the host community between 2011 and 2013. The remainder of the paper is organized as follows. Section 2 outlines the data availability and technical issues. Section 3 discusses descriptive statistics of the foreign-born and host community. Section 4 explores the impact of the foreign-born population on host community welfare. 1. DATA AND TECHNICAL ISSUES Data Sets Data availability limits which data set can be used to identify the foreign-born population and geographic location while measuring poverty. The Turkish Statistical Institute (TUIK) has been conducting three nationally representative surveys annually since 2005; the Household Income and Consumption Expenditure Survey (HICES), the Survey on Income and Living Conditions (SILC) and the Labor Force Survey (LFS). However the HICES, which is the national survey that is used to measure official poverty, 5 UNCHR, 2013pg 13 6 UNHCR (22 November 2013), UNHCR (15 September 2014) 7 UNHCR (22 March 2013), Erdogan (2014), pg 14 8 This technical issue will be discussed further later in the paper.", "output": {"entities": {"named_data": ["HICES", "Survey on Income and Living Conditions", "Household Income and Consumption Expenditure Survey", "Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 does not have migration or geographic identifiers. The SILC contains geographic identifying variables, (at the NUTS1 level) but still lacks migration variables. The data set this study uses is the Labor Force Survey (LFS) since there is an adequate availability of both migration and geographic variables. The LFS is representative at the NUTS2 level which corresponds to 26 regions in Turkey. One caveat is that income in the LFS refers to only wage income from employment, 9 and is an insufficient measure of income that should be used for welfare measurement. For example, important sources of income such as social assistance, asset liquidation, or remittances are missing. Therefore, income in the LFS is imputed with a few assumptions using information from the SILC. The NUTS1 spatial effects of the SILC are a good proxy for NUTS2 welfare dynamics in the LFS which increases the accuracy of the imputation model. However, since the original sample frame of the LFS does not account for the recent influx of foreign migrants in Turkey, the labor market characteristics of recent migrants might not be representative of the actual SUTP population. Therefore, results of the imputation could be interpreted as upper bound estimates for recent migrants. More details of survey techniques used to complete this exercise are available in the Annex.", "output": {"entities": {"named_data": ["SILC", "Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As a result, imputed poverty is measured as income poverty. Another advantage of using the LFS is the availability of CPI at the NUTS2 level in Turkey which allows for spatial deflation of different price levels across the country. Table 1. Survey Comparison and Data Availability Years Available Migration Variables Income or Consumption Geographic Identifier Spatial Deflation HICES 2003-2012 No Consumption, income National, urban / rural No SILC 2009-2012 No Income NUTS1 No LFS 2009-2013 Yes Imputed Income NUTS2 Yes However, there are other issues for consideration when using the LFS. Principally, there is a low number of sample points that are migrant households. Moreover, the study cannot identify migrant households and individuals that are specifically Syrian refugees. Foreign migrants are defined as those who were born abroad and have lived abroad for at least more than 12 months. Some Turkish-born households have also lived abroad for over a year, and these individuals are not considered to be migrants. Amongst foreign-born individuals, only the ones who have been in the country for more than 12 months are included in the sample which underrepresents the actual number of foreign migrants in the region. In addition, no specific procedure is adopted by the enumerators if the household does not speak Turkish. Given that a majority of Syrian refugees do not speak Turkish, the language barrier might result in the removal of Syrian households from the sample. Finally, refugee camps are not included in the sample frame, which limits the study to only examining recent migrants who do not live in refugee camps. 9 Wage income is only available for regular and casual employees in the LFS which accounts for around 60 % of total employment. There is no other monetary income value for the rest of the working population.", "output": {"entities": {"named_data": ["LFS", "SILC", "HICES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 Comparison Groups Six population groups are constructed based on their migrant status and geographic location (Table 2). Five out of 26 regions are defined as Near Syrian Border (NSB) regions based on their proximity to Syria as well as their popularity as a destination for migrants (see Map 1 for details). These regions are Mardin (TRC3), Sanliurfa (TCR2), Gaziantep (TCR1), Hatay (TR63), and Adana (TR62). 10 TRC3-Mardin, TCR2- Sanliurfa, TCR1-Gaziantep and TR63-Hatay are Southeastern regions of Turkey that border Syria. TR63- Adana does not border Syria but is a southern Mediterranean region that is a common destination for migrants due to abundant labor opportunities. The rest of the country includes the remaining 21 NUTS2 regions. Map 1. Near Syrian Border Regions 10 NUTS2 regions are referred with name of the largest province in each regions. The full list of provinces in each region are; Mardin-Batman-Sirnak-Siirt (TRC3), Sanliurfa-Diyarbakir (TCR2), Gaziantep-Adiyaman-Kilis (TCR1), Hatay-Kahramanmaras- Osmaniye (TR63), and Adana-Mersin (TR62).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Host community households are those whose head of household were born in Turkey, or born outside Turkey but did not live abroad for more than a year. Conversely, migrant households are defined as those whose head of household was born abroad and has lived abroad for more than 12 months. The duration of a migrant household ’ s stay in Turkey is also based on when the head of household arrived in Turkey. Three thresholds are tested: 2, 3, and 4 years. The 4 year cut-off is preferred to maximize the sample size of recent migrant households. Table 2. Population Groups for Comparison Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Geographic Location Near the Syrian Border The Rest of the Country Status Host Community Settled Migrant Households Recent Migrant Households Host Community Settled Migrant Households Recent Migrant Households Years in Turkey Head of Household born in Turkey Arrived in Turkey more than: 2, 3, or 4 years Arrived in Turkey less than: 2, 3, or 4 years Head of Household born in Turkey Arrived in Turkey more than: 2, 3, or 4 years Arrived in Turkey less than: 2, 3, or 4 years Are Syrian Refugees being captured using the LFS? While variables covering all topics of interest (migration, welfare, and geography) are available or can", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "be imputed into the LFS, it may be unclear, ex-ante, if SUTPs are adequately included in the survey. Despite a small sample of foreigners and other concerns, there is evidence that the LFS sample does include some “ recent foreign ” migrants, especially in the border regions (NUTS2) [TRC1-Gaziantep, Adiyaman, Kilis, TRC2-Sanliurfa, Diyarbakir, TRC3-Mardin, Batman, Sirnak, Siirt TR63-Hatay, Kahramanmaras, Osmaniye] (Map 1).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 3. ANALYSIS Has the increase in Syrian refugees impacted the welfare and socioeconomic conditions of the host community? Summary statistics in the previous section showed clear trends of increasing poverty among recent migrants throughout the country, both near the Syrian border and across rest of the country. While the poverty rates of recent migrant households spiked in 2013, poverty of host community households maintained a relatively constant level in the whole country. From these trends, it appears that there at least has not been an increasing trend in poverty among the host community over the latest years. The empirical model is shown in Equation 1. Regressions are estimated at the NUTS2-year level and using data from only the years 2011 and 2013. The dependent variable of interest is the host community poverty rate by region and year, where the poverty rate is based on spatially deflated imputed household income. Unlike the computation of the poverty rates, “ recent migrant ” information is not used for the analysis. Only the host community poverty rates are calculated using the LFS and the number of Syrians are taken from government sources.", "output": {"entities": {"named_data": ["LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 in 2014. The share of workers registered with social security in host community households declined but the change is not significant. In terms of the number of hours worked, and the proportion of workers in blue or white collar jobs, there is no statistical difference between 2013 and 2014. 4. CONCLUSION The movement of Syrian refugees is one of the largest passages of refugee populations in recent history. With millions of people leaving Syria and settling in Turkey, concerns about externalities onto the native population are very salient. This paper addressed the poverty impacts of SUTPs on the host communities and found no evidence that the increase in foreign-born population from 2011 to 2013 resulted in higher poverty rates among the host community. As recent literature has noted, the SUTPs have both positive and negative impacts. While some types of people may be more likely to be displaced by Syrians in the labor market, Syrians are consumers and renters, they also open businesses and create jobs. Local Turkish citizens have also benefited as employers and sellers. In some border cities, the balance of trade has improved as exports to the Middle East increased. On the other hand, analysis of only the recent migrants clearly demonstrates that the group ’ s poverty profile is worsening between 2009 and 2013.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Moreover, descriptive characteristics suggest that their conditions might have worsened in 2014. As this unprecedented event continues, the integration of Syrians into the Turkish labor market, access to public services, changing demographics, and socioeconomic impacts should be monitored closely. Especially with an increasing rate of SUTP migration to Turkey during 2014 and 2015 and the continued conflict in the region, the inflow of Syrians will be one of the most critical short, medium, and possibly long term policy issues in the country. In addition, Turkey ’ s role as a pathway to Europe for those escaping conflict in the Middle East makes the issue an international phenomenon. In this respect, the healthy incorporation of SUTPs that will protect the wellbeing of host communities while satisfying the humanitarian necessity of helping Syrians will be among the more important development issues of today and the foreseeable future.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "34 Any statistics on the imputed welfare will based on the set of imputed welfares for each household. The estimator takes the form, with R denotes the number of simulation: ܪ ෡ ൌ 1 ܴ ෍ ݄ ሺݕ ෤ ௥ ሻ ோ ௥ ୀ ଵ where ݄ ሺݕሻ is a function that converts the vector y with (log) incomes for all households into a poverty measure (such as the head-count rate or bottom 40 %), and where ݕ ෤ ௥ denotes the r-th simulated imputed welfare. Figure 6. Survey-to-Survey Imputation Methodology, an illustration For the case of Turkey, we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey. Income is used instead of consumption for this paper ’ s analysis. The model included variables related to: household demographics (age, gender, age composition, etc.), household characteristics (education, labor activity, etc.), household head ’ s characteristics (age, gender, labor, education, marital status, etc.) and household assets holding (both livestock and durables). Based on that model the simulated values of consumption (at household level) were imputed for the households in the corruption survey.", "output": {"entities": {"named_data": ["Labor Force Survey", "Survey on Income and Living Conditions survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This allowed for consistent ranking the households into welfare quintiles and cross- tabulation of welfare status with household characteristics and indicators derived from the survey data. The imputation was carried out using s2sc algorithm in STATA.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "35 Inputs: 1. Household Survey with consumption or income welfare aggregates 2. Project data / Other survey data without welfare aggregates 3. Set of harmonized common variables in both surveys Outputs: 1. Set of imputed welfare variables for project data / other survey for each household in the data 2. Imputed welfare variables can be used for poverty, distributional analysis (quintiles or more), profiling of the poor or group of interest Models: 1. Ordinary Least Squares (OLS) 2. Probit 3. Multiple Imputation (MI) Table 18. Model Specification Variables Demographic Share of children, share of adults, share of adults squared and share of old (omitted) Characteristics of head Age, gender, and level of education Interactions with urban dummy variable Level of education of the head, age of the head Geography Dummies for regions at NUTS 1 level (12 regions) Interactions with Geography Level of education of the head, age of the head interacted with regions at NUTS 1 level (12 regions) and urban-rural division 1. Validation and Robustness Check Figure 7.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household Survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "External Validation, NUTS1 Level $ 5 / day PPP – Observed (SILC) & Imputed (LFS), 2007 LFS SILC", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 6158 This paper addresses the conditions under which donor and non-state actor service provision is likely to undermine or strengthen citizens'legitimating beliefs. On the one hand, citizens may be less likely to support their government with quasi-voluntary compliance when they credit non-state actors or donors for service provision. On the other hand, the provision of goods and services by donors and non-state actors might strengthen citizens'confidence in their government and their willingness to defer to governmental laws and regulations if citizens believe that the government is essential to leveraging This paper is a product of the Poverty Reduction and Economic Management Departyment, Africa Region.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The author may be contacted at asacks @ worldbank. org. and managing these resources. The author assesses these competing hypotheses using multi-level analyses of Afrobarometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows for analysis of associations between donor and non-state actor service provision and the sense of obligation to comply with the tax authorities, the police and courts. The findings yield support for the hypothesis that the provision of services by donors and non-state actors is strengthening, rather than undermining, the relationship between citizens and the state.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "reason to believe that the provision of services by donors and non-state actors could either strengthen or weaken citizens ’ deference to government. I will identify the conditions under which these two scenarios are likely to occur. On the one hand, citizens may be less likely to support the government with deference to its laws and regulations when they credit non-state actors or donors for service provision. The provision of services by donors and non- state actors is likely to prompt citizens to question why they should pay taxes to a government that is not providing them with anything in exchange. On the other hand, the provision of goods and services by donors and non-state actors might strengthen citizens ’ legitimating beliefs and their willingness to defer to governmental laws and regulations if citizens view their government as essential to leveraging and managing these external resources. I assess these competing hypotheses using multi-level analyses of Afro- barometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows us to analyze associations between donor and non- state actor service provision and the sense of obligation to comply with the tax authorities. Third, I assess the relationship between the provision of ser- vices by donors and non-state actors and citizens ’ willingness to defer to two additional authorities, the police and courts.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afro- barometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 1). 2 For example, there is also some evidence that when tax increases are linked to improvements in public goods provision, citizens are less likely to resist the tax increases. In Ghana, the government linked increases in the VAT rate explicitly to the new public spending programs, such as the Ghana Education Trust (GET) Fund in 2002 and the National Health Insurance Scheme (NHIS) in 2003 which enjoyed broad public support. The government used strategic communication to make this link in order to avoid major public protests, such as the Kume Preko protests that greeted the introduction of the VAT in 1995 and left several people dead (Osei, 2000; Prichard, July 2009). Similarly, Ghana ’ s government linked the introduction of a talk tax on mobile phone calls to efforts to combat youth unemployment, which helped to curb public opposition (Prichard, July 2009). 3. 1 Is donor and non-state actor service provision likely to undermine the fiscal contract? We are beginning to accumulate knowledge about what government can do to influence the perception of the relationship between citizens and political authorities. We know very little about what happens once non-state actors mediate that relationship.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "impression of the state ’ s role in development and give credit to the state for helping to leverage external resources. Citizens are also likely to give the state credit where mechanisms to voice complaints about non-state actors exist and where bureaucrats are able to effectively respond to complaints. Under these conditions, non-state service provision is likely to strengthen the fiscal contract. 5 Data and Methods I explore the relationship between external service provision and deference to government using Afrobarometer survey data from 19 Sub-Saharan African countries (see Table 1). Africa is an especially good place to examine these issues because of the large amount of variation both within and across African countries in the extent to which non-state actors, donors and other states are active in service provision and the extent to which governments are relatively effective and fair. Government responsiveness, corruption and reliance on non-public resources vary considerably among localities with consequences for citizen understanding of and relationship to government (Gibson and Hoffman, 2005). This project relies on the fourth round of Afrobarometer data that surveys Africans ’ views towards democracy, economics, and civil society with random, stratified, nationally representative samples.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In 2008, trained enumerators conducted face-to-face interviews in local languages with 26, 513 respondents across 19 countries. 5 The sample is designed as a representative cross-section of all citizens of voting age in a given country. The dataset used for this paper has a multilevel structure; individuals are nested within primary sampling units (PSUs), which are nested within countries. The PSUs are the smallest, well-defined geographic units for which reliable population data are available and they tend to be socially homoge- nous, thereby producing highly clustered data. In most countries, these will be Census Enumeration Areas (Afrobarometer, 2005, 37-38). Although re- spondents were not sampled based on their ethnic affiliation, there is likely to be a high level of clustering in the dataset around ethnicity. In other work, I discuss the advantages of multilevel modeling (Levi and Sacks, 2009). Treating the dependent variable as a binary outcome and taking into account the multilevel nature of our data, I estimate random intercepts for 5I excluded Zimbabwe from the analysis because of missing data on key variables. 9", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "obtained from the multilevel logistic model. Thus, I am confident that I did not lose substantial statistical information by dichotomizing this variable. 6. 2 Independent Variables 6. 2. 1 Socio-Demographic Variables I control for standard socio-demographic variables that can affect citizens ’ acceptance of government ’ s right to make people pay taxes. A question prob- ing respondents on their household income was not included in the fourth round of Afrobarometer surveys. Asking respondents to quantify their in- come can be problematic in the context of developing economies, where in- dividuals are often embedded in barter or commodity exchange, rather than, market economies. There are, however, reasonably good proxies including whether respondents own a television, radio, car, and mobile phone, and use the internet. Age, education, employment, and urban or rural residence are demographic factors that also affect household resources. 6. 2. 2 Experience with Paying Taxes or Fees It is difficult to assess just how ubiquitous taxes are in ordinary Africans ’ lives. There has not been any systematic effort to take stock of the types and amount of taxes citizens pay across Africa. Similar to pre-modern European states, African states ’ revenue raising capacity is generally low.", "output": {"entities": {"named_data": ["Afrobarometer surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "On average, the tax-to-GDP ratio in Sub-Saharan Africa is around 21 percent, compared with the OECD average of about 32 percent. In Tanzania and Uganda, the total tax share drops to about 10 percent. Historical data suggests that the tax share of many European countries did not reach 15 percent of GDP until World War II when incomes were substantially higher than they are in many African countries (Fjeldstad and Rakner, 2003, 3). The types and amount of taxes citizens pay varies both within and be- tween countries. We do know there are taxes on agricultural crops, but the rates and processes of collection vary within countries (Kasara, 2007). User fees from electricity, water, sanitation, and other services comprise the major- ity of local revenue in South Africa (Hoffman, 2007). In Tanzania, Fjeldstad and Semboja (2001) count ten major categories of taxes, eighteen major categories of licenses, forty groups of charges and fees, and seventeen items listed as other revenue sources. In some countries including Kenya, Malawi, 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, I include a measure of whether citizens believe that a large portion of tax administrators is corrupt. Second, I in- clude a variable indicating whether citizens approve of how well their local government is handling the collection of license fees on bicycles, carts and barrows. 8 Third, both the size of a country and the size of the government may affect a government ’ s ability to detect and punish evaders. I include the 7I also include a country-level indicator of government performance, the World Bank Governance indicator of government effectiveness, in the model. This indicator measures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implemen- tation, and the credibility of the government ’ s commitment to such policies (Kaufmann, Kraay and Mastruzzi, 2006, 4). This variable is not significant at the p <. 05 level. 8I included two additional measures in the model neither of which were significant at the p < 0. 05 level. One is a measure of citizens ’ approval of how well their local government council provides citizens with the information about the councils budget (i. e. revenues and expenditures). The other, the World Bank governance indicator, control of corruption, measures the extent to which public power is exercised for private gain, as well as capture of the state by elites and private interest (Kaufmann, Kraay and Mastruzzi, 2006, 4). 13", "output": {"entities": {"named_data": ["World Bank Governance indicator"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "natural log of a country ’ s population size and government consumption as a share of GDP; the latter is the most widespread measure of government size (Adsera and Boix, 2002; Alesina and Wacziarg, 1998; Rodrik, 1998). Tax compliance is also related to the government ’ s ability to effectively detect and punish tax avoiders, tax evaders, and tax arrears. Although an imperfect measure of states ’ deterrent capacity, Afrobarometer includes two survey questions on perceptions of government enforcement and monitoring capacities. One question probes respondents on how often ordinary people who break the law go unpunished. The other probes respondents on how often officials who commit crimes go unpunished. This latter question is also a measure of perceived government fairness- the extent to which a government implements the law evenly across all social groups. 6. 2. 5 Procedural Justice I include two indicators of procedural justice. The first probes respondents on how often people are treated unequally under the law. The next taps citizens ’ perceptions of the government ’ s treatment of their ethnic group. Specifically, respondents were asked how often their ethnic group is treated unfairly by their government. 9 6. 2. 6 Donor and Non-State Actor Provision of Services I include a measure of who citizens believe is responsible for collecting income taxes.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "I expect citizens who perceive tax collection to be the responsibility of non-state actors to be less likely to be willing to pay taxes to the state ’ s tax department than citizens who perceive tax collection to be the responsibility of the state. Perceptions of the effectiveness of donor and non-state actor provision of services are assessed using the following items. Respondents were probed on how much they believe the following non-state actors and donors do to help their country: the United Nations; international donors and NGOs; international businesses and investors; China; and the United States. 9 I also include Freedom House ’ s political liberties and civil rights ratings for the 19 countries in the sample. These two variables should capture the relative equality of influence in making policy. They indicate whether citizens are able to express their voice without fear of repression and whether elections are free and fair. Neither of these variables are significant at the p < 0. 05 level. 14", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, there is reason to believe that better service delivery may affect citizens ’ willingness to defer to the tax department only through its effect on improved outcomes that matter for citizens ’ livelihoods. Unless improved services and infrastructure have a positive impact on citizens ’ welfare, indi- viduals are unlikely to credit the government for these outputs (Sacks and Levi, 2010). The Afrobarometer ’ s objective measures of service delivery only denote the presence or absence of infrastructure and services. The data do not indicate the condition of the services and infrastructure. Citizens may perceive and reward relative improvements or sanction de- teriorations in services, rather than the absolute level of service quality they receive. If services deteriorate or improve, taxpayers may alter their beliefs about governments ’ performance and should attempt to adjust their terms 10I also tested whether there is a relationship between the presence of a concrete road, health clinic, post office and electricity grid in the enumeration areas and respondents ’ willingness to pay taxes. None of these objective indicators except for the presence of an electricity grid were significant at the p < 0. 05 level. The presence of an electricity grid is negatively associated with the willingness to defer to the tax department. 17", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of trade with government. Because the dataset used for this study is cross- sectional rather than longitudinal, I am unable to examine whether citizens adjust their beliefs and behavior to relative changes in service delivery. I was only able to test whether there are associations between the absolute service quality across neighborhoods, countries and ethnic groups and deference to the tax department, police and courts. Further, citizens may not be attributing goods and services to the gov- ernment. Rather, citizens may be attributing goods and services, such as roads, electricity grids, sewage systems, health care and education to vari- ous non-state actors including the following: the private sector; NGOs and community-based groups; churches, mosques and other religious institutions; traditional leaders; and, bilateral and multilateral donors. Survey questions on the Afrobarometer only indicate the presence or absence of services and infrastructure, and the quality of these services, but these questions do not probe respondents on who they believe are providing these services. Each of the indicators of perceptions of government performance is sig- nificant at the p < 0. 05 level. Food security is positively associated with a willingness to defer to the tax department.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "appear to be a relationship between perceptions of the helpfulness of donors and non-state actors and the willingness to defer to the police and to the courts. Individuals who believe that donors and non-state actors exert too much, rather than, too little influence over one ’ s government is associated with the willingness to defer to the court and to the police. Findings also suggest that citizens who believe non-state actors are responsible for provid- ing law and order are less likely to be willing to defer to the police and to the courts than respondents who believe the state is responsible for providing law and order. 7. 4 Conclusion This paper demonstrates that the logic of the fiscal contract is relevant to a wide variety of contemporary African states. Findings from a cross-national analysis of survey data from Africa link citizens ’ legitimating beliefs — in- dicated by a willingness to defer to the tax department, the police and the courts — to a government ’ s fulfillment of a fiscal contract. Citizens who are satisfied with their government ’ s provision of services and goods are more likely to be willing to defer to the tax department, courts and police than citizens who disapprove of government service provision.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. Introduction The most common sampling approach for cross-sectional household surveys in the developing world is a stratified two-stage design (Grosh and Munoz, 1996). Following stratification based on administrative boundaries, clusters are selected in the first stage with probability proportional to size from a national census-based frame. In the second stage, a canvassing operation is conducted in the selected clusters to compile an updated list from which households are randomly selected. While this methodology is straight forward to implement in the field and reliably produces unbiased estimates, there are several downsides. The first downside is cost. The World Bank ’ s Living Standards Measurement Study team, which provides technical assistance on large-scale household surveys around the world, estimates the field listing operation increases the overall budget for data collection by 25 percent. Due to confidentiality concerns, the data collected during a field listing operation, typically the name of the household head and address or location description of dwellings, does not have any analytical applications beyond as a component of the weight calculations. 2 At a time when typical survey costs are in the USD millions, reducing a significant cost component will increase the financial sustainability of data collection. The second drawback to the traditional design relates to timeliness.", "output": {"entities": {"named_data": ["Living Standards Measurement Study"], "descriptive_data": ["national census-based frame"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 five methods, it was not possible to consider non-sampling error. This paper goes a step further by using simulations to describe the sampling error and a field experiment in an IDP camp in South Sudan to measure the total survey error of each design compared to a census, allowing for the disaggregation of the total error into sampling and non-sampling components. In addition, we attempt to separate the components of non-sampling error linked to the sample method from those common across all methods, such as interviewers selecting larger households and other issues in properly implementing the household survey protocols. The next section briefly describes each method and highlights the literature as it relates to the relevant selection methods. Section 3 describes the data set and protocols for each method included in the experiment, followed by Section 4, which discusses implementation issues. Section 5 reports the results of the analysis, and section 6 concludes with further discussion of the overall performance and areas for future research. 2. Description of Methods This paper compares five alternatives of second stage selection (satellite mapping, segmentation, grid squares, “ Qibla ” (or “ walk north ”) method, and random walk) to a human canvassing operation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In a mortality study in Iraq, Galway et al. (2012) used GIS and Google earth imagery for household sampling. The method used gridded population data for selection of clusters. The first cluster sampling stage of their study used the ‘ Create Spatially Balanced Points ’ (CSBP) function in the ArcGIS (v10) software. Boo et al. (2020) introduces a sampling design based on gridded population estimates as their sampling frame to implement a PPS design and derive sample size estimates for the number of grid cells. Assuming the grid square method is applied to the area itself rather than a selected PSU, the weights for the grid method are similar to those for segmentation, where the cells are the PSUs, but without the additional step of selecting segments. The weights can therefore be represented as 𝑤𝑤𝑖𝑖 ′ = (𝑁𝑁𝑘𝑘) ൫𝑁𝑁𝑘𝑘𝑘𝑘൯൫𝑁𝑁𝑘𝑘𝑘𝑘𝑘𝑘൯ 𝑘𝑘𝑘𝑘𝑘𝑘. 2. 4. North Method The “ Qibla method ” described in Himelein et al. (2017), or what is called in this paper the “ North method ” method, is an attempt to assign probability weights to random point selection methods. Several random point selection methods can be found in the literature, particularly in relation to epidemiological studies.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["gridded population data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Grais et al (2007) used a methodology in which the closest household to a randomly selected point is selected for a study of vaccination rates in urban Niger, though did not attempt to calculate probabilistic sampling weights. Similar approaches were used by Kondo et al (2014) in a study of the city of Sanitiago Atitlán, Kumar (2007) in urban India, and Kolbe and Hutson (2006) in Port ‐ au ‐ Prince, Haiti. Shannon et al (2012) also used such a method to select points in a study of violence in Southern Lebanon in 2008 but used the radius of a circle to define an area to be field listed, and from which buildings and then households were selected for enumeration. The circle area and building density were used to calculate probability weights. The main difference between most random point selection methods and the North Method described here is that the North Method attempts to accurately estimate the probabilities of selection.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The assumption of perfect implementation, however, is quite strong as interviewers have shown a preference for selecting respondents willing to participate in the survey (Alt, 1991), and a number of other studies found that data collected with random walk designs exhibit differences from known population statistics on gender, age, education, household size, and marital status (Bien et al. 1997, Hoffmeyer ‐ Zlotnick 2003, Blohm 2006, Eckman & Koch 2016). Probabilities of selection inherently cannot be calculated in a random walk sample design as no information is collected on how many structures are in the camp, or how likely it was that a given structure was the xth structure along any path. Random walk must then assume all structures have the same selection probability, implying constant sampling weights. Therefore, the only component of the weights for the random walk is the sub-sampling of households within a selected structure: 𝑤𝑤𝑖𝑖 ′ = 𝑁𝑁𝑗𝑗𝑗𝑗 𝑖𝑖. 2. 6. Comparison of Methods As mentioned above, stratified cluster samples with the canvassing of selected clusters is the most common sample design used to collect official socioeconomic statistics in the developing world, but in", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 other disciplines it is relatively rare. A review of published public health literature by Chen et al. (2018) found most surveys use probabilistic designs in the first stage, but random walk or similar methods in the second stage. Lupu and Michelitch (2018) suggest that the combination of random walk and quota sampling is the common approach for political science-themed surveys conducted in the developing world, with 77 percent of respondents to their expert survey using a variation on this design. Diaz de Rada and Martínez (2014) compare a combination of random walk and quota sampling (based on age and gender) to probability designs and find a more accurate estimation of age and educational attainment in the combined method than in the probability methods, but that the probability methods perform better for measuring unemployment. The authors cite the replacement protocols for the probability methods as a reason for the bias and attribute the use of quota sampling for the success in estimating age and education, compared to the gold standard of a high-quality probability sample design. There are also a limited number of papers which directly compare two or three of the methods, but none that consider this wide range of alternatives.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Chew et al. (2018) use a baseline convolutional neural network model on a gridded population sampling frame to select a sample of households in Nigeria and Guatemala. The authors found this technique to be on par with human canvassing in terms of accuracy, and to outperform other machine learning models based on crowdsource or remote sensing data. Grais et al (2007) compared an unweighted random point selection methodology to a random walk in their study of vaccination rates in urban Niger. The authors do not find statistically significant differences between the methods, though the sample size was limited and both methods were non-probabilistic. 3. Design and Field Protocols 3. 1. Experiment Design This paper makes use of a dataset from the purposefully designed methodology experiment conducted in one section of the Protection of Civilians site 1 (PoC1, Figure 1), one of the largest IDP camps in Juba, South Sudan. To generate a gold standard as the basis of comparison, a household census was conducted between August and September 2017. During this exercise, 2, 655 households were interviewed using a questionnaire designed to collect demographic information, dwelling characteristics, household consumption, and perception data. At the end of each census interview, households received a unique barcode that could be used to identify them later in the experiment.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To avoid changes in camp composition, immediately following the completion of the census fieldwork, the interviewers returned to the field to implement the experiment. Teams used each of the sample selection methods to identify which households would have been selected had that method been used for a survey. To avoid respondent fatigue, instead of re-asking the questionnaire, the interviewers simply scanned the unique bar code of the selected household. Once scanned, the barcodes created an observation in the method-specific dataset with the information captured in the census. Each sampling technique targeted about 322 interviews so that comparisons could be made between the methods using an identical sample size. There was, however, some non-response for each method if interviewers were not able to contact a household member who could provide access to the barcode, if the barcode had not been retained by the household, or if the barcode was not scanned correctly. Protocols for each individual method are listed below.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 3. 5. North Method The North Method uses RSPs to determine the selected households. RSPs are chosen from the universe of all possible points with the boundaries of the PoC camp. To implement the North Method, 322 RSPs along with replacement RSPs were chosen. These points were random geo-coordinates within camp borders (Figure 5). If the RSP lay within a structure, the corresponding structure was selected. If not, starting at the selected RSP, enumerators walked directly north, using the compass application on their tablet, until a structure was encountered. If the structure was residential, the structure was chosen to be interviewed. In the case of multiple households present in the structure, one household was randomly chosen. If the structure was not residential or if the enumerator reached the boundary of the camp, a replacement RSP was used. As it would be extremely difficult to determine the area of the shadow in the field, satellite imagery is used for these calculations. In the case of this experiment, the selection areas are calculated using Google Earth imagery taken on December 22, 2017, approximately one month after the census of households in the PoC camp. Given the dependence of the North Method on having current satellite imagery for accurate calculations, the availability of this imagery is a major consideration for this method.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The weights would be over-estimated if new structures had been built in the shadow since the imagery was taken. 3. 6. Random Walk Random Walk obtains a sample by randomly selecting starting points for enumerators with generic but unambiguous instructions to select households at regular intervals on their path. For this experiment, enumerators conducted random walks using 21 RSPs (Figure 8). Starting as near as possible to the RSP, the supervisor chose any random point (like a street corner or a school). From this point, four enumerators walked each in one of the four cardinal directions. Walking in their designated direction away from the RSP, they counted structures on both the right and the left and each selected the fifth structure for interview. Enumerators were instructed to start with the buildings on the right if two buildings were opposite to each other. To select the next structure, enumerators continued along the cardinal path, and selected the next fifth structure. If the enumerator could not proceed on its cardinal path because she had reached the boundary of the PoC camp, enumerators were instructed to turn right at a 90-degree angle and continue counting until finding the fifth dwelling. Enumerators had to conduct six interviews along their paths. 4. Implementation Issues 4. 1. Failure to Follow Survey Protocols As noted above, even if field protocols are perfectly implemented, the estimates generated from Random Walk designs are likely to be biased. Enumerators furthermore often were unable or unwilling to follow the protocols. Streets and paths were not necessarily aligned with cardinal directions and obstacles further impeded the ability to follow a straight path. Additionally, since the selection method requires enumerator judgment, it is not replicable and therefore allows enumerators greater discretion to choose which households are “ selected. ” Figure 9 shows the paths taken by two teams of enumerators from", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 implementation matters. Pooling the analysis across indicators and using satellite mapping as reference, the North Method is unbiased, while the Segmenting and Grid Square methods show minimal bias (0. 1 percent and 0. 2 percent, respectively). The Random Walk method shows 1. 2 percent bias on average across the 14 questions. In conclusion and in line with the literature, most probability-based methods perform better than non-probability methods like random walk. In addition, implementation of adherence with the survey protocol is extremely important and using appropriate methods and tools to cope with this challenge is absolutely mandatory for coming as close as possible to the theoretical results derived by the simulation for the probability-based methods. In practice – in a fragile setting like South Sudan – deviations from the survey protocol, measured as differences between the experiments and the simulations, have large influence on the actual bias of estimates.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 7. Appendix 7. 1. Simulation and Frame To compare the efficiency of the different sampling frames and designs, we will apply an empirical sampling simulation. In this type of (Monte-Carlo style) simulation, either a true or synthetic population is used as the target population. By applying a specific sampling design, and repeated sampling (usually 1, 000 repetitions) under this design, we can compare the resulting population estimates with the known true population values for each run of the simulation. The resulting distribution of these estimates is called the sampling distribution, and the average squared deviation from the underlying population value is the Mean Squared Error (MSE) or when taking its square root, the Root MSE (RMSE). To facilitate the comparison, we use the relative version expressed in percentage deviation. Empirical sampling simulations can be considered as the “ […] ultimate tool for investigators who want to know if one sampling strategy will work better than another for their population. ” (Thompson, 2012). However, this requires the underlying simulation population to replicate as realistically as possible the target population. 7. 2.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Quality Metrics A standard Measure in the assessment of a sampling designs is the Root Mean Squared Error (RMSE) and calculated as: 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = ∑ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠𝑠𝑠𝑠𝑠 1000 𝑠𝑠𝑠𝑠𝑠𝑠 = 1 1000 = ൥ 1 1000 × ඥ (𝑌𝑌 ෠ − 𝑌𝑌) 2 𝑌𝑌 ൩ × 100 Expressed here as percentage deviation from the population mean Y and calculated for each parameter of interest. Equation.. is only the empirical representation though and a result of rearranging the definition of the Mean squared Error, 𝑀𝑀𝑀𝑀𝑀𝑀൫𝑌𝑌 ෠ ൯ = 𝐸𝐸൫𝑌𝑌 ෠ − 𝑌𝑌൯ 2 = 𝐸𝐸 ൣ ൫𝑌𝑌 ෠ − 𝑌𝑌෨൯ + ൫𝑌𝑌෨ − 𝑌𝑌൯൧ 2 = 𝐸𝐸 (𝑌𝑌 ෠ − 𝑌𝑌෨) 2 + 2𝐸𝐸൫𝑌𝑌 ෠ − 𝑌𝑌෨൯൫𝑌𝑌෨ − 𝑌𝑌൯ + ൫𝑌𝑌෨ − 𝑌𝑌൯ 2 And decomposing it into 𝑀𝑀𝑀𝑀𝑀𝑀൫𝑌𝑌 ෠ ൯ = 𝑉𝑉𝑉𝑉𝑉𝑉൫𝑌𝑌 ෠ ൯ + 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 (𝑌𝑌 ෠) with 𝑌𝑌 ෠, 𝑌𝑌෨ and 𝑌𝑌 being the estimate from the sample, the mean of this estimate and the true value in the population respectively. Var is the corresponding variance, and Bias the resulting bias component, which is defined as: 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 ൫𝑌𝑌෨൯ = 𝑌𝑌෨ − 𝑌𝑌", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10044 Most refugee hosting communities are characterized by high levels of poverty with precarious livelihood conditions, low access to public services, and underdeveloped infra­structure. While the unexpected inflow of refugees might bring both constraints and opportunities for improving and maintaining local livelihoods in these communities, the understanding of these effects remains limited. Using a household level micro data set from a 2018 baseline survey of the Ethiopia Development Response to Displacement Impacts Project, this paper assesses the impact of refugee inflow on the livelihood strategies of host communities with respect to diversification and agricultural commer­cialization.", "output": {"entities": {"named_data": [], "descriptive_data": ["household level micro data set"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The endogeneity of refugee inflow is addressed by exploiting differences in factors that influence refugee arrival in the host communities. Specifically, the analysis uses potential refugee inflow as an instrument, which is the product of population density and intensity of con­flicts (number of fatalities per event) in the closest region of the origin country to the refugee camp weighted by the distance of the refugee camp to the closest region. The paper also constructs an aggregate index to proxy house­holds ’ livelihood diversification strategies. The findings show that refugee inflow brings substantial benefits to host communities by creating significant jobs, in which people engage as secondary occupations, and triggers an increasing demand for livestock products. Specifically, while no effect was found on diversification of activities such as a primary occupation and crop product sales, a 1 percent increase in refugee inflow leads to a 2. 7 percent rise in diversifica­tion of livelihood activities as a secondary occupation and a 15. 9 percent increase in the value of livestock product sales. These effects tend to be heterogeneous across refu­gee hosting regions and the gender of the household head: negative effects were mainly observed in Gambella region, which hosts the largest refugee population in the country, and male-headed households were more likely to benefit from the refugee presence for the whole sample.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The paper identifies households ’ increased engagement in different livelihood activities and access to markets as a potential mechanism for the observed effects. The findings add to the growing literature on the socioeconomic impacts of refugee inflow on host communities by showing an overall positive effect on the livelihoods and welfare of receiving communities. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at swalelign @ worldbank. org / solezena @ googlemail. com, swangsonne @ worldbank. org, and gseshan @ worldbank. org.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. Introduction We are in the midst of protracted refugee crises. According to the latest UNHCR trends report, at the end of 2020, 76 percent of refugees globally (15. 7 million) were in a protracted situation (UNHCR 2021). 2 Most refugees reside in low-income countries, and more than eight of every 10 refugees (86 percent) live in countries within territories affected by acute food insecurity and malnutrition (UNHCR 2021a). Refugee receiving host communities also tend to be poor, experience precarious livelihood conditions and face many socio-economic challenges, such as low economic status, poor access to public services, and infrastructural development. For these communities, refugees might bring both challenges and benefits. On the one hand, refugees increase competition for natural resources (e. g., wood for energy, construction, land), public services and infrastructure (e. g., education, health, water supply), and economic opportunities (e. g., traditional livelihoods, labor employment). Refugee inflow may also affect the local market by mainly depressing wages and raising product prices (Vemuru et al. 2020).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We measure livelihood diversification using two main variables: the degree of diversification of activities as a primary occupation and the degree of diversification of activities as a secondary occupation. 3 The degree of agricultural commercialization is also measured using two variables: the value from the sale of crop and livestock products. We measure refugee inflow (presence) as the number of refugees (population) in the nearest refugee camp to the household location weighted by the household's inverted distance to the camp. The impact of refugee inflow on household livelihood strategies can be causal if there are no confounding factors that affect livelihoods in host communities when refugee inflow changes. This is unlikely as refugee flow and the location of refugee camps are not random (see e. g., Baez 2011). Refugee camps are often situated close to international borders, among others, to allow for easy repatriation of the refugees when stability is restored in their countries of origin. In addition, refugees often seek shelter in the nearest refugee camp once they arrive in the host country, which is arguably true in most hosting countries as refugees often travel on foot for 2 According to UNHCR, a protracted refugee situation is a situation in which at least 25, 000 refugees from the same nationality have been in exile for at least five years in a given host country. 3 Diversification of activities is calculated using the inverse Simpson diversity index. In constructing the index, we considered both agricultural and non-agricultural livelihood activities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 hours (Ruiz and Vargas-Silva 2018). Hence, to identify the causal impact of refugee inflow on livelihood diversification and commercialization, we employ a two-stage least squares (2SLS) econometric specification strategy using potential refugee inflow as an instrument. Potential refugee inflow is constructed as the product of population density and intensity of conflicts (number of fatalities per event) in the closest region of the origin country to the refugee camp weighted by the inverted distance of the refugee camp to the closest region4 (i. e., the shortest distance to the border between the refugee camp and the bordering country of origin). Similar (weighted) instruments have been used in the literature (e. g., Baez 2011; Fallah et al. 2019) and proved to be an appropriate instrument to study the socio-economic impact of refugees on host communities. Livelihood diversification and agricultural commercialization are the two main common strategies that people in low-income countries adopt to improve or maintain their livelihood and welfare. Given the prevailing under-developed insurance market in the event of shocks, households tend to pursue several income generating activities. However, potential barriers such as low asset endowment hinder households'successful livelihood diversification (Ellis 2000; Martin and Lorenzen 2016; Loison 2015).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Specifically, a 1 percent increase of the refugees ’ presence5 leads to a 2. 7 and 15. 9 percent increase in the diversification of livelihood activities as a secondary occupation and value of livestock product sale, respectively. It should be noted that this analysis is taking place during a period where refugees in Ethiopia were prohibited by law from seeking work outside designated camps. This has changed after 2019 because of the revised Ethiopian Refugee Law. These effects tend to be heterogeneous across regions and to a limited extent, vary depending on the gender of the household head. The negative effects tend to be concentrated in Gambella, a region that hosts most of the refugee population in Ethiopia and where the refugee population is as large as the population of the region. Overall, compared to women-headed households, households with a male head seem to benefit through increased diversification of activities as a secondary 4 Region refers to the administration level 1 from the Database of Global Administrative Areas (GADM). The nearest region to the refugee camp is identified as the one that has the shortest straight distance to the refugee camp among all neighboring regions in the major refugee source countries. 5 As explained above, refugee presence is the number of refugees (population) in the nearest refugee camp to the household location weighted by the household ’ s inverted distance to the camp.", "output": {"entities": {"named_data": ["Database of Global Administrative Areas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, most of the studies only consider a subset of occupation or livelihood activities (mainly employee-based) and do not provide a full picture of the livelihood impacts of refugee presence on host communities. We consider an exhaustive set of livelihood activities in which households (individuals) in the impacted communities may engage. 7 Second, prior studies tend to focus on the different livelihood activities separately (i. e., whether the individual adult members or the household engage in each of the livelihood activities). 8 Therefore, they are unable to infer whether households are diversifying or specializing their livelihoods or are engaging more on the commercialization of activities. 9 The current paper goes beyond the allocation of labor to individual (specific) 7 As the data we used does not have a good welfare indicator (e. g., income, consumption, and assets), we could not explore the welfare impact of refugee inflow. 8 We examined households ’ engagement in individual livelihood activities as a mechanism for household livelihood strategies. 9 Generally, households tend to diversify their livelihood when facing negative shocks (e. g., conflicts, droughts) to minimize risk (Ellis 2000a, b). In the case of refugee inflow, households may either diversify or specialize as refugee inflow could be both a negative shock (through increase competition for resources, services, and employment) and a positive shock (through creating opportunities, such as high demand agricultural products, provision of cheap labor).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Figure 1: Map of regions in Ethiopia, location of refugee camps, and refugee source countries. Source: Database of the Global Administrative Areas (GADM) (https: / / gadm. org / data. html, accessed on November 20th, 2020) In Ethiopia, the Refugees and Returnees Services (RRS, former Agency for Refugees and Returnees Affairs (ARRA)) is responsible for managing refugee camps and its oversight by making sure that the commitment of the federal government is met (Nigusie and Carver 2019). Except for Eritrean refugees, most of whom are eligible for out of camp policy, arriving refugees, at the time the data was collected, were allocated to one of the 26 refugee camps spanning the five refugee hosting regions. Refugees living outside of camps represent about 10 percent of the refugees in Ethiopia (Abebe et al. 2018). The allocation tends to be based on shared identity between the refugee and the host communities and the distance of the refugee camps from the border of the source country. The South Sudanese refugees are hosted in the refugee settlements in Gambella, except the few who were relocated to the refugee camps in Benishangul-Gumuz region. Most of these refugees arrived during the civil conflict in South Sudan in 2013 (Nigusie and Carver 2019).", "output": {"entities": {"named_data": ["Database of the Global Administrative Areas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 inflow). We then use the composite variable as an instrument to identify the causal impact of refugee presence on livelihood diversification and commercialization in host communities. Figure 2: Household livelihood strategy framework under refugee inflow Source: Adapted from Nielsen et al. (2013) and Walelign and Jiao (2017) 4. Data sources The major data source of the current study is the World Bank ’ s Development Response to Displacement Impacts Project (DRDIP) 12 baseline survey from Ethiopia. The Ethiopia DRDIP survey was administered between September 2017 and August 2018. The survey covers 113 Kebeles (wards) in 16 Woredas (districts) from the top five refugee-hosting regions in Ethiopia. The selection of the sample households follows stratified random sampling with proportion to size (the number of households) using Woredas as a geographic stratum. The sample originally comprised a total of 3, 390 households, who were selected using systematic random sampling within each Woreda. We used data from 3, 375 households, as 15 of them were excluded due to missing location information (GPS).", "output": {"entities": {"named_data": ["Ethiopia DRDIP survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The sample households are located within varying distance from the nearest refugee camp (approx. 67 to 76, 665 meters) (see Figure 3). 12DRDIP aims to improve access to basic social services, expand economic opportunities, and enhance environmental management for communities hosting refugees through providing funding for community driven projects.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 Figure 3: Location of refugee camps in Ethiopia and the Ethiopia Development Response to Displacement Impacts Project (DRDIP) sample households Source: Authors ’ compilation using the database of the Global Administrative Areas (GADM) (https: / / gadm. org / data. html, accessed November 20, 2020) and the Humanitarian Data Exchange (HDX) database for the refugee location (https: / / data. humdata. org / dataset / ethiopia- refugee-camp-locations, accessed November 20, 2020). From the Ethiopia DRDIP data set, we derive two measures of livelihood diversification and two measures of agricultural commercialization (all at household level). The measures of diversification include: (i) the degree of labor diversification in different productive livelihood activities (e. g., farming, wage employment) as a primary activity (occupation), and (ii) the degree of labor diversification in different livelihood activities as a secondary activity (occupation). 13 These two outcomes were constructed using the inverse Simpson diversity index as 1 ∑ 𝑛𝑛 𝑖𝑖 𝑆𝑆𝑖𝑖 2, where 𝑆𝑆𝑖𝑖 is the share of the number of adult labor engages in 𝑖𝑖𝑡𝑡ℎ livelihood activity to total active adult household labor and 𝑖𝑖 ranges from 1 to the number of livelihood activities that a household engages in (Valdivia et al. 1996).", "output": {"entities": {"named_data": ["database of the Global Administrative Areas", "Ethiopia DRDIP data set", "Humanitarian Data Exchange"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 crop products (e. g., wheat, potatoes) and the value of five livestock products (i. e., milk, egg, butter, hides, and honey) sold in the market. 14 Several other data sources were utilized. First, the Ethiopian refugee camps location data set from the Humanitarian Data Exchange (HDX) 15 and the total number of refugees by camps from the United Nations High Commissioner for Refugees (UNHCR), Addis Ababa office. We use data from 26 official UNCHR refugee camps in Ethiopia that were operational in 2018 (see Figure 1; 3). Second, we use administrative data sets for Ethiopia and refugee source countries from the database of Global Administrative Areas (GADM). 16 We also use the conflict data set from the Armed Conflict Location and Event Data Project (ACLED) 17 and the population data from the Gridded Population of the World (GPW) data set. 18 On the basis of these data sets and the location of sample households from Ethiopia DRDIP data set, we generated the following variables: i) distance of sample households to the nearest refugee camp, the nearest region (administration level 1 in GADM) to the refugee camps, ii) distance of the refugee camps to the nearest border of the refugee source country, iii)", "output": {"entities": {"named_data": ["database of Global Administrative Areas", "Gridded Population of the World (GPW) data set", "Humanitarian Data Exchange (HDX)", "Ethiopia DRDIP data set"], "descriptive_data": ["Ethiopian refugee camps location data set"], "vague_data": ["administrative data sets"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the intensity of conflicts, and iv) population density in refugee host countries by region. Appendix B presents the list of variables along with description and summary statistics. 5. Empirical strategy To estimate the impact of refugee inflow19 on host community ’ s livelihood strategy choice, we use the following basic econometric model: 𝑌𝑌𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑅𝑅𝑅𝑅𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖 + 𝜈𝜈 + 𝜀𝜀𝑖𝑖 (1) Where, 𝑖𝑖 indexes a household, 𝑌𝑌𝑖𝑖 is an outcome variable of interest (livelihood diversification or commercialization of agriculture), 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 is the measure of refugee inflow, i. e., the refugee population (average of 2017 and 2018) in the nearest refugee camp weighted by the inverse of distance of the household to the refugee camps, 𝑋𝑋𝑖𝑖 is a set of household controls, 𝜈𝜈 is kebele fixed effects, and 𝜀𝜀𝑖𝑖 is the error term. Several variables, from the DRDIP data set, were used as controls in our model.", "output": {"entities": {"named_data": ["DRDIP data set"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Further, as Jordan and the international community develop new approaches that respond holistically to the specific needs of refugee and host communities, more evidence is needed about how gender ‐ based constraints and vulnerability affect refugee women ’ s ability to take up economic opportunities and to access the services and resources they need to enable their families to move out of poverty. Our analysis applies a gender lens to a rich set of microdata on Syrian refugees in Jordan collected by UNHCR between 2011 and 2014. As these data do not capture how the changes in policies affect refugees and the constant evolution of their situation since 2014, the analysis is not intended to directly inform current policy choices and decisions. Instead, our aim is to devise an approach that can provide greater insights into gender ‐ specific barriers, based on the premise that the experiences and potential vulnerabilities of women, men, and children are significantly different in refugee settings. We use household ‐ level data to examine the relationship between poverty and gender for Syrian refugees. Our approach is informed by a body of work in the academic literature that has used household survey data to examine the relationship between the gender of the household head and household 2 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 3 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 4 https: / / data2. unhcr. org / en / documents / download / 64568, accessed August 2, 2018. 5 https: / / reliefweb. int / sites / reliefweb. int / files / resources / 64114. pdf, accessed August 2, 2018.", "output": {"entities": {"named_data": [], "descriptive_data": ["microdata on Syrian refugees in Jordan"], "vague_data": ["household survey data", "household ‐ level data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The UNHCR data identify principal applicants for each household and our analysis examines differences in household poverty between households with a female rather than male principal applicant. 6 The principal applicant is the person who receives assistance from UNHCR for the family and is self ‐ selected or selected by the family. This definition of female headship has advantages over the way that household headship is commonly identified in household surveys. An often ‐ noted drawback of the headship variable is that female headship may reflect the enumerators ’ perception about who should be considered a family head rather than who has the most responsibility for the family ’ s welfare in practice. 7 Social norms can also affect whether female respondents self ‐ identify as household heads. For example, some Eritrean returnees who would in other cultural settings be regarded as de jure female headed (single mothers, widows, divorcees, separated women) reported being male ‐ headed. Other Eritrean female returnees who would be considered de facto heads reported headship by absent husbands or male relatives (Kibreab, 2003). Our approach is therefore to distinguish between different types of female and male principal applicant households, using a typology that reflects some of the indicators of vulnerability used by UNHCR. We find that distinguishing between different types of female principal applicant households is important in the setting of Syrian refugees in Jordan.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Most male principal applicants are one of a married couple with children whereas most female principal applicants are single care ‐ givers, single persons or living in non ‐ traditional family groups. While on average female principal applicant households are no more likely to be poor than male principal applicant ones, poverty rates for some types of households are higher when these households have a female principal applicant. Households that have formed because of the unpredictable dynamics of forced displacement, such as sibling households, unaccompanied children, and 6 Identification of the head of the case (as family groupings are referred to in the UNHCR ProGres database) is determined by who best represents the family for case management purposes. It is not assumed that the household will be best represented by a man; a woman or even a child can be a head of a case, depending on standard operating procedures. 7 Even when traditional household survey data are gathered at the individual level, the information is often collected from a single respondent. The respondent is usually the self ‐ identified ‘ most knowledgeable ’ household member, which overwhelmingly corresponds to the ‘ head ’ of the household. In the case of a household survey that solicits information on ‘ headship ’, this information is gathered often through the question: “ Who is the head of this household? ”", "output": {"entities": {"named_data": ["UNHCR ProGres database"], "descriptive_data": [], "vague_data": ["household survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 single caregivers, are an extremely vulnerable group and especially so if principal applicant is a woman or girl. Moreover, poverty gaps between male and female principal applicant ’ s for these households remain after humanitarian assistance is received. To understand how gender differentiates the poverty experienced by the Syrian refugees, we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD ‐ HV). The ProGres database for Jordan includes information on refugees ’ registration since 1935. The registration process assigns refugees a unique registration number that serves as a reference for recording data at the initial registration and in all subsequent activities, including decisions on refugee status and right of return or resettlement in a third country, as applicable. UNHCR issues refugees residing in camps a ‘ proof of registration ’ document, which they hold while they remain there. For those who live outside the camp, UNHCR provides an asylum seeker certificate stating that those on the certificate are persons of concern. The asylum seeker certificate allows Syrians to access United Nations (UN) services and assistance provided outside the camps, such as monthly cash support, nonfood goods, and healthcare (NRC and IHRC 2016).", "output": {"entities": {"named_data": ["Jordan Home Visits round 3", "Profile Global Registration System (ProGres)", "Profile Global Registration System", "asylum seeker certificate"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This assistance is crucial and has been reported to support a meal a day, a better roof, and dignity for Syrians who have fled to Jordan. 8 The JD ‐ HV database has detailed information on expenditure, sources of income, and indicators of household ‐ level welfare, for example, as reflected by recourse to coping mechanisms, standard of accommodation, or access to water, sanitation, and hygiene (WASH). JD ‐ HV data collected between October 2013 and December 2014 were first analyzed in Verme et al. (2016) who produced welfare aggregates and poverty measures to help target benefits and assistance to those most in need. Verme et al. (2016) draw attention to the precarious circumstances of Syrian refugees in Jordan and Lebanon. Around 55 percent of refugees in Jordan are vulnerable to monetary poverty and more than half are vulnerable to food shocks. Family size increases the probability of being poor, with the poverty rate almost doubling if the size of the family goes from one to two members and increasing by 17 percent when the number of children increases from one to two.", "output": {"entities": {"named_data": ["JD ‐ HV database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "UNICEF (2017) reports that for boys, child labor, school violence, and the high costs of schooling (for transportation and stationery supplies) are the main barriers to their enrollment. For girls, barriers include the distance to the nearest school, the high cost of transportation, the need to help with household chores, health problems, and families refusing to educate their daughters. According to the JD ‐ HV database, the most common reasons parents gave for their children not attending school were financial constraints (35 percent), lack of capacity in schools (29 percent), or that children were required to work to support their family (14 percent). By 2016, the enrolment rate of Syrian refugee school ‐ age children in Jordan was 83 percent, 54 percent in formal education, and 29 percent in nonformal education (World Bank 2017b). 10 Refugee Employment It has been hard for refugees to find work in Jordan. The slowdown in growth in Jordan pre ‐ dates the arrival of Syrian refugees and the economy has been increasingly unable to absorb new labor market entrants. Between 2010 and 2016, labor force inactivity increased, employment decreased, and unemployment increased in Jordan (Malaeb and Wahba, 2018).", "output": {"entities": {"named_data": ["JD ‐ HV database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To work legally in Jordan, refugees must have a work permit (Verme et al 2016). However, there is a list of professional jobs, including physicians, engineers, teachers, and workers in the services sector, that can only be done by Jordanian nationals (ILO 2015). Since the agreement of the Jordan Compact in 2016, the Government of Jordan has taken steps to open formal employment opportunities for Syrians. It has waived the fees required to obtain a work permit for Syrian refugees in a number of occupations open to foreign workers and simplified the documentation requirements. These measures have encouraged employers to regularize their workers; 10 Nonformal education services include catch ‐ up courses, dropout and basic literacy programs, and learning support services offered in Makani Centers of the United Nations Children's Fund (UNICEF) (UNICEF 2017). The Makani Centers are multifunctional spaces providing learning support, psychosocial support, and a safe environment with opportunities for play.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "32 Appendix 1. Variable Definitions Table A1. 1. Variable definitions Variable Definition Age of PA Age of the principal applicant (PA) Children under 5 1 if there are one or more children below the age of 5 (inclusive) in the household Disable 1 if there are one or more disabled persons in the household Education Categorical variable. We classified education of the PA in three groups: below years, 6-11 years, and more than 12 years of education Elderly 1 if there is one or more persons above the age of 65 (inclusive) in the household Entry status Categorical variable. ProGres reports 5 entry statuses of which we selected the three categories with the largest number of PAs: Informal, formal, and smuggled. Expenditure Raw addition of all expenditure categories, which include rent, bills, food, healthcare, education, and others. Family Type Categorical variable.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For a detailed definition see section 4 Household size Number of people included in the case records of each PA in Individual ProGress dataset Wage Income 1 if the household receives income from employment and / or daily or irregular work Income from remittances 1 if the household receives income from remittances Income per capita Raw sum of household income from all sources; work, pension, assets in Syria transfers, donations, other organizations'humanitarian aid, and other divided by household size Male Adults Number of males above 18 (inclusive) in the household Marital Status Categorical variable. The classification includes married PAs with spouse in the household, married PAs without spouse in the household, widowed, single or engaged, and divorced or separated. Proportion of female Number of female divided by the household size Location Categorical variable for 11 Governorates / cities. Ajloun City, Aqaba, Balqa, Irbid Jerash, Karak, Maan, Madaba, Mafraq, Tafilah, Zarqa. In Camp 1 if the household is located in a refugee camp Poverty before UNHCR and WFP assistance 1 if household expenditure before UNHCR plus WFP assistance is below the poverty line (JD50) Poverty before UNHCR assistance 1 if household expenditure after WFP assistance but before UNHCR assistanc is below the poverty line (JD50) Source: Authors ’ elaboration.", "output": {"entities": {"named_data": ["Individual ProGress dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 to 32 percent (see Table A1 in appendix). The total number of internally displaced people (IDP) outnumbers the stock of refugees in SSA and in the world but overall has followed a similar trend compared to the number of refugees (by country of origin). 1 Major civil wars in Central Africa mainly explained the peak in 1993 and 1994 and the increase at the end of the 1990s. Figure 1. Refugee population by origin, 1990 ‐ 2013 Note: Authors ’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014. Data from 2007 to 2013 include people in refugee ‐ like situations. Persons in refugee ‐ like situations include “ groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained ” (UNHCR 2014: 39). Refugees in Africa seem to have mainly remained in Africa. Although SSA also hosts refugees from other regions, the closeness of the ‘ blue ’ and ‘ red ’ lines in Figure 2 ‐ representing the number of refugees originating from and hosted in SSA ‐ is an indication that most refugees cross borders within Africa.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 (UNHCR, 2014), developing countries hosted 10. 1 million refugees or 86 percent of the world ’ s refugees. Contrary to what has been sometimes claimed in popular media, refugees are not “ invading ” the higher ‐ income countries. Actually, SSA has been hosting more refugees than sending them since 1990. The divergence of trends occurring in 2005 is certainly related to large inflows of refugees from North Africa and the Middle East. The second peak in 2011 corresponds to the uprisings that spread across several Arab countries (Egypt, Libya, Syria, Tunisia and Yemen), and the recent one in 2013 to the large outflows of refugees from Iraq, Syria and Yemen. Figure 2. Refugees and Internally Displaced People in SSA, 1990 ‐ 2013 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). Due to changes in classification and estimation methodology in a number of countries, 2007 figures are not fully comparable with pre ‐ 2007 figures (see also footnote 1). Gathering data on internally displaced people is much more challenging since most existing data on IDPs are incomplete or unreliable.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We only found reliable approximations from the International Displacement Monitoring Center (IDMC) between 2003 and 2013. According to IDMC, there were about 12. 5 million internally displaced people in SSA at the end of 2013 (IDMC 2014), more than one third of the total number of IDPs and more than tripling the number of refugees in SSA. Although the number of IDPs in SSA is the highest since 2007, the share of IDPs in SSA has been decreasing from 53 % in 2003.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The vulnerability of the Internally Displaced People in SSA has certainly been overlooked for too long, but the increased support provided by UNHCR is an encouraging but challenging sign in that respect. Figure 3. Refugees and Internally Displaced People in SSA, 2003 ‐ 2013 Source: Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). The annual number of IDPs is collected from the IDMC (2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2012, 2013, 2014) annual reviews.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 2. 3. The Challenges of Protracted Refugee Situations in Sub ‐ Saharan Africa At the global level, about 54 % (i. e. about 6. 3 million) refugees were in protracted refugee situation by the end of 2013 (UNHCR 2014). 2 As reported by Kreibaum (2016), the number of protracted refugee situations has increased from 22 in 1990 to 30 in 2008. These protracted situations in Africa have been characterized by Crisp (2003) as in most of the cases: i) peripherally located with poor security, unfavorable climatic conditions, and economical and political marginalized; ii) concentrating people with special needs like e. g. children and women (see Section 3); and iii) lacking basic human rights, including those covered by the provision of the 1951 refugee convention. Another distinct feature of refugees in SSA is that they are mostly hosted in organized camps. While in developing countries around one third of refugees are hosted in camps, the share raises to about 40 percent in Sub ‐ Saharan Africa (Figure 5). 3 The percentage of 76 percent in Eastern Africa and the Horn of Africa stresses again the pressing situation in this part of the world.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While camps have been recognized as posing serious challenges (Jacobsen and Crisp 1998), it is quite striking to observe that this organizational feature is not as spread in other regions of the world as in SSA. At best, only 28, 25 and 15 percent refugees are hosted in planned / managed camps in Asia, Americas, and the MENA region, respectively. Such figures are based on the most recent year available (2013) and may change significantly following the large inflows of Syrian refugees into Egypt, Lebanon, Iraq, Jordan and Turkey. Nonetheless, the differences are sufficiently striking to believe that this is a distinct feature of refugee hosting in SSA. 2 UNHCR defines a protracted refugee situation as “ one in which 25, 000 or more refugees of the same nationality have been in exile for five years or longer in a given asylum country ” (2012: 23). 3 The figures are based on refugees (including those in refugee ‐ like situation). Persons in refugee ‐ like situations include “ groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained ” (UNHCR 2014: 39). The number of refugees and people in refugee ‐ like situation for which demographic data is available does not necessarily equal the total number of refugees. However, for SSA, there is little difference between the two. We also restrict the number of refugees to those whose accommodation is known by the UNHCR (approximately 19 % in the world and 8 % for SSA).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["demographic data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Figure 5. Share of refugees hosted in camps, 2013 Source: Authors ’ presentation based on UNHCR Global Trends 2013 (UNHCR 2014). In summary, investigating the recent trends in forced displacement in Sub ‐ Saharan Africa points to the regional nature of this displacement, emphasizing the unfortunate increase in refugee movements in Eastern Africa over the most recent years. Such regional emphasis also takes some distance from the widespread view that refugees are mainly moving to Europe or other developed countries. In 2013, about 3. 7 million refugees originated from SSA but about 5. 6 million were hosted there. Most refugees from SSA remain in Africa. Refugees are mainly hosted in camps in peripheral and poor areas. The next sections will explore how refugees and hosting communities are affected by such forced displacement.", "output": {"entities": {"named_data": ["UNHCR Global Trends 2013"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 3. Refugees as Agents of Their Own Destiny 3. 1 The Composition of Africa ’ s Refugee Population and Its Consequences One of the first elements that catch the eye in Figure 6 is the difference in the composition of the refugee population in Africa compared to the rest of the world. The share of children and women among refugees is higher in Africa than elsewhere, in particular East and West Africa stand out here. This is, at least partly, a consequence of Africa ’ s younger, general population, but other forces could be at work as well, e. g. higher mortality of adult males in Africa or adult males staying behind or being separated from the rest of the household. It does mean however that, relative to other areas, more attention should be going to the needs and capacities of women and children in Africa. This means, for example, adaption of and increased supply of schooling and health services. Figure 6. The composition of refugees by age and gender, 2013 Source: Note: UNHCR statistics (UNHCR 2014). Asia excludes Australia, Japan and New Zealand. Americas exclude Canada and the United States. These percentages have been calculated by country when demographic data are available for at least 30 % of the total.", "output": {"entities": {"named_data": ["UNHCR statistics"], "descriptive_data": [], "vague_data": ["demographic data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 causes of death. The Danish Epidemiology Science Centre (1999) found severe malnutrition and high mortality in a survey of 422 refugee children in Guinea ‐ Bissau. They report higher malnutrition and higher mortality for children living in a non ‐ camp setting, compared to children living in a camp. The Goma epidemiology group (1995) found high prevalence of child mortality as well as acute malnutrition among children in refugee camps in Eastern Zaire, especially in female headed households. The magnitude of the difference between ‘ normal ’ mortality in the country under study, in the absence of conflict and the mortality in a refugee camp, depends on several parameters: the health infrastructure in the country as well as in the camp, the food available to camp and non ‐ camp residents, the frequency of visits by nurses or doctors, the intensity of the conflict (e. g. attacks on camps), and so on. Thus, the results are highly dependent on the context. For example, Singh et al (2005) do not find a difference in under 5 mortality among refugee versus non ‐ refugee households in western Uganda and South Sudan, whereas Verwimp and Van Bavel (2005) find higher child mortality and fertility among Rwanda refugees in Congo versus Rwandan women who did not became a refugee.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of 422 refugee children in Guinea ‐ Bissau"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "When general living conditions in one ’ s residence or home area are worse compared to a camp environment, e. g. because health services are available in the latter, mortality may also be lower in the camp. The strong presence of children in Africa ’ s refugee population implies that we should also look at the potential long ‐ term effects of forced displacement on survivors. Given the composition of the refugee population, such long ‐ term effects will be more important in Africa compared to elsewhere. Few studies have followed children exposed to forced displacement over a long time to directly infer the long ‐ term effects of forced displacement, in particular on health, education and labor market participation. Most studies of the long term effects of conflict use an indicator of exposure to violent conflict, but few of them have forced displacement as one of the indicators. There is however a very well established literature (see Currie and Vogl, 2013 for an overview) on the long ‐ term consequences of deprivation in early childhood which can be applied to the situation of refugees. If young children between the ages of 0 to 3 years old are exposed to malnutrition, disease, stress and violence during episodes of forced displacement, then, this literature shows that this deprivation will have negative long ‐ term effects.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 Revealingly, in Betts et al. ’ s (2013) survey of refugees in Uganda, 96 % of all interviewed households in the capital and 70 % outside the capital said they owned and used a mobile phone. They use this mobile phone to communicate with customers and suppliers, to get market information and to transfer money. Half of the urban refugees and 11 % of rural refugees also have access to the Internet. 4. Refugees As a Burden? As pointed in Section 2, most refugees in SSA are hosted in neighboring countries. Most of these hosting countries are likely among the least developed countries. It has been argued that these refugees may constitute an additional burden in terms of economic development in hosting countries (Mabiso et al. 2014). UNHCR (2014: 17) implicitly recognizes that potential burden by suggesting that the ratio of the size of the country ’ s hosted refugee population to its average income level can provide a proxy measure of the burden of hosting refugees.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of refugees in Uganda"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "SSA counts 13 out of the 20 countries hosting the larger number of refugees per 1 USD GDP (PPP) in the world. 4 While such figures stress that SSA hosts a fair share of the refugees in the world and underline that refugee flows are mainly a South ‐ South phenomenon, we shed doubt on this view of refugees described as a burden. We even argue in Section 6 that such representation is not conducive to the right policy framework in refugee ‐ hosting areas. 4 The other major host countries per USD GDP are all developing countries, with Pakistan (1st), Jordan (8th), Bangladesh (9th), Yemen (10th), Iran (14th), Lebanon (17th), and India (20th). Figure A2 provides the top 10 ranking in the world. At a global level, we should note that in 2013 “ the 40 countries with the largest number of refugees per 1 USD GDP (PPP) per capita were all members of developing regions, and included 22 Least Developed Countries ” (UNHCR 2014: 17). It should be noted that the way UNHCR computes that “ potential burden ” gives more weight to countries with very large population since is equivalent to.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 Note: Poverty is defined as the percent of the population living with less than $ 2 a day (World Development Indicators database). The annual number of refugees in each country is given by the Center for Systematic Peace (http: / / www. systemicpeace. org /). 4. 2. Lessons from Case Studies in Kenya, Tanzania, and Uganda Given the limits of cross ‐ country comparisons, we present below three short case studies on the impact of protracted refugee situations on hosting communities. These case studies were not chosen based on a systematic review but they are sufficiently close to each other to allow for comparative learning. These case studies are also those emerging from a growing literature on the quantitative assessment of the impact of refugees on hosting communities (Mabiso et al. 2014). Case Study # 1: The protracted refugee situations in Tanzania Tanzania has been known as a refugee ‐ hosting country for long due to its peaceful history and its location surrounded by conflict ‐ affected countries (Burundi, Rwanda, Uganda, Mozambique). The first president of Tanzania, Julius Nyerere, welcomed most of refugees as a sign of pan ‐ African solidarity in the post ‐ independence periods from many African nations.", "output": {"entities": {"named_data": ["World Development Indicators"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "At least, at the time of the closure of the last camp in the region of Kagera, the aid workers from UNHCR and other organizations were aware of the challenge of the transition for the hosting population and seeks to coordinate with development actors such as the United National Develop Programs or local NGOs to support the hosting population in the district of Ngara. Nevertheless, the limited resources remained a major constraint on that effort and sheds light on the institutional constraints existing to scale up such positive efforts of coordination. Case Study # 2: The protracted refugee situations in Kenya Dealing with refugees remains a relatively novel phenomenon in Kenya. It was not until the early 1990s that Kenya witnessed massive refugee influxes from Somalia, Sudan, and Ethiopia (Banki 2004). Prior to that period however, Kenya had a reputation for having generous refugee policies, which allowed the successful integration of a number of refugees from Mozambique, Uganda and Rwanda (Banki 2004). However, with the arrival of hundreds of thousands of new refugees from neighboring countries during the 1990s, the responsibility for the care of the refugees shifted from the Government of Kenya ’ s (GoK) to the international community, leaving the more inclusive policies that were prevailing before 1991.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "23 The Kakuma refugee camp was established in 1992 in response to the inflow of 23, 000 Sudanese refugees (Jamal 2000). The camp is now home to over 100, 000 refugees from South Sudan, Burundi, Ethiopia, Somalia, and the DRC (UNHCR, 2012). The ongoing unrest in South Sudan is likely to exacerbate the refugee situation in the upcoming year. Moreover, the restrictions imposed by the government on refugee movement and employment makes the Kakuma population completely dependent on assistance provided by international organizations present on the field (Jamal 2000) The evidence on the impact of refugees in Kenya is quite limited. However, the Nordic Agency for Development and Ecology (NORDECO 2010) provides a detailed description, backed by sound descriptive statistics, on the impact of Dadaab refugee camps on host communities. Despite the very different structure of the local economy, mainly driven by pastoralist livelihoods, a pattern somewhat similar to the Tanzanian case is observed. According to NORDECO (2010), the aggregated economic impact is positive. It is estimated that about USD 3 million annual income accrues to the host community thanks to livestock and milk sales to the refugee camps. Trade and employment opportunities have also been reported around Dadaab camps in Kenya.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The total economic benefits, including through savings on food purchases (including through purchases from refugees), income accruing to local contractors from assignments for the United Nations or Non ‐ Governmental Organizations or support for host communities, “ using 2010 as a reference year, are [estimated to be] around USD 14 million annually. On a per capita basis this equates to around 25 % of average annual per capita income in North Eastern province ”. This estimation corresponds to a back ‐ on ‐ the ‐ envelope approximation but it gives a sense of the major benefits to the local population. Similar to the Tanzanian case, the presence of the Dadaab refugee camps is reported to have improved the provision of local public goods such as the frequency and reach of transport services and the availability of health and social services. NORDECO also observed environmental degradation around the Dadaab camps5 but spatially restricted in an area of inherently low resource value. It seems that environmental support programs have helped limiting the collection of firewood by refugees and providing alternative fuel sources (Milner and Loescher 2004). Compared to the Tanzanian case, two main differences emerge. Less emphasis is given to the distributional effect of the refugee inflows on the hosting communities, while less pressure on prices is observed around the Dadaab refugee camps.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Both differences may actually be related to the dominance of pastoralist livelihoods. First, on the distributional dimension, NORDECO (2010) did not point to a similar substitution effect between unskilled labor or refugees. In a pastoralist environment, the low ‐ middle ‐ income group and the poor are those primarily engaged in selling their products to refugee camps. Second, contrary to Alix ‐ Garcia and Saah (2010) for Tanzania, “ the price of basic commodities such as maize, rice, wheat, sugar and cooking oil is [reported to be] at least 20 % lower in camps than in other towns in arid and semi ‐ arid parts of Kenya. The main reasons are the re ‐ sale of WFP [World Food Program] rations, access to free food by locals registered as refugees and illegal imports via Somalia ” (NORDECO 2010: 9). Another possible explanation reported by Maystadt and Duranton (2014) in the Tanzanian case, is the importance of transport services in pushing the price of traded goods down. Although focusing more on the urban function of the refugee camps and the social transformation underpinned in the hosting society, Jansen (2011) also reports similar trading activities and wealth 5 Nonetheless, such a degradation is acknowledged by NORDECO (2010) to be difficult to distinguish from general trend prevailing the region.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "25 contributions to the local communities. More than other studies, this analysis points to the transfer of physical and human capital by refugees as an important source of benefits for the local economies. Interestingly, Kreibaum (2016) provides a more quantitative approach to the issue by assessing the impact of an increase in the presence of Congolese refugees on the hosting population in the Southern and Western parts of Uganda. The results indicate a positive ‐ although small in magnitude ‐ impact on the hosts ’ welfare (consumption per adult equivalent) but with distributional effects. Those depending on wage income and transfers experienced a deterioration in welfare, suggesting labor substitutability with rural landless workers. That seems to constitute a commonality with the Tanzanian case study. In addition, increase in the provision of private education services are also found, which is consistent with the move to the so ‐ called self ‐ reliance strategy in Uganda (see below). A major contribution of this paper is to contrast these results to the Ugandan households ’ perceptions in local communities. Conditional on assuming a common trend (that could not be tested with the available data), people are found to perceive their living conditions as having worsened off in areas with a higher number of refugees.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some Key Findings from Case Studies The above case studies point to an emerging body of the literature that seeks to quantify the impact of refugees in protracted situation on the hosting economies (Alix ‐ Garcia and Saah 2010; Baez, 2011; Betts et al. 2014; Maystadt and Verwimp, 2014; Maystadt and Duranton 2014; NORDECO 2010; Kreibaum 2016). Although that literature is still in its infancy, we can seek to draw a few lessons, even if these lessons can also serve as further hypotheses to be tested. First, the three case studies underline the importance of market mechanisms. Previous literature was very much focused on the health, environmental, and security consequences of hosting refugees. These concerns still rank as first priorities when refugees cross borders. But the understanding of protracted refugee situations requires paying much more attention to the interactions between refugees and their 6 As pointed by Dryden ‐ Peterson and Hovil (2003), de facto local integration has been a common occurrence, well before 1999.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "40 Sub ‐ Saharan Africa 8604000 5895000 7055000 5406100 5068000 N. A. MENA 6230000 8000000 6675000 8592900 10892000 N. A. Asia and Pacific 4325000 2405000 3392000 2128800 5490000 N. A. (excl. Australia, Japan, New Zealand) Americas 1126000 1280000 2176000 2900000 3661000 N. A. (excl. North America) Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). As indicated in Figure A1, these data are much lower compared to those provided from 2003 by IDMC but provide a longer time series. UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "41 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Since some adjustments could take place over time, we always use the figures from the last available report.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure A2: Refugees a burden for SSA? Panel A: Not weighted by economic capacity", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Migration and Economic Mobility in Tanzania: Evidence from a Tracking Survey Kathleen Beegle The World Bank Joachim De Weerdt EDI, Tanzania Stefan Dercon Oxford University, UK We thank Karen Macours, David McKenzie, and seminar participants at the Massachusetts Avenue Development Seminar, Oxford University and the World Bank for very useful comments. All views are those of the authors and do not reflect the views of the World Bank or its member countries.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 quest for imaginative and convincing instruments for migration (see the review of the migration and poverty literature by McKenzie and Sasin, 2007, and the references therein). An additional hurdle is the need for panel data to study migration and economic mobility. The costs and difficulties in re-surveying migrants mean that attrition may be relatively high for this group and may also result in the loss of some of the most relevant households to study this process (Beegle, 2000; Rosenzweig, 2003). This paper uses unique data from a region in Tanzania to address this key question: What is the impact of physical movement out of the original community on poverty and wealth? Although we do not have experimental data, the nature of our data allows us to limit the potential sources of unobserved heterogeneity considerably. Building on a detailed panel survey conducted in the early 1990s, we re- interviewed individuals in 2004, making a notable effort to track individuals who had moved. The tracking of individuals to new locations proves crucially important for assessing welfare changes among the baseline sample. The average consumption change of individuals who migrated was more than four times higher than that of individuals who did not moved.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Those who had moved out of the Kagera Region by 2004 experienced consumption growth that was 10 times higher compared to those who remained in their original community. These averages translate into very different poverty dynamics patterns for the physically mobile and immobile. For those who stayed in the community, poverty rates drop by about 4 percentage points over these 13 years. For those who moved elsewhere within the region, poverty rates drop by about 12 percentage points, and for those who moved out of the region, they drop by 23 percentage points. Had we not tracked and interviewed people who moved out of the community – a practice found in many panel surveys – we would have seriously underestimated the extent to which poverty has gone down over the past 13 years in the Kagera Region; we would have reported poverty reduction at about half of its true value. Clemens and Pritchett (2007) raise similar concerns in the context of income growth and international migration. In addition, the data would omit the part of the population with a high information content on pathways out of poverty. Still, these statistics are not evidence that moving out of the community leads to higher income growth. As noted above, we cannot observe the counterfactual: What would income growth have been for migrants had they not migrated? We exploit some unique features of these data to address concerns about unobserved heterogeneity. First, individual fixed effects regressions for movers and stayers produce a difference-in-difference estimation of the impact of physical movement, controlling for any fixed individual factors that affect consumption. Second, we can control for initial household fixed effects in the growth rate of consumption since we observe baseline households in which some individuals migrate", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 narrative around the regressions and aims to explain why not more people migrate when benefits of doing so are so high. 2. The Setting: Tanzania and Kagera, 1994-2004 In the last decade, Tanzania has experienced a period of relatively rapid growth, attributed to liberalization, a renewed trade orientation, a stable political context, and a relatively positive business climate to boost economic performance. Real GDP growth was of the order of 4. 2 % per year between 1994 and 2004, while annual population growth was around 3. 2 % in the same period (URT, 2004). There is also evidence that growth had accelerated in the last few years compared to the 1990s. However, this growth has not been sufficiently broad-based to result in rapid poverty reduction. On the basis of the available evidence, poverty rates have declined only slightly and most of the poverty reduction progress has been made in urban areas. According to the Household Budget Survey (HBS), between 1991 and 2000 / 01, poverty declined from 39 percent to 36 percent in mainland Tanzania. The decline in poverty was steep in Dar es Salaam (from 28 % to 18 %) but minimal in rural Tanzania (from 41 % to 39 %).", "output": {"entities": {"named_data": ["Household Budget Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 3. The Data The Kagera Health and Development Survey (KHDS) was originally conducted by the World Bank and Muhimbili University College of Health Sciences (MUCHS), and consisted of about 915 households interviewed up to four times from fall 1991 to January 1994 (at 6-7 month intervals) (see World Bank, 2004, and http: / / www. worldbank. org / lsms /). The KHDS 1991-1994 serves as the baseline data for this paper. Initially designed to assess the impact of the health crisis linked to the HIV-AIDS epidemic in the area, it used a stratified design to ensure relative appropriate sampling families with adult mortality. Comparisons with the 1991 HBS suggest that in terms of basic welfare and other indicators, it can be used as a representative sample for this period for Kagera (results not shown but available upon request). The objective of the KHDS 2004 survey was to re-interview all individuals who were household members in any round of the KHDS 1991-1994 and who were alive at the last interview (Beegle, De Weerdt and Dercon, 2006). This effectively meant turning the original household survey into an individual longitudinal survey. Each household in which any of the panel individuals live would be administered the full household questionnaire.", "output": {"entities": {"named_data": ["KHDS 1991-1994", "KHDS 2004 survey", "Kagera Health and Development Survey"], "descriptive_data": [], "vague_data": ["household survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Since the set of household members at baseline have subsequently moved, and usually not as a unit, the 2004 round results in more than 2, 700 household interviews (from the baseline sample of 912 households). Although the KHDS is a panel of respondents and the concept of a ‘ household ’ after 10-13 years is a vague notion, it is common in panel surveys to consider re-contact rates in terms of households. Excluding households in which all previous members are deceased (17 households with 27 people), the field team managed to re-contact 93 % of the baseline households. This is an excellent rate of recontact compared to panel surveys in low-income countries and high-income countries. The KHDS panel has an attrition rate that is much lower than that of other well-known panel survey summarized in Alderman et al. (2001) in which the rates ranged from 17. 5 % attrition per year to the lowest rate of 1. 5 % per year. Most of these surveys in Alderman et al. (2001) covered considerably shorter time periods (two to five years). Figure 1 charts the evolution of households from baseline to 2004. One-half of all households interviewed were tracking cases, meaning they did not reside in the baseline communities. Of those households tracked, only 38 % were located nearby the baseline community. Overall, 32 % of all households were not located in or relatively nearby the baseline communities. While tracking is costly, it is an important exercise because migration and dissolution of households are often hypothesized to be important", "output": {"entities": {"named_data": ["KHDS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Respondents who were untraced were much more likely to be residing outside Kagera (43 %) compared to their counterparts who were re-interviewed (8 %). The consumption data come from an extensive consumption module administered in 1991 and again in 2004. The consumption aggregate includes home produced and purchased food and non-food expenditure. The non-food component includes a range of non-food purchases, as well as utilities, expenditure on clothing / personal items, transfers out and health expenditures. Funeral expenses and health expenses prior to the death of an ill person were excluded. Monetary levels were adjusted to account for spatial and temporal price differences, using price data collected in the Kagera survey in 1991 and 2004, and, for households outside Kagera, data from the National Household Budget Survey. Consumption is expressed in per capita, per annum terms. The poverty line is set at TZS 109, 663, calibrated to yield for our sample of respondents who remained in Kagera the same poverty rate as the 2000 / 1 National Household Budget Survey estimate for Kagera (29 %). 4. Growth, Poverty and Physical Mobility in Kagera In this section, we discuss changes in living standards overall, and the changes for four mutually exclusive groups based on residence in 2004: (i) still residing in the baseline community, (ii) residing in a", "output": {"entities": {"named_data": ["National Household Budget Survey", "Kagera survey"], "descriptive_data": [], "vague_data": ["price data", "consumption data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 neighboring community, (iii) residing elsewhere in the Kagera Region and (iv) residing outside the Kagera Region. Table 3 shows that the basic needs poverty rate declined 8 percentage points in the full sample. This figure masks significant differences in changes between subgroups based on migration. For those found residing in the baseline community, poverty rates dropped by 4 percentage points, but rates dropped by 11, 13 and 23 percentage points for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. A similar pattern is found for consumption per capita. While consumption per capita grew by $ 65 overall, it grew by only $ 30 for those found in the same community and by $ 65, $ 100 and $ 287 for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. Dividing consumption into food and non-food components gives the same result. The most basic assessment of welfare changes would have been wrong if we had focused only on individuals still residing in the community, a practice found in many panel data surveys. We would have underestimated the growth in consumption by half of its true increase. The differences in consumption changes of groups in Table 3 are statistically significant, as shown in Table 4.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Excluding respondents who have relocated would omit those with the higher rates of income growth and poverty reduction. Table 5 reports confidence intervals for the incremental samples (which are not mutually exclusive); it gives a more detailed picture of how inference on consumption growth and poverty reduction would have changed if we had not tracked movers. It is apparent that inference from a ‘ simple ’ panel survey of respondents continuing to reside within the original communities would have produced underestimates of actual consumption growth and poverty reduction in this population. These conclusions are robust across the distribution of consumption, as well as at the mean and poverty line. Panel A in Figure 2 depicts the cumulative density function for consumption per capita for those people who remained living in the same community. Panels B, C and D make the same graph for respondents found residing in neighboring communities, elswehere in Kagera Region and outside Kagera Region. As respondents were located further from their location in 1991, so the difference between the 1991 and 2004 graphs becomes more pronounced. Note how, for people who remained in the baseline community, the 1991 and 2004 distributions lie close to each other under the poverty line and diverge above it, while for other mobility categories there is more divergence at the bottom of the graph.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While both sons and daughters of the head may be expected to be more likely to stay in the community than other initial household members, patri-locality would make this probability higher for boys than for girls. In sum, this means we are using a set of six instruments. Although we can show that statistically convincing and close to identical results can be obtained by only using a subset of these instruments, we use the full set of instruments in the reported results. While our main measure of migration (Mi) is an indicator for having moved, we also substitute this for the log of the distance moved (kilometers from the original community of the location in which the individual was found in 2004, ‘ as the crow flies ’, set to 0 for non-movers). We will also extend the multivariate analysis to explore the role of moving to more urbanized areas and the role of sector movement in raising consumption growth. 6. Regression Results Table 9 presents the basic results for the initial household fixed effects (IHHFE) and 2SLS estimates (means for covariates are in Appendix Table 1). For each we estimate using an indicator for having moved and a measure of distance of the move. The 2SLS estimates in column (3) and (4) use the six instruments defined above. In Table 10, we present the first stage results of regressions explaining migration or the distance traveled in migration.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1 Introduction Company owners and managers make two decisions with important implications in the labor market: what skills are demanded and who to hire. On the demanded skills, a driver of trends in employment is the changing demand for soft skills (Heckman and Kautz (2012), Weidmann and Deming (2021)). However, we know relatively little about what kinds of soft skills employers value in modern entry- level jobs (Heller and Kessler, 2022). On the decision of who to hire, it is in the best interest of companies to hire based on workers productivity. However, several studies have documented the existence of labor market discrimination in a wide range of contexts (Bertrand and Duflo (2017), Neumark (2018)) and it remains unclear how discrimination operates throughout the hiring process and how the existent empirical evidence on discrimination is linked to economic theory (Bertrand and Duflo, 2017). We conducted a correspondence study in 2023 using a large online job platform to assess demand for soft skills in the context of hiring discrimination in Malaysia. Malaysia is a particularly interesting setting because it is an upper-middle-income economy, home to multiple ethnicities representing large shares of the population, and previously documented gender gaps in labor force participation and wages.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["large online job platform"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of dependability (Heller and Kessler, 2022). In a recent study, Karpowitz et al. (2023) show how these soft skills interact with discriminatory practices. They find that assigning leadership roles to women in a classroom setting reduces gender discrimination. Similarly, Kaas and Manger (2012) use a correspondence study to show that presenting soft information, such as reference letters with information on conscientiousness and agreeableness, seems to mitigate discrimination. Second, unlike most correspondence studies, we exploit data on firm characteristics and decisions at multiple stages of the hiring process to conduct a rich heterogeneity analysis. Most studies are only able to observe if the candidate receives an interview offer. Hangartner et al. (2021) is an interesting exception, which tracks online employers ’ actions and collects information that allows them to study hiring decisions. We instead observe five stages in the hiring process: 1) if employers reject an application, 2) if they visit an applicant ’ s profile, 3) the number of visits to each profile, 4) if employers contact the candidate, and 5) if they offer an interview. These five outcomes provide a rich preview into the hiring decision.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In addition, we observe several characteristics of the firm and job that are not typically observable to researchers, which we will use to conduct a rich heterogeneity analysis. Most importantly, we observe the number of applicants applying to a specific job, and the number of similar jobs posted on the platform. This unique data facilitates our third contribution, in which we analyze the role of labor market competition in hiring discrimination, an area which to date has not been widely explored. de Haan et al. (2017) show that discrimination against disadvantaged groups is more likely in the presence of competition of workers from a non-discriminated group than in a non-competitive scenario. Along these lines, we hypothesize that firms will discriminate less often when there is a low supply of applicants. Unlike de Haan et al. (2017), we uniquely observe quality indicators of the applicant pool, which we use to test our hypothesis that discrimination decreases when the relative quality of applicants in the pool is low. Furthermore, we exploit our unique data to test whether firms discriminate less often when there is high demand for specific job positions. We know of no studies that have previously considered competition on the demand side. The rest of the paper is organized as follows. Section 2 provides background information of Malaysia. Section 3 details the experimental design, and section 4 provides summary statistics of the data. Section 5 explores if there is discrimination in the Malaysian labor market, section 6 studies 4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These degree- based specializations were selected after initially characterizing job advertisements posted between April 11-17, 2022 on the job portal. At that time, 671 scraped jobs met the following criteria: full time, entry level, bachelor ’ s degree required, 0-1 years of experience. These jobs were then categorized into the aforementioned degrees. The remaining 15 % of total jobs were determined to be too specialized and those kinds of jobs were excluded from the study. All resumes have a bachelor ’ s degree conferred by the same university, one of the most prestigious and multi-ethnic institutions in Malaysia. In Malaysia, students from a particular ethnicity might attend specific colleges. Hence, the decision of using one institution for all candidates prevents potential associations of perceived quality of an institution to ethnicity. In total, 90 candidate profiles were created (3 ethnicities x 2 genders x 3 soft skills x 5 industries = 90 profiles). Each degree has 18 unique candidate profiles allowing for all possible combinations of ethnicity, gender, and emphasized soft skill. For example, 6 of the 18 mechanical engineering applicant profiles are Chinese, 6 are Malay, and 6 are Indian. For a given ethnicity, half are female and half male. Among the 3 Chinese female applicants within a degree, each is uniquely assigned one of the three soft skills traits to be emphasized (leadership, teamwork, or none), and similarly for 7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3. 2 Job Search and Classification Every Friday from May 12 to July 21, 2023 we scraped all job ads posted within the previous seven days from the job site. Then we filtered and kept job ads that met the criteria: full time, entry level position or requiring at most 1 year of experience. Relevant jobs were then classified into one of the five degree-based specializations. Each job was assigned a degree-based specialization using the area of specialization reported in the ad. For the accounting degree we used job ads classified as “ Accounting / Finance ”. For the business administration degree we used job ads with special- izations: ‘ Admin / Human Resources ’, ‘ Sales / Marketing ’, ‘ Customer Service ’ or ‘ Logistics / Supply Chain ’. For the computer science degree we used the specializations: ‘ Tech & Helpdesk Support ’ or ‘ Computer / Information Technology ’. For electrical engineering we used specializations that are related to ‘ Electronical ’, ‘ Electronics ’ or ‘ Other engineering ’. If the position had the word ‘ engineer ’ and the industry of the company was related to Electronical or Electronics, we also classified the ad into the electrical engineering degree. Finally, for mechanical engineering we used specializations related to mechanical, industrial or chemical engineering and specializations related to oil and gas.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the power calculations performed to determine the sample size. 3. 3 Randomized Assignment and Job Applications Each week we randomly selected 300 job ads from the sample of ads meeting our inclusion criteria. Among these 300 ads, each job ad was randomly assigned to a single applicant profile. Job ads are stratified to ensure that our treatment and comparison units are balanced on key variables. We use two variables for our strata: company size and company location. Company size is a dummy variable that takes the value of 1 if the company has up to 50 employees, and takes the value of 0 if companies have 51 or more employees. Company location is a dummy variable that takes the value of 1 if the company is located in greater Kuala Lumpur, the capital and largest metropolitan area in Malaysia, and 0 otherwise. 6 Our stratified randomization procedure guarantees balance in the assignment of job profiles to specific characteristics of companies. The application process was carried out manually from May 17 to July 28, 2023. At the beginning of each week, a research assistant was given a list of randomly assigned jobs for each applicant profile. Applications were completed on Mondays, Wednesdays, and Fridays of every week (with day of the week randomly assigned).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Applications were submitted during the same time span each of these days (8am-12pm CT). 7 The order of applications (grouped by profile) each day was also randomized. The application process is straightforward, it consists of submitting the application and completing an optional pitch. However, some jobs have a mandatory pre-scan questionnaire. For these type of ads we standardized answers and recorded the job ads that implemented these questionnaires. 3. 4 Monitoring Job Applications Job applications were monitored using a web scraping algorithm. For each job application, the following data was scraped from the website every Tuesday, Thursday, and Saturday (between 8am- 12pm CT): 1. Number of times the profile was viewed by the employer 6The locations we classify as Greater Kuala Lumpur are: Kuala Lumpur, Putrajaya, Petaling Jaya, Klang / Port Klang, Kajang / Bangi / Serdang, Subang Jaya, Ampang, Cyberjaya, Seremban, Selangor, Selangor- Others, Selayang, Semenyih, Shah Alam / Subang, and Central. 7If the website is under maintenance, which is common, applications will be delayed until the website is available. 10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ethnicity: For Chinese name candidates is 4 days, for Malay name candidates 5 days and for Indian name candidates 7 days. We find this evidence consistent with a hypothesis of the existence of statis- tical discrimination since employers can be taking more time to collect information of discriminated groups, or sorting applications. Companies that conduct pre-scan questionnaires in the application process are less likely to discriminate against Indian-sounding name candidates in the profile visit outcome. We do not find heterogeneous effects for location or for engineering jobs. Heterogeneity results for gender discrimination are presented in tables 21 to 26. Companies located in Kuala Lumpur and small companies are less likely to visit female profiles than male profiles. There is no effect in any of the other outcomes of the hiring process. We do not find heterogeneous effects for high-paying jobs, for companies with low processing time, for companies with pre-scan questionnaires or jobs in engineering. 6 Do Soft Skills Matter? In this section we explore if soft skills are relevant in the labor market and how soft skill signals affect ethnic and gender discrimination.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The role of soft information has been previously raised by Kaas and Manger (2012) that find that soft information on conscientiousness and agreeableness mitigate the discrimination practices. 6. 1 Response to Soft Skills Does the labor market respond to signals of soft skills (leadership / teamwork / neither)? If so, what is the extent of the response? To answer these questions, we use the soft skill signal that we randomly assigned to each profile. We can test if soft skills are differentially relevant in the labor market using specification 6: (6) yi = θ0 + 2 X k = 1 θkSik + εi Again, the outcome yi and error term εi are defined as in specification 1. The soft skills we want to test are leadership and teamwork, in comparison to a control soft skill that we call ‘ neither ’. Sk is the soft skills variable, where k = { 0, 1, 2}. That is, Sk is a dummy variable that takes the value of 1 for soft skill k (e. g. leadership) and 0 for other soft skills (e. g. teamwork and our counterfactual soft 25", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 Conclusions We conducted a correspondence study using an online job platform in Malaysia. We tested for ethnic discrimination, gender discrimination and the value of signaling soft skills in the labor market. Unlike many correspondence studies, the data allow us to observe different stages in the hiring process. We observe if the employer rejects an application, visits the profile of a candidate, number of times the profile is visited, if they contact them and if they offer an interview. Uniquely, we observe competition in the labor market on both the demand and supply sides. We do not find evidence of gender discrimination in the hiring process. Malaysia ’ s observed differential wages and labor force participation rates by gender do not seem to be associated with discrimination or human capital accumulation. More research is needed to determine why women in the Malaysian labor market have lower employment rates and wages. We find that Indian and Malay sounding name profiles are discriminated against in comparison to Chinese-sounding name profiles. There is discrimination along all the hiring process variables we observe. Malay and Indian candidates are 8 and 9 percentage points less likely to receive an interview offer relative to a Chinese candidate. Discrimination for both ethnicities is also present in other outcomes.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Poverty remains widespread and social indicators are well below the average for Sub-Saharan Africa. Chad is ranked 173 among the 177 countries surveyed in the 2006 UNDP Human Development Report. The incidence of poverty (defined as the proportion of households with annual spending below what is necessary to meet minimal needs) is estimated at 55 percent according to a 2003 household survey; an estimated four-fifths of the population of about 8. 8 million is living on less than a dollar a day3. Of the population over 15 years old, more than 73 percent (and 76 percent of women) are illiterate. Access to potable water has improved over past years, but is still limited to one out of three people in 2005. Less than two percent of the population has access to electricity and only 1021 kilometers of roads has been paved on a surface area of over 1. 2 million square kilometers. As already mentioned, Chad has recently become oil producing country; however, the economy remains largely agricultural and pastoral. About 80 percent of the country ’ s population lives in rural areas and continue to make their living4 from agriculture and livestock. Cotton is the principal cash crop, employing about 300, 000 families.", "output": {"entities": {"named_data": [], "descriptive_data": ["2003 household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "29 should be entered immediately into the accounts; and (c) Payment must be recorded as soon as they are made. Budget implementation should be reviewed periodically to ensure that programs are implemented effectively and to identify any financial or policy derailment. The review of budget execution should cover financial, physical and other performance indicators. Development budgets are often beset by implementation problems because of insufficient implementation capacities and other factors such as delays in mobilizing external financing, overoptimistic implementation schedules or difficulties in importing supplies. It is thus important to have in place mechanisms for reviewing the most significant or problematic projects. These could consist of a regular monthly or quarterly review of projects within the line ministries and a midyear review involving line ministries and central agencies29. The government has taken steps to improve the tracking of budget expenditure until the intended destination, particularly investments spending, for which a tracking survey was entrusted in 2005 with the Ministry of infrastructures. In addition, the ministries took themselves certain internal initiatives, in particular in health and education sectors, but the action plans of these ministries were not updated as envisaged in 2005, and there are neither reliable benchmark, nor quantitative targets as regards improvement of the arrival of the expenditure at intended destination in these sectors.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["tracking survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A survey was carried out in the health sector (health centers) with the support of the World Bank in 2005, but has not been yet validated. In the rural development sector, where the first expenditure tracking survey between the decentralized center and services was to be carried out in 2005 on PEFA funds, the survey is yet to be carried out. It is critical that adequate management measures are gradually put in place to stop the current waste of resources. These measures include, inter alia, the accounting improvement of material, improvement of the inventory and delivery control, and especially a transparent planning of the deliveries, including, the posting of the received deliveries, their comparison with the planned deliveries and their certification by users within each service. It would also be desirable that in each ministry, an action plan is prepared for the implementation of these measures, on the basis of existing ones, and with target indicators as regards improvement of the arrival of the expenditure at intended destination. The social ministries could usefully open the way in this field, on the basis of some projection already carried out. Without a quantitative and qualitative improvement of the arrival of the expenditure at their final recipient, the increase in the budgetary appropriations to the priority sectors will hardly be translated into substantial concrete results on the ground. 29 It is difficult to make accurate forecasts for the implementation of certain programs or developments in economic parameters such as inflation or interest rate. Some immediate needs that were not foreseen during budget execution may appear during budget execution.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["expenditure tracking survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10100 This paper explores the impact of refugee return on social cohesion using data from Burundi, a country that expe­rienced high levels of repatriation during the 2000s. It uses a nationwide survey conducted in 2015 and relies on geographic features of the communities for identification purposes. The results suggest varying impacts of refugee return on different aspects of social cohesion. The stronger effects, suggest that refugee return has a negative impact on the feeling that community members help each other, could borrow money for emergencies from non-household members and feeling that the community is peaceful.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["nationwide survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The estimated impacts on measures of reconciliation, post-con­flict justice, trust and participation in community groups are mostly statistically insignificant. The paper also explores how these effects differ across different sub-samples based on ethnic composition, land scarcity and attitudes towards return. The results highlight the possible role of new migra­tion-related societal divisions (i. e. returnees versus stayees) in affecting post-return social cohesion. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at carlos. vargas-silva @ compas. ox. ac. uk.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 agreements that did not fully satisfied either party (Ndayirukiye and Takeuchi 2014). This tension related to land, and who has a claim to the land, can lead to social tensions in communities with higher levels of return. Figure 3 – Refugees in Tanzania in 2005 by province of origin in Burundi Note: The number in brackets is the number of refugees in Tanzania in 2005 which was originally from the given province in Burundi. This information comes from (UNHCR 2021b). The number in parenthesis is that number as a percentage share of the population of the provinces as estimated in 1990 Burundi Census. An important question for our hypotheses is the degree to which there is evidence of migration-related societal divisions in the country. There is no direct quantitative evidence on identity issues (i. e. returnees versus stayees), but we have data on attitudes towards emigration, remittances and return that can provide insights on these identities and even be a proxy for migration-related identity in some cases. Overall, attitudes towards emigration and return are mixed and show that there is scope for the existence of migration-related divisions. In Table 1 we report the share of respondents who agreed with different statements regarding emigration, remittances and return.", "output": {"entities": {"named_data": ["1990 Burundi Census"], "descriptive_data": ["data on attitudes towards emigration, remittances and return"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Finally, we divide the communities based on land scarcity before the 1993 conflict in order to explore the possible role of posterior rules regarding land provision to returnees. Respondents in communities that had more and less pre-war land available have broadly similar attitudes towards return. 4. Research design 4. 1 The survey We collected the data for this project during January to March 2015 as part of a nationwide survey on issues related to migration for the Labour Market Impacts of Forced Migration (LAMFOR) project. The survey had two components. First, a household survey in which 15 households were interviewed in 100 communities (i. e. sous-collines) across the 17 provinces of the country. Second, a community survey in which a local leader was interviewed in each of the 100 communities. The number of communities selected in each province was based on information from the 2008 Census. Figure 4 indicates the location of the communities surveyed. Figure 4 – Location of communities surveyed in Burundi Note: Geolocation of the 100 communities (i. e. sous-collines) sampled in the survey. Each community corresponds to a dot. Fifteen households and a local leader were interviewed in each community. The number of communities selected in each province was based on information from the 2008 Census. In the analysis below we focus on rural areas.", "output": {"entities": {"named_data": ["2008 Census"], "descriptive_data": [], "vague_data": ["household survey", "community survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Where 𝑌𝑌𝑖𝑖 represents one of the indicators of social cohesion explained above, 𝛿𝛿𝑗𝑗 is the province indicator, 𝑅𝑅𝑐𝑐 is the share of returnees in the community, 𝐻𝐻𝑖𝑖 indicates a series of household level controls and 𝐶𝐶𝑐𝑐 are a series of community level of controls. In the main regressions we estimate the share of returnees in the community, using the information from the survey (i. e. share who are returnees), but in the robustness section we show that results are robust to the use of an alternative indicator in which the information is provided by a community leader. The Appendix (Table A2) includes the descriptive statistics for the control variables. We present results for the full sample and divided by communities with lower / higher ethnic diversity, less / more pre-1993 war land availability and better / worse attitudes towards return. In the robustness checks we also present the results if we limit the analysis to stayees only. Limiting the sample in this way does not affect the main results of the paper. 4. 4 Identification As mentioned above, Tanzania mandated the return of all Burundian refugees from the 1993 conflict. Returnees also had a very strong incentive to return to their communities of origin as this was the place in which they were entitled to land, a very scarce resource in the country.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "UNHCR (2021a) projects that the number of returnees will reach 141, 000 in 2021, up from 41, 000 in 2020. There are no datasets such as the one used in this study to explore the impact of post-2015 returnees on social cohesion and it is not possible to determine the degree to which our findings our applicable to this new context. However, it is possible to explore similarities and differences between the two contexts. Looking at a UNHCR report about the latest wave of returnees, it states that “ almost all returnee households rely on food obtained from their own gardens (93 %) and / or fields- households struggle to get food during the period they do not produce. 81 % of households declared that they are not satisfied with their level of food security because of the low dietary diversity ” (UNHCR 2021a). The report also suggests that “ 88 % of returnee heads of households are subsistence farmers, but most of them declared not having the adequate resources to produce their land. ” This high level of dependence on agriculture and prevalence of food insecurity are similar to the ones in our dataset for returnees and suggests that tensions related to access to agricultural land could also be present for post-2015 returnees. There are also signs of potential differences between current dynamics and the pre-2015 period.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 I. Introduction After two decades of multiparty democracy, Mali was viewed as a democratic success story. The fifth presidential elections were scheduled to take place in March 2012 and another peaceful and democratic transfer of power was widely anticipated. Reality was different, however. A secessionist movement sparked by a Kel Tamasheq1 rebellion led to a political and constitutional crisis culminating in a coup d ’ état in March 2012 and an attempt to take over the country by force. The three northern regions of Gao, Timbuktu and Kidal became occupied by various rebel and Islamist factions until early 2013, when a coalition composed of the Malian Army, French troops and the ECOWAS-led African-led International Support Missions to Mali (AFISMA) recaptured the occupied areas. 2 After months of insecurity in the North and two violent attacks in Bamako, a Peace Accord was signed in May and June 2015 between the government and different actors involved in the rebellion. The Accord established a joint vision for peace and prosperity predicated on demobilization and disarmament, the devolution of authority to local governments, and the establishment of conditions for restoring stability and economic recovery in northern Mali. In spite of the Accord, the regions of Gao, Kidal and Timbuktu remain in a state of prolonged crisis, with high levels of insecurity and weak governance.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Without army protection most parts of the North, especially Kidal remain inaccessible to those working for the central and local government. 3 Armed bandits are active and IED explosions as well as violent attacks on the MINUSMA peacekeeping forces are regular occurrences. Under these circumstances, data collection is very difficult. INSTAT, the National Bureau of Statistics, has not been in a position to collect information from northern Mali since the beginning of the crisis. To our knowledge, our surveys implemented by a private survey entity, GISSE, are the only systematic and representative effort to collect data in north Mali since the crisis. They offer a unique database providing a crucial perspective that would otherwise not be reflected in academic analyses and policy level decision-making, and a perspective that is indispensable in any attempt at understanding the situation in Northern Mali. 4 Preceding the Accord on Peace and Reconciliation in Mali (Accord pour la paix et la réconciliation au Mali) (hereafter, the Peace Accord) of May and June 2015, four peace accords had been signed between the government and Toureg and Arab armed groups in 1 Kel Tamasheq (those who speak Tamasheq) is synonymous for Tuareq. 2 Francis David (2013): The regional impact of the armed conflict and French intervention in Mali. NOREF, Norwegian Peacebuilding Resource Centre. 3 Assessing Recovery and Development Priorities in Mali ’ s Conflict-Affected Regions. Draft Report of the Joint Assessment Mission for Northern Mali (January 2016), p. 15-16. 4 All data can be downloaded from www. gisse. org.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The objective of the survey was to analyze perceptions and priorities with regard to the Peace Accord, to analyze perceptions of security, to determine access to basic infrastructure and school attendance, to understand nutrition levels, and to measure household asset ownership. In addition to this baseline survey, the World Bank was sponsoring a mobile phone survey among IDPs in Bamako, returnees in Gao, Kidal and Timbuktu town and refugees in camps in Niger and Mauritania. This survey interviewed 500 respondents on a monthly basis. In the August 2015 round of this survey, questions about perceptions and priorities with regard to the Peace Accord were included. This paper also makes use of a subset of the responses obtained from that survey, particularly those from refugees in Niger (n = 80) and Mauritania (n = 100) as these sub-populations who live outside Mali ’ s borders are important stakeholders in the peace process whose opinions risk being ignored. 15 To select a household in a village or neighborhood for the baseline survey, random selection was used: the enumerator divided the locality into two parts and selected five 15 For a more elaborate description of this mobile phone survey, see: Etang Ndip, A., J. Hoogeveen and J. Lendorfer (2016). Socioeconomic Impact of the Crisis in Mali on Displaced People. Journal of Refugee Studies.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["mobile phone survey", "baseline survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 households in each. To select a starting point the enumerator used a code of the day16 and chose every second house in rural areas and every fifth house in urban areas. The selection of individuals within the household to answer the questionnaire was conducted as follows: the head of household (male or female) was selected to answer the first part of the questionnaire dealing with general questions about the households. Using the roster of household members which was compiled during the first part of the interview, another member of the household aged 18 or above was selected randomly to answer the second part of the questionnaire in which perception questions were asked. Alternation between male and female was ensured. The survey thus generated data that are reflective of the opinions of those aged 18 and above in northern Mali. To assess the representativeness of the data, which were collected under rather challenging circumstances, the ethnic composition of the sample was compared with the ethnic composition in the North as reported by the 2009 Census.", "output": {"entities": {"named_data": ["2009 Census"], "descriptive_data": ["roster of household members"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Similarly, regional representation of the sample was found to be in line with actual regional distribution of the population in the north. 16 The code of the day is the sum of the two figures of the date, i. e. if it ’ s the 25th of August the code of the day is 2 + 5 = 7. The enumerater will chose house number 7 as a starting point. Arab 3 % Tamashek 32 % Songhai 45 % Peulh / Foulbe 7 % Other ethnicities 13 % Figure 2: Ethnic composition of the North, 2009 Census Arab 5 % Tamashek 36 % Songhai 49 % Peulh / Foul be 7 % Other ethnicities 3 % Figure 3: Ethnic composition of sample", "output": {"entities": {"named_data": ["2009 Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 In addition to the 500 households and 180 refugees, 50 local authorities were interviewed. Of those, 18 are based in Goa, 22 in Timbuktu and 10 in Kidal. Included were 38 village chiefs, 11 mayors and 1 local notable. Table 3: Authorities interviewed by function and by region (%) Gao Kidal Timbuktu Total Village chief 13 8 17 38 Mayor 5 1 5 11 Local notability 0 1 0 1 Total 18 10 22 50 The authorities interviewed are all men aged between 30 and 86. Forty-four percent are either just literate or have no education at all. In Gao, authorities have a higher level of education compared with Kidal and Timbuktu. The majority (almost 39 %) of respondents in Gao have a high school education, 22 % are literate and 17 % have primary education. In Kidal, 80 % of respondents have no education at all. In Timbuktu, about 32 % of the surveyed authorities are literate, 27 % have a high school education and 23 % a primary education.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 III. The 2015 Accord on Peace and Reconciliation in Mali Preceding the Peace Accord ’ s signature, talks were held in Algiers between six armed groups congregated in two broad coalitions – the CMA (Coordination des Mouvements de l ’ Azawad) and the Platform – and the Malian government. Al-Qaida in the Islamic Maghreb (AQIM) and the Movement for Unity and Jihad in West Africa (MUJAO), both foreign-origin jihadist groups, were excluded from the talks although they were controlling most of the North from June 2012 to January 2013. 17Another security actor that emerged after the negotiations began, and which was not represented, is the Imghad Tuareg and Allies Self-Defense Group (GATIA), 18 a pro-government self-defense group, fighting those groups seeking greater autonomy and or an independent state of Azawad. Despite the fact that there were at a minimum eight different armed groups fighting in northern Mali at one point or another since the rebellion was sparked in 2012, there are only three signatories to the Peace Accord: the Malian Government, the “ Platform ” and the CMA. The Platform coalition consists of the Coordination des Mouvements et Fronts Patriotiques de Résistance (CM-FPR), the Coalition du Peuple pour l ’ Azawad (CPA) and a faction of the Mouvement Arabe de l ’ Azawad (MAA).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The CMA consists of the Movement National de Liberation de Azawad or the Azawad National Liberation Movement (MNLA), which is the main secular Tuareg separatist group, the Haut Conseil pour l ’ Unité de l ’ Azawad or High Council for the Unity of Azawad (HCUA), which is an Islamist group led by Touareg traditional leaders formerly associated with the Ansar Dine jihadist group, and the Mouvement Arabe de l ’ Azawad or Azawad Arab Movement (MAA), the main Arab separatist group. 19 Initially, the CMA did not sign the Peace Accord. The Platform coalition of armed groups took over Ménaka in Gao region in April 2015, prompting the CMA to refuse to join the signing ceremony on 15 May 2015. The CMA set as condition for signing the withdrawal by the Platform from Ménaka. Following collective international mediation initiatives, the Platform announced its immediate withdrawal from Ménaka on 18 June and on 19 June the Government of Mali lifted arrest warrants against 15 leaders of the CMA. Subsequently, on 20 June, Sidi Brahim Ould Sidatt from MAA-CMA signed the Peace Accord on behalf of the CMA. On 23 June, Mali ’ s President Ibrahim Boubacar Keïta met with the leadership 17 Report of the Secretary General on the Situation in Mali (22 Sept 2015): https: / / minusma. unmissions. org / sites / default / files / 150928_sg_report_sept_2015_en. pdf. 18 Reeve, Richard (2015): Devils in the Detail: Implementing Mali ’ s New Peace Accord, Oxford Research Group. 19 http: / / www. responsibilitytoprotect. org / index. php / crises / crisis-in-mali.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Worry about economic conditions after a recession can make companies more cautious about hiring, which generates temporary forms of recruitment. Finally, the process of technological change, especially the development of digital communications, is a key factor in justifying new forms of non-standard employment. The expansion of services and global supply chains is inextricably linked to technological advances. The new information technologies, the higher quality and the lower cost of infrastructure and the logistical and transport improvements, allow companies to compare, organize and manage production in a more diversified way in territorial terms. At the same time, new communication technologies have allowed the generation of new forms of work, such as work on internet platforms or work on demand through digital applications. In this sense, technological developments allow companies to assemble teams of workers who develop activities in any part of the world through a virtual network (Brews and Tucci, 2004). The most recent development of online recruitment services, such as\"eLance\"and\"oDesk\"enables the search for workers who can be subcontracted, performing their activities in virtual mode. 3.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Sources of information This work includes a descriptive analysis that allows us to identify how the prevalence of non- standard employment (NSE) has evolved in the last two decades in different regions of the planet, as well as the evolution of the profile of workers in those roles, in terms of their educational level, salary per hour, and type of tasks performed. This analysis was based mainly on periodic surveys of households that included information regarding the employment and educational situation of individuals. Although the denomination of this type of surveys varies from country to country, in all the cases analyzed there is usually a survey of annual or higher frequency that includes information required to identify the labor status of the individuals as well as to analyze the salary profile and education of the employed. However, it should be noted that the identification of the type of work relationship (standard or non- standard) is frequently limited in these data sources. Indeed, it is only possible to identify part-time employment and temporary employment (not in all cases) within the non-standard forms of employment mentioned in the previous section.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["periodic surveys of households"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 7 of 51 In this sense, it is necessary to draw attention to the fact that the non-standard employment statistics presented below are not homogeneous among countries. In countries where both forms of non- standard employment were identified, we define non-standard employment as those occupations that satisfy at least one of the conditions, that is, either corresponds to a part-time occupation or a temporary job. In countries where temporary employment was not identified in the data, our non- standard employment category will coincide with part-time employment. Note that in either case, as other non-standard employment modalities are not identified, the indicators presented in this paper indicate a lower level with respect to the true dimension of the phenomenon. The only aspect addressed that required the use of additional information was the analysis linked to the profile of tasks that are developed in the framework of non-standard jobs. To carry out this analysis, the information available in the O * NET (Occupational Information Network) database was used in conjunction with the Household surveys. This database provides information referring to the content of tasks of the occupations.", "output": {"entities": {"named_data": ["O * NET (Occupational Information Network) database"], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Since 2003, O * NET data have been collected in the United States for approximately 1, 000 occupations based on the Standard Occupational Classification (SOC), and it has been updated periodically from then until 2014. 5 Following Acemoglu and Autor (2011), Hardy et al. (2015) and Apella and Zunino (2017), five measures of content or intensity of main tasks performed by workers are constructed: non-routine cognitive analytical and interpersonal, routine cognitive and manual and non-routine manual. The definition of the type of task performed by the worker is associated with the risk of automation and therefore its implication in terms of earned wage. While routine, and especially manual, are susceptible of automation, those non-routine tasks, especially cognitive tasks (both analytical and interpersonal) not only are not exposed to the risk of automation but also could be complemented by automation, increasing the productivity of the workers. 4. NSE trends in Latin America and the Caribbean and Europe and Central Asia As was mentioned in the previous section, the available data sources limit the statistical analysis presented below to two types of NSE: temporary employment and part-time employment. At the same time, it was not possible in all cases to identify the employees whose employment relationship is temporary.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In this sense, it is necessary to take into consideration that all indicators of prevalence of NSE and its profile will be limited to a subset of this kind of workers. 4. 1 Latin America and the Caribbean This section focuses on the Latin America and the Caribbean region, where a set of 9 countries, that we consider representing the different realities of the region in an exhaustive way, was analyzed. Specifically, the analysis was conducted for Argentina, Brazil, Bolivia, Chile, El Salvador, Mexico, Peru, Dominican Republic and Uruguay. 5O * NET is the successor of DOT (Dictionary of Occupational Titles) which is no longer updated. O * NET was launched in 1998 on the basis of the BLS Occupational Employment Statistics codes. In 2003, it was changed to SOC which implies that the consistent measures of task content are calculated from 2003.", "output": {"entities": {"named_data": ["BLS Occupational Employment Statistics"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 8 of 51 In the 9 cases mentioned, it was possible to identify part-time workers, while temporary workers were only identified in the cases of Argentina, Brazil, Chile, Mexico and El Salvador. 4. 1. 1 Variation of the NSE as a percentage of total employment The prevalence of NSE in the total employment has not shown very significant variations in the countries considered in the last two decades (Figure 1). Indeed, most of the countries analyzed show non-standard employment registers similar to those observed in the mid-1990s. The exceptions where the variation is a little more relevant are Brazil and Uruguay, where there are contractions in the incidence of the NSE of the order of 10 and 5 percentage points respectively and Mexico, where there is an increase of 5 percentage points. Figure 1: Prevalence of NSE among salaried employees. (Mid-90s / Mid-2010s) Source: Own calculations based on Household surveys Analyzing the prevalence of NSE by types of occupations, considering the ISCO classification at one digit, we find a quite similar pattern across countries. Indeed, in most of the considered countries, the “ Elementary Occupations ” are the category where the prevalence of NSE is higher.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 9 of 51 Figure 2: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) Source: Own calculations based on Household surveys When these results are analyzed by the type of NSE, we find significant differences between the trends in Part-time and Temporary employment. On the one hand, there is a stable or growing prevalence of part-time employment among the salaried employees, where Uruguay is the only exception, characterized by a decrease in the incidence of this type of employment (Figure 3). In the cases of Peru and Bolivia, we find almost the same prevalence of part-time employment as two decades ago while in the remaining countries (Argentina, Brazil, Chile, Mexico, El Salvador and the Dominican Republic) there is a greater prevalence of part-time employment. Likewise, the prevalence of temporary employment shows a downward trend in most of the analyzed countries in the last 20 years (Figure 4). In fact, three of the five countries in which temporary employment could be identified show a significant drop in the prevalence of this type of employment (Argentina, Brazil, and Chile). El Salvador presents a stable incidence of temporary employment, while Mexico is the only country in our sample for which there is an increase in this type of NSE.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 10 of 51 Source: Own calculations based on Household surveys Figure 4: Prevalence of Temporary employment among salaried employees (Mid-90s / Mid-2010s) Source: Own calculations based on Household surveys Analyzing the evolution of non-standard employment according to their age profile, we found a slight increase in the share of the older groups (Figure 5 and 6). This slight aging in the profile of non- standard workers is observed in both part-time and temporary employment. This finding is striking since, in principle, it was expected that the non-standard modalities of employment would show an increasing participation of the younger groups of the population. However, this change in the age composition of NSE is consistent with the age profile observed in total employment. In fact, the 0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % Argentina Brazil Peru Dominican Republic El Salvador Starting point 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % Argentina Brazil Chile Mexico El Salvador Starting Point Ending Point", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 5: Part-time employment by age group. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Uruguay Brazil Chile Peru Bolivia Dominican Republic Mexico El Salvador > 64 years old 50 ‐ 64 40 ‐ 49 25 ‐ 39 15 ‐ 24", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 12 of 51 Figure 6: Temporary employment by age group. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys. In the case of temporary employment, there is evidence of increasing female participation in the five countries analyzed, with large variations in several cases. Indeed, in the five countries for which we identified temporary workers, while temporary employment was predominantly male in the 1990s, today women show a participation higher than 50 % in all cases. Figure 7: Temporary employment by age group. (Mid-1990s / Mid-2010s) employment by gender (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Brazil Chile Mexico El Salvador > 64 years old 50 ‐ 64 40 ‐ 49 25 ‐ 39 15 ‐ 24 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Uruguay Brazil Chile Peru Bolivia Dominican Republic Mexico El Salvador Female Male", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 13 of 51 Figure 8: Temporary employment by gender. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys 4. 1. 2 Profile of Non-Standard Employment The next objective of this work is to investigate possible changes in the profile of non-standard employment, usually associated with lower productivity and greater vulnerability. The analysis of the employment profile was made based on access to social security benefits, education level, labor income and the task content performed by the workers. From the point of view of social security, we find that NSE shows a higher prevalence of informality compared with SE. For instance, the average prevalence of informality for our set of countries among NSE in the ending point of the study is 40 % while the prevalence among SE is 20 %. In this sense, a rise in the prevalence of NSE could be associated to a big set of workers without access to social security benefits. From a dynamic perspective, there is no a common trend across countries regarding the prevalence of informality among NSE workers (Figure 9). Indeed, several countries (Uruguay, Brazil, Chile, and Peru) registered a small decrease in the prevalence of informality among NSE but there is another set of countries for which the opposite is observed (Mexico, El Salvador, Bolivia and Argentina).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It is important to note that the changes in the prevalence of informality among SE workers between the starting and ending point of the study show a very similar dynamic compared with the observed dynamic for NSE (the prevalence of informality among SE workers is presented in the Annex II of the paper). Then, the trend in the prevalence of informality among NSE mainly reflects the overall trend of informality in the labor market instead of a specific characteristic of NSE. Figure 9: Prevalence of informality among NSE workers. (Mid-1990s / Mid-2010s) 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Brazil Chile Mexico El Salvador Female Male", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 14 of 51 Source: Own calculations based on Household surveys From the education perspective, an improvement in the profile of workers employed as NSE can be observed in the countries analyzed, even though there are some exceptions. In fact, in most of the countries of our sample, the prevalence of workers with secondary and tertiary education increases in detriment of workers with a lower educational level. However, there are some differences by type of non-standard employment. In the case of part-time employment (Figure 10), several countries show an obvious rise in the prevalence of workers with secondary and tertiary education (Argentina, Brazil, Peru, and Mexico). There is a second group of countries that presents a decrease in the prevalence of workers at the secondary level, but, this decrease is more than compensated by the greater incidence of tertiary workers, so that, taken together, workers with secondary or higher education increased their share within part-time employees (Uruguay and Chile). Therefore, we can also conclude from this group that part-time employment presents a better educational profile today than two decades ago. In the Dominican Republic and El Salvador, we find a rise in the prevalence of workers with secondary education but a decrease in the share of workers at the tertiary level.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 15 of 51 Figure 10: Education profile of Part-time employees. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys On the other hand, in the case of temporary employment, the improvement of the educational profile is more evident and generalized than in part-time employment (Figure 10). In fact, in all the cases in which temporary employment was identified, taken together, the share of workers at the secondary or higher education increased in proportion in the last two decades. The countries in which the improvements in the education profile are less deep are Chile, where the participation of workers with secondary education decreased in the last two decades, even though this decrease is more than compensated by the greater proportion of tertiary workers, and El Salvador, where the share of workers with tertiary education remains almost constant. 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % Argentina Uruguay Brazil Chile Peru Bolivia Dominican Republic Mexico El Salvador Incomplete primary (starting point) Incomplete primary (ending point) Primary (starting point) Primary (ending point) Secondary (starting point) Secondary (ending point) Tertiary (starting point) Tertiary (ending point)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 16 of 51 Figure 11: Education profile of Temporary employees. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys However, it should be noted that the improvement of the educational profile of workers is a generalized trend in the countries considered and cannot be considered a specific characteristic of non-standard employment since it is also observed in standard wage employment and self-employed workers. Statistics regarding the educational profile of standard employees are included in Annex II of the paper. Figure 12 presents the Kernel distribution of the labor income per working hour by country and type of employment. When we analyze what has happened at the salary level and distribution in the period under analysis, two important conclusions emerge. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. This growth in wages is simply a consequence of the economic growth experienced by these economies. Note that this average wage increase is also observed in the counterpart of standard employment in all cases.. 6 The second trend identified as generalized in the countries of study is the increase in the variance of the wage distribution. In fact, in most of the countries considered, non-standard employment wages currently show a significantly greater dispersion than that registered a decade ago.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The only two exceptions are Chile and Peru in which the variance of the wage distribution of NSE shows a small decrease. 6As mentioned above, standard employment is calculated as the total number of employees who do not identify with any of the non-standard categories (temporary or part-time). However, since there are non-standard forms of employment not identified in the database, our standard employment category could, in fact, incorporate non-standard workers. 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % Argentina Brazil Chile Mexico El Salvador Incomplete primary (starting point) Incomplete primary (ending point) Primary (starting point) Primary (ending point) Secondary (starting point) Secondary (ending point) Tertiary (starting point) Tertiary (ending point)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 19 of 51 Source: Own calculations based on Household surveys Finally, we analyze the trends in the task content performed by non-standard workers following the task methodology following the methodology proposed by Acemoglu and Autor (2011). Figure 13 presents the variation in the task content index by non-standard workers vis-a-vis standard workers. A first general view suggests that non-routine cognitive task content of jobs (both analytical and interpersonal) increased in both NSE and SE even though we have some exceptions. Indeed, the only countries where non-standard employment shows a less intense profile in non-routine cognitive analytical tasks are Peru and the Dominican Republic. Additionally, Chile and El Salvador show a virtually null change in the intensity of this kind of tasks. In the case of standard employment, the change in the profile towards non-routine cognitive analytical tasks is even more obvious (the Dominican Republic is the only exception). A similar scenario is recorded in the case of the intensity of non-routine cognitive interpersonal tasks, even though in this case the trend is more pronounced in both, standard and non-standard employment. Additionally, the trends in SE and NSE are more correlated for this type of tasks. The evolution of the intensity in the routine cognitive tasks in the last two decades in NSE presents a much more heterogeneous picture.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 25 of 51 higher followed by the “ Professionals ” and “ Skilled agricultural, forestry and fishery workers ”. On the contrary, “ Managers ”, “ Technicians ”, “ Craft and related trades workers ” and “ Plant and machine operators and assemblers ” are the types of occupations with a lower incidence of NSE in the region. Figure 16: Prevalence of NSE among salaried employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Figure 17: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) Source: Own calculations based on Household surveys. As in the case of Latin American countries, the observed prevalence of NSE across types of occupations suggests a strong heterogeneity across non-standard employees. Actually, as we will analyze in detail in next sections, the productivity and task profile of workers in the categories of “ Professionals ” and “ Elementary workers ” is very different, even though both types of occupations are characterized by a higher prevalence of non-standard employment arrangements. 0 % 5 % 10 % 15 % 20 % 25 % 30 % Russia Georgia Kyrgyz Republic Turkey Armenia Albania Moldova starting point ending point 0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % 40 % 45 % 50 % Managers Professionals Technicians Clerical Support Workers Services and Sales Workers Skilled", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 27 of 51 Figure 18: Prevalence of Part-time employment among salaried employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys. Figure 19: Prevalence of Temporary employment among salaried employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys The older age profile of part-time employment in most of the analyzed countries of Eastern Europe and Asia is consistent with the age profile observed for total employment. In fact, the mean age of the labor force rose 4. 5 years on average in the countries included in the study. That is, the older age profile of NSE workers reflects the aging trend of the overall labor force. 0 % 2 % 4 % 6 % 8 % 10 % 12 % 14 % 16 % Russia Georgia Kyrgyz Republic Turkey Armenia Albania Moldova Starting point Ending point 0 % 5 % 10 % 15 % 20 % 25 % Georgia Kyrgyz Republic Turkey Armenia starting point ending point", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 28 of 51 Figure 20: Part-time employment by age groups. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Figure 21: Temporary employment by age groups. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys A less clear picture is observed in the case of temporary employment. Indeed, in the four countries where temporary employees are identified, we observe different dynamics in the age profile. On the one hand, Kyrgyzstan and Turkey do not evidence significant changes in the age profile of temporary workers in the last 10 / 15 years. On the other hand, Georgia and Armenia present changes in the age profile but in opposite directions. Armenia shows today a higher share of the older groups within temporary employees while Georgia evidences a younger profile of temporary workers. Finally, analyzing the composition of non-standard employment by gender, we have a heterogeneous picture by types of non-standard employment in the levels but with a similar trend (Figure 22 and 23).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 30 of 51 Figure 23: Temporary employment by gender. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys In summary, the evidence does not suggest a clear trend across countries regarding the prevalence of non-standard employment but rather identifies a heterogeneous panorama by countries and types of non-standard employment. 4. 2. 2 Profile of Non-Standard Employment In this section, we investigate possible changes in the profile of non-standard employment, usually associated with lower productivity and greater vulnerability. Like in the case of the LAC countries, the analysis of the employment profile was made based on the education, wages and the content of tasks performed. 9 From the educational point of view, a general tendency can be observed in the countries analyzed to improve the profile of workers linked to non-standard work contracts. In fact, in most of the countries in our sample, it is observed that taken together, the prevalence of workers with secondary and tertiary educational levels increases to the detriment of workers with a lower educational level.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 32 of 51 Figure 25: Education profile of Temporary employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Note: SP indicates the starting point of the analysis and EP states de ending point. It should be noted, however, that, like in the case of Latin American countries, the improvement in the educational profile of workers is a generalized trend in the countries considered and cannot be considered a specific characteristic of non-standard employment since it is also observed in standard wage employment and self-employed workers. Statistics regarding the educational profile of standard employees are included in the Annex of the paper. When we analyze what has happened at the salary level in the period of consideration (Figure 26), two important conclusions emerge. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. This growth in wages is simply a consequence of the economic growth experienced by the economies. Note that this average wage increase is also observed in the counterpart of standard employment in all cases. 10 10As mentioned above, standard employment is calculated as the total number of employees who do not identify with any of the non-standard categories (temporary or part-time).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 36 of 51 In fact, part-time employment shows a more generalized trend towards non-routine cognitive tasks, where Russia and Georgia are the only exceptions (Figure 29). There is also observed a generalized growth in the intensity of routine cognitive tasks, Russia being the only exception. Finally, a less intense profile in manual tasks (both routine and non-routine) is clearly observed only in the cases of Albania and Moldova. The analysis of the task profile of temporary employees is much more limited since it only includes three countries. In these three cases, there is no observed a change towards a more intense profile in non-routine cognitive tasks. Indeed, only Kyrgyzstan shows a rise in the intensity of non-routine cognitive interpersonal tasks. Additionally, a lower intensity is observed in routine cognitive tasks in Georgia and Kyrgyzstan.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Therefore, although the available evidence is very limited, we do not observe a shift towards a more intense task profile in cognitive activities in the case of temporary workers. 11 Figure 27: Variation in the task content performed by NSE and SE. (Change from the late 90s) Non-Routine Cognitive Analytical 11 Evidence of a higher intensity of the cognitive tasks in the ECA countries is presented in Keister and Lewandowski (2016). ‐ 1 ‐ 0, 5 0 0, 5 1 1, 5 Russia Georgia Kyrgyzstan Armenia Albania Moldova NSE SE", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 MPI, constructed in Admasu et al. (2021), to capture the deprivations of forcibly displaced individuals and their gendered lives. The paper proceeds as follows. Section 2 reviews the existing literature to provide the background and motivation for the analysis, including a summary of the different country contexts covered by the case studies in this paper. Section 3 outlines the measurement strategy for deconstructing the MPI used for analysis and its limitations, followed by Section 4, which introduces the data. Section 5 presents the findings, first for deprivation results at the individual level and then results evaluating intrahousehold inequalities. Concluding remarks are discussed in Section 6. 2 Background and Literature Review 2. 1 Individual-level measures of gender and multidimensional poverty Individual-level analyses of multidimensional poverty have mostly centered around children, with various studies analyzing the relevance of indicators for children (aged 0- 17 years), 2 as well as other age ranges. The MPI has also been used to better understand gender issues, for example, Batana (2008) implemented a women ’ s MPI in Sub-Saharan Africa. Bhutan ’ s Gross National Happiness measures (2010, 2015), Vijaya et al. (2014), and Klasen and Lahoti (2016) are implemented at the individual level.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Women ’ s Empowerment in Agriculture Index (WEAI) (Alkire et al. 2012) uses individual-level data, and the linked Gender Parity Index reflects inequalities across women and men ’ s deprivation scores within the same household. Alkire, Apablaza and Jung (2014) design and implement an exploratory individual-level MPI for 31 European countries over six waves of data using EU-SILC data sets, finding no cases in which are women significantly less poor than men, and in many cases, they are significantly poorer. Espinoza-Delgado and Klasen (2018) create an individual-level MPI to understand differences in poverty between women and men in Nicaragua, finding similar overall incidence, but much higher intensity of poverty among women. Bessell (2015) and Pogge and Wisor (2016) explore deeply contextual gendered poverty measures and elucidate the ways that participatory consultations can inform the design and uses of gendered measures. Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa. Alkire, Ul Haq, and Alim (2019) use individual-level data alongside MPI data to expose gendered and intrahousehold differences among MPI poor and non-poor children.", "output": {"entities": {"named_data": ["EU-SILC data sets", "global MPI", "Women ’ s Empowerment in Agriculture Index", "Gender Parity Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 In this study, we apply their techniques to understand gender differences among adults as well. 2. 2 Intrahousehold analyses of multidimensional poverty The literature on multidimensional poverty measurement and intrahousehold analysis is limited. Espinoza-Delgado and Klasen (2018) propose an individual-based multidimensional poverty measure for Nicaragua and estimate gender gaps in headline statistics. Klasen and Lahoti (2016) question the neglect of intrahousehold inequality in multidimensional poverty indices by comparing a standard household-level MPI and an individual-level MPI to the MPIs proposed by Alkire and Santos (2014) and UNDP (2014), finding that females recorded a far higher poverty rate when using the individual measure and that age differentials in poverty were also larger. We follow their work of investigating poverty in the indicators for which individual data is available and compare the achievements of men and women and boys and girls living together.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This allows us to avoid relying on gender of the head to derive conclusions about gender gaps, while also recognizing the high prevalence of female-headed households among the displaced and large differences across countries between households based on the gender of the head (Admasu et al. 2021). 2. 3 Country contexts Sub-Saharan Africa is one of the most conflict-affected regions in the world, with one- third of the total number of conflicts taking place in the region (Pape et al. 2018). At the end of 2018, IDMC estimated that 16. 8 million people in Africa were internally displaced because of conflicts and violence, which amounts to 40 % of the global number. All five countries included in this study are among the most conflict-affected in the region and rank in the top 12 countries with the highest number of conflict-induced displaced population (Pape et al. 2018). East Africa (which includes four of the five countries considered in this study) is the sub-region with the largest number of internally displaced people and refugees, accounting for 22 % of the global number. Ethiopia, Somalia, and South Sudan are three of the top five countries in the region with the greatest number of new displacements since 2019 (IDMC 2020).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 3 Methodology 3. 1 The A-F method and individual deprivations The Multidimensional Poverty Index (MPI) used in this paper was first presented, with a full methodological discussion, in Admasu et al. (2021). Here we present a general overview of the measure for the individual-level and intrahousehold analyses. The MPI is constructed based on the Alkire-Foster (AF) method of multidimensional poverty measurement (Alkire and Foster 2011). Three key statistics characterize any MPI: incidence or headcount ratio (H), which is the proportion of the population who are multidimensionally poor; intensity (A), which is the average share of weighted indicators in which multidimensionally poor people are deprived; and adjusted headcount ratio (M0 or MPI), which is the product of the incidence and intensity (MPI = H × A). The AF method uses a dual-cutoff counting approach to poverty measurement. Having fixed relative weights across indicators that sum to 100 %, it first identifies who is deprived in each indicator, then sums up the weighted deprivations each person experiences into a deprivation score. A person is identified as poor if their deprivation score meets or exceeds a cross-dimensional poverty cutoff that is greater than 0 and less than or equal to 100 %. It then aggregates this information to compute society-level MPI, incidence, and intensity.", "output": {"entities": {"named_data": ["Multidimensional Poverty Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The MPI can be decomposed by any groups for which the data are representative and broken down by indicator to show the composition of multidimensional poverty, adding to the policy relevance of the analysis. To tackle individual-level and intrahousehold analyses, we build on the work of Alkire, Ul Haq and Alim (2019). The focus is on individual deprivations, and we call the persons with individual-level data in each indicator the eligible household members. For example, children aged 6-16 years might be eligible for deprivations in terms of school attendance, but not those older or younger. For individual-level indicators, we identify who and how many household members are deprived: their gender and their age, and what proportion of eligible household members are deprived. This is a powerful and potentially informative steppingstone for analysis. Consider two households, each of which has five eligible members with data on nutrition. The aggregation rule in this example is that if any household member is undernourished then the household is undernourished. So, both households are deprived in terms of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 Bank Account No member has a bank or mobile money account. 1 / 12 Many of the indicators align with goals identified in the 2030 Agenda for Sustainable Development, such as no hunger, good health, access to quality education, clean water and sanitation, and decent work, as well as indicators that are especially relevant for displaced people, such as possession of legal identification, physical safety, and food security. The focus on gendered dynamics justifies health indicators related to pregnancy care, combining information on prenatal care, assisted delivery, and early marriage. A full discussion of the MPI ’ s indicator selection can be found in Admasu et al. (2021). We focus on six of these 15 indicators that use individual-level data – viz years of schooling, school attendance, pregnancy care, early marriage, legal identification, and unemployment. Our intrahousehold analysis drops the two health indicators due to data limitations; the question about age at marriage was only asked to the household head in Ethiopia, Nigeria, and South Sudan, whereas in Somalia and Sudan, it was applied to more members than the head.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["health indicators"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The pregnancy care indicator is also excluded from the intrahousehold inequalities analysis as the reference populations for the analysis did not permit rigorous statistical testing. 3. 3 Limitations With a few exceptions, gendered MPIs have been designed using indicators that are present in standard survey instruments, which themselves struggle with normative challenges (Alkire 2018). To create improved gendered MPIs, in which people ’ s poverty can be compared across gender and age or the life cycle, research must develop “ comparable ” definitions of capability deprivation that matter to people in different age cohorts or different life situations. Reliable indicators comparing men and women ’ s income, ownership of assets, and decision-making powers in the same household are difficult, as are those measuring decent work. Health indicators also differ by gender, change across the life cycle, and vary across family structures and disability status. The MPI constructed in Admasu et al. (2021) has the same weaknesses as these measurement paradoxes, but it remains a step in the right direction. We aim to mitigate the limitations of this household-level measure by unpacking the deprivations of indicators available at the individual level, disaggregating those deprivations by gender", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "24 According to Table 12, all countries but Sudan show a significant relationship for school- age children ’ s experience of intrahousehold inequality and their displacement status, although sample sizes mean that only Nigeria and Somalia ’ s results are robust. In Nigeria, most children experiencing intrahousehold inequality reside in non-displaced households – although it is crucial to note that these children constitute most of the school-age children (71. 1 %), and the levels of intrahousehold inequality are nonetheless far higher than anticipated if displacement status had no effect. In Somalia, displaced children are significantly more likely to experience intrahousehold inequality in school attendance, as they constitute 63. 5 % of school-age children experiencing intrahousehold inequality even though they only make up 35. 3 % of the school-age children population in the sample. For the years of schooling indicator, the overall lack of intrahousehold inequality among the MPI poor in years of schooling obscures meaningful or robust differences by displacement status. Gender and displacement status appear to jointly have significant impacts in school attendance in Ethiopia, Somalia, and South Sudan. In Northeast Nigeria, it appears that displacement status has larger effects than gender. In Somalia, forcibly displaced school children experience intrahousehold inequality more often than non-displaced children, to the disadvantage of girls.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 The Government of Chad subscribes to this logic as evidenced by the application decree of Chad ’ s Asylum Law signed in 2023, 1 and the National Response Plan to the Impact of the Sudanese Crisis which is under preparation. The Law and the Plan promote the local integration of refugees, aim to avoid settling refugees in permanent camps and promote self-sufficiency. They offer refugees the right to own land, to engage in formal employment and commercial activities, to move freely, and to access to banking services. While participation is the stated policy objective, the reality is that previous arrivals are almost exclusively living in camps, and that the new arrivals live in “ organized sites ” (as humanitarians now call them), presumably to cope with massive arrivals but with little concrete evidence for the onward movement of refugees. This note explores the size of this four-way benefit (for refugees, hosts, the Chadian state and the international community) by estimating how much could be saved on aid for basic needs consumption by enabling refugees from Sudan to realize their economic potential. For its empirical work the note draws primary on data from the ECOSIT4 survey. 2 In 2018 – 19, Chad became one of the first countries in Africa to capture refugees and host communities in its national household survey.", "output": {"entities": {"named_data": ["ECOSIT4 survey"], "descriptive_data": [], "vague_data": ["national household survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Refugees and Host Communities Household Survey expanded the national Household Consumption and Informal Sector Survey to include a representative sample of refugees and host communities, including Sudanese and host communities located in the east of the country. The remainder of this note is organized as follows. Section 2 presents a short discussion of the literature on the economic participation of refugees. Section 3 compares the characteristics of newly arrived refugees from Sudan with previous arrivals for whom survey data is available, to find that both groups are highly comparable. Section 4 uses the existing data to explore how the basic needs refugees are covered from own-income. Sections 5 and 6 dig deeper by exploring econometrically the correlates of higher incomes of refugees. A discussion of the results and their policy implications follows in section 7, after which section 8 concludes. 2. Benefits of economic participation of refugees Whether or not the arrival of Sudanese refugees in Chad contributes to economic growth is of limited immediate relevance as concerns about the safety of fellow humans drive the response. Nor does any decision maker suggest that hosting refugees is a development strategy Chad should pursue.", "output": {"entities": {"named_data": ["Refugees and Host Communities Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "But, given the presence of thousands of refugees in the country, the constrained fiscal space the authorities face, and the likelihood that international assistance for refugees will taper in the future, an imminent policy question is how to ensure that refugees can be hosted in a sustainable manner, without becoming a fiscal burden in the future. The fear that refugees are (or might become) a fiscal burden is driven by a broadly held perspective about forcibly displaced persons in general, and refugees in particular, namely that they are humanitarian subjects, vulnerable and worthy of public assistance (Betts and Collier 2017). This perspective is not universal, however. The economic contributions of refugees have been extolled for years, from posters 1 https: / / www. ecoi. net / en / file / local / 2091861 / 645b938a4. pdf. 2 Enquête sur la Consommation des ménages et le Secteur Informel au Tchad. The survey was carried out jointly with the National Statistics Office (Institut national de la statistique, des études économiques et démographiques, INSEED) and the UNHCR in Chad.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. Introduction Violent conflict is one of the most important development challenges facing the world today. The incidence of wars has decreased in recent years (Harbom and Wallensteen 2009). However, the legacy of violence persists in many regions, affecting millions of men, women and children (Geneva Declaration Secretariat 2008, UNHCR 2008). The economic, political and social consequences of violence are far-reaching. Violent forms of conflict kill, injure and displace people, destroy physical capital and infrastructure and change the ways in which societies are organized. These effects will have considerable consequences for the long-term human capital accumulation of populations exposed to violence. This is well visible in the fact that no conflict-affected country will reach the Millennium Development Goals by 2015 (DFID 2009): conflict-affected countries contain one-third of those living in extreme poverty, and are responsible for almost one-half of child mortality in the world (Collier, 2007, DFID 2009). They also account for 42 % of all out-of-school children (28 million children), even though only 18 % of all children in the world of primary school age live in conflict-affected countries (UNESCO 2011). The objective of this paper is to examine one important channel linking violent conflict and development outcomes: the level and access to education of children living in contexts of conflict and violence.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 household displacement and the destruction of household dwellings during the violent events. We are also able to measure conflict intensity across time – including peaks of violence at various stages of the conflict – at the district level from event data on violence intensity during the conflict in Timor Leste. We focus on primary school effects because only a small percentage of the Timorese population attended secondary school. Our results show mixed evidence for the impact of violent conflict on educational outcomes. Mirroring some of the findings of Bellows and Miguel (2006) and others, we find evidence for a rapid recovery of the education sector in Timor Leste, and of educational outcomes, particularly for girls. However, in line with emerging results in the micro-level literature, we find that the 1999 wave of violence in Timor Leste – as well as peaks of violence in the 1970s and 1980s – resulted in negative effects on primary school attendance and attainment. This effect is particularly strong for boys. We attribute the first result to a process of educational catch-up among girls in Timor Leste that started before the conflict and continued despite the conflict.", "output": {"entities": {"named_data": [], "descriptive_data": ["event data on violence intensity during the conflict"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The second result is likely to be due to large rates of grade repetition and of delay entry that were exacerbated by the need to remove boys from school due to the negative economic effects of the conflict on households more exposed to the violence. The paper is structured as follows. In section 2, we present a literature review on the impact of violent conflict on development outcomes in general and education in particular. Section 3 provides a descriptive background of the conflict in Timor Leste and the country ‘ s education sector. In section 4, we describe the datasets, discuss our identification strategy and present some descriptive results. Section 5 discusses our empirical results, as well as a range of robustness checks. Section 6 concludes the paper. 2. Literature review An emerging body of literature has provided valuable empirical evidence on the effects of violent conflict on income and consumption levels, and more generally on the welfare of populations living in areas of violence (Ibáñez and Moya 2009, Justino and Verwimp 2006, Verwimp and Bundervoet 2008). A significant number of studies have also examined the health impact of violent conflict, finding that violence results in negative health effects in terms of lower height-for-age and lower nutritional outcomes among children that will generate long-term consequences on future outcomes. 2 2 See Alderman et al. (2006) for Zimbabwe, Bundervoet et al. (2009) for Burundi, Akresh and Verwimp (2006), Akresh, Verwimp and Bundervoet (2007) and Akresh and de Walque (2008) for Rwanda and Guerrero-Serdán (2009) for Iraq.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 The evolution of education in Timor Leste has been characterized by three distinct periods, coinciding with (i) the Portuguese colonial rule (from early 1500s to 1975), (ii) the Indonesian occupation (from 1975 to 1999) and (iii) the UNTAET administration (from October 1999 until independence in May 2002). Under Portuguese colonial rule, education was administered via the Catholic Church. Churches were the major providers of education and schooling was mostly available to the elite in urban areas. When, in 1975, Indonesia invaded the country, literacy rates were extremely low, at around five percent (UNDP 2002). Gender disparities were also very large. The Indonesian government planned to expand education access to the whole population of Timor Leste. Education was used as a means to control the population, and the Portuguese and Tetum languages were abolished. Under the Indonesian education system, children had to enroll in primary school by the age of 7, and were supposed to finish primary school at 12 years old (grade 6). In 1994, basic education was made compulsory up to low secondary school (nine grades of education up to age 15).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The data shows that cohorts that were of school age during the Indonesian occupation achieved higher level of education than older cohorts (i. e. those born in the 1970s compared to those born in the 1960s or before) testifying for an increasing trend as expected. The figure shows that despite the increasing trend a large fraction of individuals have low education levels. Interestingly, all curves start to drop after the 1987 cohort. This decreasing trend is observed among individuals aged 20 or younger in 2007 and provides evidence of a mismatch between the grade attended and the grade that they should have achieved at their age. 4 This is caused by a persistent sluggishness in grade achievement due to the high level of delayed entry to school and high rates of repetition. 5 The impact of the conflict in its different phases and the subsequent reconstruction efforts on schooling levels of children in Timor Leste is therefore unclear. The early years of violence coincided with an education for all policy in which quantity was preferred to quality. In addition, the 1999 violence that followed the withdrawal of Indonesian troops led to the destruction of schools and the removal of children from school. The reconstruction program implemented after 1999 tried to counteract this destruction, and achieved fast progress. However, the education sector was still in very poor shape. In the next section, we investigate in more detail the effects of the conflict on educational outcomes of boys and girls in Timor Leste. 4. Identification strategy and data description 4 Those born in 1992 are 15 in 2007. So they might have at most completed grade 9 and this justifies part of the drop in the curves as the grade completed is right censored. 5 The high levels of school delay are also confirmed by the figures on gross and net enrolment ratios calculated using the TLSS 2001 and 2007: primary gross enrolment ratio was 105 percent in 2001 and 128 percent in 2007, while net enrolment ratios were 74 and 94 percent, respectively, in 2001 and 2007.", "output": {"entities": {"named_data": ["TLSS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Commission for Reception, Truth and Reconciliation (CAVR). 7 This information has been collected from deponents to the Commission ‘ s statement-taking process. 8 We make use of data on the number of killings that occurred during the war in order to derive patterns and variation of violence in Timor Leste over time and across space. We use this data to identify districts and years that experienced high and low violence-intensity, both at the start of the occupation and following the withdrawal of Indonesian troops in 1999. This allows us to estimate both the impact of the first years of the conflict and the impact of the last wave of violence in 1999. 4. 1. Identification strategy: The impact of violence on school attendance in 2001 We first investigate the short-term impact of the 1999 violence. The empirical questions being addressed are: (i) whether the violence in 1999 imperiled school attendance9 and school grade deficit, and (ii) whether different channels of exposure to conflict – displacement and house destruction – affected boys and girls and different age groups differently. 4. 1. 1. Primary school attendance and grade deficit rates in 2001 We make use of information in TLSS 2001 collected at the individual and household levels on displacement and house destruction to identify conflict-affected individuals.", "output": {"entities": {"named_data": ["TLSS 2001"], "descriptive_data": ["data on the number of killings that occurred during the war"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We have constructed two different variables that try to account for the degree of severity of the conflict. 10 The first variable identifies individuals belonging to households that were displaced due to the 1999 wave of violence (all members displaced). The second variable identifies individuals in households that report having their house completely destroyed by the violent attacks in 1999. The TLSS 2001 contains also useful retrospective information on school attendance and grade attained across three different academic years: 1998 / 99, 1999 / 00 and 2000 / 01. We are 7 Commission for Reception, Truth and Reconciliation & Benetech Human Rights Data Analysis Group. ― Human Rights Violations Database. ‖ 9 February, 2006. Website: http: / / www. hrdag. org / resources / timor-leste_data. shtml. 8 There may be potential sample biases in the statement taking procedure given the voluntary nature of the process. It is possible that those living in more remote or mountainous areas, those living far away from the areas where the statements were taken, the sick, old and disabled and those with no access to the media or means of mass communication have a lower probability of being part of the sample. By contrast, those more active in local communities are more likely to have provided a testimony.", "output": {"entities": {"named_data": ["Human Rights Violations Database", "TLSS 2001"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In order to address these potential sample biases, the CAVR supplemented its documentation with reports produced by Amnesty International and Fokupers (a local NGO). The information contained in these reports was then included into the HRVD database. 9 Note that we do not analyse school completion in 2001 because most children that were of school age in 1999 were still in school in 2001. 10 The questions we used are ― Was [NAME] displaced outside E. Timor in 1999? ‖, and ― Was the [BUILDING] damaged in the violence of 1999? ‖. 14 % of the whole sample surveyed in 2001 report having been displaced, while 26 % report that their house was destroyed. Within our sample of school age children, these figures are 16 % and 25 %, respectively. We have made sure that buildings that are reported to having been destroyed were used for living purposes only.", "output": {"entities": {"named_data": ["HRVD database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 4. 1. 2. Empirical strategy In order to make use of the retrospective information on school attendance provided in the dataset, we have constructed what we have called an ‘ ad hoc panel ’, whereby we exploit the time-variation of the variables of interests (age, attendance status and grade attained by the respondents) by reshaping the cross-sectional structure of the TLSS 2001 dataset. In this way, we are able to obtain observations for each individual over three academic years. All key education variables are time- variant, while other individuals and households characteristics are time-invariant. Within these three years, we focus our analysis on individuals that were of primary school age (between 7 and 12 years old) in each year. In practice, we keep all children aged at minimum 7 years old in 1998 and at maximum 12 years old in 2000. As a consequence, our panel data contains children aged 8-11 years in 1999, the year of the violence. 11 Since we are interested in looking at different effects across groups of individuals, we have split the sample between boys and girls and between younger children (aged 8-9 in 1999) and older children (aged 10-11 in 1999).", "output": {"entities": {"named_data": ["TLSS 2001 dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We estimate the following equation: [2] where are our outcome variables, i. e. attendance rate (which is a binary variable12) or grade deficit. and are year dummies respectively for year 2, the year of the 1999 violence, and for year 3, the first year of the post-conflict period. The model includes individual fixed effects,. The variable is the random error. All standard errors are clustered at the village level. As discussed above, we identify violence-affected individuals using two different measures,, with j = 1, 2 depending on which measure is included in the specification. The first one is whether the individual was displaced with the whole household. The second is whether the individual reports that her house was completely destroyed during the 1999 violence. We allow the violence measure to interact with both year dummies. The estimation of our specification above is 11 We have also tried to keep a larger sample that includes those children in primary school age in the year of the violence (i. e. between 7 and 12 in 1999). This means including individuals aged 6 in year 1 and aged 13 in year 3. The inclusion of these latter individuals may generate ‗ spurious ‘ results as they are not of primary school age. We have estimated the model using both samples. We find that the estimates using the larger sample are similar to those obtained with the sample of children aged between 8 and 11 in 1999. The larger sample generates more statistically significant results but we have decided to opt for the most restrictive sample to avoid inclusion of ‗ tails ‘ of the age distribution that are not of primary school age. 12 We estimate our model with a linear probability model.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 of intense conflict. We examine also the overall impact of the 25 years of conflict on educational outcomes. This analysis allows us to consider the full long-term impact of the conflict on educational outcomes of different generations of children in Timor Leste. 4. 2. 1. The educational impact of the 1999 wave of violence We exploit variation in the number of killings over time and across districts to identify conflict affected individuals. Our intention here is to analyze whether individuals exposed to the violence during their primary school age show different primary school completion rates eight years after the end of the war, relative to those not affected by the conflict. The outcome variable in which we are interested is whether individuals completed primary school in 2007. Figure 7 shows average primary school attainment for all individuals in our sample. The graph shows an increasing trend in primary school completion across cohorts and a progressive reduction of the gender gap. The gap among the younger cohort (those born after 1987) is almost zero. The drop in the curve for the younger cohort confirms the presence of significant delays in school attendance. For the purpose of this analysis, we use the TLSS 2007 dataset and the HRVD dataset contained in the CAVR data publication.", "output": {"entities": {"named_data": ["TLSS 2007 dataset", "HRVD dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As explained in section 4, the HRVD dataset contains data on the number of human rights violations occurred since the start of the conflict in 1975 until its end in 1999 for each district. The types of violations recorded are killings, deaths due to deprivation and disappearances. We use only the number of killings to identify years and districts affected by the conflict. We exclude the deaths due to deprivation because the districts in which this occurred may very likely not be those where the conflict was most intense, but were simply places were the victims were hiding as a consequence of escaping from the troops, and died for starvation. In addition, since killings are less likely to affect entire families than deaths due to deprivation, there is a lower underreporting bias attached to the former measure relative to the latter one (Silva and Ball 2006). We also exclude disappearances as, according to HRVD data, they do not show enough time and geographical variation in order to identify individuals more or less exposed to the conflict. We believe that the number of killings proxies quite well the intensity of the conflict across time and space as their occurrence largely tracked the movements of the Indonesian military operations. The other two types of violations do not seem to show the same pattern (Silva and Ball 2006). For the same reason, we believe that it proxies quite well the destruction of houses and infrastructure and the displacement of people given the way in which the last wave of violence occurred (i. e. the scorch-earth technique employed by Indonesian troops as they moved towards West Timor).", "output": {"entities": {"named_data": ["HRVD dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, boys can get caught by adverse impacts of violence in ways that still remain under-researched. The evidence for Timor Leste suggests that boys were very vulnerable to the educational effects of violence. This result implies that much more attention must be paid to understanding how children are affected by violent conflict and the different roles girls and boys assume during and after the conflict. The third implication is that conflict is not a uniform phenomenon. Violent conflict affects different people and different aspects of their welfare through different channels. In the case of Timor Leste, and in line to evidence from other conflicts such as Colombia (see Ibáñez and Moya 2009 for instance), displacement has particularly adverse effects on educational outcomes. The recently released Education for All Global Monitoring report by UNESCO (2011) portrays displaced populations as the hidden victims of conflict. A significantly disproportional number of displaced children are out of school (even in comparison to conflict-affected populations in the same country), while enrolment rates among displaced populations across the world average around 69 % for primary school and 30 % for secondary school. The analysis of Timor Leste confirms the extreme disadvantage that displaced populations face in terms of lost educational opportunities. This is likely to affect generations of boys in Timor Leste, possibly perpetuating the risks associated with renewed conflict in the future.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 implemented the program in Irbid and Mafraq Governorates, which host over 47 % of the Syrian refugees in the country. In Lebanon, the programs were implemented in Zahle, West Bekaa, Chouf, Jezzine and Saida where one-third of the population are Syrian refugees live. Participants enrolled in courses that aligned with their private interests as well as market demand and sectors in which refugees could legally work. Courses were implemented by local training providers and lasted two to eight weeks. Topics included aluminum fabrication and installation, woodworking and carpentry, food and dairy processing, electrical repair, beautician, light construction rehabilitation, mechanical repair, artisanal manufacturing, greenhouse maintenance, and drip irrigation installation and repair. Although a small number of sessions trained only members of one nationality — partially due to employment restrictions-- a majority mixed host-refugee groups. On average, each group contained an approximate mix of 65 % hosts and 35 % refugees. Theoretical Motivation: Literature Review: Jobs programs are often utilized to not only promote economic outcomes, but also social cohesion goals. As delineated in the World Development Report 2013, there are two main pathways for jobs to promote social cohesion. One pathway is indirect.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 this oversubscribed list. Selection into the training group was based on a “ vulnerability score ” that gave priority to younger, female and unemployed individuals. Despite this approach, intake was “ fuzzy ”- participants were ordered by their vulnerability score, with the most vulnerable entering up until capacity. In some intakes, individuals with comparatively high scores were not taken into the program. In others, individuals with comparatively low scores were included. We construct our treatment and control groups from these intake decisions. Data were collected from members of the host and refugee communities in each country. 4 In both Jordan and Lebanon, the intervention was implemented on a rolling basis. As soon as one training cycle was completed, another would begin. Data were collected in three waves during each training cycle. First, during an “ outreach ” phase, where data were collected in order to assign treatment status. Second, at “ baseline ”, which occurred before the training had begun but after treatment assignment was known. Third, data were collected at “ endline ”, immediately following the end of the training. Data collection for those assigned to the treatment and control followed the same pattern. 5 Outreach and baseline data collection took place less than a week apart and were collected between July 2018 and September 2019.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Between outreach and baseline, one full survey round was collected due to potential survey fatigue and on the understanding that nothing of importance would likely change in such a short period. Basic demographic information, such as age, gender, marital status and employment status were collected at outreach. At baseline, additional indicators were collected, relating to the behavior, attitudes, opinions and personalities of the participants. The only exception to this is data on optimism, which were collected at both outreach and endline. This allowed us to test whether or not the intake decision had effects, even before the training began. Endline data were collected between July 2018 and November 2019 and repeated the combined outreach and baseline surveys and experiments. Variables: We collected a range of survey and experimental indicators in order to assess our key research questions and associated hypotheses: 6 Economic and life optimism: We collected two survey questions about optimism at outreach, baseline and endline. These questions ask individuals to rank their expectation that their life and economic situation will be better in one year than it is now. Answers are 4 In addition, data were collected from Palestinian Refugees in Lebanon (PRL).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["data on optimism"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Due to our research design and the complexity of introducing a third grouping who would play outgroup in the behavioral experiments regardless of partner identity, we exclude PRL from the main analyses presented here. 5 Data were also collected six months after the end of the training but this was heavily disrupted due to the outbreak of the COVID-19 pandemic. In Lebanon, this resulted in a change to the method of data collection (from in-person to telephone) and in Jordan, an end to data collection entirely. In Jordan, this had a more pronounced effect on the control group, due to the scheduling of data collection and implementation of restrictions in Jordan. Given these complexities, we do not present results from these analyses. 6 In addition, we attempted to collect information on the extent of social and economic interactions between hosts and refugees. At baseline, almost 95 % of respondents in both the treatment and control group reported such interactions. For this reason, we do not include this information in these analyses.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 scored on a Likert scale running from 0 (significantly worse) – 10 (significantly better). The survey questions on optimism were collected at outreach, baseline and endline. Employment status: Due to slight differences in access to labor markets for refugees in Jordan and Lebanon and differences in how we were able to ask about employment status, we tabulate employment status as whether or not an individual is employed. Participants were asked at outreach about their employment status, and, in subsequent rounds, whether or not this had changed. This variable is coded 0 for not currently employed and 1 for employed. Economic scarcity: We collect survey questions on individual perceptions on: ability to meet current needs; ability to meet future needs; expectation that access to jobs is fair; expectation that salaries are fair; and belief that unfair access to labor markets fuels tensions. Ability to meet current and future needs are coded on a Likert scale running from 1 (completely unable) to 5 (fully able). The “ fairness ” indicators are coded: 0 (unfair) or 1 (fair). Whether or not competition around employment contributes to tensions is captured on a 1 (not at all) to 5 (absolutely) Likert scale.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Intergroup behaviors: We collect data from two one-shot incentivized behavioral games: the dictator game (a division game where players choose how to split a prize) and stag hunt (which gives players the chance to cooperate). 7 In each wave of data collection, players were randomized, at the session-level, to play either with a partner from the host or refugee community in that country. For example, a Lebanese player could be paired with a Lebanese or a Syrian resident in Lebanon but not with a Jordanian or a Syrian resident in Jordan. Partner identities were re-randomized between the waves so that not all players played with a partner of the same identity in both rounds. We made clear that partners were not individuals in the same room and, at endline, that the partner was not the same partner from baseline. A hint was given about the partner ’ s identity based on dialectic differences in the words for common foods, along with a small amount of innocuous information (approximate age, favorite hobby and marital status). 8 Sample intakes and partner assignments by data collection wave are shown in Table 1. This prime relies on a minor, and subtle, difference in dialects in settings with an otherwise high degree of cultural similarity.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 the region. Third, again due to cultural closeness, the nature of these variations is known across the region. 9 Additional measures: We collected usual socio-economic and demographic information, including: age, gender, marital status and education. In addition, we collected data on self- reported risk preference and a short-form personality survey to ascertain GRIT among participants. Noting that a small number of participants did not answer all survey questions, we undertake a regression-based data interpolation process to complete the dataset. 10 Table 1: Partner Assignment and Sample Sizes by Treatment and Community Status Host Refugee Ingroup Outgroup Ingroup Outgroup T C T C T C T C Outreach / Baseline 219 48 203 48 147 72 147 49 Endline 179 34 222 37 148 45 133 51 We present summary statistics of demographic data and other covariates for the baseline (Top) and endline (Bottom) for Jordan in Table 2 and for Lebanon in Table 3. [TABLES 2 AND 3 ABOUT HERE] Identification: The “ fuzzy ”, treatment intake is not random. As can be seen in Table 1, there are some elements of attrition from the sample. The sample decreases by about 10 % from baseline to endline.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We, therefore, first examine whether or not there is structure, both, to selection into the treatment group and attrition, which could undermine our econometric approach, where we rely on difference-in-difference estimators. Imbalance between treatment and control groups could undermine the key assumption of parallel trends. For example, given that men and women face different barriers in the labor market, we should not expect employment to evolve in the same way for men and women after the treatment. We would expect to observe a difference-in-differences for a treatment group where women are more common than in the reference group, even without the program. To test for imbalances, we run a simple regression of treatment and attrition indicators at baseline on the socio-economic and demographic controls, GRIT indicators, self-reported optimism, employment status and risk. Table 4 (Column 1 for the treatment analysis, Column 2 for the attrition analysis) shows some signs of structure. In particular, host status and risk preferences are significantly different between treatment and control, with 9 For example, “ hummus ” is used to refer to chickpeas in general but can also be used for the dish involving mashed chickpeas, tahini, lemon and garlic in Lebanon. In other dialects, some qualifiers are required to specify this dish (e. g. hummus ne ’ em, or smooth hummus). This is akin to identifying a British or American individual using similar variations in foodstuffs such as courgette / zucchini; coriander / cilantro; etc. 10 Specifically, we regress variables with missing observations on the list of all variables with a complete record. We then use the predicted values from this regression to populate the missing variables. Where appropriate, predicted values are rounded to the nearest integer and within answer codes of that variable. In a second round, this process is repeated on the full set of actual and predicted values from the first stage.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["GRIT indicators"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 employment status and education level also significant at 10 %. Marital status and education are important predictors of attrition. As we might expect, these imbalances suggest some threats that, if left uncorrected, could undermine the parallel trends assumption of difference-in-difference estimators. That said, we see no sign of differences between treatment and control, or attritors and non-attritors, over the key GRIT personality features. This suggests that members of the treatment group are not, for example, more motivated to succeed than members of the control group. To account for these biases, we generate a series of inverse probability weights to balance the data. These weights define the probability of an individual with particular characteristics (e. g. host or refugee status) being in each of the treatment and control groups at baseline and endline and are used to rebalance the data in order to closer support the parallel trends assumption. Results are shown in Column 3 of Table 4. Following weighting, data balances on all key factors, including nationality. This suggests that the parallel trends assumption is more reasonable under the weighted dataset than in the raw treatment / control data. 11 Based on these analyses, we conclude that it is safe to use weighted OLS-based approaches.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 degree of group bias. Thus, should the program reduce bias, 𝜓𝜓7 < 0. 𝑋𝑋is the same 𝑛𝑛 × 𝑘𝑘 matrix of control variables. 𝜓𝜓 is a 𝑘𝑘 × 1 vector of regression coefficients; and 𝜔𝜔 is the idiosyncratic error. We employ two small deviations from these approaches to produce the full set of results. First of all, as we do not have two sets of control variables from outreach to baseline, we run a fixed effects analysis to understand the impact of assignment to treatment status on life and economic optimism. Second, due to a data collection error in the field, indicators of economic scarcity were not collected from all of the control group at baseline. Instead, we seek to approximate the effect of treatment on these indicators by triangulating comparisons in two dimensions. First, we test whether or not these indicators improved for the treatment group from baseline to endline. Second, we test whether or not there are differences between the treatment and control groups at endline. This stops short of causality but still reveals interesting information about the dynamics at play. We produce five outputs for each analysis, with the exception of the economic scarcity indicators. First, we use uncontrolled OLS. Second, we introduce control variables. Third, we remove the controls but add inverse probability weights.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10099 Situations of forced displacement create unique challenges for social cohesion because of the major disruption of social dynamics among both displaced persons and host communi­ties. This paper uses a sequential mixed method approach to analyze the relationship between hosting displaced persons and perceptions of social cohesion in eastern Democratic Republic of Congo. First, participatory research methods in focus groups empowered participants to pro-duce a locally driven definition of social cohesion. The results from these exercises inform the quantitative assessment by dictating measurement strategies when analyzing original surveys.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Combining almost 50, 000 responses to 11 cross-sectional surveys between 2017 and 2021, displacement is neg­atively associated with perceptions of social cohesion in aggregate. But at the individual level, those who report hosting displaced populations in their communities often have higher perceptions of social cohesion. These results are strongest among respondents who self-report hosting IDPs as opposed to refugees, but important heterogeneity across indicators, local context, and gender should guide policy meant to promote social cohesion in forced displacement. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at ppham @ hsph. harvard. edu.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["cross-sectional surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "itative exercise empowered focus group participants to guide the research and conceptualization. The results from these exercises then dictating the definition and measurement strategy for social cohesion in the quantitative assessment. The participatory research strategy was based mainly on structured focus group discussions. The project conducted consultations in seven territoires with the objective to develop localized understandings of what elements were important to social cohesion in eastern DRC. Participants were selected from civil society and the public sector. 96 individuals participated in these exercises (Table 1), 55 % of whom were men and 45 % of whom were women. 6 Location (Groupement) Date Number Participants Goma City 06 Oct 2017 11 Bukavu City 13 Oct 2017 13 Nyabibwe (Kalehe) 12 Oct 2017 14 Ishungu & Lughendo (Kabare) 12 Oct 2017 15 Kamisimbi (Walungu) 16 Oct 2017 18 Wassa (Walikale) 20 Oct 2017 12 Biiri (Masisi) 03 Nov 2017 13 Table 1: Descriptive Information on Focus Groups The focus group discussions began with an open discussion on social cohesion designed to ascertain participants familiarity with the concept. The facilitator further asked participants to write down words or concepts participants related to social cohesion. These words were written on individual post-its, which were then posted on a wall.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Participants then grouped words, building a concept map to visually represent their common understanding of social cohesion. Following the development of the concept map, the facilitator broke participants into smaller groups and were asked to conceive of a fictional yet realistic person that exists in their contexts. 6Focus groups were conducted in Swahili and French. Focus groups were transcribed to French for analysis. 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In aggregate, we expect to observe a negative relationship between the percentage of the population that reports displacement, but at the individual level we expect that experience with hosting may, in certain circumstances, be positively associated with perceptions of social cohesion, regarding perceptions of relationships and solidarity. 4 Displacement and Social Cohesion: Survey Evidence To empirically evaluate the relationships between hosting displaced populations and social cohe- sion, this paper analyzes a series of surveys of civilian adults conducted in eastern DRC. 8 Each survey uses a multi-stage cluster sampling strategy capturing all territoires9 in North Kivu, South Kivu and Ituri provinces. The final sampling units are randomly selected adults above the age of 18 to avoid bias toward men and / or heads of households. Multiple attempts are made over the course of one day to contact selected respondents and if necessary, appointments are made for in- terview. Surveys are enumerated by Congolese college students or professionals and interviews are conducted by members of the same gender and ethnicity as respondents to minimize enumerator- induced response bias. Further methodological details have been published (Vinck, Pham, Bindu, Bedford & Nilles 2019) elsewhere and additional details and sample size calculation are detailed in Appendix.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["surveys of civilian adults"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These surveys are part of a long-term data collection effort by the research team (Vinck & Pham 2014) and were collected separately from the focus group discussions. The focus group discussions thus did not directly influence the design of the surveys, but rather directed the analysis strategy of the surveys that our team has collected at regular intervals in eastern DRC. The survey data are analyzed in two ways. First, 11 surveys collected between 2017 and 2021 8Eastern DRC is a site of ongoing violence, raising a number of ethical, methodological, and practical concerns about collecting data. We discuss the ethical protections we implemented when collecting this survey data in the Appendix, Section B. 9Territoires are sub-provincial administrative units. Additional details on the structure of administrative units are available in the Appendix, Section A. 1. 21", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "groupement level data cannot measure the character of the displacement in any meaningful way, but analysis at the individual level can. 5. 2 Individual Level Relationships Two survey waves posed additional questions on local displacement dynamics. While these ad- ditional questions restrict comparison with other survey waves, they provide the opportunity to unpack the mixed results found in the aggregate analysis. Poll 14 is a special survey that only sam- ples cities (Ville de Goma, Ville de Beni, Ville de Butembo, Ville de Bukavu, Ville d ’ Uvira, Ville de Bunia and Irumu in particular) while Poll 15 is a representative sample of all territoires in the three provinces. 11 These survey waves are labeled as “ Cities ” and “ General ” samples in the indi- vidual analysis. Analyzing these two surveys together enables the comparison of relationships by the local context, which may distort the impact that hosting has on perceptions of social cohesion. Figure 5 plots the coefficients from series of logistic regressions to account for the binary na- ture of the dependent variables. The regressions include Province fixed effects and groupement clustered standard errors to account for unmeasured context-specific dynamics. Responses are weighted by the inverse proportion of selection at the territoire in each regression.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey waves"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The relation- ships between displacement dynamics and perceptions of each manifestation of social cohesion are analyzed separately by running 12 models for each independent variable (6 regressions for each sample). The regressions are correlations and should not be interpreted causally. Hosting status and displacement flows are likely related to perceptions of social cohesion in indirect ways and the structure of the survey data limit the ability to specify the channels through which these relation- ships run. Each regression controls for characteristics that may influence respondents ’ perceptions of social cohesion outside of the presence of IDPs or refugees in the local community such as province, gender, age, marital status, level of education, employment, and exposure to violence. 11Poll numbers correspond to the number wave in our larger project, as described and shown in Table 2. 28", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The paper extends the GTAP 8 database by separating Lebanon, Jordan, Syria, Iraq, and West Bank and Gaza from the rest of the Western Asia aggregate and Algeria and Libya from the rest of North Africa. Kuwait, Qatar, Bahrain, Saudi Arabia, UAE, and Oman are aggregated into a GCC composite group. In addition, the 57 sectors in the GTAP 8 database are aggregated into 22 sectors based on their importance for the countries in the MENA region (Table 1). The resulting MENA-specific database contains 26 countries, among which are the six Levant economies of interest in this paper (Turkey, Lebanon, Syria, Iraq, Jordan, and Egypt) and the rest of the developing MENA countries (Table 1). The procedure used to construct the individual country information employs data from several sources. The UN Statistics Division data for 2007 is the source for the six components of GDP – agriculture, hunting, forestry, and fishing (ISIC A-B); mining, manufacturing, and utilities (ISIC C-E); construction (ISIC-F); transport, storage, and communication (ISIC I); wholesale, retail trade, restaurants and hotels (ISIC G-H); and other activities (ISIC J-P).", "output": {"entities": {"named_data": ["GTAP 8 database"], "descriptive_data": ["MENA-specific database"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We sourced bilateral trade value data from WITS and bilateral tariff data from a medley of sources, presented in Appendix Table A1. As part of this procedure, all entries in the two composite regions (rest of Western Asia and rest of Northern Africa) were split and assigned the split values to the newly created economies, while all entries for the two composite regions from the GTAP database were removed from the database. Each entry was split using the most thematically relevant external source. Sectoral GDP shares were used to split consumption and production values, trade data were used to split export and import values, and tariff information was used to assign tariff values. Export shares were used to split further production and consumption information into the final set of industries presented in Table 1. For internal consistency purposes, the required accounting relationships were imposed on the split database 8", "output": {"entities": {"named_data": ["WITS", "GTAP database"], "descriptive_data": ["bilateral tariff data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These tariff rate modifications are essential for this analysis as suggested by the substantial differences between the tariff rates available in the GTAP 8 database, especially those implied for Jordan, Iraq, Lebanon, and Syria (Figure 1), and the updated tariff rates, presented by country, product, and source in Appendix Tables B1-B6. Since the GTAP tariffs attributed to Jordan, Iraq, Lebanon, and Syria are composite rates, they do not correspond to the actual trade profile of these countries. Therefore, the new tariff rates differ from the GTAP ones both because of differences in the tariff lines and trade composition. By contrast, the tariff information on Egypt and Turkey in the GTAP 8 database represents relatively accurately existing preferences (Figure 1). 3. Simulation design The pre-war efforts for deeper trade integration in the Levant are reflected in the pre-simulation analysis. Starting from the newly constructed database, the pre-simulation analysis implements the deep trade initiatives discussed by the Levant countries prior to the onset of the Syrian war in 2011. The context for these reforms and the shocks associated with each of these reforms are presented in section 3. 1.", "output": {"entities": {"named_data": ["GTAP 8 database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The updated database from the pre-simulation analysis, which represents an integrated Levant in a peaceful alternative world, is the starting point for the simulation analysis of the Syrian conflict and the spread of ISIS as well as the disintegration of the deep regional trade ties. The design of the war and disintegration scenarios are presented in section 3. 2. 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the degree to which the global response should include a development element. We find that the average stood at around 10. 3 years at the end of 2015, with a median duration of 4 years, and significant sensitivity to a few situations. Such numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete protracted situations. To produce these numbers, we rely on the Population Statistics Database compiled and main- tained by UNHCR. The database records the number of “ persons of interest ” to UNHCR in each year since 1951 and for each situation, where a situation consists of a pair host-origin countries. The calculation of duration of exile is obtained under a no-turnover assumption, whereby a de- crease in the number of refugees for any given situation is fully attributed to exits from refugee status, while increases are assumed to be fully accounted for by new cases. Although such ap- proach tends to over-estimate the true duration of exile, the lack of individual-level data on regis- tration precludes refining the estimate further. Attempts to estimate similar statistics have been limited.", "output": {"entities": {"named_data": ["Population Statistics Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In a 2004 note to its Executive Com- mittee, UNHCR established the average at 17 years at the end of 2003 (Executive Committee of the High Commissioner ’ s Programme 2004). This number has been widely quoted by media, ac- tivists, humanitarian agencies, and development institutions (Milner 2014; United Nations 2016; UNHCR 2015). The rest of the paper is organized as follows. Section 1 gives some definitions and background information on the refugee population. In section 2, we provide some summary statistics from our main source of data, the UNHCR Population Statistics Database. Section 3 describes the method followed to construct duration statistics and presents a few stylized facts. The results of our anal- ysis are presented in section 4. Section 5 concludes. 1 Background: Definitions and Data Under the terms of the 1951 Convention Relating to the Status of Refugees – henceforth the Convention – later amended by the 1967 Protocol, a refugee is a person, who “ owing to a well-founded fear of being persecuted for reasons of race, religion, nationality, membership of a particular social group or political opinion, is outside the country of his nationality, and is unable to, or owing to such fear, is unwilling to avail himself of the protection of that country. ” Data on refugees and asylum seekers are collected by individual countries, international orga- 3", "output": {"entities": {"named_data": ["UNHCR Population Statistics Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "nizations such as UNHCR, and national and international non-governmental organizations. Data is compiled from a number of sources, including but not restricted to individual registration of refugees and asylum seekers (information typically includes name, gender, date of birth, country of origin, marital status, and place of displacement), tracking of population movement in situa- tions where the movement is fluid or continuous, standardized surveys such as Living Standards Measurement Study (LSMS) surveys, Labor Force Surveys (LFS), Demographic and Health Sur- veys (DHS), and Multiple Indicator Cluster Surveys (MICS), administrative records and registries. Yet, data collection is a difficult exercise, due to both methodological issues (UNHCR 2014) and practical challenges, especially in situations of heightened insecurity or mass refugee situations. To date, UNHCR maintains the most comprehensive statistical database under a uniform methodology. UNHCR publishes annual data on refugee flows and stocks by countries of resi- dence and origin dating back to 1951, shortly after the Office was established. UNHCR publishes annual statistical reports ranging from “ Global Trends ”, “ Mid-year trends ”, “ Asylum trends ”, to a “ Statistical Yearbook ”. There is a consensus that these data provide the most reliable source of information (Sarzin 2016).", "output": {"entities": {"named_data": ["Labor Force Surveys", "Multiple Indicator Cluster Surveys", "Living Standards Measurement Study"], "descriptive_data": ["individual registration of refugees and asylum seekers"], "vague_data": ["administrative records"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our analysis exclusively uses data from the 1951-2015 UNHCR Population Statistics Reference database (extracted September 18 2015). Data were provided for 173 countries: 77 percent of these data were based on individual refugee registration, 13 percent on estimates, 5 percent on combined estimation and registration, and 5 percent on other sources. The data are structured as follows: for each situation, the database records annual numbers of persons of concern, which comprise “ Refugees (including refugee-like situations) ”, “ Asylum seekers ”, “ Internally Displaced Persons ”, “ Returnees ”, “ Stateless ” persons, and “ Others of concern ”. A situation is a pair country of origin / country of destination. For example, Somali refugees in Kenya account for one situation, Somali refugees in Ethiopia for another, and South Sudanese refugees in Kenya for yet another. Furthermore, a situation is considered major if it involves more than 25, 000 people. It is referred to as protracted if it is major for at least 5 continuous years. The database, and therefore our analysis, is limited to refugees under UNHCR protection. It does not include asylum seekers, i. e. individuals who have sought international protection under the 1951 Convention but whose claims for refugee status have not yet been determined, and persons in “ refugee-like situations ”, i. e. individuals outside their country or territory of origin who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained (e. g., undocumented Rohingya originating from Myanmar 4", "output": {"entities": {"named_data": ["UNHCR Population Statistics Reference database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "in Bangladesh). The data set we use does not cover the 5. 1 million Palestinian refugees who are under the man- date of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UN- RWA). The definition of Palestinian refugees in international law is distinct from other refugees. Palestinian refugees are people “ whose normal place of residence was Mandatory Palestine be- tween June 1946 and May 1948, who lost both their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict ”. Importantly, their patrilineal descendants are also considered refugees regardless of citizenship (UNRWA 2009). For the purpose of the analysis, we do not include Internally Displaced Persons (IDPs), who are defined as “ persons who have been forced or obliged to flee or leave their home or place of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disas- ters, and who have not crossed an internationally recognized border ” (United Nations 2004). The categories “ returnees ”, “ Stateless ”, and “ Others of concern ” are also not included.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This allows us to determine the earlier possible date of arrival for each cohort of current refugees. As an illustration, we go back to the Somalia-Kenya situation. In 2015, the number of Somali refugees in Kenya amounted to 418, 844 persons. The first year such number was reached was in 2011. The no-turnover assumption implies that all current refugees have been in exile for 3 years. But in 2010, the number of refugees was 353, 208 persons. We conclude that the difference between 2010 and 2011 represents 74, 104 new refugees in 2010, who have hence been in exile for four years. In 2009, the number was at 310, 458: the difference between 2009 and 2010 (42, 750) are therefore people who have been in exile for five years, etc. We repeat the exercise for each year until the beginning of the crisis in 1991. This gives us a num- ber of new arrivals for each year since 1991. On this basis we can calculate average and median durations for this situation. Next, we aggregate all situations and consider one single “ global refugee population ”. We have for each year a number of people who arrived in a variety of situations and make up a global flow. We can hence calculate global average and median durations. For each year, we can also break down the flow across countries of arrival. We can construct similar lower-bound envelopes starting from any “ current year ” which we choose as a reference point. For example, we can apply the same protocols to evaluate average and median durations as of 1994 (the lower-bound envelope is depicted by the dash-dot line in Figure 3). This allows us to follow the variation of aggregate mean and median averages over time. 10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The countries that are the largest source of refugees include: Afghanistan, Syria, Somalia, South Sudan, Sudan, Democratic Republic of Congo, Central African Republic, Iraq, Myanmar, and Eritrea (UNHCR, 2017). These countries account for the vast majority of refugees worldwide. We will also consider three countries in Europe that are an important source of asylum seekers in Europe, and whose recognition rate is low, and account for many returnees – these are Kosovo, Serbia and Albania. Is return possible and safe, and if so, does the economic situation bode well to provide essentials, namely a job, housing, education and reliable public services? It is helpful to identify three groups of countries: ones that are still mired in war and / or high ‐ intensity civil conflicts, countries where medium ‐ intensity conflict persist and are already in the process of rebuilding, and countries which are not in conflict. According to the Armed Conflict Survey (IISS 2017), high ‐ intensity conflict is defined by frequent (daily) armed clashes between governments, government forces and insurgents, or among non ‐ state armed groups that control territory.", "output": {"entities": {"named_data": ["Armed Conflict Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Medium ‐ intensity, on the other hand refers to “ regular armed clashes between governments, government forces and insurgents. ” (IISS 2017) Within the first category, according to the Armed Conflict Survey in 2016 are Afghanistan, Syria, Somalia, South Sudan, and Iraq, while Myanmar, Central African Republic, Democratic Republic of the Congo, and Sudan are in medium ‐ intensity conflict. (Eritrea is not included in the Armed Conflict Survey in 2017, discussed in Annex). Minorities in Albania, Kosovo and Serbia are often the objects of discrimination but countries are not in conflict. A capsule summary of the security and social situation in each of the other countries included in this survey can be found in Annex 2. The survey illustrates how impractical return is today in the countries in conflict, such as Syria, Afghanistan, and Iraq, and voluntary return in large 36 Best practice and new methods in return policy, July 2017, http: / / www. bamf. de / SharedDocs / Meldungen / EN / 2017 / EMN / 20170504 ‐ emnjahrestagung ‐ rueckkehr. html", "output": {"entities": {"named_data": ["Armed Conflict Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Internally displaced women are extremely vulnerable to rape by armed men, “ including government soldiers and militia. ” Protection for them basically does not exist58. According to UNHCR, Somalia has over 1. 4 million internally displaced people. In the year 2016, some 24, 500 refugees and asylum ‐ seekers were registered in Somalia. Refugee return from Kenya began in 2014. Voluntary repatriation number is close to 40, 000 Somali nationals from 2014 to December 201659. Economic Opportunity According to Berlin ‐ based Transparency International, Somalia is one of the world ’ s most corrupt countries. Improved governance could enable Somalia ’ s economy to grow on the basis of its oil and gas reserves. Ongoing droughts continue to drive hungry and thirsty refugees to surrounding countries, and large parts of the population are in need of humanitarian aid. The agriculture sector contributes to over two ‐ thirds of its GDP while industry only makes up for 7 % in 201360. According to the IMF, Somalia has a very high youth 57 EIU Syria economy: Quick View ‐ Wheat harvest set to fall short of government forecast, July 2017 58 Human Rights Watch, Somalia Events of 2016, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / somalia 59 UNHCR, Somalia, http: / / reporting. unhcr. org / node / 2550? y = 2016 # year 60 CIA The World FactBook, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / so. html", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "48 unemployment rate, contributing to irregular migration and participation in extremist activities, including Al ‐ Shabaab. Joining militant jihadist group is viewed as another form of employment61. Social Services The lack of infrastructure and basic service hinders IDP settlements. On top of that “ urban areas are being overwhelmed with new arrivals62 ”. Access to basic needs, such as health and education, are unmet. South Sudan Security South Sudan ’ s civil war began in December 2013 and continues with serious abuses against civilians. A peace agreement was signed in August 2015 but the ceasefire was not achieved63. On May 25th, 2017, South Sudan President declared a ceasefire. According to the World Report by Human Rights Watch, South Sudanese “ government soldiers killed, raped and tortured civilians as well as destroying and pillaging civilian property during counterinsurgency operations in the southern and western parts of the country, and both sides committed abuses against civilians in and around Juba and other areas. UN Special Advisor on the Prevention of Genocide Adma Dieng said the ongoing violence had transformed into an “ ethnic war ” and warned of a “ potential for genocide64 ” On top of the precarious living situation, security and logistical challenges posed constraints to the delivery of much ‐ needed humanitarian assistance 65.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "According to Council on Foreign Relations, the estimated number of people killed since December 2013 is over 50, 000, and over 1. 6 million people are internally displaced66. Economic Opportunity South Sudan has abundant natural resources. Before its oil production fell sharply, the government relies on oil for its revenue. It also has very fertile soils and abundant water supplies. South Sudan has struggled with economic development since its independence and its economic conditions have deteriorated since January 2012 when the government decided to 61 IMF, Six Things to Know about Somalia ’ s Economy, April 11, 2017, http: / / www. imf. org / en / News / Articles / 2017 / 04 / 11 / NA041117 ‐ Six ‐ Things ‐ to ‐ Know ‐ About ‐ Somalia ‐ Economy 62 UNHCR, Somalia, http: / / reporting. unhcr. org / node / 2550? y = 2016 # year 63 Human Rights Watch, World Report, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / south ‐ sudan 64 Human Rights Watch, World Report, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / south ‐ sudan 65 UNHCR, South Sudan http: / / reporting. unhcr. org / node / 2553 66 Council on Foreign Relations (CFR) https: / / www. cfr. org / global / global ‐ conflict ‐ tracker / p32137 #! / conflict / civil ‐ war ‐ in ‐ south ‐ sudan", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "49 shut down its oil production67. UNHCR reported that in 2016, South Sudan ’ s economic situation deteriorated further and the cost of living rose exponentially68. Social Service South Sudan has very high mortality caused by AIDS and high risk of infection of diseases. Education expenditure is low and the literacy rate is also very low (27 % in 2009). South Sudan has little infrastructure. According to the CIA Factbook, there are approximately 200 kilometers of paved roads. Electricity is produced mostly by costly diesel generators. Goods and services are mostly imported from surrounding countries. 69 Sudan Security Similar to South Sudan, Sudan ’ s government forces have raped, killed civilians, and destroyed hundreds of villages. In September 2016 UN found that violence has displaced up to 190, 000 people and many of them are not accessible to humanitarian agencies 70. Government forces and armed rebels in Southern Kordofan and the Blue Nile continue to be engaged in armed conflict for the fifth year in 2016. Civilians in populated areas were subject to indiscriminate bombing, especially during March through June in 2016. Citizens also face arbitrary detentions, ill ‐ treatment, and torture. Sudan ’ s National Intelligence and Security Service is known for detaining activists, students, lawyers, doctors and those who are perceived to be needed in some capacity by the government71.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Many detainees are facing ill ‐ treatment. Females are subjected to sexual harassment by security officers. According to UNHCR, there are 2. 7 million people of concern in Sudan (includes refugees, asylum ‐ seekers, IDPs, returned refugees, returned IDPs, stateless persons, and other concern), a lower number than 2015. About 37, 000 refugees returned to Sudan in 2016. At the same time, there were over 2 million IDPs, over 420, 000 refugees and over 16, 000 asylum ‐ seekers in other countries. Economic Opportunity Oil output in Sudan has been low due to civil war, poor infrastructure, and low productivity. Compared to South Sudan, Sudan also has fewer oil resources; nevertheless, it still has abundant resources72. Sudan ranks 186th of 190 states in the World Bank ’ s Doing Business 67 CIA Factbook, South Sudan, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / od. html 68 UNHCR, South Sudan http: / / reporting. unhcr. org / node / 2553 69 CIA Factbook, South Sudan, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / od. html 70 UNHCR, Sudan, http: / / reporting. unhcr. org / node / 2535 71 Human Rights Watch, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / sudan 72 EIU Country Outlook, Sudan, June 19th 2017", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "50 Rankings, with particularly poor scores for starting a business, getting electricity and registering property. Agriculture slowed in 2016 and is expected to do better in 2017. Lack of rain affects the outcome of the agricultural sector. CIA ’ s Factbook73 reports that 46. 5 % of Sudan ’ s population is living below the poverty line. Only 35 % of its population has access to electricity. Social Service It is difficult for the staff of international humanitarian aid agencies to obtain travel permits in Sudan, severely hampering the relief effort. The Democratic Republic of the Congo (DRC) Security The security situation in the Democratic Republic of Congo has been poor since 2012. An attempt to integrate a Tutsi rebel group into the Congolese military failed and prompted the defection and formation of the M23 armed group. The renewed conflict led to large population displacement and human rights abuses. Furthermore, the President of the DRC, Joseph Kabila is barred from running for a third term, but the DRC government has delayed national election originally slated for November 2016. The failure to hold election fueled sporadic street protests by Kabila ’ s opponents74. Although a deal signed by representatives of Kabila ’ s ruling party said the presidential election would be held before the end of 2017, the President himself has not endorsed the deal.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Government officials repeatedly banned opposition demonstrations, fired teargas and live bullets at peaceful protesters, shut media outlets, and prevent opposition leaders from moving freely75. According to UNHCR, there are over 2 million IDPs in the DRC, and over 450, 000 refugees. In year 2016, there were about 13, 000 returned refugees and 619, 000 returned IDPs. “ Dozens of armed groups remained active in eastern Congo, many of their commanders have been implicated in war crimes, including ethnic massacres, killing of civilians, rape, forced recruitment of children and pillage. 76 ” Economic Opportunity 73 CIA the World Factbook, Sudan, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / su. html 74 CIA, the World Factbook, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / cg. html 75 Human Rights Watch, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / democratic ‐ republic ‐ congo 76 Human Rights Watch, DRC, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / democratic ‐ republic ‐ congo", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9826 Despite the many simultaneous deprivations faced by forcibly displaced communities, such as food insecurity, inadequate housing, or lack of access to education, there is little research on the level and composition of multi­dimensional poverty among them, and how it might differ from that of host communities. Relying on house­hold survey data from selected areas of Ethiopia, Nigeria, Somalia, South Sudan, and Sudan, this paper proposes a Multidimensional Poverty Index (MPI) that captures the overlapping deprivations experienced by poor individuals in contexts of displacement.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using the MPI, the paper presents multi-country descriptive analysis to explore the relationships between multidimensional poverty, displace­ment status, and gender of the household head. The results reveal significant differences across displaced and host com­munities in all countries except Nigeria. In Ethiopia, South Sudan, and Sudan, female-headed households have higher MPIs, while in Somalia, those living in male-headed house­holds are more likely to be identified as multidimensionally poor. Lastly, the paper examines mismatches and overlaps in the identification of the poor by the MPI and the $ 1. 90 / day poverty line, confirming the need for complementary measures when assessing deprivations among people in con­texts of displacement. This paper is a product of the Gender Global Theme. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at atybogale @ worldbank. org, sabina. alkire @ qeh. ox. ac. uk, uekhator @ worldbank. org, fanni. kovesdi @ qeh. ox. ac. uk, juliethsa @ iadb. org, and sophie. scharlin-pettee @ qeh. ox. ac. uk.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 regards to who is poor, how poor they are, and the composition of their poverty. Based on the Alkire-Foster (AF) method, the index provides a summary measure of poverty for the population that can be disaggregated by displacement status and gender of the household head to analyze the variation in deprivations. The MPI can be further broken down by indicator to show the proportion of the population who are poor and deprived in each area. These features of the MPI can inform better policy responses, with interventions and programs targeting the most deprived communities and indicators with the highest headcount ratios. The paper proceeds as follows. Section 2 of the paper reviews some of the existing literature to provide the background and motivation for the analysis, including a summary of the different country contexts covered by the data analyzed in this paper. Section 3 outlines the Alkire- Foster method and the selected dimensions and indicators used to construct the MPI, followed by Section 4, which introduces the data. Section 5 presents the findings, first for results at the national level and then results disaggregated by displacement status. Section 6 analyzes differences in multidimensional poverty by gender of the household head to improve understanding of the gendered aspects of multidimensional poverty in these contexts.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Section 7 compares the MPI results with monetary poverty, with some concluding remarks discussed in Section 8. 2. Background and literature review 2. 1 Poverty and forced displacement By 2019, there were about 51 million IDPs across the world, most of them – 46 million – displaced by conflict and violence, with around five million displaced due to natural disasters (IDMC 2020). According to estimates by UNHCR (2020), the number of refugees reached over 20 million as of the end of 2019. While in many cases, conflict and natural disasters have been temporary, resulting in fluctuations in the number of people fleeing their homes in any given country, the global number of IDPs and refugees has grown almost every year over the last two decades. UNHCR estimates that the number of refugees has doubled over the last ten years. Nearly all IDPs live in low- and middle-income countries, and many have experienced secondary displacement. Overall, about half live in urban areas, with one-fourth in major urban areas (i. e., populations exceeding 300, 000). Since almost all IDPs are in developing countries, governments are often resource-constrained in terms of providing assistance and access to services, and in some cases, government authorities may be a cause of displacement (World Bank Group 2020).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For refugees, the situation is more varied, with most staying close to their country of origin while a smaller minority fled to countries further away. UNHCR (2020) estimates that three-quarters of all refugees were hosted by neighboring countries. To reflect the increase in forced displacement over the last decade and enable sustainable and long-term solutions to refugee situations, the UN Statistical Commission approved a new indicator, SDG Indicator 10. 7. 4, in early 2020 to measure and track the “ proportion of population who are refugees, by country of origin ” (UNHCR 2020). While the specific challenges for displaced communities depend on the country or host community context, often, in new locations, key challenges confronting IDPs and refugees include food insecurity, lack of livelihood opportunities, and tensions and competition over resources with host communities. The multiplicity of deprivations faced by displaced", "output": {"entities": {"named_data": ["SDG Indicator 10. 7. 4"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Children and adolescents ’ long-term exposure to households with a more equalized division of domestic labor, as a result of violent or political conflict, warrants further investigation. 2. 3 Country contexts The countries with subnational regions covered in this study, using data from 2017 or 2018, are Ethiopia, Nigeria, Somalia, South Sudan, and Sudan. All are located in Sub-Saharan Africa, have undergone or are currently involved in armed conflict, and are affected by environmental issues such as drought, famine or flooding. Despite some commonalities, each faces a unique set of social, political and economic challenges, which cannot be accurately covered in this study. However, to contextualize the findings, a brief introduction of the country context is presented alongside the poverty estimates by the $ 1. 90 / day measure and the global Multidimensional Poverty Index (MPI). 3 3 An international measure of acute multidimensional poverty, aligined with the 2030 Agenda, that captures deprivations in health, education, and living standards for more than 100 countries (Alkire and Jahan 2018; Alkire, Kanagaratnam and Suppa 2020).", "output": {"entities": {"named_data": ["global Multidimensional Poverty Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Pregnancy Care A woman who gave birth in the last 2 years did not visit a clinic while pregnant or have a trained assistant during delivery 1 / 16 Physical Safety Any member feels unsafe at home or walking alone14 1 / 16 Early Marriage A member was married before age 19 1 / 16 Living Standards Garbage Disposal Main method of solid waste disposal is dumping, burying in own compound, burning, or other 1 / 24 Drinking Water Main source of drinking water is unsafe, or it takes more than 20 minutes (round-trip) to get water15 1 / 24 Electricity It does not have electricity 1 / 24 Cooking Fuel Main energy source for cooking is solid fuels 1 / 24 Housing It is an unimproved housing type 1 / 24 Sanitation Main toilet facility is unimproved, or shared with other households16 1 / 24 Financial Security Unemployment Any member 15 or older is unemployed and looking for work17 1 / 12 Legal Identification No member has a form of legal identification 1 / 12 Bank Account No member has a bank or mobile money account 1 / 12 The MPI presented here uses equal nested weights with all four dimensions considered to be equally important, and all indicators within a dimension receiving an equal share of the total weight.", "output": {"entities": {"named_data": ["mpi"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The cross-dimensional poverty cut-off is defined as k = 50 %, with those deprived in half or more of the weighted indicators identified as multidimensionally poor. 4. Data Data on forcibly displaced populations are scarce, with many household surveys excluding refugees and IDPs from the sample framework. To ensure that MPI results are representative of these communities and that they can be disaggregated for comparative analysis, an initial review of possible data sets was conducted. Feasibility was determined based on the availability of sufficient sample sizes for forcibly displaced persons for quantitative analyses, as well as inclusion of many of the indicators (on health, education, living standards, etc.) 14 A household is deprived if the respondent reports feeling moderately or very unsafe when alone at home, walking alone after dark, or walking around during the day. In Sudan, the indicator on the ‘ feeling safe from crime and violence when at home ’ was not available, and the indicator only considers answers to the questions on safety when walking alone. 15 Unprotected dug well, unprotected spring, carts with tank, tanker-truck, surface water, or other are considered as unsafe water sources according to international guidelines. See https: / / washdata. org / monitoring / drinking-water. 16 Pit latrine without slab, bucket, hanging toilet, and no facility (open defecation) are considered as unimproved sanitation facilities according to international guidelines. See https: / / washdata. org / monitoring / sanitation. 17 According to the ILO definition, those who did not participate in employment in the last four weeks (and have no work to return to) are actively looking for work and are available to start, or those currently waiting to start work are classed as unemployed. See https: / / www. ilo. org / ilostat-files / Documents / description_UR_EN. pdf.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 5. 2 Composition of poverty Unpacking the headline numbers further, important patterns emerge about the composition of multidimensional poverty among forcibly displaced and host communities in these countries. Overall, the censored headcount ratios (proportion of people who are poor and deprived in a given indicator) are lower among non-displaced communities than among refugees and IDPs, but there are large differences in which indicators are the most salient in different countries. The indicators with the largest difference between the two populations are bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria. These findings reinforce the need for policies and programming that take into account the measured experiences of IDPs and refugees. In this way, the MPI can function both as tool to monitor, track, and bear witness to the lived experiences of forcibly displaced communities, as well as advise on evidence-based interventions that address the needs of the local population. Figure 1 shows the censored headcounts of each indicator in Sudan ’ s MPI, with large differences appearing by displacement.", "output": {"entities": {"named_data": [], "descriptive_data": ["High Frequency Surveys"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "MPI of IDP communities is considerably higher than that of the host communities, so it follows that the censored headcount ratios display a similar gap. The difference by indicator is particularly noticeable in the living standards dimension, where the electricity, cooking fuel, housing, and bank account indicators show over 34 percentage point difference between the censored headcount ratios for the IDP and host communities. Figure 1. Censored headcounts of each indicator in the MPI, by displacement status in Sudan (2018)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). As the Sudanese sample surveys IDPs living in the Abu Shouk and El Salam camps, we must understand these deprivations with the background that these settlements were created as emergency and crisis responses rather than durable, long-term solutions (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019). Although Sudan does have a national electric grid that supplies electricity to the urban and peri-urban areas of the nearby city El Fasher, IDP communities living in the camps report limited connection to the city ’ s electricity supply, reflected in the high deprivations in the electricity and cooking fuel indicators. The ad-hoc construction of dwellings in the two camps explains why 71 % of the IDP households in Abu Shouk and 65 % in El Salam live in tukuls or other permanent mud or wood structures (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019: p. 50), both of which register as unimproved housing types.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Bank branches in El Fasher have limited capital for small businesses as their headquarters in Khartoum regard the area as too great a risk and IDPs themselves as riskier investments than their host community peers (UN- HABITAT 2009: p. 8). Exposure to unclean cooking fuels and inadequate housing can lead to poor health outcomes, while lack of access to electricity and a bank account further excludes individuals from labor market integration and livelihood opportunities that would empower forcibly displaced persons to overcome their multiple, overlapping deprivations. Clearly, displacement status puts individuals at a greater risk of poverty than their host community neighbors, and we can unpack those risks in greater detail using the MPI. Results can also be broken down to show the percentage contribution of each indicator to multidimensional poverty (see Figure 2). Among refugees in Ethiopia, lack of a bank account is the largest contributor to poverty, while among host communities, the largest contributor is years of schooling.", "output": {"entities": {"named_data": ["MPI"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 6. Gender differences in multidimensional poverty Next we examine differences in multidimensional poverty outcomes by the gender of the household head. Existing literature points out the limitation of household level MPI analysis in masking the intrahousehold distribution of deprivations, and thus being less sensitive to gender based differences in individual outcomes within the family unit, which might lead to underestimation of inequality and gender gaps (Espinoza-Delgado and Klasen 2018; Franco 2017; Klasen and Lahoti 2020, Rodriguez, 2016). However, as the MPI identifies poverty at the household level, our initial analysis focuses on disaggregated results by the gender of the household head. 19 We acknowledge that this approach has several limitations since most women reside in male-headed households, and the composition of households can change after displacement due to separation of family members, and widowhood. Regardless, the analysis at the household level remains relevant given the high prevalence of female-headed households that emerge after displacement, with the analysis showing large differences across countries between households based on the gender of the head.", "output": {"entities": {"named_data": [], "descriptive_data": ["High Frequency Surveys"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Table 6: Percentage of displaced individuals deprived in selected indicators by gender Ethiopia South Sudan Sudan Male Female Male Female Male Female Years of schooling 55 78 * * * 36 63 * * * 32 46 * * * School attendance 16 19 * * 21 29 23 23 Early marriage 3 13 * * * 8 75 * * * 6 50 * * * Unemployment 7 5 * * * 2 0 * 3 3 Legal id 45 46 48 74 * * * 10 10 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Asterisks indicate statistical significance of mean differences between male and female at 1 % * * *, 5 % * * and 10 % * levels. Returning to patterns of household headship, Figure 5 breaks down the variation in censored headcount ratios among refugee households in Ethiopia, depending on the gender of the household head.", "output": {"entities": {"named_data": ["High Frequency Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "22 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). To uncover the main drivers of the observed gender based differences in multidimensional poverty at country level, we study the absolute contribution of the gender difference in each indicator to the overall household gender gap (see Figure 6), calculated as the difference between the censored headcount ratio for males versus females. We find that in Ethiopia, the gender gap that disadvantages female-headed households is mostly driven by the difference in financial insecurity measures (lack of legal ID and bank account) and health measures (early marriage, physical safety, and food insecurity), which is further reinforced by the differences in the living standard and education measures. Female-headed refugee households are more food insecure, live in unimproved housing, have lower access to electricity, are more likely to be married at an early age, and have lower access to legal identification and a bank account. In South Sudan, gender gap that disadvantages female-headed households is mainly explained by the differential in the financial insecurity and health measures, but cumulative gaps in the living standard and education indicators also contribute to the overall gap at the household level.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "E Nigeria Somalia Sudan Host community Refugees Host community IDP Host community IDP Host community IDP No earners 40 % 51 % 62 % 50 % 32 % 64 % 7 % 45 % Remittance recipients only 7 % 8 % 76 % 69 % 16 % 37 %-- Female single earner 12 % 52 % 23 % 37 % 35 % 70 % 24 % 55 % Male single earner 8 % 23 % 4 % 14 % 24 % 50 % 8 % 39 % Majority female earners 3 % 57 % 9 % 28 % 9 % 28 % 15 % 45 % Equal contribution 10 % 24 % 17 % 16 % 23 % 63 % 8 % 41 % Majority male earners 5 % 16 % 15 % 19 % 20 % 52 % 9 % 39 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Overall, the results show that besides gender, displacement status and the number of household contributors plays a key role in the identification and level of poverty. In comparison with female-headed non-displaced households, more female-headed displaced households are classified as multidimensionally poor.", "output": {"entities": {"named_data": ["High Frequency Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "26 value of consumption flow of durable goods. 22 While monetary poverty can measure temporal resource holdings, multidimensional poverty, as a more comprehensive measure, includes chronic and exacerbating sources of poverty. This difference explains the existence of mismatches between individuals identified as monetary versus MPI poor, which are often more prominent in poorer countries (Evans et al 2020). This section examines these differences in the contexts of displacement. Table 9. Percentage of the sample in each poverty category: Rows sum to 100 % Non-poor by both measures Only Monetary Poor Only Multidimensional Poor Monetary and multidimensional poor Ethiopia 38 % 23 % 12 % 27 % N. E Nigeria 13 % 69 % 4 % 15 % Somalia 20 % 32 % 14 % 34 % Sudan 33 % 47 % 4 % 17 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). This table presents the distribution of households in each of the categories in the columns. Thus, each row adds up to 100 %. South Sudan is excluded from this analysis as monetary data is not available for the country.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The lack of overlap might be explained by the relatively recent start of the displacement situation in 2014, when Boko Haram appeared in the north-eastern part of the country. Pape et al (2018) identify two groups of IDPs in this situation: one group representing 74 % of the IDP population that was more engaged in wages and non-farm business before displacement, and another group representing about 26 % of the population, that had significantly more unemployed women. Most of the displaced populations from the first group live in host communities with good access to basic services such as sanitation and water, and safety nets. However, they are disproportionally more likely to be female-headed households and lack access to education, health services, and may face more stringent labor-market barriers. In other words, this group has relatively better housing conditions, but may lack short-term resources that reduce their consumption expenditure. 22 In summary, expenditure in these three categories is computed based on the quantities and prices of a selected list of items in each category. See more details about the computation of the consumption aggregate in Appendix A of the Somali Poverty Profile (Pape et al, 2017). A similar procedure was followed in the other countries of analysis.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "% 83 % 28 % 9 % 14 % Equal contribution 93 % 81 % 83 % 16 % 17 % 17 % Majority male earners 78 % 56 % 57 % 19 % 15 % 16 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 8. Conclusion This paper contributes to the literature by analyzing multidimensional poverty among refugees and internally displaced populations. We observe that forcibly displaced communities are poorer than host communities in each of the five countries ’ sub-populations covered in the surveys, with the difference in incidence between displaced and non-displaced population ranging between 15 and 19 percentage points in South Sudan and Somalia to over 30 percentage points in Ethiopia and Sudan. Displaced communities also experience greater deprivations in nearly every indicator, although there is significant variation in which indicators are the most salient, with having a bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria showing the largest differences between the two populations. The results also indicate gender differences in the experience of multidimensional poverty, with female-headed households more likely to be poor than male-headed households in most of the countries. In addition, displaced households headed by women have a higher incidence of poverty and MPI than non-displaced female-headed households. Particularly, female-headed households in camps have higher multidimensional poverty and intensity compared to their counterparts living outside camps. Dissaggregating further, we find heterogeneity among de facto and de jure female heads. This variation lends itself to further research questions about", "output": {"entities": {"named_data": ["High Frequency Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 1 ‐ Number of Forcibly Displaced Persons (1951 ‐ 2015) Source: Constructed from UNHCR population data (http: / / popstats. unhcr. org / en / time_series). Note: 2015 data are mid ‐ year and lower than end of year data. 5 http: / / www. unhcr. org / en ‐ us / figures ‐ at ‐ a ‐ glance. html. 0 10000000 20000000 30000000 40000000 50000000 60000000 70000000 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR population data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Figure 4 ‐ Research on Migration, Refugees and IDPs (% of total hits) Source: Authors ’ estimations based on Econpapers, SSRN and Google Scholars searches. This phenomenon can be explained by essentially two factors. The first relates to the humanitarian ‐ development nexus. For the longest time, refugees and IDPs remained the quasi monopoly of humanitarian organizations whose mandate is essentially the humanitarian protection of refugees and IDPs. These organizations are not typically staffed by economists and analysists but by field workers and lawyers. There was, therefore, little demand for hard economics on forced displacement for a very long time. This is changing as development organizations typically staffed by economists have started to work on forced displacement situations. The second factor relates to lack of good data. As we will see in the data section, data collection of mobile populations is complex and the main organizations in charge of data collection of refugee and IDPs data are humanitarian organizations that do not necessarily have the complex skills required for issues like sampling, questionnaire design and data analysis and have a duty to protect data by mandate. This, in turn, has resulted in very few micro data that would be both of good quality and accessible to researchers.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 outcome because of preferences for known odds to unknown odds. This ambiguity aversion violates the postulates of the subjective expected utility theory and offers a possible insight into why some people may choose to live under a conflict where risks are known rather than escaping elsewhere where risks and conditions are unknown. A more recent critique to the EU model includes Khaneman and Tversky (1979) prospect theory. The central theme of the critique is that people underweight outcomes that have very low probability of occurring and overweight outcomes that have a very high probability (this is called the certainty effect). Considering equal weighting as in EU theory can lead to the Allais paradox (Allais, 1953) where different choice frameworks can lead to opposite conclusions about dominance of alternative choices. Khaneman and Tversky (1979) also showed that, for negative prospects, preferences are reversed as compared to positive prospects (this is called the reflection effect). Therefore, “ (…) certainty increases the aversiveness of losses as well as the desirability of gains ” (Khaneman and Tversky, 1979, p. 269).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Hence, the authors introduce the notion of decision weight ߨ to weigh the importance that people give to different probabilities so that the expected utility function becomes ܧሺ ܷ ሻ ൌ ෍ ߨ ൣ ݌ ൫ܧ ௝ ൯൧ ܷ ሺݔ ௝ ሻ ௡ ௝ ୀ ଵ One can also study decision making in a game theory setting. Expected utility can be looked at as a one ‐ person game but the value added of game theory in the context of forced displacement relates to multiple ‐ person games. Suppose that actions taken by individuals under conflict situations affect the actions of others and ultimately one ’ s own action (the classic prisoner ’ s dilemma for example). This is what game theory is good in modeling and it could provide valuable contributions to the study of collective behavior under forced displacement situations (see for example Zeager and Bascom, 1996). New branches of economics such as neuroeconomics and behavioral economics, which combine elements of psychology and neuroscience with elements of economics, offer alternative new avenues to the construction of utility models in a forced displacement context. For example, neuroeconomics developed a hierarchical module oriented approach (Sanfey et al., 2006) whereby individuals take decisions in a hierarchical manner where multiple systems of specialized processing modules transform specific inputs into outputs in organized decision stages. This process can be observed in people ’ s brains with scans and can be modeled empirically using specifically designed questionnaires. This literature shows how the short ‐ term decision process is different from the long ‐ term process in terms of how we value potential", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 people may also gain from conflict in terms of income and wealth and this may explain why some people do not move. The defining attributes of the alternative choices are very different from any other model and the task of economics is to understand what these defining attributes should be. In terms of independent variables, “ push ” factors become more important than “ pull ” factors in forced displacement models. The intensity of a conflict may be more important than the income opportunities in potential destination areas. In addition to the classic socioeconomic variables, risk aversion, stress, anxiety, other traits of personality and behavioral factors in general have to be well understood and measured. Hence, one could think of four essential blocks of independent variables including individual or household socioeconomic characteristics, “ push ” factors, “ pull ” factors and behavioral factors. Also, access to and dissemination of information related to the conflict in the place of origin but also in the potential places of destination may be crucial for people to make choices. This is where social psychology, behavioral economics and neuroeconomics may offer insights into such choices. Forced displacement data are also unusual in their form.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Forced displacement data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Deciding on whether to flee or not to flee a conflict (the migration choice) can be an individual or household choice and risk coping strategies may include temporary migration, shuttling between places, migration of only selected members of the households or migration of the whole household. This implies that individuals may stay put throughout the period observed, join or leave the household during the period, or have several episodes of out and immigration. Households may decide to leave and come back several times. In econometric terms, this means that longitudinal data may be left and right censored and have spells within. They are therefore the most complex set of panel data possible and require particular treatment of data and modeling. Survival or duration models can usually accommodate many of these complexities but it is very rare to find similar data sets used in published articles. Collecting such type of data is also not obvious, particularly if conflict is intense and survey areas cannot be reached. This is an issue where empirical economics could provide a real contribution by defining the optimal data format and adapting panel models to this format. Macro models Macroeconomics has attempted to model forced migration using models borrowed from the trade and economic migration literature such as the gravitational model (Echevarria and Gardeazabal, 2016) or used other macro models to test the impact of refugees on trade (White and Tadess, 2010). A more recent body of work is adapting trade models to take into account stochastic shocks in a dynamic framework (Cameron et al., 2007; Artuc et al., 2008). These are rational expectations models that are able to model the unpredictability of shocks, and recent work has tried to adapt these models to the context of violent", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["longitudinal data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These people are then expected to return to their place of origin once the conflict is over and governments are typically over optimistic about the duration of civil conflicts and about return of IDPs. In some cases, governments also have an interest in denying the very existence of IDPs for political purposes. Therefore, little time is spent in surveying IDPs or trying to find durable solutions in the place where they migrated. Moreover, national censuses are usually conducted every ten years and statistical agencies have little incentives to revise censuses, master samples and sample survey structure for situations that are perceived as short ‐ term. In most cases, new surveys are suspended or carried out under the pre ‐ crisis frameworks and, in either case, information on IDPs is not collected or poorly collected. This leaves specialized government agencies or international organizations in charge of IDP statistics (and care). However, unlike refugees, the IDPs do not benefit from a specialized international agency such as the UNHCR. IDP assistance is currently provided by a multitude of organizations including ministries of interior, specialized government agencies, the UNHCR, the International Organization for Migration (IOM), the UN Office for Humanitarian Affairs (UN ‐ OCHA), specialized NGOs and others. Some of these organizations collect information on IDPs and make this information public while others collect information that is not published and others do not collect information and focus on providing assistance. Most data collected are for the simple purpose of counting IDPs and do not include individual or", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["national censuses"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 household socioeconomic information. In some cases, socioeconomic information is collected at the individual or household level but more often information is collected at the community level. It is extremely rare to have unit record data sets on IDPs, which explains why we found only 18 studies in the Econpaper repository as already documented. Recognizing the problem of scarcity and availability of information on IDPs, international organizations have set up in several countries coordination mechanisms to count IDPs usually coordinated by IOM, UN ‐ OCHA or the UNHCR. There are also global efforts to centralize this information on the part of organizations such as the UNHCR, UN ‐ OCHA, the international Displacement Monitoring Centre (iDMC) or the Joint IDP Profiling Services (JIPS). These efforts are making good progress on harmonizing counts of IDPs but remain short of establishing proper data collection systems that could deliver in the years to come unit data of quality for research. Hence, research on IDPs remains constrained by lack of data, lack of a blueprint on how to collect data and lack of an organization dedicated to IDP data collection.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some data challenges common to refugees and IDPs Irrespective of the specific questions related to refugee and IDP data, there are also general questions that refer to the forcibly displaced in general and that are distinct from data collection of regular populations or even migrant populations. We explore here selected issues including sampling, unit of analysis, welfare measurement, multidimensional aspects, and the measurement of risks and vulnerabilities. Sampling. As mentioned, the UNHCR is really the only statistical agency for refugees and the UNHCR registry the only population census. As for any other populations, sampling requires the preparation of a master sample that derives from the population census. With various degrees of knowledge and accuracy, this is also what happens with refugees. However, the master sample is more difficult to construct than for regular populations because refugees live in camps and outside camps and are diluted in a host population with different types of arrangements. Some households rent, others stay at relatives ’ places, other live in makeshift shacks and others stay in camps. The information available in the UNHCR registry (the census) can also be quite inaccurate, as already discussed, and the degree of accuracy changes for different groups of refugees. Stratification by urban and rural areas, a typical approach in sampling, may mean little for a population that is mostly in urban areas whether in camps or outside camps. Refugees and IDPs are also mobile and more difficult to track over time than other populations. Several statistical institutes worldwide have developed methodologies to track and measure mobile populations such as herders, nomads or homeless people. However, tracking refugees from other countries has been in the", "output": {"entities": {"named_data": ["UNHCR registry"], "descriptive_data": [], "vague_data": ["population census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 capacity to offer these services. Many may be at risk of violence or trafficking. These are very important aspects from the perspective of welfare economists interested in measuring well ‐ being but these measurements are complex and not usually included in multidimensional indicators of deprivation or poverty. The dimensions of deprivations to consider are more numerous and more complex to measure. Again, there is very little research in welfare economics dedicated to the special needs of these populations. Risks and vulnerabilities. The analysis of risk and vulnerability is also much more complex in the context of the forcibly displaced. Welfare economics has only approached these topics recently, in the past decade or so. Essentially, the idea is to measure the risk of being poor or falling poor in the future using cross ‐ section or panel data studying spells of poverty over time. This is work that requires accurate and complex data sets that would be rarely available in a refugee or IDP context. More importantly, the nature of the problem changes. Refugees and IDPs are by definition more at risk and more vulnerable than regular populations and these vulnerabilities are not only linked to skills and efforts but to legal status, discrimination, limited mobility and other factors that are unique or much more acute with refugees and IDPs.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["cross ‐ section or panel data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Lebanon. It was the obvious step forward in overcoming these problems and the answer to the increasing demand for evidence around the Syrian crisis. The study benefitted from a unique and unprecedented set of data. The UNHCR registry data in Jordan and Lebanon were among the better quality registry data available worldwide and the UNHCR also conducted home visits in Jordan that, at the time of the study, covered over a third of all refugees. There were also sample surveys in both Jordan and Lebanon that were small in size but representative of the population present in the registry. The home visits and the surveys included questions on income and expenditure that could be used for the welfare assessment. Using these data, the study addressed ten questions defined as follows: 1) Who are the refugees?; 2) How different are refugees from “ regular ” populations?; 3) How poor are refugees?; 4) What are the main predictors of refugees ’ welfare and poverty?; 5) How vulnerable are refugees from a monetary and non ‐ monetary perspective?; 6) Do poverty and vulnerability statuses overlap?; 7) How effective are refugee assistance programs?; 8) What is the potential for alternative policies?; 9) How does welfare compare across countries and data sets?; 10) How transferable are", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR registry data"], "vague_data": ["sample surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These findings were also echoed in the context of other developing countries such as India (Allard et al., 2022) and Zimbabwe (Mabugu, Maisonnave, Henseler, Chitiga-Mabugu, & Makochekanwa, 2023). In the MENA economies ’ context, the evidence is considerably sparcer. Early work on the impact of the COVID-19 pandemic on labor markets in the MENA region re- lied on high-frequency phone surveys and highlight important job losses among wage workers and an uneven impact across industries (Krafft, Assaad, & Marouani, 2021, 2022). Providing evidence from Labor Force Surveys in the Islamic Republic of Iran, Dang and Salehi-Isfahani (2023) find that the pandemic exacerbated the pre-existing low participation of females in the labor force. Wahby and Assaad (2023), on the other hand, focus on the impact of the pandemic on Syrian refugees in Jordan and find a divergence in job finding and separation rates of Syrian refugees relative to their Jorda- nian hosts after the onset of the pandemic. Focusing on cross-border commuters in the West Bank and Gaza, Adnan and Etkes (2022) find that undocumented commuters benefited relative to their documented peers after the pandemic, as Israeli policies inadvertently created incentives for employers to favor the former. This sharply con- trasts the results by Borjas and Cassidy (2020) on the impact of the pandemic on immigrants in the United States. The rest of this paper is organized as follows. Section 2 provides background information on labor markets in the West Bank and Gaza, as well as background in- formation on the COVID-19 pandemic and government responses. Section 3 describes the data. Section 4 discusses our methodology. Section 5 presents the main regression results and investigates heterogeneous effects. Section 6 provides robustness checks. Finally, we provide concluding remarks in Section 7. 4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Labor Force Surveys", "high-frequency phone surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 Background Information 2. 1 Characteristics of West Bank and Gaza ’ s labor markets The labor markets of the West Bank and Gaza exhibit features typical of the broader Middle East and North Africa (MENA) region, but also have attributes that are highly unique. Additionally, important differences exist between the West Bank and Gaza. This section provides an overview of these characteristics. We use data from the Labor Force Surveys (LFS) of the West Bank and Gaza and we focus on 20-59 years old men. In Section 3. 1, we provide more information about the data sources and sample selection. We divide each labor market into five mutually exclusive and jointly exhaustive states: public sector employment, private formal sector employment, private informal sector employment, unemployment, and out of labor force. 1 We focus our discussion exclusively on men, as women ’ s labor force participation in both the West Bank and Gaza is very low, never reaching values above 25 %. This low participation rate is common in MENA countries and makes the role of the pandemic on women ’ s labor market outcomes relatively less important than other, more relevant structural factors.", "output": {"entities": {"named_data": ["Labor Force Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, permit-holding status is neither sufficient for, nor necessary to, working in Israel. As of 2019Q4, just under a fifth of West Bank residents had the right to work in Israel and the occupied territories, but almost a quarter among them were not commuting across the border for work. The vast majority of such individuals are holders of Israeli or Jerusalem IDs. Conversely, among those who do commute to Israel and occupied territories, 17 % do not hold valid permits or IDs. Likewise, permit-holding status does not logically affect the formality status of the commuter. The frequent border crossings between the West 2This is derived from authors ’ own calculations using the 2016 Jordan Labor Market Panel Survey. 3It is worth noting that the LFS is representative of the residents of the West Bank and Gaza, whose work may not lie in the country. 6", "output": {"entities": {"named_data": ["LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Bank and Israel and occupied territories strongly suggest that commuters would be differently affected during the pandemic when border closures were enacted. Another unique feature is the presence of a large refugee population in the West Bank and Gaza. However, it is important to note that refugees in this context are de- fined quite differently from other contexts. Here, not only the individuals immediately displaced are considered refugees, but also their patrilineal descendants, even if born many decades later. In particular, the LFS dataset follows the United Nations Relief and Works Agency (UNRWA) definition of refugees, which is “ persons whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948, and who lost both home and means of livelihood as a result of the 1948 conflict, ” as well as “ the descendants of Palestine refugee males, including adopted children ” (UNRWA, 2023). Consequently, most refugees are indistinguishable in socio-economic outcomes and labor market behavior from non-refugees. However, residence in refugee camps does make a significant difference. As of 2019Q4, 5 % of the West Bank ’ s residents live in refugee camps, as do 14 % in Gaza.", "output": {"entities": {"named_data": ["LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "After a short respite in November, Israel imposed a third lockdown from December to February the next year. Gradual easing continued throughout March. During this lockdown, vaccination was rolled out, including to commuters with valid permits. Given this context, we expect the main shock to occur in 2020Q2 in the West Bank, with negative effects of smaller magnitudes to persist over the next quarters. In Gaza, we expect another major shock in 2020Q3 that carries over to Q4. We expect the commuters ’ outcomes to instead track events in Israel and occupied territories more closely, with a major shock in 2020Q2, recovery in Q3, and another dip in Q4. 3 Data 3. 1 Data sources and definitions We employ data from the Labor Force Surveys (LFS) of the West Bank and Gaza, collected by the Palestinian Central Bureau of Statistics and prepared by the Economic Research Forum. The surveys are conducted on a quarterly basis, covering periods from 2000 onwards. The LFS is meant to represent all households whose ordinary residence is in the West Bank and Gaza, though their place of work need not be. The LFS is representative at the region level (respectively of the West Bank and Gaza), as well as at the level of locality types (urban, rural, and refugee camps) (Palestine- Labor Force Survey, LFS, 2021). Importantly for the purpose of our analysis, the data have a panel dimension, en- abling the study of labor market transitions. The sample rotation scheme is described 9", "output": {"entities": {"named_data": ["Labor Force Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "prior to the pandemic, a period characterized by less distinct time trends in the labor market. Since the focus of the paper is on the impact of COVID on labor market stock and dynamics, we also restrict the sample of interest to include only prime-aged working adults (aged 20 to 59). The dataset contains standard variables expected of a labor force survey, including those denoting employment, unemployment, and inactivity. It also contains information on the intensive margin of the labor supply, including hours worked and full-time and part-time status. Information on employment sector, industry, contract status, health insurance coverage, and mode of work (distinguishing between employees and self-employed, for example) is also available, allowing us to construct indicators of formality and to differentiate different modes of employment. Information on occupation is also available, but only at the level of 2-digit ISCO-08 classification. This information is enough to distinguish between white- and blue-collar occupations but it is not enough to observe additional relevant pandemic-related job characteristics such as the degree of contact with the public. 3. 2 Descriptive statistics Figure (1) tracks the evolution of labor market stocks in the West Bank and Gaza respectively over time.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A significant increase in non-participation among men can be clearly seen in the West Bank in 2020Q2, mostly at the expense of the private infor- mal sector. The labor market then quickly bounces back. By 2020Q3, labor market stocks in the West Bank appear indistinguishable from pre-pandemic periods. Gaza, on the other hand, experienced three consecutive quarters of depressed employment from 2020Q2 to 2020Q4. Non-participation spiked twice, first in 2020Q2 and then in 2020Q4, corresponding respectively to the initial lockdown orders and the subsequent outbreak in Gaza. Recovery also appears to be slow and uneven. Figure (3) shows the labor market flows. We exploit the specific panel structure of the LFS dataset, described in Section 3. 1 by focusing on one cohort of the same respon- dents who were surveyed in 2019Q1, 2019Q2, 2020Q1, 2020Q2, and finally 2020Q4. This cohort of individuals allows us to observe labor market transitions into the pan- demic; to compare with a period over the same quarters in 2019; and, finally, to observe their recovery outcomes in 2020Q4. Overall, Figure (3) shows two labor markets with high levels of churning. On average, 29 % of individuals in the sample would change their labor market states after just one quarter. These churns are especially prominent between informal employment and unemployment, and in Gaza between unemployment and non-participation. The figure also illustrates the significant differences between the West Bank and Gaza in labor market dynamics, differences already observed in the labor market stocks presented in Figure (1). In addition to the significant flows between unemployment 11", "output": {"entities": {"named_data": ["LFS dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the West Bank and Gaza. Out of all the estimated coefficients for each period for both outcomes (job loss and job gain), we only find a negative effect on job gain in 2019Q4, which is very small in magnitude (1 percentage point). Taken altogether, the results presented in this section bolster our confidence that we are correctly identifying the effects of the pandemic shock on labor market outcomes. Figure 12: Placebo effect on labor market flows Notes: The figure shows the output of a placebo test with a set-up analogous to Figures 6 and 9. We perform the same regression as specified in Equation (2). Our sample includes data from 2018Q2 to 2020Q1 and assumes that the pandemic started in 2019Q2. Therefore, the post-pandemic period refers to the quarters between 2019Q2 to 2020Q1. The analysis is restricted to men aged 20-59. 7 Conclusion This paper examines the effect of the pandemic on labor markets in the West Bank and Gaza using quarterly labor market data provided by national labor force surveys. With a focus on men ’ s labor market outcomes, this paper sheds light on how labor markets in the West Bank and Gaza adjusted to the COVID-19 shock examining adjustments at the extensive (employment) and intensive (hours of work) margins.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["national labor force surveys", "quarterly labor market data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "One of the main contributions of this paper is the use of panel data which allows us to examine the effect of the pandemic on labor market transitions — job loss and job gain rates — in addition to the effect on labor market stocks. Studying both stocks and flows provides a comprehensive framework to analyze the impact of the pandemic on labor markets and allows for a better understanding of the underlying mechanisms 30", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1 POST-CONFLICT TRANSITIONS WORKING PAPER NO. 16 Population Size, Concentration, and Civil War. A Geographically Disaggregated Analysis * Håvard Hegre Centre for the Study of Civil War, PRIO (CSCW) Clionadh Raleigh CSCW, PRIO & University of Colorado at Boulder Abstract Why do larger countries have more armed conflict? This paper surveys three sets of hypotheses forwarded in the conflict literature regarding the relationship between the size and location of population groups: Hypotheses based on pure population mass, on distances, on population concentrations, and some residual state-level characteristics. The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events. The analysis covers 14 countries in Central Africa. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper develops a statistical method to analyze this type of data. The analysis confirms several of the hypotheses. World Bank Policy Research Working Paper 4243, June 2007 The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post- Conflict Transitions Project can be found at http: / / econ. worldbank. org / programs / conflict).", "output": {"entities": {"named_data": ["Armed Conflict Location and Events Dataset", "ACLED"], "descriptive_data": [], "vague_data": ["conflict event data", "geographically disaggregated data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in these papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http: / / econ. worldbank. org. * Contact author: Håvard Hegre; CSCW, PRIO, Hausmanns gate 7, N-0187 Oslo, Norway. Email: hhegre @ prio. no. Thanks to Joachim Carlsen for writing a program to create the dataset used in the analysis, to Siri Aas Rustad for research assistance, and to Kristian Gleditsch, Anke Hoeffler, Pat Regan, Mike Ward, Nils Weidmann and Jen Ziemke for valuable comments. The research has been funded by the Research Council of Norway, grant no. 163115 / V10. WPS4243 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 population density across a state, the ecological inference issue is alleviated as we directly test the propensity of any population group to experience a conflict. Through disaggregation, we may succeed in supporting explanations based on variables such as the distance from the capital and the overall size of the country's population if we know at which locations conflicts occur. If conflicts are located mainly at some distance from countries'capitals, we might infer that large countries have more conflicts because of the difficulties of projecting governmental power. If they are located in population concentrations irrespective of location relative to the capita, other explanations should be sought. The paper makes use a new dataset called ACLED (Armed Conflict Location and Events Dataset) to allow for this type of disaggregated analysis. The dataset currently codes the location of all reported conflict events in 14 countries in Central Africa in the 1960 – 2004 period. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper suggests some adaptions to a statistical method to allow for analyzing data at this level of analysis. Related to the size of populations is their distributions. The Democratic Republic of Congo, for instance, is not only characterized by being enormously large, but also shows tremendous variation in population densities.", "output": {"entities": {"named_data": ["Armed Conflict Location and Events Dataset", "ACLED"], "descriptive_data": [], "vague_data": ["conflict event data", "geographically disaggregated data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 however, since they cannot distinguish between a country where the population is concentrated in one cluster covering 10 % of the territory and one where the population is concentrated in two clusters of 5 % each, but with a considerable geographical distance between them. Population and conflict geography in the Democratic Republic of Congo (DRC) corresponds to these arguments regarding population concentration and dispersion. Concentrations of language-based minorities are evident throughout eastern DRC. Due to the limited access of the government, the close proximity to international borders, and the dense population concentrations, these concentrated minorities have a higher potential of conflict than other, more accessible, sparsely populated areas of DRC. Figure 1 shows the population concentrations in 1990 (CIESIN data) for Central Africa. Heavily populated areas are shaded in deeper tones of red / grey. Civil conflict in DRC has overwhelmingly occurred in the eastern portion of the state, which is the most densely populated area and also geographically peripheral to the capital, Kinshasa. Of the eleven Congolese rebel groups accounted for in the dataset used in this paper, all have operated either exclusively or partially in the eastern and southern areas of DRC.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["CIESIN data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Proposition 6 Concentration and Dispersion: The risk of civil war events at a location increases more strongly in local population concentrations in locations distant from the capital of countries. 2. 5 Residual State-Level Mechanisms We have pointed out a set of location-specific factors, each of which imply a positive relationship between country size and national-level risk of armed conflict. But it is not certain that such location-specific factors are the only relevant ones. The size of a country itself may affect risk over and beyond what is implied by sheer population size, distance, and population distributions. If the economies of scale with respect to defense are sufficiently large, the risk of conflict events at a location at a given distance from the capital may be lower the larger is the country (Collier & Hoeffler, 2002). Moreover, large countries may rather be more conflictual than small ones for several reasons. Fearon & Laitin (2003: 81), for instance, note that insurgency will be favored when potential rebels", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Proposition 7 State-Level Effects of Population Size: The risk of civil war events at a location varies with the size of the population of the country to which the location belongs, controlling for the local effects. 3 Research Design 3. 1 Unit of Analysis To distinguish between the different theoretical statements regarding how population sizes, population concentrations and locations relate to risk of conflict, we need to investigate exactly where conflicts occur. We have created a dataset using a Geographic Information Systems (GIS) program which converted large territories into smaller portions of 8. 6 km x 8. 6 km, totaling 74 square kilometers. Each of these grid squares are our units of observation (we will refer to them as squares). This approach is similar to that of Buhaug & Rød (2006), with two important differences. First, their squares are much larger (100x100km). Second, they code the dependent variable considerably more crudely than is done in the ACLED dataset described below. Buhaug & Rød (2006) use the `scope'and `location'variables in the Uppsala / PRIO dataset.", "output": {"entities": {"named_data": ["ACLED dataset", "Uppsala / PRIO dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Although much more suited to geographically disaggregated analysis than other datasets, this location dataset has some limititations, It does not record changes over time in the center location and extent of conflicts, and it reports the total extent of the conflict zone without distinguishing between areas that saw repeated and extensive fighting and those that only experienced scattered activities or individual events far from the center of the conflict. 3. 2 Disaggregated Dependent Variable: ACLED The ACLED dataset (Raleigh & Hegre, 2005) deals with these problems. The dataset takes the PRIO / Uppsala Armed Conflicts Dataset as its point of departure. The dataset is limited to events within conflicts that fall within the Uppsala conflict definition; conflicts involving two parties, one of which is a government, and fighting resulting in at least 25 battle deaths. 3 ACLED is designed to parse out both the temporal and spatial actions of rebels and governments within civil wars. 3See the PRIO / Uppsala Armed Conflict Data codebook for more information (Strand, Wilhelmsen & Gleditsch, 2004).", "output": {"entities": {"named_data": ["PRIO / Uppsala Armed Conflicts Dataset", "ACLED dataset"], "descriptive_data": [], "vague_data": ["location dataset"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 The fundamental unit of observation in ACLED is the event. Figure 2 illustrates the ACLED data for Central Africa for the 1980s and the 1990s. Each location of a conflict event is represented by a symbol. In several of these locations, multiple events occured over the periods. Events always involve two actors – a rebel group and a government – and are coded to occur at a specific point location and on a specific day. Most of the events are battles, but the dataset also records other activities. The dataset includes information on and distinguishes between six types of events: battles resulting in no change of territory, battles resulting in a transfer of territory to the rebel actor, battles", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 resulting in government forces recapturing rebel held territory, establishment of a rebel base or headquarters, rebel activity that is not battle related (e. g. presence or the killing of civilians), and territorial transfers. The dataset consists of 4, 145 battle events for the 1960 – 2004 period. In the present analysis, we use 2, 530 of these. The remaining events were dropped as they either were in countries not included in the analysis, or because information was missing for one of the key variables. Each conflict event is associated with geographic coordinates and a date of occurrence. This information allows for spatial and temporal modeling of conflict events. The dataset used in this article covers 14 countries in Central Africa. 6 of them had a conflict in the 1960 – 2004 period according to the Uppsala / PRIO Armed Conflict Dataset (Gleditsch et al., 2002): Angola, Burundi, Republic of Congo (Brazzaville), Democratic Republic of Congo (Zaire), Rwanda, and Uganda.", "output": {"entities": {"named_data": ["Uppsala / PRIO Armed Conflict Dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The remaining 8 did not have a conflict: Cameroon, Central African Republic, Equatorial Guinea, Kenya, Malawi, Tanzania, and Zambia. 3. 3 Handling temporal and spatial dependence Both the squares and the conflicts events are obviously not fully independent – all events within one conflict are related to each other as an action in one location leads to a later retaliation by the opposing party or to further advances in proximate locations. Events in one conflict may also affect the likelihood of other conflicts, such as the spillover of the conflict in Rwanda into Eastern DRC. The statistical model employed to analyze these data must handle the dependence between observations. We will do this by explicitly modeling the probability of an event in a location as a function of preceding events in the same and in adjacent squares. We can do this since we know both the precise date and the precise geographic location of each event. We use an adaption of the calendar-time Cox regression model presented in Raknerud & Hegre (1997) for this purpose. In Cox regression, the dependent variable is the transition between `states of nature'-- the transition from peace to conflict in a square. A central concept is the hazard function, () t λ, which is closely related to the concept of transition probability: () t t Δ λ is approximately the probability of a transition in the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The observations of positive events contain more information than the non-event observations We therefore sample asymmetrically: We sample all of the transition events and 1. 0 % of the non-transition events. 3. 4 Disaggregated Independent Variables Local level data on land, population, and elevation is available in the geospatial format of raster files with a resolution of 1km. Using Geographic Information systems (GIS), attributes from raster and point data are associated with the grid square in which they lie. In this way, spatial data is georeferenced to a location that is defined by the grid cell. This process results in a data structure in which each row has within it combined information on a square defined by the grid, the national level information in which is it located, and () () () ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ ∑ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ = = ∉ = t X t X t d d j j p j R i d j j p j w w t β β 1 1 exp exp at out breaks war a | square a in war Pr", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 the local data on physical geography and population from the raster data. These data can then be imported into statistical programs for analysis. We aggregate all data up to a grid of 8. 6x8. 6km squares. Each grid square is assigned attributes of the country it is in along with information from data disaggregated to the level of the individual squares. Figure 3 illustrates this grid as a fictive country somewhat smaller than the average size in our dataset (50x50 squares, or 430x430 km) with a fairly representative but stylized population distribution. The country has three major cities, one of which is the capital, and two smaller ones. A rebel group has its headquarters at the Eastern border. The ACLED data for the Central African conflicts were aggregated up to the 8. 6x8. 6km squares and merged with information on other explanatory variables aggregated to the same level.", "output": {"entities": {"named_data": ["ACLED data"], "descriptive_data": [], "vague_data": ["raster data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Distance from Rebel Group Headquarters We coded the location of the headquarters of the rebel groups participating in the conflicts under study, and calculated the distance from each square to the most proximate rebel group headquarters (we do not know a priori which rebel group or government that will act in a particular square). As the `distance from capital'variable, it was coded as the distance in terms of squares and log-transformed. Border Square We coded squares as border squares if a national border runs through it. Such squares belong to more than one country and are not straightforward to code. We coded national- level information for border squares according to the following rule: A border square was considered to belong to the country that was most frequent among the eight neighboring squares. In tie cases, we assigned nationality randomly between the tied countries. Interaction country-square population This variable was created to test the population settlement pattern hypothesis. It is an interaction between population count at a location (square) as a portion of the country's total population. Road type Road type is a variable by ESRI that is available in the Digitial Chart of the World Data. It is a high resolution dataset at 1: 1, 000, 000 scale and consists of arcs which indicate road mass.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A number of different road indicators are available and we choose road line type to use in the analysis. Road type is defined by the following: The reference category (0) points out squares with dual lane / divided highways, other primary roads, or road connectors within urban areas (types 1 or 8 in the ESRI dataset). The second category include secondary roads (type 2), and the third combines squares with informal or tertiary roads (tracks, trails or footpaths) or no road registered at all (types 3 and 0, respectively, in the ESRI dataset). Figure 4 overlays the types of roads in the original dataset before our recategorization. The shaded area represents the portion of Africa for which we code", "output": {"entities": {"named_data": ["ESRI dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 6832 This paper presents findings from the impact evaluation of the Economic Empowerment of Adolescent Girls and Young Women (EPAG) project in Liberia. The EPAG project was launched by the Liberian Ministry of Gender and Development in 2009 with the goal of increasing the employment and income of 2, 500 young Liberian women by providing livelihood and life skills training and facilitating their transition to productive work. The analysis in this paper is based on data collected during two rounds of quantitative surveys in 2010 and 2011, the second of which was conducted six months after the classroom-based phase of the training program ended.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["quantitative surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Strong impacts are found on the employment and earnings outcomes of program participants, relative to a control group of non-participants. The EPAG program increased employment by 47 percent and earnings by 80 This paper is joint product of the Poverty Reduction and Economic Management Unit, Africa Region and the Social Protection and Labor Unit, Human Development Network.. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at schakravarty @ worldbank. org. percent. In addition, the impact evaluation documents positive effects on a variety of empowerment measures, including access to money, self-confidence, and anxiety about circumstances and the future. The evaluation finds no net impact on fertility or sexual behavior. At the household level, there is evidence of improved food security and shifting attitudes toward gender norms. These results reinforce the highly positive feedback received from focus group discussions with program participants. Finally, preliminary cost-benefit analysis indicates that the budgetary cost of the EPAG business development training for young women is equivalent to the value of three years of the increase in income among program beneficiaries. These preliminary results provide strong evidence for further investment and research into young women ’ s livelihood programs in Liberia.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1. Introduction Sub-Saharan Africa is the youngest region in the world, and the world ’ s fastest-growing. Between 2010 and 2025, the number of people between 15 and 24 will grow to 250 million – a net increase of nearly 50 percent. Over the next decade, roughly one million young people will enter the labor market each month in Sub-Saharan Africa. However, these young people often enter the labor market too early and unprepared. Although access to education is growing, illiteracy remains high and schooling low: among the 32 Sub-Saharan African countries in the Barro-Lee (2010) data set, nearly 40 percent of women aged 15 and above have received no education at all; and the most recent (2007-2011) statistics in the World Bank ’ s Edstats data reveal that female literacy is less than 60 percent, on average. 1 This lack of preparedness contributes to a growing problem of youth unemployment. Quantifying the level of unemployment is bedeviled by lack of data and measurement issues. Household and labor force surveys throughout Africa usually record unemployment rates of less than 10 percent, 2 but those figures belie the extent of underemployment and vulnerability. Those same surveys indicate that the vast majority of working adults have insecure work in the informal sector, on the family farm, or in less- productive or unremunerated labor.", "output": {"entities": {"named_data": ["Barro-Lee", "Edstats data"], "descriptive_data": [], "vague_data": ["Household and labor force surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The most recent labor force survey from Liberia mirrors these statistics: there are roughly 1. 1 million people in the workforce, of whom 195, 000 (about 18 percent) are engaged in wage employment; the remaining 900, 000-plus workers (82 percent) are considered in vulnerable employment, working for themselves or working unpaid for their own households (LISGIS 2010). Among young women (15-24) in Liberia, the unemployment rate is 8 percent, double the rate among young men (LISGIS 2010). Most of these gaps can be explained by differences across individuals, especially in educational attainment, skills training, and years of experience. But segregation, market segmentation, and discrimination do play a role in determining these individual characteristics. Women have fewer opportunities for education or training, less access to credit, a larger share of domestic responsibilities, and less independence and control over their own lives. In Liberia, women comprise half of the employed, but only about one-quarter of paid employment (LISGIS 2011). Fourteen years of civil war in Liberia devastated the country ’ s infrastructure and institutions, and left a generation of young people with very low levels of education and training. Girls were particularly disadvantaged.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In 2003, almost 60 % of young girls and 40 % of young boys had no formal schooling (ILO / UNICEF 2005). Data from the Demographic and Health survey shows that more than 40 percent of adult women have no education, compared to fewer than 20 percent of men, while 23 percent of women and 44 percent of men have some secondary schooling (DHS 2007). Happily, access to education is rising rapidly, especially for girls: according to the Liberian labor force survey from 2010, the ratio of girls to boys enrolled in primary school has risen from 72 in 2000 to 90 in 2009. Enrollment levels and sex ratios are lower among older children and youth, as they become increasingly engaged in 1 Both are unweighted averages; Barro-Lee comprises 32 countries with data from 2010; Edstats comprises 43 countries with data from 2007-2011. 2 Defined as without work, available for work, and actively looking for work (LISGIS 2010). 2", "output": {"entities": {"named_data": ["Barro-Lee", "Demographic and Health survey", "Liberian labor force survey", "Edstats"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "household and productive work. Just over one-third of young (15-24-year-old) Liberians are in the labor force. Young women are more likely than young men to be out of the labor force because they are engaged in household duties (30. 4 percent v. 18. 9 percent), and young men are more likely to be out of the labor force because they are still in school (75. 8 percent v. 63. 0 percent) (LISGIS 2010). Given their initial disadvantage relative to their male peers, and the sources of this disadvantage in social norms, market failures, and poorly functioning institutions, adolescent girls may require targeted policy and program efforts to achieve better outcomes. However, as in many other post-conflict situations, emergency skills training and public works programs in Liberia have targeted male youth ex- combatants, likely reinforcing rather than reducing adolescent girls ’ disadvantage. The few skills training programs for adolescent girls, run largely by NGOs, have focused on traditional female skills (such as sewing, soap production, tie-dyeing) for which the market is already well-supplied.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Programs designed to enhance employment among young people can focus on the supply side of the labor market, including skills training programs for both wage employment and entrepreneurship training; or programs to augment demand, such as wage subsidies, public works, and community service programs; or programs to help the labor market clear, such as job search assistance and placement services. In addition, it may be that the constraints facing young people are not in the labor market itself, but in other markets, such as for credit. The vast majority of jobs programs have focused on the supply side: training programs make up about 79 percent of over 600 cases included in the World Bank ’ s Youth Employment Inventory database. 3 Although rigorous and general evidence of success is limited, it seems that successful skills training programs share a few key features: they are responsive to local market conditions, they provide more than just technical skills in a specific area (including, for example, “ life skills ”), and they include ancillary services that alleviate other constraints preventing successful labor market integration (e. g., access to credit). Among the most celebrated are the Jovenes programs that provide demand-driven technical training, plus social skills that help in the labor market, plus internships.", "output": {"entities": {"named_data": ["Youth Employment Inventory database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These programs have had positive impacts on wages and employment, especially for young women and those from more disadvantaged backgrounds (Ibarrarán and Rosas 2009). This paper evaluates the first round of the EPAG (“ Economic Empowerment of Adolescent Girls and Young Women ”) skills training program implemented by the Government of Liberia from 2010 to 2011. EPAG was designed to alleviate the barriers to entering the labor market faced by young women, while avoiding the shortfalls of previous skills training programs offered in Liberia. The program combined six months of classroom-based technical and life skills training, with a focus on skills with high market demand, followed by six months of follow-up support to enter wage employment or start a business. Roughly 1200 young women aged 16-27 participated in the first round of EPAG. 3 See http: / / www. youth-employment-inventory. org /. Also, although focused on a narrower time frame and using a different selection criterion, the joint ILO / World Bank Inventory of Policy responses to the Financial Crisis (http: / / www. ilo. org / crisis-inventory) finds that “ about 78 percent of reported policy measures focused on the supply side ”, essentially through training (ILO / WB 2012). 3", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The outcomes of interest for this impact evaluation fall into three categories. First, we are interested in the economic outcomes of EPAG participants in terms of employment, earnings, and savings and investment behaviors. Second, because of the program ’ s focus on young women, a variety of non- economic outcomes are explored. Early evidence suggests that empowerment in one realm (e. g., schooling) can have important impacts on other realms, such as early pregnancy, prevalence of STDs, and risky behaviors such as transactional sex (Baird 2010). Related to these non-economic outcomes are measures of social empowerment, including mobility, decision-making, and self-confidence, which are thought to strengthen women ’ s agency, or capacity to exert choice over decisions involving herself, her family, and her community. Finally, we investigate a third set of outcomes on spillover effects. Evidence suggests that women tend to invest more of their income in their families, especially their children, than do men (e. g. World Bank 2001, Hoddinott 1995, Pitt 1998, Borges 2007). Advocates frequently argue for increased investment in adolescent girls by pointing to the potential for spillovers onto other family members (including future children).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our evaluation explicitly investigated these spillovers by including household-level indicators such as food security and attitudes of the household head toward gender norms. Our results show strong impacts on economic outcomes, including large and statistically significant increases in employment and earnings of EPAG participants. We show mixed results on empowerment- related outcomes, and very little evidence of spillovers on non-participants. Self-assessed measures of self-confidence show huge gains, as does ownership and control over monetary resources such as savings. The remainder of the paper is organized as follows: Section 2 describes the EPAG project including some of its innovative design features and implementation details. Section 3 reviews the methodology of the evaluation and Section 4 presents results on the three groups of outcomes discussed above: economic, empowerment, and spillovers. Section 5 includes a short discussion of cost-effectiveness. Section 6 presents a series of robustness checks and Section 7 concludes with a discussion of next steps and policy implications. 2. The EPAG Project The EPAG project is part of a larger Adolescent Girls Initiative (AGI) administered by the World Bank with support from the Nike Foundation and the Governments of Australia, the United Kingdom, Norway, Denmark, and Sweden. Launched in Washington DC in October 2008, the AGI was spearheaded by President Ellen Johnson Sirleaf, who signed on to undertake the initiative ’ s first pilot project in Liberia. The Liberian pilot was launched in March 2010 and has served as a role model to seven subsequent pilot projects in Rwanda, South Sudan, Nepal, Afghanistan, Haiti, Jordan, and Lao PDR. Under the global AGI, young women and adolescent girls are given a package of skills training and complementary services in order to facilitate their successful transition to employment. In the case of EPAG, the intervention consisted of a six month phase of classroom-based training, followed by a six 4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "month placement and support phase in which the trainees were supported in their transition to self or wage employment. Upon recruitment, the participants are assigned to a\"Job Skills (JS)\"track or a\"Business Development Services (BDS)\"track. When possible, the participant's track preference was honored; however, the demand for the Job Skills track greatly exceeded the supply, so the remaining trainees were placed into the BDS track. In the first round of training, the proportion of Job Skills track places was limited to 35 % of the total training places available given the expectation that few wage jobs will be available in the Liberian job market. The Job Skills track provided training in six areas: 1) hospitality, 2) professional cleaning / waste management, 3) office / computer skills, 4) professional house / office painting, 5) security guard services, and 6) professional driving. These areas were determined based on independent labor market assessments, a review of the available market data, and input from EPAG ’ s private sector partners. All Job Skills trainees received training in entrepreneurship skills as well. The BDS training taught young women how to identify micro-enterprise opportunities based on an assessment of market needs, and how to grow and manage any existing businesses they already had.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["available market data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The curriculum included entrepreneurship principles, market analysis, business management, customer service, money management, and record-keeping. The EPAG program was implemented by four NGOs who were selected by the Liberian Ministry of Gender and Development through a competitive bidding process: the Community Empowerment Program (CEP), Liberia Entrepreneurial and Economic Development (LEED), the International Rescue Committee (IRC), and the American Refugee Committee (ARC). Two of these organizations (ARC and IRC) further subcontracted to four Liberian NGOs. 4 The service providers were responsible for developing training curricula, identifying training venues, 5 making arrangements for childcare services, assisting with the mobilization of the nine target communities, and participating in the recruitment of training participants. The EPAG program differed from many training programs in a number of ways. First, performance bonuses were awarded to training providers that successfully place their graduates in jobs or micro- enterprises. The bonus was the last payment that the service providers received under their contracts. These were paid about 12 months after the start of training, or around the same time as the midline survey. Second, a variety of contests and competitions were also held among EPAG trainees (such as attendance prizes, quizzing contests, business plan competitions, etc.).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Third, the EPAG program was designed around the girls'needs: service providers held both morning and afternoon sessions, to accommodate the participants'busy schedules; trainings were held in the communities where the girls reside; and every site offered free childcare. Fourth, frequent and unannounced monitoring visits by MoGD staff ensured that the service providers created and maintained a high-quality learning 4 There are: National Adult Education Association of Liberia (NAEAL), Community Empowerment Sustainable Program (CESP), EduCare, and Children ’ s Assistance Program (CAP). 5 A total of 19 training venues were used during the first round of training. They were chosen with the following considerations in mind: 1. Girls ’ safety, so that the buildings are not so isolated or otherwise dangerous, raising security concerns for girls. 2. Conducive atmosphere for learning, spacious and sanitary with access to water and latrine facilities. Reasonably outside community noise concentration. 3. Proximity to community center and to security posts such as police depots. 4. Accessible to girls from various parts of the community. 5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "environment. Any issues discovered during these monitoring visits were brought to the attention of service providers and resolved swiftly in conjunction with the project coordination team at MoGD. Eligibility: The EPAG program was targeted to young women who: i) were age 16 to 27, ii) possessed basic literacy and numeracy skills, iii) were not enrolled in school within several months prior of the program initiation, and iv) resided in one of nine target communities in and around Monrovia. 6 These eligibility criteria stemmed from the project's objectives to reach young women at an early enough age to significantly improve the trajectory of their working years, to focus on girls who already had the basic literacy and numeracy skills needed to succeed in the labor market, and to avoid incentivizing applicants to drop out of school.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The literacy requirement in particular, although basic, made the EPAG program out of reach for many of the most vulnerable young Liberian women; the requirement reflected a deliberate choice on the part of program designers in the face of a tradeoff between serving the most vulnerable and serving those who could most readily make use of this relatively short training program. 7 The team recognized that it was difficult, if not impossible, to require documentation from the applicants to verify each of the eligibility criteria (especially age, since many Liberians do not have any official form of identification). Hence the application process relied primarily on self-reported data. To counter the likelihood that applicants would give false information in order to gain entry into the program, the eligibility criteria were not made public; the mobilization and outreach campaigns did not specify the precise age or education requirements for the program. During the recruitment events, each applicant had to physically present herself, fill out an application form specifying her age, education history, and residence. A simple literacy and numeracy assessment was also administered at the time of application. Beyond these basic eligibility criteria, no further selection criteria were applied, and program managers did not choose whom to train.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A “ randomized pipeline ” design was adopted in which eligible candidates were assigned by lottery to one of two rounds of the program, where those assigned to the second round would serve as a control group for the evaluation. Every applicant who met the eligibility criteria had an equal chance of participating based on the lottery results. Retention and Attendance: After dividing EPAG trainees into two rounds, or cohorts, for the training, the first round of training was held from March 2010 to February 2011. The classroom training was held from March through August, during which the project achieved a 95 % retention rate (far higher than similar programs in Liberia and elsewhere), and an average attendance rate of nearly 90 % during the classroom training phase. EPAG trainees were given incentives to participate and to make the most of their training: they signed\"Trainee Commitment Forms\"at the start of the training, they were paid small stipends and a completion bonus contingent upon attendance, they were offered free childcare at every training site, they were assisted to open a savings account at a local bank in which to save their 6 Bassa Community, Battery Factory, Bentol, Doe Community, New Kru Town, Old Road, Red Light, and West Point in Montserrado County and Kakata in Margibi County. 7 To alleviate the burden of this requirement, the second round of the EPAG program included a preliminary basic literacy program to help otherwise eligible young women to improve their literacy and numeracy skills in advance of entering the program. 6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "stipend money, and they were formed into small groups or\"EPAG teams\", each with a coach or mentor, to foster support networks and boost attendance. 3. Methodology 3. 1. Research design The impact evaluation of the EPAG project uses a randomized controlled trial, in which eligible applicants to the program were randomly assigned to participate in one of two cohorts (or “ rounds ”) of training. The treatment group is defined as those who were offered a space in the first round of training and the control group comprises those assigned to the second round. Selection into the training rounds was performed on a computer (using Excel) and was stratified by the track choice of the applicant (job skills versus business development skills), community, and service provider. Data were collected using three quantitative household surveys (baseline, midline, and endline) and two sets of qualitative focus group discussions (one after each round of training). A timeline of the impact evaluation is depicted in Figure 1. During both the baseline and midline surveys, the head of the household in which the EPAG participant was residing was also interviewed, in order to examine potential spillover effects of the program on non-treated household members.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["quantitative household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The household head interviews also provided an opportunity to gather useful household-level information (including assets, housing characteristics, and household head characteristics) to be used as control variables in our analysis. The baseline survey was conducted before participants were informed of the outcome of the randomization, and the midline survey was conducted one year after the baseline survey, one month after completion of the first round of training and before the start of the second round. The results in this paper are based on a panel data set that includes data from the baseline and midline surveys as well as administrative and monitoring data from the program implementation team. Note that although the midline data collection occurred just one month after the completion of the intervention, the intervention itself includes 6 months of post-training follow-up; hence the midline survey captures outcomes of the first cohort 7 months after they exited the classroom-based training. This timing was necessary to prevent too long of a delay between cohorts; the second cohort of trainees started immediately after the midline survey was completed. Future analysis using endline data will examine the trends in outcomes of both groups after Round 1 completion, but will not involve comparison against a pure control group of non-participants. 3. 2. Sample and attrition Following the communications and outreach campaigns in each of the nine target communities, during which 2, 106 young women were originally recruited to be a part of the EPAG program, 8 a randomized 8 The original recruitment effort fell short of the target of enrolling 2500 participants. Hence, before the second round of training, another recruitment effort was launched and an additional 617 girls were enrolled. These girls are not included in the impact evaluation since they were not subject to the initial random assignment. 7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data set"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "assignment process was conducted to assign the trainees to the first or second round of training. 9 Of those entered into the random selection, 1273 young women were assigned to the first round of training, with the remaining 808 to serve as a control group (the control group would participate in the second round of training starting in July 2011). Of the 1273 assigned to treatment, 118 women were not found or chose not to participate after they were selected. 10 In order to fill at least some of these slots, 39 young women from the control group were randomly issued as replacements, resulting in a modified control group of 769 individuals. In the end, 1191 young women entered the first round of training. 11 The assignment process and all post-randomization modifications are summarized in Figure 2. Table 1 reports the baseline and midline survey response rates leading to the sample used for the analysis in this paper. The target sample for both the baseline and midline survey consisted of the original 2106 EPAG recruits, of which 1989 were successfully interviewed during the baseline survey. 12 At midline, 1736 were interviewed, including 56 who were not interviewed at baseline. For our analysis, we drop individuals who were excluded from the randomization or who were manually re-assigned from control to treatment as replacements after the randomization.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["baseline survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These 25 were included in the surveys but have been dropped from the analysis because they were not assigned randomly. 10 The reasons given for not entering the training included: 1) they were back in school, 2) they had moved to a distant location, 3) they were seriously ill, 4) they had found full-time work, 5) they were not interested or able to make such a big time commitment, or 6) they could not be located despite numerous efforts. 11 This includes 1155 of the original 1273 assigned to treatment, plus 36 out of the 39\"replacements\"- young women from the control group who were offered a chance to be reassigned to round 1. 12 Note that a previously released baseline report for this evaluation was based on 2008 observations. However, after cleaning, 4 were found to be duplicate observations and 15 were not found in the program data and hence were dropped from the midline analysis. This leaves 1989 baseline observations that are included in the midline analysis. 13 These are cases in which the adolescent girl was interviewed but the household head interview was not conducted because the head was not available, could not be found, or declined to take part. 14 This suggests that the loss of the 118 young women who were selected but declined to take part, and the 60 who started but did not complete the program, does not bias the results. 8", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Yit is the outcome of interest for individual i and time t. Treati is an indicator which is equal to 1 for treated individuals and 0 otherwise. Postt is an indicator equal to 1 for midline observations and 0 for baseline. Xit is a vector of controls at baseline (t = 0), including individual characteristics (education, age, current pregnancy, marital, parental, and orphan status) and household characteristics (sex of household head and household size). β1 is the coefficient of interest that defines the “ impact ” of the program on individuals in the treatment group. The model also includes dummy variables for the communities where the program was implemented (and where the trainees resided) as well as the program track (business skills or job skills) to which the respondent was assigned. Finally, in order to control for household wealth, we compute an index based on household asset ownership at baseline using multiple component analysis, similar to the method described in Filmer and Pritchett (2001). After constructing the index, which includes thirteen household assets and six indicators of housing conditions, we control for the quintile of household ’ s overall asset position in all regressions. We augment this basic specification with an individual fixed effects model and find that the results are almost identical.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For binary dependent variables, we estimate both linear probability models and probit models analogous to the one given in equation (1), again finding that the results are nearly identical across all outcomes. We cluster the standard errors by classroom in all models, and to account for the interaction effect for variables such as (PosttxTreati) we additionally follow Ai and Norton (2003) to correct the standard error for interaction terms in probit models. 3. 4. Baseline characteristics Table 2 presents baseline balance tests for survey respondents from the treatment and control groups. 15 In addition to confirming the success of the randomization, as judged by the very few significant differences between the two groups, the table provides a vivid profile of the average EPAG participant. The study population has an average age of 23 years, with 55 % falling between 20 and 24 years. The majority have never been married, while 29 % are cohabiting with a partner and only 5 % are married. The majority of the study population has started or completed high school, which is consistent with the program ’ s target group of young women with basic literacy and numeracy, and with the program ’ s goal not to encourage girls to drop out of school.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Thirty-eight percent of the sample was already engaged in at least one income-generating activity (IGA) at baseline. This is consistent with the national figures from the 2007 DHS survey, which found labor force participation rates of 34 % for women aged 15-19 and 49 % for women ages 20-24. It is also consistent with the Liberian 2010 Labor Force Survey, which found labor force participation rates of 25 % for women aged 15-19 and 47 % for women aged 20-24. For the purposes of this study, to be consistent with program objectives and the Liberian context, our definition of income-generating activity encompasses the full range of activities through which people earn money, including paid employment, either formal or informal, and self-employment in small business or through petty trade. The most common types of IGAs reported at baseline were petty trade, including 15 The balance tests are run on the same sample as will be used in the impact analysis in Section 4, that is, the subset of individuals for whom we have a panel. Balance tests run on the full sample of baseline survey respondents, regardless of whether they also participated in the midline survey, confirm the same findings. A report summarizing the balance tests on the full sample, including comparisons to nationally representative data, is available upon request from the authors. 9", "output": {"entities": {"named_data": ["2007 DHS survey", "Liberian 2010 Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "street vending (48 % of those with at least 1 IGA), food processing for sale, including baking, cooking, and drying (16 %), and home production of crops, livestock, and fish (11 %). It is important to note that the EPAG program was not targeted toward the most vulnerable segments of Liberian society, but rather toward young women with enough education to be able to benefit from a training program of this nature. Based on comparisons from the 2007 CWIQ (Core Welfare Indicators Questionnaire) survey, the average EPAG participant is more educated, more literate, more likely to be engaged in an income-generating activity, and owns more assets than the average Liberian women of the same age group: mobile phone ownership was high (63 %), as was the proportion reporting that they had some money of their own (79 %). Even compared to other similar residents of Monrovia, the EPAG participants are better educated and have higher income. A strong sense of female empowerment at baseline emerges from the sections of the survey instrument having to do with self-confidence and agency.", "output": {"entities": {"named_data": ["Core Welfare Indicators Questionnaire", "CWIQ"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "sexual encounter. Finally, about 10 % of study respondents have experienced a forced sexual encounter in their lifetimes, which is consistent with the national figure of 13 % for this age group (DHS 2007). 17 Table 2B presents baseline balance tests for the household characteristics of respondents in the study sample. More than 40 % of households are headed by females, and the average household has slightly fewer than five members. The mothers of EPAG respondents tended to have very low education levels (almost 60 % had never been to formal school), while the fathers had more variance in their education (about a quarter had no schooling, but over 60 % had at least some secondary education). In both treatment and control households, a high proportion of school-aged children are in fact enrolled in school. Less than half of young people aged 13-30 in study households have any employment. Housing conditions are also similar across experimental groups. Tables 2A and 2B include a representative, but not exhaustive, list of indicators that were tested for balance by the authors. We conclude that the presence of very few significant differences between the individual or household characteristics between the two experimental groups indicates a high degree of internal validity for the study. 3. 5.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, the program itself was designed to fit within the young women ’ s lives, specifically with regard to their employment, education, and childcare duties. Training schedules were flexible, to allow participants to continue with their pre-existing educational and income-generating activities (and many participants did report continuing with these activities) and free childcare was provided. Hence, at least for these three dimensions of life, there would not have been much incentive to change one ’ s behavior prior to starting the program. While these explanations do not erase concerns about anticipatory behavior, they at least mitigate them. The generalizability of these results is also limited by the differences between the EPAG target group and the population of young women in Liberia. First, a high proportion of adolescent girls and young women in Liberia are illiterate or have very low literacy, while the participants recruited for the EPAG 17 Gender-based violence questions were administered in line with international ethical protocols, with additional informed consent procedures and referral mechanisms as needed. 18 See Ashenfelter (1978). 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "project had to meet a basic minimum literacy level in order to qualify for admission to the program; whereas only half of the 15-29-year-old female respondents to the nationally-representative Core Welfare Indicators Questionnaire (CWIQ) survey report that they can read and write (LISGIS 2007). Fewer than two percent of the girls in the EPAG study responded that they had no education, which is much lower than in the CWIQ survey. Second, the majority of adolescent girls and young women in Liberia reside in rural areas, whereas the survey participants were residing in urban and peri-urban areas, where access to basic social services may be much more improved. Consequently, the results are not representative of adolescent girls and young women in Liberia overall. The results are neither indicative of the average Liberian girl and young woman; nor are they indicative of the average Liberian girl or young woman in the project communities. They are only indicative of the average girl and young woman who are part of the EPAG project. Finally, many of the variables that we examine in this study are measures of self-assessed levels of satisfaction or belief. These are entirely subjective variables, and are subject to significant measurement error. There is considerable evidence that the wording of these questions can affect the answers given, as can the order in which the questions are asked.", "output": {"entities": {"named_data": ["Core Welfare Indicators Questionnaire", "CWIQ survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "cope with the emergencies that arise all too often. 20 To facilitate successful coping and provide a safe place to save, the EPAG program assisted each participant to set up a savings account at a local bank if she did not already have one. Table 5 presents midline survey results that indicate that the treatment group were nearly 50 percentage points more likely to have savings than the control group, and were saving on average LD 2500 (nearly US $ 35) more than the control group. EPAG graduates were also twice as likely as the control group to have outstanding loans (six percent v. three percent), and have loans from formal lenders (five percent v. two percent), 21 although the overall rate of obtaining credit remains extremely low. 4. 3. Empowerment The Adolescent Girls ’ Initiative is based on the hypothesis that livelihood and life skills training for young women will improve their lives in more than just narrowly-defined economic dimensions. In addition, evidence is increasing that these soft skills are also essential for success in employment (Borghans et al. 2008).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Surveys of employers consistently find that more than hard, technical skills, employers value these harder-to-quantify skills of honesty and integrity, problem-solving ability, work ethic, communication skills, the ability to work productively with others, responsibility and dependability (Blom and Hobbs 2007). The AGI program has focused on the development and measurement of these softer attributes that matter for employment as well as those that matter more to the individual, such as self-confidence and empowerment. Despite the challenges of measuring such subjective outcomes, the survey instruments included panels of questions designed to elucidate a nuanced picture of the personality and psychosocial characteristics that are most relevant for labor market success. Table 6A presents results on empowerment and decision-making. The first series of questions have to do with control over resources, spending decisions and earnings. Respondents were asked how much control they had over how to spend their own earnings; also, whether they had money of their own for basic uses that they alone could decide how to use, without having to ask for permission. The EPAG baseline survey found that respondents reported a high degree of control over resources even before the program started.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Surveys of employers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Because of this, the program impacts on control over resources were small, albeit statistically significant: 80 percent of respondents at baseline said they controlled their own resources, with a seven percentage point increase for the treatment group (relative to control). Similarly, 80 percent of those engaged in income-generating activities said that they controlled the money they earned. The midline results indicated that among the treatment group, this had increased by roughly eight percentage points. As shown in the second panel, EPAG graduates report that they worry less than those in the control group. They are less likely to worry about their jobs or incomes or that they won ’ t be able to pay for basic necessities, and those with partners are less worried about their relationships breaking up. The impact on subjective well-being, as measured by a series of questions about the respondent ’ s satisfaction with various dimensions on her life, indicate that EPAG was most 20 See for example Ashraf et. al. (2010); Morcos and Sebstad (2010); and Austrian and Ghati (2010). 21 “ Formal ” loans are those from banks, credit groups, susu, or money lenders; “ informal loans ” are those from parents, friends, relatives, or business partners. 16", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In contrast, the self-assessed entrepreneurial score is based on questions asking how well the respondent believes she could perform a series of six tasks related to starting or running a business, and can be considered a task-oriented measure of self-efficacy. The aggregate measure of entrepreneurial ability increased by roughly nine percentage points among EPAG beneficiaries relative to those in the control group, equivalent to a quarter of a standard deviation. Enhancing participants ’ self-confidence to perform these tasks was one of the main immediate objectives of the BDS training program. Table 6B summarizes the results on a series of questions on attitudes and self-confidence that were added during the midline survey only (hence no panel analysis is possible). EPAG graduates report a more positive attitude: they feel more in control and more comfortable, and they have greater confidence in their own business abilities as well as in their personal and social lives. They are also more confident than the control group in their personal relationships with spouses and partners, consistent with the findings in Table 6A.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Both treatment and control group respondents report equally high confidence in their ability to return to school “ should [she] decide to do so. ” These findings from the quantitative impact evaluation complement the results from a set of qualitative focus group discussions that were held with participants at the end of the 6 months of classroom training. Twenty-five percent of the trainees from Round 1 participated in a total of 34 focus group discussions that covered a variety of topics including their satisfaction with the program and their empowerment in both social and economic realms. The trainees overwhelmingly voiced a high degree of satisfaction with the training, and trainers commented on how the motivation or “ seriousness ” of the participants grew over the 6 month period. The trainees credited the transport allowance and free childcare in particular as features that facilitated their full participation; as one trainee commented, 22 Questions adapted from the Adolescent Self-Regulation Inventory, developed and validated for youth in the United States by Moilanen, 2006. The questions were revised and translated into an 11-item for the Liberian context. In the future, we plan to conduct basic testing on this scale on internal consistency and reliability. 17", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "their earnings on household expenses than men. Most of these studies have focused on adult women, specifically married women with children. It is not known whether the same holds true for young women, who may have other spending priorities, have less experience in managing households, and have younger children. Given the large increases in employment and earnings documented above, the EPAG program serves as a good setting to examine these types of spillovers. The evaluation included detailed interviews with the heads of the household in which EPAG participants were residing. The purpose of the household questionnaires was precisely to examine the hypothesis that investing in young girls would benefit her household. A secondary hypothesis was that EPAG participation may change gender-related attitudes in the participants ’ households. Household data was collected for 1601 out of the 1622 individuals who were interviewed at both baseline and midline; this same sample of 1601 individuals serves as the basis for both the individual and household level analysis in this paper. The estimated impact of the program on a broad range of household outcomes is summarized in Tables 8 and 9. Panel A of Table 8 examines the household size.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The household and family dynamics in Liberia, as in other African settings, can be complex, with families often sending their children to live with relatives who are more able to provide for their schooling and basic needs. Young parents, in particular, often leave home to migrate for work, leaving their children back home with relatives until they are able to establish themselves and send for their children. If the economic success of the EPAG participant allowed her to bring non-resident family members into her household (including but not limited to her own children), then overall household size may have been expected to increase as a result of the program. This does not appear to have happened, at least in the short term. The results in Table 8 show that overall household size was not affected by the program. It is possible that the increase in earnings due to EPAG was too small, or too short-lived, to have induced the kinds of migrations described above. Other measures of household well-being, including food security and asset ownership, reflect shorter- term investments that might be influenced by the economic success of EPAG participants. Panel B of Table 8 shows the impact of EPAG on a broad range of food security measures.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Household food security was measured using two methods: dietary frequency of high-value protein-rich foods, and two subjective questions on food shortages adapted from USAID ’ s FANTA questions (Coates 2007). On two of the four of the dietary frequency questions and on both of the food shortage questions, the treatment groups ’ dietary situation improved as a result of the EPAG program. Weekly consumption of fish and meat rose significantly by four percentage points in treatment households (from a high baseline value of 84 % for meat / chicken and 90 % for fish), and weekly consumption of dairy and eggs did not change significantly. Household heads report worrying less about insufficiency of household food supplies, and the reported incidence of household members going to bed hungry also decreased in treatment households relative to control. Combined, the impacts across these indicators portray a situation of improved food security and dietary composition, consistent with the hypothesis that the increased earnings of the EPAG participants were spent in part on food. 20", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "any change in the amount of time spent on domestic work, 25 we have no evidence that these shifting norms have affected the division of labor in practice. Further research would be needed to disentangle the issues of changing gender norms in theory versus practice, and attitudes toward norms in general versus those at play in one ’ s own household. 5. Cost Effectiveness Although the first task of any evaluation is to demonstrate the effectiveness of an intervention – that is, whether or not the program actually has a measurable and attributable impact – this is not enough to recommend the program to policymakers. This requires also that the program can show that it is worth spending scarce public resources to do it. Ideally, a program worth doing will be both effective and cost- effective. One can measure cost-effectiveness in terms of the number of physical outputs produced or outcomes achieved, e. g. the number of people employed per dollar spent, or one can measure achievements in terms of the value of the benefits acquired relative to the amount of money spent. In the case of the EPAG, the unit cost of training in Round 1 was roughly $ 1200 for the Business Skills track and $ 1650 for the Job Skills track.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The EPAG Job Skills track consisted of more hours of training, and required purchasing practical equipment for each trade area. Some Job Skills trainers, especially those with more specialized skills and experience, were also more costly. The estimated unit costs cover trainer salaries (EPAG has mostly college-educated trainers), training materials (not including curriculum development), training venue rental, administrative and overhead costs of the training provider, childcare costs, event costs (job fairs, etc.), stipends to mentors, trainee transport allowances and completion bonuses. The costs also cover the withheld incentive payment to the training provider, based on how many trainees find employment. Although high relative to most developing-country budgets, these costs are well within the range of the Jovenes youth training programs implemented in Latin America (cf. Ibarrarán and Rosas 2009). The Jovenes programs were estimated to cost between $ 700 and $ 2000 per participant, depending on the country (Betcherman 2007). Not only were the costs of the EPAG program within international norms, the program itself is also cost-effective.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6. Robustness Checks 6. 1. Sample attrition Many challenges were encountered during survey implementation, due to the context – the densely populated and impoverished communities in and around Monrovia are difficult settings in which to find and track respondents — as well as the transience of a young study population. Despite vigorous efforts to track and interview each individual in the sample, a certain amount of survey attrition was expected. As the survey response rates in Table 1 show, 1622 (or 80 %) of the individuals in the study sample were successfully interviewed in both the baseline and midline surveys. Another 305 respondents were interviewed at baseline but not at midline and hence are not in the panel used for the analysis in this paper. 27 This survey attrition, while not much higher than other program evaluations in Africa, may cause concern that the results of this evaluation are biased, especially if the loss to follow up is correlated with individual characteristics that might affect the outcomes. To address this concern, Table 10 presents regressions on the likelihood of panel inclusion, that is, the likelihood of being interviewed at both baseline and midline. The first column indicates that treated individuals are significantly more likely than control to have been interviewed twice. This result persists even after controlling for individual characteristics and community dummies.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Columns 2, 3, and 4 show that age, school attendance, and employment status at baseline are all correlated with survey attrition. To further investigate whether these characteristics lead to differential attrition between the treatment and control groups, we interact treatment with particular characteristics at baseline. Columns 5 and 6 show that, conditional on being in the control group, the most likely predictor of attrition is having a child. By itself, being a mother does not predict attrition, but when interacted with treatment, we see that control group mothers are more likely to have dropped out of the panel than their treated counterparts. Employment at baseline, while positively correlated with attrition, does not differentially affect treated and untreated individuals. Because the differential attrition between treatment and control groups may bias our results, we use Inverse Probability Weighting (IPW) as outlined in Wooldridge (2002) to adjust the estimates of our key outcomes, using the inverse probability of inclusion in the panel as a probability weight. As a first step, we use the probit model in Column (6) of Table 10 to regress the likelihood of being observed twice on baseline individual characteristics, including those likely to affect attrition, such as employment and parental status. In the second step, we use the inverse of the predicted values from that probit model as probability weights to redo the difference-in-difference regressions for our key outcomes of interest. This method gives more weight to the individuals with the highest chance of attrition, giving them more influence on the estimate of the impact than those with a low probability of attrition. The results are reported in Table 11. The results show a high degree of similarity between the original (unadjusted) and the adjusted estimates. Across all outcomes, the point estimates and standard errors vary only slightly. 27 Of these 305 cases, nine are dropped from the attrition analysis because the household head was not interviewed at baseline, hence the household level control variables are not available. 23", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The EPAG program, which delivered six months of classroom-based skills training followed by six months of job placement support for either self or wage employment, led to a 47 % increase in employment and 80 % growth in earnings, relative to a randomly selected control group of non-participants. The program ’ s Business Skills track had markedly higher impacts on employment and earnings than the Job Skills track, which focused on wage employment. These impacts vary somewhat but remain consistently positive and significant across almost all communities, educational backgrounds, and wealth levels. The highest impacts were obtained for those in the middle of the wealth distribution, and for girls with higher educational levels, which is consistent with the program ’ s initial screening for young women with basic literacy who would be able to make use of a classroom-based skills course. These strong impacts on employment and earnings translated into positive impacts in other realms of the participants ’ lives. Our results show striking improvements in various empowerment measures, including access to and control over monetary resources, including savings, where the program led to a sizeable difference of 35 USD in savings between treated and control individuals. The study also documents significant improvements in a wide range of subjective outcomes including measures of worry, life satisfaction, self-regulation, self-confidence, and self-perceptions of social abilities. In the area of fertility and sexual behaviors, the results paint a somewhat more nuanced picture. The EPAG program had no discernible effect on the desired number of children or on the actual number of children, conditional on having any children. There was a weak reduction in the likelihood of having any children, and a stronger increase in the likelihood of being pregnant, even after excluding those who were pregnant at baseline. On net, these impacts appear to cancel each other out, consistent with a hypothesis that treated individuals waited until the end of the EPAG program to become pregnant. The third main area of outcomes looks at household-level measures. Consistent with the wide body of literature on the benefits to the household of women ’ s increased resources, the results show a 24", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, an endline survey was conducted after the second round of the EPAG program as per the timeline depicted in Figure 1. Examination of this endline survey data will permit a descriptive analysis of the outcomes of the first group of trainees 12 months after they completed the EPAG program, as well as examination of the outcomes of the second batch of trainees. The second round included not only the control group from this impact evaluation but also newly recruited participants who were offered brief basic literacy and numeracy training program prior to program entry. Work is already underway to design and implement the third round of EPAG, with a substantial redesign of the Job Skills track, an emphasis on reaching younger girls with lower literacy, and expansion to communities outside of Monrovia. If the high success rates found in this study are replicated for these future cohorts, the EPAG program should serve as a model for policy makers in Africa and the world seeking to improve lives and livelihoods of all youth, male and female. 25", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["endline survey data", "endline survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Religious School Enrollment in Pakistan A Look at the Data Tahir Andrabi1 Pomona College Jishnu Das The World Bank Asim Ijaz Khwaja Harvard University Tristan Zajonc Harvard University Abstract Bold assertions have been made in policy reports and popular articles on the high and increasing enrollment in Pakistani religious schools, commonly known as madrassas. Given the importance placed on the subject by policy makers in Pakistan and those internationally, it is troubling that none of the reports and articles reviewed based their analysis on publicly available data or established statistical methodologies. This paper uses published data sources and a census of schooling choice to show that existing estimates are inflated by an order of magnitude. Madrassas account for less than 1 percent of all enrollment in the country and there is no evidence of a dramatic increase in recent years. The educational landscape in Pakistan has changed substantially in the last decade, but this is due to an explosion of private schools, an important fact that has been left out of the debate on Pakistani education. Moreover, when we look at school choice, we find that no one explanation fits the data.", "output": {"entities": {"named_data": [], "descriptive_data": ["census of schooling choice"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While most existing theories of madrassa enrollment are based on household attributes (for instance, a preference for religious schooling or the household ’ s access to other schooling options) the data show that among households with at least one child enrolled in a madrassa, 75 percent send their second (and / or third) child to a public or private school or both. Widely promoted theories simply do not explain this substantial variation within households. 1 Corresponding Author: Tahir Andrabi (tandrabi @ pomona. edu). This study would not have been possible without the enthusiasm and continuous support we received from Tara Vishwanath. Charles Griffin first encouraged us to look at the data. We thank Veena Das, Shehla Andrabi, Sehr Jalal, Ritva Reinikka and Carolina Sánchez for their encouragement and to Hedy Sladovich for her excellent editorial suggestions. The paper has also benefited from comments by Ismail Radwan, Naveeda Khan, Shahzad Sharjeel and Shanta Devarajan. The research department of the World Bank provided funding for this study through the Knowledge for Change trust fund. The findings, interpretations and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent. Working papers describe research in progress by the authors and are published to elicit comments and to further debate. Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized WPS3521", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 The only publicly available sources of data to document patterns of enrollment and available educational options for Pakistani families are household-based surveys. These are the official 1998 Census of Population (Government of Pakistan) 3, the 1991, 1998, and 2001 rounds of the Pakistan Integrated Household Survey4, and a 2003 census of schooling choice conducted by our research team. The fact that three sources use different definitions of madrassa enrollment, and were collected at different times by individuals with very different institutional affiliations provides independent verification of enrollment estimates and allows us to determine the sensitivity of our results. The household data tell us whether a child is enrolled full-time in a madrassa, but not whether a child goes for an hour on any given day to study the Quran. Therefore this data does not confound full-time with part-time attendees — a child who attends a public school during the day and a madrassa in the evening is recorded as enrolled in a public school. This is an important distinction since parents might use a modicum of madrassa or mosque based education to teach their children about religion. Consequently, if we contrast these household-based numbers with numbers from establishment-based reports, discrepancies can arise. From virtually any policy perspective, including evening quran classes in enrollment figures seems misguided.", "output": {"entities": {"named_data": ["Pakistan Integrated Household Survey", "1998 Census of Population"], "descriptive_data": ["2003 census of schooling choice"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Regrettably, until now almost all enrollment numbers cited have been based on establishment surveys which do just that. These data sources show that around 200, 000 children were enrolled full-time in madrassas before 2001. Since 2001, our school census suggests that these numbers may have increased somewhat, although the experience varies across districts. To put this number in context, total primary enrollment (grades 1-5) in public and private schools stood at 17. 4 million in 2003 (Government of Pakistan, Ministry of Finance, 2003). The choice of madrassa schooling viewed as either the percentage of eligible children or the percentage of enrolled children, is statistically insignificant for the average Pakistani household. Enrollment in madrassas accounts for approximately 0. 3 percent of all children between the ages of 5 and 19. Given that the overall enrollment rate for this age group is roughly 42 percent, this represents less than 0. 7 percent of all enrolled children, an order of magnitude less than the 33 percent cited by the International Crisis Group report (2002). 3 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 to provide statistics for private versus public enrollment. 4The PIHS is the equivalent of the widely used Living Standard Measurement Surveys (LSMS) implemented in various countries. See http: / / www. worldbank. org / lsms for extensive notes on the 1991 PIHS. See also www. statpak. gov. pk for information on the census and the Federal Bureau of Statistics data.", "output": {"entities": {"named_data": ["PIHS", "Living Standard Measurement Surveys"], "descriptive_data": ["census of private schools"], "vague_data": ["school census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 3. 1 Data Sources We use three different types of data to verify our estimates and determine how sensitive they are to changes in definition and the year of the survey. Two sources are nationally representative, but date from 2001 or before, the third is data from a census of households carried out by the authors in 2003 as part of a project on educational choice. The first source is the “ long ” form of the population census in 1998, which is a large sample-based survey with information on enrollment. This survey is representative at the level of the district and region (rural or urban) and provides comprehensive coverage of the entire country. 7 We use this data to examine enrollment patterns across districts. The second type of data, based on household surveys, are different rounds of the Pakistan Integrated Household Survey (PIHS) carried out in 1991, 1998 and 2001. While the data is not as extensive as the census, it contains detailed household information on schooling and income, and has been used extensively by researchers both in Pakistan and the United States. Finally, we use the census of schooling choice among households that our research team conducted in August 2003 (referred to as the project on “ Learning and Educational Achievement in Punjab Schools ”, or LEAPS).", "output": {"entities": {"named_data": ["Pakistan Integrated Household Survey"], "descriptive_data": ["census of schooling choice among households"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These data are only for three districts in the province of Punjab, but is very recent, was conducted by an independent team of academics, and is a complete census of all households in the selected villages. Consequently, it yields sufficient madrassa enrollment to examine correlations with household attributes in a meaningful manner (this data source provides information on four times as many children as the PIHS). Table A2 in the appendix shows how these different data sources are used in the paper. Each source asks about madrassa enrollment in a slightly different but comparable way. The population census (1998) asks about the field-of-education (“ What is name ’ s field of education? ”) with options that include (for instance) engineering, medicine, or religious education. This question is also asked of all literate adults irrespective of their current enrollment status, allowing for comparisons in the stock of religious education over time. The PIHS rounds ask, “ What type of school is name currently attending? ” with options that include government school, private school, or deeni-madrassa (religious schooling). Finally, the LEAPS census directly asks, “ Is the child enrolled in a madrassa or an Islamic education school? ” Fortunately these different questions all give rise to similar numbers. This is reassuring since it suggests that any one particular result is not driven by the specific question or definition that was used. 7 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 (www. statpak. gov. pk).", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": ["census of private schools"], "vague_data": ["population census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 There may be grounds for skepticism about these estimates for madrassa enrollment. Since the data were collected prior to 2001, geopolitical changes after September 11 could have led to greater madrassa enrollment. In addition, the household-based survey faces the usual problems of accurately estimating a low-probability event — although enrollment is less than 1 percent in these surveys, the sampling error is large (see Bauman, 2001, for a description of similar problems in estimating home-schooling in the United States). Finally, while the census of populations does not face the problem of small samples, it is not that recent (1998) and some may have reservations regarding the quality of government data. 10 The LEAPS census of schooling choice conducted in 2003 provides a rough check on these numbers (see appendix for details). This census was conducted in three districts of Punjab and villages were chosen randomly based on the criterion that each village must have at least one private school. Typically, this means that the villages lies somewhere between fully urban and fully rural populations and are not representative of the districts that they are in. Estimates from the LEAPS census show that as a percentage of enrolled children, the numbers in two of the three districts are slightly higher than those of the population census.", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": [], "vague_data": ["census of populations", "household-based survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the third (Rahim Yar Khan) there is a large difference, with the census reporting that 1 percent of all school-going children attended madrassas, and the LEAPS showing that the fraction is closer to 3. 7 percent (Table II). There are three potential explanations for this difference. First, the LEAPS data is not representative of the district and could be off the mark for districts with wide variation in madrassa enrollment across rural and urban samples. Second, the experience of the last five years could have varied dramatically across districts — in some, the enrollment fractions did not change and in others it increased substantially. Third, the data could point to systematic problems with the census estimates from certain districts, or the statistical problems that arise when we try to estimate low-probability events. 3. 3 Explaining the Differences A number of reasons could account for differences between the estimates presented here and those in the popular press. 1. Differences in the sampling unit. Our estimates are all based on household surveys — an interviewer goes to a household and asks about the enrollment status of every child. Some census estimates of home rather than religious schooling in the United States — the former ranges from 1 to 2 percent (Bauman 2001) while the latter is closer to 8 percent (National Center for Education Statistics, 2001). 10 In our own analysis, we find the quality of the data generated by the Federal Bureau of Statistics in Pakistan to be consistently high. We have used the FBS Census of Private Schools (PEIP, 2000) to guide our fieldwork and feasibility study for LEAPS and found it tallied with the situation on the ground quite well, even in remote villages.", "output": {"entities": {"named_data": ["FBS Census of Private Schools", "LEAPS"], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 4. 1 Variation across Districts Pakistan is divided administratively into four provinces with 102 districts — Punjab, Balochistan, North-West Frontier Provinces (NWFP), and Sindh — plus the federal capital Islamabad, the Federally Administered Tribal Areas (FATA), the federally administered Northern Areas and Azad Jammu and Kashmir (AJK). The four provinces or Punjab, Balochistan, Sindh and NWFP, together with Islamabad, account for more than 97 percent of the population. Geographically, parts of Balochistan, the NWFP and FATA border Afghanistan. Sindh and Balochistan are sparsely populated provinces, with the exception of Karachi in Sindh, which is the single biggest metropolis in the country with a population approaching 10 million. We use data from the population census, 1998, as well as the census of private schooling, 2000, to provide estimates of madrassa, private, and government school enrollment in each district except for those in the province of FATA. The geographical dispersion of madrassa enrollment depends on how we define madrassa prevalence. There are three alternatives. We could present a geographical breakdown of the total number of children enrolled in madrassas. This number is related to the total population of the district, and may thus reflect only the size of the district relative to others.", "output": {"entities": {"named_data": [], "descriptive_data": ["population census, 1998", "census of private schooling, 2000"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Another option is to use the equivalent of the gross enrollment ratio (GER), defined as the total enrollment divided by the number of “ eligible ” children — in this case, children between the ages of 5-19. This statistic provides an estimate of the “ penetration ” of madrassas, but it does not take into account the overall enrollment decision of the family. Thus, a district with two children enrolled in madrassas, and 20 children enrolled in private or public schools out of a total of 100 children will have exactly the same gross enrollment ratio (GER) as a district with two children enrolled in madrassas and 98 children enrolled in regular schools. To the extent that we want to distinguish between these two districts, a third statistic, the ratio of children enrolled in madrassas to total enrollment (the madrassa fraction of enrollment or MFOE), can also be used. The picture changes dramatically depending on whether we use the raw numbers or the ratio of children enrolled in madrassas to total enrollment. However, since enrollment in madrassas is highly correlated with total enrollment, there is little difference in the pattern of madrassa enrollment whether we use the GER or the fraction of enrolled children in madrassas. Figure 1a shows the number of children enrolled in madrassas for every district in the country. As expected, numbers are closely linked to population size — the three most populated districts account for one-quarter of the enrollment, with the bulk of enrollment in large urban", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 between 1948 and 1965, with increases in the percentage of adults with religious education in the cohorts born after this date. There is also wide geographical dispersion in the prevalence of madrassa education in Pakistan. Although all districts report that less than 2. 5 percent of children in the relevant age group (children between the ages of 5 and 19) are going to madrassas, the Pashto speaking belt that borders Afghanistan stands out in terms of the popularity of madrassas as an educational choice. The notion that the madrassa movement coincided with resistance to the Soviet invasion of Afghanistan is supported by the 1998 data from the population census. The increase in the stock of religiously educated individuals starts with the cohort that came of age in 1979 (the year of the Soviet invasion of Afghanistan) and the largest increase is for the cohort co-terminus with the rise of the Taliban. Combined with the fact that the largest enrollment percentage in Pakistan is in the Pashtun belt bordering Afghanistan, this suggests events in neighboring Afghanistan influence madrassa enrollment. Is there something intrinsic about Pashtun sensibility or tribal culture that leads to higher madrassa enrollment? The differentiation of the Pashtun and non-Pashtun districts does not extend to Pashtun and non-Pashtun households in the LEAPS data.", "output": {"entities": {"named_data": ["LEAPS data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We find no evidence that Pashtun households are more likely to send their children to Madrassas compared to the rest of the sample, suggesting that geopolitical factors and geographical proximity to Afghanistan matter more than cultural preferences. 16 Similarly there is no evidence for religiosity or household preference-based models of madrassa enrollment. The radical religiosity argument suggests that children are more likely to be sent to madrassas when the family favors a radical brand of Islam. If true, what are we to make of the fact that more than 75 percent of all households with a child in a madrassa also send a child to a public or private school? In a multivariate context we checked whether households identified as “ radically Islamic ” were more likely to send their child to a madrassa. 17 Again, we found no 16 The data from the LEAPS census asked about ethnic and caste identity, and households that classified themselves as “ Pathan ” or “ Afghani ” were used to represent Pashtun households. In line with the usual residential patterns of individuals with Pashtun backgrounds, most of these households are in district Attock in the North of Punjab. 17 In a largely Islamic country it is difficult to find good measures of religiosity.", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "No data on religiosity was collected as part of the census and a more recent and detailed household survey that includes information on time-use elicits little variation — everyone reports high mosque attendance and regular prayers. An alternative, suggested by David Evans at Harvard University, which we pursue here, is to use recent developments in the use of “ names. ” Research by Fryer and Leavitt (2004) demonstrates the increasing use of names to define race identity in the United States. We postulate that households who named (at least) one child “ Osama ” (also spelt Usamah, Usamma or Usama) are more likely to favor a radical brand of Islam. The use of the name Osama was minimal until 1998, and then peaks in 1998 and 2001, following disruptive events. Of course, the naming of the child may reflect name recognition rather", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "List of acronyms CAR Central African Republic DHS Demographic and Health Surveys DRC Democratic Republic of Congo DTM Displacement Tracking Matrix FCS Fragile and Conflict-affected Situations GIDD Global Internal Displacement Database GIS Geographic Information Systems IASC Inter-Agency Standing Committee ICRC International Committee of the Red Cross IDMC Internal Displacement Monitoring Centre IDPs Internally Displaced Persons ILO IOM International Labour Organization International Organization for Migration IRRS International Recommendations for Refugee Statistics JIPs Joint IDP Profiling Service LSMS Living Standards Measurement Study MICS Multiple Indicator Cluster Surveys NGOs Non-Governmental Organizations NRC Norwegian Refugee Council OCHA Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat OAU Organization of African Unity ODA Official Development Assistance OECD Organisation for Economic Co-operation and Development SDG Sustainable Development Goal SKOPE Somalia Knowledge for Operations and Political Economy SuTPs Syrians under Temporary Protection UAV Unmanned Aerial Vehicle UNDP United Nations Development Programme UNHCR United Nations High Commissioner for Refugees UNITAR United Nations Institute for Training and Research UNOSAT UNITAR ’ s Operational Satellite Applications Programme UNRWA United Nations Relief and Works Agency for Palestine Refugees in the Near East UNSD United Nations Statistical Commission WFP World Food Programme", "output": {"entities": {"named_data": ["Multiple Indicator Cluster Surveys", "International Recommendations for Refugee Statistics", "Demographic and Health Surveys", "Global Internal Displacement Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "displaced due to armed conflict, situations of generalized violence and violations of human rights. 28 Data on IDPs monitored by IDMC are disaggregated and currently published separately for conflict-induced displacement and disaster-induced displacement. 29 At the country level the IOM ’ s Displacement Tracking Matrix (DTM) 30 provides data on IDPs in both conflict and natural disaster settings (activated in all major natural disaster contexts in recent years). Global data on conflict-induced internal displacement reflect variations in how IDPs are defined across situations. There is no consensus on how far a person must flee in order to be considered internally displaced. The definition of internal displacement for nomadic populations, which account for a significant share of IDPs in the Horn of Africa and increasingly in the Sahel, is open to controversy. 31 Moreover, while some countries register IDP children born in displacement (e. g. Azerbaijan, Cyprus and Georgia), other countries do not (IDMC 2015). The crafting of a definition for IDPs and its application in a particular context may be heavily influenced by local and national politics in conflict and post-conflict countries, as well as the direct link between estimates of displaced populations and humanitarian assistance, which can lead to both over- and under-reporting.", "output": {"entities": {"named_data": ["Displacement Tracking Matrix"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In other contexts, deregistration signifies not the achievement of a durable solution but rather the end of state or international support (IDMC 2015). 33 The absence of a clear and operational approach to defining the ‘ end ’ of internal displacement may be one of the factors behind the continued overall increase in the global numbers of IDPs. Lack of clarity around when displacement ends also leaves room for political manipulation. Governments may find it politically expedient to artificially prolong IDP status by deterring returns or local integration, for example in Azerbaijan and Georgia to promote claims over territory (Beau 2003). In other contexts, national 28 UNHCR ’ s IDP data focus only on internally displaced populations to which it extends protection or assistance. IDMC coverage of IDP data is more expansive and in 2015 included additional data on: (a) 26 countries accounting for 4. 5 million IDPs including some significant IDP hosting countries (Turkey, India, Ethiopia, Bangladesh and Kenya); and (b) IDPs in countries where UNHCR is active who are not protected or assisted by the agency. In 2015, IDMC ’ s aggregate figure for conflict-induced internal displacement was 3. 3 million higher than UNHCR ’ s aggregate figure for IDPs protected or assisted by the agency. 29 IDMC ’ s 2016 report presents both data sets alongside each other.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["IDP data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In certain contexts, there can be significant overlaps in these two groups; however data systems may be maintained separately for conflict-induced displacement and natural disasters (e. g. in Afghanistan) leading to possible gaps or double counting if these categories are combined. 30 The IOM Displacement Tracking Matrix (DTM) is a system to track and monitor displacement and population mobility. It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route. It has been active in over 40 countries since its inception in 2004. See http: / / www. globaldtm. info /. 31 This is typically defined as nomads not having access to their traditional routes, but routes can vary. 32 IDMC has recently adjusted their methodology to facilitate greater comparability across situations and improvements are reflected in IDMC ’ s end-2015 data. 33 This is not necessarily a problem if the purpose of the registration system is to delineate entitlements to assistance rather than to determine status.", "output": {"entities": {"named_data": ["IOM Displacement Tracking Matrix"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": " The Sri Lanka: Emergency Northern Recovery Project aimed to support government efforts to resettle IDPs in the Northern Province by creating an enabling environment through: (i) emergency assistance to IDPs; (ii) a work-fare program; and (iii) rehabilitation and reconstruction of essential public and economic infrastructure. The project closed in December 2013 and was rated satisfactory.  The Mitigate the Impact of Syrian Displacement on Jordan Project assisted the government to maintain access to essential healthcare services and basic household needs for the Jordanian population affected by the influx of Syrian refugees. The project closed in July 2014 and implementation was rated satisfactory.  The ongoing Azerbaijan IDP Living Standards and Livelihoods Project aims to improve living conditions and increase economic self-reliance of targeted IDPs.  The ongoing Lebanon Municipal Services Emergency Project addresses urgent community priorities in selected municipal services, targeting areas most affected by the influx of Syrian refugees in order to mitigate the impact on host communities, including: (i) provision of high priority municipal services and initiatives that promote social interaction and collaboration; and (ii) larger works to rehabilitate / develop critical infrastructure in the areas of solid waste management, roads improvement, water and sanitation and community infrastructure.  The ongoing Jordan Emergency Services and Social Resilience Project aims to assist municipalities and host communities to address the immediate service delivery", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "impacts of Syrian refugees and strengthen municipal capacity to support local economic development.  The FATA Temporarily Displaced Persons Emergency Recovery Project in Pakistan will promote child health, and strengthen emergency response safety net delivery systems in the affected Federally Administered Tribal Areas (FATA) by promoting the early recovery of approximately 120, 000 displaced families through cash grants.  The Great Lakes Displaced Persons and Border Communities Program is a regional program under preparation to target IDPs, refugee and host populations in the Democratic Republic of Congo (DRC) and Zambia with investments in socio-economic services, livelihood support, land access and social cohesion.  The Development Response to Displacement Impacts Project in the Horn of Africa will help improve access to basic social services, expand economic opportunities, and enhance environmental management for communities hosting refugees in target areas of Ethiopia, Uganda and Djibouti. The project is the first phase of an expanded program to include other countries affected by forced displacement. C.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Historical trends and patterns of forced displacement: Insights from available global data This section provides an overview of the available global data on conflict-induced forced displacement, drawing largely on UNHCR ’ s published data on asylum-seekers, refugees and IDPs. 41 Data are presented visually in a series of figures to highlight the scope and character of the current global forced displacement crisis and identify historical trends and anomalies. These data largely focus on the scale and trends in conflict-induced displacement (i. e. the numbers of forced displaced) with some coverage of other elements such as demographics, location and accommodation. Globally, there has been an unprecedented increase in the numbers of displaced people over the last decade, largely explained by the expansion in the number of reported IDPs. Historical data show a substantial increase in the numbers of forced displaced (see Figure 3), however the expanding geographical scope and quality of displacement monitoring systems are likely to account for much of the increase in forced displacement figures. The numbers of refugees under UNHCR ’ s mandate have recorded a number of variations over time, peaking in the early 1990s (at a level 10 percent over 2015 numbers) with the conflict and displacement associated with the end of the Cold War. The number of Palestinian refugees steadily has increased steadily over time, largely as a result of natural growth. IDP numbers (for which the underlying data are the least robust) have recorded the largest progression as a consequence of: (a) the expanded scope of monitoring efforts (IDPs were not counted before 1989 and methodologies were 41 UNHCR ’ s data only include IDPs protected or assisted by the agency.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["global data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 4: Refugees and Asylum-Seekers by Migratory Path 1951 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 5: Significant Historical Crises as a Share of Total Forced Displaced 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes IDPs protected or assisted by UNHCR, asylum-seekers and refugees. Excludes IDPs not protected or assisted by UNHCR and Palestinian refugees under UNRWA ’ s mandate. Figure 6: Top 15 Countries of Origin end-2015 Source: IDMC Global Report on Internal Displacement 2016, UNHCR Global Trends 2015, UNRWA A small number of countries carry the burden of hosting the majority of refugees. Historically since 1991, 15 asylum countries, overwhelmingly in the developing world, have hosted more than 50 percent of refugees and asylum-seekers (see Figure 7). 44 By the end of 2015, while almost all countries in the world were hosting refugees, the burden was unevenly shared (see Figure 8). Only seven countries hosted more 44 Major host countries are identified based on the cumulative number of refugees and asylum-seekers over the period 1991-2015.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "than 500, 000 refugees, together accounting for half of all refugees and people in refugee-like situations (excluding Palestine refugees). Major refugee hosting countries are typically the neighbors of countries of origin. For example, Syria ’ s neighbors (Turkey, Lebanon, and Jordan) together accounted for 27 percent of total refugee numbers; Afghanistan ’ s neighbors (Pakistan and the Islamic Republic of Iran) together accounted for 16 percent; and Somalia ’ s and South Sudan ’ s neighbors (Ethiopia, Kenya and Uganda) together accounted for 11 percent. Some countries (Lebanon, Jordan and Turkey) are hosting a particularly large share of refugees relative to their population (see Figure 10). 45 However, in all other countries, the number of refugees as a percentage of the population is 3 percent or lower, and most often below 1 percent. Figure 7: Top 15 Host Countries as a Share of Total Refugees and Asylum-Seekers 1991 – 2015 Source: UNHCR Statistical Online Population Database Note: Includes refugees, people in refugee-like situations and asylum-seekers. Excludes Palestinian refugees under UNRWA ’ s mandate. 45 Nauru is a special case since the Australian government funds the offshore processing center where refugees and asylum-seekers intercepted at sea are detained pending determination of their status.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the case of refugees, host countries rarely facilitate naturalization, only a minority of refugees ever gets resettled in third countries and voluntary repatriation is frequently not a realistic option for several reasons. International law provides for three possible durable solutions for refugees, including integration within the area of displacement, repatriation to their home country or resettlement in a third country; refugee status can also cease when there are no longer compelling reasons for an individual to refuse to avail themselves of the protection of their country of origin. In 2015 only 119, 265 refugees under UNHCR ’ s mandate were either resettled, naturalized53 or ceased to be refugees; and there were only 201, 415 voluntary returns, mostly Afghanistan, Sudan, Somalia and CAR (see Figure 17). These statistics highlight the significant gap between the unprecedented numbers of refugees and the capacity of the international community to provide durable solutions. For the 85 percent of refugees hosted in developing countries, there are only minute prospects for resettlement. Figure 17: Durable Solutions Relative to Refugee Stock 2015 Source: UNHCR Global Trends 2015 Global statistics that show the low rate of refugee returns masks the variation in returns over historical periods and across displacement crises — with significant voluntary returns for some countries.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 18: Significant Refugee Returns by Country of Origin 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Countries selected based on their cumulative returns of refugees over the period 1991-2015. Return does not necessarily lead to the full reintegration of a person into their home country or area of origin. In the absence of global data on the success of reintegration following return, data on returns appear to be taken as indication of sustainable return. In reality, many returnees face impediments to reintegration and continue to have specific economic and social vulnerabilities linked to their displacement. They may not be able to reclaim land, access sufficient financial resources (e. g. accumulated during their displacement) or reestablish social networks in areas of origin, which are critical factors for successful reintegration (World Bank 2015). Sustainable refugee return is therefore not a one-off event but a process that provides returnees with adequate safety, housing, livelihoods and services that address their specific vulnerabilities and reduce the likelihood of secondary displacement (World Bank 2015). Figure 19: Voluntary Returns of Refugees 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of refugees and people in refugee- like situations protected or assisted by UNHCR.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 20: Returns of IDPs Protected or Assisted by UNHCR 1993 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of IDPs and people in IDP-like situations assisted and protected by UNHCR. Consequently, the average length of protracted refugee situations has increased over the past two decades according to UNHCR estimates (see Table 2). UNHCR estimates that the average length of ongoing", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes high-income (OECD and non-OECD) countries. Excludes Palestinian refugees under UNRWA ’ s mandate.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 22: Numbers of Refugees in Ongoing Refugee Situations end-2014 Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes Palestinian refugees under UNRWA ’ s mandate. D. Data on asylum-seekers, refugees and IDPs: sources, applications and credibility In this section, a distinction is made between: (a) the collection of source data; and (b) the compilation of data across sources (within a country or across countries). In general, there is a delineation of roles between data collectors and data compilers, however there are organizations, such as UNHCR, IOM and the Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat (OCHA), 59 that are involved in both data collection and compilation activities. Data collection: Sources for refugees, asylum-seekers and IDPs60 Collection of primary data on forcibly displaced persons is generally undertaken by national governments through their national statistical offices, line ministries or immigration agencies. However, where countries lack the capacity to undertake this work, they may rely on international organizations as well as international and local NGOs to collect data or undertake estimates. 61 In general, governments tend to collect data on refugees in developed countries, while UNHCR and NGOs tend to collect data on refugees in developing countries.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Humanitarian organizations such as UNHCR and OCHA as well as international organizations such as IOM are also involved in the collection of data on IDPs, often involving international and local 59 OCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. See http: / / www. unocha. org /. 60 This section draws heavily on the “ Report of Statistics Norway and the Office of the United Nations High Commissioner for Refugees on statistics on refugees and IDPs ” presented at the UNSD in March 2015. 61 The number of countries where UNHCR exclusively collects data on refugees declined from 76 in 2010 to 72 in 2014, while the proportion of countries where refugee data were exclusively provided by governments gradually increased over the same period from 33 to 38 percent. In 2014, the proportion of countries where data were provided through collection conducted jointly by governments and UNHCR was 15 percent, while in the remaining proportion (13 percent), refugee data were provided exclusively by NGOs and other organizations. In 2014, more than 173 countries and territories provided data on refugees.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "NGOs. Initiatives such as JIPS, a collaborative project of UN and NGO actors, have also been established to support governments and operational organizations to design and implement data collection processes. There are a variety of data sources for generating statistics on forced displacement, each of which has strengths and weaknesses. Despite the significant challenges, large amounts of data are collected and disseminated every year. The main data sources and methods for the generation of statistics on forcibly displaced populations include: (a) registration of refugees and asylum-seekers; (b) registration of IDPs; (c) profiling of IDPs; (d) population movement tracking systems; (e) national population censuses; (f) sample surveys; (g) border crossings; (h) administrative records and registers; (i) general population registers; and (j) a variety of estimation methods for producing statistics when adequate and reliable data on individuals are unavailable (UNSD 2014). Several of these data sources might be used together to triangulate estimates of stocks and flows for a particular displacement situation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["general population registers", "sample surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some data sources (such as registration systems and population censuses) are more appropriate for estimating stocks of asylum-seekers, refugees and IDPs at a particular point in time, while other data sources (such as population tracking systems and border crossings) are more appropriate for estimating flows over a specific period. In general, there is a lack of comprehensive and up-to-date data available on all stocks and flows for a particular displacement situation (see Table 4). Consequently, data on flows might be used to estimate stocks, for example in the absence of government data, the stock of refugees in many industrialized countries is estimated by UNHCR based on 10 years of individual asylum-seeker recognition. And, especially in the case of IDPs, changes in the total population combined with some contextual analysis, may be used to deduce estimates of new internal displacement or returns. However, these approximations are flawed unless data on all other flows (births, deaths, repatriation etc.) are also available, which is not usually the case. Even a static figure for the stock of IDPs in a particular location might obscure substantial flows including new displacement and returns. Moreover, there are no common definitions of the various stocks and flows, and therefore the risk of double counting or gaps cannot be discounted.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population censuses", "population tracking systems"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For example, IDPs who subsequently cross international borders may be counted as both IDPs and refugees (e. g. in the case of the Syrian displacement crisis). Table 4: Stocks and Flows Stock Increases Decreases Asylum- seekers  New applications for asylum, separately identifying individuals who were previously IDPs  Positive decisions (convention status, complementary protection status)  Rejected  Otherwise closed Refugees  Spontaneous arrivals (group recognition, temporary protection, individual recognition), separately identifying individuals who were previously IDPs  Resettlement arrivals  Births  Administrative corrections  Repatriation  Resettlement  Cessation  Naturalization  Deaths  Administrative corrections IDPs  New internal displacement  Births  Administrative corrections  Cross border flight, becoming an asylum-seeker or refugee  Return  Settlement elsewhere in the country  Local integration  Administrative corrections Source: UNHCR Global Trends, IDMC Forced Displacement Data Model", "output": {"entities": {"named_data": ["UNHCR Global Trends"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Moreover, some refugees may not register because they are unaware that they should, and others may be reluctant to do so because they are skeptical of the integrity of the registration process (e. g. fair access to entitlements or opportunities for durable solutions) or lack confidence in protection measures. Individuals in irregular migration flows may also choose not to apply for asylum due to fear of declaring themselves to the authorities. A significant challenge with refugee registers is keeping them up to date. Individual registration can provide a robust snapshot of the stock of refugees and asylum-seekers, but registers need to be updated regularly to reflect flows, i. e. increases in refugee and asylum-seeker numbers (births, new arrivals) and decreases (deaths, departures, durable solutions). In situations of sudden mass influxes, existing registration capacity may not be adequate and the scope of registration data is then rationalized. 65 Additionally, it may not be possible to capture all demographic changes in the case of highly mobile populations.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee registers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "If registration is linked to the provision of services or other entitlements, there may be strong incentives to register births and new arrivals and weak incentives to deregister, leading to the inflation of the register over time or even instances of fraud and abuse (e. g. multiple registration, “ borrowing children ” etc.) (UNHCR 2003). Consequently, data from a refugee register may overestimate the number of refugees, requiring periodic corrective action through the verification of records. For example, in 2014 a verification of registration records for Somali refugees in the Dadaab camps in Kenya led to the deactivation of tens of thousands of records for individuals that are believed to have returned spontaneously to Somalia (UNHCR 2015). Additional problems with refugee registers include security concerns or inclement weather preventing refugees from accessing registration sites (UNHCR 2003) and the application of data protection principles. Registration of IDPs Individual registration is not as common a method of estimating numbers of IDPs as it is for refugees.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee register"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In principle, individual registration of IDPs is not used to determine the ‘ status ’ of an IDP, since IDPs have the same rights and entitlements as other citizens and do not need to apply or be granted a special legal status. 66 Rather, registration of IDPs can provide a basis for: (a) establishing the number, location, and key demographic characteristics of displaced populations; (b) providing protection and assistance; (c) keeping track of family relationships; (d) preventing fraudulent access to scarce humanitarian assistance; (e) facilitating the issuance of temporary identity cards to replace lost personal documentation (Brookings 2008); and (f) providing social security benefits. 67 Full IDP registration by international organizations is not 62 By the end of 2014, individual refugee registration was the source of about 77 percent of the data on refugees; estimation accounted for 13 percent of data, combined estimation and registration for 5 percent and other sources for 5 percent (UNHCR 2016). 63 UNHCR may undertake registration activities when national governments do not have the capacity to do so. 64 Additional data can also be recorded such as education and occupation. 65 Insufficient budgetary resources, staff, training or materials. 66 Countries with national legislation that provides a legal status for IDPs are an exception to this international standard. 67 The scope of data collected depends on the objectives of the registration exercise, for example in Kenya, registration of individuals displaced by the 2007 and 2008 post-election violence excluded ‘ integrated ’ IDPs, i. e. those who had", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "When national or international actors provide assistance, there may be an incentive for people to register in camps even if they are staying elsewhere, or to register in multiple locations (Brookings 2011). 69 Furthermore, registration data provide only a snapshot of the stock of IDPs at a particular point in time and may be out of date if registers are not maintained regularly. Registration methodologies can vary across displacement situations. For example, families may be registered rather than individuals and the population estimated based on an assumption of average family size, which can differ among organizations (UNSD 2014). IDPs may be required to present documentation, meet specific criteria or re-register periodically to maintain their benefits, which affects aggregate numbers (IDMC 2015). For example, in Ukraine, pre-requisites for IDP registration (including valid documentation, arrival from a recognized conflict zone and permanent residence registration in recognized conflict zone) means that people displaced within a non-government controlled area, people displaced from a non- recognized conflict zone in a government controlled area, unaccompanied children or people without current / valid identification are not counted as IDPs (IDMC 2015).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["registration data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Furthermore, in some contexts, registration as an IDP can expire after a prescribed timeframe without regard to whether the person achieved a durable solution (e. g. after five years in Russia). The accuracy of IDP registers is greatly impacted by political considerations, particularly a government ’ s willingness to acknowledge internal displacement and to enable the humanitarian community to respond. Some countries may be reluctant to acknowledge the presence of IDPs, or may be inclined to understate numbers to demonstrate progress in military operations or limit assistance provided to IDPs. For example, in Kenya, registration of individuals displaced by the 2007 and 2008 post-election violence excluded ‘ integrated ’ IDPs, i. e. those who had sought refuge with host communities or rented accommodation in urban areas, as assistance was limited to registered IDPs. Alternatively, aggregate numbers of IDPs in particular countries may be inflated to suggest a deterioration of the situation or to maximize humanitarian assistance. Therefore, access to IDP areas and the willingness of IDPs to be counted may be largely dependent on government policies. These political considerations can lead to disagreements on the data, undermine cooperation and in some cases even lead to reduced humanitarian funding. Profiling of IDP situations Profiling of IDP situations is a collaborative process aimed at generating reliable data that can be broadly agreed upon. As a collaborative process, it can be a crucial tool for generating agreement on persistent questions such as who is recognized as internally displaced within a given context, what are the most prevalent vulnerabilities caused by displacement, and how do IDPs fare compared to host populations. In sought refuge with host communities or rented accommodation in urban areas, as assistance was limited to registered IDPs. 68 However, in many conflict-affected countries, governments lack the basic capacity to maintain Civil Registration and Vital Statistics (CRVS) systems including the registration of births and deaths in non-displacement situations, let alone the registration of IDPs displaced due to natural disasters or conflict. 69 IOM has introduced biometric registration systems in South Sudan, Sudan, DRC and Nigeria to circumvent these problems.", "output": {"entities": {"named_data": ["Civil Registration and Vital Statistics"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "this way, profiling of IDP situations aims to underpin advocacy, protection and assistance activities as well as support the achievement of durable solutions by informing joint strategies between government, humanitarian and development actors. Profiling provides an overview of displacement-affected populations through the collection and analysis of minimum core data (number of IDPs, disaggregated by location, age and sex) and where possible additional quantitative and qualitative data (causes of displacement, patterns of displacement, protection concerns, humanitarian needs, vulnerabilities, and aspirations and prospects for durable solutions). Profiling may utilize data collection techniques at individual, household and community levels, often combining population estimation methods, a review of secondary data, focus group discussions, household surveys and key informant interviews targeted specifically at forcibly displaced populations (UNSD 2014). 70 Profiling methods focus on displacement situations, rather than only on displaced populations, and therefore includes comparisons to conditions in the host population. IDMC estimates that humanitarian profiling data forms the basis for 18 of their 60 country estimates and around 63 percent of their annual estimates (IDMC 2015), with the largest volume of data on conflict-induced internal displacement provided by OCHA followed by IOM. There are several practical challenges associated with IDP profiling exercises in displacement situations. Insecurity or terrain may impede access to displaced populations in conflict-affected or hard to reach areas.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["humanitarian profiling data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For example, profiling exercises in Pakistan do not cover all IDPs or areas affected by displacement due to insecurity, and in Afghanistan profiling of IDPs by UNHCR underestimates the scale of the initial displacement as IDPs are only interviewed once displacement sites are accessible, if they are profiled at all (IDMC 2015). Additional challenges include unwillingness of IDPs to participate due to fear of persecution, and mobile populations (IDMC 2008). There may also be political pressures to inflate or reduce numbers. Population movement tracking systems In situations where the movement of displaced populations is fluid or continuous, a movement tracking system can be a useful tool for providing rough estimates of population flows, including recurrent displacements. Movement tracking systems are useful for monitoring fluid population movements (including spontaneous and organized, internal and cross-border, and returns and resettlement) in remote or inaccessible routes and locations (including displacement sites, places of origin, and places of return and resettlement). UNHCR, IOM and other organizations have developed methods for tracking and monitoring movements of IDPs in over 30 countries, particularly in cases of disaster-induced displacement, but also in some cases of conflict-induced displacement (UNSD 2014).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These systems employ a combination of data collection techniques including key respondent interviews, focus group discussions, registration, observations and physical counts, samplings and other statistical methodologies. For examples, UNHCR ’ s population tracking systems identifies and trains local NGOs to monitor key locations such as IDP settlements, bus stations and roads to report on movements. The accuracy of data from movement tracking systems is subject to several caveats. These include: limited access to locations and routes due to insecurity; vast geographical areas to monitor; mixed population flows that include refugees, IDPs, pastoral and seasonal movements and economic migrants; massive population flows that overwhelm monitoring capacity; disinclination of individuals to provide information when there is no assistance being offered; pressures from communities to inflate figures to maximize future assistance; and political pressures to suppress accurate reporting on IDP movements. Additionally, due to the fluid nature of displacement in many contexts and the likelihood of recurring displacements, it is not possible to use movement data to provide estimates of population stocks. Population censuses National population and housing censuses often provide the most comprehensive source of population data and offer the potential for estimating numbers of forcibly displaced people. To estimate the size of displaced populations a census would need to include questions on country (and / or place) or birth, year of (internal) 70 Other data collection methods may be used such as movement tracking systems, registration, big data etc.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["movement tracking systems", "population tracking systems"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "migration, and reason for movement including asylum / refugee protection (or conflict-induced internal migration) (UNHCR 2016), or a specific question to identify IDPs or refugees. However, not all censuses cover refugees and asylum-seekers (if foreigners are considered outside the scope of the census or because they are considered a special category), 71 nor is it common practice for national censuses to include questions related to forced displacement. 72 Nevertheless, there are several examples of national censuses that have included relevant questions on forced displacement. 73, 74 In the case of protracted internal displacement situations, IDPs are likely to be included in national censuses; however, census instruments may be subject to manipulation for political purposes. There are several drawbacks of population censuses including their cost, the significant training required for enumerators to ensure consistent answers to questions on forced displacement, impediments to field operations and data processing (such as weather conditions and technical problems), the relative infrequency with which they are carried out, and the long processing time before data and statistics become available, which have consequences for the timeliness of data. Moreover, often censuses are not conducted in contested territory or conflict zones where many displaced persons reside, and this limits the completeness of the data.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population censuses"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Nevertheless, censuses are the only nation-wide source of population data for potentially providing estimates of displaced populations, especially those in non-camp settings, and often provide a basis for sampling frames for survey instruments (UNHCR 2016). Capturing displacement situations in official statistics also increases their visibility. Sample surveys Sample surveys can potentially provide a rich source of data on forcibly displaced populations. Compared with censuses and registers, sample surveys allow more detailed questions to be asked about the characteristics and situations of households. If survey instruments identify displaced populations based on individuals ’ and households ’ self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status (UNSD 2014). There are opportunities to mainstream forced displacement into international survey instruments, but this has only been done in a handful of cases. Several standardized international sample surveys have been designed for special purposes including the Living Standards Measurement Study (LSMS), 75 Labor Force 71 The UN census recommendations for the 2010 World Population and Housing Census Programme stipulate that refugees and IDPs living in camps should be counted and their numbers disaggregated in population statistics, however there is no requirement to separately distinguish displaced people living outside of camps (UNHCR 2016).", "output": {"entities": {"named_data": ["World Population and Housing Census Programme"], "descriptive_data": [], "vague_data": ["population statistics"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The United Nations Economic Commission for Europe ’ s (UNECE) guidelines include a question on reason for migration, population with a refugee-like background and IDPs as non-core topics / questions (UNHCR 2016). 72 While most countries include questions on country of birth and citizenship, only about 40 percent include a question on year of migration, less than a quarter include a question on reason for international migration, and about a fifth include a question on the reason for internal migration (UNHCR 2016). 73 E. g. Kyrgyz Republic 1999 (refugee status), West Bank and Gaza 2007 (refugee status), Zambia 2000 and 2010 (purpose of stay), Germany 1970 (federal refugee identity card), Greece 2001 (reason for settling in Greece), Sudan and South Sudan 2008 (type of household including IDP and refugee), Liberia 1990 (ever displaced by war since 1990), Uganda 2014 (refugees). 74 UNHCR is collaborating with the Statistics Norway on systematically embedding forcibly displaced peoples in national statistics exercises and collaborates with national authorities and with UNFPA in various countries on the design of census exercises that include refugees, IDPs, returnees and stateless persons. 75 LSMS is a household survey program housed in the Bank's Development Research Group that provides technical assistance to national statistical offices in the design and implementation of multi-topic household surveys covering household behavior, welfare and interactions with government policies. All data gathered through the LSMS is published online in the Bank ’ s Central Microdata Catalog.", "output": {"entities": {"named_data": ["LSMS", "Central Microdata Catalog"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Surveys, 76 Demographic and Health Surveys (DHS), 77 and Multiple Indicator Cluster Surveys (MICS). 78 The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner (UNSD 2014). There are only a few cases where modules or questions on forced displacement have been integrated into survey instruments (most notably in Somalia, Uganda, West Bank and Gaza, Azerbaijan, Bosnia and Herzegovina, Serbia, Ghana, as well as health surveys in Albania, Ukraine and Moldova). There are several challenges associated with ‘ mainstreaming ’ forced displacement into household surveys: (a) there is huge demand for adding sector-specific or thematic modules to international surveys; (b) it is relatively difficult to convince national statistical agencies to modify their county-specific surveys; (c) disaggregating survey results by specific vulnerable groups (e. g. refugees, IDPs, migrant populations) requires these distinctions to be integrated into the sampling frame and sometimes there is insufficient information to do this or a lack of resources to expand the sample size; (d) lack of access to displacement- affected areas; and (e) difficulties associated with integrating an inherently political topic into less controversial surveys.", "output": {"entities": {"named_data": ["Multiple Indicator Cluster Surveys", "Demographic and Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Border crossings The registration of people crossing internationals borders is conducted in many countries, and in some cases these data are used to estimate migration flows. Identifying refugees among people crossing borders is a significant challenge, particularly if individuals decide not to apply for asylum or refugee status (UNSD 2014). Additional problems associated with the collection of data on border crossings include: (a) difficulties distinguishing migrants from other people crossing a border, such as tourists, commuters, traders and truck drivers; (b) lack of capacity of many border posts and officials to handle large migration flows; (c) less scrutiny and diligence of emigration flows compared with immigration flow; and (d) lack of tight controls at most borders and the high incidence of undocumented or irregular crossings (UNSD 2014). Administrative records and registers Many countries have administrative records or registers of immigrants that could generate statistics on asylum-seekers and refugees. In particular, data on residence permits issued to refugees or asylum- seekers could be used to generate statistics on both flows and stocks of refugees. 79 For example, Eurostat collects and disseminates data on residence permits granted to those with refugee status and subsidiary protection (UNSD 2014).", "output": {"entities": {"named_data": [], "descriptive_data": ["data on residence permits"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "General population registers In a small but growing number of countries, information from the central population register is the main source of migration statistics. 80 While population registers may generate statistics on both internal and international migration (if they record changes of residence, and international arrivals and departures) they do not typically record reasons for movement. However, it may be possible to link data from the central population register to those from immigration or border authorities to identify refugees and asylum- 76 Using standard ILO definitions, Labor Force Surveys collect data on work-related issues and provide a basis for measuring employment and unemployment indicators. They are typically conducted monthly in developed countries and quarterly or annually in developing countries. 77 Supported by USAID and implemented by ICF International, the DHS Program has collected, analyzed and disseminated data on population, health, HIV and nutrition through more than 300 surveys in over 90 countries. 78 MICS is an initiative of UNICEF that assists countries in collecting and analyzing health and education data in order to fill data gaps for monitoring the situation of children and women. 79 Many refugee hosting countries issue a form of identification, either specific to refugees or based on national identification documents or those issued to non-national residents. In many cases where such documents are not issued, refugee identity cards are issued in collaboration with UNHCR. 80 A population register provides a mechanism for the continuous recording of selected data on the resident population including a unique identification number, date of birth, sex, marital status, place of birth, place of residence, citizenship and language and possibly also socio-economic data, such as occupation or education.", "output": {"entities": {"named_data": ["Labor Force Surveys", "DHS Program"], "descriptive_data": ["central population register"], "vague_data": ["national identification documents", "population register"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "seekers. 81 There are several challenges associated with using central population registers to estimate refugee and asylum-seeker populations, including: consistency of the definition of refugees with the definition in the 1951 Convention and 1967 Protocol; difficulty and cost of establishing and maintaining a population register (UNSD 2014); and confidentiality safeguards. Compilation of statistics on forcibly displaced populations Several international organizations are involved in the compilation, analysis and dissemination of statistics on forced displacement including UNHCR, 82 Eurostat, IDMC, OCHA, International Committee of the Red Cross (ICRC), 83 WFP84 and IOM. Each of these actors has their own thematic focus and specific objectives, and applies their own methodologies. Asylum-seekers and refugees UNHCR is the principal organization responsible for the compilation, analysis and dissemination of data on asylum-seekers and refugees. UNHCR maintains a publicly available statistical online database85 with data for the period 1951-2014 on refugees (including people in refugee-like situations), asylum-seekers (pending cases), returned refugees, IDPs protected or assisted by UNHCR, returned IDPs previously protected or assisted by UNHCR, stateless persons and others of concern to UNHCR, disaggregated by country of origin and asylum. 86 Data are also provided on demographics, location, asylum-seekers (refugee status determination and monthly data) and resettlement.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["central population registers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "UNHCR regularly publishes statistical reports, including “ Global Trends ”, “ Mid-Year Trends ”, “ Asylum Trends ” and “ Statistical Yearbook ”. Additionally, UNHCR hosts interagency information sharing portals for significant emergencies. 87 These portals provide data on populations of concern at regional and country levels, including time series data, demographics, location and accommodation information. 88 Eurostat compiles and publishes data on asylum (applications and decisions) and managed migration in European Union member countries. Countries and national and international NGOs also publish these statistics, based on sources of various completeness, quality and timeliness (UNSD 2014). There are sometimes substantial inconsistencies between the numbers published by different organizations for the same country, including high-income countries with good statistical systems, usually due to differences in definitions, times and statistical methods, including the mixing of data on flows and stocks (UNSD 2014). There are several challenges associated with the compilation of data on asylum-seekers and refugees.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Significant among these are: (a) the lack of capacity of national statistical agencies in many developing countries to collect robust data on refugees; (b) weak or incomplete monitoring of refugees dispersed within host communities; (c) lack of capacity to maintain up to date information on refugees (reflecting new arrivals, 81 General population registers may also provide opportunities for more elaborate analysis of the integration of refugees in asylum countries, as the data could be linked to other administrative registers, for example on labor and education (UNSD 2014). 82 UNHCR collects, compiles and publishes data on asylum-seekers, refugees and IDPs protected or assisted by UNHCR, including populations in refugee-like or IDP-like situations. 83 Established in 1863, the ICRC ’ s mission is to ensure humanitarian protection and assistance for victims of armed conflict and other situations of violence.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["General population registers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ICRC ’ s work is based on the Geneva Conventions of 1949, their Additional Protocols, its Statutes — and those of the International Red Cross and Red Crescent Movement — and the resolutions of the International Conferences of the Red Cross and Red Crescent. 84 WFP is the food assistance branch of the United Nations and the world's largest humanitarian organization addressing hunger and promoting food security. 85 See: popstats. unhcr. org. 86 IDP data are only included from 1998 onwards. 87 See: http: / / data. unhcr. org. Currently the Burundi situation, Yemen (regional refugee and migrant response plan), DRC regional refugee response, Mediterranean (refugees / migrants emergency response), CAR, Côte d ’ Ivoire, Syria Emergency, Sahel Emergency, South Sudan Situation, Horn of Africa Emergency, and the Liberia Portal. 88 IOM ’ s new Global Migration Data Analysis Centre provides limited data on global migration trends such as data on asylum application in Europe and selected countries (including demographics, country of origin, and country of asylum).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["IDP data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Consequently, data may be aggregated across situations and countries even though the source data were generated for different purposes and using different methodologies. (b) Very few national governments collect or report up-to-date data on IDPs. Political will as well as the resources and capacities to carry out effective and timely data collection vary across countries. In 2015, only five governments responded to IDMC requests for data. 90 Consequently, data on internal displacement is outdated in several countries and is at risk of becoming outdated in others, including countries like Afghanistan with large IDP populations (IDMC 2016). 91 Problems of outdated and ‘ decaying ’ data are especially problematic in protracted displacement situations — international organizations reallocate resources to more visible or pressing displacement crises (IDMC 2016). (c) Limited official standards and guidance on how to collect data on IDPs in the field. IDMC may rely on more than one source in some countries (each gathering data for different purposes and using different definitions and methodologies with little or no coordination) and so double counting and gaps cannot be excluded (IDMC 2015).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "IDMC reported that changes in the way their sources collect and analyze their data led to dramatic adjustments in 2014 figures, e. g. in Côte d ’ Ivoire a profiling exercise led to a four-fold increase in IDMC ’ s estimate, and in Nigeria improvements in the national capacity to collect information led to a 70 percent decrease in IDMC ’ s estimate (IDMC 2015). (d) The lack of complete data for most countries. The fluidity of population movements, insecurity and other access restrictions (lack of transport infrastructure, high logistical costs and government restrictions) make primary data collection almost impossible in many areas (IDMC 2015). Consequently, data collectors often focus on IDPs in relatively stable, secure and accessible places (e. g. camps) and estimations in the most difficult areas rely on ‘ local informers ’ (e. g. local authorities, NGOs etc.) who may or may not have the capacity to provide adequate numbers, or which leaves data collection and reporting subject to the influence of parties to the conflict (IDMC 2016). Figures do not always capture ‘ invisible ’ IDPs that are living in individual accommodation dispersed in host communities, and therefore figures are 89 The statistics of UNHCR on IDPs are limited to countries (numbering 24 in 2013) where the organization is engaged in assisting or protecting IDPs. 90 Azerbaijan, Bosnia and Herzegovina, Georgia, Ireland and Mexico (IDMC 2016) 91 IDMC notes that outdated or ‘ decaying ’ data are a problem in 12 of the 53 conflict- or violence-affected countries it monitors (Armenia, Bangladesh, Congo, Cyprus, Guatemala, Macedonia, Nepal, Papua New Guinea, Thailand, Togo, Turkey and Uganda), accounting for approximately 20 percent of IDPs worldwide (IDMC 2016).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "IDMC reports that of the 52 countries it monitored in 2015, it was only able to obtain data on new displacements in 20 countries, 92 on returns in 20 countries, on integration in one country, on resettlement in two countries, on children born in displacement in two countries and on deaths in one country; no data was obtained for any county on cross-border flight in 2015 (IDMC 2016). Moreover, existing systems for collecting data on refugees and asylum-seekers make it difficult to know how many were formerly IDPs, and it is possible for some people to be simultaneously counted in both categories, e. g. in the case of the Syrian displacement crisis (IDMC 2016). If no data on returns are available, IDMC risks overstating the number of IDPs (IDMC 2015).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Disaggregated data on IDPs who are not protected or assisted by UNHCR are collected by other agencies, including IOM, but data are not comprehensive and therefore not published in IDMC ’ s global reports. 93 The difficulties of collecting disaggregated data on locations of IDPs are compounded by the fluidity of IDP movements — IDPs might suffer multiple displacements or they might resort to changing locations as a coping strategy (e. g. moving between their homes and place of displacement or testing different locations before deciding where to stay) (Brookings 2011). In recent years, efforts have been made to improve data collection for IDPs living outside of camps by employing a range of techniques including: (a) profiling; (b) household surveys; (c) collecting information on IDPs who come to camps to visit family members or collect relief items; and (d) community outreach programs (Brookings 2013).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Disaggregated data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, there is no consensus or agreed best practices on the use of these methods in different contexts or stages of displacement (Brookings 2013). 92 The absence of data on new displacement may simply mean that no displacement has taken place (IDMC 2016). 93 IDMC reports that data, disaggregated by age and sex, were available for 15 of the 60 countries it monitored in 2014, however these data were not comprehensive and are not published. Additionally, in some countries there are data provided by IOM on IDP populations by location from which the urban or rural character of the population may be inferred (e. g. if the camp is located in the capital), but data are not comprehensive and not published. While the majority of humanitarian profile data does not typically cover IDPs living outside of camp or camp-like settings (the large majority of IDPs), IOM ’ s DTM in countries such as Nigeria, Iraq, Yemen and Libya do include information about those residing in host communities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["humanitarian profile data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 5: Coverage of Published Data on Location, Accommodation and Demographics 2015 Population end- 2015 (millions) Urban or Rural Location Accommodatio n Age Sex Refugees and people in refugee-like situations 16. 1 85 % 83 % 58 % 68 % Other people of concern to UNHCR 47. 8 (including 37. 5 IDPs) 70 % 47 % 25 % 39 % UNHCR ’ s total population of concern 63. 994 73 % 56 % 33 % 46 % IDPs monitored by IDMC but not included in UNHCR's data 3. 3 0 % 0 % 0 % 0 % Source: UNHCR Global Trends 2015 Note: Other people of concern to UNHCR include asylum-seekers, IDPs and people in IDP-like situations protected or assisted by UNHCR, stateless persons, and ‘ other ’. Overall robustness of current data The robustness of data is difficult to estimate. A review of data collection and compilation methodologies shows broad variations in terms of the accuracy and reliability of the global estimates of forced displacement that are widely used. Headline figures on forced displacement are significant in shaping public opinion and are critical for sound decision making, both to inform the allocation of resources and to design effective humanitarian and development responses. However, the available estimates are potentially misleading and should not be referred to without appropriate caveats and qualifiers.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": " In Somalia, the Mogadishu Household Survey covered both residential areas and IDP camps, collecting data on expenditures, demographics and living conditions [survey completed; analysis forthcoming]. As part of the Somalia Knowledge for Operations and Political Economy (SKOPE) initiative, the Puntland Household Survey will also cover both residential and IDP populations [ongoing].  An IDP study in South Sudan [ongoing] aims to assess the economic needs of IDPs and host communities in urban areas, covering livelihoods, water and sanitation, infrastructure as well as intentions and conditions to return. Basic information about education, employment and general health variables will also be collected.  The Iraq Crisis Response Study [ongoing] will assess the impact of the Islamic State and oil price-related crises on IDPs and households left behind in IS controlled areas.", "output": {"entities": {"named_data": ["Iraq Crisis Response Study", "Mogadishu Household Survey", "Puntland Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The study will conduct an ex-ante micro- simulation using pre-crisis household data (IHSES 2012) and macroeconomic projections for 2014 to gauge the distributional impact of the crises across groups (e. g. individuals and / or households, sectors, IDPs and host communities) and space (e. g. urban / rural, governorates).  The Economic and Social Impact Assessment for Kurdistan Region of Iraq [completed in 2015] provides an analysis of the impact of displaced people on access to and quality of service delivery across several sectors.  The Lebanon Economic and Social Impact Assessment of the Syria Conflict [completed in 2013] provides an analysis of the impact of displaced people on access to and quality of service delivery across several sectors.  The Bank and UNHCR undertook a welfare assessment of Syrian refugees living in Jordan and Lebanon [completed in 2016] focusing on welfare, poverty and vulnerability.", "output": {"entities": {"named_data": ["IHSES 2012"], "descriptive_data": ["pre-crisis household data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The study: (i) compares the socio- economic profile of refugees with that of the Syrian population before the crisis and with the hosting populations of Jordan and Lebanon; (ii) provides a welfare and vulnerability assessment of Syrian refugees including a poverty profile, the socio-economic characteristics of higher poverty and where pockets of deep poverty are located; (iii) analyzes key drivers of welfare and poverty; and (iv) models monetary and non- monetary vulnerability.  In Lebanon, Jordan and Iraq, the Bank is leading an initiative to evaluate the socio-economic impact of the regional crises on the welfare of Syrian refugees and host communities in neighboring countries [ongoing]. Data on living conditions, access to services and economic opportunities, coping strategies and economic status are to be collected via a specialized household survey and a sub-component of the survey will be carried out on a semi-annual basis to continue to monitor and adapt support as needed.  A recent Bank paper, “ Turkey ’ s Response to the Syrian Refugee Crisis and the Road Ahead ” [completed in 2015] assessed the impact of Syrian refugees on host areas in various sectors. It found that the presence of Syrian refugees is placing a strain on municipal services, housing rental markets, social relations, and education services for Turkish households.", "output": {"entities": {"named_data": [], "descriptive_data": ["specialized household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Impacts on the labor market are unclear; initial research suggests that there has been a supply shock to informal labor markets. This has had a large-scale impact on the employment of natives in the informal sector. At the same time, research suggests there has been a boost to formal employment for the Turks, but this has been uneven: the low educated and women experience net displacement from the labor market and, together with those in the informal sector, declining earning opportunities.  The “ Socio-economic Assessment of the Impact of Syrians under Temporary Protection (SuTPs) on Turkish Hosting Communities ” [ongoing], to be undertaken in partnership with the Government of Turkey, will include a nationally representative household survey with SuTP and local Turkish households including camp and non-camp environments. The questionnaire will cover welfare (assets, income, expenditure), municipal services, labor and employment, education, social networks and quality of life. F. Options to improve forced displacement statistics Significant efforts are needed to enhance the reliability, comparability, quality and scope of the global data on forced displacement. In particular, more robust estimates are needed of the scale (stocks, flows and locations) and typology (demographics, location and accommodation) of forced displacement crises. This requires substantial improvements in the rigor of data collection and compilation methodologies including: (a) Harmonization of definitions and methodologies used in the collection and analysis of statistical data on forced displacement — covering stocks and flows of refugees, asylum- seekers and IDPs — to ensure comparability across regions and countries;", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(b) Disaggregation and geo-mapping of data by location (current location and location of habitual residence; urban, peri-urban or rural location), accommodation (organized camp versus non- camp), and demographics (age and sex); (c) Expanded coverage of data collection exercises to include all areas of affected countries (security permitting); (d) Improved coverage and detailed data on displaced populations living outside of organized camps; (e) Improved coverage of ‘ flows ’, i. e. new displacement, durable solutions (returns, integration, resettlement), births, deaths, and in the case of IDPs, the numbers that flee across international borders becoming refugees; (f) Systematic data collection beginning from the earliest moment following displacement, following up as populations disperse, and continuing until sustainable / durable solutions have been achieved; and (g) Better aggregation, analysis and presentation of forced displacement data currently compiled separately by UNHCR, IOM, IDMC and UNRWA. Additional efforts are required to address the gaps in the data required for development policy and planning. These data are critical for informing the design of development policies and assistance programs.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["forced displacement data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Survey instruments enable detailed questions to be asked about the characteristics and situations of households, and if they identify displaced populations based on self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status. Additionally, more innovative tools and technologies for data collection, analysis and compilation should be explored and leveraged. For example, new methodologies (such as high resolution satellite imagery and unmanned drones) may expand the coverage of data collection efforts in insecure or inaccessible areas. Additionally, new techniques could be explored to improve the collection of robust data on flows of refugees and IDPs. Organizations such as the World Bank, UNHCR, IOM and IDMC are already exploring and in some cases are beginning to use more innovative data collection tools. These techniques include: 99 Several standardized international sample surveys have been designed for special purposes including Living Standards Measurement Studies, Labor Force Surveys, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys. The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner", "output": {"entities": {"named_data": ["Labor Force Surveys", "Multiple Indicator Cluster Surveys", "Demographic and Health Surveys", "Living Standards Measurement Studies"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(a) High frequency sample surveys using smartphone and cellular technologies. For example, a high-frequency survey initiative in Somalia employs a dynamic questionnaire loaded onto smartphones, which enables data to be collected from household interviews in 60 minutes. This approach was developed to overcome the challenges of insecurity, limited data gathering capacity and budgetary constraints. (b) The use of mobile phones to conduct surveys or follow up interviews following face-to-face household surveys. For example, a Bank paper on the impact of the 2012 crisis in Mali on IDPs, refugees and returnees used information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones. This combination provided a mechanism to monitor the impact of conflict on hard-to-reach populations who at times live in areas inaccessible to enumerators. And in Sierra Leone and Liberia, the Bank supported the use of mobile phones to collect key socio-economic data on the effects of the Ebola virus. (c) Crowdsourcing data on displacement. Platforms such as the Kenyan Ushahidi has crowd- sourced data on displacement in Kenya and eastern DRC by encouraging IDPs and host communities to report incidents using their mobile phones or the internet, including information about living conditions. The platform references these reports geo-spatially. (d) Geo-mapping of data on displaced populations and affected host communities.", "output": {"entities": {"named_data": [], "descriptive_data": ["face-to-face household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Geographic Information Systems (GIS) and geospatial analysis can be used to map, monitor and analyze data on forced displacement. Triangulation of this information with socio-economic and other indicators can provide a rich source of data and enable insights into underlying patterns and trends over time. (e) Use of big data (mobile phone data, news scraping, social media). IDMC is pursuing big data approaches to capturing displacement data in real time in order to report on displacement situations as they are happening and to provide updates on how they are evolving (IDMC 2015). These data are not necessarily representative but can be used in conjunction with other methods to triangulate trends. For example, the Swedish NGO, Flowminder, has pioneered the use of de-identified data from mobile operators to track population displacement caused by natural disasters such as earthquakes in Haiti in 2010 and Nepal in 2015, and these techniques may also have applications in conflict-induced displacement crises. 100 (f) High-resolution satellite imagery and unmanned drones. High resolutions satellite imagery can be used to map physical structures in refugee and IDP camps including changes to the number and type of these over time, support the remote detection of displaced populations in hard to reach or insecure settings; and conduct rapid assessments during or immediately after a mass displacement (Harvard Humanitarian Initiative 2014).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["de-identified data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In settings with clearly distinguishable individual structures, these methods can also be reasonably accurate for the purposes of rapid estimation of displaced population (Checchi, et al. 2013). 101 The use of unmanned drones is also becoming more popular as the cost of this technology falls. This technique has been used by UNHCR to update its estimates of IDPs in Somalia ’ s Afgooye corridor (IDMC 2015) and by IOM to monitor disaster-induced displacement in Haiti, including the use of Unmanned Aerial Vehicles (UAVs) in collaboration with UNOSAT. (g) Open data initiatives. There are several initiatives to provide free and open data that enable Internet users to independently mine and analyze data and generate customized summaries, charts and visualizations. For example, the Bank has provided free, open access to its development data since the launch of its Open Data Initiative in 2010, however there is little open data on asylum-seekers, refugees and IDPs. JIPS has developed a web-based platform that allows users to explore, analyze and visualize profiling data online, and IDMC has 100 See http: / / www. flowminder. org /. 101 These methods are not effective in settings with connected structures, a complex pattern of roofs or multi-level buildings, as are prevalent in urban areas. Additionally, cloud cover and dense foliage can also obscure structures.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "recently introduced the first version of their Global Internal Displacement Database (GIDD) that allows users to explore, filter and sort IDMC ’ s data to produce graphs and tables, and export underlying data. 102 Such platforms need to incorporate safeguards to protect the privacy and confidentiality of individuals ’ data. UNHCR and Statistics Norway are currently leading an initiative to improve forced displacement statistics with the participation of national statistical agencies. This process began with the presentation of the “ Report on Statistics on Refugees and IDPs ” at the 46th session of the UN Statistical Commission in March 2015, 103 followed by an international conference in Turkey in October 2015. 104 The conference set in motion a process for national statistical agencies to collaborate to develop a set of recommendations that both countries and international organizations can use to improve data collection, reporting, data disaggregation, and overall quality, including the preparation of International Recommendations for Refugee Statistics (IRRS). Progress on this agenda was discussed at the 47th session of UNSD held in New York in March 2016, where it was recommended that the expert group should also include IDPs in its scope of work (UNSD 2016). 105 The current initiative is focused on refugees, asylum-seekers and IDPs but would ideally be extended to host communities and returnees.", "output": {"entities": {"named_data": ["Global Internal Displacement Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Institutional arrangements would need to delineate data collector and data compiler roles, as well as reflect the following principles to ensure sustainability: (a) Pursue additional activities within the overall framework of existing initiatives to ensure coherence with the activities of other actors. (b) Primary responsibility for data collection rests with national statistical agencies (with adequate arrangements in the case of IDPs to mitigate political risks). (c) Definitions and methodologies should be harmonized across countries, through a process managed under the auspices of the UN Statistical Commission. (d) Agencies such as UNHCR and IDMC can play a leading role in ensuring quality, providing technical assistance as may be needed, and aggregating data for global analyses. 102 See http: / / www. internal-displacement. org / database. 103 See http: / / unstats. un. org / unsd / statcom / doc15 / 2015-9-RefugeeStats-E. pdf. 104 See conference documentation at http: / / www. efta. int / seminars / refugee. 105 See http: / / unstats. un. org / unsd / statcom / 47th-session / documents / 2016-14-Refugee-statistics-E. pdf.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 7825 This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at assaad @ umn. edu. The impact of the growth of the local supply of public schools in the post-Colonial period on intergenerational mobility in education is a first-order question in the Arab World.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This question is examined in Jordan using a unique dataset that links individual data on own schooling and par­ents ’ schooling for adults, from a household survey, with the supply of schools in the subdistrict of birth at the time the individual was of age to enroll, from a school census. The identification strategy exploits the variation in the supply of basic and secondary public schools across cohorts and subdistricts of birth in Jordan, controlling for year and subdistrict-of-birth fixed effects and interactions of gov­ernorate and year-of-birth fixed effects. The findings show that the local availability of basic public schools does, in fact, increase intergenerational mobility in education. For instance, a one standard deviation increase in the supply of basic public schools per 1, 000 people reduces the father- son and mother-son associations of schooling by 18 – 20 percent and the father-daughter and mother-daughter asso­ciations by 33 – 44 percent. However, an increase in the local supply of secondary public schools does not seem to have an effect on the intergenerational mobility in education.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey", "school census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 Over the past three or four decades, the Arab world has experienced a massive expansion in educational attainment. According to the Barro and Lee educational attainment dataset, seven out of the top 20 countries in terms of increase in number of years of schooling from 1980 to 2010 were Arab countries (Barro and Lee 2013). 1 Jordan, the subject of this paper, had the seventh highest increase in educational attainment in the world, with an increase of about five years in the average years of schooling over the period. This increase is widely believed to be attributable to a massive public investment in the supply of schooling in the postindependence period in the context of a state-led development model, which virtually guaranteed employment in the public sector for graduates (Assaad 2014; Saleh 2016). The rapid increase in educational attainment has continued unabated despite the fall in returns to education that accompanied the demise in the state-led model and its employment guarantee schemes (Pritchett 2001). A slew of recent literature on the drivers of the Arab Spring protests, some of which occurred in Jordan, has identified the low economic returns to this massive increase in education as the single most important cause of the uprisings (Goldstone 2011; Campante and Chor 2012a, 2012b, 2014; Sanborn and Thyne 2014).", "output": {"entities": {"named_data": ["Barro and Lee educational attainment dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Another way to view the connection between low return on education and youth frustrations is that the rapid intergenerational mobility in education has failed to yield similar mobility in either income or social status. This delinking between educational and occupational mobility has been documented for Egypt by Binzel and Carvalho (2013). While there is no similar work on Jordan, this article contributes to this agenda by documenting the first step in this process, which is the link between public investment in schooling and the educational mobility across generations. 1. This is based on version 2. 0 of the Barro-Lee dataset for educational attainment among the total population 15 and older.", "output": {"entities": {"named_data": ["Barro-Lee dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 The article employs a unique data source from Jordan, the 2010 Jordan Labor Market Panel Survey (JLMPS 2010), which includes information on parents ’ schooling for every adult in the sample, along with the 2010 School Census produced by the Jordanian Ministry of Education (Hashemite Kingdom of Jordan, 2010). The school census provides the subdistrict, type, and date of establishment of every school in Jordan, allowing us to measure the local supply of each type of schools in each subdistrict in every year (under the presumption that there were no significant school closures or changes in type over time, which is likely the case). The exposure of an individual in the JLMPS 2010 sample to the supply of public schooling is then determined by the number of sex-appropriate basic (or secondary) public schools (per 1, 000 individuals) that were available to them in their subdistrict of birth at the time they were of age to enroll in that school level (six years of age for basic and 15 years for secondary). The richness of the data set makes it the first in the Middle East to allow such a study.", "output": {"entities": {"named_data": ["2010 School Census", "2010 Jordan Labor Market Panel Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 One could think of Jordan ’ s growth of local supply of public basic and secondary schools as a progressive public investment in human capital that increases the human capital production for children of marginal parents in terms of income and educational attainment. Those are parents who would have chosen higher investment in the human capital of their offspring, but were constrained by the limited supply of schools in their subdistricts of residence and could not afford to send their children to more distant schools outside their jurisdiction or to provide them with homeschooling. However, the increase in public schools is expected to have less of an effect on richer or more educated parents, who are expected to provide education to their children regardless of the availability of schools in their subdistricts either by sending their children to distant schools or through homeschooling. On average, however, the increase in the local supply of public schools is expected to reduce the intergenerational correlation of educational attainment or enhance intergenerational educational mobility. III. DATA Two new and unique data sources are employed in the empirical analysis. First, the Jordan Labor Market Panel Survey of 2010, carried out by the Economic Research Forum in cooperation with the Jordanian Department of Statistics, is a rich source of information on all aspects of the Jordanian labor market (JLMPS 2010).", "output": {"entities": {"named_data": ["Jordan Labor Market Panel Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Most importantly for the purpose of the analysis is the fact that the survey provides individual-level data on own schooling and parents ’ schooling for all adults in the sample, which is quite rare in household surveys from developing countries. Also, the survey provides the actual years of schooling completed and not only the highest educational degree attained, which allow observing the schooling variable with precision.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Second, each individual in the JLMPS restricted sample is matched to the 2010 Jordanian school census. The matching process determines for each individual the number of sex- appropriate public basic and secondary schools per 1, 000 individuals available in the individual ’ s subdistrict of birth when the individual was of age to accede to this educational level (six years of age for the basic level and 15 years of age for the secondary level). 7 A school is considered sex-appropriate for a female if it is a girls ’ or a mixed school and for a male if it is a boys ’ or mixed school. The empirical analysis is also performed by entering boys ’, girls ’, and mixed schools separately. 8 Measuring the local supply of public schools at the subdistrict of birth of the individual (i. e., the child) mitigates potential endogeneity originating from parents who had a higher taste for schooling moving to subdistricts where public schooling was more abundant when their child was of school age, although it is not possible to rule out that parents might have moved across subdistricts prior to the birth of their child.", "output": {"entities": {"named_data": ["JLMPS restricted sample", "2010 Jordanian school census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The JLMPS sample is restricted, after matching it to the 2010 school census, to individuals born in Jordan who are aged 25 to 70 in 2010 and who have nonmissing information on age, subdistrict of birth, years of schooling, father ’ s schooling, mother ’ s schooling, and local supply of schools in subdistrict of birth. 9 These exclusions resulted in a sample of 4, 139 males and 4, 131 females, which are referred to as the male and female full samples, respectively. 7. Because of the absence of annual estimates of subdistrict populations, the population used to normalize the supply of schooling at the subdistrict level is the 2004 population of the subdistrict. There are 86 subdistricts in Jordan. If subdistrict populations are growing at different rates, this could introduce some measurement error of the true supply of schooling available to different cohorts. 8. Secondary schools include both general and vocational secondary schools. Public schools include schools under the jurisdiction of: (i) Ministry of Education, (ii) Ministry of Higher Education, (iii) Ministry of Defense, (iv) Ministry of Social Development, (v) Ministry of Religious Endowments (Awqaf), and (vi) UNRWA. 9. The original sample size of all individuals who are aged 25 to 70 years in 2010 and are born in Jordan is 8, 312 observations. The sample restrictions on the missing values result in the exclusion of 34 observations (missing age), 1 observation (missing father ’ s schooling), and 7 observations (missing mother ’ s schooling).", "output": {"entities": {"named_data": ["JLMPS", "2010 school census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "23 and females. 10 The results are shown in table S. 1 in the supplemental appendix. The baseline effects of basic schools on educational attainment and their effects on mobility are stronger and often more significant than those estimated in table 4 for both males and females. A second concern is that Jordan received a large influx of Palestinian refugees in the aftermath of the 1967 Arab-Israeli War. While refugees benefited from the UNRWA basic schools, their educational attainment and intergenerational educational mobility were perhaps subject to a different set of constraints than those facing other Jordanians. Thus, as a robustness check, individuals who are likely to be Palestinian refugees were excluded from the sample. Since the JLMPS 2010 does not allow directly identifying Palestinian refugees who are now mostly Jordanian citizens, two indirect methods were employed to identify individuals who are likely to be Palestinian refugees. Method 1 excludes individuals born in subdistricts where the percentage of individuals who were ever enrolled (or are currently enrolled) in an UNRWA school exceeds ten percent out of all individuals below 36 years of age in the sample. Method 2 excludes individuals born in subdistricts where the percentage of UNRWA schools exceeds ten percent of the total number of schools. The results for the restricted sample according to both methods are shown in tables S. 2 and S. 3 respectively.", "output": {"entities": {"named_data": ["JLMPS 2010"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Overall, the results remain unchanged from those in table 4. A third concern is that the samples of males and females that are employed in the analysis may include adult siblings who belong to the same household. This could introduce intra- household serial correlation among these observations. Thus, as a robustness check, the sample is restricted to males and females who are household heads or their spouses, hence excluding adult 10. This results in excluding 381 “ movers ” among males and 371 “ movers ” among females, or roughly 9 percent of the original samples.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 8012 This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at jhoogeveen @ worldbank. org or at mariacristina. rossi @ econ. unito. it or ds1289 @ georgetown. edu. This paper uses a unique data set to analyze the migration dynamics of refugees, returnees, and internally displaced people during the Northern Mali conflict.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 3. Data The data used in this paper have been collected through the Listening to Displaced People Survey (LDPS). 4 The baseline face-to-face interviews were executed between June and August 2014. The following 12 monthly interviews – from August 2014 until August 2015- were conducted using mobile phones. 5 The original sample comprised 501 respondents (51 % Male, 49 % Female) and was divided between internally displaced people (IDPs) located in the capital city Bamako, 6 refugees living in refugee camps in Mauritania and Niger, as well as returnees living in the regional capitals Gao, Timbuktu and Kidal in Northern Mali. This survey did not collect information on individuals who were never displaced. The attrition rate was very low, always around 1-2 % per wave. We need to stress that the locations were not randomly selected. Bamako was selected because it hosted a large number of IDPs. Furthermore, the main cities in the north of Mali were chosen to obtain a large sample of returnees given the funds available. Finally, a refugee camp was located in Niger since bureaucratic issues did not allow the inclusion of a camp in Burkina Faso. Nevertheless, households were selected randomly within each location.", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Having returned (early) to Northern Mali did not always lead to a stable condition either: the transition probabilities from returnee to IDP or to refugee are 1. 1 % and 0. 2 % respectively. As we can see from Figure 1, the majority of the sample is Songhai and Kel Tamasheq (almost everybody identified themselves as Muslim). There are clear differences in migration decisions between ethnic groups. The reaction of most of Arab and Kel Tamasheq origin was to leave the country, while most Songhai people preferred to go south, to Bamako, or, by the time of our survey, had already returned to Northern Mali. In fact, as pointed out in (Etang-Ndip et al., 2015), IDPs and returnees have a similar ethnic composition because 94 % of returnees in our sample were IDPs. Far fewer returnees in the sampled cities of Gao, Tombouctou and Kidal returned from refugee camps in the neighboring countries for the simple reason that most refugees used to live in towns and villages outside the regional capitals of Northern Mali. Displaced Refugee Returnee Total Tamasheq Arab Songhai Peulh Bella Other Analytic weights used Source: LDPS 2014-15 Figure 1: Ethnic composition", "output": {"entities": {"named_data": ["LDPS 2014-15"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 5. 3 Desire to return (Y / N) Keeping our attention on refugees and IDPs, we wanted to deepen our understanding about their migration plans. In particular, we would like to discover which characteristics are associated with the desire to return to Northern Mali. To this end, we estimated a probit model using the same regressors as in the previous sections. The dependent variable was set equal to one when the respondent was considering the possibility to eventually go back to the North, zero otherwise. The estimated marginal effects have been reported in Table 3 for all respondents (Column 1-2), as well as for only the household heads or their spouses (Column 3-4). The strongest predictor of a planned future return was refugee status: individuals living abroad in refugee camps were up to 25 percentage points more willing to go back than IDPs. Joining this result with those on unemployment presented in the descriptive statistics, we may wonder whether this desire to go back home may have resulted from a more general malaise experienced by these respondents forced to migrate and halted in a limbo not fully integrated with the local community and labor market.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["descriptive statistics"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 5. 5 Intention to return (Y / N) – Fixed-Effect This last empirical section represents an attempt to use the panel dimension of our data set to estimate causal effects. In particular, for 12 consecutive waves, refugees and IDPs were asked whether they were considering going back to Northern Mali in the subsequent month. We tried to test how employment, security and expectations affect these decisions. We did so by estimating a fixed-effect linear probability model (LPM). The estimated coefficients are shown in Table 6. Columns 1 and 2 have been estimated using the whole sample, while only respondents who were the household heads or the spouses were included in the regressions presented in Columns 3 and 4. The main conclusion is that being employed reduces the intention to go back to the regions in Northern Mali by around 8 percentage points. This result persists across all specifications, even when we control for immigration status, i. e., whether the individual is a refugee or an IDP. Indeed, refugees are more likely to be willing to go back. Estimating the same model for refugees and IDPs separately does not change our conclusions. In line with the previous findings, whether an individual felt safe during the day (or at night) did not affect the likelihood of planning to go back.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, another regressor indicates that security may still be pivotal: those who owned a weapon were up to 30 percentage points more likely to plan to go back. In addition to this, it is quite surprising that, if the respondent thought that the Northern Mali crisis was improving, he or she was less likely to plan a return to that area. From a technical point of view, we should point out that we have used an LPM even if the dependent variable was a binary outcome. This choice has been made since in this linear model it is straightforward to add fixed-effects. Furthermore, the coefficients can be interpreted as average partial effects. A simple logit or probit model would not have allowed the inclusion of individual fixed-effects because of the incidental parameter problem. An alternative approach would have been to estimate a conditional logit model. However, since the distribution of the fixed effects is unknown, it would not have been possible to estimate the average partial effects in this model, but only the effect of the regressors on the log-odds ratio. 13 We conclude by stressing that the monthly phone interviews were relatively short, so we did not have a rich panel data set. This may have led to omitted variable biases. Indeed, there may still be time varying factors which could have affected both the probability of being employed and the respondents ’ intentions to go back. Nevertheless, we believe that our model managed to control for 13 See (Wooldridge, 2010) page 639. Conclusions from the conditional logit model are qualitatively similar.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data set"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 7402 This paper is a product of the Social Protection and Labor Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at xdelcarpio @ worldbank. org and mathiswagner @ gmail. com. Currently 2. 5 million Syrians fleeing war have found refuge in Turkey, making it the largest refugee-hosting country worldwide.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turk­ish Labour Force Survey to assess their labor market impact. Syrian refugees are overwhelmingly employed informally, since they were not issued work permits, making their arrival a well-defined supply shock to informal labor. Consistent with economic theory our instrumental variable estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupa­tional upgrading, there are increases in formal employment for the Turkish- though only for men without completed high school education. Women and the high-skilled are not in a good position to take advantage of lower cost informal labor. The low educated and women experience net dis­placement from the labor market and, together with those in the informal sector, declining earning opportunities.", "output": {"entities": {"named_data": ["Labour Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. INTRODUCTION Refugees pose a massive moral, political and economic challenge for potential host countries. 1 The scale of the challenge is larger than ever, with 60 million people forcibly displaced by conflicts across the world (UNHCR, 2014). War in Syria has produced more refugees than any other conflict of the past two decades: around 4. 6 million have fled the country, with an additional 7. 6 million internally displaced. 2 About 2. 5 million Syrians have found refuge in Turkey, making it the largest refugee-hosting country worldwide. This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turkish Labour Force Survey to assess the impact on Turkish employment and wages. The large majority (85 percent) of Syrians have left the refugee camps and entered the Turkish labor market. 3 They are overwhelmingly employed informally, since they were not issued work permits. This makes their arrival a well-defined supply shock to informal labor, and a particularly good context in which to test the predictions of basic economic theory. We instrument for refugee flows using travel distance between 13 origin governorates in Syria and 26 Turkish subregions (338 origin-destination pairs). This allows us to also control for distance from the Syrian border, and thus any confounding factors that are correlated with proximity to Syria.", "output": {"entities": {"named_data": ["Turkish Labour Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 permits and subsequently for the right to work. In practice, this is a long and cumbersome process and by late 2015 at most several thousand had been issued. 14 The economic impact of Syrian refugees in Turkey extends beyond changes in the potential labor supply of informal workers in important ways. There has been extensive humanitarian aid provided to the refugees, overwhelmingly by the Turkish government. Reportedly, by early 2015 the Turkish state had spent $ 6 billion (with total outside contributions $ 300 million). 15 Much of these funds have been spent on food, various services, non-food items such as medicines, clothing, shelter, and housing-related goods. In particular, there are 20 accommodation centers (camps) in 10 cities in Turkey. 2. 2 Data Sources We use the Turkish Household Labor Force Survey (LFS) micro-level data sets compiled and published by the Turkish Statistical Institute. The data contains a rich set of labor market variables along with individual-level characteristics and the region of residence. We primarily rely on two years of LFS data: 2011 (just before the arrival of the refugees) and 2014 (the last year available). 16 By design the LFS does not contain any information on Syrian refugees.", "output": {"entities": {"named_data": ["Turkish Household Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Disaster and Emergency Management Presidency of Turkey (AFAD) provides information on the number of Syrian refugees. The numbers used in this paper are taken from Erdogan (2014), who draws on information from AFAD and the Ministry of Interior and reports the number of refugees by NUTS 2 subregion. To construct our instrument we use the Syrian Labor Force Survey for 2010 (the year before the beginning of the war). Finally, Google Maps was used to derive the travel distance between each governorate in Syria and the most populous city in each NUTS 2 subregion in Turkey. 14 Most recently, in January 2016, labor market access for Syrian refugees in Turkey was eased considerably. Importantly, they now can benefit from vocational training under the Turkish Employment Agency, employers will be able have to Syrians comprise up to 10 percent of their staff, and seasonal workers are exempted from the work permit, see http: / / www. resmigazete. gov. tr / eskiler / 2016 / 01 / 20160115-23. pdf. It is of course too early to evaluate the impact of these legislative changes. 15 Hurriyet Daily News (February 2015) http: / / www. hurriyetdailynews. com / turkey-urges-worlds-help-on- syrian-refugees-as-spending-reaches-6-billion. aspx? pageID = 238 & nID = 78951 & NewsCatID = 359. 16 Starting with 2014 there was a change in the design of the Household Labour Force Survey to ensure full compliance with European Union standards. This has caused some difficulty in making comparisons across years. However, our identification strategy does not use aggregate variation across years for identification and should hence be unaffected by the changes to the design of the survey.", "output": {"entities": {"named_data": ["Household Labour Force Survey", "Syrian Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 1 provides descriptive statistics for the years 2011 and 2014. 18 Labor force participation is very low in Turkey, around 54 percent of the working-age population in 2011, though it has been rising. The reason is that female labor force participation is particularly low at about one-third. The majority of employment is private sector, around one-third of the working-age population, compared to 6 percent employed in the public sector. There are a large number of unpaid workers (7 percent) and unemployment is at 5 percent of the working-age population, an unemployment rate of about 10 percent. School attendance has been rising over the period, from 12 to 16 percent of the working-age population, and the fraction retired has been steady at about 5 percent. Correspondingly, educational attainment has been rising though still 13 percent of the working-age population has no formal education, 57 percent at least completed primary education but not high school, and high school completion has risen from 30 to 34 percent. 17 Of those who have an irregular workplace 60 percent are agricultural workers, 14 percent work in construction, 7 percent in transportation and 5 percent in retail and in manufacturing each, and 3 percent as household employees. 18 Note that in 2014 new regulations for the Household LFS were carried out within the framework of European Union criteria. Consequently, statistics are not necessarily entirely comparable across years. Since we do not use aggregate time-series variation for identification this does not affect our empirical strategy, see Section 3. For those interested, the Turkish Statistical Institute provides consistent time-series on their website.", "output": {"entities": {"named_data": ["Household LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 though somewhat lower than that of the Turkish at 30- 50 percent. Child labor is also quite prevalent, though there have been extensive efforts made to ensure that refugee children attend school. 19 Publicly available information on refugees comes from an AFAD survey of 2, 700 households in June and July 2013. Figure 2, using data from AFAD (2013), provides an overview of the Syrian governorates from which the refugees to Turkey originated. The refugees primarily come from northwest Syria. The largest source regions are Aleppo (36 percent), Idleb (21 percent) al-Raqqah (11 percent), Lattakia (9 percent), and Hamah (8 percent). Consistent with travel distance being a good predictor of refugee flows to Turkey, 80 percent of respondents report that they chose to flee to Turkey, instead of another country, due to the ease of transportation. The refugees in Turkey, unlike the later 2015 refugee flows to Western Europe, are nearly 50 percent female. Slightly over 50 percent are minors (under the age of 18). These facts reflect that to large extent Syrian families fled to Turkey together.", "output": {"entities": {"named_data": ["AFAD survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Then the absolute refugee-induced wage change is given by Δݓഥ ൌ ݓഥଵ െ ݓഥ ଴ ൌ ൫ ݁ ஓ ෝ ೢ ∗ ଶ െ 1൯ ∗ ݓഥ ଴. 3. 3 Instrument To allow for a causal interpretation of the impact of refugee flows, see equations (1) and (4), we instrument for the ratio of refugees to working-age population (ܴ ௥ ௧ሻ. 24 Our instrumenting strategy is based on the idea that travel distance, from the Syrian governorate from which the refugee is fleeing to each potential destination Turkish subregion, is a key determinant of refugee location decisions. We use Google Maps to calculate the travel distance Tsr from each Syrian governorate capital (s), to the most populous city in each Turkish NUTS 2 subregion (r). The instrument for the number of refugees at a given point of time in each Turkish subregion is given by: ܫ ܸ ௥ ௧ ൌ ෍ 1 ܶ ௦ ௥ ߨ௦ ܴ ௧ ௦, (5) where Rt is the total number of registered Syrians in Turkey in a year and ߨ௦ the fraction of the Syrian population that lived in each governorate in 2010 (pre-war). 25 Since all our 23 The education categories are at most primary school, secondary school, and higher education.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The age categories are 15 – 19, 20 – 24, 25 – 29, 30 – 34, 35 – 39, 40 – 44, 45 – 49, 50 – 54, 55 – 59, and 60 – 64 years. There are 183 groups since we exclude groups containing less than 40 observations. 24 An additional advantage of the IV approach is that it helps deal with measurement problems. Despite the improved measures of refugee numbers in Turkey by subregion starting in 2014, there is likely considerable measurement error, resulting in attenuation bias in the OLS estimates. For the IV estimates to be consistent, it is only necessary that- conditional on the fixed effects and control variables- the flows of Syrian refugees are uncorrelated with the instrument. 25 Using data from AFAD (2013) we can also weight the aggregate refugee numbers using the Syrian source governorates of refugees in 2012-13 (see Figure 2). Results are qualitatively robust to this alternative instrument and first-stage F-statistics about the same. We prefer the use of the pre-war distribution of population in Syria,", "output": {"entities": {"named_data": [], "descriptive_data": ["data from AFAD"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Second, the Turkish LFS asks respondents whether they had previously lived in a different province (one of Turkey ’ s 81 NUTS 3 regions), and if so in what year they moved to their current province. We estimate the impact of refugees on the probability a native moved to a subregion in the past year. Table 9 reports OLS and IV estimates of the impact of refugee on net population growth in subregion (Columns 1 and 2) and gross population inflows (Columns 3 and 4). Net population growth is estimated at the level of NUTS 2 subregions. Population inflows to a subregion are estimated at the individual level (and standard errors clustered by subregion- year). All regressions include subregion and year fixed effects and a year-specific control for log distance from the Syrian border. The first column presents the estimates for the whole sample, subsequent columns for different sub-samples by gender, age and education. For the full sample the net population growth in a subregion is positively correlated with refugee flows, while the IV point estimate is negative (though neither estimate is statistically significant). The probability of a Turkish person migrating to a subregion is negatively correlated with refugee flows (the OLS estimate is highly statistically significant). The IV estimate is of a similar magnitude, but no longer statistically significant. This same pattern broadly holds for both women and men. The only other statistically significant IV estimates are a decrease in the population aged 15 – 24, an age group that is likely more mobile, and of those with medium educational attainment. There is also a decrease in the inflow of low", "output": {"entities": {"named_data": ["Turkish LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "26 education Turkish to a subregion. In sum, there is some evidence that the inflow of Syrian refugees results in a decrease in the number of Turkish living in a subregion. The evidence, however, is weak and the impact unlikely to be very large. 5. PLACEBO TESTS AND ROBUSTNESS CHECKS 5. 1 Placebo Tests The key threat to the validity of our instrument is that there are subregion specific economic trends that are correlated with the instrument, and not fully controlled for by the inclusion of the log distance of a Turkish subregion from the Syrian border. A priori this seems unlikely since the instrument is also based on travel distances, but we can test for the existence of such trends in a pre-period. Specifically, we run regressions that are analogous to those reported in Tables 5, 6 and 7 using data from the LFS 2009 and 2011. As a placebo test we pretend that the Syrian refugees had arrived between 2009 and 2011, rather than between 2011 and 2014, to see if the instrument is correlated with Turkish outcomes in this pre-period. Table 10a presents the results of our placebo tests. For the overall sample there is no statistically significant trend that is correlated with subsequent (instrumented) refugee flows in formal or informal employment, or in log wages.", "output": {"entities": {"named_data": [], "descriptive_data": ["data from the LFS 2009 and 2011"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As expected the positive correlation between refugee flows and 2011 school attendance rates is significant at the one percent significance level (controlling for the gender, age and education composition of a subregion the point estimate is- 0. 17). Even once we instrument for refugee flows the correlation remains significant at the one percent significance level (a point estimate of- 0. 30). However, once we control for the log distance for the Syria border there is no longer a statistically significant relationship, in either the OLS or IV, between school attendance rates in 2011 and subsequent refugee flows. This suggests that, on account of the inclusion of our distance from the border control, we can rule out the 2012 education reform confounding our estimates. 5. 3 Robustness to Varying Sample of Turkish NUTS 2 Subregions Throughout this paper we use all 26 NUTS 2 subregions of Turkey for identification. However, the results are robust to varying the particular sample of subregions. We report results for two alternative samples. First, we drop the Gaziantep subregion from the estimation. Gaziantep has the highest refugee to population ratio among all regions and reportedly towns with a refugee share of over 30 percent. The inclusion of Gaziantep may skew results if there are any non-linearities in the impact of refugees. Second, we follow Ceritoglu et al. (2015) in only considering nine subregions of Turkey. These are the five Syrian border regions of southeastern Anatolia (Hatay, Gaziantep, Sanliurfa, Mardin, and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["school attendance rates"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Women experience particularly pronounced displacement in the informal sector and no formal job gains. In contrast, for men displacement in the informal sector is fully offset by employment growth in formal sector, with no net job losses. Clearly, our main findings do not depend on the particular sample of subregions we analyze, and importantly are robust to restricting the analysis to a more homogenous group of regions. 6. CONCLUSIONS This paper combines newly available data on the 2014 distribution of 1. 6 million Syrian refugees across subregions of Turkey and the Turkish LFS, to assess the impact on Turkish labor market conditions. The Syrian refugees in Turkey are overwhelmingly employed informally, since they were not issued work permits, and so their arrival was a well-defined supply shock to informal labor. Consistent with economic theory our IV estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupational upgrading, there are increases in formal employment for the Turkish. This increase though only occurs among men without completed high school education. The employment patterns of women and the high-skilled mean they are not in a good position to take advantage of lower cost 36 Results are also robust to dropping all subregions with close to no refugees.", "output": {"entities": {"named_data": ["Turkish LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Anderson, J. (2011): “ The Gravity Model, ” Annual Review in Economics, 3 (1), 133 – 160. Angrist and Pischke (2009). Mostly Harmless Econometrics, Princeton University Press. AFAD (Disaster and Emergency Management Presidency of Turkey) (2013). Syrian Refugees in Turkey, 2013: Field Survey Results. Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015a). “ The Impact of Refugee Crisis on Host Labor Markets: The Case of the Syrian Refugee Crisis in Turkey. ” IZA Discussion Paper 8841. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015b). “ The Impact of Refugee Crises on Firm Dynamics and Internal Migration: Evidence from the Syrian Refugee Crisis in Turkey, ” mimeo. Aydemir, Abdurrahman and Murat Kırdar (2013). “ Quasi-Experimental Impact Estimates of Immigrant Labor Supply Shocks: The Role of Treatment and Comparison Group Matching and Relative Skill Composition, ” IZA Discussion Paper 7161. Baez, J. (2011). “ Civil Wars Beyond their Borders: The Human Capital and Health Consequences of Hosting Refugees. ” Journal of Development Economics 96 (2) November: 391 – 408.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "33 In school: an indicator for whether the respondent currently attends regular education (schooling). This does not preclude also being employed. Retired: an indicator for a respondent who declares that they are not engaged in job search because they are retired. Full / part-time employment: an indicator for whether a person works full or part-time for all people in private sector employment (see above definition of employment). Full-time employment is defined as usual working hours of 30 or more hours per week, part-time employment as usual working hours of less than 30 hours per week. We do not use the indicator provided in the LFS data since there seems to be some confusion in which category 30 hours per week falls (with these evenly divided between full and part-time). Education: we classify people into three education categories. Low education is defined as those with no completed formal education. Medium education is defined as those with at least completed primary education but no high school completion. Higher education is defined as people who have at least completed high school.", "output": {"entities": {"named_data": ["LFS data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Wages: the earnings measure we use is the response to the question “ how much did you earn from your main job activity during the last month? ” In the LFS 2011 there is further information on how much of that income was irregular, for example a bonus payment, but the LFS 2014 no longer provides that breakdown. There is also a measure of the “ number of hours per week worked in the main job ” (both usual and total hours), which can be used to construct hourly wages. Since the hours worked measure does not correspond exactly to the earnings measure and introduces additional measurement error, our preferred wage measure is the monthly wage. We exclude wage observations were respondents report having usual working hours of less than 14 or more than 84 hours per week.", "output": {"entities": {"named_data": ["LFS 2014", "LFS 2011"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 6832 This paper presents findings from the impact evaluation of the Economic Empowerment of Adolescent Girls and Young Women (EPAG) project in Liberia. The EPAG project was launched by the Liberian Ministry of Gender and Development in 2009 with the goal of increasing the employment and income of 2, 500 young Liberian women by providing livelihood and life skills training and facilitating their transition to productive work. The analysis in this paper is based on data collected during two rounds of quantitative surveys in 2010 and 2011, the second of which was conducted six months after the classroom-based phase of the training program ended. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["quantitative surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Strong impacts are found on the employment and earnings outcomes of program participants, relative to a control group of non-participants. The EPAG program increased employment by 47 percent and earnings by 80 This paper is joint product of the Poverty Reduction and Economic Management Unit, Africa Region and the Social Protection and Labor Unit, Human Development Network.. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at schakravarty @ worldbank. org. percent. In addition, the impact evaluation documents positive effects on a variety of empowerment measures, including access to money, self-confidence, and anxiety about circumstances and the future. The evaluation finds no net impact on fertility or sexual behavior. At the household level, there is evidence of improved food security and shifting attitudes toward gender norms. These results reinforce the highly positive feedback received from focus group discussions with program participants. Finally, preliminary cost-benefit analysis indicates that the budgetary cost of the EPAG business development training for young women is equivalent to the value of three years of the increase in income among program beneficiaries. These preliminary results provide strong evidence for further investment and research into young women ’ s livelihood programs in Liberia. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1. Introduction Sub-Saharan Africa is the youngest region in the world, and the world ’ s fastest-growing. Between 2010 and 2025, the number of people between 15 and 24 will grow to 250 million – a net increase of nearly 50 percent. Over the next decade, roughly one million young people will enter the labor market each month in Sub-Saharan Africa. However, these young people often enter the labor market too early and unprepared. Although access to education is growing, illiteracy remains high and schooling low: among the 32 Sub-Saharan African countries in the Barro-Lee (2010) data set, nearly 40 percent of women aged 15 and above have received no education at all; and the most recent (2007-2011) statistics in the World Bank ’ s Edstats data reveal that female literacy is less than 60 percent, on average. 1 This lack of preparedness contributes to a growing problem of youth unemployment. Quantifying the level of unemployment is bedeviled by lack of data and measurement issues. Household and labor force surveys throughout Africa usually record unemployment rates of less than 10 percent, 2 but those figures belie the extent of underemployment and vulnerability. Those same surveys indicate that the vast majority of working adults have insecure work in the informal sector, on the family farm, or in less- productive or unremunerated labor. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Barro-Lee", "Edstats data"], "descriptive_data": [], "vague_data": ["Household and labor force surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The most recent labor force survey from Liberia mirrors these statistics: there are roughly 1. 1 million people in the workforce, of whom 195, 000 (about 18 percent) are engaged in wage employment; the remaining 900, 000-plus workers (82 percent) are considered in vulnerable employment, working for themselves or working unpaid for their own households (LISGIS 2010). Among young women (15-24) in Liberia, the unemployment rate is 8 percent, double the rate among young men (LISGIS 2010). Most of these gaps can be explained by differences across individuals, especially in educational attainment, skills training, and years of experience. But segregation, market segmentation, and discrimination do play a role in determining these individual characteristics. Women have fewer opportunities for education or training, less access to credit, a larger share of domestic responsibilities, and less independence and control over their own lives. In Liberia, women comprise half of the employed, but only about one-quarter of paid employment (LISGIS 2011). Fourteen years of civil war in Liberia devastated the country ’ s infrastructure and institutions, and left a generation of young people with very low levels of education and training. Girls were particularly disadvantaged. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In 2003, almost 60 % of young girls and 40 % of young boys had no formal schooling (ILO / UNICEF 2005). Data from the Demographic and Health survey shows that more than 40 percent of adult women have no education, compared to fewer than 20 percent of men, while 23 percent of women and 44 percent of men have some secondary schooling (DHS 2007). Happily, access to education is rising rapidly, especially for girls: according to the Liberian labor force survey from 2010, the ratio of girls to boys enrolled in primary school has risen from 72 in 2000 to 90 in 2009. Enrollment levels and sex ratios are lower among older children and youth, as they become increasingly engaged in 1 Both are unweighted averages; Barro-Lee comprises 32 countries with data from 2010; Edstats comprises 43 countries with data from 2007-2011. 2 Defined as without work, available for work, and actively looking for work (LISGIS 2010). 2 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Liberian labor force survey", "Demographic and Health survey", "Barro-Lee", "Edstats"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "household and productive work. Just over one-third of young (15-24-year-old) Liberians are in the labor force. Young women are more likely than young men to be out of the labor force because they are engaged in household duties (30. 4 percent v. 18. 9 percent), and young men are more likely to be out of the labor force because they are still in school (75. 8 percent v. 63. 0 percent) (LISGIS 2010). Given their initial disadvantage relative to their male peers, and the sources of this disadvantage in social norms, market failures, and poorly functioning institutions, adolescent girls may require targeted policy and program efforts to achieve better outcomes. However, as in many other post-conflict situations, emergency skills training and public works programs in Liberia have targeted male youth ex- combatants, likely reinforcing rather than reducing adolescent girls ’ disadvantage. The few skills training programs for adolescent girls, run largely by NGOs, have focused on traditional female skills (such as sewing, soap production, tie-dyeing) for which the market is already well-supplied. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Programs designed to enhance employment among young people can focus on the supply side of the labor market, including skills training programs for both wage employment and entrepreneurship training; or programs to augment demand, such as wage subsidies, public works, and community service programs; or programs to help the labor market clear, such as job search assistance and placement services. In addition, it may be that the constraints facing young people are not in the labor market itself, but in other markets, such as for credit. The vast majority of jobs programs have focused on the supply side: training programs make up about 79 percent of over 600 cases included in the World Bank ’ s Youth Employment Inventory database. 3 Although rigorous and general evidence of success is limited, it seems that successful skills training programs share a few key features: they are responsive to local market conditions, they provide more than just technical skills in a specific area (including, for example, “ life skills ”), and they include ancillary services that alleviate other constraints preventing successful labor market integration (e. g., access to credit). Among the most celebrated are the Jovenes programs that provide demand-driven technical training, plus social skills that help in the labor market, plus internships. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Youth Employment Inventory database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These programs have had positive impacts on wages and employment, especially for young women and those from more disadvantaged backgrounds (Ibarrarán and Rosas 2009). This paper evaluates the first round of the EPAG (“ Economic Empowerment of Adolescent Girls and Young Women ”) skills training program implemented by the Government of Liberia from 2010 to 2011. EPAG was designed to alleviate the barriers to entering the labor market faced by young women, while avoiding the shortfalls of previous skills training programs offered in Liberia. The program combined six months of classroom-based technical and life skills training, with a focus on skills with high market demand, followed by six months of follow-up support to enter wage employment or start a business. Roughly 1200 young women aged 16-27 participated in the first round of EPAG. 3 See http: / / www. youth-employment-inventory. org /. Also, although focused on a narrower time frame and using a different selection criterion, the joint ILO / World Bank Inventory of Policy responses to the Financial Crisis (http: / / www. ilo. org / crisis-inventory) finds that “ about 78 percent of reported policy measures focused on the supply side ”, essentially through training (ILO / WB 2012). 3 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The outcomes of interest for this impact evaluation fall into three categories. First, we are interested in the economic outcomes of EPAG participants in terms of employment, earnings, and savings and investment behaviors. Second, because of the program ’ s focus on young women, a variety of non- economic outcomes are explored. Early evidence suggests that empowerment in one realm (e. g., schooling) can have important impacts on other realms, such as early pregnancy, prevalence of STDs, and risky behaviors such as transactional sex (Baird 2010). Related to these non-economic outcomes are measures of social empowerment, including mobility, decision-making, and self-confidence, which are thought to strengthen women ’ s agency, or capacity to exert choice over decisions involving herself, her family, and her community. Finally, we investigate a third set of outcomes on spillover effects. Evidence suggests that women tend to invest more of their income in their families, especially their children, than do men (e. g. World Bank 2001, Hoddinott 1995, Pitt 1998, Borges 2007). Advocates frequently argue for increased investment in adolescent girls by pointing to the potential for spillovers onto other family members (including future children). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our evaluation explicitly investigated these spillovers by including household-level indicators such as food security and attitudes of the household head toward gender norms. Our results show strong impacts on economic outcomes, including large and statistically significant increases in employment and earnings of EPAG participants. We show mixed results on empowerment- related outcomes, and very little evidence of spillovers on non-participants. Self-assessed measures of self-confidence show huge gains, as does ownership and control over monetary resources such as savings. The remainder of the paper is organized as follows: Section 2 describes the EPAG project including some of its innovative design features and implementation details. Section 3 reviews the methodology of the evaluation and Section 4 presents results on the three groups of outcomes discussed above: economic, empowerment, and spillovers. Section 5 includes a short discussion of cost-effectiveness. Section 6 presents a series of robustness checks and Section 7 concludes with a discussion of next steps and policy implications. 2. The EPAG Project The EPAG project is part of a larger Adolescent Girls Initiative (AGI) administered by the World Bank with support from the Nike Foundation and the Governments of Australia, the United Kingdom, Norway, Denmark, and Sweden. Launched in Washington DC in October 2008, the AGI was spearheaded by President Ellen Johnson Sirleaf, who signed on to undertake the initiative ’ s first pilot project in Liberia. The Liberian pilot was launched in March 2010 and has served as a role model to seven subsequent pilot projects in Rwanda, South Sudan, Nepal, Afghanistan, Haiti, Jordan, and Lao PDR. Under the global AGI, young women and adolescent girls are given a package of skills training and complementary services in order to facilitate their successful transition to employment. In the case of EPAG, the intervention consisted of a six month phase of classroom-based training, followed by a six 4 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "month placement and support phase in which the trainees were supported in their transition to self or wage employment. Upon recruitment, the participants are assigned to a\"Job Skills (JS)\"track or a\"Business Development Services (BDS)\"track. When possible, the participant's track preference was honored; however, the demand for the Job Skills track greatly exceeded the supply, so the remaining trainees were placed into the BDS track. In the first round of training, the proportion of Job Skills track places was limited to 35 % of the total training places available given the expectation that few wage jobs will be available in the Liberian job market. The Job Skills track provided training in six areas: 1) hospitality, 2) professional cleaning / waste management, 3) office / computer skills, 4) professional house / office painting, 5) security guard services, and 6) professional driving. These areas were determined based on independent labor market assessments, a review of the available market data, and input from EPAG ’ s private sector partners. All Job Skills trainees received training in entrepreneurship skills as well. The BDS training taught young women how to identify micro-enterprise opportunities based on an assessment of market needs, and how to grow and manage any existing businesses they already had. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["available market data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The curriculum included entrepreneurship principles, market analysis, business management, customer service, money management, and record-keeping. The EPAG program was implemented by four NGOs who were selected by the Liberian Ministry of Gender and Development through a competitive bidding process: the Community Empowerment Program (CEP), Liberia Entrepreneurial and Economic Development (LEED), the International Rescue Committee (IRC), and the American Refugee Committee (ARC). Two of these organizations (ARC and IRC) further subcontracted to four Liberian NGOs. 4 The service providers were responsible for developing training curricula, identifying training venues, 5 making arrangements for childcare services, assisting with the mobilization of the nine target communities, and participating in the recruitment of training participants. The EPAG program differed from many training programs in a number of ways. First, performance bonuses were awarded to training providers that successfully place their graduates in jobs or micro- enterprises. The bonus was the last payment that the service providers received under their contracts. These were paid about 12 months after the start of training, or around the same time as the midline survey. Second, a variety of contests and competitions were also held among EPAG trainees (such as attendance prizes, quizzing contests, business plan competitions, etc.). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Third, the EPAG program was designed around the girls'needs: service providers held both morning and afternoon sessions, to accommodate the participants'busy schedules; trainings were held in the communities where the girls reside; and every site offered free childcare. Fourth, frequent and unannounced monitoring visits by MoGD staff ensured that the service providers created and maintained a high-quality learning 4 There are: National Adult Education Association of Liberia (NAEAL), Community Empowerment Sustainable Program (CESP), EduCare, and Children ’ s Assistance Program (CAP). 5 A total of 19 training venues were used during the first round of training. They were chosen with the following considerations in mind: 1. Girls ’ safety, so that the buildings are not so isolated or otherwise dangerous, raising security concerns for girls. 2. Conducive atmosphere for learning, spacious and sanitary with access to water and latrine facilities. Reasonably outside community noise concentration. 3. Proximity to community center and to security posts such as police depots. 4. Accessible to girls from various parts of the community. 5 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "environment. Any issues discovered during these monitoring visits were brought to the attention of service providers and resolved swiftly in conjunction with the project coordination team at MoGD. Eligibility: The EPAG program was targeted to young women who: i) were age 16 to 27, ii) possessed basic literacy and numeracy skills, iii) were not enrolled in school within several months prior of the program initiation, and iv) resided in one of nine target communities in and around Monrovia. 6 These eligibility criteria stemmed from the project's objectives to reach young women at an early enough age to significantly improve the trajectory of their working years, to focus on girls who already had the basic literacy and numeracy skills needed to succeed in the labor market, and to avoid incentivizing applicants to drop out of school. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The literacy requirement in particular, although basic, made the EPAG program out of reach for many of the most vulnerable young Liberian women; the requirement reflected a deliberate choice on the part of program designers in the face of a tradeoff between serving the most vulnerable and serving those who could most readily make use of this relatively short training program. 7 The team recognized that it was difficult, if not impossible, to require documentation from the applicants to verify each of the eligibility criteria (especially age, since many Liberians do not have any official form of identification). Hence the application process relied primarily on self-reported data. To counter the likelihood that applicants would give false information in order to gain entry into the program, the eligibility criteria were not made public; the mobilization and outreach campaigns did not specify the precise age or education requirements for the program. During the recruitment events, each applicant had to physically present herself, fill out an application form specifying her age, education history, and residence. A simple literacy and numeracy assessment was also administered at the time of application. Beyond these basic eligibility criteria, no further selection criteria were applied, and program managers did not choose whom to train. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A “ randomized pipeline ” design was adopted in which eligible candidates were assigned by lottery to one of two rounds of the program, where those assigned to the second round would serve as a control group for the evaluation. Every applicant who met the eligibility criteria had an equal chance of participating based on the lottery results. Retention and Attendance: After dividing EPAG trainees into two rounds, or cohorts, for the training, the first round of training was held from March 2010 to February 2011. The classroom training was held from March through August, during which the project achieved a 95 % retention rate (far higher than similar programs in Liberia and elsewhere), and an average attendance rate of nearly 90 % during the classroom training phase. EPAG trainees were given incentives to participate and to make the most of their training: they signed\"Trainee Commitment Forms\"at the start of the training, they were paid small stipends and a completion bonus contingent upon attendance, they were offered free childcare at every training site, they were assisted to open a savings account at a local bank in which to save their 6 Bassa Community, Battery Factory, Bentol, Doe Community, New Kru Town, Old Road, Red Light, and West Point in Montserrado County and Kakata in Margibi County. 7 To alleviate the burden of this requirement, the second round of the EPAG program included a preliminary basic literacy program to help otherwise eligible young women to improve their literacy and numeracy skills in advance of entering the program. 6 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The household head interviews also provided an opportunity to gather useful household-level information (including assets, housing characteristics, and household head characteristics) to be used as control variables in our analysis. The baseline survey was conducted before participants were informed of the outcome of the randomization, and the midline survey was conducted one year after the baseline survey, one month after completion of the first round of training and before the start of the second round. The results in this paper are based on a panel data set that includes data from the baseline and midline surveys as well as administrative and monitoring data from the program implementation team. Note that although the midline data collection occurred just one month after the completion of the intervention, the intervention itself includes 6 months of post-training follow-up; hence the midline survey captures outcomes of the first cohort 7 months after they exited the classroom-based training. This timing was necessary to prevent too long of a delay between cohorts; the second cohort of trainees started immediately after the midline survey was completed. Future analysis using endline data will examine the trends in outcomes of both groups after Round 1 completion, but will not involve comparison against a pure control group of non-participants. 3. 2. Sample and attrition Following the communications and outreach campaigns in each of the nine target communities, during which 2, 106 young women were originally recruited to be a part of the EPAG program, 8 a randomized 8 The original recruitment effort fell short of the target of enrolling 2500 participants. Hence, before the second round of training, another recruitment effort was launched and an additional 617 girls were enrolled. These girls are not included in the impact evaluation since they were not subject to the initial random assignment. 7 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data set"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "assignment process was conducted to assign the trainees to the first or second round of training. 9 Of those entered into the random selection, 1273 young women were assigned to the first round of training, with the remaining 808 to serve as a control group (the control group would participate in the second round of training starting in July 2011). Of the 1273 assigned to treatment, 118 women were not found or chose not to participate after they were selected. 10 In order to fill at least some of these slots, 39 young women from the control group were randomly issued as replacements, resulting in a modified control group of 769 individuals. In the end, 1191 young women entered the first round of training. 11 The assignment process and all post-randomization modifications are summarized in Figure 2. Table 1 reports the baseline and midline survey response rates leading to the sample used for the analysis in this paper. The target sample for both the baseline and midline survey consisted of the original 2106 EPAG recruits, of which 1989 were successfully interviewed during the baseline survey. 12 At midline, 1736 were interviewed, including 56 who were not interviewed at baseline. For our analysis, we drop individuals who were excluded from the randomization or who were manually re-assigned from control to treatment as replacements after the randomization. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["baseline survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These 25 were included in the surveys but have been dropped from the analysis because they were not assigned randomly. 10 The reasons given for not entering the training included: 1) they were back in school, 2) they had moved to a distant location, 3) they were seriously ill, 4) they had found full-time work, 5) they were not interested or able to make such a big time commitment, or 6) they could not be located despite numerous efforts. 11 This includes 1155 of the original 1273 assigned to treatment, plus 36 out of the 39\"replacements\"- young women from the control group who were offered a chance to be reassigned to round 1. 12 Note that a previously released baseline report for this evaluation was based on 2008 observations. However, after cleaning, 4 were found to be duplicate observations and 15 were not found in the program data and hence were dropped from the midline analysis. This leaves 1989 baseline observations that are included in the midline analysis. 13 These are cases in which the adolescent girl was interviewed but the household head interview was not conducted because the head was not available, could not be found, or declined to take part. 14 This suggests that the loss of the 118 young women who were selected but declined to take part, and the 60 who started but did not complete the program, does not bias the results. 8 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Yit is the outcome of interest for individual i and time t. Treati is an indicator which is equal to 1 for treated individuals and 0 otherwise. Postt is an indicator equal to 1 for midline observations and 0 for baseline. Xit is a vector of controls at baseline (t = 0), including individual characteristics (education, age, current pregnancy, marital, parental, and orphan status) and household characteristics (sex of household head and household size). β1 is the coefficient of interest that defines the “ impact ” of the program on individuals in the treatment group. The model also includes dummy variables for the communities where the program was implemented (and where the trainees resided) as well as the program track (business skills or job skills) to which the respondent was assigned. Finally, in order to control for household wealth, we compute an index based on household asset ownership at baseline using multiple component analysis, similar to the method described in Filmer and Pritchett (2001). After constructing the index, which includes thirteen household assets and six indicators of housing conditions, we control for the quintile of household ’ s overall asset position in all regressions. We augment this basic specification with an individual fixed effects model and find that the results are almost identical. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For binary dependent variables, we estimate both linear probability models and probit models analogous to the one given in equation (1), again finding that the results are nearly identical across all outcomes. We cluster the standard errors by classroom in all models, and to account for the interaction effect for variables such as (PosttxTreati) we additionally follow Ai and Norton (2003) to correct the standard error for interaction terms in probit models. 3. 4. Baseline characteristics Table 2 presents baseline balance tests for survey respondents from the treatment and control groups. 15 In addition to confirming the success of the randomization, as judged by the very few significant differences between the two groups, the table provides a vivid profile of the average EPAG participant. The study population has an average age of 23 years, with 55 % falling between 20 and 24 years. The majority have never been married, while 29 % are cohabiting with a partner and only 5 % are married. The majority of the study population has started or completed high school, which is consistent with the program ’ s target group of young women with basic literacy and numeracy, and with the program ’ s goal not to encourage girls to drop out of school. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Thirty-eight percent of the sample was already engaged in at least one income-generating activity (IGA) at baseline. This is consistent with the national figures from the 2007 DHS survey, which found labor force participation rates of 34 % for women aged 15-19 and 49 % for women ages 20-24. It is also consistent with the Liberian 2010 Labor Force Survey, which found labor force participation rates of 25 % for women aged 15-19 and 47 % for women aged 20-24. For the purposes of this study, to be consistent with program objectives and the Liberian context, our definition of income-generating activity encompasses the full range of activities through which people earn money, including paid employment, either formal or informal, and self-employment in small business or through petty trade. The most common types of IGAs reported at baseline were petty trade, including 15 The balance tests are run on the same sample as will be used in the impact analysis in Section 4, that is, the subset of individuals for whom we have a panel. Balance tests run on the full sample of baseline survey respondents, regardless of whether they also participated in the midline survey, confirm the same findings. A report summarizing the balance tests on the full sample, including comparisons to nationally representative data, is available upon request from the authors. 9 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["2007 DHS survey", "Liberian 2010 Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "street vending (48 % of those with at least 1 IGA), food processing for sale, including baking, cooking, and drying (16 %), and home production of crops, livestock, and fish (11 %). It is important to note that the EPAG program was not targeted toward the most vulnerable segments of Liberian society, but rather toward young women with enough education to be able to benefit from a training program of this nature. Based on comparisons from the 2007 CWIQ (Core Welfare Indicators Questionnaire) survey, the average EPAG participant is more educated, more literate, more likely to be engaged in an income-generating activity, and owns more assets than the average Liberian women of the same age group: mobile phone ownership was high (63 %), as was the proportion reporting that they had some money of their own (79 %). Even compared to other similar residents of Monrovia, the EPAG participants are better educated and have higher income. A strong sense of female empowerment at baseline emerges from the sections of the survey instrument having to do with self-confidence and agency. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Core Welfare Indicators Questionnaire"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "sexual encounter. Finally, about 10 % of study respondents have experienced a forced sexual encounter in their lifetimes, which is consistent with the national figure of 13 % for this age group (DHS 2007). 17 Table 2B presents baseline balance tests for the household characteristics of respondents in the study sample. More than 40 % of households are headed by females, and the average household has slightly fewer than five members. The mothers of EPAG respondents tended to have very low education levels (almost 60 % had never been to formal school), while the fathers had more variance in their education (about a quarter had no schooling, but over 60 % had at least some secondary education). In both treatment and control households, a high proportion of school-aged children are in fact enrolled in school. Less than half of young people aged 13-30 in study households have any employment. Housing conditions are also similar across experimental groups. Tables 2A and 2B include a representative, but not exhaustive, list of indicators that were tested for balance by the authors. We conclude that the presence of very few significant differences between the individual or household characteristics between the two experimental groups indicates a high degree of internal validity for the study. 3. 5. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, the program itself was designed to fit within the young women ’ s lives, specifically with regard to their employment, education, and childcare duties. Training schedules were flexible, to allow participants to continue with their pre-existing educational and income-generating activities (and many participants did report continuing with these activities) and free childcare was provided. Hence, at least for these three dimensions of life, there would not have been much incentive to change one ’ s behavior prior to starting the program. While these explanations do not erase concerns about anticipatory behavior, they at least mitigate them. The generalizability of these results is also limited by the differences between the EPAG target group and the population of young women in Liberia. First, a high proportion of adolescent girls and young women in Liberia are illiterate or have very low literacy, while the participants recruited for the EPAG 17 Gender-based violence questions were administered in line with international ethical protocols, with additional informed consent procedures and referral mechanisms as needed. 18 See Ashenfelter (1978). 11 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "cope with the emergencies that arise all too often. 20 To facilitate successful coping and provide a safe place to save, the EPAG program assisted each participant to set up a savings account at a local bank if she did not already have one. Table 5 presents midline survey results that indicate that the treatment group were nearly 50 percentage points more likely to have savings than the control group, and were saving on average LD 2500 (nearly US $ 35) more than the control group. EPAG graduates were also twice as likely as the control group to have outstanding loans (six percent v. three percent), and have loans from formal lenders (five percent v. two percent), 21 although the overall rate of obtaining credit remains extremely low. 4. 3. Empowerment The Adolescent Girls ’ Initiative is based on the hypothesis that livelihood and life skills training for young women will improve their lives in more than just narrowly-defined economic dimensions. In addition, evidence is increasing that these soft skills are also essential for success in employment (Borghans et al. 2008). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Surveys of employers consistently find that more than hard, technical skills, employers value these harder-to-quantify skills of honesty and integrity, problem-solving ability, work ethic, communication skills, the ability to work productively with others, responsibility and dependability (Blom and Hobbs 2007). The AGI program has focused on the development and measurement of these softer attributes that matter for employment as well as those that matter more to the individual, such as self-confidence and empowerment. Despite the challenges of measuring such subjective outcomes, the survey instruments included panels of questions designed to elucidate a nuanced picture of the personality and psychosocial characteristics that are most relevant for labor market success. Table 6A presents results on empowerment and decision-making. The first series of questions have to do with control over resources, spending decisions and earnings. Respondents were asked how much control they had over how to spend their own earnings; also, whether they had money of their own for basic uses that they alone could decide how to use, without having to ask for permission. The EPAG baseline survey found that respondents reported a high degree of control over resources even before the program started. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Because of this, the program impacts on control over resources were small, albeit statistically significant: 80 percent of respondents at baseline said they controlled their own resources, with a seven percentage point increase for the treatment group (relative to control). Similarly, 80 percent of those engaged in income-generating activities said that they controlled the money they earned. The midline results indicated that among the treatment group, this had increased by roughly eight percentage points. As shown in the second panel, EPAG graduates report that they worry less than those in the control group. They are less likely to worry about their jobs or incomes or that they won ’ t be able to pay for basic necessities, and those with partners are less worried about their relationships breaking up. The impact on subjective well-being, as measured by a series of questions about the respondent ’ s satisfaction with various dimensions on her life, indicate that EPAG was most 20 See for example Ashraf et. al. (2010); Morcos and Sebstad (2010); and Austrian and Ghati (2010). 21 “ Formal ” loans are those from banks, credit groups, susu, or money lenders; “ informal loans ” are those from parents, friends, relatives, or business partners. 16 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In contrast, the self-assessed entrepreneurial score is based on questions asking how well the respondent believes she could perform a series of six tasks related to starting or running a business, and can be considered a task-oriented measure of self-efficacy. The aggregate measure of entrepreneurial ability increased by roughly nine percentage points among EPAG beneficiaries relative to those in the control group, equivalent to a quarter of a standard deviation. Enhancing participants ’ self-confidence to perform these tasks was one of the main immediate objectives of the BDS training program. Table 6B summarizes the results on a series of questions on attitudes and self-confidence that were added during the midline survey only (hence no panel analysis is possible). EPAG graduates report a more positive attitude: they feel more in control and more comfortable, and they have greater confidence in their own business abilities as well as in their personal and social lives. They are also more confident than the control group in their personal relationships with spouses and partners, consistent with the findings in Table 6A. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Both treatment and control group respondents report equally high confidence in their ability to return to school “ should [she] decide to do so. ” These findings from the quantitative impact evaluation complement the results from a set of qualitative focus group discussions that were held with participants at the end of the 6 months of classroom training. Twenty-five percent of the trainees from Round 1 participated in a total of 34 focus group discussions that covered a variety of topics including their satisfaction with the program and their empowerment in both social and economic realms. The trainees overwhelmingly voiced a high degree of satisfaction with the training, and trainers commented on how the motivation or “ seriousness ” of the participants grew over the 6 month period. The trainees credited the transport allowance and free childcare in particular as features that facilitated their full participation; as one trainee commented, 22 Questions adapted from the Adolescent Self-Regulation Inventory, developed and validated for youth in the United States by Moilanen, 2006. The questions were revised and translated into an 11-item for the Liberian context. In the future, we plan to conduct basic testing on this scale on internal consistency and reliability. 17 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "their earnings on household expenses than men. Most of these studies have focused on adult women, specifically married women with children. It is not known whether the same holds true for young women, who may have other spending priorities, have less experience in managing households, and have younger children. Given the large increases in employment and earnings documented above, the EPAG program serves as a good setting to examine these types of spillovers. The evaluation included detailed interviews with the heads of the household in which EPAG participants were residing. The purpose of the household questionnaires was precisely to examine the hypothesis that investing in young girls would benefit her household. A secondary hypothesis was that EPAG participation may change gender-related attitudes in the participants ’ households. Household data was collected for 1601 out of the 1622 individuals who were interviewed at both baseline and midline; this same sample of 1601 individuals serves as the basis for both the individual and household level analysis in this paper. The estimated impact of the program on a broad range of household outcomes is summarized in Tables 8 and 9. Panel A of Table 8 examines the household size. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The household and family dynamics in Liberia, as in other African settings, can be complex, with families often sending their children to live with relatives who are more able to provide for their schooling and basic needs. Young parents, in particular, often leave home to migrate for work, leaving their children back home with relatives until they are able to establish themselves and send for their children. If the economic success of the EPAG participant allowed her to bring non-resident family members into her household (including but not limited to her own children), then overall household size may have been expected to increase as a result of the program. This does not appear to have happened, at least in the short term. The results in Table 8 show that overall household size was not affected by the program. It is possible that the increase in earnings due to EPAG was too small, or too short-lived, to have induced the kinds of migrations described above. Other measures of household well-being, including food security and asset ownership, reflect shorter- term investments that might be influenced by the economic success of EPAG participants. Panel B of Table 8 shows the impact of EPAG on a broad range of food security measures. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Household food security was measured using two methods: dietary frequency of high-value protein-rich foods, and two subjective questions on food shortages adapted from USAID ’ s FANTA questions (Coates 2007). On two of the four of the dietary frequency questions and on both of the food shortage questions, the treatment groups ’ dietary situation improved as a result of the EPAG program. Weekly consumption of fish and meat rose significantly by four percentage points in treatment households (from a high baseline value of 84 % for meat / chicken and 90 % for fish), and weekly consumption of dairy and eggs did not change significantly. Household heads report worrying less about insufficiency of household food supplies, and the reported incidence of household members going to bed hungry also decreased in treatment households relative to control. Combined, the impacts across these indicators portray a situation of improved food security and dietary composition, consistent with the hypothesis that the increased earnings of the EPAG participants were spent in part on food. 20 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "any change in the amount of time spent on domestic work, 25 we have no evidence that these shifting norms have affected the division of labor in practice. Further research would be needed to disentangle the issues of changing gender norms in theory versus practice, and attitudes toward norms in general versus those at play in one ’ s own household. 5. Cost Effectiveness Although the first task of any evaluation is to demonstrate the effectiveness of an intervention – that is, whether or not the program actually has a measurable and attributable impact – this is not enough to recommend the program to policymakers. This requires also that the program can show that it is worth spending scarce public resources to do it. Ideally, a program worth doing will be both effective and cost- effective. One can measure cost-effectiveness in terms of the number of physical outputs produced or outcomes achieved, e. g. the number of people employed per dollar spent, or one can measure achievements in terms of the value of the benefits acquired relative to the amount of money spent. In the case of the EPAG, the unit cost of training in Round 1 was roughly $ 1200 for the Business Skills track and $ 1650 for the Job Skills track. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The EPAG Job Skills track consisted of more hours of training, and required purchasing practical equipment for each trade area. Some Job Skills trainers, especially those with more specialized skills and experience, were also more costly. The estimated unit costs cover trainer salaries (EPAG has mostly college-educated trainers), training materials (not including curriculum development), training venue rental, administrative and overhead costs of the training provider, childcare costs, event costs (job fairs, etc.), stipends to mentors, trainee transport allowances and completion bonuses. The costs also cover the withheld incentive payment to the training provider, based on how many trainees find employment. Although high relative to most developing-country budgets, these costs are well within the range of the Jovenes youth training programs implemented in Latin America (cf. Ibarrarán and Rosas 2009). The Jovenes programs were estimated to cost between $ 700 and $ 2000 per participant, depending on the country (Betcherman 2007). Not only were the costs of the EPAG program within international norms, the program itself is also cost-effective. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6. Robustness Checks 6. 1. Sample attrition Many challenges were encountered during survey implementation, due to the context – the densely populated and impoverished communities in and around Monrovia are difficult settings in which to find and track respondents — as well as the transience of a young study population. Despite vigorous efforts to track and interview each individual in the sample, a certain amount of survey attrition was expected. As the survey response rates in Table 1 show, 1622 (or 80 %) of the individuals in the study sample were successfully interviewed in both the baseline and midline surveys. Another 305 respondents were interviewed at baseline but not at midline and hence are not in the panel used for the analysis in this paper. 27 This survey attrition, while not much higher than other program evaluations in Africa, may cause concern that the results of this evaluation are biased, especially if the loss to follow up is correlated with individual characteristics that might affect the outcomes. To address this concern, Table 10 presents regressions on the likelihood of panel inclusion, that is, the likelihood of being interviewed at both baseline and midline. The first column indicates that treated individuals are significantly more likely than control to have been interviewed twice. This result persists even after controlling for individual characteristics and community dummies. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Columns 2, 3, and 4 show that age, school attendance, and employment status at baseline are all correlated with survey attrition. To further investigate whether these characteristics lead to differential attrition between the treatment and control groups, we interact treatment with particular characteristics at baseline. Columns 5 and 6 show that, conditional on being in the control group, the most likely predictor of attrition is having a child. By itself, being a mother does not predict attrition, but when interacted with treatment, we see that control group mothers are more likely to have dropped out of the panel than their treated counterparts. Employment at baseline, while positively correlated with attrition, does not differentially affect treated and untreated individuals. Because the differential attrition between treatment and control groups may bias our results, we use Inverse Probability Weighting (IPW) as outlined in Wooldridge (2002) to adjust the estimates of our key outcomes, using the inverse probability of inclusion in the panel as a probability weight. As a first step, we use the probit model in Column (6) of Table 10 to regress the likelihood of being observed twice on baseline individual characteristics, including those likely to affect attrition, such as employment and parental status. In the second step, we use the inverse of the predicted values from that probit model as probability weights to redo the difference-in-difference regressions for our key outcomes of interest. This method gives more weight to the individuals with the highest chance of attrition, giving them more influence on the estimate of the impact than those with a low probability of attrition. The results are reported in Table 11. The results show a high degree of similarity between the original (unadjusted) and the adjusted estimates. Across all outcomes, the point estimates and standard errors vary only slightly. 27 Of these 305 cases, nine are dropped from the attrition analysis because the household head was not interviewed at baseline, hence the household level control variables are not available. 23 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The EPAG program, which delivered six months of classroom-based skills training followed by six months of job placement support for either self or wage employment, led to a 47 % increase in employment and 80 % growth in earnings, relative to a randomly selected control group of non-participants. The program ’ s Business Skills track had markedly higher impacts on employment and earnings than the Job Skills track, which focused on wage employment. These impacts vary somewhat but remain consistently positive and significant across almost all communities, educational backgrounds, and wealth levels. The highest impacts were obtained for those in the middle of the wealth distribution, and for girls with higher educational levels, which is consistent with the program ’ s initial screening for young women with basic literacy who would be able to make use of a classroom-based skills course. These strong impacts on employment and earnings translated into positive impacts in other realms of the participants ’ lives. Our results show striking improvements in various empowerment measures, including access to and control over monetary resources, including savings, where the program led to a sizeable difference of 35 USD in savings between treated and control individuals. The study also documents significant improvements in a wide range of subjective outcomes including measures of worry, life satisfaction, self-regulation, self-confidence, and self-perceptions of social abilities. In the area of fertility and sexual behaviors, the results paint a somewhat more nuanced picture. The EPAG program had no discernible effect on the desired number of children or on the actual number of children, conditional on having any children. There was a weak reduction in the likelihood of having any children, and a stronger increase in the likelihood of being pregnant, even after excluding those who were pregnant at baseline. On net, these impacts appear to cancel each other out, consistent with a hypothesis that treated individuals waited until the end of the EPAG program to become pregnant. The third main area of outcomes looks at household-level measures. Consistent with the wide body of literature on the benefits to the household of women ’ s increased resources, the results show a 24 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, an endline survey was conducted after the second round of the EPAG program as per the timeline depicted in Figure 1. Examination of this endline survey data will permit a descriptive analysis of the outcomes of the first group of trainees 12 months after they completed the EPAG program, as well as examination of the outcomes of the second batch of trainees. The second round included not only the control group from this impact evaluation but also newly recruited participants who were offered brief basic literacy and numeracy training program prior to program entry. Work is already underway to design and implement the third round of EPAG, with a substantial redesign of the Job Skills track, an emphasis on reaching younger girls with lower literacy, and expansion to communities outside of Monrovia. If the high success rates found in this study are replicated for these future cohorts, the EPAG program should serve as a model for policy makers in Africa and the world seeking to improve lives and livelihoods of all youth, male and female. 25 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["endline survey", "endline survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "presented by equation 3 where Rit is instrumented by the instrument defined in equation 2. Since we use all provinces in the main IV estimations, i is defined as i = (1,..., 81) while t is defined as t = (2011, 2014). Following Del Carpio and Wagner (2015), we use the year-specific natural logarithm of the distance to the closest border crossing, LDit as a control variable. The year-specific distance control is defined as LDi2011 = 0 and LDi2014 = LDi. Yit = a + ρRit + Pi + Tt + βLDit + eit (3) Our second strategy is to estimate a linear difference-in-differences model with province level fixed effects for all outcomes, which is the method used by Ceritoglu et al. (2017). Their approach defines the years 2012 and 2013 as treatment years and the previous years as pre-treatment. We do exclude 2014 in the DD model since Syrian refugees have been spreading across Turkey from 2014 onwards, while they were more concentrated near the border areas that we define as the treatment region in 2012 and 2013. 4 In effect, the DD estimates use treatment years that are completely excluded from the IV model: 2012 and 2013. A second issue in the DD specification is the definition of the control area. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The weight given to each province in constructing the synthetic control is based on pre-treatment outcomes. We use the pre-treatment average of the outcome dimension, Y, the unemployment rate, employment rate and the import and export per capita of the province to determine the degree of similarity between control group provinces and the treated provinces, which in turn determines the weight assigned to control provinces. The unemployment and employment rates are included to control for the general economic performance while trade values are added to control for the degree of ’ openness ’ of the province. 6 The treated unit i = 1 is constructed by taking the mean of the outcome variables in the provinces hosting refugees in 2012 or 2013. 5 Data We use several data sources for the analysis. The IV estimations use data from years 2011 and 2014 while the DD estimations use data from 2009 to 2014. The numbers of refugees up to 2012 are treated as 0. The refugee data for 2012 and 2013 are obtained from UNHCR ’ s official weekly statements in December. Data on the number of refugees in 2014 is from Erdo ˘ gan (2014), who uses statements released by the Ministry of the Interior to compile his data. All refugee data we use in the analysis is provided at the level of 81 provinces. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There were in total 1, 6 million refugees by November 2014. Data on the number of new firms and their ownership characteristics and value are provided by the Turkish Chamber of Commerce. 7 Data on total sales and gross profits are obtained from the Turkish Ministry of Science, Industry and Technology. Other economic indi- cator variables, such as population and unemployment rates, are obtained from Turkish Statistics. Since Syrians usually have guest status rather than resident status during the period of analysis, they are not counted in official statistics such as province population and unemployment rates. Turkey is officially divided into 81 provinces and that is the level of our analysis and variables throughout. The Chamber of Commerce provides data on the number of new firms and the num- ber of new foreign-owned firms at the provincial level. Enterprises defined as firms do 11 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The data on total gross profits and net sales acquired from the Turkish Ministry of Science, Industry and Technology are compiled from administrative taxation data and was provided upon request by the ministry. The key difference from the Chamber of Commerce data is that the sales and profits data include all businesses including self- proprietorships. 9 Data were provided for the years between 2010 and 2014 and are re- ported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by definition and they are likely to be less accurate and complete for smaller firms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets which includes sales and profit figures. 10 We scale the variables according to province size by dividing sales and profits by the pop- ulation of the provinces. If we use sales and profits in absolute terms, we get qualitatively similar results. The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we 12 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Chamber of Commerce data"], "vague_data": ["administrative taxation data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There is a clear increase in the entry of new foreign firms which may be driven by refugees ’ enterpreneurship. Cheaper low-skilled labor may have helped all businesses cutting costs. Balkan and Tumen (2016) had also found a decline in prices and attribute their finding to lower labor costs, which may also be one of the mechanisms driving our results. Gross profits and sales also appear to have gone up, which would be consistent with an increase in demand. As noted by Maystadt and Verwimp (2014), heterogeneous effects on specific subgroups of the native population should be expected from refugee crises. In case of the Syrian refugee crisis in Turkey, the business activity in hosting region appears to have benefited. For a complete picture of the effects of the Syrian refugee crisis on local economies in Turkey, further research will be needed on market activity, health and longer term effects. More specifically for the line of research this study focused on, further analysis using micro-level firm data would be needed to understand how firms adjust their activity, 24 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The instrument becomes even weaker if we use refugee to population ratios rather than the number of refugees, therefore we use the absolute number of refugees throughout. 4Including 2014 data in the DD estimations generally results in more statistically significant and larger coefficients but does not change the direction of the results. 5The weights are calculated using the Stata package synth provided by the authors. We use the option nested to calculate the weights through the nested optimization procedure described in Abadie et al. (2011). 6Tunceli ’ s 2011 export and import values are missing for 2011, therefore only 2009 and 2010 values could be used in calculating Tunceli ’ s average. 7The Chamber of Commerce also provides information on the number of firms that shut down. However, reporting exits is not mandatory and the indicator is therefore less reliable. We found no significant effects in both the IV and DD estimates on the number of firms that shut down. 8Since the number of new foreign firms is 0 in several observations, we add 1 to the value. As an alternative, we used the hyperbolic inverse sine transformation which does not have the same problem with 0s as log transformation and found similar results (Burbidge et al., 1988). 9Publicly available data from the Ministry of Science, Industry and Technology indicate that less than 10 % of total revenue is from micro-establishments. Most of the net sales and gross profits reported stem from larger firms that should be included in the Chamber of Commerce data. 10All firms exceeding 200, 000 Turkish Liras (ca. $ 85, 000) in sales are obligated to report detailed balance sheets. Smaller firms may still report their balance sheets but would be doing so on a voluntary 25 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The notion that volunteering may affect positively youth ’ s sense of social cohesion has gained policy relevance since the publication of the World Development Report 2013: On Jobs, which stresses that in countries affected by conflict situations, creating the types of productive opportunities that strengthen social cohesion can help reduce the volatility of economic growth and achieve international development goals by defusing tensions and building trust among the different communities involved. This paper provides novel empirical evidence on the impact of volunteering on enhancing social cohesion values in Lebanon, a country with a fragile and highly complex political, religious and social landscape, as well as high degrees of social and economic exclusion among its young population. To our knowledge, this is the first impact evaluation that rigorously addresses this research question in Lebanon and in the Middle East and North Africa (MENA) region. The main results show that youth who were selected to participate in a volunteering program that consisted of 80 hours of inter-community volunteering activities and 20 hours of soft skills training were more likely to report higher and improved values of social cohesion in the short term. In specific, they were more likely to report higher tolerance values as well as a stronger sense of belonging to Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 SECTION 1: CONTEXT & INTERVENTION Lebanon ’ s political development system since Independence has been heavily influenced by its confessional system. While originally established to balance the competing interests of Lebanon ’ s diverse religious communities, it is seen as an impediment to inclusive growth and effective governance (World Bank, 2016), and has been closely tied to the economic and social inclusion challenges facing Lebanese youth today. The confessional system of governance has heavily impeded the equitable and efficient distribution of investments and public services. Provision and targeting of public services tend to be guided by considerations of confessional quotas and electoral geography rather than needs- based service delivery that favors the poor. In the absence of effective state institutions, sectarian organizations have played a key role in the provision of social services such as education, health, and welfare support to the most vulnerable groups linked to their electorates, thus deepening a sense of discriminatory and inequitable system (World Bank, 2016; Kraft et al., 2008). Regional disparities are stark, with the bulk of the poor living in peripheral areas (particularly the North and the South), with visible inequality in access to and quality of social services. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "According to the 2012 Institutional Profiles report, the quality of public services and its territorial coverage, which was weak to being with, have significantly deteriorated since 2006. 1 A combination of rising poverty, rising insecurity, and deteriorating public services have further strained inter-communal relations and contributed to deteriorations in social cohesion. Many Lebanese youth do not trust their state and become disillusioned as they are not able to affect their own life or contribute productively to society at large. 2 Political and civic engagement is reported to be low (Status of Women in the Middle East and North Africa Survey Project, 2010). 3 In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and large numbers of refugees, many Lebanese 1 On the quality of public services indicator, Lebanon ’ s score declined from 2. 5 in 2006 to 0. 8 in 2012 on a 4-point scale. On the territorial coverage indicator, its score went down from 2. 7 in 2006 to 1. 5 in 2012. 2In a Gallup World Poll, Lebanese reported low confidence in (a) their national government (37 percent) and the judiciary, (b) the honesty of elections (15 percent), and (c) the honesty of government (4 percent) (World Bank, 2016). 3 According to the SWMENA survey, only 18 percent of Lebanese women are members of an organization, compared to 34 percent of men. Men are more likely to be members of a political organization than women (21 percent of men vs. 7 percent of women), whereas women are more likely to be active in religious groups and charity organizations than men. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Middle East and North Africa Survey Project", "Gallup World Poll", "SWMENA survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Challenges around skill mismatch exacerbate exclusion, as many young Lebanese lack the skills and competencies demanded by private sector employers, particularly ‘ soft skills ’. To address some of these challenges, the Lebanese government (GOL) identified volunteerism as a mechanism to enable diverse youth to work together for improved community assets and service delivery as well as increased employability. In September 2012, the GOL issued a Decree (Number 8924 / 2012) that created a new extra curriculum program that requires secondary school students to complete 60 hours of civil work. In addition, the Ministry of Social Affairs (MOSA), through its Volunteering Department, launched annual action plans for the implementation of youth volunteer summer camps across Lebanon. 4 Father ’ s education and residence (region and location of school) are the two largest contributors to inequality of opportunity in students ’ math test scores, accounting for 44 and 23 percent of total inequality, respectively (World Bank, 2016). 5 According to the 2013 Gallup Poll, 90 percent of respondents in Lebanon agreed with the statement that knowing people in high positions is critical to getting a job. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Gallup Poll"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 as self-confidence, responsibility, and respect. Additionally, the curriculum includes workplace readiness skills, such as interviewing and time management. SECTION 2: STUDY TIMELINE & DATA A quasi experimental impact evaluation design was embedded into the NVSP. As mentioned before, the NVSP received 38 applications from eligible NGOs. Per well-developed selection criteria, 8 the highest 22 ranked proposals were selected to receive funding. Each of the 38 proposals included a list of 50 youth (the minimum number of youth set by the NVSP) who would benefit from the project if selected for funding. However, as mentioned before, the 22 selected projects benefited a total of 1, 296 youth, exceeding the set target of 1, 100 volunteers. Of the 50 volunteers included in each of the 38 proposals, 22 youth per proposal were randomly selected to participate in the impact evaluation study. Therefore, the initial sample size of the study comprised a total of 825 youth: 473 youth who served as the treatment group (representing the 22 selected NGOs that received NVSP funding) and 352 youth who served as the comparison group (representing the 16 non-selected NGOs). However, two NGOs refused to participate in the study once informed that their proposals had not been selected for funding. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Therefore, the final sample size consisted of 759 youth, of which 473 treatment and 286 comparison. Detailed baseline data were collected through face-to-face interviews from July to September 2015 prior to implementation. The actual implementation varied between projects and ranged between the second half of August and end of December 2015. Sampled youth from both selected and non- selected NGOs were invited to fill out a questionnaire with detailed information on volunteers ’ socio-economic backgrounds, education levels, interests and attitudes towards volunteering, employment, soft skills, as well as social cohesion values. Follow-up data were collected between November 2016 and March 2017, approximately one year following the start of implementation, through phone and face-to-face interviews. The questionnaire contained the same modules asked and collected at baseline. Despite the high 8 Proposals were ranked based on four main selection criteria: institutional appraisal (25 points), technical appraisal (40 points), project impact (25 points), and financial appraisal (10 points). There was also a fifth criterion related to sustainability of volunteering activities, which was assigned a bonus score (5 points). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The report focuses on intent-to-treat (ITT) estimates, measuring the impact of offering volunteering opportunities and soft skills training independently of actual take-up. 9 We estimate the following individual-level intent-to-treat regression: 𝑌 ௜ ௧ ൌ 𝛼 + 𝜇 ௡ ൅ 𝛽𝑇௧ + 𝛾𝐷 ௜ + 𝛿ሺ𝑇 ∗ 𝐷ሻ ௜ ௧ + 𝜀 ௜ ௧ (1) where 𝑌 ௜ ௧ is the outcome of interest for respondent i in period t, 𝑇௧ is a post-treatment year binary variable, 𝐷 ௜ is a binary variable for being assigned to the treatment, and 𝜇 ௡ is a fixed effect for NGOs. 𝛼 represents the baseline average for the outcome of interest for non-selected youth. 𝛽 is the difference in after-and- before intervention in outcomes for non-selected youth. 𝛽 ൅ 𝛿 is the difference in after-and- before intervention in outcomes for selected youth. 𝛾 is the difference in 9 Due to some procurement delays that caused a big time-lag between baseline data collection and actual NGO project implementation, many of the volunteers who belonged to selected NGOs and who were randomly selected to participate in the impact evaluation study dropped out after their baseline data were collected and were replaced by other volunteers. Project monitoring data reveal that 23 percent of volunteers assigned to treatment did not actually end up participating in the NVSP. Given the relatively high number of non-compliance, we are unable to perform Local Average Treatment Effects (LATE) analyses to understand the impact of participating in NVSP on outcomes of interest. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Project monitoring data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 outcomes between selected and non-selected youth at baseline. 𝛿 is the DiD estimator. 𝜀 ௜ ௧ is a mean-zero error term. Standard errors are robust and allow for intra-cluster correlation at the NGO level. 10 The DiD estimator can be derived from the above regression as follows: E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 1ሻ = 𝛼 + 𝜇 ௡ ൅ ���. 1 + 𝛾. 1 + 𝛿 (1. 1) + E (𝜀 ௜ ଵ | 𝐷 ௜ ൌ 1ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽 + 𝛾 ൅ 𝛿 E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 1ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽. 0 + 𝛾. 1 + 𝛿 (0. 1) + E (𝜀 ௜ ଴ | 𝐷 ௜ ൌ 1ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛾 E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 0ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽. 1 + 𝛾. 0 + 𝛿 (1. 0) + E (𝜀 ௜ ଵ | 𝐷 ௜ ൌ 0ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽 E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 0ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽. 0 + 𝛾. 0 + 𝛿 (0. 0) + E (𝜀 ௜ ଴ | 𝐷 ௜ ൌ 0ሻ = 𝛼 ൅ 𝜇 ௡ Hence, the Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "DiD estimate is (E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 1ሻ- E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 1ሻ) – (E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 0ሻ- E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 0ሻሻ ൌ ሺ 𝛽 ൅ 𝛿ሻ െ 𝛽 ൌ 𝛿. The DiD estimator relies on the “ Equal Trend Assumption ” that does not require both selected and non-selected youth to be on average balanced at baseline on key observable & unobservable characteristics. Table 1 shows that both groups differ on some key characteristics. Non-selected youth are more likely to be older, more educated (hold more academic degrees), come from Beqaa and Nabatiye, and have parents with intermediate education (grade 7 to 9). Selected youth are more likely to be males, younger, students, come from Mount Lebanon and the North, and have mothers with university education. Both groups appear balanced on key outcomes related to soft skills, tolerance values, and labor market outcomes. The exception is that non-selected youth exhibited a better sense of belonging to the Lebanese community and selected youth were more likely to have been unpaid employees (interns) at the time of baseline data collection. In addition to comparing means of observable characteristics, the study also tested for the differences in the statistical distributions of key outcomes using two sample Kolmogorov-Smirnov tests of the equality of distributions. Results indicate that the only key outcome for which there is a statistically significant difference in its distribution between the treatment and comparison groups at baseline is the sense of belonging to the Lebanese community. The largest difference between the distribution functions in the direction that the comparison group contains larger values 10 Standard errors are clustered at the NGO level because that was the unit of allocation into treatment and comparison groups. Abadie et al. 2017 argue that clustering is generally needed even if NGO fixed effects are included in the regression. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "FE remove the effect of individual-specific time-invariant characteristics so as the net effect of the predictor variable on outcome variables can be assessed, per the following equation: 𝑌 ௜ ௧ ൌ 𝛼 + 𝜇 ௡ + 𝛽𝑇௧ + 𝛾𝐷 ௜ + 𝛿ሺ𝑇 ∗ 𝐷ሻ ௜ ௧ + 𝜃ଶ𝐸ଶ ൅.. ൅ 𝜃 ௡ 𝐸 ௡ ൅ 𝜀 ௜ ௧ (3) where 𝐸 ௡ is entity n (i. e. the individual volunteer). Since they are binary (dummies), there are n-1 included in the model (i. e. 758 individual volunteers). 𝜃ଶ is the coefficient for the binary regressors (the 758 volunteers). Additionally, we propose dealing with attrition in two ways. First, we utilize the standard “ Manski Bounds ” approach (Horowitz and Manski, 2000) by imputing upper and lower bound estimates for missing data on estimated outcomes of interest at follow-up, where lower bound estimates take the lowest possible value and upper bound estimates take the highest possible value for individuals who could not be tracked over time. This allows us to provide the two extreme possible scenarios for estimated impacts had data been successfully collected for attritors. Second, we use the Inverse Probability Weighting (IPW) procedure to establish narrower bounds that might provide a better sense of whether there is a robust treatment effect. This entails first estimating a probit model that predicts the probability of data being observed (i. e. not attrition) using a set of covariates at Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 baseline that were found to be uncorrelated with the treatment in Table 1. 12 Observations are then weighted by the inverse of their probability of having data observed. Therefore, those who had a small chance of being observed are given increased weight, to compensate for those similar observations who are missing. The pseudo R-squared from the probit model suggests that those baseline covariates explain about 8 percent of the probability of data being observed. A Wald test confirmed that those variables are jointly statistically different from zero (the P-value is 0. 000). However, this still leaves a large percentage of attrition (around 92 percent) unexplained. 13 Therefore, we note that the results in the following section should be interpreted with caution. We present results in the next section for four specifications. Specification 1 presents OLS estimates from equation 1. Specification 2 presents results that control for individual fixed effects from equation 3. Specification 3 presents OLS estimates for the full sample by imputing missing observations for attritors at follow-up using lower and upper bound estimates. Specification 4 presents OLS estimates with the estimated constructed weights. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 do selected volunteers increase their teamwork / leadership and their communication skills? Do they increase their self-esteem / self-satisfaction? iii) As a result of their assignment to NVSP volunteering experience, are selected youth more likely to find a job than non-selected ones? (a) IMPACTS ON SOCIAL COHESION VALUES The two main indicators that measure improvements in social cohesion values are tolerance and a sense of belonging to the Lebanese community. Measuring social cohesion values in large-scale surveys is challenging. We are unable to use extensive measures, but rely instead on brief measures adapted from Harb (2010). The tolerance measure relies on a series of 12 questions, each of which is ranked on a four-point scale, which makes the total possible score range between 12 and 48 points. The sense of belonging to the Lebanese community measure consists of 18 questions, each of which is ranked on a seven-point scale, which makes the total possible score range between 18 and 126. Thus, higher scale values indicate higher tolerance values and a stronger sense of belonging to the Lebanese community. Both values are internally standardized so that they have a mean of 0 and a standard deviation (S. D.) of 1 in the comparison group at baseline. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "They have also been piloted ahead of data collection to test their validity and reliability. Results in table 4 indicate that assignment to NVSP has no impact on the three indicators for soft skills among selected youth for specifications 1 & 2 (see the 𝛿 estimate for columns 1, 2, & 3). The leadership skills measure appears to have worsened for both selected and non-selected youth over time, which is somewhat puzzling given that both groups are active volunteers and members in their communities (see the 𝛽 ൅ 𝛿 estimate and the 𝛽 estimate for column 1). Any changes for the communication and confidence scores one year following NVSP were not statistically 15 The selection of the indicator for this study was based on its extensive utilization (to maximize the chance for the scale to be reliable when calculating Cronbach ’ s Alpha with the data of the pilot), on the availability of detailed information regarding how the indicator was designed, and of how the scales should be interpreted once data have been collected. 16 This scale had been tested with youth aged 12-18 showing high levels of internal consistency. Additionally, it was a relatively simple scale with no need for special training to administer it or to analyze the results of the scale. 17 These skills include: awareness of one ’ s own styles of communication; understanding and valuing different styles of communication; practicing empathy; adjusting one ’ s own styles of communication to match others'styles. (communicative adaptability); and communication of essential information; Interaction management. 18 This scale has been used extensively in the psycho-social / soft skills literature, ensuring possible comparability with other studies of the soft skills literature. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 significant for both selected and non-selected youth (see the 𝛽 ൅ 𝛿 estimate and the 𝛽 estimate for columns 2 & 3). Lack of results also holds for specification 3, where imputing missing data with upper and lower bound estimates to account for the potential bias introduced by attrition did not alter the lack of impact of the program, as well as for specification 4 (see the 𝛿 estimate). Figures 1, 2, and 3 plot the distribution of soft skills scores at baseline for both selected and non- selected youth. The figures indicate that scores across the three skills are concentrated towards the end of the scale, suggesting that soft skills training offered by NVSP might have been ineffective or too basic for this pool of volunteers. 19 Indeed, as table 1 shows, a high percentage of selected youth (71 percent) and non-selected youth (63 percent) had taken previous training in soft skills prior to NVSP. Results from a process evaluation conducted separately support this explanation. The majority of NVSP volunteers in focus group discussions and interviews mentioned that they would have welcomed more advanced trainings on soft skills, as well as on technical topics and job-relevant skills that can support their employability. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These may include skills on how to write a CV, prepare for job interviews, business and entrepreneurial skills to start a business, etc. In this regard, the mechanism for improving social cohesion values appears to have come from inter-community volunteering activities, rather than improvements in soft skills. (c) IMPACTS ON LABOR MARKET OUTCOMES While the NVSP was designed primarily to improve social cohesion values among participating Lebanese youth, it was also hoped that engaging them in volunteering activities, coupled with soft skills training, would enhance their employability and thus increase their chances of employment. At baseline, half of the selected and non-selected volunteers were active and searching for a job. Among them, 49 percent reported being unemployed, 31 percent wage employed, 13 percent employed in unpaid jobs, and 7 percent self-employed (see table 1). Those active volunteers were older in age than the rest of volunteers who reported being inactive in the study ’ s sample (with an average age of 21 and closer to labor market insertion). One year later, it appears that many of 19 Our interpretation that offered soft skills are likely too basic for this pool of volunteers is provided given the scale that we used in the questionnaire to test their knowledge on soft skills. We cannot rule out the possibility that had we used a different scale, we might have found an impact, either negative or positive. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 6158 This paper addresses the conditions under which donor and non-state actor service provision is likely to undermine or strengthen citizens'legitimating beliefs. On the one hand, citizens may be less likely to support their government with quasi-voluntary compliance when they credit non-state actors or donors for service provision. On the other hand, the provision of goods and services by donors and non-state actors might strengthen citizens'confidence in their government and their willingness to defer to governmental laws and regulations if citizens believe that the government is essential to leveraging This paper is a product of the Poverty Reduction and Economic Management Departyment, Africa Region. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "reason to believe that the provision of services by donors and non-state actors could either strengthen or weaken citizens ’ deference to government. I will identify the conditions under which these two scenarios are likely to occur. On the one hand, citizens may be less likely to support the government with deference to its laws and regulations when they credit non-state actors or donors for service provision. The provision of services by donors and non- state actors is likely to prompt citizens to question why they should pay taxes to a government that is not providing them with anything in exchange. On the other hand, the provision of goods and services by donors and non-state actors might strengthen citizens ’ legitimating beliefs and their willingness to defer to governmental laws and regulations if citizens view their government as essential to leveraging and managing these external resources. I assess these competing hypotheses using multi-level analyses of Afro- barometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows us to analyze associations between donor and non- state actor service provision and the sense of obligation to comply with the tax authorities. Third, I assess the relationship between the provision of ser- vices by donors and non-state actors and citizens ’ willingness to defer to two additional authorities, the police and courts. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afro- barometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 1). 2 For example, there is also some evidence that when tax increases are linked to improvements in public goods provision, citizens are less likely to resist the tax increases. In Ghana, the government linked increases in the VAT rate explicitly to the new public spending programs, such as the Ghana Education Trust (GET) Fund in 2002 and the National Health Insurance Scheme (NHIS) in 2003 which enjoyed broad public support. The government used strategic communication to make this link in order to avoid major public protests, such as the Kume Preko protests that greeted the introduction of the VAT in 1995 and left several people dead (Osei, 2000; Prichard, July 2009). Similarly, Ghana ’ s government linked the introduction of a talk tax on mobile phone calls to efforts to combat youth unemployment, which helped to curb public opposition (Prichard, July 2009). 3. 1 Is donor and non-state actor service provision likely to undermine the fiscal contract? We are beginning to accumulate knowledge about what government can do to influence the perception of the relationship between citizens and political authorities. We know very little about what happens once non-state actors mediate that relationship. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "impression of the state ’ s role in development and give credit to the state for helping to leverage external resources. Citizens are also likely to give the state credit where mechanisms to voice complaints about non-state actors exist and where bureaucrats are able to effectively respond to complaints. Under these conditions, non-state service provision is likely to strengthen the fiscal contract. 5 Data and Methods I explore the relationship between external service provision and deference to government using Afrobarometer survey data from 19 Sub-Saharan African countries (see Table 1). Africa is an especially good place to examine these issues because of the large amount of variation both within and across African countries in the extent to which non-state actors, donors and other states are active in service provision and the extent to which governments are relatively effective and fair. Government responsiveness, corruption and reliance on non-public resources vary considerably among localities with consequences for citizen understanding of and relationship to government (Gibson and Hoffman, 2005). This project relies on the fourth round of Afrobarometer data that surveys Africans ’ views towards democracy, economics, and civil society with random, stratified, nationally representative samples. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "obtained from the multilevel logistic model. Thus, I am confident that I did not lose substantial statistical information by dichotomizing this variable. 6. 2 Independent Variables 6. 2. 1 Socio-Demographic Variables I control for standard socio-demographic variables that can affect citizens ’ acceptance of government ’ s right to make people pay taxes. A question prob- ing respondents on their household income was not included in the fourth round of Afrobarometer surveys. Asking respondents to quantify their in- come can be problematic in the context of developing economies, where in- dividuals are often embedded in barter or commodity exchange, rather than, market economies. There are, however, reasonably good proxies including whether respondents own a television, radio, car, and mobile phone, and use the internet. Age, education, employment, and urban or rural residence are demographic factors that also affect household resources. 6. 2. 2 Experience with Paying Taxes or Fees It is difficult to assess just how ubiquitous taxes are in ordinary Africans ’ lives. There has not been any systematic effort to take stock of the types and amount of taxes citizens pay across Africa. Similar to pre-modern European states, African states ’ revenue raising capacity is generally low. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Afrobarometer surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "On average, the tax-to-GDP ratio in Sub-Saharan Africa is around 21 percent, compared with the OECD average of about 32 percent. In Tanzania and Uganda, the total tax share drops to about 10 percent. Historical data suggests that the tax share of many European countries did not reach 15 percent of GDP until World War II when incomes were substantially higher than they are in many African countries (Fjeldstad and Rakner, 2003, 3). The types and amount of taxes citizens pay varies both within and be- tween countries. We do know there are taxes on agricultural crops, but the rates and processes of collection vary within countries (Kasara, 2007). User fees from electricity, water, sanitation, and other services comprise the major- ity of local revenue in South Africa (Hoffman, 2007). In Tanzania, Fjeldstad and Semboja (2001) count ten major categories of taxes, eighteen major categories of licenses, forty groups of charges and fees, and seventeen items listed as other revenue sources. In some countries including Kenya, Malawi, 11 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, I include a measure of whether citizens believe that a large portion of tax administrators is corrupt. Second, I in- clude a variable indicating whether citizens approve of how well their local government is handling the collection of license fees on bicycles, carts and barrows. 8 Third, both the size of a country and the size of the government may affect a government ’ s ability to detect and punish evaders. I include the 7I also include a country-level indicator of government performance, the World Bank Governance indicator of government effectiveness, in the model. This indicator measures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implemen- tation, and the credibility of the government ’ s commitment to such policies (Kaufmann, Kraay and Mastruzzi, 2006, 4). This variable is not significant at the p <. 05 level. 8I included two additional measures in the model neither of which were significant at the p < 0. 05 level. One is a measure of citizens ’ approval of how well their local government council provides citizens with the information about the councils budget (i. e. revenues and expenditures). The other, the World Bank governance indicator, control of corruption, measures the extent to which public power is exercised for private gain, as well as capture of the state by elites and private interest (Kaufmann, Kraay and Mastruzzi, 2006, 4). 13 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["World Bank Governance indicator"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "natural log of a country ’ s population size and government consumption as a share of GDP; the latter is the most widespread measure of government size (Adsera and Boix, 2002; Alesina and Wacziarg, 1998; Rodrik, 1998). Tax compliance is also related to the government ’ s ability to effectively detect and punish tax avoiders, tax evaders, and tax arrears. Although an imperfect measure of states ’ deterrent capacity, Afrobarometer includes two survey questions on perceptions of government enforcement and monitoring capacities. One question probes respondents on how often ordinary people who break the law go unpunished. The other probes respondents on how often officials who commit crimes go unpunished. This latter question is also a measure of perceived government fairness- the extent to which a government implements the law evenly across all social groups. 6. 2. 5 Procedural Justice I include two indicators of procedural justice. The first probes respondents on how often people are treated unequally under the law. The next taps citizens ’ perceptions of the government ’ s treatment of their ethnic group. Specifically, respondents were asked how often their ethnic group is treated unfairly by their government. 9 6. 2. 6 Donor and Non-State Actor Provision of Services I include a measure of who citizens believe is responsible for collecting income taxes. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "I expect citizens who perceive tax collection to be the responsibility of non-state actors to be less likely to be willing to pay taxes to the state ’ s tax department than citizens who perceive tax collection to be the responsibility of the state. Perceptions of the effectiveness of donor and non-state actor provision of services are assessed using the following items. Respondents were probed on how much they believe the following non-state actors and donors do to help their country: the United Nations; international donors and NGOs; international businesses and investors; China; and the United States. 9 I also include Freedom House ’ s political liberties and civil rights ratings for the 19 countries in the sample. These two variables should capture the relative equality of influence in making policy. They indicate whether citizens are able to express their voice without fear of repression and whether elections are free and fair. Neither of these variables are significant at the p < 0. 05 level. 14 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, there is reason to believe that better service delivery may affect citizens ’ willingness to defer to the tax department only through its effect on improved outcomes that matter for citizens ’ livelihoods. Unless improved services and infrastructure have a positive impact on citizens ’ welfare, indi- viduals are unlikely to credit the government for these outputs (Sacks and Levi, 2010). The Afrobarometer ’ s objective measures of service delivery only denote the presence or absence of infrastructure and services. The data do not indicate the condition of the services and infrastructure. Citizens may perceive and reward relative improvements or sanction de- teriorations in services, rather than the absolute level of service quality they receive. If services deteriorate or improve, taxpayers may alter their beliefs about governments ’ performance and should attempt to adjust their terms 10I also tested whether there is a relationship between the presence of a concrete road, health clinic, post office and electricity grid in the enumeration areas and respondents ’ willingness to pay taxes. None of these objective indicators except for the presence of an electricity grid were significant at the p < 0. 05 level. The presence of an electricity grid is negatively associated with the willingness to defer to the tax department. 17 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of trade with government. Because the dataset used for this study is cross- sectional rather than longitudinal, I am unable to examine whether citizens adjust their beliefs and behavior to relative changes in service delivery. I was only able to test whether there are associations between the absolute service quality across neighborhoods, countries and ethnic groups and deference to the tax department, police and courts. Further, citizens may not be attributing goods and services to the gov- ernment. Rather, citizens may be attributing goods and services, such as roads, electricity grids, sewage systems, health care and education to vari- ous non-state actors including the following: the private sector; NGOs and community-based groups; churches, mosques and other religious institutions; traditional leaders; and, bilateral and multilateral donors. Survey questions on the Afrobarometer only indicate the presence or absence of services and infrastructure, and the quality of these services, but these questions do not probe respondents on who they believe are providing these services. Each of the indicators of perceptions of government performance is sig- nificant at the p < 0. 05 level. Food security is positively associated with a willingness to defer to the tax department. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "appear to be a relationship between perceptions of the helpfulness of donors and non-state actors and the willingness to defer to the police and to the courts. Individuals who believe that donors and non-state actors exert too much, rather than, too little influence over one ’ s government is associated with the willingness to defer to the court and to the police. Findings also suggest that citizens who believe non-state actors are responsible for provid- ing law and order are less likely to be willing to defer to the police and to the courts than respondents who believe the state is responsible for providing law and order. 7. 4 Conclusion This paper demonstrates that the logic of the fiscal contract is relevant to a wide variety of contemporary African states. Findings from a cross-national analysis of survey data from Africa link citizens ’ legitimating beliefs — in- dicated by a willingness to defer to the tax department, the police and the courts — to a government ’ s fulfillment of a fiscal contract. Citizens who are satisfied with their government ’ s provision of services and goods are more likely to be willing to defer to the tax department, courts and police than citizens who disapprove of government service provision. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This study aims to assess the consequences of forced migration on ethnic diversity and conflict in Sub-Saharan Africa. We combine a unique dataset on refugee camps with individual data from the Afrobarometer Surveys across 23 African countries for the 2005 — 2016 period. We construct two standard measures of ethnic diversity: indices of ethnic fractionalization (EF) and ethnic polarization (EP). Ethnic fractionalization measures the probability that two individuals drawn from the society at random will belong to two different ethnic groups and thus increases with the number of ethnic groups present. Ethnic polarization captures antagonism between individuals and is maximized when the society is divided into two equally sized and distant ethnic groups. Although these indices have been widely used, little variation over time has been found, making causal inference difficult. The innovative aspect of our analysis is that we use data on the precise locations of refugee camps, their yearly size, and — most importantly — their annual composition in terms of countries of origin. Combined with the Ethnic Power Relations- Ethnicity of Refugees 2019 dataset, we are able to predict changes in ethnic diversity induced by refugee inflows. We then assess the relationship between refugee diversity and the likelihood of conflict. In an additional analysis, we also assess how refugee-induced changes in diversity affect the incidence of theft and violence, participation in protests, and perceptions of ethnic attachment, inter-personal trust, and institutional trust. Other studies have investigated the links between displacement and social conflict or social co- 3 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Afrobarometer Surveys", "Ethnic Power Relations- Ethnicity of Refugees 2019"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using Afrobarometer ’ s geocoded surveys, we focus on clusters as our unit of observation. 10 Our sample consists of 7, 547 such locations and 76, 518 individuals in 23 countries in Sub-Saharan Africa. “ The sampling universe normally includes all citizens age 18 and older. As a standard practice, they [we] exclude people living in institutionalized settings, such as students in dormitories, patients in hos- pitals, and persons in prisons or nursing homes. ” (Afrobarometer, https: / / afrobarometer. org / surveys − and − methods / sampling − principles) Since the sampling frame is based on recent censuses, with the aim of representing all citizens of voting age in a given country, the Afrobarometer samples are unlikely to include refugees. Note also that “ the sample design is a clustered, stratified, multi-stage, 8We explain the construction of theses indices in Section 4. 2. 9We test the robustness of our results with a smaller (40 km) and a larger (120 km) radius in Section 5. 3. This choice of buffer size assures us that between 75 percent and virtually all refugee camps fall within a cluster buffer. Other studies relying on Afrobarometer data construct buffers ranging from 25 km (e. g., Michaelopoulos and Papaioannou (2011), investigating ethnic-specific pre-colonial institutional structures) to 100 km (e. g., McGuirk and Burke (2020a), analyzing the impact of food-price shocks on conflict). 10Afrobarometer is a pan-African research network conducting public attitude surveys on democracy, governance, the economy, and society in African countries that are repeated on a regular basis (Afrobarometer, 2020). 10 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location. Area stratifi- cation reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analyzed. ” Afrobarometer provides geocoded data for 6 rounds, which correspond to the 1991 – 2016 period, with the information on an individual ’ s ethnicity available from round 3 (corresponding to 2005 – 2006). We therefore restrict our analysis to the 2005 – 2016 period. The selection of countries is driven by data availability. Among the 33 countries with available Afrobarometer data, we exclude Botswana, Cape Verde, Lesotho, Madagascar, Mauritius, Sao Tome and Principe, South Africa, and Swaziland, for which no data is available on refugee camps or from the EPR-ER. We also exclude Sudan since the question on individual ethnicity is not asked in this country ’ s survey. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["EPR-ER"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The countries in our sample are Benin, Burkina Faso, Burundi, Cameroon, Gabon, Ghana, Guinea, Ivory Coast, Kenya, Liberia, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, Tanzania, Togo, Uganda, Zambia, and Zimbabwe. As described in Table B. 1, we also incorporate information on the quality of our refugee data, which is determined by comparison with official UNHCR bilateral data. Below we describe how these data have been used to define our main variables of interest and present some descriptive statistics in Table B. 2. 11 Conflict. In Equation 1, we first relate variation in ethnic diversity with data on conflict from ACLED (Linke et al., 2010). Two main definitions are used: the incidence of conflict and the intensity of conflict. Incidence is captured by an indicator equal to one if conflict occurred in a particular year within a pre-defined buffer around cluster j. Intensity is measured by summing the number of conflict events occurring in a particular year within the same buffer area. A conflict event is defined as a single altercation wherein force is used by one or more groups for a political end (Linke et al., 2010). We further describe events (non-exclusively) as violent events, non-violent events, violence against civilians, and riots. In our main analysis, we focus on violent conflicts (Section 5. 1) and report results for other outcomes as robustness tests (Section 5. 3). In doing so, we follow a recent and large literature that has combined the ACLED dataset with geographically disaggregated data in Africa (Besley and Reynal-Querol, 2014; Berman and Couttenier, 2015; Michaelopoulos and 11Panel A of Table B. 2 shows descriptive statistics for the data from refugee-hosting areas specifically, whereas panel B of Table B. 2 shows descriptive statistics for our data in all covered areas. 11 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data on conflict from ACLED"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Papaioannou, 2016; Berman et al., 2017; Harari and Ferrara, 2018; Eberle et al., 2020; McGuirk and Burke, 2020b). As a further robustness check, we also use data on conflict incidence and intensity from the UCDP, which uses a more conservative definition of conflict. The UCDP dataset is manually curated and compiled with automated computer assistance (Sundberg and Melander, 2013). The UCDP defines an armed conflict event as “ an incident where armed force was used by an organized actor against another organized actor, or against civilians, resulting in at least one direct death at a specific location and a specific date ” (Pettersson et al., 2020). We extract daily event observations from the UCDP dataset if the location of the actual event is exactly known, the event location is within a radius of less than 25 km around a known point, or at least the administrative district where the event happened is known. As pointed out by Eberle et al. (2020), the UCDP events are more likely to capture violence between large-scale and more structured groups. Table B. 2 shows that on average, conflict events seem to occur more in refugee-hosting areas. This is of course not a causal interpretation but a simple correlation. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["UCDP dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "that are at a maximum distance of 80 km from the 7, 547 clusters. 12 Figure 1 shows the locations of these refugee camps and clusters. Clusters are represented in green, while clusters in the vicinity of a refugee camp are represented in red. Refugee camps are designated with a red + sign. There are some important limitations associated with this data. First, the data only provides information on refugees residing in camps monitored by the UNHCR. In Figure B. 7, we combine the UNHCR refugee camp data on the annual number of refugees and the UNHCR official statistics on refugees (which includes people in refugee-like situations) at the country level. 13 Although the overall trends match, our constructed dataset clearly underestimates the true refugee population in Africa, which is not surprising since our camp-specific data does not contain dispersed refugees or refugees living outside of camps. While our data seem to represent quite fairly the number of refugees in camps, there is significant heterogeneity across countries. Based on the visual inspection of Figure B. 8, the quality of the refugee data appears to be less reliable for the following countries in our sample: Gabon, Mali, Senegal, and Togo. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR official statistics on refugees"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 1: Data and Descriptive Statistics: Clusters, Refugee Camps, and Conflicts Revised refugee diversity indices. We first use Afrobarometer data to construct standard indices of diversity, namely the EF and the EP indices (Bazzi et al., 2019; Esteban and Ray, 1994). The EF index describes the probability that two randomly selected individuals from a given location belong to two different ethnic groups (Alesina et al., 2003, 2016; Gomes, 2020b). The EF index can be defined as EFjt = Njt X e = 1 get (1 − get), (2) where Nj is the number of ethnic groups in cluster j at time t and get is the population share of 14 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ethnic group e at time t. It can also be expressed as one minus the Herfindahl index (Alesina et al., 2016). The EP index gives more weight to intergroup differences at the expense of within group homo- geneity. It can be defined as (Esteban and Ray, 1994, 1999; Montalvo and Reynal-Querol, 2005) 14 EPjt = Nrt X e = 1 (g2 et) (1 − get). (3) We compute this index for each cluster at the time of each Afrobarometer survey to assess how refugee-induced changes in diversity differ from standard indices of diversity. In order to construct the revised refugee diversity indices according to ethnicity e, we first combine information about the country of origin of refugees hosted in refugee camps c in year t with the data from the EPR-ER 2019 dataset. The EPR-ER records the ethnic composition of refugee stocks originating from neighboring countries and countries in proximity to each other (maximal distance between country borders ≤ 950 km) with at least 2, 000 refugees and provides the ethnic composition of refugees (Vogt and Girardin, 2015). More specifically, the EPR-ER dataset gives us the share of refugees from ethnic group e moving from country o to country d at year t. The EPR-ER data gives us the three main ethnic groups. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Afrobarometer survey", "EPR-ER 2019 dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There is one limit to our approximation in Equation 4. The ethnic composition of refugees in each year t for a given origin – destination pair of countries obtained from the EPR-ER database is assumed to be homogeneous across camps of the same origin – destination pair of countries for the refugees at year t. This may seem to be a strong assumption; however, the risk of misallocating refugees is reduced as the annual variation in the EPR-ER is generated by just a few dominant groups for a given origin – destination pair and the geographical distribution of refugees by country of origin is highly influenced by the proximity to their countries of origin. 16 As can be seen from panel A of Table B. 2, in refugee-hosting areas, on average, both EF and the EP seem to increase quite significantly when they are revised by incorporating the number of refugees in an 80-km buffer: the mean value of the standard EF index is 25. 58 %, while the mean value of the revised refugee EF index is 37. 90 %. The mean value of the standard EP index is 10. 11 %, while the mean value of the revised refugee EP index is 14. 07 %. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["EPR-ER database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 3 also shows that there is considerable variation in both indexes within our sample when averaging these indexes at the regional level over the period of investigation. 16It is possible that our approximation is noisy and could potentially induce non-random measurement errors. In Section 4. 3, we propose an instrumental variable approach and estimate Shareodet from the EPR-ER data using a gravity model. Our findings concerning the number of ethnic groups across time for a given origin – destination pair are in line with the EPR-ER data. It seems that refugees of a given origin – destination pair mainly belong to two major ethnic groups. This also means that the variation in diversity in refugee-hosting areas is coming from the refugee composition at the camp level. Figure B. 5 shows the movements of refugees from origin to destination countries under scrutiny. Somalia, the Democratic Republic of Congo, Liberia, South Sudan, and Sudan are major source countries for refugees, while Kenya, Tanzania, Uganda, Zambia, and Ghana appear to be countries hosting most refugees. Representing refugees in camps per ethnic group for the top 5 asylum countries over the sample period, Figure B. 9 shows that there is considerable variation in ethnic composition across camps. 17 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["EPR-ER data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Linking Ethnic Data from Africa", "EPR-ER dataset", "Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "at the same level with a similar categorization process; instead, the information can relate to an individual ’ s linguistic ethnicity, dialect, or an ethnic group encompassing several languages. In our main analysis, we use LEDA ’ s binary linking at the “ dialect ” level, based on the minimum linguistic distance to link these ethnic groups. 17 This involves computing a value corresponding to the shortest path (see Equation A. 1) between ethnic groups using a language tree. In our case, “ dialect ” is the level defined to match the two groups (see Figure A. 1 from M ¨ uller-Crepon et al. (2020) for a Ghanaian case). 18 We further describe the use of the LEDA software package in Section Appendix A. 1. 4. 3 An instrumental variable approach In Section 4. 1, we acknowledged that non-random measurement errors might be a concern. Another major identification challenge is the risk that our revised measures of diversity are biased due to the selection of hosting areas by refugees. We should first acknowledge that the ability of refugees to select their places of residence is much more limited than economic migrants. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, we can- not exclude the possibility that refugees would sort non-randomly into areas with particular ethnic characteristics. 19 In order to address this potential endogeneity, we implement an instrumental variable (IV) ap- proach. We are particularly concerned about certain ethnic groups from certain countries of origin moving to destination countries with similar ethnic characteristics. Such endogenous selection would be reflected in the EPR-ER data. To deal with the plausibly endogenous nature of the resulting refugee EF and EP indices, we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data. The predicted (and plausibly exogenous) number of refugees by ethnic group e is then used to create other (plausibly exogenous) diversity indices to be used as instrumental variables. More specifically, we estimate the following gravity model: 17We also use this method to link data from EPR-ER on the ethnicities of refugees with data from the Murdock Atlas on their historical homeland (Section 4. 3). 18As a robustness check (Section 5. 3), we use an alternative linkage based on the relations between sets of language nodes associated with two groups. 19Another source of selection may come from the fact ethnic groups are more likely to be displaced when they share territory with regime supporters in their countries of origin (Lacina et al., 2017). Since similar ethnic groups are likely to share common borders (Michaelopoulos and Papaioannou, 2016), it is not impossible to think conflict might spill over through this channel. 19 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["EPR-ER data", "EPR-ER", "Murdock Atlas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "REFodet = αod + γe + τt + β1Conflictot − 1 + β2Conflictet − 1 + β3Distanceed + ϵodet, (5) where REFodet is the stock of refugees of ethnic group e from country o in country d at year t. As we have data on yearly refugee stocks and would like to estimate the changes in these stocks over time using a gravity model, we include origin – destination fixed effects αod so that identification is based only on changes in stock over time (Zylkin, 2019). 20 We also include time τt and ethnic group fixed effects γe. Here we obtain data on the ethnicity of refugees from Murdock ’ s Atlas, which provides a map of ethnographic regions for Africa and the historical homelands of refugees (Murdock, 1967). To match ethnic groups across datasets, we again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity from the EPR-ER dataset and, later, with data from Afrobarometer. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Murdock ’ s Atlas", "LEDA21", "EPR-ER dataset", "Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Indeed, despite the recent literature rejecting the conflictive impact of refugees in hosting areas (Zhou and Shaver, 2021), the magnitude of our coefficients might be explained by the confounding presence of refugees. Columns (5) and (6) further introduce climatic controls. Column (6) corresponds to Equation 1 and refers to our benchmark specification. Columns (1) and (2) show that without incorporating the changes in ethnic diversity induced by refugees we would not be able to identify a relationship between diversity and violent conflicts. In column (3), the revised refugee fractionalization index has a negative and significant coefficient, while the revised refugee polarization index has a positive and significant effect on the incidence of violent conflicts. In columns (2), (4), and (6), our coefficients of interest are of the same order of magnitude when the number of refugees is controlled for. Our results are not altered by incorporating rainfall and temperature anomalies (columns (5) and (6)), but the estimates become slightly more precise. 21 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5. 5 Ethnic diversity at different levels Despite the use of sampling weights in the construction of the diversity indices, we have no guarantee that our diversity indices are representative at the local level. Although similar ethnic diversity indices have been used at the local level (Nunn and Wantchekon, 2011; Rohner et al., 2013; Robinson, 2017; Desmet et al., 2020; Gomes, 2020b, a; Hodler et al., 2020), we cannot exclude the possibility that a lack of representativeness at the local level introduces some noise into our estimates. Ideally, we would have liked to construct our local diversity indices based on census data. However, such data are not available on an annual basis and only a minority of African countries include ethnicity questions on their censuses (Robinson, 2017). Robinson (2017) highlights other benefits but also warns against the risk of using non-random samples or of the size of samples introducing significant errors. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["census data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As summarized by Robinson (2017), “ fortunately, Afrobarometer respondents comprise stratified random samples at all levels, making population estimates based on them unbiased: thus, the major concern with using Afrobarometer sample data to construct demographic measures is unbiased measurement error. ” Based on a comparison of census-based and survey-based diversity indexes across five African countries, Robinson (2017, 224) found that a “ sample-based measure tends to underestimate the overall degree of diversity compared to census data ”. In theory, this should make it more difficult to observe the true relationship between ethnic diversity and some outcomes at the local level. Diversity indices are more likely to be measured with noise in highly diverse communities at the local level. We nonetheless argue that such a concern should not be overestimated, for three reasons. First, such noise cannot easily explain the contrast between the coefficients corresponding to the pre-revised and revised indices and the opposite results found for the revised refugee fractionalization and the revised polarization. This set of results can be explained by the fact that our identification comes from the annual changes in refugees flows. Second, the IV approach is likely to deal with the measurement errors if they are correlated with our main variables of interest. Our IV estimates therefore capture a local average treatment effect coming from the plausibly exogenous increase in annual refugee flows of particular ethnic groups. The similarity of the IV results to the OLS results supports this interpretation. Third, at the cost of introducing attenuation bias30, we also aggregate the number of conflict events at the regional level. Lines B and C of Table 7 confirm the negative and positive effects found for the revised fractionalization and polarization indexes, respectively, whether or not 30Another risk highlighted by Robinson (2017) is the fact that ethnic diversity may also capture different theoretical mechanisms at aggregated levels. 34 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer sample data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of the groups and the distances between them. For instance, (Bazzi et al., 2019) shows that polariza- tion increases ethnic attachment. Others have highlighted the reduction in trust, either interpersonal trust or institutional trust Alesina and Ferrara (2002); Beugelsdijk and Klasing (2016). To assess the importance of alternative explanations, we first replicate our analysis using individual data on violence. In addition to participation in protests, we follow McGuirk and Burke (2020b) in using the Afrobarometer survey data on interpersonal crime and physical assault. We then assess the relationship between the revised refugee diversity indices and alternative individual outcomes such as ethnic vs. national identity, generalized trust, trust in neighbors, and institutional trust (trust in government). The questions from the Afrobarometer mentioned below are used as a proxy for these outcomes: 32 1 Attack: Over the past year, how often (if ever) have you or anyone in your family: Been physically attacked? 2 Crime: Over the past year, how often (if ever) have you or anyone in your family: Feared crime in your own home? 3 National identity: Let us suppose that you had to choose between being a [Ghanaian / Kenyan / etc.] and being a [respondent ’ s identity group]. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Afrobarometer survey data"], "descriptive_data": [], "vague_data": ["individual data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As a robustness check, we also use the first type of linkage: binary linking based on the relations between sets of language nodes associated with two groups. This is done using the “ setlink ” function of LEDA. With this function, the two groups are linked to each other as soon as they share any 2 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure A. 2: Injective relations We also isolate many-to-one (bijective) relations. In this case, we have to aggregate the Afro- barometer ethnicities with their unique and more aggregated correspondence in the UNHCR refugee camps data (See Figure A. 3). Figure A. 3: Bijective relations The remaining correspondences are either (i) one-to-many (bijective) but opposite to Figure A. 3 (i. e., many ethnicities from the UNHCR refugee camps data correspond to one ethnicity from the Afrobarometer) or (ii) many-to-many relations. For both cases, we apply a more pragmatic approach: a. In both cases, we disregard ethnicities that do not appear either in the Afrobarometer or in the UNHCR refugee camps data. This means that for the remaining ethnicity that has no counterpart in either the Afrobarometer or the UNHCR refugee camps data, we simply keep the name of the ethnicity as such, i. e., this information is not dropped. b. Then, after ignoring ethnicities that have no occurrence in our datasets, we check whether the one-to-many or the many-to-many relation has not boiled down to a one-to-one resp. many-to- one relation again. If so, we can treat them as above. c. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For the remaining one-to-many relations, we keep these ethnicities in the Afrobarometer as such and consider them as a single ethnic group. Some manual treatment can even further improve 4 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 International migration — the movement of people across national borders — has important economic, social, and political implications. Despite the recent emergence of a dynamic literature, empirical analysis of migration flows and their impact lags behind the policy debate and the theoretical literature. The main reason is the absence of comprehensive and reliable data on international migration patterns and migrant characteristics at either the aggregate or the household level. The objective of this article is to use data from more than one thousand national censuses and population registers to estimate a complete global origin – destination migration matrix for each decade over 1960 – 2000. These 226 * 226 matrices, comprising every country, major territory, and dependency around the world, are divided into periods corresponding to the last five completed census rounds. The gender dimension of international migration over this period is also presented. The primary source of the raw data is the United Nations Population Division ‘ s Global Migration Database, created through the collaboration of the United Nations Population Division, the United Nations Statistics Division, the World Bank, and the University of Sussex (United Nations [2008]). This unique data repository comprises 3, 500 individual census and population register records1 for more than 230 destination countries and territories over the last five decades. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Global Migration Database"], "descriptive_data": [], "vague_data": ["national censuses and population registers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 The main contributions of this article lie in identifying and overcoming these challenges in order to construct a consistent and complete set of origin – destination matrices of international migrant stocks for 1960 – 2000, disaggregated by gender. The starting point is a master set of 226 origin or destination countries and regions. Despite border changes, all migrants are assigned to this master set so that migrations can be meaningfully tracked over time. These assignments, especially in cases where only aggregate data are available, are made using several alternative propensity measures based either on a destination country ‘ s propensity to accept international migrants or on an origin country ‘ s propensity to send migrants abroad. Cases of omitted data occur when destination countries do not collect or publicly disseminate the information on migrants. When data from census rounds are missing altogether, the approach taken depends on the extent of the omission (see appendices 3 and 4). When sufficient data are available for other decades, interpolation is used. When not enough data are available, propensity measures are used to generate bilateral data. When a gender breakdown is missing, gender splits are calculated based on supplementary statistics or other data in the matrices (see appendix 5). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The resulting migration matrices should be viewed as work in progress, but they are an important step in an ongoing global effort to improve migration data. The matrices can be readily updated as additional or superior information surfaces, and they can easily be extended to include future census rounds. Bilateral datasets of international migration are rare. Attempts to create them have focused almost exclusively on industrialized countries as destinations because these countries have more accurate and more frequently produced data. Harrison and others (2003) calculate bilateral remittances for the countries of the Organisation for Economic Co-operation and Development (OECD) together with the 27 largest nonmembers. These estimates are based on international bilateral migrant stock data that the authors also provide, although many of the data are derived from the Trends in International Migration (OECD 2002). This report, published annually since 1973, was arguably the most comprehensive guide to international migration for many years and has been the basis for many studies (see, for example, Mayda 2007). More recently, the OECD has developed a database that provides a comprehensive overview of migration to OECD countries in 2000 (OECD 2008). These data are disaggregated by a number of covariates including age, gender, educational attainment, and place of birth. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Trends in International Migration"], "descriptive_data": ["international bilateral migrant stock data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Another series of papers, again concentrating on the OECD, examines the brain drain in 1990 and 2000 (see, for example, Docquier and Marfouk 2006); migrants ‘ gender (Docquier, Lowell, and Marfouk 2009); age of entry (Beine, Docquier, and Rapoport 2007); and the medical brain drain (Bhargava and Docquier 2007). Parsons and others (2007) construct a matrix encompassing the entire world for the 2000 census round. Until now, this was the most comprehensive global overview of bilateral migrant movements. Ratha and Shaw (2007) use an earlier version of the dataset in a paper focusing on migration between developing countries (generally referred to as South – South migration in the literature) and bilateral remittance flows. The data in the current article reveal several important patterns. Between 1960 and 2000, the global migrant stock rose from 92 million to 165 million, but fell as a share of world population, from 3. 05 percent to 2. 71 percent. A large share of the stock in 1960 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Database. Where both censuses and population registers are available, censuses receive priority. Censuses, generally conducted decennially, are retrospective tools for surveying an entire population (or in some cases, a representative sample) at a single point in time. In addition to their universal coverage, their greatest strength is the inclusion of questions on place of birth and nationality. Censuses also typically aim to enumerate the resident population, whether documented or undocumented (Bilsborrow and others 1997). So although some migrants have a strong incentive to provide false information to enumerators, many undocumented migrants will be captured in these matrices. 7 The size and scope of the census questionnaires vary enormously, both over time and in different destination countries. And there is potential variation in the quality of censuses both across countries and over time. Richer countries have many resources at their disposal to design questionnaires, train interviewers, employ statisticians, and disseminate results. Researchers have little choice but to accept the data at face value. However, where the underlying census is clearly substandard (when there are errors that are obviously not coding errors or not easily corrected), these data are omitted from the analysis. Popular in many parts of Europe, population registers are continuous reporting systems providing up-to-date demographic and socioeconomic information for everyone surveyed. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["censuses", "population registers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Typically, registers have evolved over time (from parish records, for example). They were never developed specifically to record international migration information, and they vary considerably across countries. For example, the laws under which individuals are classified as migrants and the conditions under which they are inscribed or deregistered differ greatly (Bilsborrow and others 1997). The Raw Data The Global Migration Database is a vast collection of destination country data sources detailing migrant stocks from numerous origin countries and regions (United Nations [2008]). Compiling and maintaining the underlying primary sources require herculean efforts to scour the key census collections of the world and enter the data manually. In total, the database comprises records from some 3, 500 separate censuses from more than 230 migrant destination countries and territories, by sex and age. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Global Migration Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Last, specific adjustments are made in the case of Germany and the Republic of Korea. For Germany, bilateral data are available only by nationality. However, these data fail to take adequate account of the large number of ethnic Germans who arrived from other countries between 1944 and 1950 (mainly expellees) and those who arrived after1950 (mainly resettlers). Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3). In the case of Korea, data by nationality are readily available for each census round. However, these data fail to account for the large numbers of migrants from the People ‘ s Democratic Republic of Korea living in the Republic of Korea. Since the United Nations Trends in International Migrant Stock details the total migrant stock in the Republic of Korea by the country of birth definition and because citizenship is rarely granted to people from outside, it is simply assumed that the nationality data were comparable to the foreign-born definition. The nationality total was then subtracted from the UN total and the remaining migrants were assigned to the People ‘ s Democratic Republic of Korea. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["German 2005 micro-census", "United Nations Trends in International Migrant Stock"], "descriptive_data": [], "vague_data": ["bilateral data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Recording and Recoding There is little standardization in the recording and dissemination practices for censuses across destination countries. 14 The level of detail with which destination countries record and disseminate migration data depends on the design of the original questionnaire. Some census questionnaires ask for a specific country of birth and others simply ask for a general geographic region, such as Africa. Even if the original questionnaire asked detailed questions, some countries disseminate data only on how many residents were born abroad or have foreign citizenship. In general, three types of migrant origin are observed in the disseminated census data:  Specific geographic regions: Some of these correspond to exactly one of the 226 countries and territories in the master list. Others pertain to localities that tend to be obscure territories, islands, or regions, such as the Isle of Man or Ceuta.  Aggregate geographic regions: These correspond to two or more countries or territories in the master list. They can be continents (such as Africa), parts of continents (such as South Asia), political alliances (European Union), or other classifications (such as Other Ex-French Africa; Algeria, Tunisia, and Morocco; and Melanesia). The data for these aggregate regions need to be allocated to the 226 countries in the master list. The details of the procedures are discussed below.  Miscellaneous categories: These include refugees, stateless, and born at sea. There are generally no geographic correspondences for these. Thousands of geographic regions and categories emerged from the more than one thousand individual destination country sources chosen for the analysis. The vast majority of these are repetitions that refer to identical geographic locations using different 14 The United Nations (1998) has developed recommendations aimed at promoting standardized recording practices across countries. Until such practices are followed uniformly, harmonization will remain a key issue in understanding and comparing migration statistics. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "An alternative version of the database that has been mapped to the United Nations (2006, 2009) Trends in International Migrant Stock database is available from the authors. These data are standardized over time in terms of the years to which they refer. { Table 3 here} Calculating Missing Gender Splits Although common in the underlying data, bilateral migration data disaggregated by gender are sparser than aggregate migrant totals (see table 1). An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices. Similar to the allocation from aggregated categories in the Global Migration Database to specific origins in the master list, two measures are used for calculating gender splits; they are described in appendix 5. Combining Migrant Definitions Only a single definition of a migrant (foreign born or foreign citizen) can be applied to each destination country in the final matrices. Switching definitions over time 17 The subregions used for the disaggregations are the 21 UN regions (see http: / / unstats. un. org / unsd / methods / m49 / m49regin. htm, with the countries of Oceania aggregated into a single subregion. They do not match the large World Bank regions used in the analysis in section IV. 18 While this propensity measure is clearly inappropriate, less than 1 percent of all migrants and observations are assigned on this basis. This method is included so that every migrant in the underlying data is accounted for. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock", "Global Migration Database"], "descriptive_data": [], "vague_data": ["bilateral migration data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 { Tables 4 and 5 here} Among the first six categories that are based on raw census data, three categories (raw scaled, R & R not scaled, and R & R scaled) are constructed through the summation of bilateral raw numbers and disaggregations of some aggregate categories in the original censuses. Since these categories together constitute around 45 percent of migrants in each census round, the original bilateral portion of each cell was compared with the final number assigned to them after the various calculations as a check on accuracy. For each decade, therefore, the overall percentage contribution of the raw bilateral data to the total is calculated (table 6). 23 In each census round, at least 92 percent of all those categories are derived from the raw data. { Table 6 here} Simulating Missing Data Finally, to examine the reliability of the estimated missing census data and test the methodologies, several scenarios are assumed. All bilateral observations for a single year for four countries (Australia, United States, Switzerland, and Chile) in different parts of the world are deleted and the missing cells are filled using one of five methods. 24 The first simulation assumes that all bilateral data for 2000 are missing but that the total number of migrants is available. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["raw census data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 corridor, so only aggregate numbers can be compared. For this comparison, mid-year estimates of the world migrant stock for 1990 – 2000 are taken from the 2008 edition and estimates for the earlier censuses, 1960 – 1980, are taken from the 2005 edition (table 8). The analysis subtracts the estimated number of refugees from the total mid-year estimates of the world migrant stock from the Trends in International Migrant Stock database to yield the net number of migrants in each decade. These numbers are then compared with the decadal estimates generated through this project, both the total and the net, after subtracting estimates of migrants within the Soviet Union for 1960 – 1980 (data for 1990 and 2000 should be directly comparable) and the number of ethnic German migrants added to the German censuses. { Table 8 here} The aggregate estimates are remarkably close (the two net totals), differing at most by around 1 million migrants, except in 1990. There are several possible explanations for these differences. First, the census totals from the current work may not match because censuses do not always make allowances for temporary workers. For example, Singapore ‘ s official 2000 census records 563, 430 foreign-born migrants. The United Nations, however, reports 1, 351, 806 foreign-born migrants for 2000. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Second, there are cases where the current study reports data by nationality, but the corresponding figure in the Trends in International Migrant Stock refers to the foreign born. This situation generally arises when a census does not report the number of foreign-born migrants on a bilateral basis. Examples include Austria and Côte d ‘ Ivoire. Third, differences in the years to which the data refer can generate large disparities. For example, this study uses the 1966 data for Australia, whereas Trends in International Migrant Stock reports data for 1970. Overall, however, the fact that the totals are remarkably close in every decade adds credence to the estimates here. IV. THE EVOLUTION OF GLOBAL BILATERAL MIGRATION The greatest strengths of the global migration matrices are their bilateral coverage, the number of decades covered, and the disaggregation by gender. These data are too rich for a full analysis of all movements between all pairs of countries. Instead, this section summarizes the major trends in the evolution of bilateral migrant stocks, based primarily on World Bank regions. 25 Global Trends The migration matrix for the 1960 census round reflects a realigning world in the postcolonial era. Over the 1960-2000 period, the composition of world migration 25 Appendix 1 details the World Bank regions: South Asia, East Asia and Pacific, Sub-Saharan Africa, Latin America and the Caribbean, Europe and Central Asia, and Middle East and North Africa. High-income Middle East and North Africa refers to the predominantly oil producing countries in the Persian Gulf (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) and to Israel. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "such as education and caste. We also construct measures of social proximity between a migrant ’ s place of birth and each possible destination, using detailed available data on ethnicity, caste, language, and religion. We also investigate a number of factors that may influence the choice of migration destination but have not received much attention in the existing literature. Fafchamps and Shilpi (2009) have shown that the subjective welfare cost of geographical isolation is high. To investigate this issue, we include regressors controlling for population density and for the average distance to various amenities. Fafchamps and Shilpi (2008) have further shown that migrants are concerned with their welfare relative to that of their birth district as well as to that in their destination location. We examine whether relative welfare considerations influence the choice of migration destination. Additional controls include distance and prices. The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal. The diverse terrain of Nepal along with geographical variation in amenities makes it ideal for our study. The mountainous nature of Nepal means that the country faces daunting challenges in the provision of transport and energy infrastructure. These challenges are unique to Nepal, however. Similar constraints are faced by many developing countries — or regions within such countries. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["2001 population Census data"], "descriptive_data": [], "vague_data": ["LSMS survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "If many people migrate to a specific location, such as the capital city, this is likely to affect wages, incomes, and access to amenities in that location. 7 This would generate a potential endogeneity bias due to the fact that incomes and amenities in that location result in part from the decision of many migrants to locate there. To eliminate this bias, we use past data to estimate the income regression. More precisely, let T be the period for which we have income information and T + t the period at which we 5The dropped observation corresponds to the location of origin M h ii which, as explained earlier, we do not include in the analysis since including M h ii would mean de facto including the decision of whether to migrate or not. 6McFadden (1974) has shown that, in multiple choice problems of the kind studied here, the application of logit estimation is justified if (1) the errors in each latent choice equation follow the extreme value distribution and (2) errors are independent across choices. See Train (2003), Chapter 3 for a detailed discussion. The estimation of models with correlated errors across choices requires either multiple integration or the use of Bayesian estimation techniques relying on Gibbs sampling. With a choice of over 70 possible destinations, multiple integration is out of the question. Gibbs sampling remains a possibility but would require extensive programming. We choose instead to keep the logit approach but to correct the standard errors for possible correlation in errors across choices. In our case the possible efficiency gain achieved by Bayesian methods does not appear to justify the programming cost. 7The effect could be negative — e. g., congestion — or positive — e. g., agglomeration externalities. 10 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["past data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To this effect, we replace yh i with yh i / yi in equation (1) and proceed as outlined above. If migration decisions are based on relative rather than absolute income, then the coefficients of eδs − eδi and (eηs − eηi) zh should be positive and significant only when they are computed using yh i / yi. In addition to relative and absolute income differences, the analysis also examines the re- spective roles of various location characteristics such as housing and food prices, availability of public services, and density of human settlement. 8An alternative strategy for the estimation of pre-migration income distribution in cross-section data is sug- gested by Bayer, Khan and Timmins (2008). 11 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In contrast, districts of origin are distributed widely across the country. This reflects the fact that much work migration is from remote rural areas to towns and cities. The main characteristics of work migrants are reported in Table 1, together with those of non- migrant adult males. We see that work migrants are on average younger and better educated. The census contains detailed information about ethnicity, language, and religion. In the Nepal census, the term ‘ ethnicity ’ is used to capture a hodgepodge of caste and tribal distinctions. The census distinguishes up to 103 ethnic categories. Most of these categories only account for a tiny proportion of the total population. In terms of the total adult population, the most common ethnic categories are Chhetri, Brahmin, and Newar who, together, account for 35 % of 13 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Nepal census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "standard fixed or random effect approach is not sufficient to ensure correct inference; clustering standard errors by individual is necessary. This is what we do. Having described how the dependent variable is constructed, we turn to regressors. We begin by describing how we construct an estimate of g E [yhs | zh], the level of income (or consumption) yh s that a migrant with characteristics zh can expect to earn in district s. To construct such estimate, we use the 1995 / 96 NLSS data. The reason for using the 1995 / 96 data instead of the 2002 / 3 NLSS survey is to avoid reverse causation, i. e., migration causing a change in income patterns. Migrants are unlikely to be able to accurately predict the evolution of incomes in each district over time. Income and consumption levels observable before migration are thus a reasonable starting point. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["1995 / 96 NLSS data", "NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using the NLSS data we begin by estimating a regression of the form: yk s = δs + α (ak s − a) + βs (Ek s − Es) + χs (Hk s − Hs) + vk s (4) where yk s is the log of income (or consumption) of household k residing in district s, coefficients δs, βs and χs vary by district, ak s stands for the age and age squared of the household head, Ek s is the education level of the head measured in years of completed education, and Hk s = 1 if the head belongs to what we have earlier classified as a high caste (i. e., Brahmin, Chhetri or Newar). Since income or consumption are expressed in logs, βs and χs can be thought of as education and high caste premia, respectively. Female headed households are excluded from the regression since the focus is on migrant males. Vector a denotes the average age and age squared of observations across the sample. Variables E and Hs denote the district-specific averages of Ek s and Hk s. By demeaning regressors, we ensure that eδs measures the unconditional, district- specific average of yk s. Marital status, household size, and other household characteristics are 15 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["NLSS data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Formula (5) can be decomposed into two parts: eδs, which measures the average income level in district s, and eηszh ≡ eβs (Eh s − Es) + eχs (Hh s − Hs) which captures individual-specific variation in income. Migration models predict that, other things being equal, the choice of migration destination should depend on g E [yhs | zh]. This means that if we regress the choice of destination separately on eδs and eηszh, they should have the same coefficient. The same methodology is used to construct other variables that may affect the choice of 9The literature has often emphasized that migrations often serve an important role in household formation. For migrants, the prospect of forming a large, successful household is likely to be one of the purposes of migration. 10The 1995 / 96 NLSS survey adopted the following sampling strategy. Within each district a small number of wards were selected at random. Within each ward, 12 randomly selected households were interviewed. Because the wards differ widely in terms of population, applying sampling weights is essential in order to obtain consistent estimates of δs. 16 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "destination. Building on a growing literature documenting the relationship between subjective welfare and relative income, Fafchamps and Shilpi (2008) show that Nepalese households care about their consumption level relative to that of others in the same location. If this is the case, it is conceivable that migrants choose their destination not so much for the absolute gain in income it may provide but for the gain in relative status that would ensue. For instance, if returns to education and ability are higher in an urban setting, an educated individual may improve his relative position in society by moving from a rural to an urban setting. To investigate this possibility, we estimate equation (4) using the log of relative income (or relative consumption) as dependent variable and construct a predicted relative income measure using the same formula (5). These are shown in the second panel of Table 1. Theories of work migration predict that individuals move to increase their utility or welfare. The 1995 / 96 NLSS asked respondents a number of questions regarding their subjective satisfac- tion level with various dimensions of consumption — namely, food, clothing, housing, health care, and child schooling. They were also asked their subjective satisfaction with their level of total income. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["NLSS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "expect the prices of many manufactures to do as well. The 1995 / 96 NLSS collected information on the quantity and price paid for rice by individual households. From this we compute a unit price per Kg. The log of the district median is used as our price index proxy. To construct an index of housing costs, we take advantage of a section of the 1995 / 96 NLSS survey focusing on housing. The survey collected information on hypothetical and actual house rental values of each household together with house characteristics such as square footage, number and type of rooms, quality of materials, and the availability of various utilities. We use these data to construct an hedonistic index of housing costs for each district. Let rk s be the house rental price paid (or estimated) by household h in district s and let xh s denote a vector of house characteristics. We estimate a regression of the form: log rk s = as + bxh s + ek s to obtain estimates of eas, the housing cost premium in each district s. Regression results are shown in Table A1 in appendix. Many house characteristics are significant with the expected sign, e. g., larger, better built houses with better in-house amenities are worth more. District price differentials are large and jointly significant. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["NLSS", "NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(2009) have shown that, in Nepal, subjective welfare is negatively associated with geographical isolation. Census data on total population and population density in each district are used as proxies for urbanization and geographical proximity: the denser the population, the less geographically isolated individuals are likely to be. We also include data on the average elevation in each district. Nepal being a mountainous country, the higher the average elevation of a district, the more costly it is to build roads, raising transport and delivery costs to the district. Ceteris paribus, we expect migrants to seek out districts with a higher population density and a lower elevation. 4 Econometric results 4. 1 Univariate analysis We now investigate the choice of migration destination. We begin with simple univariate analysis. Variables are of the form ∆ h is = xh s − xh i where i is the district of origin of migrant h and s is each of 74 possible districts of destination. We examine the average value of ∆ h is for the destination district and compare it to the value of ∆ h is for alternative destinations. For instance, let xh s be population density in district s. The average value of ∆ h is for the actual destination of the migrant tells us whether the destination district is more densely populated than the district of origin. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Census data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The comparison between ∆ h is for actual and hypothetical destinations tells us whether the actual district of destination is more densely populated than alternative destinations. Results are presented in Table 3 for all variables used in the analysis. We begin with district log income eδs. We have two estimates of eδs, one obtained using reported income data, and the other based on reported consumption data. Given that most respondents to the NLSS survey are self-employed, measurement error is typically larger for income than for consumption. We see that our estimates of log income and consumption eδs are on average 20 % and 8 % higher in 20 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["NLSS survey"], "descriptive_data": [], "vague_data": ["reported consumption data", "reported income data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The univariate analysis showed that migrants on average move to destinations where they are on average less likely to find people like them. The results presented in Table 4 present a different picture. Conditional on the other regressors, the ethnicity and language proximity indices are significant with the anticipated sign: social proximity between the migrant and the population of the destination district is higher than in alternative destinations. The religion proximity index is not significant. Taken together, these results suggest that, conditional on material benefits from migration, migrants prefer to move to a destination where they integrate more easily — and possibly enjoy network benefits in terms of access to jobs and housing (Munshi 2003, Beaman 2006). 24 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "include the rice price — which appears with the wrong sign but is only marginally significant — and elevation and population density — which are no longer significant. Comparing Tables 7 and 5, we find that in the smaller NLSS 2002 / 3 dataset none of the anticipated consumption variables is statistically significant. Other results are as before. 4. 4 Magnitude To assess the relative magnitude of our results, we multiply coefficients estimated in Tables 4 and 5 by the standard deviation of their respective regressors. We then average over the various regressions reported in Tables 4 and 5. Calculations are summarized in Table 8. The larger the value, the more influence the regressor has on the choice of a destination district. We see that the most important regressors in terms of magnitude are travel time to the near- est road, elevation, language similarity, and the price of rice. Consumption variables have an effect on migration destination that is smaller in magnitude: a one standard deviation increase in anticipated relative consumption, for instance, has an effect on destination that corresponds to a third of the effect of a one standard deviation in elevation — and one-sixth of a one stan- dard deviation in distance from the nearest road. Income variables have a negligible effect on migration decisions. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["NLSS 2002 / 3 dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These calculations confirm our earlier assessment. 5 Conclusion Combining data from a household survey and an 11 % census of the population, we have estimated destination choice regressions for Nepalese internal migrants. Results show that population density, social proximity, and access to amenities exert a strong influence on migrants ’ choice of destination. These results confirm earlier work on the factors affecting the subjective welfare cost of isolation (Fafchamps and Shilpi, 2008). 29 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey", "11 % census of the population"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It is difficult to draw causal inference from observational data. This study is no exception. The results presented here are nevertheless sufficiently suggestive to cast doubt on the theory that the choice of migration destination is driven primarily by income differentials. Other factors seem to play a strong — and probably more important — role. References 1. Adams, Richard, Remittances, Investment, and Rural Asset Accumulation in Pakistan, 30 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["observational data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 as the Demographic and Health Surveys (DHS), though researchers have reduced under-reporting by providing special training to interviewers, placing greater emphasis on respondents ’ privacy and safety, and allowing women multiple opportunities to disclose their experiences (Ellsberg et al., 2001a; Garcia Moreno et al., 2003; Ellsberg et al., forthcoming). Estimates of the magnitude of the problem Population-based surveys have found that between 10-70 % of women report being physically assaulted by an intimate male partner at some point in their lives (Heise, Ellsberg and Gottemoeller, 1999). See Annex A for estimates from many recent population based studies (Ellsberg et al., forthcoming). Findings from a multi-country study on domestic violence and women ’ s health carried out by the World Health Organization in fifteen sites and ten countries found that between 13-62 % of women had experienced physical violence by a partner over the course of their lifetime, and between 3-29 % of women reported violence within the past year (Figure 1. 1). Figure 1. 1. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": [], "vague_data": ["Population-based surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Percentage of women who reported sexual violence by an intimate partner (ever), physical violence by an intimate partner (ever), and physical violence by an intimate partner in the past 12 months. 50 % 59 % 30 % 27 % 34 % 13 % 31 % 50 % 62 % 34 % 33 % 47 % 23 % 41 % 37 % 20 % 10 % 14 % 6 % 17 % 23 % 47 % 23 % 29 % 31 % 6 % 23 % 40 % 42 % 49 % 3 % 18 % 19 % 16 % 8 % 19 % 16 % 8 % 13 % 29 % 3 % 17 % 25 % 13 % 15 % Bangladesh (Urban) Bangladesh (Province) Brazil (Urban) Brazil (Province) Ethiopia (Province) Japan (Urban) Namibia (Urban) Peru (Urban) Peru (Province) Thailand (Urban) Thailand (Province) Tanzania (Urban) Tanzania (Province) Serbia Samoa sexual violence ever physical violence ever physical violence past 12 months Source: Unpublished data from the WHO Multi-Country Study on Women ’ s Health and Domestic Violence Against Women. The final published comparative report is forthcoming. Cited with permission. Prevalence data on sexual violence is even more limited than physical violence. However, evidence suggests that a substantial proportion of girls and women have experienced child sexual abuse, forced sex and other forms of sexual coercion in virtually every setting of the world. For example, population-based studies have asked about “ forced ” sexual debut among sexually experienced young people and found rates from 7 % (New Zealand), to 46 % (in the Caribbean) (Heise and Garcia Moreno, 2002). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A growing number of low and middle-income countries have tried to improve the law enforcement response to gender-based violence by training professionals, reorganizing police and courts, and trying to provide a more comprehensive response to survivors. Evidence of effectiveness is relatively limited; most well-evaluated initiatives come from high-income countries, and the lessons learned may not be applicable to developing countries. Evaluations of law enforcement reforms in low and middle income countries have typically been limited to case study approaches drawing from police records (notorious for under-reporting), qualitative perspectives from key informant interviews, intermediate outcomes such as changes in attitudes and knowledge among police and judges, and interviews with small numbers of women who have sought legal redress. Population-based data collection, control groups, or follow-up among more than a handful of survivors are rare. Nonetheless, the following initiatives illustrate the types of efforts that have produced important lessons learned. Training personnel in the police and judiciary and other parts of the justice system Throughout the world, organizations have launched efforts to improve the knowledge, attitudes, and practices of justice sector personnel regarding gender-based violence. Some law enforcement institutions organize training internally, as did South Africa following passage of the 1998 Domestic Violence Act (Usdin et al., 2000). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["police records"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In other settings, NGOs such as Rozan in Pakistan (Rashid, 2001), Profamilia in the Dominican Republic (Guedes et al., 2002), and the Musasa Project in Zimbabwe have trained law enforcement personnel on issues related to gender-based violence. Elsewhere, governments have collaborated with the United Nations to provide training and support for the police and judiciary. For example, ILANUD is a joint institute of the government of Costa Rica and the United Nations that works with governmental agencies throughout Latin America to improve the work of prosecutors, judges, lawyers, police and other professionals in criminal justice generally, and gender-based violence specifically (Villanueva, 1999; ILANUD, n. d.). Most of these initiatives have been evaluated using key informant interviews and pre and post questionnaires before and after training-if they have been evaluated at all. Nonetheless, training appears to be both constructive and urgently needed (Rashid, 2001; Villanueva, 1999). Other lessons learned include the finding that changing attitudes of law enforcement is a challenging, long-term process. The quality of the trainings ’ content and the skills of the trainer are essential. Training appears to be most effective when all levels of personnel (especially high-level officials) participate, and when training is backed up with changes throughout the institution, such as policies, procedures, adequate resources, and continual monitoring and evaluation. Special police stations or cells for crimes against women All-women police stations began in Brazil and were later tried in other countries in Latin America and Asia. As of 2003, for example, Nicaragua had 17 police stations for women and children (called Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Unfortunately, evidence suggests that without system-wide reforms and support, single training sessions or even routine screening policies rarely produce long-term changes in the quality of care for survivors (McLeer et al., 1989; Heise, Ellsberg, and Gottemoeller, 1999). Instead, Heise and colleagues argue that the most effective way to improve the health care response is to use a “ systems approach ” involving reforms throughout the organization. Typically, these initiatives include changes in norms, policies and protocols, infrastructure upgrades to ensure private consultations, training all staff (including managers), ensuring that providers have adequate resources such as referral networks and directories, and strengthening the ability of staff to provide emergency services such as danger assessment, safety planning, emotional support, STI prophylaxis, and emergency contraception. In settings where adequate referral services do not exist, health programs sometimes offer specialized services such as counseling, legal aid and women ’ s support groups. The International Planned Parenthood Federation, Western Hemisphere Region (IPPF / WHR) carried out an initiative illustrating the “ systems approach ” in four member associations in Latin America, namely: Profamilia (the Dominican Republic), INPPARES (Peru), and PLAFAM (Venezuela), with some participation from BEMFAM (Brazil). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Quantitative and qualitative baseline, midterm and follow-up studies concluded that the initiative improved provider attitudes and practices; strengthened patient privacy and confidentiality; increased detection of women who experienced physical and sexual abuse; improved the overall quality of women ’ s health care; and benefited survivors through the provision of specialized services such as legal aid, counseling and support groups (Guedes, Bott, and Cuca, 2002; Guedes et al., 2002; Bott, Guedes, and Guezmes, forthcoming). This initiative benefited from generous funding from international donors, and it might be difficult for other organizations to replicate the project in its entirety; however, IPPF / WHR has disseminated a large body of recommendations and tools designed to help organizations in low-income settings build on their experiences. Routine screening (also called routine enquiry) Research indicates that without routine screening, providers typically identify only a fraction of women requiring assistance with physical or sexual abuse. Routine screening for violence has increasingly been considered the standard of care within women ’ s health services in the United States and other industrialized countries (American Medical Association, 1992; Buel, 2001). However, a vigorous debate has erupted over the benefits and risks of routine screening, particularly in resource-poor settings (Ramsay et al., 2002; Garcia Moreno, 2002). Some argue that routine screening may harm women in settings where providers are unprepared to respond appropriately, where privacy and confidentiality cannot be ensured, and where adequate referral services do not exist. In many settings, providers blame victims of gender-based violence-without an appreciation of gender issues or human rights-and may Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "29 evaluations have been conducted (Renton et al., 2000; Shaw, 2000; Shaw, 2002a; Shaw, 2002b; Paine et al., 2002; White, Greene and Murphy, 2003; Interagency working Group, 2003). For example, the Medical Research Council (from the United Kingdom) conducted an external evaluation of program effectiveness in the Gambia using key informant interviews, focus groups, and a KAP (knowledge, attitudes and practices) survey in four villages, including two program sites and two controls. That study found that the Gambia program improved self-reported attitudes and behaviors related to violence against women. Specifically, the program reduced the social acceptability of wife-beating at the community level and appeared to produce a corresponding drop in that behavior. Qualitative findings from other Stepping Stones sites suggest similar benefits. Program H (Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru) is being carried out by four NGOs. It aims to change gender norms and sexual behaviors in Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru (Barker, 2003; White, Green and Murphy, 2003; Guedes, 2004). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["KAP (knowledge, attitudes and practices) survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The initiative includes four components, namely: a) training professionals to work with young men in the area of health and gender-equity using a set of manuals and videos; b) social marketing of condoms; c) promoting health services; and d) evaluating changes in gender norms. In 2002, PROMUNDO and Horizons began a 2-year evaluation to measure the effectiveness of two different approaches, compared to a control site. Researchers have developed a\"Gender-Equitable Men\"(Leichert) scale with 24 items for measuring attitudes. Methods include pre and post-tests as well as a six-month follow-up community-based survey. In addition, they are gathering qualitative information among men and their female partners. Preliminary results suggest that the program has been successful at increasing gender equitable norms and reducing behavior that puts men at increased risk of HIV / AIDS. ReproSalud (Peru): Manuela Ramos launched ReproSalud in 1995 as a USAID-funded rural reproductive health program. ReproSalud used participatory rural appraisal (PLA) to help women's groups identify women's reproductive health needs and to organize community meetings to design strategies to address those needs. Domestic violence and forced sex within marriage emerged as important problems in those communities. In response, ReproSalud organized workshops for women and men on gender issues, carried out community awareness campaigns and established a microcredit program for women. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["community-based survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "33 recommendations (Jewkes, 2000). Their efforts contributed to the Employment of Educators Act and new Department of Education guidelines, both of which were introduced in 2000. These regulations mandate dismissal of educators found guilty of sexual or physical assault, or of having a sexual relationship with a student. They also define penalties for failing to report abuse. It remains to be seen whether these measures will have the intended impact. After the act was passed, Human Rights Watch (2001) suggested that the South African government needed to do more to increase awareness of the law among school principals and to strengthen enforcement. Institutional reform Efforts to improve the institutional response to gender-based violence range from sensitization and training of staff, sexual harassment policies, curriculum reform, school-wide anti-violence awareness campaigns, counseling and referrals, and broader efforts to reduce discrimination against girls and improve school safety. Initiatives to increase female enrolment by improving girls ’ safety at and on the way to school As mentioned earlier, parental concerns about girls ’ safety in school appears to lower female school enrolment in settings such as South Asia, Africa and the Middle East. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some initiatives have addressed these concerns by establishing single sex schools, hiring more female teachers, building separate latrines or canteens for girls, reducing the distance that girls must travel in order to receive an education, and / or providing in-service gender sensitivity training to teachers, principals and inspectors (UNICEF, 2004). For example, the UNICEF African Girls Education Initiative (AGEI) used a combination of these approaches, (along with other strategies) to boost girls ’ enrolment in 34 African countries (UNICEF, 2003a). Evidence of this project ’ s effectiveness was limited in many sites, largely due to limitations in the evaluation design. While some demonstrated significant enrolment increases (for example, 15 % in Guinea, 12 % in Senegal, and 9 % in Benin) in relatively short periods of time, the extent to which this was due to the project impact was not clear. Overall, however, the experience of this project suggests that addressing concerns about girls ’ safety and reducing the risk of sexual harassment and violence in schools is not only a high priority for parents, but also a potentially promising way to improve girls ’ access to education in selected settings (UNICEF, 2003b). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Improving attitudes, knowledge, skills and practices of educators Many initiatives have aimed to improve educators ’ attitudes, knowledge and practices in regards to gender discrimination, sexual violence and sexual harassment. Few have been well documented or evaluated. A comparative study of HIV / AIDS education in three African countries found evidence that awareness and responses to sexual harassment in the Uganda sites was markedly better than those in Botswana and Malawi; researchers attributed this to the Ugandan government ’ s efforts to curb sexual harassment in schools (Bennell, Hyde, and Swainson, 2002). The South African National Department of Education (in collaboration with international organizations) has developed a training module for educators (South African National Department of Education, 2001). Composed of eight interactive workshops and other materials, the module aims to increase educators ’ awareness of sexual harassment and gender violence, highlight the links between violence and HIV / AIDS and increase the safety of the school environment. The module is a professional development tool, rather than a part of the national curriculum. It has been field tested in some sites, and according to some reports is being rolled out nationwide. In other settings, schools have trained educators to teach courses promoting gender-equitable norms and nonviolence among students. For example, a consortium of researchers and advocates field-tested the\"Gender and conflict\"Model Curriculum in South Africa (Dreyer et al., 2001; Guedes, 2004) to compare a\"whole school\"approach (which trained the entire primary school staff, including principals and auxiliary staff) with a “ trainer of trainers ” approach (which trained two teachers from each school and Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "38 Expanding social services for women and children In many countries, public and private institutions have worked to improve social services for women and children who experience violence. In some settings, NGOs and coalitions such as the Nicaraguan Network of Women against Violence have spearheaded these initiatives (Velzeboer et al., 2003). In other settings, governments have promoted institutional reforms by establishing ministries, departments or agencies devoted to the advancement of women, including Mexico, Jamaica, Guatemala, Bolivia, Peru (Center for Reproductive Laws and Policy, 2000); these agencies often work to strengthen comprehensive services for survivors of gender-based violence. For example, in El Salvador, the Salvadoran Institute for the Development of Women is a government agency that coordinates the “ Program to Strengthen the Family ” (Programa de Saneamiento de la Relación Familiar), a multi-sectoral effort among public and private institutions (Valdez, 1999). In some settings, such as Nicaragua and Costa Rica, coalitions of government agencies and NGOs develop National Plans to improve the network of services for women and children affected by violence (Velzeboer et al., 2003). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "At a community level, social service initiatives accompanied by substantial outreach efforts have sometimes increased the proportion of women who know what services exist and where, as well as the numbers of women who seek help, but little scientific research has explored the impact of expanded social services on violence prevention. Batterer programs Increasingly, NGOs and governments have attempted to reduce violence against women by organizing treatment programs for batterers, aimed at changing their attitudes and behaviors. Most are run by NGOs, but they often depend on court- mandated attendance as an alternative to criminal sanctions. Most batterer programs have been carried out in high-income countries, but increasingly they have been implemented by developing country NGOs, such as the Instituto Noos in Brazil (White, Greene, and Murphy, 2003) and CORIAC in Mexico (Morrison and Biehl, 1999). Many studies have evaluated these programs ’ effectiveness in high-income countries, but most evaluations have been methodologically flawed. The only randomized controlled trial to date was carried out by the United States Navy, which found no reduction in abuse compared with controls (Dunford, 2000). Battered women often identify treatment or counseling for their husbands as a high priority (e. g. Ellsberg, 2001b), but it remains to be seen whether cost-effective strategies for changing batterer behavior can be found. Shelters Many researchers and advocates have called on governments and donors to invest in shelters for women who experience gender-based violence. Typically, these facilities offer emergency refuge as well as counseling, medical and legal assistance, job training, telephone hotlines, and other services. Most rigorous evaluation studies on the effectiveness and quality of shelters come from settings such as the Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, subsequent multivariate analysis and qualitative research found that the link between micro-credit and violence was more complex (Hashemi, Schuler and Riley, 1996; Schuler, Hashemi and Badal, 1998). While participation in microcredit programs appeared to increase women ’ s empowerment over time, levels of violence did not decline, and in some cases even rose. Ultimately, researchers concluded that-- similar to other types of empowerment initiatives-- micro-credit programs appear to work in two directions at once. On the one hand, they reduce women's vulnerability to violence by strengthening their access to resources and making women's lives more public; on the other hand, they may increase the risk of violence by challenging patriarchal norms and escalating conflict in the household. Some micro-credit programs are trying to reduce the potential risks of exacerbating violence associated with micro-credit. For example, RADAR (South Africa) has integrated HIV / AIDS and gender-based violence prevention into an existing microcredit program for women in poor rural communities. Since RADAR is designed as a prospective, randomized community intervention trial, it may-- in the future-- contribute to a richer understanding of how to provide the benefits of micro-credit while mitigating the risks (RADAR, n. d.). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Following the approach used for the Global Costing of Refugee Inclusion, successful inclusion is defined as earning sufficient income to be no longer poor and to consume more than the (international) poverty line. This definition opens two tracks for investigation: first, how much aid would be needed if the policy objective were to bring refugee consumption up to the poverty line. The answer to this question is found by identifying the poverty gap for refugees. This opens the second track which explores the factors that determine, or at least that are associated with, the size of the poverty gap. The note is organized as follows. The next section discusses data and presents some key descriptive statistics on refugees and host communities in Uganda. This is followed by a methodological section discussing how own income and aid are complements and how an analysis of poverty gaps informs about the need for assistance. This is followed by two analytical sections. The first identifies refugee poverty gaps, and assistance needs for refugees with distinct characteristics. The following section estimates how much has been saved by including refugees in the economy and explores how more could be saved. Conclusions follow. 2 The poverty numbers in World Bank (2019) are based on the official poverty line adopted in Uganda in 1997. There was a need to update this line as it was too old and producing a very low poverty rate. For example, using this line produced a national poverty rate of about 21 percent in 2019 / 20 compared to more the than 40 percent international poverty rate using the USD 2. 15 2017 PPP daily poverty line. In order to address this criticism, the poverty line was revised by the Uganda Bureau of Statistics in 2021, but it is not available for the 2018 Refugee and Host Communities Household Survey used in this note. Instead, we are using the international poverty line throughout. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["2018 Refugee and Host Communities Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 Figure 10. Selected characteristics across Ugandan and refugee households, % Source: RHCS 2018, WB staff calculations. A simple comparison between refugees and Ugandan households demonstrates that refugees lag with regards to selected characteristics found to narrow the poverty gap. For example, refugees are less likely to have access to land than Ugandans. If refugees have access to land, the majority do not own it, but have user rights. The size of land also differs a lot among Ugandan and refugee households. Most Ugandans have at least 0. 05 hectare per capita, while the majority of refugees have less than 0. 05 hectare per capita. Refugee heads of household are also less likely to work and less likely to be literate compared to their Ugandan counterparts. Refugees have higher shares of children and elderly in household size compared to Ugandans. For example, among almost 60 percent of refugee households, more than half of the household members are children and elderly compared to 42 percent of households among Ugandans. Economic inclusion dividend When a development approach to hosting refugees is followed and refugees earn incomes, there are two key beneficiaries. Refugees themselves, who gain dignity, financial autonomy and pathways to self-reliance. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["RHCS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "And the international community, which can provide less humanitarian aid. 9 It is possible to estimate how much money is saved in humanitarian assistance thanks to the inclusion of refugees in Uganda ’ s economy. For this, return to Figure 8 to observe that the savings in assistance between the no income and current scenarios are US $ 150 per refugee per year (US $ 343 – US $ 193). Multiplied by 1. 5 million refugees that reside in the country, the annual savings are US $ 225 million. If the aim is to bring the consumption of all refugees to at least the international poverty line, rather than spending US $ 515 million on basic humanitarian needs assistance every year, US $ 290 million would be needed to ensure all refugees have a dignified life. Unfortunately, refugees receive less than US $ 290 million in assistance, because despite working and after receiving assistance many remain poor with levels of consumption that fall below the poverty line. Humanitarian aid is falling short in Uganda, and refugees bear the burden for it. This burden is well-documented in a 2023 document by the Uganda Refugee Operation which explores the impact of underfunding by humanitarian agencies: it points to high 9 There is potentially a third beneficiary: the Government of Uganda which might be rewarded by the international community with additional financing in return for its inclusive refugee policies. Assessing this, and how these potential rewards compare to the costs associated with economic inclusion, falls outside the scope of this paper. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A recent global hazard analysis generated a comprehensive database of hazard events during 1975- 2007 from observed data and of event probabilities from geo-physical models (ISDR 2009). We combined this hazard information with city-specific population projections 2 It should be noted that well-documented evidence for such externalities is quite scarce. Their importance is usually taken as given: “ A building collapse may create externalities in the form of economic dislocations and other social costs in addition to the economic loss suffered by the owner. The owners may not have taken these consequences into account when evaluating specific mitigation measures. Consider the following example. A building toppling off its foundation after an earthquake could break a pipeline and cause a major fire, which would damage other homes that had not been affected by the earthquake in the first place. “ Kuenreuther and Roth (1998). See also www. quakesmart. org / index. php? option = com_content & view = article & id = 92 & Itemid = 209. But some experiences have been documented: “ As shown by research on the Great Hanshin-Awaji Earthquake, including that conducted by the Architectural Institute of Japan, Architectural Institute of Japan (1997), houses with inferior earthquake-resistant quality triggered large negative externalities in the neighborhood. For example, broken fragile houses blocked transportation networks, thereby preventing effective fire fighting and, by severing lifelines, they made recovery more difficult. ” (Nakagawaa et al. 2007). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "19 As property prices in the worst affected areas reduced the most, low income households responded by moving into low-rent housing being offered in these locations. On the other hand, middle income households moved away to avoid risk, and the wealthy, for whom insurance and self-protection was the most affordable, did not change where they lived. Poor people “ sort ” into low rent locations – which are often at higher risk to natural hazards. The problem is particularly acute in developing countries where there is a divide between the formal and informal markets for land. While formal developments may respect land use regulations, informal settlements are often located in hazard prone locations, such as on hill slopes, close to river banks, or near open drains and sewers. In Dhaka for example, informal settlements are developing across the metropolitan area, with many residents lacking basic public services and in locations at risk from flooding. In fact, most informal settlements do not have access to a public toilet within 100 meters, and 7, 600 households in 44 slums live within 50m of the river (World Bank 2005, Dhaka Urban Poverty Assessment). For the city of Bogotá, we use the same database discussed earlier to examine if poor people are at greater risk from natural hazards – particularly earthquakes. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In our employment arm, we offer gainful employment in the form of a surveying assignment for an average of three days per week for two months. 1 The surveying task requires workers to walk through their blocks four times per day tallying the various activities their neighbors are engaged with and consumes approximately 2. 5 hours per workday, resulting in a form of part-time employment. The job is designed to embody the key features inherent to ‘ work ’. Drawing from the economics literature, workers must exert real effort and their task occupies a meaningful portion of their work day. Drawing from the sociology literature, the work involves some degree of sociability and purpose in the completion of a productive task. Employment lasts for eight weeks, a long duration given the scarce daily labor opportunities that arise in our setting. Relative to this employment arm, our control arm receives no work and a small fee for weekly survey participation. A comparison of the control to the employment arm therefore yields the psychosocial benefits of the employment intervention. In order to estimate the non-pecuniary psychosocial value of employment, we include a cash treatment arm, in which no work is offered, but a large fee (equivalent to that received by those in the employment arm) for weekly survey participation is provided. We work in the Rohingya refugee camps, situated upon the southern tip of Bangladesh. 1We obtained formal permissions from camp administration to engage our study participants in this manner through our NGO partner, Pulse Bangladesh. 1 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "An additional 33 blocks were assigned to the cash group, where participants earned 450 taka (USD $ 5. 30) per week as compensation for survey participation. Finally, 83 blocks were assigned to a work group, where we offered participants gainful employment. We compensated participants in this treatment arm with 150 taka (USD $ 1. 77) per day of work. Households were assigned an average of three days of work per week, resulting in 450 taka per week on average over the course of the eight weeks and thereby equivalent to that received by the cash group. All participants were aware of the randomization process: enumerators described the three arms and displayed the random number to the participant as it appeared on their tablet, assigning the participant to his or her treatment group. Employment intervention details We now turn to the nature of the employment we offer. Employees were asked to engage in a data collection exercise in which they completed time-use sheets describing the activities of fifteen unnamed, same-sex neighbors of their choosing four times per day. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We asked that households complete their work on specific days to which they were as- signed: work schedules varied week to week, averaging three days weekly. To ensure com- pliance with the work schedule, we stationed a tamper-proof box in a preselected household within each block (the facilitator household) and informed participants that they should submit their tasks into the box at the end of each assigned workday. The facilitator would slip an additional piece of paper into the box at the end of the day to bookend that day ’ s set of submissions, and the respondent ’ s submission was marked late if it was inserted after the bookend. Facilitators were compensated with an additional 50 taka per week for their services, and had no access to the materials inside the box. Along with dropping offtheir submissions at the end of each workday, participants were instructed to visit the facilitator ’ s home on their designated ‘ collection day ’ each week. The facilitator made their home available for a few hours on this day so the enumerator could complete the check-ins with the block ’ s five respondents and pay the participants their respective amounts in a relatively private setting. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "required to purchase other basic staple foods such as salt and vegetables. Given that the WFP provisions are the only reliable rations that refugees receive, we approximate a cash transfer of 450 taka per week to at least double potential weekly consumption. Relative to the wealth refugees possess, 450 taka per week is likewise sizeable: average baseline savings is 195 taka, with the median refugee reporting zero taka in savings. Average baseline borrowing (typically in the form of store credit) is 1, 600 taka, with a median of 600 taka. Refugees have no economically meaningful assets that may be more common among the rural poor, such as land or cattle, given the unanticipated displacement which forced them from their homes. Relative to other employment opportunities, average reported pay is 300 taka per day for less than three days. The monthly cash transfer is therefore more than double what a refugee might expect from alternative employment if he or she is fortunate enough to secure a job. 4 Data Collection and Survey Instruments Timeline and survey instruments We collected data via a baseline, commencing in November 2019, and endline survey, commencing in February 2020, as well as seven midline surveys conducted prior to payment disbursal each week. These weekly surveys collected a small subset of well-being outcomes. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In an effort to ensure that our temporary interventions had no unintended negative mental health consequences on our participants, we also con- ducted a final short followup survey six weeks after the interventions concluded (Appendix Figure A1. We had 2 % attrition at endline and followup, neither differential by treatment arm (Appendix Table A1). Main outcome variables Our primary outcome of interest is psychosocial well-being, which we assess through an index of seven mental and social health measures, henceforth re- ferred to as the psychosocial (PS) index: depression, stress, life satisfaction, locus of control, sociability, self-worth, and stability. Our measures of depression, stress, life satisfaction, and locus of control are drawn from standard screening tools (PHQ-9, Cohen ’ s Perceived Stress Scale, Diener ’ s Satisfaction With Life Scale, and the Levenson Multidimensional Internal Locus of Control Scales, respectively) adapted for sensitivity to the Rohingya camp context. The PHQ, our depression screening tool, has been validated against antidepressant medica- tion (L ¨ owe et al., 2006) and employed in the cross-section among refugee populations (Poole et al., 2018) as well as in experimental evaluations of psychotherapy programs in South Asia (Patel et al., 2017; Bhat et al., 2021). For sociability, we inquire about the number of interactions that participants have had 11 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "throughout the day prior to the survey day. We develop our own questions around self- worth rather than employing the more standard Rosenberg Self-Esteem Scale, which we found inappropriate given the Rohingya ’ s recent experiences. Specifically, we construct an index of self-worth from two questions designed to elicit respondents ’ beliefs about how they contribute to their family and community. Finally, we adapt the Cantril Self-Anchoring Striving Scale (Cantril, 1965) to measure how stable respondents feel in their present lives and in the future. We additionally examine the impacts of each treatment on physical health, cognitive function, economic decision making, time-use, and consumption. We capture respondents ’ sense of physical health by asking how many days they have fallen sick in the past thirty days and cognitive function by employing a digit-span memory test and a series of basic arithmetic problems. We explore economic decision making along two dimensions: incentivized time preferences (Andreoni and Sprenger, 2012; Gin ´ e et al., 2018) and incentivized risk preferences (Holt and Laury, 2002). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We measure time-use through the number of hours in the previous day a respondent reports spending idle, as well as the amount of time spent on a variety of other common activities one might do in the camps (including bathing, market, chores, collection of rations, eating, child-rearing, sitting at tea stalls, praying, sleeping, visiting friends / relatives, playing games, playing sport, sitting idle). Finally, we ask respondents how much they consume, borrow and save over the past week. We further consider changes in perceptions on gender and power in two ways. First, we generate a Household Power Index, composed of a set of questions on perceptions of gendered decision-making and intimate partner violence. The questions are drawn from Haushofer and Shapiro (2016), which are themselves adapted from the Demographic Health Surveys. In addition, we produce a Work Rights Index, composed of questions around whether respondents feel that women should be allowed to work inside or outside the home or the camp block. Each outcome is described in greater detail in Appendix C. The frequency with which each outcome is collected is also presented in Appendix C. Multiple hypothesis testing We utilize two approaches to address the issue of multiple hypothesis testing. First, we present our primary outcome, psychosocial well-being, as an inverse-covariance weighted index variable following Anderson (2008). We also generate index variables for other outcomes in which this is possible, such as the cognitive index, the household power index, and the work rights index. Our second strategy is to report the sharpened False Discovery Rate (FDR) q-values for all outcomes within a particular table, which control for the expected proportion of rejections that are type I errors, likewise 12 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Demographic Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "which increases significantly under both treatment arms. In other words, the psychosocial value to employment appears to be driven largely by the non-pecuniary dimensions of the employment experience. 5. 4 Impacts of employment on reported physical health, cognitive function, and economic decision-making The positive effects of employment extend to other measures beyond psychosocial health. Table 3 presents results on reported physical health, cognitive function, and incentivized measures of risk and time preference. We observe a significant increase in the days reported healthy. This effect may be due to ‘ real ’ health improvements from increased exercise (which has also been documented to translate to improved mental health (Herbert et al. (2020))) from the employment task or ‘ perceived ’ health improvements in which improved psychoso- cial well-being translates into feeling less physically ill. Should the channel be exercise, we may expect health improvements to grow over time. Our weekly data on days healthy sug- gests this is not the case: we observe the treatment effect on health from the first week of working, and the gap remains steady throughout the following two months (Appendix Figure A3). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["weekly data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The employment arm also significantly improves cognitive function as measured through an index of memory and basic arithmetic tests, a finding consistent with a large psychol- ogy literature documenting the relationship between cognitive processes and depression (Semkovska et al., 2019). As with physical health, improvements to cognitive function are unlikely to be a direct product of the employment task itself, which was specifically de- signed to require no literacy or mathematical skill. Rather, these results are suggestive of a downstream impact to reducing depression through the experience of employment. Finally, we find no change in time preferences: treated individuals are no more or less likely to discount the future relative to control counterparts, although results may have differed had we engaged participants in an effort or consumption-based time preference game rather than a financial one. However, we find a substantial increase in risk tolerance among the employed. A greater preference for risk-taking may be indicative of employment serving as a form of psychological ‘ insurance ’ that allows participants the mental bandwidth to exercise greater risk. This is consistent with the positive impacts of employment on stability as well as with a key motive underlying universal basic income (UBI) in the developing world (Banerjee, Niehaus, and Suri, 2019). Interestingly, however, we document no parallel increase in risk tolerance in the cash transfer arm. Our result on risk preference also echoes a potential consequence of depression and anxiety described in Ridley et al. (2020), although empirical 15 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Having experienced the work task and therefore able to realistically value the work, we offer individuals in the employment arm an additional [surprise] week of work at a series of wages following the incentivized Becker-DeGroot-Marschak (BDM) method. We inform participants that we have a limited amount of funds remaining and are therefore unable to pay everyone their previous wage. This strategy realistically motivates the reservation wage elicitation exercise and makes clear that there will be no further opportunities for work. We piloted this exercise extensively. To maximize comprehension, we employ a multiple price list strategy, embed repeated confirmations, and conduct a trial run of the exercise for each respondent before the real exercise; this mimics the procedure employed in Burchardi et al. (2021) for which participants in another low-income country field context exhibited high comprehension. For those individuals who express willingness to work at a wage of zero, we offer an alternative option of answering a brief survey at the end of the week for a small, randomized fee; we then use the fraction of respondents who are willing to forego this paid option and instead work for free as an estimate of the proportion of respondents who have a negative reservation wage of at least the foregone magnitude. 16 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Second, we complement our psychosocial index with measures that are not vulnerable to experimenter demand. Demand effects are unlikely to alter one ’ s cognitive ability as measured through the arithmetic questions and memory tests of our cognitive index. Our risk and time preference games are incentivized with meaningful stakes (respondents gamble with a minimum of 1. 20 USD in the risk preference game and trade off3. 50 USD today with higher amounts tomorrow in the time preference game), stake sizes that de Quidt, Haushofer, and Roth (2018) have found effectively eliminate demand effects. Perhaps employed individuals feel a need to impress the enumerator, as their proximate employer, in a way cash recipients do not. This may lead to reporting better mental and physical health and investing greater effort in the cognitive tasks. However, we find that life satisfaction increases substantially for both groups, inconsistent with a differential desire to impress among the employed. We also observe patterns of treatment effects within our validated PHQ-9 module that are inconsistent with experimenter demand (Appendix Table 12The signaling value of the certificate may have been diminished if other employers learned about the nature of the certificate distribution. Our time in the field suggests this is unlikely: we randomized certificate distribution at the block level to limit spillovers, only five people in each block of ˜ 200 adults was involved in the experiment, and job opportunities were scarce. 13The certificate read “ I engaged with Pulse Bangladesh to do data collection ”. It was written this way in order to be generic enough to apply to all the individuals in the experiment, all of whom were providing us data from the weekly surveys. 18 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Such a channel would be consistent with psychology literature on behavioral activation, or the act of scheduling structured activities as a means of combatting depression (Cuijpers, van Straten, and Warmerdam (2007)). To explore this question, we supply a random subset of the employed with a calendar marking every date of work (Appendix Figure A5). The re- mainder receive a blank calendar and are instead informed weekly about their schedule. We find no impact of a schedule on respondent well-being or decision-making (Appendix Table A7). Despite this exercise, we cannot causally estimate the role of the structure alone on well-being, as the structure imposed by regular employment is coextensive with employment itself. Indeed, our measure of stability, which asks respondents how secure they feel at the moment and expect to feel in the future, increases substantially among the employed relative to both control and cash arms. Time use Does employment improve well-being by allowing participants to substitute time away from unsavory or psychosocially costly activities? Appendix Table A8 presents how cash and work arms use their time. We document no significant difference between the two arms in the number of hours that respondents report spending across a variety of activities. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Notably, while we cannot rule out that time outside due to employment may play a role (e. g., fresh air may boost one ’ s mood), our time-use data indicates that the average refugee already spends at least three hours outside per day, with no measurable difference between employed and cash arms. As we are powered to detect changes of at least twenty minutes for each activity, our results suggest that large substitutions away from unsavory activities are unlikely to be driving the improvements in psychosocial well-being, insofar as the respondent recalls. 1718 We also investigate whether those who were more idle prior to being employed benefit more from employment. We find no impact along this margin, suggesting that the elimination of boredom per se is not the driving force behind the psychosocial value of employment (Appendix Table A10). 17Most respondents do not track their day by time, making collection of reliable time use data challenging (though recent literature documents the broader unreliability of such data). We piloted a variety of strategies, and settled on asking respondents how much time they spent on a set of activities in the previous day. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["time-use data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure A4: Participation certificate to boost ‘ resume ’ CERTIFICATE THIS ACKNOWLEDGES THAT I engaged with Pulse Bangladesh to do data collection Notes: The wording of the certificate was made such that it could be applied to both arms; cash-only arms participated in weekly surveys along with all other experiment participants, so technically also engaged in data collection for our project. 60 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "C Details on outcome measures Outcome Variable Descriptions Psychological Well-being PHQ9 The standardized total score of 9 questions from the Patient Health Questionnaire-9 (PHQ9) Life Satisfaction Index A standardized average of survey responses to four questions from Di- ener ’ s standardized scale, responses made along a seven-point Likert scale. Stress Index The standardized total score from three elements of adapted from the Cohen Stress scale. “ How many of the last 7 days have you [been able to fall asleep peacefully / felt nervous / felt frustrated]? ” Sociability (Total) The total number of conversations in the past day with adults. Sociability (Positive) The total number of conversations in the past day with adults that the respondent felt were positive. Self-Worth Index The standardized total score from the responses on a scale from 1 to 10 to two questions: “ Think of a person you know who you most respect and who brings greatest value to your [family / community]. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Patient Health Questionnaire-9"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "If that person is a 10, where would you put yourself? ” Locus of Control The standardized total score from responses to four locus of control questions. “ In the last 7 days, how many days did you feel that to a great extent your life is controlled by accidental / chance happenings... ” Allocation Decision Game Indicator (yes / no) for response to an offer to participate an allocation committee to decide how money is spent. Participants are offered the opportunity to make a resource allocation decision for their community or have another individual (an NGO worker, an “ expert ”, or another refugee) make the decision. Stability Index The standardized total score from responses to two stability questions using a Cantril ladder. “ How secure [do you feel / think you will feel] [at present / five years from now] ” Physiological Index An inverse-covariance weighted average of PHQ, Stress, Life Satisfac- tion, Sociability (Total), Self-Worth, Locus of Control, and Stability indices. Gender Dynamics Gender Perceptions- Work The standardized total score of two questions regarding women ’ s work, “ How often would you agree that women should be allowed to work for a living [inside / outside] the block? ” 64 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Gender Perceptions- Violence (IPV) The standardized total score of five questions regarding norms for inti- mate partner violence (IPV) from the Demographic and Health Survey (DHS) (The important decisions in the family should be made only by the men of the family. How often would you agree? The wife has the right to express her opinion even when she disagrees with what her husband is saying. How often would you agree? A wife should tolerate being beaten by her husband in order to keep the family together. How often would you agree? A husband has the right to beat his wife. How often would you agree? It is more important to send a son to school than it is to send a daughter. How often would you agree?). Financial Well-being Savings Response to the question “ How much money do you currently have in savings? ” During the collection surveys (midlines) this question instead asked “ How much money did you save in the past week? ” Borrowing Total amount of money the household has borrowed. Economic Decision Making Risk Preference Measured using incentivized responses to the multiple price list deci- sions adapted from Holt-Laury and Sprenger (2002). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Outcome Variable Collection Periods Basline Midline Weekly Endline Psychological Well-being PHQ9 X X Life Satisfaction Index X X Stress Index X X X Sociability (Total) X X X Sociability (Positive) X X X Self-Worth Index X X Locus of Control X X Allocation Decision Game X X Stability Index X X Physiological Well-being Index X X Gender Dynamics Gender Perceptions- Work X X Gender Perceptions- Violence (IPV) X X Financial Well-being Savings X X ∗ X Borrowing X X Economic Decision Making Risk Preference X X Time Preference X X Other Outcomes Cognitive Ability X X ∗ X Physical Health X X ∗ X Notes: The “ Baseline ” survey was conducted with respondents before treatment assignment was revealed. The “ Midline ” survey were questions asked immediately after treatment assignments were disclosed after the baseline survey, but before the work task had begun. “ Weekly ” surveys were conducted after each week of work (if any). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "28 In section three of this study, we could not find taxes and regulations among the potential barriers to sales and employment growth that clearly separated the ups from the downs in the formal sec- tor. We also showed that the large sector has become a smaller share of the economy rather than a bigger share due to self-employment growth and due to the process of de-industrialisation. In substance, the CIS-7 may fit the developing countries scenario better than the transitional countries scenario in the Schneider and Klinglmair (2004) regressions. If this is the case, we should expect that the growth of the informal sector negatively contributes to growth. The data we have do not contradict this hypothesis given that the shadow economy has been on the rise during the recession period and has stabilised during the growth period. This digression on informality suggests that self-employment may partially act as an host to in- formal and illegal activities especially during recessions where self-employment may constitute a refuge for small informal and illegal businesses. Self-employment is also evidently a sector of ne- cessity for those who wish to keep health and pension records alive and do not want to formally register anywhere else. In times of growth this sector may instead function as a first step to for- mality, an entry gate to the formal sector given its lower entry barriers and taxes. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Has this hap- pened during the recent growth period in the CIS-7? Explaining labor market flows The key to understanding output and employment growth in the CIS-7 is evidently the relation- ship between self-employment which is largely informal and employees in the formal sector. And the transition from non wage (informal) to wage (formal) labor can occur thanks to 1) A migra- tion of workers from self-employment to wage labor or 2) Endogenous growth of self- employment turning into SMEs and generating formal employment. We have in fact introduced one further dimension of labor market segmentation, the wage / non-wage labor divide. Labor flows between these different states may contribute to explain the employment puzzle. For this purpose, we turn to Moldova, a country that in many respects could be considered as the average scenario in our CIS-7 sample. Moldova is also the only country that disposes of a consis- tent longitudinal panel survey between 1997 and 2002 which can be used to assess labor market flows during the growth period and test some hypotheses on the evolution of the labor market. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We use the whole longitudinal sample and the restricted panel sample to compute the statistics, the transitional probabilities and the probit regressions presented below. 19 In figure 4. 1, we show the distribution of the population across working categories and between 1998 ad 2002. The employed population has marginally increased in percentage of the total popu- lation. A migration of workers has also occurred from wage labor to non wage labor and this mi- gration has taken place mostly within agriculture. The most significant change in fact occurred among rural workers with farmers growing very significantly at the expenses of agricultural em- ployees. Non agricultural labor has remained practically unchanged during the period while agri- cultural employment has increased marginally. This phenomenon occurred during the post-1998 recession (1998-1999) and during the subsequent growth period (2000-2002). In table 4. 4, we report the population structure by category20. It is visible the constant growth of private agriculture and the constant decline of employment in agricultural enterprises in both the public and private sectors. Among non-agricultural enterprises, there is a growth in the private sector and a decline in the pubic sector suggesting a migration of workers between the two sectors 19 See http: / / www. statistica. md / for details on the survey. 20 Categories are identified on the basis of the main source of income of respondents. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 11055 The paper examines the early integration of Ukrainian ref­ugee students into Italy ’ s education system following the Russia ’ s invasion of Ukraine in 2022. Using administrative and survey data, the study presents enrollment trends, aca­demic performance, and barriers to educational integration. Findings from the analysis indicate that Ukrainian refugees face lower enrollment rates, higher absenteeism, and lower test scores than other students, particularly in subjects requiring language proficiency. Despite these challenges, teachers often recommend Ukrainian refugee students for advanced educational tracks, thus revealing their optimism about the potential of these students. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["administrative and survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Language barriers, mental health challenges, and uncertain futures are identi­fied as major obstacles to integration. The study highlights the importance of tailored interventions, such as psycho­logical support and more dedicated teaching time, to foster refugee students ’ academic and social inclusion. This paper is a product of the Development Data Group, Development Economics and the Social Protection and Labor Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at michela_carlana @ hks. harvard. edu; pcastaing @ worldbank. org; mtestaverde @ worldbank. org; and mtiberti @ worldbank. org. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The long-term consequences of these trends are signiϐicant, as diminished educational outcomes and social isolation can hinder successful integration into host communities. Conversely, sustained social and educational integration efforts are vital for positive outcomes. For instance, studies indicate that long-term integration can be hampered by social, economic, and institutional barriers (Chiswick and Miller, 2014), while interventions focused on language support and community engagement can lower these barriers (Ozden and Wagner, 2020). Further, speciϐic interventions aimed at removing obstacles to education for children on the move can contribute signiϐicantly to better integration outcomes (Schuettler and Caron, 2020). This paper beneϐits from key information coming from administrative data on educational records of Ukrainian refugees in Italy for the academic years 2021-2022 to 2023-2024 for grades 6 to 13. This provides a unique opportunity to examine enrollment, attendance, test performance, and other indicators of integration into the Italian educational system. Supplemented by survey data collected in 2023-2024, this study offers an overview of the challenges and opportunities faced by Ukrainian students in secondary schools and highlights areas for potential policy development. This study advances the literature by adding empirical evidence on the short- to medium-term educational impacts of displacement on young refugees within a European host country, offering insights into the role of education policy in mitigating human capital losses. It also contributes to discussions on human development by identifying factors that support or hinder integration, highlighting pathways for improving educational and social outcomes for refugee students. Results highlight that despite gradual improvements, enrollment rates remain signiϐicantly lower among refugees compared to native and other foreign students. Ukrainian refugees also demonstrate higher absenteeism and lower academic performance, particularly in subjects requiring language proϐiciency such as Italian and English. However, good performance in mathematics suggests potential strengths linked to their prior educational backgrounds. Despite these challenges, teachers seem to be more inclined to recommend Ukrainian refugees for high-track education compared to other newly arrived foreigners, indicating potential optimism about their academic capabilities. The Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 3. Data Sources and Methods 3. 1. Administrative data on educational outcomes in Italy Two administrative data sources represent the backbone of this paper. These are the administrative data obtained from the Ministry of Education (MoE) for academic years 2021-22, 2022-23 and 2023- 24, and standardized test score data from the Italian National Institute for the Evaluation of the Educational System (INVALSI) for the 2022-2023 academic year. These datasets offer valuable insights into the educational outcomes of students in Italy, including Ukrainian refugees who entered the Italian school system following the invasion in 2022. Deϐinitions. In both datasets, students are categorized into ϐive demographic groups based on their nationality and timing of entry into the Italian educational system. These groups are Italian students, Ukrainian refugee students, non-refugee Ukrainian students, newly arrived foreign students, and other foreign students. Among Ukrainian students, the distinction between refugees and non- refugees is based on their enrollment date in the Italian education system. Ukrainian refugees are deϐined as Ukrainian students who enrolled in Italian schools after February 2022. In this paper, Ukrainian refugees are labeled “ Ukr post-Feb 2022 “, while non-refugee Ukrainians are labeled “ Ukr pre-Feb 2022 “. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["standardized test score data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This distinction is also applied to foreign students, with those entering the system after February 2022 referred to as “ Migrant post-Feb 2022 ” and others referred as “ Migrant pre-Feb 2022 ”. 7 Administrative data from MoE. The Ministry data includes information for all students who enrolled at any point during the academic year. 8 This information covers academic year, grade, gender, birth date, birthplace, citizenship. They also include school-speciϐic information such as the name and identifying code of the institution where the student is enrolled. Furthermore, the dataset includes a variety of school outcome variables, including grades in English, Italian, Mathematics, overall GPA calculated as the average across all subjects, behavior scores from grade 9 to grade 12, guidance council evaluations from lower secondary school, records of absences, late entries, and early exits. Given the timing of this study, data for academic year 2022-23 are the most complete. For academic year 2022-2023, school enrollment data at the provincial level was provided for 4, 269, 348 enrolled students across the 8 years of Italian lower and upper secondary school, encompassing both public and private institutions. The dataset includes nearly all students in the country irrespective of their citizenship. 9 Table 1 shows the distribution of the different groups of students by grade. In the 7 For ease of reference, we refer to non-Italian and non-Ukrainian students as migrants. However, we acknowledge that some of these students may be refugees or displaced students. 8 At the time of writing this paper, both MoE and INVALSI data were not fully available and, as such, only the information on enrollment was used for the academic year 2023-24. 9These numbers do not include students enrolled in Provincial centers for adult education (CPIA). See footnote 5. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["INVALSI data"], "descriptive_data": [], "vague_data": ["school enrollment data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 measured by the number of days missed during the academic year, can function as an indicator of school attachment or integration for refugee students. To address potential biases from teacher subjectivity, the analysis of score disparities relies on INVALSI test results. The standardized and anonymized nature of these tests helps mitigate subjectivity in assessment. First, the results section presents some summary statistics of the main outcomes across the different categories of students. Second, we use the administrative data to analyze empirically how Ukrainian refugees and newly arrived foreigners compared to other students as regards their education performance. This estimation is based on an OLS model with the following econometric speciϐication: 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 + 𝛽𝛽1 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 + 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 + 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 + 𝑓𝑓𝑔𝑔 + 𝑓𝑓𝑠𝑠 + 𝑓𝑓𝑙𝑙 + ϵigs (1) where 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 represents the outcome of interest (such as test scores, absenteeism, or high-track recommendation) for student i in school s, in grade g, and with language l. The variable 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 is a dummy indicating whether the student is a Ukrainian refugee, and 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 indicates if the student is a newly arrived foreigner. 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 represents the Economic, Social, and Cultural Status of the student, and 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 indicates the student ’ s gender. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 coefϐicients from this estimation. The results indicate that Ukrainian refugees are consistently more absent than other foreign students who entered the Italian education system around the same time. Lower test score performance The evidence suggests that Ukrainian refugees in Italy face important learning gaps across all subjects. Figure 4 reports the INVALSI test scores by topic and category of students. 23 The educational disparity is particularly pronounced between Ukrainian refugees and Italian students, but signiϐicant gaps also exist between refugees and both Ukrainian nationals and foreign students who were enrolled in Italian schools before February 2022. However, Ukrainian refugees tend to have INVALSI scores comparable to migrant students who joined the educational system after February 2022. Notably, Figure 4 shows that Ukrainian refugees perform better in mathematics than recent migrants but score lower in Italian. Figure 4-INVALSI scores in Grades 8, 10, and 13 (Source: INVALSI, a. y. 2022-23) Table 3 presents the regression estimates that control for various potential confounding factors. The results indicate that both Ukrainian refugees and recent migrants score lower across all subjects. In mathematics, both groups score 16 points less than the rest of the sample. As expected, given their relatively short time in Italy, their performance in Italian is notably weaker. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "19 children and 61 % of caregivers prefer to remain in Italy. Adolescents between the ages of 15 and 19 express a greater desire to continue living in Italy compared to children aged between 9 and 14. Another survey conducted across Europe from June to December 2022 shows that only 8 % of Ukrainian refugees planned to settle outside Ukraine (Adema et al., 2024). Compared to other foreign children in Italy, the aspirations of Ukrainian refugees to return to Ukraine seems signiϐicantly higher: indeed, a recent study from ISTAT on children 11 to 19 years old shows that only 11 % of foreign children wish to return to their home country (ISTAT, 2024). The relatively strong desire to return to Ukraine can have negative effects in refugee parents'educational decisions, particularly in encouraging their children to learn the language of the host country and in enrolling in school (Dryden-Peterson et al., 2019; Zengin and Atas-Akdemir, 2020). Figure 6- Aspirations and identity of refugee caregivers and students (Source: World Bank Survey on Ukrainian refugees in Italy) Many students facing uncertain futures try to stay connected to both educational systems. Findings from the World Bank survey indicate that 25 % of children are engaging in online Ukrainian schooling while being enrolled and attending Italian schools. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The lack of certainty means children are trying to prepare for further studies (e. g., in universities) in two separate systems with varying requirements. The survey results indicate that students enrolled in both systems spend as much time in Italian schools as those attending only Italian schools, averaging 31 hours per week. However, students participating in both systems spend an additional 8 hours per week on online Ukrainian classes. This puts an extra burden on these children. Connectedness to Italy is correlated with demographic characteristics and social environment of refugee children. Additionally, Table 6 shows that making new friends in the country of destination and speaking Italian are strongly associated with higher connection to Italy. The mental distress resulting from displacement is a key barrier to educational integration for many Ukrainian refugees in Italy. The link between poor mental health and low school attendance and performance is widely acknowledged in the literature (see Fiining et al., 2019 for a systematic review). In the World Bank survey data, children and caregivers reported signs of mental distress, with 16 % of children and 24 % of refugee caregivers reported experiencing psychological distress 61 % 35 % 31 % 68 % 50 % 36 % 23 % 68 % 59 % 26 % 26 % 59 % Would like to continue living in Italy Would like to move back to Ukraine Feel strongly connected to Italy Feel strongly connected to Ukraine Caregivers (N = 283) Children between 9 and 14 years old (N = 141) Children between 15 and 20 years old (N = 96) Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["World Bank survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "displaced children and adolescents residing in host communities, rather than in refugee camps. Particularly, VenRePs-Kids collects data on 2, 556 households including 1, 338 Colombian and 918 Venezuelan households, respectively. The study collects rich and comprehensive data on children ’ s and adolescent ’ s development including anthropometric measures, vo- cabulary ability tests, and socio-emotional and mental health assessments. It also collects data on risk behaviors, time use, social integration measures, prosocial behaviors, and parents and caregivers sociodemographics, among other dimensions. 2 The study includes Colombian children and adolescents as the comparison group to high- light the developmental differences of Venezuelan forcibly displaced children. This choice stems from the fact that comparing Venezuelan children in Colombia with their counter- parts remaining in Venezuela is impractical due to the latter ’ s exposure to a severe eco- nomic and humanitarian crisis, marked by limited access to services and food. This envi- ronment severely hampers their potential for normal human development. Additionally, many Venezuelan children and adolescents have spent more of their lives in Colombia than in Venezuela. Therefore, Colombian children and adolescents serve as the most ap- propriate benchmark for assessing the developmental gaps of their Venezuelan peers. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["VenRePs-Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This paper analyzes data from the initial wave of VenRePS-Kids, conducted from Oc- tober to December 2022, to outline key demographics and stylized facts about forcibly displaced children and adolescents. Initially, we examine the human development dis- parities of forcibly displaced Venezuelan children and adolescents in comparison to their Colombian counterparts. Our approach to human development is broad, covering physi- cal, cognitive, socio-emotional, and mental health aspects. Additionally, we complement our analysis by exploring differences in food security, social cohesion, and the economic status of parents. Although our analysis is descriptive, it represents a crucial initial step 2Venezuelan households are defined as those where both parents and their children have a Venezuelan nationality. Colombian households are composed of Colombian citizens only. 3 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["VenRePS-Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "vices. Our findings reveal significant delays in the physical and cognitive development of Venezuelan minors compared to their Colombian peers. Specifically, we observe a 0. 3 standard deviation difference in Body Mass Index (BMI), indicative of nutritional status, and a 12-percentage point difference in the Peabody Vocabulary Test scores, which as- sesses receptive vocabulary and verbal ability. Surprisingly, our analysis does not identify any disparities in socio-emotional and mental health between the two groups. This out- come is unexpected, given the high incidence of socio-emotional and mental health chal- lenges among forcibly displaced populations. The absence of discernible gaps in these areas could be attributed to the non-exposure of Venezuelan migrants to warfare, or it may reflect the vulnerabilities of the Colombian population, which has its own extensive history of internal forced displacement and violence. When examining the role of time of settlement, regularization status, and service access on the developmental disparities between Venezuelan and Colombian minors, we un- cover two significant facts. On the one hand, the gaps in both cognitive and physical development are diminishing over time. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "III THE VENREPS-KIDS STUDY In this section, we offer a detailed overview of the VenRePs-kids Study, covering its de- sign, implementation, the questionnaire utilized, and the primary outcomes that will be employed to evaluate the human development disparities between forcibly displaced children and adolescents and their peers in host communities. III. A Design Location. Our study is conducted in Medell ´ ın, Colombia ’ s second-largest city, following Bogot ´ a. Medell ´ ın was chosen for this study because it hosts the third-largest Venezue- lan migrant population in the country, trailing only Bogot ´ a and C ´ ucuta, as indicated by the 2018 population census data. Additionally, previous research has demonstrated that survey response rates among migrants in Medell ´ ın are notably high. For instance, a na- tionally representative survey of Venezuelan migrants conducted in 2018 — which was representative across Colombia — revealed that Medell ´ ın had the highest response rates among migrants, whereas Bogot ´ a recorded the lowest (Ib ´ a ˜ nez et al. 2022). This finding supports the decision to focus our study exclusively on Medell ´ ın, also considering the challenges and high costs associated with tracking a highly mobile population longitu- dinally in previous research efforts. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["2018 population census data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A map highlighting Medell ´ ın ’ s geographic position and the locations of the households interviewed for this study is provided in Figure 2, offering visual context to our research setting. Representativeness and stratification. VenRepS-Kids is designed to be representative of two groups of youth. The first group consists of Colombian children and adolescents, aged 5 to 17, born to Colombian parents. The second group encompasses Venezuelan migrant children and adolescents of the same age range, born to Venezuelan parents, who mi- grated to Colombia between 2016 and 2020. The sample was further stratified by gender and socioeconomic levels, using Colombia ’ s neighborhood income-based classification system that ranges from 1 to 6, where six indicates the wealthiest neighborhoods. Our 13 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 2. Location of Households in the VenReP-Kids Sample Notes: The figure depicts the exact geographic location of all the households in the VenRePs-Kids study. Green and blue dots depict the location of Colombian and Venezuelan households, respectively. The map in the upper right corner illustrates the location of Medell ´ ın (blue pin) with the department of Antioquia (highlighted in red). survey focuses on strata 1 through 4, intentionally omitting strata 5 and 6 to avoid bias toward higher-income groups which are less likely to include migrants in need of sup- port. 14 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "cents. The universe of blocks (“ manzanas ”) was stratified by socioeconomic strata and a representative sample of blocks was selected at random without replacement. To en- sure a sample representative of Colombian children by age group, stratum, and sex, we followed a multi-stage random sampling process. 8 As mentioned earlier, one of the biggest constraints in characterizing the role of forced migration in children ’ s human development within developing countries is the difficulty of finding a representative sample of those migrants. This is specially true in contexts where migrants are not hosted in refugee camps, but are integrated in local communi- ties, which account for 80 % of refugees worldwide (Climate Center 2022). We address these difficulties, leveraging all available information on Venezuelan settlements across the country to construct the largest possible comprehensive listing. The listing included data on Venezuelan settlements from all available sources, such as the 2018 population census, migrant organizations, and settlements identified by iMMAP, a non-profit orga- nization. iMMAP uses multiple sources, including OIM, United Nations, local migrant organizations, and satellite images, to identify Venezuelan settlements geographically. 9 Hence, to create our sampling frame, our field team verified the geographic location of all the Venezuelan settlements in-person and implemented a snowball sampling procedure in all the settlements found. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["2018 population census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5. Time Use and Child Labor: The fifth module examined how children spend their leisure time, their involvement in child labor, and interactions with peers. 6. Pro-social Preferences and Migration Outlook: The sixth module concentrated on adolescents ’ pro-social behaviors, such as altruism and trust, and explored their expectations and intentions regarding migration. 7. Socio-emotional and Mental Health: The final module involved the administration of various scales to assess socio-emotional well-being and mental health, includ- ing trauma, behavioral problems, anxiety, and depression. The scales include the Trauma Symptom Checklist for Young Children (TSCYC), Strengths and Difficulties Questionnaire (SDQ), General Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire (PHQ-9). All these scales and the corresponding outcomes that we evaluated are described in the next subsection. The survey also employed the Peabody vocabulary test to evaluate the cognitive devel- opment of all participating children and adolescents. A summary of the survey modules is depicted in Table A. 1. III. C Sample comparability While Medell ´ ın ranks as the third city with the highest migration in Colombia, it is crucial to recognize the degree to which migrants arriving in the city differ from those migrating to other regions in Colombia. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Trauma Symptom Checklist for Young Children", "Strengths and Difficulties Questionnaire", "General Anxiety Disorder Scale", "Patient Health Questionnaire"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Ideally, we would conduct a comparative analysis of the characteristics of migrants residing in Medellin versus those in other parts of the coun- try to discern the extent of these differences. However, the lack of comprehensive data regarding the living conditions of this population makes such analysis unfeasible. To explore how this population compares with other migrant groups in the country, we turn to the only two available data sources on migrants. First, we use the Venezuelan Refugees Panel Survey (VenRePS), conducted by Ib ´ a ˜ nez et al. (2022), which captures a 19 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Venezuelan Refugees Panel Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "representative sample of undocumented migrants in Colombia ’ s major cities as of 2020. 10 This survey encompasses information on the socioeconomic status, health, well-being, access to services, and labor market outcomes of adult undocumented migrants. Second, we compare our sample with the Administrative Venezuelan Migrant Registry (RAMV), a nationwide census of undocumented Venezuelan migrants conducted by the Colom- bian government in 2018. 11 This census surveyed Venezuelan households regarding their socioeconomic conditions and the labor market characteristics of the household head. We compare the household characteristics and the labor market outcomes of the house- hold heads in our sample with those in VenRePS and RAMV surveys in Table A. 3. 12 We observe that households in the VenRePS-Kids survey are smaller on average and have a greater number of children living in the household. The latter is anticipated since one of the eligibility criteria to participate in our survey is the presence of at least one child in the household. Furthermore, the household heads in our sample are disproportionately female and more likely to be married, aligning with the family structure targeted in our sampling frame. Regarding labor outcomes, household heads in our sample are more likely to be employed and engaged in the informal sector compared to those surveyed in VenRePS and the RAMV census. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["VenRePS", "Administrative Venezuelan Migrant Registry", "VenRePS-Kids survey", "RAMV surveys", "Administrative Venezuelan Migrant Registry (RAMV)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Despite notable disparities in the characteristics of our sample compared to the VenRePS and RAMV samples, understanding the interaction of these attributes within a frame- work of self-selection among migrants into Medellin is challenging. Moreover, it is cru- cial to note two primary distinctions between our survey and the VenRePS and RAMV surveys. Firstly, the inherent differences in the sampling frames of each survey stem from their distinct measurement objectives. Second, both surveys were conducted at different times compared to our survey. The RAMV survey was undertaken in 2018 in response 10See Ib ´ a ˜ nez et al. (2022) for specific survey and sampling details. 11Refer to Ib ´ a ˜ nez et al. (2022) for further details. 12Since the VenRePS and RAMV surveys lack information regarding children and adolescents within households, our analysis concentrates only on the household and household head characteristics that are available in all three surveys. 20 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["RAMV survey", "VenRePS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "to the Venezuelan migratory crisis, whereas the VenRepPS survey was conducted during the pandemic in 2020. These temporal inconsistencies result in varying sample composi- tions across surveys, diverging from the landscape we observe in 2022. Notably, forced migrants in the VenReps Kids survey migrated during the crisis but have since remained in the country for several years, potentially leading to disparities in household integration outcomes. IV GENERAL DESCRIPTIVE STATISTICS IV. A Key characteristics of adults Table 2 provides descriptive statistics for the adults in our study, encompassing the pri- mary caregiver, mother and father (if residing with the child), and the individual finan- cially responsible for the child (should they be different from the aforementioned per- sons). Typically, the roles of primary caregiver and financial provider are fulfilled by either the mother or the father. The table is organized into three panels for clarity: Panel A details key individual characteristics, Panel B outlines adults ’ access to services, and Panel C focuses on labor market characteristics. Within the table, columns (1) and (2) present average values for adults from Colombia and Venezuela, respectively, while the final column displays the results of mean difference tests between these two groups, with standard errors noted in brackets. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["VenRepPS survey", "VenReps Kids survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 3. Wealth Index Distribution (a) Wealth Index- Total 0. 05. 1. 15. 2. 25 k-Density- 5 0 5 10 15 Wealth Index Colombian Venezuelan Venezuelan: Before migrating (b) Adequate Housing Materials (% of Total) 0 1 2 3 4 k-Density 0. 2. 4. 6. 8 1 Average- Dwelling Material Colombian Venezuelan Venezuelan: Before migrating (c) Asset Ownership (# Total) 0. 05. 1. 15 k-Density 0 10 20 30 40 Total Assets Colombian Venezuelan Venezuelan: Before migrating (d) Access to Services (% of Total) 0 1 2 3 k-Density 0. 2. 4. 6. 8 1 Average- Access to Services Colombian Venezuelan Venezuelan: Before migrating Notes: Panel (a) presents the distribution of the wealth index for Colombian and Venezuelan households in our sample in 2022 and pre-migration. Wealth Index is an index measure of the household ’ s cumula- tive living standard constructed following The Demographic and Health Surveys (DHS) methodology. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Due to the variations in weight and height ratios among children and adolescents according to gender and age, the benchmarks for determining nutritional status are specifically tailored to these factors. We adhere to WHO guidelines to calculate the standardized BMI values for minors. A BMI exceeding one standard deviation (SD) from the mean suggests overweight, while a BMI less than minus one SD indicates underweight. 16 Health status is assessed through a binary variable, assigned a value of one if the caregiver has reported any health issues such as disease or chronic pain, accidents, dental pain, surgical interventions, or preg- 14For the Colombian households the wealth index is measured with contemporaneous data. 15This procedure restricts the sample to the common support of the propensity score for being a forced migrant and weights observations for Colombian kids by a non-parametric function of the propensity score. This procedure has been shown to increase the estimate ’ s efficiency. 16Furthermore, a BMI greater than 2SD is indicative of obesity risk, and less than- 2SD signals a risk of severe thinness. 33 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of difficulty, ranging from those suitable for children as young as 2 and a half years old to more complex items for individuals over 18 years old. 19 The Peabody Vocabulary Test is calibrated with a mean standard score of 100 and a standard deviation of 15, placing scores between 85 and 115 within the average range. This test, in its Spanish edition, has been validated for use in Colombia. Additionally, to ensure relevance and accuracy for Venezuelan nationals, we conducted a preliminary validation by administering the test to a sample of Venezuelan mothers in our study. This step confirmed that the words used in the test held consistent meanings for participants from Venezuela. Figure B. 1 depicts the distribution of PVTS scores for Venezuelan and Colombian chil- dren and adolescents in our sample. This visualization indicates that Venezuelan minors consistently score lower on the PVTS compared to their Colombian peers across the en- tire score distribution. In Table 6, we present the average disparities in percentile rank on the Peabody scale, revealing that Venezuelan children and adolescents, who are forcibly displaced, score approximately 12 p. p. lower than their Colombian counterparts. The difference is meaningful and in turn translated into Venezuelan minors falling into the a higher likelihood of having extremely low, moderately low, and low score categories. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Additionally, these findings remain consistent even when adjusting for a range of control variables pertaining to the individual, their parents, and grandparents, underlining the robustness of these observations. V. C Socioemotional and mental health development To explore the differences in mental health and socioemotional development across Colom- bian and Venezuelan minors we use multiple scales. For children aged 5-10 years, the Trauma Symptoms Checklist for Young Children is employed. This 90-item question- 19Children only respond to items within their “ critical range ”, determined by a lower limit called the “ base item ” and an upper limit called the “ ceiling item ”. The base item, marking the starting point, is determined by the individual ’ s chronological age in years (date of test administration- date of birth). Once the child correctly answers 8 consecutive questions, they reach the ” Base ”. Subsequently, upon making 6 mistakes within 8 consecutive questions, the ceiling is established. The direct score is calculated as the item number where the test ends (ceiling item) minus the number of errors from the highest base to the end of the ceiling. 36 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "to severe based on the score. Moreover, depression is screened with the Patient Health Questionnaire, a 9-item tool administered directly to adolescents. These instruments collectively gauge a broad spectrum of psychological and emotional states, including post-traumatic stress, emotional disturbances, behavioral issues, inat- tention, peer relationships, prosocial behavior, anxiety, and depression, offering a com- prehensive view of the mental health and socioemotional development of the minors in our study. Figures B. 3 and B. 4 depict the distribution of the raw scores for Venezuelan and Colom- bian minors for each of the four scales. Surprinsingly, we do not observe any stinking differences on the distribution of any of these scores across groups. We are also not able to distinguish statistical differences between Colombian and Venezuelan children in any of the scales, when we estimate the specification highlighted in equation (1) as illustrated in Table 7. This is an unexpected result considering that typically, forcibly displaced pop- ulations have a high prevalence of socioemotional and mental health issues, but might be related to the fact that Venezuelan migrants have not faced war (as many forced migrants have in other contexts) directly and as such, these issues are less prevalent. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Another ex- planation of these results could be the vulnerability of the Colombian population who in many cases has also a long history of internal forced displacement and violence. V. D Social Cohesion We also delve into the differences in secondary outcomes among Colombian and Venezue- lan adolescents concerning social cohesion. We focus on assessing altruism, trust, iden- tity towards specific domains, networks, and experiences of discrimination. To measure altruism and trust, we employ the questions from the Global Preference Survey, a tool developed by Falk et al. (2022) to elicit risk, time, and social preferences. Specifically, to measure altruism we ask the adolescents how much of a fictional endowment would they be willing to donate to a good cause. To measure trust, we include the 7-itme ques- 38 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Global Preference Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Colombian and Venezuelan migrant children and adolescents. VII Discussion In this study, we analyze novel and unique data on forcibly displaced migrants and hosts, focusing on children and adolescents, to highlight the disparities in human development between them. The structure our analysis in two parts. In the first part, we characterize the main trends in the data. We show that forcibly displaced households have a wealth distribution skewed towards lower values relative to Colombian households. This is likely explained by the assets ownership loss that forcibly displaced households expe- rienced after the migration episode. We also identify meaningful lags in human capital accumulation between Colombian and Venezuelan children and adolescents of approxi- mately 1 year. We further note that the Colombian government ’ s supportive policies for Venezuelan forced migrants are evident through high levels of service access and pro- gram participation for migrants. Nevertheless, it remains surprising that participation is not higher, suggesting significant potential for improvement in increasing sisb ´ en and health insurance enrollments. In a second part of our analysis, we document sizeable lags in physical and cognitive de- velopment of Venezuelan children and adolescents, relative to their Colombian counter- parts. However, we were not able to identify any gaps in the socioemotional and mental health between the two groups. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 As displacement crises are largely unpredictable, all the studies surveyed in this paper are evaluations conducted ex-post. In theory, a few of the crises studied could have been predicted but it would not be possible to allocate individuals to treated and non-treated groups randomly given that, by the definition of forced displacement we provided, people are fleeing violence, persecution or high levels of insecurity or uncertainty. Consequently, none of the papers reviewed is based on a Randomized Controlled Trial (RCT). Due to the randomness of the decision to leave (because of conflict, violence, insecurity or major political events) and / or the random allocation of displaced people in the country of destination (by policy or by default), some authors argue that they are in the presence of natural experiments. All authors do, however, address the question of endogeneity and, if one searches for a common thread, these evaluations would be better described as quasi-natural experiments. The basic model used by the literature is a model of the following form: 𝑦 ௜ ൌ 𝛼 ൅ 𝛽𝐹𝐷 ௜ ൅ 𝛾𝐹𝐸 ௜ ൅ 𝜀 ௜ Where i is the unit of observation, y is one of the four outcomes described, FD is the forced displacement shock and FE are fixed effects. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Most papers with few exceptions use standard OLS estimators or some of its variants (Table 2). Two papers use general equilibrium models (Bodvarsson, Van den Berg, and Lewer 2008; Hercowitz and Yashiv 2002) and two papers simply compare means between treated and non-treated groups resulting in simple difference estimations (Card, 1990 and Alix-Garcia and Bartlett, 2015). [Table 2] The unit of observation varies depending on the data at hand. Most studies rely on household survey data where individuals or households are the unit of observations and most studies include some regional dimension (more frequently administrative areas). Where longitudinal or panel data are available time is also included. Other choices for unit of observations include skills or education level, various types of population groups (based on gender, age etc.), and, in a few cases, economic sectors, industry or labor market segments. The use of fixed effects varies. Some papers use the full set of parameters depicting units of observation (for example, household, region and time fixed effects in equations where the unit of observation is constructed using household, region and time). Other papers use subsets of these parameters whereas other papers introduce variables that are not used to identify the unit of observation. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Very few papers provide explanations for these choices and there is no clear common approach to this choice. There are also only a handful of papers that discuss estimations of the error term and choices made in this regard. The two prevalent evaluation methods used by these studies are Differences-in Difference (DD) methods and linear elasticities models. In the first case, the variable of interest (FD) is a discrete status variable (generally a pre / post- treated / non-treated interaction term) and the coefficient of interest measures the impact on outcomes in the presence or absence of displaced people. In the second case, the model is typically in log form and is based on a shock variable that measures the intensity of the shock such as the number or share of refugees per geographical unit. In this case, the coefficient measures the elasticity of outcomes to the intensity of displacement. A few papers conduct simple differences illustrating results graphically or in tabular form. A few papers use ordinary matching methods (Alix-Garcia and Bartlett 2015, Aydemir and Kirdar 2018, Murard and Sakalli 2017; Mayda et al. 2017) and three papers use Synthetic Matching Methods (Peri and Yasenov 2017; Borjas 2017; Makela 2017). We could not find any paper using a discontinuity design. 12 The essential ingredients used to measure the population shock are the number or presence of forcibly displaced persons, the size of the host population and the distance of the displaced from host communities if the displaced are clustered in camps or other forms of independent settlements. The literature covering high-income countries tends to focus on labor markets and the host population is often defined in terms of 12 Schumann (2014) is an exception, but only looks at the impacts on municipality size. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Sarvimaki (2011) uses the elements of the government ’ s placement policy as instruments (i. e. the proportion of a municipality ’ s population speaking Swedish and the hectares of potential agricultural land). Other authors focus instead on the counterfactual group testing alternative designs of the control group, sometimes including placebo groups and other times recurring to matching methods. The choice of matching methods varies from ordinary methods such as nearest neighbor to more recent advances such as Synthetic Control Methods (Abadie and Gardeazabal, 2003). The inclusion of fixed effects is common to almost all papers although the choice of fixed effects can be very different, as described above. Only one paper uses Fixed Effects (FE) and Random Effects (RE) formal models in conjunction and tests for differences (Esen and Binatli 2017). Cross-section econometrics is, by far, the method of choice even if time is included into the equations but we also found three papers employing time-series models (Carrington and de Lima 1996, Makela 2017, Fakih and Ibrahim 2015). Only few papers are able to exploit panel data (Foged and Peri 2015, Depetris-Chauvin and Santos 2017) and several of them use the same data set (Maystadt and Duranton 2018, Maystadt and Verwimp 2014; Ruiz and Vargas-Silva 2015, 2016, 2017). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Not all cross-sectional studies have multiple rounds of comparable data, covering the period before and after the crisis. When comparing impacts between locations within a country, cross-sectional data also usually does not allow to capture impacts on those who moved out and to differentiate impacts between those who were already there before the shock and those who moved in afterwards. Some of the models based on administrative areas qualify as spatial econometrics models in that they use estimation methods that derive from this literature and are published in spatial econometrics journals. Studies that compare different areas within a country are not only confronted with the potential endogeneity of the size and skill composition of the inflow and the choice of destination, but also with the endogenous reactions of the host community. Local workers might respond to the labor supply shock by dropping out of the labor force, investing in education, occupational upgrading or moving to other areas and diffusing the impact of the inflow. Even if local workers do not respond to wage variations, capital flows may equalize capital / labor ratios within the country, labor-intensive industries might move towards the regions with a high refugee or IDP influx or firms might use more labor-intensive production technologies. The reactions of the host country workers, investors and firms are medium-to long-term in nature and will play less of a role in the short-term if there are large, sudden and geographically concentrated inflows. Some of the papers explicitly analyze these potential channels, notably migration of local workers, and, to a lesser extent, occupational upgrading. Outmigration of hosts is a critical complement to the labor market analysis and excluding this outcome can lead to an underestimation of the impacts of forced displacement on the labor market outcomes of natives. The papers we reviewed that looked at tasks complexities and the question of substitution vs complementarities between refugees and natives found occupational upgrading among Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In order to address this issue, one has to consider a labor supply model that is able to measure both effects separately whereas most papers confound these two effects into one. Foged and Peri (2015) is one of the exceptions, as their paper looks at the intensive margin (fraction of year worked). Rozo and Sviastchi (2018) include the number of hours worked, and Ruiz and Vargas-Silva (2017) look at the changes in number of hours dedicated to a task (including employment outside the household). The second question relates to possible spurious correlations generated by how variables are combined in models. Linear models that use ratios of two variables as dependent variable (think of average prices or wages, employment rates or consumption per capita) and the denominator of this ratio as independent variables (think of the share of refugees on host communities or household size) can produce spurious correlations (Kronmal 1993). This is noted and addressed in Clemens and Hunt (2017) who show how addressing this issue change results for several studies in the literature covered here. Indeed, almost all models reviewed use the same population or household size on both sides of the equations. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Finally, the expenditure shock which we discussed in the theory section (international aid or an increase in public spending associated with the forced displacement crisis) is considered by only a handful of papers. This is a possible confounding factor of the impact of forced displacement on host communities and one that is not easily addressed with the use of fixed effects. This is clearly a shortcoming of this literature that will require increased attention in the future. 4. Meta-analysis of empirical results 4. 1 Data The literature review covers 49 papers spanning over a period of 29 years. We were not able to find published papers prior to the work by Card in 1990, which effectively started this literature, and there is a relatively low interest in this topic between 1990 and 2011 with only one or two papers published per year. With the Syrian crisis starting in 2011 and the EU crisis in 2015 the number of papers per year increased by several fold. Most of the papers and results considered in this review are therefore very recent (Figure 2). We used academic databases and search engines (EconLit, Social Science Research Network, JSTOR, Google Scholar) and searched websites of institutions with relevant working paper series (NBER, IZA, ERF and others). Relevant unpublished papers were included by searching agendas of workshops and conferences organized during the past few years. From the papers reviewed, we selected a total of 762 results summarized in Table 3. The results database was compiled as follows. For each paper we focused on the results that the authors considered the main and Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 conclusions on the role of individual characteristics. As for employment, results on wages could not be predicted by basic theory and are in clear contrast with popular beliefs. 5. Conclusion The paper reviewed 49 empirical studies that focused on estimating the impact of forced displacement on host communities. This literature covers 17 different displacement situations in high, medium and low- income countries covering the impact on the labor and consumer markets. A total of 762 results have been used for the meta-analysis. To our knowledge, this is the first comprehensive review of this literature. The empirical modeling analysis highlighted the main traits of this literature. By definition, all studies operate ex-post, after the displacement crisis has taken place. The unexpected nature of the crisis and the randomness of the allocation of displaced persons are two elements used to defend the natural experiment assumption. However, all papers address the central question of endogeneity. The instrumental variable approach is the dominant method to address endogeneity issues and instruments tend to focus on either distance from the shock or previous location of migrants. Double difference and linear elasticity models are the dominant choice of estimation models with matching and placebo counterfactuals often supporting these choices. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Looking also at the impact of IDPs in Colombia on wages, Morales (2017) uses a labor force survey, census data and registry data to study short and long-term effects as follows: 𝑆ℎ𝑜𝑟𝑡 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ௧ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ௧ ି ଵ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ௧ ൅ 𝜆 ௠ 𝑋 ௠ ௧ ൅ 𝛾௧ ൅ 𝛿 ௠ ൅ 𝛿 ௠ 𝑇 ൅ 𝜀 ௜ ௠ ௧ 𝐿𝑜𝑛𝑔 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ൅ 𝜆 ௠ 𝑋 ௠ ൅ 𝛿 ஽ ௠ ൅ 𝜀 ௜ ௠ where y is the log of wages, i, m, and i are individuals, municipalities and time respectively, 𝑋 ௜ ௠ ௧ are individual controls, 𝑋 ௠ ௧ is the log of total population or other municipality controls, 𝛾௧ and 𝛿 ௠ are time and municipality fixed effects, 𝛿 ௠ 𝑇 are municipality time trends, 𝛿 ஽ ௠ are department fixed effects and d is the inflow of IDPs defined as 𝑑 ௠ ௧ ൌ 100 𝑝𝑜𝑝 ௠ ௧ 𝑓 ௠ ௧ where 𝑓 ௠ ௧ is the total number of IDPs arriving in municipality m at time t. The same variable without the t subscript is used for the long-run effects equation. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey", "census data", "registry data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The third proxy for economic activity is given by per capita growth of night light, computed using satellite data from the National Oceanic and Atmospheric Administration (NOAA). 6 Night light data has the benefit 6Satellite data is available for a shorter time period, 1992-2013. 9 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["satellite data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of being available on a yearly basis independently of the quality of local statistical offices and data gathering. While it comes with its own problems it can shed light on local economic activity where gathering of statistical data is incomplete. 7 This makes it a great fit for measuring growth in a context of civil conflict. Conflict incidence is measured through the number of battle-related deaths from UCDP / PRIO dataset. We run the following regression for country i at time t: git = β × incidenceit + µi + ηt + ϵit (1) where git is economic performance per capita growth of country i in year t, incidenceit is conflict incidence, µi and ηt are respectively country and year fixed effects. A cross-country analysis as in equation (1) bears considerable potential for both reverse causality and omitted variable bias. Thus, a priori, a convincing causal link is hard to establish. However, here we expect the resulting bias to be small for two rea- sons. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["UCDP / PRIO dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, the cross-country literature has not found that negative, contemporaneous shocks to growth systematically lead to violence. 8 Second, we have run a large number of robustness checks by adding time trends or lagged growth to our specification and controlling for rainfall shocks directly. 9 The upshot from this is not only that results remain significant but also that the estimated coefficients barely change. This does not mean that a causal link from falling growth to conflict can be ruled out. But it is unlikely to drive the macro relationship we see in the data. In order to further explore the relationship between violence and country-level out- put we run two specifications of the model described above. In the first model conflict in country i at time t is defined by any violence, i. e. if at least one battle related deaths occurs. In the second specification, conflict is defined by a higher threshold, by 0. 008 deaths per 1000 population. 10 We expect to get different results from the two specifications. From the analysis of Figure 1 we know that economic damage of civil war increases with the severity of conflict. The estimated impact from the second model should therefore be more acute. Table 1, panel A and B, reports the results. Each column contains one of our measures for economic growth. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In all specifications, conflict incidence correlates nega- tively with country-level economic performance. The estimated coefficients of conflict incidence are statistically significant and negative. We also find that the coefficients 7For a discussion see Henderson et al. (2012). In order to calculate light per capita we use popu- lation data that is provided by a World Bank dataset. 8The standard reference here is Miguel et al. (2004). Ciccone (2011) shows that high rainfall levels three years earlier seem to be best predictors of conflict in the reduced form. Miguel and Satyanath (2011) argue that lagged negative growth shocks are a predictor of conflict onset. In any case, there is no evidence from this literature that contemporaneous growth declines cause conflict. Bazzi and Blattman (2014) corroborate the view that the relationship between income shocks and conflict is not straightforward. They do not find evidence of an effect of price shocks on conflict onset and only weak evidence on incidence. 9Results from this are presented in the Appendix. 10We take the threshold from Mueller (2016) who shows that a threshold like this leads to a similar number of coded civil wars as the threshold of 1000 battle-related deaths often used in the conflict literature. In the context here, this is a conservative approach as it is not the threshold which yields the biggest difference between conflict and non-conflict countries. 10 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["World Bank dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 Internal Conflicts and Refugees A particularly serious aspect of internal conflicts is the human suffering they generate. This is not only those who are killed or injured in conflict but the large number of people who are forced to leave their homes. The issue of refugees has received particular attention in Western media in recent years as refugee flows from Northern Africa, the Middle East and Afghanistan are increasingly reaching Europe. These refugee streams are linked to a severe humanitarian crisis with considerable funding needs for international donors and heavy strains on host countries. 21 The current refugee crisis, however, is in no way unique. Civil war has always been closely linked to humanitarian crisis and refugee streams are one way to capture this. In this section we provide a cross-country analysis aimed at investigating how the stock of refugees evolves when a civil conflict hits a country. In the analysis we will focus entirely on showing changes in the stock of refugees across time to illustrate the dimensions involved. We will base our later analysis on these population movements. We exploit country-level data gathered from several sources. Data about refugees is provided by the UNHCR Population Statistics Database. The database provides in- formation about UNHCR ’ s populations of concern from the year 1951 up to 2014. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR Population Statistics Database"], "descriptive_data": [], "vague_data": ["country-level data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This database lists seven categories: refugees, asylum-seekers, returned refugees, internally displaced persons (IDPs), returned IDPs, stateless persons and others of concern. For each group the database provides yearly information about their composition by loca- tion of residence and origin. We exploit only the data on “ refugees ”. 22 In particular, we are interested in the annual stock of refugees for each country of residence, i. e. how many people with refugees status have left their home country each year. We focus on these numbers as they appear to be the most comparable across time and countries. However, this is likely to capture only the tip of the iceberg in some cases. The number of IDPs is extremely high in some instances but cannot be captured with the same level of confidence as refugees generally. 23 Cross-country data about conflict is provided by the UCDP / PRIO. As for the index of country-level economic activity, we use again information provided by the Penn World Table and World Bank databases. As mentioned above, our aim is to explore the dynamics of refugees during conflicts. In other words, we attempt to answer several questions. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Penn World Table", "World Bank databases"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Do people run away from their home country when a conflict breaks out? Does the seriousness of the conflict matter in this decision? In which phase of the conflict do they leave? When do refugees come back to their home country? In order to answer these questions, we look at the impact of conflict incidence on 21We will return to these issues in the policy section. 22According to the UNHCR definition, this category includes “ individuals recognized under the 1951 Convention relating to the Status of Refugees; its 1967 Protocol; the 1969 OAU Convention Governing the Specific Aspects of Refugee Problems in Africa; those recognized in accordance with the UNHCR Statute; individuals granted complementary forms of protection; or those enjoying temporary protection; and people in a refugee-like situation ”. 23The UNHCR Global Trends Report 2014 provides evidence that confirms this hypothesis. About 59. 5 million people were forcibly displaced worldwide by the end of year 2014. Among them, 19. 5 million were refugees and 38. 2 million were IDPs. 28 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The third graph represents the kernel density of our productivity loss measure for observations with positive loss, which represents around 11 percent of country-year observations in our dataset. It gives an idea of the distribution of this variable across country-years. The distribution shows a long and thin upper tail driven by countries with repetitive and highly intense conflict history like Afghanistan or the Lebanon. The average productivity loss is 15 percent in this sample and one fourth of all country-year observations are associated with losses of more than 20 percent of productivity. Even if these estimates were drastically overestimated they indicate that the long run impact of mass violence through this channel could be substantial. 5. 2 Macro Evidence In this subsection, we investigate the correlation between the aggregate loss measure and output. For this purpose, we use the height loss measure from the previous sub- section to estimate the marginal effect of an extra cm loss on log GDP. This serves two objectives. First, we explore whether the micro evidence can be used as a conduit for understanding the long-term damage to output from conflict. Second, we check whether the aggregate loss in output that we get is consistent with the micro estimates of marginal economic return to health. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We follow Besley and Mueller (2015a) and analyze international investment flows in a fixed effects Pseudo Poisson framework. The investment flow at the country level is given by E { xit} = exp (αc + γ0 ∗ peaceit + log Xt) (9) where xit is the inflow of investment in country i in year t. We use the exposure model which controls for global investment flows through Xt. The regression with total inflows as an exposure variable can be thought of as modeling the annual rate of investment inflows into a country in each year. 40 The variable peaceit is a dummy that takes a value of 1 in all years with peace. We lag this variable by one year to allow for the fact that investment needs some planning and will not react immediately to changes in the host country. We expect γ0 > 0 if inflows increase after the end of conflict. It is likely that effects of violence will be most visible if the conflict has been intense in terms of battle related deaths per capita. Yet, the right cut-offfor the peace dummy is a priori not clear. India, for example, is coded as in conflict throughout the period if we choose a very low threshold. Choosing a higher cut-offmeans we treat low intensities as experiencing no conflict. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In all these cases violence probably did not affect the entire economy notably. In what follows we focus on positive net flows, i. e. we subtract outflows from inflows and code negative numbers as 0s. Our results are robust to using gross inflows but as these are not provided by all sources. 40See Frome (1983) for a discussion of using the Poisson model to study rates. For a general discus- sion of count data models, see Cameron and Trivedi (2013). Our results are also robust to using year fixed effects instead of exposure. 41The reason is that the OECD data, the Dutch Central Bank data and the UN data allows us to distinguish between net flows and gross flows. 42We also distinguish two different ways of calculating the cut-offof intensity using contemporaneous and average population in a country. In total we therefore have 14 different estimates per cut-off. 43Each coefficient is also estimated quite precisely at this cut-off. 50 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Dutch Central Bank data"], "vague_data": ["OECD data", "UN data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Within-country risk falls signif- icantly in peacetime. The effect is also economically meaningful- about one quarter of a standard deviation in the case of short term risks. In columns (2) and (5) we show the specification in which we add a dummy for the first five years of recovery and fragile peace. The coefficient on fragile peace is positive and of similar size in both cases. Mid-term risk is evaluated significantly higher in periods that are followed by conflict. In columns (3) and (6) we include the fitted values gained from a regression of fragility on refugees and political exclusion. Again the fitted values predict higher risk evaluations by ONDD. The estimate is not very precise but quantitatively large both for short- and mid-term evaluations. A rise in the fitted risk by 10 percentage points coincides with an increase in risk evaluations by 0. 08 to 0. 16. Evaluations like this have real-life repercussions as they are used to decide on insurance premiums. Our results signal a clear margin for policy. Attracting foreign investment appears to be a lot harder if a government excludes or even discriminates against parts of the population and refugees have not returned to their homes. We argue that this is true even if investors only care about stability. In this view, investment can be attracted to a country through policies that de-escalate conflict and commit the warring parties to peace in the period right after violence stops. 56 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "violent conflict. Besley and Mueller (2015a) argue that foreign investors seem to know that growth volatility changes with strong executive constraints and therefore react significantly to their adoption. In summary, the literature suggests that a lack of constraints on executive power at the country level could play a key role in building inequalities across regions and ethnic groups. In the absence of strong executive constraint, we expect regions populated by ethnic groups that have access to executive power to perform better relative to others due to ethnic favoritism. Conversely, excluded ethnic groups should experience relatively worse economic performance compared to other groups in the absence of such constraints. 46 To test these hypothesis we use data on ethnic groups ’ access to executive power and night light intensity from the GROWup Research Front-End (RFE Release 2. 0) dataset and executive constraint data from the Polity IV dataset. We use night light intensity as a proxy for economic activity at the ethnic group level. 47 Night light data has the benefit of being available on a yearly basis and of being measured at the local level where there is poor availability of statistical data. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["GROWup Research Front-End", "Polity IV dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As in previous sections we follow Henderson et al. (2012) who argue that the relationship between GDP and night light at the country level can be expressed fairly well in a constant elasticity model in which an increase of night light by 1 percent implies an increase of GDP of about 0. 25 percent. Hodler and Raschky (2014) also look at the relationship between log nighttime light intensity and log GDP at the regional level using the panel data of regional GDP per capita assembled by Gennaioli et al. (2013) 48 and they confirm that the relationship is linear and also find an elasticity of around 0. 3. Access to political power is ranked on a scale from 1 to 7 in the GROWup dataset. Ethnic groups are\"powerful\"(monopoly of power or dominant group in power), have access to central power through a formal system of power sharing (as\"Senior\"or\"Ju- nior\"partner) or are “ excluded ” from power (self excluded, powerless or discriminated). Strong executive constraint is measured as a dummy indicating whether or not we have executive parity or subordination of the executive at the country level, a value 7 for “ xconst ” variable in Polity IV dataset. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["GROWup dataset", "Polity IV dataset"], "descriptive_data": ["panel data of regional GDP per capita"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 19: Trust and Conflict in the Cross Section The problem with a lack of trust and strong group identities is that they penetrate and pervert formal institutions. The ethnic politics analyzed by Burgess et al. (2015) is just one example. Shayo and Zussman (2011), for example, use data from Israeli small claims courts to show that Arab and Jewish judges displayed significant judicial ingroup bias. Furthermore, this bias is strongly associated with terrorism intensity in the vicinity of the court in the year preceding the ruling. Confidence-building is also a crucial ingredient for the establishment of a fertile investment climate, which in turn is a trigger of economic development post conflict. This is the core message of the World Bank Report by Mills and Fan (2006). An important role of increasing trust doubtlessly goes to the media. It has been shown, for example, that hate radio in Rwanda played a critical role in the extent of ethnic violence during the genocide. 59 Other research has shown that media coverage can have strong effects on political preferences more generally. 60 Perhaps the most direct proof of the crucial role played by the media in the post-conflict situation comes from DellaVigna et al. (2014). The authors exploit variation in radio reception of na- tionalistic Serbian radio in border regions in Croatia. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data from Israeli small claims courts"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Appel and Loyle (2012) analyze the role of Post Conflict Justice (PCJ) institutions in attracting FDI in post-conflict countries. They show that post- conflict states that adopt PCJ are more likely to receive higher levels of FDI compared with post-conflict states that refrain from implementing these institutions. 71 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 7253 This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at jlendorfer @ worldbank. org and jhoogeveen @ worldbank. org. This paper analyzes the impact of the 2012 crisis in Mali on internally displaced people, refugees and returnees. It uses information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["face-to-face household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 trust in the government and its institutions and perspectives on conflict resolution. By analyzing the impact of the crisis on welfare, the consequences of returning home versus remaining in displacement and by comparing immediate with longer term impacts, this paper contributes to the literature on refugee, IDP and returnee populations. The paper combines data from a face-to-face baseline survey with information collected via mobile phone interviews from respondents identified during the baseline. This innovative approach to data collection makes it possible to collect welfare data with high frequency (monthly) – important in a volatile crisis situation – and allows measuring changes over time. It also permits following displaced and refugee households once they return, even if they return to areas that are inaccessible to enumerators. The remainder of this paper is organized as follows. Section 2 provides a brief overview of the methodology, the sample and sample selection. Section 3 discusses the characteristics of the displaced and returnees, looking specifically at ethnic composition, place of origin, household size, education, asset ownership and employment status. Section 4 considers how the crisis affected food consumption, employment, assets and school attendance. Section 5 is devoted to the specificities of returnees who turn out to be, on aggregate, less affected by the crisis and better off than IDPs or refugees. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["face-to-face baseline survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Section 6 presents the differing views of IDPs, refugees and returnees on what happened during the crisis and prospects for peace. Section 7 concludes the paper. 2. The Listening to Displaced People Survey The Listening to Displaced People Survey (LDPS) combines a baseline face-to-face survey with mobile phone follow-up interviews. During the baseline survey respondents were identified and information on household and respondent characteristics was collected. Once the baseline interview was completed, respondents were given a mobile phone and started to receive, at monthly intervals, phone interviews from a call center in Bamako. During these phone interviews structured questions were asked about welfare of the household. Phone interviews are standard practice in developed countries and they are increasingly being used in less developed countries, as the coverage of cell phone networks expands. Not only do these kinds of surveys allow for low cost, high frequency representative data collection (Hoogeveen et Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 implemented in six areas: Bamako, the regional capitals of Gao, Timbuktu, and Kidal as well as one refugee camp in Mauritania and one in Niger. Bamako was selected because it is home to a large number of IDPs. The refugee camps were selected to obtain a sample of refugees. Returnees were identified in the regional capitals of Timbuktu, Gao and Kidal where the phone network was (still) functional. The approach to selecting respondents differed by location and depended on the availability of pre-existing population information.  Bamako: Listing information of all households with IDPs was obtained from the International Organization for Migration (IOM). Based on this data 10 districts were selected and in each district 10 households were randomly identified.  Gao, Timbuktu and Kidal: No listing data was available and the cities were divided into different sectors. The enumerator was assigned a starting point in a sector, a direction (North, South, East, West) and based on the code of the day 9 the enumerator selected the first household. If the code of the day was 4, the enumerator would choose the 5th house to conduct the first interview. No more than 6 houses were to be interviewed from one starting point. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 All data collection is done by GISSE a research institute in Bamako. The anonymized unit record data of the baseline and the monthly surveys can be downloaded from www. gisse. org. The response rate for the phone interviews has been very high (Table 1): after 6 rounds of monthly interviews the original sample is almost entirely intact. The low level of attrition demonstrates that mobile phone samples can be maintained over prolonged periods without being unduly affected by (non-random) respondent drop-out. 3. Characteristics of the Displaced and Returnee Population According to the 2009 population census, the two most sizeable ethnic groups in northern Mali are the Songhai (45 %) and Kel Tamasheq (32 %)-- see Table 2. The crisis brought about an ethnic divide, which is reflected in the composition of the three sub-samples. The majority of IDPs and returnees are Songhai (75 % and 71 % respectively), while the majority of refugees are Kel Tamasheq. Results suggest that the decision of where to flee was determined by ethnicity: Kel Tamasheq and Arabs left the country; Songhai fled towards Bamako. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["2009 population census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 2: Ethnic composition of IDPs, refugees, returnees in the North Ethnicity IDPs in Bamako (%) Refugees Niger (%) Refugees Mauritania (%) Returnees (%) Total I + R + R (%) Ethnic composition of the North (%) Songhai 75 21- 71 43 45 Kel Tamasheq 12 56 69 12 38 32 Arab 3- 28 4 11 3 Peulh 4 21- 6 4 7 Other 6 11 3 7 4 12 Total (%) 100 100 100 100 100 100 Total (n) 100 81 100 220 501 1, 268, 009 Source: Listening to Displaced People Survey, 2014 and 2009 Population and Housing Census. The ethnic composition of IDPs and returnees is almost identical. This is a reflection of the fact that 94 % of returnees were displaced within Mali. Only 6 % returned from outside the country. The reason why few returned refugees are in the returnee sub-sample is explained by their place of residence prior to the crisis: only 5 % of the refugees in Mauritania and Niger lived in Timbuktu town before their displacement; 2 % lived in Gao town and 1 % in Kidal town. The remaining 92 % lived in 27 different towns and villages in northern Mali, locations not covered by the survey. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Displaced People Survey", "Housing Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 displacement. 55 % of the returnees reported to have been employed before the crisis and 36 % in June 2014. Over time the employment situation among the displaced has improved steadily and by December 2014 more people reported being employed than prior to the crisis. All the returnees were able to regain employment after returning. The employment situation of IDPs, returnees, and refugees in Niger is steadily improving; only for refugees in Mauritania does one notice a steady decrease, with 100 % reporting no employment during January and February. Source: Listening to Displaced People Survey, 2014 and 2015. The ownership of livestock and consumer durables was reduced significantly as a consequence of the crisis. Table 7 demonstrates this by showing the Tropical Livestock Units (TLU) 12 owned prior to the crisis and in June 2014 as well as the percentage of ‘ yes ’ responses on a question whether a given asset was owned by the household. 13 The loss on livestock has been enormous particularly amongst IDPs and refugees who lost respectively more than 90 % and 75 % of their animals. 12 TLU is a common unit to describe livestock numbers of various species as a single figure that expresses the total amount of livestock present – irrespective of the specific composition. 13 This was a ‘ yes / no ’ question meaning that if 56 % of the Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 Figure 10: Children aged 7-12 attending school (%) Source: Listening to Displaced People Survey, 2014 and 2015. 5. Challenges Faced by Returnees The results suggest that returnees were less affected by the crisis than IDPs and refugees. This is reflected in data on asset and livestock ownership, but also in the information pertaining to exposure to violence. Returnees reported fewer victims; fewer returnees reported to have lost income as a consequence of the crisis; more of their children were able to continue schooling; and relative to IDPs and refugees, fewer perceived being poorer in June 2014 than before the crisis. Returnees are also the group that feels most secure, that has high levels of trust in the Malian army and police and that has a positive attitude towards most government policies. 88 88 97 79 76 78 99 88 92 55 74 72 91 98 92 96 100 85 90 91 96 90 86 87 87 95 89 76 75 90 94 92 97 86 73 98 Bamako Gao Timbuktu Kidal Niger Mauritania IDPs Returnees Refugees 14-Aug 14-Oct 14-Nov 14-Dec 15-Jan 15-Feb Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 Figure 11: Timing of Return (%) Source: Listening to Displaced People Survey, 2014. 94 % of the returnees were displaced inside Mali and 6 % outside the country. 67 % went to Bamako, 11 % in Koulikoro, 9 % to Ségou, 5 % to Mopti and 3 % went elsewhere (Kidal, Gao and Sikasso). The majority returned between June and October 2013 a period that followed the signing of a peace deal between the interim government and rebel factions to allow presidential elections to be held in July (first round) and August (second round) 2013. In October security in the North worsened again and ever since the number of people returning has been very limited. The main challenges reported by returnees in June 2014 were (i) poverty and food insecurity; (ii) lack of infrastructure (including lack of safe drinking water) and (iii) unemployment. 11 % of the returnees stated not to be facing any challenges (Figure 12). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Results suggest that 66 % of the returnees trust the Malian police and army most when it comes to providing security in the North. Almost half believe that the Malian army is brave and well trained. The vast majority of returnees believe that the government ’ s policies regarding reconciliation, security and social cohesion are good or very good. They also support the government ’ s approach towards decentralization and providing infrastructure such as access to potable water and electricity. As the next section will illustrate this differs strongly with the opinions of refugees. 6. Prospects for Peace IDPs, refugees and returnees have comparable opinions with regard to the requirements for peace: (i) addressing the ongoing crisis, (ii) improving security and (iii) reconciliation. Although there is agreement on what needs to be done, there is little consensus on what happened during the crisis, who the culprits are and who the main victims. Figure 15: What is the most important problem the Government needs to resolve today? (%) Source: Listening to Displaced People Survey, 2014. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "23 86 % of the IDPs, 91 % of the refugees and 88 % of the returnees are confident or fully confident that a coalition like this would be capable of providing security. Source: Listening to Displaced People Survey, 2014. In an open-ended question on who can be trusted most when it comes to ensuring security in the North, survey results suggest that the majority of refugees in Mauritania (86 %) trust the armed rebel groups as opposed to the army or police. This does not hold for refugees in Niger of whom 75 % trust the army and police. Similar results hold for IDPs and returnees, who put much more confidence in state authorities when it comes to securing the North: most trust is placed in the army and police (72 % of the IDPs and 66 % of the returnees) while little to no trust is placed in armed rebel groups (3 % of IDPs, 1 % of returnees). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "25 less cohesion. The increase in solidarity is less apparent among refugees where the majority (53 %) stated that the crisis had no effect on solidarity. Figure 18: Levels of trust, by group (June) (%) Source: Listening to Displaced People Survey, 2014. Perceptions that different groups have of others are important elements of peace. When asking for the degree to which neighbors, other villagers and people from other ethnic groups can be trusted the survey finds positive outcomes. Although all groups trust people from other ethnic groups slightly less, the general level of trust is high and it remains stable over time. Finally, consider how IDPs, refugees and returnees envision the future of Mali. The majority of refugees in Mauritania vie for an independent or autonomous North, while the majority of IDPs, returnees and refugees in Niger wish to see full government control over the North. 20 20This contradicts, in part, findings of an Afrobarometer perception survey on causes and consequences of the conflict in Mali conducted in December 2013. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Displaced People Survey", "Afrobarometer perception survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "26 Source: Listening to Displaced People Survey, 2014. 95 % of the refugees in Mauritania envision an independent or autonomous North and 26 % of the refugees in Mauritania even state the independence of Azawad (= the north) as a main condition for returning home. Stark differences can also be observed with regard to the discussion around a possible federalist solution for the North that was ongoing when the monthly phone interviews were conducted in October. As illustrated in the Figure 20 below, 80 % of the refugees in Mauritania support a federalist solution, while the majority of IDPs, returnees and refugees in Niger are not in favor. Of those who do not support a federalist solution (96 % of the IDPs, 88 % of the refugees in Niger and 95 % of the returnees), the majority of IDPs (61 %) and returnees (70 %) as well as 38 % of the refugees in Niger suggest decentralization as a possible solution to resolve the conflict. 13 % of the refugees in Niger also mention war and 27 % the integration of the North. Nonetheless, 49 % of IDPs, 86 % of returnees and 89 % of refugees believe a stable and sustainable peace accord can be achieved. by 4 % of the population living Timbuktu, 2 % in Gao and nobody in Kidal. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Afrobarometer finds that the vast majority of Malians want their country to remain a single and unified nation and that the attempt by armed groups to create a breakaway state in Mali ’ s northern territories is decisively rejected. See Afrobarometer Policy Paper 10 (Dec 2013). This difference with the Afrobarometer survey can be explained by the fact that the latter survey only focused on Malians inside the country and did not take the views of refugees into account. 5 93 2 6 86 75 20 2 3 94 Independence of the North Autonomy of the North Establish full government control over the North Figure 19: How do you envision the future of Mali? IDPs Refugees Niger Refguees Mauritania Returnees Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "27 Source: Listening to Displaced People Survey, 2014. 7. Conclusion The 2012 crisis in northern Mali led to widespread displacement. The welfare of the displaced – and those who returned – is monitored by combining a baseline survey with structured monthly follow-up interviews carried out by mobile phone. This innovative approach allows tracking changes in welfare with high frequency – even for those who returned to areas that are insecure and inaccessible to enumerators. After 6 rounds of follow-up interviews attrition rates are very low (more than 99 % response rate), demonstrating that it is possible to collect robust and representative data from hard-to-reach, conflict-affected populations. The results show that those who fled were better educated, better off and less affected by violence than the average population in the North. Those who fled lost significant amounts of durable goods (20-60 %) and livestock (50-90 %); many of their children ended up being taken out of school and their welfare (measured subjectively and by the number of meals consumed) declined considerably. Over time, the impact of the crisis on welfare has lessened and by February 2015 the majority of eligible children of the displaced were going to school and levels of employment and number of meals consumed were at pre-crisis levels. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 INTRODUCTION In 2015, an estimated 2. 2 million Syrians Under Temporary Protection (SUTPs) were residing in Turkey, the majority arriving in the country over the last 4 years. 2 Turkey ’ s national population is roughly 75 million; recent refugees account for approximately 3 percent of the population. For a country that has never experienced such a large-scale, sudden inflow of foreigners, demographic changes in the composition of the population and labor force will yield unprecedented implications. This paper examines, as data allows, the relationship between the size of the foreign-born population and host community poverty rates in Turkey. First, this paper finds the poverty rates of ‘ recent migrants ’ near the Syrian border (NSB) significantly increased from 2009 to 2013. Second, the number of foreign-born households being captured by the Labor Force Survey (LFS) is expanding, which suggests a growing number of foreign households that are likely to be Syrians. Third, with respect to poverty, the results show no negative impacts on the host community as a result of the increasing size of the foreign-born population. The impact of SUTPs has been both positive and negative. Overall, a significant negative impact on host communities ’ welfare is not observed in the data. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This paper ’ s scope of analysis includes the country as a whole using a nationally representative survey. While regional case studies may reveal salient stresses on public services and job displacement, nationally, there is no significant impact. Over the period of 2009 to 2013, the poverty rates of host community households have stayed relatively stable near the Syrian border; despite the high poverty rates experienced among the recent migrants. By country of origin, the displacement of Syrians is one of the largest in recent history. As a result of the civil war that began in 2011, Syrians started to leave their homes and look for safety in neighboring countries across the region. By November 2015, about 4. 3 million Syrians were seeking refuge in primarily Turkey, Lebanon, Jordan, Iraq, and the Arab Republic of Egypt. 3 The only other time in the last half century that the world experienced a larger group of refugees from a single country is the case of Afghan refugees during the 1980s to 1990s. Refugee displacements of this size are rare. Consequently, they are not well studied and their impacts are not well understood. Moreover, the case of Afghan refugees in Pakistan is different, since they were stigmatized to a larger extent, which limited their movement in Pakistan. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["nationally representative survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While there is a large literature on the role of immigrants on the native-born population in terms of labor market competition, there is a limited amount of studies that examine the effect from displaced populations. Many conclusions from the traditional literature on the study of immigrants ’ impact on natives cannot be applied to the case of Syrians in Turkey. There are many differences between the inflow of Syrians and other flows of extended family and economic immigrants. First, the sheer volume of Syrian refugees and the short time- frame in which they entered Turkey is unprecedented. For the case of Syrians in Turkey, or displaced populations in general, large movements of refugees are not restricted due to humanitarian reasons. Second, formal immigration processes are controlled, limited, and regulated by destination countries. Therefore, results from literature on “ immigrants ” are very different than a focus on displaced or refugee populations. Recent literature on the labor market effects of SUTPs estimates negative impacts on host community employment rates. The negative displacement results are largest for the young, women, informal workers, 2 United Nations High Commissioner for Refugees (UNHCR) – Syrian Regional Refugee Response, Inter-agency Information Sharing Portal 3 (UNHCR) – Syrian Regional Refugee Response, Inter-agency Information Sharing Portal Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Inter-agency Information Sharing Portal 3"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 and the less educated (Ceritoglu, Tunculer, Torun, and Tumen, 2015; Del Carpio and Wagner, 2015). The economic effects of SUTPs not only vary across different segments of the labor market, there are also strong regional differences in their economic effects. Using synthetic modelling methods, Ozturkler and Goksel (2015) estimate the impact of Syrian refugees on local prices, wages, inflation, and services in 10 cities with large refugee populations. Some of the salient negative effects have been increases in rental prices, increases in inflation at border cities, illegal hiring by small business, and decreases in wages. However, in some cities (Gaziantep, Adana, Kahramanmaras, and Mardin), the presence of refugees has improved the trade balance, and economic activity in these areas are projected to increase as economic integration with MENA deepens. Orhan and Gundogar (2015) also note both positive and negative aspects of SUTPs. A primary contribution of this paper is the estimation of poverty at the sub-national level and among population groups of interest. Since migration, geographic, and welfare variables do not exist in a single data set, imputation techniques are required to overcome these limitations and to compute household level poverty. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The imputation of income poverty is done using the Turkish Labor Force Survey (LFS), and with information and modeling parameters determined from the Survey of Income and Living Conditions (SILC). More details and validation of this methodology is discussed throughout this paper. While explicit identification of Syrians in available surveys is not feasible, there is evidence of an increase in the amount of foreign-born individuals that is being captured in the LFS. The arrival year of foreign-born migrants is available in the data which allows for identification of “ Settled Migrants ” and “ Recent Migrants ”. The latter is used as a proxy for Syrian refugees for the purposes of this paper. National official surveys that are conducted under-report the refugee population. Yet, since about 10 percent of Syrian refugees are in camps and the remaining are residing throughout the country, it is not surprising that they are accessible to interviews by the LFS. Despite data limitations, there are strong and significant trends in the poverty rates for the recent foreign- born, especially for those near the Syrian border. In 2013, recent migrants near the Syrian border were the poorest group4 in Turkey. While this statistic in itself is not initially surprising, fluctuating welfare trends of recent migrants over time is noteworthy. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Turkish Labor Force Survey", "Survey of Income and Living Conditions"], "descriptive_data": [], "vague_data": ["National official surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In previous years, migrant households in Turkey tend to have much lower poverty on average than even the host community. Across comparison groups and time, the poverty rates of recent migrants is higher than among the host community in only one instance: in 2013 near the Syrian border. This sudden change in the historically stable pattern implies that the LFS is able to capture at least a part of the incoming SUTPs who have significantly different socioeconomic profiles in comparison the previous economic migrants. Throughout history, immigration to Turkey has been relatively limited and consisted mostly of those of Turkish heritage. In the early 20th century, immigration was encouraged by the government as a method to increase the population. Since 1970, immigration has slowed down and has been even discouraged at times. Many immigrants to Turkey are of Muslim Turkish background, since the government prioritized preserving a national identity. This is likely why “ migrants ” had very similar or even lower poverty rates than the host community. The sharp degradation of welfare among recent migrants in 2013 illustrates the severity of poverty that is arising very likely from a growing population of Syrian refugees. The Syrian refugee inflow to Turkey 4 Based on grouping by host community, established migrant, recent migrant. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 began in April 2011 and has been continuing at an increasing pace as the conflict in Syria expands. 5 The Turkish government has provided a tremendous amount of support in the form of shelter and essential items to help sustain the livelihood of large numbers of refugees. However, aid funds are not limitless and refugees face hardships that will persist over the long-term. The refugee camp population in Turkey has been stable since March of 2013 as the physical capacity of the camps have been exhausted. 6 This saturation has resulted in a steep increase in the number of Syrians living outside camps across Turkey. The proportion of Syrian refugees living outside camps increased from 53 percent to 87 percent between March 2013 and November 2014. 7 In addition, even though refugees living outside camps continue to be concentrated near the Syrian border (64 percent), the dispersion of Syrians across the country has expanded, especially in major urban centers such as Istanbul and Ankara. The results in this paper are limited to 2013 due to changes in the 2014 LFS that make poverty estimations incomparable to previous years. 8 Therefore our results may provide only a partial insight into the impact of SUTPs, since the dispersion of Syrians across Turkey has increased greatly in 2014 and 2015. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The descriptive characteristics of foreign-born and host community households are still comparable, and therefore allow for comparisons between 2013 and 2014. It is possible to make some inferences on welfare changes by looking at changes in host community employment rates and labor market characteristics. Overall, this paper finds no negative effects on host community welfare from an increasing population of SUTPs. As other authors have stated, the influx of SUTPs has had both positive and negative impacts. It seems on average, the host community has been strong and adaptive, and not negatively impacted. This is not to disregard that real strains do exist in some regions where the SUTP population is very large. Nor do these results undermine findings of displacement effects in the labor market that certain types of workers are experiencing. However, on average nationally, we do not see a systematic decline in the welfare of the host community between 2011 and 2013. The remainder of the paper is organized as follows. Section 2 outlines the data availability and technical issues. Section 3 discusses descriptive statistics of the foreign-born and host community. Section 4 explores the impact of the foreign-born population on host community welfare. 1. DATA AND TECHNICAL ISSUES Data Sets Data availability limits which data set can be used to identify the foreign-born population and geographic location while measuring poverty. The Turkish Statistical Institute (TUIK) has been conducting three nationally representative surveys annually since 2005; the Household Income and Consumption Expenditure Survey (HICES), the Survey on Income and Living Conditions (SILC) and the Labor Force Survey (LFS). However the HICES, which is the national survey that is used to measure official poverty, 5 UNCHR, 2013pg 13 6 UNHCR (22 November 2013), UNHCR (15 September 2014) 7 UNHCR (22 March 2013), Erdogan (2014), pg 14 8 This technical issue will be discussed further later in the paper. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Household Income and Consumption Expenditure Survey", "Survey on Income and Living Conditions", "Labor Force Survey", "HICES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 does not have migration or geographic identifiers. The SILC contains geographic identifying variables, (at the NUTS1 level) but still lacks migration variables. The data set this study uses is the Labor Force Survey (LFS) since there is an adequate availability of both migration and geographic variables. The LFS is representative at the NUTS2 level which corresponds to 26 regions in Turkey. One caveat is that income in the LFS refers to only wage income from employment, 9 and is an insufficient measure of income that should be used for welfare measurement. For example, important sources of income such as social assistance, asset liquidation, or remittances are missing. Therefore, income in the LFS is imputed with a few assumptions using information from the SILC. The NUTS1 spatial effects of the SILC are a good proxy for NUTS2 welfare dynamics in the LFS which increases the accuracy of the imputation model. However, since the original sample frame of the LFS does not account for the recent influx of foreign migrants in Turkey, the labor market characteristics of recent migrants might not be representative of the actual SUTP population. Therefore, results of the imputation could be interpreted as upper bound estimates for recent migrants. More details of survey techniques used to complete this exercise are available in the Annex. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["SILC", "Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As a result, imputed poverty is measured as income poverty. Another advantage of using the LFS is the availability of CPI at the NUTS2 level in Turkey which allows for spatial deflation of different price levels across the country. Table 1. Survey Comparison and Data Availability Years Available Migration Variables Income or Consumption Geographic Identifier Spatial Deflation HICES 2003-2012 No Consumption, income National, urban / rural No SILC 2009-2012 No Income NUTS1 No LFS 2009-2013 Yes Imputed Income NUTS2 Yes However, there are other issues for consideration when using the LFS. Principally, there is a low number of sample points that are migrant households. Moreover, the study cannot identify migrant households and individuals that are specifically Syrian refugees. Foreign migrants are defined as those who were born abroad and have lived abroad for at least more than 12 months. Some Turkish-born households have also lived abroad for over a year, and these individuals are not considered to be migrants. Amongst foreign-born individuals, only the ones who have been in the country for more than 12 months are included in the sample which underrepresents the actual number of foreign migrants in the region. In addition, no specific procedure is adopted by the enumerators if the household does not speak Turkish. Given that a majority of Syrian refugees do not speak Turkish, the language barrier might result in the removal of Syrian households from the sample. Finally, refugee camps are not included in the sample frame, which limits the study to only examining recent migrants who do not live in refugee camps. 9 Wage income is only available for regular and casual employees in the LFS which accounts for around 60 % of total employment. There is no other monetary income value for the rest of the working population. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["LFS", "HICES", "SILC"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 Comparison Groups Six population groups are constructed based on their migrant status and geographic location (Table 2). Five out of 26 regions are defined as Near Syrian Border (NSB) regions based on their proximity to Syria as well as their popularity as a destination for migrants (see Map 1 for details). These regions are Mardin (TRC3), Sanliurfa (TCR2), Gaziantep (TCR1), Hatay (TR63), and Adana (TR62). 10 TRC3-Mardin, TCR2- Sanliurfa, TCR1-Gaziantep and TR63-Hatay are Southeastern regions of Turkey that border Syria. TR63- Adana does not border Syria but is a southern Mediterranean region that is a common destination for migrants due to abundant labor opportunities. The rest of the country includes the remaining 21 NUTS2 regions. Map 1. Near Syrian Border Regions 10 NUTS2 regions are referred with name of the largest province in each regions. The full list of provinces in each region are; Mardin-Batman-Sirnak-Siirt (TRC3), Sanliurfa-Diyarbakir (TCR2), Gaziantep-Adiyaman-Kilis (TCR1), Hatay-Kahramanmaras- Osmaniye (TR63), and Adana-Mersin (TR62). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Host community households are those whose head of household were born in Turkey, or born outside Turkey but did not live abroad for more than a year. Conversely, migrant households are defined as those whose head of household was born abroad and has lived abroad for more than 12 months. The duration of a migrant household ’ s stay in Turkey is also based on when the head of household arrived in Turkey. Three thresholds are tested: 2, 3, and 4 years. The 4 year cut-off is preferred to maximize the sample size of recent migrant households. Table 2. Population Groups for Comparison Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Geographic Location Near the Syrian Border The Rest of the Country Status Host Community Settled Migrant Households Recent Migrant Households Host Community Settled Migrant Households Recent Migrant Households Years in Turkey Head of Household born in Turkey Arrived in Turkey more than: 2, 3, or 4 years Arrived in Turkey less than: 2, 3, or 4 years Head of Household born in Turkey Arrived in Turkey more than: 2, 3, or 4 years Arrived in Turkey less than: 2, 3, or 4 years Are Syrian Refugees being captured using the LFS? While variables covering all topics of interest (migration, welfare, and geography) are available or can Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "be imputed into the LFS, it may be unclear, ex-ante, if SUTPs are adequately included in the survey. Despite a small sample of foreigners and other concerns, there is evidence that the LFS sample does include some “ recent foreign ” migrants, especially in the border regions (NUTS2) [TRC1-Gaziantep, Adiyaman, Kilis, TRC2-Sanliurfa, Diyarbakir, TRC3-Mardin, Batman, Sirnak, Siirt TR63-Hatay, Kahramanmaras, Osmaniye] (Map 1). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 3. ANALYSIS Has the increase in Syrian refugees impacted the welfare and socioeconomic conditions of the host community? Summary statistics in the previous section showed clear trends of increasing poverty among recent migrants throughout the country, both near the Syrian border and across rest of the country. While the poverty rates of recent migrant households spiked in 2013, poverty of host community households maintained a relatively constant level in the whole country. From these trends, it appears that there at least has not been an increasing trend in poverty among the host community over the latest years. The empirical model is shown in Equation 1. Regressions are estimated at the NUTS2-year level and using data from only the years 2011 and 2013. The dependent variable of interest is the host community poverty rate by region and year, where the poverty rate is based on spatially deflated imputed household income. Unlike the computation of the poverty rates, “ recent migrant ” information is not used for the analysis. Only the host community poverty rates are calculated using the LFS and the number of Syrians are taken from government sources. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 in 2014. The share of workers registered with social security in host community households declined but the change is not significant. In terms of the number of hours worked, and the proportion of workers in blue or white collar jobs, there is no statistical difference between 2013 and 2014. 4. CONCLUSION The movement of Syrian refugees is one of the largest passages of refugee populations in recent history. With millions of people leaving Syria and settling in Turkey, concerns about externalities onto the native population are very salient. This paper addressed the poverty impacts of SUTPs on the host communities and found no evidence that the increase in foreign-born population from 2011 to 2013 resulted in higher poverty rates among the host community. As recent literature has noted, the SUTPs have both positive and negative impacts. While some types of people may be more likely to be displaced by Syrians in the labor market, Syrians are consumers and renters, they also open businesses and create jobs. Local Turkish citizens have also benefited as employers and sellers. In some border cities, the balance of trade has improved as exports to the Middle East increased. On the other hand, analysis of only the recent migrants clearly demonstrates that the group ’ s poverty profile is worsening between 2009 and 2013. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Moreover, descriptive characteristics suggest that their conditions might have worsened in 2014. As this unprecedented event continues, the integration of Syrians into the Turkish labor market, access to public services, changing demographics, and socioeconomic impacts should be monitored closely. Especially with an increasing rate of SUTP migration to Turkey during 2014 and 2015 and the continued conflict in the region, the inflow of Syrians will be one of the most critical short, medium, and possibly long term policy issues in the country. In addition, Turkey ’ s role as a pathway to Europe for those escaping conflict in the Middle East makes the issue an international phenomenon. In this respect, the healthy incorporation of SUTPs that will protect the wellbeing of host communities while satisfying the humanitarian necessity of helping Syrians will be among the more important development issues of today and the foreseeable future. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "34 Any statistics on the imputed welfare will based on the set of imputed welfares for each household. The estimator takes the form, with R denotes the number of simulation: ܪ ෡ ൌ 1 ܴ ෍ ݄ ሺݕ ෤ ௥ ሻ ோ ௥ ୀ ଵ where ݄ ሺݕሻ is a function that converts the vector y with (log) incomes for all households into a poverty measure (such as the head-count rate or bottom 40 %), and where ݕ ෤ ௥ denotes the r-th simulated imputed welfare. Figure 6. Survey-to-Survey Imputation Methodology, an illustration For the case of Turkey, we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey. Income is used instead of consumption for this paper ’ s analysis. The model included variables related to: household demographics (age, gender, age composition, etc.), household characteristics (education, labor activity, etc.), household head ’ s characteristics (age, gender, labor, education, marital status, etc.) and household assets holding (both livestock and durables). Based on that model the simulated values of consumption (at household level) were imputed for the households in the corruption survey. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Survey on Income and Living Conditions survey", "Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This allowed for consistent ranking the households into welfare quintiles and cross- tabulation of welfare status with household characteristics and indicators derived from the survey data. The imputation was carried out using s2sc algorithm in STATA. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "35 Inputs: 1. Household Survey with consumption or income welfare aggregates 2. Project data / Other survey data without welfare aggregates 3. Set of harmonized common variables in both surveys Outputs: 1. Set of imputed welfare variables for project data / other survey for each household in the data 2. Imputed welfare variables can be used for poverty, distributional analysis (quintiles or more), profiling of the poor or group of interest Models: 1. Ordinary Least Squares (OLS) 2. Probit 3. Multiple Imputation (MI) Table 18. Model Specification Variables Demographic Share of children, share of adults, share of adults squared and share of old (omitted) Characteristics of head Age, gender, and level of education Interactions with urban dummy variable Level of education of the head, age of the head Geography Dummies for regions at NUTS 1 level (12 regions) Interactions with Geography Level of education of the head, age of the head interacted with regions at NUTS 1 level (12 regions) and urban-rural division 1. Validation and Robustness Check Figure 7. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household Survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "External Validation, NUTS1 Level $ 5 / day PPP – Observed (SILC) & Imputed (LFS), 2007 LFS SILC Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. Introduction The most common sampling approach for cross-sectional household surveys in the developing world is a stratified two-stage design (Grosh and Munoz, 1996). Following stratification based on administrative boundaries, clusters are selected in the first stage with probability proportional to size from a national census-based frame. In the second stage, a canvassing operation is conducted in the selected clusters to compile an updated list from which households are randomly selected. While this methodology is straight forward to implement in the field and reliably produces unbiased estimates, there are several downsides. The first downside is cost. The World Bank ’ s Living Standards Measurement Study team, which provides technical assistance on large-scale household surveys around the world, estimates the field listing operation increases the overall budget for data collection by 25 percent. Due to confidentiality concerns, the data collected during a field listing operation, typically the name of the household head and address or location description of dwellings, does not have any analytical applications beyond as a component of the weight calculations. 2 At a time when typical survey costs are in the USD millions, reducing a significant cost component will increase the financial sustainability of data collection. The second drawback to the traditional design relates to timeliness. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Living Standards Measurement Study"], "descriptive_data": ["national census-based frame"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 five methods, it was not possible to consider non-sampling error. This paper goes a step further by using simulations to describe the sampling error and a field experiment in an IDP camp in South Sudan to measure the total survey error of each design compared to a census, allowing for the disaggregation of the total error into sampling and non-sampling components. In addition, we attempt to separate the components of non-sampling error linked to the sample method from those common across all methods, such as interviewers selecting larger households and other issues in properly implementing the household survey protocols. The next section briefly describes each method and highlights the literature as it relates to the relevant selection methods. Section 3 describes the data set and protocols for each method included in the experiment, followed by Section 4, which discusses implementation issues. Section 5 reports the results of the analysis, and section 6 concludes with further discussion of the overall performance and areas for future research. 2. Description of Methods This paper compares five alternatives of second stage selection (satellite mapping, segmentation, grid squares, “ Qibla ” (or “ walk north ”) method, and random walk) to a human canvassing operation. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In a mortality study in Iraq, Galway et al. (2012) used GIS and Google earth imagery for household sampling. The method used gridded population data for selection of clusters. The first cluster sampling stage of their study used the ‘ Create Spatially Balanced Points ’ (CSBP) function in the ArcGIS (v10) software. Boo et al. (2020) introduces a sampling design based on gridded population estimates as their sampling frame to implement a PPS design and derive sample size estimates for the number of grid cells. Assuming the grid square method is applied to the area itself rather than a selected PSU, the weights for the grid method are similar to those for segmentation, where the cells are the PSUs, but without the additional step of selecting segments. The weights can therefore be represented as 𝑤𝑤𝑖𝑖 ′ = (𝑁𝑁𝑘𝑘) ൫𝑁𝑁𝑘𝑘𝑘𝑘൯൫𝑁𝑁𝑘𝑘𝑘𝑘𝑘𝑘൯ 𝑘𝑘𝑘𝑘𝑘𝑘. 2. 4. North Method The “ Qibla method ” described in Himelein et al. (2017), or what is called in this paper the “ North method ” method, is an attempt to assign probability weights to random point selection methods. Several random point selection methods can be found in the literature, particularly in relation to epidemiological studies. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["gridded population data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Grais et al (2007) used a methodology in which the closest household to a randomly selected point is selected for a study of vaccination rates in urban Niger, though did not attempt to calculate probabilistic sampling weights. Similar approaches were used by Kondo et al (2014) in a study of the city of Sanitiago Atitlán, Kumar (2007) in urban India, and Kolbe and Hutson (2006) in Port ‐ au ‐ Prince, Haiti. Shannon et al (2012) also used such a method to select points in a study of violence in Southern Lebanon in 2008 but used the radius of a circle to define an area to be field listed, and from which buildings and then households were selected for enumeration. The circle area and building density were used to calculate probability weights. The main difference between most random point selection methods and the North Method described here is that the North Method attempts to accurately estimate the probabilities of selection. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Chew et al. (2018) use a baseline convolutional neural network model on a gridded population sampling frame to select a sample of households in Nigeria and Guatemala. The authors found this technique to be on par with human canvassing in terms of accuracy, and to outperform other machine learning models based on crowdsource or remote sensing data. Grais et al (2007) compared an unweighted random point selection methodology to a random walk in their study of vaccination rates in urban Niger. The authors do not find statistically significant differences between the methods, though the sample size was limited and both methods were non-probabilistic. 3. Design and Field Protocols 3. 1. Experiment Design This paper makes use of a dataset from the purposefully designed methodology experiment conducted in one section of the Protection of Civilians site 1 (PoC1, Figure 1), one of the largest IDP camps in Juba, South Sudan. To generate a gold standard as the basis of comparison, a household census was conducted between August and September 2017. During this exercise, 2, 655 households were interviewed using a questionnaire designed to collect demographic information, dwelling characteristics, household consumption, and perception data. At the end of each census interview, households received a unique barcode that could be used to identify them later in the experiment. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To avoid changes in camp composition, immediately following the completion of the census fieldwork, the interviewers returned to the field to implement the experiment. Teams used each of the sample selection methods to identify which households would have been selected had that method been used for a survey. To avoid respondent fatigue, instead of re-asking the questionnaire, the interviewers simply scanned the unique bar code of the selected household. Once scanned, the barcodes created an observation in the method-specific dataset with the information captured in the census. Each sampling technique targeted about 322 interviews so that comparisons could be made between the methods using an identical sample size. There was, however, some non-response for each method if interviewers were not able to contact a household member who could provide access to the barcode, if the barcode had not been retained by the household, or if the barcode was not scanned correctly. Protocols for each individual method are listed below. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 3. 5. North Method The North Method uses RSPs to determine the selected households. RSPs are chosen from the universe of all possible points with the boundaries of the PoC camp. To implement the North Method, 322 RSPs along with replacement RSPs were chosen. These points were random geo-coordinates within camp borders (Figure 5). If the RSP lay within a structure, the corresponding structure was selected. If not, starting at the selected RSP, enumerators walked directly north, using the compass application on their tablet, until a structure was encountered. If the structure was residential, the structure was chosen to be interviewed. In the case of multiple households present in the structure, one household was randomly chosen. If the structure was not residential or if the enumerator reached the boundary of the camp, a replacement RSP was used. As it would be extremely difficult to determine the area of the shadow in the field, satellite imagery is used for these calculations. In the case of this experiment, the selection areas are calculated using Google Earth imagery taken on December 22, 2017, approximately one month after the census of households in the PoC camp. Given the dependence of the North Method on having current satellite imagery for accurate calculations, the availability of this imagery is a major consideration for this method. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The weights would be over-estimated if new structures had been built in the shadow since the imagery was taken. 3. 6. Random Walk Random Walk obtains a sample by randomly selecting starting points for enumerators with generic but unambiguous instructions to select households at regular intervals on their path. For this experiment, enumerators conducted random walks using 21 RSPs (Figure 8). Starting as near as possible to the RSP, the supervisor chose any random point (like a street corner or a school). From this point, four enumerators walked each in one of the four cardinal directions. Walking in their designated direction away from the RSP, they counted structures on both the right and the left and each selected the fifth structure for interview. Enumerators were instructed to start with the buildings on the right if two buildings were opposite to each other. To select the next structure, enumerators continued along the cardinal path, and selected the next fifth structure. If the enumerator could not proceed on its cardinal path because she had reached the boundary of the PoC camp, enumerators were instructed to turn right at a 90-degree angle and continue counting until finding the fifth dwelling. Enumerators had to conduct six interviews along their paths. 4. Implementation Issues 4. 1. Failure to Follow Survey Protocols As noted above, even if field protocols are perfectly implemented, the estimates generated from Random Walk designs are likely to be biased. Enumerators furthermore often were unable or unwilling to follow the protocols. Streets and paths were not necessarily aligned with cardinal directions and obstacles further impeded the ability to follow a straight path. Additionally, since the selection method requires enumerator judgment, it is not replicable and therefore allows enumerators greater discretion to choose which households are “ selected. ” Figure 9 shows the paths taken by two teams of enumerators from Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 implementation matters. Pooling the analysis across indicators and using satellite mapping as reference, the North Method is unbiased, while the Segmenting and Grid Square methods show minimal bias (0. 1 percent and 0. 2 percent, respectively). The Random Walk method shows 1. 2 percent bias on average across the 14 questions. In conclusion and in line with the literature, most probability-based methods perform better than non-probability methods like random walk. In addition, implementation of adherence with the survey protocol is extremely important and using appropriate methods and tools to cope with this challenge is absolutely mandatory for coming as close as possible to the theoretical results derived by the simulation for the probability-based methods. In practice – in a fragile setting like South Sudan – deviations from the survey protocol, measured as differences between the experiments and the simulations, have large influence on the actual bias of estimates. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 7. Appendix 7. 1. Simulation and Frame To compare the efficiency of the different sampling frames and designs, we will apply an empirical sampling simulation. In this type of (Monte-Carlo style) simulation, either a true or synthetic population is used as the target population. By applying a specific sampling design, and repeated sampling (usually 1, 000 repetitions) under this design, we can compare the resulting population estimates with the known true population values for each run of the simulation. The resulting distribution of these estimates is called the sampling distribution, and the average squared deviation from the underlying population value is the Mean Squared Error (MSE) or when taking its square root, the Root MSE (RMSE). To facilitate the comparison, we use the relative version expressed in percentage deviation. Empirical sampling simulations can be considered as the “ […] ultimate tool for investigators who want to know if one sampling strategy will work better than another for their population. ” (Thompson, 2012). However, this requires the underlying simulation population to replicate as realistically as possible the target population. 7. 2. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Quality Metrics A standard Measure in the assessment of a sampling designs is the Root Mean Squared Error (RMSE) and calculated as: 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = ∑ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠𝑠𝑠𝑠𝑠 1000 𝑠𝑠𝑠𝑠𝑠𝑠 = 1 1000 = ൥ 1 1000 × ඥ (𝑌𝑌 ෠ − 𝑌𝑌) 2 𝑌𝑌 ൩ × 100 Expressed here as percentage deviation from the population mean Y and calculated for each parameter of interest. Equation.. is only the empirical representation though and a result of rearranging the definition of the Mean squared Error, 𝑀𝑀𝑀𝑀𝑀𝑀൫𝑌𝑌 ෠ ൯ = 𝐸𝐸൫𝑌𝑌 ෠ − 𝑌𝑌൯ 2 = 𝐸𝐸 ൣ ൫𝑌𝑌 ෠ − 𝑌𝑌෨൯ + ൫𝑌𝑌෨ − 𝑌𝑌൯൧ 2 = 𝐸𝐸 (𝑌𝑌 ෠ − 𝑌𝑌෨) 2 + 2𝐸𝐸൫𝑌𝑌 ෠ − 𝑌𝑌෨൯൫𝑌𝑌෨ − 𝑌𝑌൯ + ൫𝑌𝑌෨ − 𝑌𝑌൯ 2 And decomposing it into 𝑀𝑀𝑀𝑀𝑀𝑀൫𝑌𝑌 ෠ ൯ = 𝑉𝑉𝑉𝑉𝑉𝑉൫𝑌𝑌 ෠ ൯ + 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 (𝑌𝑌 ෠) with 𝑌𝑌 ෠, 𝑌𝑌෨ and 𝑌𝑌 being the estimate from the sample, the mean of this estimate and the true value in the population respectively. Var is the corresponding variance, and Bias the resulting bias component, which is defined as: 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 ൫𝑌𝑌෨൯ = 𝑌𝑌෨ − 𝑌𝑌 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The endogeneity of refugee inflow is addressed by exploiting differences in factors that influence refugee arrival in the host communities. Specifically, the analysis uses potential refugee inflow as an instrument, which is the product of population density and intensity of con­flicts (number of fatalities per event) in the closest region of the origin country to the refugee camp weighted by the distance of the refugee camp to the closest region. The paper also constructs an aggregate index to proxy house­holds ’ livelihood diversification strategies. The findings show that refugee inflow brings substantial benefits to host communities by creating significant jobs, in which people engage as secondary occupations, and triggers an increasing demand for livestock products. Specifically, while no effect was found on diversification of activities such as a primary occupation and crop product sales, a 1 percent increase in refugee inflow leads to a 2. 7 percent rise in diversifica­tion of livelihood activities as a secondary occupation and a 15. 9 percent increase in the value of livestock product sales. These effects tend to be heterogeneous across refu­gee hosting regions and the gender of the household head: negative effects were mainly observed in Gambella region, which hosts the largest refugee population in the country, and male-headed households were more likely to benefit from the refugee presence for the whole sample. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The paper identifies households ’ increased engagement in different livelihood activities and access to markets as a potential mechanism for the observed effects. The findings add to the growing literature on the socioeconomic impacts of refugee inflow on host communities by showing an overall positive effect on the livelihoods and welfare of receiving communities. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at swalelign @ worldbank. org / solezena @ googlemail. com, swangsonne @ worldbank. org, and gseshan @ worldbank. org. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. Introduction We are in the midst of protracted refugee crises. According to the latest UNHCR trends report, at the end of 2020, 76 percent of refugees globally (15. 7 million) were in a protracted situation (UNHCR 2021). 2 Most refugees reside in low-income countries, and more than eight of every 10 refugees (86 percent) live in countries within territories affected by acute food insecurity and malnutrition (UNHCR 2021a). Refugee receiving host communities also tend to be poor, experience precarious livelihood conditions and face many socio-economic challenges, such as low economic status, poor access to public services, and infrastructural development. For these communities, refugees might bring both challenges and benefits. On the one hand, refugees increase competition for natural resources (e. g., wood for energy, construction, land), public services and infrastructure (e. g., education, health, water supply), and economic opportunities (e. g., traditional livelihoods, labor employment). Refugee inflow may also affect the local market by mainly depressing wages and raising product prices (Vemuru et al. 2020). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We measure livelihood diversification using two main variables: the degree of diversification of activities as a primary occupation and the degree of diversification of activities as a secondary occupation. 3 The degree of agricultural commercialization is also measured using two variables: the value from the sale of crop and livestock products. We measure refugee inflow (presence) as the number of refugees (population) in the nearest refugee camp to the household location weighted by the household's inverted distance to the camp. The impact of refugee inflow on household livelihood strategies can be causal if there are no confounding factors that affect livelihoods in host communities when refugee inflow changes. This is unlikely as refugee flow and the location of refugee camps are not random (see e. g., Baez 2011). Refugee camps are often situated close to international borders, among others, to allow for easy repatriation of the refugees when stability is restored in their countries of origin. In addition, refugees often seek shelter in the nearest refugee camp once they arrive in the host country, which is arguably true in most hosting countries as refugees often travel on foot for 2 According to UNHCR, a protracted refugee situation is a situation in which at least 25, 000 refugees from the same nationality have been in exile for at least five years in a given host country. 3 Diversification of activities is calculated using the inverse Simpson diversity index. In constructing the index, we considered both agricultural and non-agricultural livelihood activities. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 hours (Ruiz and Vargas-Silva 2018). Hence, to identify the causal impact of refugee inflow on livelihood diversification and commercialization, we employ a two-stage least squares (2SLS) econometric specification strategy using potential refugee inflow as an instrument. Potential refugee inflow is constructed as the product of population density and intensity of conflicts (number of fatalities per event) in the closest region of the origin country to the refugee camp weighted by the inverted distance of the refugee camp to the closest region4 (i. e., the shortest distance to the border between the refugee camp and the bordering country of origin). Similar (weighted) instruments have been used in the literature (e. g., Baez 2011; Fallah et al. 2019) and proved to be an appropriate instrument to study the socio-economic impact of refugees on host communities. Livelihood diversification and agricultural commercialization are the two main common strategies that people in low-income countries adopt to improve or maintain their livelihood and welfare. Given the prevailing under-developed insurance market in the event of shocks, households tend to pursue several income generating activities. However, potential barriers such as low asset endowment hinder households'successful livelihood diversification (Ellis 2000; Martin and Lorenzen 2016; Loison 2015). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Specifically, a 1 percent increase of the refugees ’ presence5 leads to a 2. 7 and 15. 9 percent increase in the diversification of livelihood activities as a secondary occupation and value of livestock product sale, respectively. It should be noted that this analysis is taking place during a period where refugees in Ethiopia were prohibited by law from seeking work outside designated camps. This has changed after 2019 because of the revised Ethiopian Refugee Law. These effects tend to be heterogeneous across regions and to a limited extent, vary depending on the gender of the household head. The negative effects tend to be concentrated in Gambella, a region that hosts most of the refugee population in Ethiopia and where the refugee population is as large as the population of the region. Overall, compared to women-headed households, households with a male head seem to benefit through increased diversification of activities as a secondary 4 Region refers to the administration level 1 from the Database of Global Administrative Areas (GADM). The nearest region to the refugee camp is identified as the one that has the shortest straight distance to the refugee camp among all neighboring regions in the major refugee source countries. 5 As explained above, refugee presence is the number of refugees (population) in the nearest refugee camp to the household location weighted by the household ’ s inverted distance to the camp. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Database of Global Administrative Areas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, most of the studies only consider a subset of occupation or livelihood activities (mainly employee-based) and do not provide a full picture of the livelihood impacts of refugee presence on host communities. We consider an exhaustive set of livelihood activities in which households (individuals) in the impacted communities may engage. 7 Second, prior studies tend to focus on the different livelihood activities separately (i. e., whether the individual adult members or the household engage in each of the livelihood activities). 8 Therefore, they are unable to infer whether households are diversifying or specializing their livelihoods or are engaging more on the commercialization of activities. 9 The current paper goes beyond the allocation of labor to individual (specific) 7 As the data we used does not have a good welfare indicator (e. g., income, consumption, and assets), we could not explore the welfare impact of refugee inflow. 8 We examined households ’ engagement in individual livelihood activities as a mechanism for household livelihood strategies. 9 Generally, households tend to diversify their livelihood when facing negative shocks (e. g., conflicts, droughts) to minimize risk (Ellis 2000a, b). In the case of refugee inflow, households may either diversify or specialize as refugee inflow could be both a negative shock (through increase competition for resources, services, and employment) and a positive shock (through creating opportunities, such as high demand agricultural products, provision of cheap labor). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Figure 1: Map of regions in Ethiopia, location of refugee camps, and refugee source countries. Source: Database of the Global Administrative Areas (GADM) (https: / / gadm. org / data. html, accessed on November 20th, 2020) In Ethiopia, the Refugees and Returnees Services (RRS, former Agency for Refugees and Returnees Affairs (ARRA)) is responsible for managing refugee camps and its oversight by making sure that the commitment of the federal government is met (Nigusie and Carver 2019). Except for Eritrean refugees, most of whom are eligible for out of camp policy, arriving refugees, at the time the data was collected, were allocated to one of the 26 refugee camps spanning the five refugee hosting regions. Refugees living outside of camps represent about 10 percent of the refugees in Ethiopia (Abebe et al. 2018). The allocation tends to be based on shared identity between the refugee and the host communities and the distance of the refugee camps from the border of the source country. The South Sudanese refugees are hosted in the refugee settlements in Gambella, except the few who were relocated to the refugee camps in Benishangul-Gumuz region. Most of these refugees arrived during the civil conflict in South Sudan in 2013 (Nigusie and Carver 2019). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Database of the Global Administrative Areas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 inflow). We then use the composite variable as an instrument to identify the causal impact of refugee presence on livelihood diversification and commercialization in host communities. Figure 2: Household livelihood strategy framework under refugee inflow Source: Adapted from Nielsen et al. (2013) and Walelign and Jiao (2017) 4. Data sources The major data source of the current study is the World Bank ’ s Development Response to Displacement Impacts Project (DRDIP) 12 baseline survey from Ethiopia. The Ethiopia DRDIP survey was administered between September 2017 and August 2018. The survey covers 113 Kebeles (wards) in 16 Woredas (districts) from the top five refugee-hosting regions in Ethiopia. The selection of the sample households follows stratified random sampling with proportion to size (the number of households) using Woredas as a geographic stratum. The sample originally comprised a total of 3, 390 households, who were selected using systematic random sampling within each Woreda. We used data from 3, 375 households, as 15 of them were excluded due to missing location information (GPS). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Ethiopia DRDIP survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The sample households are located within varying distance from the nearest refugee camp (approx. 67 to 76, 665 meters) (see Figure 3). 12DRDIP aims to improve access to basic social services, expand economic opportunities, and enhance environmental management for communities hosting refugees through providing funding for community driven projects. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 crop products (e. g., wheat, potatoes) and the value of five livestock products (i. e., milk, egg, butter, hides, and honey) sold in the market. 14 Several other data sources were utilized. First, the Ethiopian refugee camps location data set from the Humanitarian Data Exchange (HDX) 15 and the total number of refugees by camps from the United Nations High Commissioner for Refugees (UNHCR), Addis Ababa office. We use data from 26 official UNCHR refugee camps in Ethiopia that were operational in 2018 (see Figure 1; 3). Second, we use administrative data sets for Ethiopia and refugee source countries from the database of Global Administrative Areas (GADM). 16 We also use the conflict data set from the Armed Conflict Location and Event Data Project (ACLED) 17 and the population data from the Gridded Population of the World (GPW) data set. 18 On the basis of these data sets and the location of sample households from Ethiopia DRDIP data set, we generated the following variables: i) distance of sample households to the nearest refugee camp, the nearest region (administration level 1 in GADM) to the refugee camps, ii) distance of the refugee camps to the nearest border of the refugee source country, iii) Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Humanitarian Data Exchange (HDX)", "database of Global Administrative Areas", "Gridded Population of the World (GPW) data set", "Ethiopia DRDIP data set"], "descriptive_data": ["Ethiopian refugee camps location data set"], "vague_data": ["administrative data sets"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the intensity of conflicts, and iv) population density in refugee host countries by region. Appendix B presents the list of variables along with description and summary statistics. 5. Empirical strategy To estimate the impact of refugee inflow19 on host community ’ s livelihood strategy choice, we use the following basic econometric model: 𝑌𝑌𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑅𝑅𝑅𝑅𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖 + 𝜈𝜈 + 𝜀𝜀𝑖𝑖 (1) Where, 𝑖𝑖 indexes a household, 𝑌𝑌𝑖𝑖 is an outcome variable of interest (livelihood diversification or commercialization of agriculture), 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 is the measure of refugee inflow, i. e., the refugee population (average of 2017 and 2018) in the nearest refugee camp weighted by the inverse of distance of the household to the refugee camps, 𝑋𝑋𝑖𝑖 is a set of household controls, 𝜈𝜈 is kebele fixed effects, and 𝜀𝜀𝑖𝑖 is the error term. Several variables, from the DRDIP data set, were used as controls in our model. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["DRDIP data set"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Further, as Jordan and the international community develop new approaches that respond holistically to the specific needs of refugee and host communities, more evidence is needed about how gender ‐ based constraints and vulnerability affect refugee women ’ s ability to take up economic opportunities and to access the services and resources they need to enable their families to move out of poverty. Our analysis applies a gender lens to a rich set of microdata on Syrian refugees in Jordan collected by UNHCR between 2011 and 2014. As these data do not capture how the changes in policies affect refugees and the constant evolution of their situation since 2014, the analysis is not intended to directly inform current policy choices and decisions. Instead, our aim is to devise an approach that can provide greater insights into gender ‐ specific barriers, based on the premise that the experiences and potential vulnerabilities of women, men, and children are significantly different in refugee settings. We use household ‐ level data to examine the relationship between poverty and gender for Syrian refugees. Our approach is informed by a body of work in the academic literature that has used household survey data to examine the relationship between the gender of the household head and household 2 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 3 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 4 https: / / data2. unhcr. org / en / documents / download / 64568, accessed August 2, 2018. 5 https: / / reliefweb. int / sites / reliefweb. int / files / resources / 64114. pdf, accessed August 2, 2018. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["microdata on Syrian refugees in Jordan"], "vague_data": ["household ‐ level data", "household survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The UNHCR data identify principal applicants for each household and our analysis examines differences in household poverty between households with a female rather than male principal applicant. 6 The principal applicant is the person who receives assistance from UNHCR for the family and is self ‐ selected or selected by the family. This definition of female headship has advantages over the way that household headship is commonly identified in household surveys. An often ‐ noted drawback of the headship variable is that female headship may reflect the enumerators ’ perception about who should be considered a family head rather than who has the most responsibility for the family ’ s welfare in practice. 7 Social norms can also affect whether female respondents self ‐ identify as household heads. For example, some Eritrean returnees who would in other cultural settings be regarded as de jure female headed (single mothers, widows, divorcees, separated women) reported being male ‐ headed. Other Eritrean female returnees who would be considered de facto heads reported headship by absent husbands or male relatives (Kibreab, 2003). Our approach is therefore to distinguish between different types of female and male principal applicant households, using a typology that reflects some of the indicators of vulnerability used by UNHCR. We find that distinguishing between different types of female principal applicant households is important in the setting of Syrian refugees in Jordan. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Most male principal applicants are one of a married couple with children whereas most female principal applicants are single care ‐ givers, single persons or living in non ‐ traditional family groups. While on average female principal applicant households are no more likely to be poor than male principal applicant ones, poverty rates for some types of households are higher when these households have a female principal applicant. Households that have formed because of the unpredictable dynamics of forced displacement, such as sibling households, unaccompanied children, and 6 Identification of the head of the case (as family groupings are referred to in the UNHCR ProGres database) is determined by who best represents the family for case management purposes. It is not assumed that the household will be best represented by a man; a woman or even a child can be a head of a case, depending on standard operating procedures. 7 Even when traditional household survey data are gathered at the individual level, the information is often collected from a single respondent. The respondent is usually the self ‐ identified ‘ most knowledgeable ’ household member, which overwhelmingly corresponds to the ‘ head ’ of the household. In the case of a household survey that solicits information on ‘ headship ’, this information is gathered often through the question: “ Who is the head of this household? ” Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR ProGres database"], "descriptive_data": [], "vague_data": ["household survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This assistance is crucial and has been reported to support a meal a day, a better roof, and dignity for Syrians who have fled to Jordan. 8 The JD ‐ HV database has detailed information on expenditure, sources of income, and indicators of household ‐ level welfare, for example, as reflected by recourse to coping mechanisms, standard of accommodation, or access to water, sanitation, and hygiene (WASH). JD ‐ HV data collected between October 2013 and December 2014 were first analyzed in Verme et al. (2016) who produced welfare aggregates and poverty measures to help target benefits and assistance to those most in need. Verme et al. (2016) draw attention to the precarious circumstances of Syrian refugees in Jordan and Lebanon. Around 55 percent of refugees in Jordan are vulnerable to monetary poverty and more than half are vulnerable to food shocks. Family size increases the probability of being poor, with the poverty rate almost doubling if the size of the family goes from one to two members and increasing by 17 percent when the number of children increases from one to two. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["JD ‐ HV database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "UNICEF (2017) reports that for boys, child labor, school violence, and the high costs of schooling (for transportation and stationery supplies) are the main barriers to their enrollment. For girls, barriers include the distance to the nearest school, the high cost of transportation, the need to help with household chores, health problems, and families refusing to educate their daughters. According to the JD ‐ HV database, the most common reasons parents gave for their children not attending school were financial constraints (35 percent), lack of capacity in schools (29 percent), or that children were required to work to support their family (14 percent). By 2016, the enrolment rate of Syrian refugee school ‐ age children in Jordan was 83 percent, 54 percent in formal education, and 29 percent in nonformal education (World Bank 2017b). 10 Refugee Employment It has been hard for refugees to find work in Jordan. The slowdown in growth in Jordan pre ‐ dates the arrival of Syrian refugees and the economy has been increasingly unable to absorb new labor market entrants. Between 2010 and 2016, labor force inactivity increased, employment decreased, and unemployment increased in Jordan (Malaeb and Wahba, 2018). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["JD ‐ HV database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To work legally in Jordan, refugees must have a work permit (Verme et al 2016). However, there is a list of professional jobs, including physicians, engineers, teachers, and workers in the services sector, that can only be done by Jordanian nationals (ILO 2015). Since the agreement of the Jordan Compact in 2016, the Government of Jordan has taken steps to open formal employment opportunities for Syrians. It has waived the fees required to obtain a work permit for Syrian refugees in a number of occupations open to foreign workers and simplified the documentation requirements. These measures have encouraged employers to regularize their workers; 10 Nonformal education services include catch ‐ up courses, dropout and basic literacy programs, and learning support services offered in Makani Centers of the United Nations Children's Fund (UNICEF) (UNICEF 2017). The Makani Centers are multifunctional spaces providing learning support, psychosocial support, and a safe environment with opportunities for play. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For a detailed definition see section 4 Household size Number of people included in the case records of each PA in Individual ProGress dataset Wage Income 1 if the household receives income from employment and / or daily or irregular work Income from remittances 1 if the household receives income from remittances Income per capita Raw sum of household income from all sources; work, pension, assets in Syria transfers, donations, other organizations'humanitarian aid, and other divided by household size Male Adults Number of males above 18 (inclusive) in the household Marital Status Categorical variable. The classification includes married PAs with spouse in the household, married PAs without spouse in the household, widowed, single or engaged, and divorced or separated. Proportion of female Number of female divided by the household size Location Categorical variable for 11 Governorates / cities. Ajloun City, Aqaba, Balqa, Irbid Jerash, Karak, Maan, Madaba, Mafraq, Tafilah, Zarqa. In Camp 1 if the household is located in a refugee camp Poverty before UNHCR and WFP assistance 1 if household expenditure before UNHCR plus WFP assistance is below the poverty line (JD50) Poverty before UNHCR assistance 1 if household expenditure after WFP assistance but before UNHCR assistanc is below the poverty line (JD50) Source: Authors ’ elaboration. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Individual ProGress dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 to 32 percent (see Table A1 in appendix). The total number of internally displaced people (IDP) outnumbers the stock of refugees in SSA and in the world but overall has followed a similar trend compared to the number of refugees (by country of origin). 1 Major civil wars in Central Africa mainly explained the peak in 1993 and 1994 and the increase at the end of the 1990s. Figure 1. Refugee population by origin, 1990 ‐ 2013 Note: Authors ’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014. Data from 2007 to 2013 include people in refugee ‐ like situations. Persons in refugee ‐ like situations include “ groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained ” (UNHCR 2014: 39). Refugees in Africa seem to have mainly remained in Africa. Although SSA also hosts refugees from other regions, the closeness of the ‘ blue ’ and ‘ red ’ lines in Figure 2 ‐ representing the number of refugees originating from and hosted in SSA ‐ is an indication that most refugees cross borders within Africa. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 (UNHCR, 2014), developing countries hosted 10. 1 million refugees or 86 percent of the world ’ s refugees. Contrary to what has been sometimes claimed in popular media, refugees are not “ invading ” the higher ‐ income countries. Actually, SSA has been hosting more refugees than sending them since 1990. The divergence of trends occurring in 2005 is certainly related to large inflows of refugees from North Africa and the Middle East. The second peak in 2011 corresponds to the uprisings that spread across several Arab countries (Egypt, Libya, Syria, Tunisia and Yemen), and the recent one in 2013 to the large outflows of refugees from Iraq, Syria and Yemen. Figure 2. Refugees and Internally Displaced People in SSA, 1990 ‐ 2013 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). Due to changes in classification and estimation methodology in a number of countries, 2007 figures are not fully comparable with pre ‐ 2007 figures (see also footnote 1). Gathering data on internally displaced people is much more challenging since most existing data on IDPs are incomplete or unreliable. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We only found reliable approximations from the International Displacement Monitoring Center (IDMC) between 2003 and 2013. According to IDMC, there were about 12. 5 million internally displaced people in SSA at the end of 2013 (IDMC 2014), more than one third of the total number of IDPs and more than tripling the number of refugees in SSA. Although the number of IDPs in SSA is the highest since 2007, the share of IDPs in SSA has been decreasing from 53 % in 2003. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The vulnerability of the Internally Displaced People in SSA has certainly been overlooked for too long, but the increased support provided by UNHCR is an encouraging but challenging sign in that respect. Figure 3. Refugees and Internally Displaced People in SSA, 2003 ‐ 2013 Source: Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). The annual number of IDPs is collected from the IDMC (2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2012, 2013, 2014) annual reviews. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 2. 3. The Challenges of Protracted Refugee Situations in Sub ‐ Saharan Africa At the global level, about 54 % (i. e. about 6. 3 million) refugees were in protracted refugee situation by the end of 2013 (UNHCR 2014). 2 As reported by Kreibaum (2016), the number of protracted refugee situations has increased from 22 in 1990 to 30 in 2008. These protracted situations in Africa have been characterized by Crisp (2003) as in most of the cases: i) peripherally located with poor security, unfavorable climatic conditions, and economical and political marginalized; ii) concentrating people with special needs like e. g. children and women (see Section 3); and iii) lacking basic human rights, including those covered by the provision of the 1951 refugee convention. Another distinct feature of refugees in SSA is that they are mostly hosted in organized camps. While in developing countries around one third of refugees are hosted in camps, the share raises to about 40 percent in Sub ‐ Saharan Africa (Figure 5). 3 The percentage of 76 percent in Eastern Africa and the Horn of Africa stresses again the pressing situation in this part of the world. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While camps have been recognized as posing serious challenges (Jacobsen and Crisp 1998), it is quite striking to observe that this organizational feature is not as spread in other regions of the world as in SSA. At best, only 28, 25 and 15 percent refugees are hosted in planned / managed camps in Asia, Americas, and the MENA region, respectively. Such figures are based on the most recent year available (2013) and may change significantly following the large inflows of Syrian refugees into Egypt, Lebanon, Iraq, Jordan and Turkey. Nonetheless, the differences are sufficiently striking to believe that this is a distinct feature of refugee hosting in SSA. 2 UNHCR defines a protracted refugee situation as “ one in which 25, 000 or more refugees of the same nationality have been in exile for five years or longer in a given asylum country ” (2012: 23). 3 The figures are based on refugees (including those in refugee ‐ like situation). Persons in refugee ‐ like situations include “ groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained ” (UNHCR 2014: 39). The number of refugees and people in refugee ‐ like situation for which demographic data is available does not necessarily equal the total number of refugees. However, for SSA, there is little difference between the two. We also restrict the number of refugees to those whose accommodation is known by the UNHCR (approximately 19 % in the world and 8 % for SSA). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["demographic data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Figure 5. Share of refugees hosted in camps, 2013 Source: Authors ’ presentation based on UNHCR Global Trends 2013 (UNHCR 2014). In summary, investigating the recent trends in forced displacement in Sub ‐ Saharan Africa points to the regional nature of this displacement, emphasizing the unfortunate increase in refugee movements in Eastern Africa over the most recent years. Such regional emphasis also takes some distance from the widespread view that refugees are mainly moving to Europe or other developed countries. In 2013, about 3. 7 million refugees originated from SSA but about 5. 6 million were hosted there. Most refugees from SSA remain in Africa. Refugees are mainly hosted in camps in peripheral and poor areas. The next sections will explore how refugees and hosting communities are affected by such forced displacement. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 3. Refugees as Agents of Their Own Destiny 3. 1 The Composition of Africa ’ s Refugee Population and Its Consequences One of the first elements that catch the eye in Figure 6 is the difference in the composition of the refugee population in Africa compared to the rest of the world. The share of children and women among refugees is higher in Africa than elsewhere, in particular East and West Africa stand out here. This is, at least partly, a consequence of Africa ’ s younger, general population, but other forces could be at work as well, e. g. higher mortality of adult males in Africa or adult males staying behind or being separated from the rest of the household. It does mean however that, relative to other areas, more attention should be going to the needs and capacities of women and children in Africa. This means, for example, adaption of and increased supply of schooling and health services. Figure 6. The composition of refugees by age and gender, 2013 Source: Note: UNHCR statistics (UNHCR 2014). Asia excludes Australia, Japan and New Zealand. Americas exclude Canada and the United States. These percentages have been calculated by country when demographic data are available for at least 30 % of the total. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["demographic data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 causes of death. The Danish Epidemiology Science Centre (1999) found severe malnutrition and high mortality in a survey of 422 refugee children in Guinea ‐ Bissau. They report higher malnutrition and higher mortality for children living in a non ‐ camp setting, compared to children living in a camp. The Goma epidemiology group (1995) found high prevalence of child mortality as well as acute malnutrition among children in refugee camps in Eastern Zaire, especially in female headed households. The magnitude of the difference between ‘ normal ’ mortality in the country under study, in the absence of conflict and the mortality in a refugee camp, depends on several parameters: the health infrastructure in the country as well as in the camp, the food available to camp and non ‐ camp residents, the frequency of visits by nurses or doctors, the intensity of the conflict (e. g. attacks on camps), and so on. Thus, the results are highly dependent on the context. For example, Singh et al (2005) do not find a difference in under 5 mortality among refugee versus non ‐ refugee households in western Uganda and South Sudan, whereas Verwimp and Van Bavel (2005) find higher child mortality and fertility among Rwanda refugees in Congo versus Rwandan women who did not became a refugee. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of 422 refugee children in Guinea ‐ Bissau"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "When general living conditions in one ’ s residence or home area are worse compared to a camp environment, e. g. because health services are available in the latter, mortality may also be lower in the camp. The strong presence of children in Africa ’ s refugee population implies that we should also look at the potential long ‐ term effects of forced displacement on survivors. Given the composition of the refugee population, such long ‐ term effects will be more important in Africa compared to elsewhere. Few studies have followed children exposed to forced displacement over a long time to directly infer the long ‐ term effects of forced displacement, in particular on health, education and labor market participation. Most studies of the long term effects of conflict use an indicator of exposure to violent conflict, but few of them have forced displacement as one of the indicators. There is however a very well established literature (see Currie and Vogl, 2013 for an overview) on the long ‐ term consequences of deprivation in early childhood which can be applied to the situation of refugees. If young children between the ages of 0 to 3 years old are exposed to malnutrition, disease, stress and violence during episodes of forced displacement, then, this literature shows that this deprivation will have negative long ‐ term effects. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 Revealingly, in Betts et al. ’ s (2013) survey of refugees in Uganda, 96 % of all interviewed households in the capital and 70 % outside the capital said they owned and used a mobile phone. They use this mobile phone to communicate with customers and suppliers, to get market information and to transfer money. Half of the urban refugees and 11 % of rural refugees also have access to the Internet. 4. Refugees As a Burden? As pointed in Section 2, most refugees in SSA are hosted in neighboring countries. Most of these hosting countries are likely among the least developed countries. It has been argued that these refugees may constitute an additional burden in terms of economic development in hosting countries (Mabiso et al. 2014). UNHCR (2014: 17) implicitly recognizes that potential burden by suggesting that the ratio of the size of the country ’ s hosted refugee population to its average income level can provide a proxy measure of the burden of hosting refugees. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of refugees in Uganda"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "SSA counts 13 out of the 20 countries hosting the larger number of refugees per 1 USD GDP (PPP) in the world. 4 While such figures stress that SSA hosts a fair share of the refugees in the world and underline that refugee flows are mainly a South ‐ South phenomenon, we shed doubt on this view of refugees described as a burden. We even argue in Section 6 that such representation is not conducive to the right policy framework in refugee ‐ hosting areas. 4 The other major host countries per USD GDP are all developing countries, with Pakistan (1st), Jordan (8th), Bangladesh (9th), Yemen (10th), Iran (14th), Lebanon (17th), and India (20th). Figure A2 provides the top 10 ranking in the world. At a global level, we should note that in 2013 “ the 40 countries with the largest number of refugees per 1 USD GDP (PPP) per capita were all members of developing regions, and included 22 Least Developed Countries ” (UNHCR 2014: 17). It should be noted that the way UNHCR computes that “ potential burden ” gives more weight to countries with very large population since is equivalent to. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 Note: Poverty is defined as the percent of the population living with less than $ 2 a day (World Development Indicators database). The annual number of refugees in each country is given by the Center for Systematic Peace (http: / / www. systemicpeace. org /). 4. 2. Lessons from Case Studies in Kenya, Tanzania, and Uganda Given the limits of cross ‐ country comparisons, we present below three short case studies on the impact of protracted refugee situations on hosting communities. These case studies were not chosen based on a systematic review but they are sufficiently close to each other to allow for comparative learning. These case studies are also those emerging from a growing literature on the quantitative assessment of the impact of refugees on hosting communities (Mabiso et al. 2014). Case Study # 1: The protracted refugee situations in Tanzania Tanzania has been known as a refugee ‐ hosting country for long due to its peaceful history and its location surrounded by conflict ‐ affected countries (Burundi, Rwanda, Uganda, Mozambique). The first president of Tanzania, Julius Nyerere, welcomed most of refugees as a sign of pan ‐ African solidarity in the post ‐ independence periods from many African nations. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["World Development Indicators"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "At least, at the time of the closure of the last camp in the region of Kagera, the aid workers from UNHCR and other organizations were aware of the challenge of the transition for the hosting population and seeks to coordinate with development actors such as the United National Develop Programs or local NGOs to support the hosting population in the district of Ngara. Nevertheless, the limited resources remained a major constraint on that effort and sheds light on the institutional constraints existing to scale up such positive efforts of coordination. Case Study # 2: The protracted refugee situations in Kenya Dealing with refugees remains a relatively novel phenomenon in Kenya. It was not until the early 1990s that Kenya witnessed massive refugee influxes from Somalia, Sudan, and Ethiopia (Banki 2004). Prior to that period however, Kenya had a reputation for having generous refugee policies, which allowed the successful integration of a number of refugees from Mozambique, Uganda and Rwanda (Banki 2004). However, with the arrival of hundreds of thousands of new refugees from neighboring countries during the 1990s, the responsibility for the care of the refugees shifted from the Government of Kenya ’ s (GoK) to the international community, leaving the more inclusive policies that were prevailing before 1991. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "23 The Kakuma refugee camp was established in 1992 in response to the inflow of 23, 000 Sudanese refugees (Jamal 2000). The camp is now home to over 100, 000 refugees from South Sudan, Burundi, Ethiopia, Somalia, and the DRC (UNHCR, 2012). The ongoing unrest in South Sudan is likely to exacerbate the refugee situation in the upcoming year. Moreover, the restrictions imposed by the government on refugee movement and employment makes the Kakuma population completely dependent on assistance provided by international organizations present on the field (Jamal 2000) The evidence on the impact of refugees in Kenya is quite limited. However, the Nordic Agency for Development and Ecology (NORDECO 2010) provides a detailed description, backed by sound descriptive statistics, on the impact of Dadaab refugee camps on host communities. Despite the very different structure of the local economy, mainly driven by pastoralist livelihoods, a pattern somewhat similar to the Tanzanian case is observed. According to NORDECO (2010), the aggregated economic impact is positive. It is estimated that about USD 3 million annual income accrues to the host community thanks to livestock and milk sales to the refugee camps. Trade and employment opportunities have also been reported around Dadaab camps in Kenya. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The total economic benefits, including through savings on food purchases (including through purchases from refugees), income accruing to local contractors from assignments for the United Nations or Non ‐ Governmental Organizations or support for host communities, “ using 2010 as a reference year, are [estimated to be] around USD 14 million annually. On a per capita basis this equates to around 25 % of average annual per capita income in North Eastern province ”. This estimation corresponds to a back ‐ on ‐ the ‐ envelope approximation but it gives a sense of the major benefits to the local population. Similar to the Tanzanian case, the presence of the Dadaab refugee camps is reported to have improved the provision of local public goods such as the frequency and reach of transport services and the availability of health and social services. NORDECO also observed environmental degradation around the Dadaab camps5 but spatially restricted in an area of inherently low resource value. It seems that environmental support programs have helped limiting the collection of firewood by refugees and providing alternative fuel sources (Milner and Loescher 2004). Compared to the Tanzanian case, two main differences emerge. Less emphasis is given to the distributional effect of the refugee inflows on the hosting communities, while less pressure on prices is observed around the Dadaab refugee camps. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Both differences may actually be related to the dominance of pastoralist livelihoods. First, on the distributional dimension, NORDECO (2010) did not point to a similar substitution effect between unskilled labor or refugees. In a pastoralist environment, the low ‐ middle ‐ income group and the poor are those primarily engaged in selling their products to refugee camps. Second, contrary to Alix ‐ Garcia and Saah (2010) for Tanzania, “ the price of basic commodities such as maize, rice, wheat, sugar and cooking oil is [reported to be] at least 20 % lower in camps than in other towns in arid and semi ‐ arid parts of Kenya. The main reasons are the re ‐ sale of WFP [World Food Program] rations, access to free food by locals registered as refugees and illegal imports via Somalia ” (NORDECO 2010: 9). Another possible explanation reported by Maystadt and Duranton (2014) in the Tanzanian case, is the importance of transport services in pushing the price of traded goods down. Although focusing more on the urban function of the refugee camps and the social transformation underpinned in the hosting society, Jansen (2011) also reports similar trading activities and wealth 5 Nonetheless, such a degradation is acknowledged by NORDECO (2010) to be difficult to distinguish from general trend prevailing the region. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "25 contributions to the local communities. More than other studies, this analysis points to the transfer of physical and human capital by refugees as an important source of benefits for the local economies. Interestingly, Kreibaum (2016) provides a more quantitative approach to the issue by assessing the impact of an increase in the presence of Congolese refugees on the hosting population in the Southern and Western parts of Uganda. The results indicate a positive ‐ although small in magnitude ‐ impact on the hosts ’ welfare (consumption per adult equivalent) but with distributional effects. Those depending on wage income and transfers experienced a deterioration in welfare, suggesting labor substitutability with rural landless workers. That seems to constitute a commonality with the Tanzanian case study. In addition, increase in the provision of private education services are also found, which is consistent with the move to the so ‐ called self ‐ reliance strategy in Uganda (see below). A major contribution of this paper is to contrast these results to the Ugandan households ’ perceptions in local communities. Conditional on assuming a common trend (that could not be tested with the available data), people are found to perceive their living conditions as having worsened off in areas with a higher number of refugees. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some Key Findings from Case Studies The above case studies point to an emerging body of the literature that seeks to quantify the impact of refugees in protracted situation on the hosting economies (Alix ‐ Garcia and Saah 2010; Baez, 2011; Betts et al. 2014; Maystadt and Verwimp, 2014; Maystadt and Duranton 2014; NORDECO 2010; Kreibaum 2016). Although that literature is still in its infancy, we can seek to draw a few lessons, even if these lessons can also serve as further hypotheses to be tested. First, the three case studies underline the importance of market mechanisms. Previous literature was very much focused on the health, environmental, and security consequences of hosting refugees. These concerns still rank as first priorities when refugees cross borders. But the understanding of protracted refugee situations requires paying much more attention to the interactions between refugees and their 6 As pointed by Dryden ‐ Peterson and Hovil (2003), de facto local integration has been a common occurrence, well before 1999. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "40 Sub ‐ Saharan Africa 8604000 5895000 7055000 5406100 5068000 N. A. MENA 6230000 8000000 6675000 8592900 10892000 N. A. Asia and Pacific 4325000 2405000 3392000 2128800 5490000 N. A. (excl. Australia, Japan, New Zealand) Americas 1126000 1280000 2176000 2900000 3661000 N. A. (excl. North America) Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). As indicated in Figure A1, these data are much lower compared to those provided from 2003 by IDMC but provide a longer time series. UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "41 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Since some adjustments could take place over time, we always use the figures from the last available report. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure A2: Refugees a burden for SSA? Panel A: Not weighted by economic capacity Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Migration and Economic Mobility in Tanzania: Evidence from a Tracking Survey Kathleen Beegle The World Bank Joachim De Weerdt EDI, Tanzania Stefan Dercon Oxford University, UK We thank Karen Macours, David McKenzie, and seminar participants at the Massachusetts Avenue Development Seminar, Oxford University and the World Bank for very useful comments. All views are those of the authors and do not reflect the views of the World Bank or its member countries. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 quest for imaginative and convincing instruments for migration (see the review of the migration and poverty literature by McKenzie and Sasin, 2007, and the references therein). An additional hurdle is the need for panel data to study migration and economic mobility. The costs and difficulties in re-surveying migrants mean that attrition may be relatively high for this group and may also result in the loss of some of the most relevant households to study this process (Beegle, 2000; Rosenzweig, 2003). This paper uses unique data from a region in Tanzania to address this key question: What is the impact of physical movement out of the original community on poverty and wealth? Although we do not have experimental data, the nature of our data allows us to limit the potential sources of unobserved heterogeneity considerably. Building on a detailed panel survey conducted in the early 1990s, we re- interviewed individuals in 2004, making a notable effort to track individuals who had moved. The tracking of individuals to new locations proves crucially important for assessing welfare changes among the baseline sample. The average consumption change of individuals who migrated was more than four times higher than that of individuals who did not moved. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Those who had moved out of the Kagera Region by 2004 experienced consumption growth that was 10 times higher compared to those who remained in their original community. These averages translate into very different poverty dynamics patterns for the physically mobile and immobile. For those who stayed in the community, poverty rates drop by about 4 percentage points over these 13 years. For those who moved elsewhere within the region, poverty rates drop by about 12 percentage points, and for those who moved out of the region, they drop by 23 percentage points. Had we not tracked and interviewed people who moved out of the community – a practice found in many panel surveys – we would have seriously underestimated the extent to which poverty has gone down over the past 13 years in the Kagera Region; we would have reported poverty reduction at about half of its true value. Clemens and Pritchett (2007) raise similar concerns in the context of income growth and international migration. In addition, the data would omit the part of the population with a high information content on pathways out of poverty. Still, these statistics are not evidence that moving out of the community leads to higher income growth. As noted above, we cannot observe the counterfactual: What would income growth have been for migrants had they not migrated? We exploit some unique features of these data to address concerns about unobserved heterogeneity. First, individual fixed effects regressions for movers and stayers produce a difference-in-difference estimation of the impact of physical movement, controlling for any fixed individual factors that affect consumption. Second, we can control for initial household fixed effects in the growth rate of consumption since we observe baseline households in which some individuals migrate Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 narrative around the regressions and aims to explain why not more people migrate when benefits of doing so are so high. 2. The Setting: Tanzania and Kagera, 1994-2004 In the last decade, Tanzania has experienced a period of relatively rapid growth, attributed to liberalization, a renewed trade orientation, a stable political context, and a relatively positive business climate to boost economic performance. Real GDP growth was of the order of 4. 2 % per year between 1994 and 2004, while annual population growth was around 3. 2 % in the same period (URT, 2004). There is also evidence that growth had accelerated in the last few years compared to the 1990s. However, this growth has not been sufficiently broad-based to result in rapid poverty reduction. On the basis of the available evidence, poverty rates have declined only slightly and most of the poverty reduction progress has been made in urban areas. According to the Household Budget Survey (HBS), between 1991 and 2000 / 01, poverty declined from 39 percent to 36 percent in mainland Tanzania. The decline in poverty was steep in Dar es Salaam (from 28 % to 18 %) but minimal in rural Tanzania (from 41 % to 39 %). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Household Budget Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Since the set of household members at baseline have subsequently moved, and usually not as a unit, the 2004 round results in more than 2, 700 household interviews (from the baseline sample of 912 households). Although the KHDS is a panel of respondents and the concept of a ‘ household ’ after 10-13 years is a vague notion, it is common in panel surveys to consider re-contact rates in terms of households. Excluding households in which all previous members are deceased (17 households with 27 people), the field team managed to re-contact 93 % of the baseline households. This is an excellent rate of recontact compared to panel surveys in low-income countries and high-income countries. The KHDS panel has an attrition rate that is much lower than that of other well-known panel survey summarized in Alderman et al. (2001) in which the rates ranged from 17. 5 % attrition per year to the lowest rate of 1. 5 % per year. Most of these surveys in Alderman et al. (2001) covered considerably shorter time periods (two to five years). Figure 1 charts the evolution of households from baseline to 2004. One-half of all households interviewed were tracking cases, meaning they did not reside in the baseline communities. Of those households tracked, only 38 % were located nearby the baseline community. Overall, 32 % of all households were not located in or relatively nearby the baseline communities. While tracking is costly, it is an important exercise because migration and dissolution of households are often hypothesized to be important Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["KHDS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Respondents who were untraced were much more likely to be residing outside Kagera (43 %) compared to their counterparts who were re-interviewed (8 %). The consumption data come from an extensive consumption module administered in 1991 and again in 2004. The consumption aggregate includes home produced and purchased food and non-food expenditure. The non-food component includes a range of non-food purchases, as well as utilities, expenditure on clothing / personal items, transfers out and health expenditures. Funeral expenses and health expenses prior to the death of an ill person were excluded. Monetary levels were adjusted to account for spatial and temporal price differences, using price data collected in the Kagera survey in 1991 and 2004, and, for households outside Kagera, data from the National Household Budget Survey. Consumption is expressed in per capita, per annum terms. The poverty line is set at TZS 109, 663, calibrated to yield for our sample of respondents who remained in Kagera the same poverty rate as the 2000 / 1 National Household Budget Survey estimate for Kagera (29 %). 4. Growth, Poverty and Physical Mobility in Kagera In this section, we discuss changes in living standards overall, and the changes for four mutually exclusive groups based on residence in 2004: (i) still residing in the baseline community, (ii) residing in a Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Kagera survey", "National Household Budget Survey"], "descriptive_data": [], "vague_data": ["consumption data", "price data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 neighboring community, (iii) residing elsewhere in the Kagera Region and (iv) residing outside the Kagera Region. Table 3 shows that the basic needs poverty rate declined 8 percentage points in the full sample. This figure masks significant differences in changes between subgroups based on migration. For those found residing in the baseline community, poverty rates dropped by 4 percentage points, but rates dropped by 11, 13 and 23 percentage points for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. A similar pattern is found for consumption per capita. While consumption per capita grew by $ 65 overall, it grew by only $ 30 for those found in the same community and by $ 65, $ 100 and $ 287 for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. Dividing consumption into food and non-food components gives the same result. The most basic assessment of welfare changes would have been wrong if we had focused only on individuals still residing in the community, a practice found in many panel data surveys. We would have underestimated the growth in consumption by half of its true increase. The differences in consumption changes of groups in Table 3 are statistically significant, as shown in Table 4. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Excluding respondents who have relocated would omit those with the higher rates of income growth and poverty reduction. Table 5 reports confidence intervals for the incremental samples (which are not mutually exclusive); it gives a more detailed picture of how inference on consumption growth and poverty reduction would have changed if we had not tracked movers. It is apparent that inference from a ‘ simple ’ panel survey of respondents continuing to reside within the original communities would have produced underestimates of actual consumption growth and poverty reduction in this population. These conclusions are robust across the distribution of consumption, as well as at the mean and poverty line. Panel A in Figure 2 depicts the cumulative density function for consumption per capita for those people who remained living in the same community. Panels B, C and D make the same graph for respondents found residing in neighboring communities, elswehere in Kagera Region and outside Kagera Region. As respondents were located further from their location in 1991, so the difference between the 1991 and 2004 graphs becomes more pronounced. Note how, for people who remained in the baseline community, the 1991 and 2004 distributions lie close to each other under the poverty line and diverge above it, while for other mobility categories there is more divergence at the bottom of the graph. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While both sons and daughters of the head may be expected to be more likely to stay in the community than other initial household members, patri-locality would make this probability higher for boys than for girls. In sum, this means we are using a set of six instruments. Although we can show that statistically convincing and close to identical results can be obtained by only using a subset of these instruments, we use the full set of instruments in the reported results. While our main measure of migration (Mi) is an indicator for having moved, we also substitute this for the log of the distance moved (kilometers from the original community of the location in which the individual was found in 2004, ‘ as the crow flies ’, set to 0 for non-movers). We will also extend the multivariate analysis to explore the role of moving to more urbanized areas and the role of sector movement in raising consumption growth. 6. Regression Results Table 9 presents the basic results for the initial household fixed effects (IHHFE) and 2SLS estimates (means for covariates are in Appendix Table 1). For each we estimate using an indicator for having moved and a measure of distance of the move. The 2SLS estimates in column (3) and (4) use the six instruments defined above. In Table 10, we present the first stage results of regressions explaining migration or the distance traveled in migration. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1 Introduction Company owners and managers make two decisions with important implications in the labor market: what skills are demanded and who to hire. On the demanded skills, a driver of trends in employment is the changing demand for soft skills (Heckman and Kautz (2012), Weidmann and Deming (2021)). However, we know relatively little about what kinds of soft skills employers value in modern entry- level jobs (Heller and Kessler, 2022). On the decision of who to hire, it is in the best interest of companies to hire based on workers productivity. However, several studies have documented the existence of labor market discrimination in a wide range of contexts (Bertrand and Duflo (2017), Neumark (2018)) and it remains unclear how discrimination operates throughout the hiring process and how the existent empirical evidence on discrimination is linked to economic theory (Bertrand and Duflo, 2017). We conducted a correspondence study in 2023 using a large online job platform to assess demand for soft skills in the context of hiring discrimination in Malaysia. Malaysia is a particularly interesting setting because it is an upper-middle-income economy, home to multiple ethnicities representing large shares of the population, and previously documented gender gaps in labor force participation and wages. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["large online job platform"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In addition, we observe several characteristics of the firm and job that are not typically observable to researchers, which we will use to conduct a rich heterogeneity analysis. Most importantly, we observe the number of applicants applying to a specific job, and the number of similar jobs posted on the platform. This unique data facilitates our third contribution, in which we analyze the role of labor market competition in hiring discrimination, an area which to date has not been widely explored. de Haan et al. (2017) show that discrimination against disadvantaged groups is more likely in the presence of competition of workers from a non-discriminated group than in a non-competitive scenario. Along these lines, we hypothesize that firms will discriminate less often when there is a low supply of applicants. Unlike de Haan et al. (2017), we uniquely observe quality indicators of the applicant pool, which we use to test our hypothesis that discrimination decreases when the relative quality of applicants in the pool is low. Furthermore, we exploit our unique data to test whether firms discriminate less often when there is high demand for specific job positions. We know of no studies that have previously considered competition on the demand side. The rest of the paper is organized as follows. Section 2 provides background information of Malaysia. Section 3 details the experimental design, and section 4 provides summary statistics of the data. Section 5 explores if there is discrimination in the Malaysian labor market, section 6 studies 4 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These degree- based specializations were selected after initially characterizing job advertisements posted between April 11-17, 2022 on the job portal. At that time, 671 scraped jobs met the following criteria: full time, entry level, bachelor ’ s degree required, 0-1 years of experience. These jobs were then categorized into the aforementioned degrees. The remaining 15 % of total jobs were determined to be too specialized and those kinds of jobs were excluded from the study. All resumes have a bachelor ’ s degree conferred by the same university, one of the most prestigious and multi-ethnic institutions in Malaysia. In Malaysia, students from a particular ethnicity might attend specific colleges. Hence, the decision of using one institution for all candidates prevents potential associations of perceived quality of an institution to ethnicity. In total, 90 candidate profiles were created (3 ethnicities x 2 genders x 3 soft skills x 5 industries = 90 profiles). Each degree has 18 unique candidate profiles allowing for all possible combinations of ethnicity, gender, and emphasized soft skill. For example, 6 of the 18 mechanical engineering applicant profiles are Chinese, 6 are Malay, and 6 are Indian. For a given ethnicity, half are female and half male. Among the 3 Chinese female applicants within a degree, each is uniquely assigned one of the three soft skills traits to be emphasized (leadership, teamwork, or none), and similarly for 7 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3. 2 Job Search and Classification Every Friday from May 12 to July 21, 2023 we scraped all job ads posted within the previous seven days from the job site. Then we filtered and kept job ads that met the criteria: full time, entry level position or requiring at most 1 year of experience. Relevant jobs were then classified into one of the five degree-based specializations. Each job was assigned a degree-based specialization using the area of specialization reported in the ad. For the accounting degree we used job ads classified as “ Accounting / Finance ”. For the business administration degree we used job ads with special- izations: ‘ Admin / Human Resources ’, ‘ Sales / Marketing ’, ‘ Customer Service ’ or ‘ Logistics / Supply Chain ’. For the computer science degree we used the specializations: ‘ Tech & Helpdesk Support ’ or ‘ Computer / Information Technology ’. For electrical engineering we used specializations that are related to ‘ Electronical ’, ‘ Electronics ’ or ‘ Other engineering ’. If the position had the word ‘ engineer ’ and the industry of the company was related to Electronical or Electronics, we also classified the ad into the electrical engineering degree. Finally, for mechanical engineering we used specializations related to mechanical, industrial or chemical engineering and specializations related to oil and gas. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the power calculations performed to determine the sample size. 3. 3 Randomized Assignment and Job Applications Each week we randomly selected 300 job ads from the sample of ads meeting our inclusion criteria. Among these 300 ads, each job ad was randomly assigned to a single applicant profile. Job ads are stratified to ensure that our treatment and comparison units are balanced on key variables. We use two variables for our strata: company size and company location. Company size is a dummy variable that takes the value of 1 if the company has up to 50 employees, and takes the value of 0 if companies have 51 or more employees. Company location is a dummy variable that takes the value of 1 if the company is located in greater Kuala Lumpur, the capital and largest metropolitan area in Malaysia, and 0 otherwise. 6 Our stratified randomization procedure guarantees balance in the assignment of job profiles to specific characteristics of companies. The application process was carried out manually from May 17 to July 28, 2023. At the beginning of each week, a research assistant was given a list of randomly assigned jobs for each applicant profile. Applications were completed on Mondays, Wednesdays, and Fridays of every week (with day of the week randomly assigned). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Applications were submitted during the same time span each of these days (8am-12pm CT). 7 The order of applications (grouped by profile) each day was also randomized. The application process is straightforward, it consists of submitting the application and completing an optional pitch. However, some jobs have a mandatory pre-scan questionnaire. For these type of ads we standardized answers and recorded the job ads that implemented these questionnaires. 3. 4 Monitoring Job Applications Job applications were monitored using a web scraping algorithm. For each job application, the following data was scraped from the website every Tuesday, Thursday, and Saturday (between 8am- 12pm CT): 1. Number of times the profile was viewed by the employer 6The locations we classify as Greater Kuala Lumpur are: Kuala Lumpur, Putrajaya, Petaling Jaya, Klang / Port Klang, Kajang / Bangi / Serdang, Subang Jaya, Ampang, Cyberjaya, Seremban, Selangor, Selangor- Others, Selayang, Semenyih, Shah Alam / Subang, and Central. 7If the website is under maintenance, which is common, applications will be delayed until the website is available. 10 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ethnicity: For Chinese name candidates is 4 days, for Malay name candidates 5 days and for Indian name candidates 7 days. We find this evidence consistent with a hypothesis of the existence of statis- tical discrimination since employers can be taking more time to collect information of discriminated groups, or sorting applications. Companies that conduct pre-scan questionnaires in the application process are less likely to discriminate against Indian-sounding name candidates in the profile visit outcome. We do not find heterogeneous effects for location or for engineering jobs. Heterogeneity results for gender discrimination are presented in tables 21 to 26. Companies located in Kuala Lumpur and small companies are less likely to visit female profiles than male profiles. There is no effect in any of the other outcomes of the hiring process. We do not find heterogeneous effects for high-paying jobs, for companies with low processing time, for companies with pre-scan questionnaires or jobs in engineering. 6 Do Soft Skills Matter? In this section we explore if soft skills are relevant in the labor market and how soft skill signals affect ethnic and gender discrimination. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The role of soft information has been previously raised by Kaas and Manger (2012) that find that soft information on conscientiousness and agreeableness mitigate the discrimination practices. 6. 1 Response to Soft Skills Does the labor market respond to signals of soft skills (leadership / teamwork / neither)? If so, what is the extent of the response? To answer these questions, we use the soft skill signal that we randomly assigned to each profile. We can test if soft skills are differentially relevant in the labor market using specification 6: (6) yi = θ0 + 2 X k = 1 θkSik + εi Again, the outcome yi and error term εi are defined as in specification 1. The soft skills we want to test are leadership and teamwork, in comparison to a control soft skill that we call ‘ neither ’. Sk is the soft skills variable, where k = { 0, 1, 2}. That is, Sk is a dummy variable that takes the value of 1 for soft skill k (e. g. leadership) and 0 for other soft skills (e. g. teamwork and our counterfactual soft 25 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 Conclusions We conducted a correspondence study using an online job platform in Malaysia. We tested for ethnic discrimination, gender discrimination and the value of signaling soft skills in the labor market. Unlike many correspondence studies, the data allow us to observe different stages in the hiring process. We observe if the employer rejects an application, visits the profile of a candidate, number of times the profile is visited, if they contact them and if they offer an interview. Uniquely, we observe competition in the labor market on both the demand and supply sides. We do not find evidence of gender discrimination in the hiring process. Malaysia ’ s observed differential wages and labor force participation rates by gender do not seem to be associated with discrimination or human capital accumulation. More research is needed to determine why women in the Malaysian labor market have lower employment rates and wages. We find that Indian and Malay sounding name profiles are discriminated against in comparison to Chinese-sounding name profiles. There is discrimination along all the hiring process variables we observe. Malay and Indian candidates are 8 and 9 percentage points less likely to receive an interview offer relative to a Chinese candidate. Discrimination for both ethnicities is also present in other outcomes. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Poverty remains widespread and social indicators are well below the average for Sub-Saharan Africa. Chad is ranked 173 among the 177 countries surveyed in the 2006 UNDP Human Development Report. The incidence of poverty (defined as the proportion of households with annual spending below what is necessary to meet minimal needs) is estimated at 55 percent according to a 2003 household survey; an estimated four-fifths of the population of about 8. 8 million is living on less than a dollar a day3. Of the population over 15 years old, more than 73 percent (and 76 percent of women) are illiterate. Access to potable water has improved over past years, but is still limited to one out of three people in 2005. Less than two percent of the population has access to electricity and only 1021 kilometers of roads has been paved on a surface area of over 1. 2 million square kilometers. As already mentioned, Chad has recently become oil producing country; however, the economy remains largely agricultural and pastoral. About 80 percent of the country ’ s population lives in rural areas and continue to make their living4 from agriculture and livestock. Cotton is the principal cash crop, employing about 300, 000 families. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["2003 household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "29 should be entered immediately into the accounts; and (c) Payment must be recorded as soon as they are made. Budget implementation should be reviewed periodically to ensure that programs are implemented effectively and to identify any financial or policy derailment. The review of budget execution should cover financial, physical and other performance indicators. Development budgets are often beset by implementation problems because of insufficient implementation capacities and other factors such as delays in mobilizing external financing, overoptimistic implementation schedules or difficulties in importing supplies. It is thus important to have in place mechanisms for reviewing the most significant or problematic projects. These could consist of a regular monthly or quarterly review of projects within the line ministries and a midyear review involving line ministries and central agencies29. The government has taken steps to improve the tracking of budget expenditure until the intended destination, particularly investments spending, for which a tracking survey was entrusted in 2005 with the Ministry of infrastructures. In addition, the ministries took themselves certain internal initiatives, in particular in health and education sectors, but the action plans of these ministries were not updated as envisaged in 2005, and there are neither reliable benchmark, nor quantitative targets as regards improvement of the arrival of the expenditure at intended destination in these sectors. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["tracking survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A survey was carried out in the health sector (health centers) with the support of the World Bank in 2005, but has not been yet validated. In the rural development sector, where the first expenditure tracking survey between the decentralized center and services was to be carried out in 2005 on PEFA funds, the survey is yet to be carried out. It is critical that adequate management measures are gradually put in place to stop the current waste of resources. These measures include, inter alia, the accounting improvement of material, improvement of the inventory and delivery control, and especially a transparent planning of the deliveries, including, the posting of the received deliveries, their comparison with the planned deliveries and their certification by users within each service. It would also be desirable that in each ministry, an action plan is prepared for the implementation of these measures, on the basis of existing ones, and with target indicators as regards improvement of the arrival of the expenditure at intended destination. The social ministries could usefully open the way in this field, on the basis of some projection already carried out. Without a quantitative and qualitative improvement of the arrival of the expenditure at their final recipient, the increase in the budgetary appropriations to the priority sectors will hardly be translated into substantial concrete results on the ground. 29 It is difficult to make accurate forecasts for the implementation of certain programs or developments in economic parameters such as inflation or interest rate. Some immediate needs that were not foreseen during budget execution may appear during budget execution. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["expenditure tracking survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10100 This paper explores the impact of refugee return on social cohesion using data from Burundi, a country that expe­rienced high levels of repatriation during the 2000s. It uses a nationwide survey conducted in 2015 and relies on geographic features of the communities for identification purposes. The results suggest varying impacts of refugee return on different aspects of social cohesion. The stronger effects, suggest that refugee return has a negative impact on the feeling that community members help each other, could borrow money for emergencies from non-household members and feeling that the community is peaceful. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["nationwide survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The estimated impacts on measures of reconciliation, post-con­flict justice, trust and participation in community groups are mostly statistically insignificant. The paper also explores how these effects differ across different sub-samples based on ethnic composition, land scarcity and attitudes towards return. The results highlight the possible role of new migra­tion-related societal divisions (i. e. returnees versus stayees) in affecting post-return social cohesion. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at carlos. vargas-silva @ compas. ox. ac. uk. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 agreements that did not fully satisfied either party (Ndayirukiye and Takeuchi 2014). This tension related to land, and who has a claim to the land, can lead to social tensions in communities with higher levels of return. Figure 3 – Refugees in Tanzania in 2005 by province of origin in Burundi Note: The number in brackets is the number of refugees in Tanzania in 2005 which was originally from the given province in Burundi. This information comes from (UNHCR 2021b). The number in parenthesis is that number as a percentage share of the population of the provinces as estimated in 1990 Burundi Census. An important question for our hypotheses is the degree to which there is evidence of migration-related societal divisions in the country. There is no direct quantitative evidence on identity issues (i. e. returnees versus stayees), but we have data on attitudes towards emigration, remittances and return that can provide insights on these identities and even be a proxy for migration-related identity in some cases. Overall, attitudes towards emigration and return are mixed and show that there is scope for the existence of migration-related divisions. In Table 1 we report the share of respondents who agreed with different statements regarding emigration, remittances and return. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["1990 Burundi Census"], "descriptive_data": ["data on attitudes towards emigration, remittances and return"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Finally, we divide the communities based on land scarcity before the 1993 conflict in order to explore the possible role of posterior rules regarding land provision to returnees. Respondents in communities that had more and less pre-war land available have broadly similar attitudes towards return. 4. Research design 4. 1 The survey We collected the data for this project during January to March 2015 as part of a nationwide survey on issues related to migration for the Labour Market Impacts of Forced Migration (LAMFOR) project. The survey had two components. First, a household survey in which 15 households were interviewed in 100 communities (i. e. sous-collines) across the 17 provinces of the country. Second, a community survey in which a local leader was interviewed in each of the 100 communities. The number of communities selected in each province was based on information from the 2008 Census. Figure 4 indicates the location of the communities surveyed. Figure 4 – Location of communities surveyed in Burundi Note: Geolocation of the 100 communities (i. e. sous-collines) sampled in the survey. Each community corresponds to a dot. Fifteen households and a local leader were interviewed in each community. The number of communities selected in each province was based on information from the 2008 Census. In the analysis below we focus on rural areas. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["2008 Census"], "descriptive_data": [], "vague_data": ["household survey", "community survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Where 𝑌𝑌𝑖𝑖 represents one of the indicators of social cohesion explained above, 𝛿𝛿𝑗𝑗 is the province indicator, 𝑅𝑅𝑐𝑐 is the share of returnees in the community, 𝐻𝐻𝑖𝑖 indicates a series of household level controls and 𝐶𝐶𝑐𝑐 are a series of community level of controls. In the main regressions we estimate the share of returnees in the community, using the information from the survey (i. e. share who are returnees), but in the robustness section we show that results are robust to the use of an alternative indicator in which the information is provided by a community leader. The Appendix (Table A2) includes the descriptive statistics for the control variables. We present results for the full sample and divided by communities with lower / higher ethnic diversity, less / more pre-1993 war land availability and better / worse attitudes towards return. In the robustness checks we also present the results if we limit the analysis to stayees only. Limiting the sample in this way does not affect the main results of the paper. 4. 4 Identification As mentioned above, Tanzania mandated the return of all Burundian refugees from the 1993 conflict. Returnees also had a very strong incentive to return to their communities of origin as this was the place in which they were entitled to land, a very scarce resource in the country. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "UNHCR (2021a) projects that the number of returnees will reach 141, 000 in 2021, up from 41, 000 in 2020. There are no datasets such as the one used in this study to explore the impact of post-2015 returnees on social cohesion and it is not possible to determine the degree to which our findings our applicable to this new context. However, it is possible to explore similarities and differences between the two contexts. Looking at a UNHCR report about the latest wave of returnees, it states that “ almost all returnee households rely on food obtained from their own gardens (93 %) and / or fields- households struggle to get food during the period they do not produce. 81 % of households declared that they are not satisfied with their level of food security because of the low dietary diversity ” (UNHCR 2021a). The report also suggests that “ 88 % of returnee heads of households are subsistence farmers, but most of them declared not having the adequate resources to produce their land. ” This high level of dependence on agriculture and prevalence of food insecurity are similar to the ones in our dataset for returnees and suggests that tensions related to access to agricultural land could also be present for post-2015 returnees. There are also signs of potential differences between current dynamics and the pre-2015 period. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 I. Introduction After two decades of multiparty democracy, Mali was viewed as a democratic success story. The fifth presidential elections were scheduled to take place in March 2012 and another peaceful and democratic transfer of power was widely anticipated. Reality was different, however. A secessionist movement sparked by a Kel Tamasheq1 rebellion led to a political and constitutional crisis culminating in a coup d ’ état in March 2012 and an attempt to take over the country by force. The three northern regions of Gao, Timbuktu and Kidal became occupied by various rebel and Islamist factions until early 2013, when a coalition composed of the Malian Army, French troops and the ECOWAS-led African-led International Support Missions to Mali (AFISMA) recaptured the occupied areas. 2 After months of insecurity in the North and two violent attacks in Bamako, a Peace Accord was signed in May and June 2015 between the government and different actors involved in the rebellion. The Accord established a joint vision for peace and prosperity predicated on demobilization and disarmament, the devolution of authority to local governments, and the establishment of conditions for restoring stability and economic recovery in northern Mali. In spite of the Accord, the regions of Gao, Kidal and Timbuktu remain in a state of prolonged crisis, with high levels of insecurity and weak governance. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Without army protection most parts of the North, especially Kidal remain inaccessible to those working for the central and local government. 3 Armed bandits are active and IED explosions as well as violent attacks on the MINUSMA peacekeeping forces are regular occurrences. Under these circumstances, data collection is very difficult. INSTAT, the National Bureau of Statistics, has not been in a position to collect information from northern Mali since the beginning of the crisis. To our knowledge, our surveys implemented by a private survey entity, GISSE, are the only systematic and representative effort to collect data in north Mali since the crisis. They offer a unique database providing a crucial perspective that would otherwise not be reflected in academic analyses and policy level decision-making, and a perspective that is indispensable in any attempt at understanding the situation in Northern Mali. 4 Preceding the Accord on Peace and Reconciliation in Mali (Accord pour la paix et la réconciliation au Mali) (hereafter, the Peace Accord) of May and June 2015, four peace accords had been signed between the government and Toureg and Arab armed groups in 1 Kel Tamasheq (those who speak Tamasheq) is synonymous for Tuareq. 2 Francis David (2013): The regional impact of the armed conflict and French intervention in Mali. NOREF, Norwegian Peacebuilding Resource Centre. 3 Assessing Recovery and Development Priorities in Mali ’ s Conflict-Affected Regions. Draft Report of the Joint Assessment Mission for Northern Mali (January 2016), p. 15-16. 4 All data can be downloaded from www. gisse. org. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The objective of the survey was to analyze perceptions and priorities with regard to the Peace Accord, to analyze perceptions of security, to determine access to basic infrastructure and school attendance, to understand nutrition levels, and to measure household asset ownership. In addition to this baseline survey, the World Bank was sponsoring a mobile phone survey among IDPs in Bamako, returnees in Gao, Kidal and Timbuktu town and refugees in camps in Niger and Mauritania. This survey interviewed 500 respondents on a monthly basis. In the August 2015 round of this survey, questions about perceptions and priorities with regard to the Peace Accord were included. This paper also makes use of a subset of the responses obtained from that survey, particularly those from refugees in Niger (n = 80) and Mauritania (n = 100) as these sub-populations who live outside Mali ’ s borders are important stakeholders in the peace process whose opinions risk being ignored. 15 To select a household in a village or neighborhood for the baseline survey, random selection was used: the enumerator divided the locality into two parts and selected five 15 For a more elaborate description of this mobile phone survey, see: Etang Ndip, A., J. Hoogeveen and J. Lendorfer (2016). Socioeconomic Impact of the Crisis in Mali on Displaced People. Journal of Refugee Studies. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["mobile phone survey", "baseline survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Similarly, regional representation of the sample was found to be in line with actual regional distribution of the population in the north. 16 The code of the day is the sum of the two figures of the date, i. e. if it ’ s the 25th of August the code of the day is 2 + 5 = 7. The enumerater will chose house number 7 as a starting point. Arab 3 % Tamashek 32 % Songhai 45 % Peulh / Foulbe 7 % Other ethnicities 13 % Figure 2: Ethnic composition of the North, 2009 Census Arab 5 % Tamashek 36 % Songhai 49 % Peulh / Foul be 7 % Other ethnicities 3 % Figure 3: Ethnic composition of sample Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["2009 Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 In addition to the 500 households and 180 refugees, 50 local authorities were interviewed. Of those, 18 are based in Goa, 22 in Timbuktu and 10 in Kidal. Included were 38 village chiefs, 11 mayors and 1 local notable. Table 3: Authorities interviewed by function and by region (%) Gao Kidal Timbuktu Total Village chief 13 8 17 38 Mayor 5 1 5 11 Local notability 0 1 0 1 Total 18 10 22 50 The authorities interviewed are all men aged between 30 and 86. Forty-four percent are either just literate or have no education at all. In Gao, authorities have a higher level of education compared with Kidal and Timbuktu. The majority (almost 39 %) of respondents in Gao have a high school education, 22 % are literate and 17 % have primary education. In Kidal, 80 % of respondents have no education at all. In Timbuktu, about 32 % of the surveyed authorities are literate, 27 % have a high school education and 23 % a primary education. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 III. The 2015 Accord on Peace and Reconciliation in Mali Preceding the Peace Accord ’ s signature, talks were held in Algiers between six armed groups congregated in two broad coalitions – the CMA (Coordination des Mouvements de l ’ Azawad) and the Platform – and the Malian government. Al-Qaida in the Islamic Maghreb (AQIM) and the Movement for Unity and Jihad in West Africa (MUJAO), both foreign-origin jihadist groups, were excluded from the talks although they were controlling most of the North from June 2012 to January 2013. 17Another security actor that emerged after the negotiations began, and which was not represented, is the Imghad Tuareg and Allies Self-Defense Group (GATIA), 18 a pro-government self-defense group, fighting those groups seeking greater autonomy and or an independent state of Azawad. Despite the fact that there were at a minimum eight different armed groups fighting in northern Mali at one point or another since the rebellion was sparked in 2012, there are only three signatories to the Peace Accord: the Malian Government, the “ Platform ” and the CMA. The Platform coalition consists of the Coordination des Mouvements et Fronts Patriotiques de Résistance (CM-FPR), the Coalition du Peuple pour l ’ Azawad (CPA) and a faction of the Mouvement Arabe de l ’ Azawad (MAA). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The CMA consists of the Movement National de Liberation de Azawad or the Azawad National Liberation Movement (MNLA), which is the main secular Tuareg separatist group, the Haut Conseil pour l ’ Unité de l ’ Azawad or High Council for the Unity of Azawad (HCUA), which is an Islamist group led by Touareg traditional leaders formerly associated with the Ansar Dine jihadist group, and the Mouvement Arabe de l ’ Azawad or Azawad Arab Movement (MAA), the main Arab separatist group. 19 Initially, the CMA did not sign the Peace Accord. The Platform coalition of armed groups took over Ménaka in Gao region in April 2015, prompting the CMA to refuse to join the signing ceremony on 15 May 2015. The CMA set as condition for signing the withdrawal by the Platform from Ménaka. Following collective international mediation initiatives, the Platform announced its immediate withdrawal from Ménaka on 18 June and on 19 June the Government of Mali lifted arrest warrants against 15 leaders of the CMA. Subsequently, on 20 June, Sidi Brahim Ould Sidatt from MAA-CMA signed the Peace Accord on behalf of the CMA. On 23 June, Mali ’ s President Ibrahim Boubacar Keïta met with the leadership 17 Report of the Secretary General on the Situation in Mali (22 Sept 2015): https: / / minusma. unmissions. org / sites / default / files / 150928_sg_report_sept_2015_en. pdf. 18 Reeve, Richard (2015): Devils in the Detail: Implementing Mali ’ s New Peace Accord, Oxford Research Group. 19 http: / / www. responsibilitytoprotect. org / index. php / crises / crisis-in-mali. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Worry about economic conditions after a recession can make companies more cautious about hiring, which generates temporary forms of recruitment. Finally, the process of technological change, especially the development of digital communications, is a key factor in justifying new forms of non-standard employment. The expansion of services and global supply chains is inextricably linked to technological advances. The new information technologies, the higher quality and the lower cost of infrastructure and the logistical and transport improvements, allow companies to compare, organize and manage production in a more diversified way in territorial terms. At the same time, new communication technologies have allowed the generation of new forms of work, such as work on internet platforms or work on demand through digital applications. In this sense, technological developments allow companies to assemble teams of workers who develop activities in any part of the world through a virtual network (Brews and Tucci, 2004). The most recent development of online recruitment services, such as\"eLance\"and\"oDesk\"enables the search for workers who can be subcontracted, performing their activities in virtual mode. 3. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Sources of information This work includes a descriptive analysis that allows us to identify how the prevalence of non- standard employment (NSE) has evolved in the last two decades in different regions of the planet, as well as the evolution of the profile of workers in those roles, in terms of their educational level, salary per hour, and type of tasks performed. This analysis was based mainly on periodic surveys of households that included information regarding the employment and educational situation of individuals. Although the denomination of this type of surveys varies from country to country, in all the cases analyzed there is usually a survey of annual or higher frequency that includes information required to identify the labor status of the individuals as well as to analyze the salary profile and education of the employed. However, it should be noted that the identification of the type of work relationship (standard or non- standard) is frequently limited in these data sources. Indeed, it is only possible to identify part-time employment and temporary employment (not in all cases) within the non-standard forms of employment mentioned in the previous section. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["periodic surveys of households"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 7 of 51 In this sense, it is necessary to draw attention to the fact that the non-standard employment statistics presented below are not homogeneous among countries. In countries where both forms of non- standard employment were identified, we define non-standard employment as those occupations that satisfy at least one of the conditions, that is, either corresponds to a part-time occupation or a temporary job. In countries where temporary employment was not identified in the data, our non- standard employment category will coincide with part-time employment. Note that in either case, as other non-standard employment modalities are not identified, the indicators presented in this paper indicate a lower level with respect to the true dimension of the phenomenon. The only aspect addressed that required the use of additional information was the analysis linked to the profile of tasks that are developed in the framework of non-standard jobs. To carry out this analysis, the information available in the O * NET (Occupational Information Network) database was used in conjunction with the Household surveys. This database provides information referring to the content of tasks of the occupations. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Since 2003, O * NET data have been collected in the United States for approximately 1, 000 occupations based on the Standard Occupational Classification (SOC), and it has been updated periodically from then until 2014. 5 Following Acemoglu and Autor (2011), Hardy et al. (2015) and Apella and Zunino (2017), five measures of content or intensity of main tasks performed by workers are constructed: non-routine cognitive analytical and interpersonal, routine cognitive and manual and non-routine manual. The definition of the type of task performed by the worker is associated with the risk of automation and therefore its implication in terms of earned wage. While routine, and especially manual, are susceptible of automation, those non-routine tasks, especially cognitive tasks (both analytical and interpersonal) not only are not exposed to the risk of automation but also could be complemented by automation, increasing the productivity of the workers. 4. NSE trends in Latin America and the Caribbean and Europe and Central Asia As was mentioned in the previous section, the available data sources limit the statistical analysis presented below to two types of NSE: temporary employment and part-time employment. At the same time, it was not possible in all cases to identify the employees whose employment relationship is temporary. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 8 of 51 In the 9 cases mentioned, it was possible to identify part-time workers, while temporary workers were only identified in the cases of Argentina, Brazil, Chile, Mexico and El Salvador. 4. 1. 1 Variation of the NSE as a percentage of total employment The prevalence of NSE in the total employment has not shown very significant variations in the countries considered in the last two decades (Figure 1). Indeed, most of the countries analyzed show non-standard employment registers similar to those observed in the mid-1990s. The exceptions where the variation is a little more relevant are Brazil and Uruguay, where there are contractions in the incidence of the NSE of the order of 10 and 5 percentage points respectively and Mexico, where there is an increase of 5 percentage points. Figure 1: Prevalence of NSE among salaried employees. (Mid-90s / Mid-2010s) Source: Own calculations based on Household surveys Analyzing the prevalence of NSE by types of occupations, considering the ISCO classification at one digit, we find a quite similar pattern across countries. Indeed, in most of the considered countries, the “ Elementary Occupations ” are the category where the prevalence of NSE is higher. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 9 of 51 Figure 2: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) Source: Own calculations based on Household surveys When these results are analyzed by the type of NSE, we find significant differences between the trends in Part-time and Temporary employment. On the one hand, there is a stable or growing prevalence of part-time employment among the salaried employees, where Uruguay is the only exception, characterized by a decrease in the incidence of this type of employment (Figure 3). In the cases of Peru and Bolivia, we find almost the same prevalence of part-time employment as two decades ago while in the remaining countries (Argentina, Brazil, Chile, Mexico, El Salvador and the Dominican Republic) there is a greater prevalence of part-time employment. Likewise, the prevalence of temporary employment shows a downward trend in most of the analyzed countries in the last 20 years (Figure 4). In fact, three of the five countries in which temporary employment could be identified show a significant drop in the prevalence of this type of employment (Argentina, Brazil, and Chile). El Salvador presents a stable incidence of temporary employment, while Mexico is the only country in our sample for which there is an increase in this type of NSE. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 5: Part-time employment by age group. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Uruguay Brazil Chile Peru Bolivia Dominican Republic Mexico El Salvador > 64 years old 50 ‐ 64 40 ‐ 49 25 ‐ 39 15 ‐ 24 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 13 of 51 Figure 8: Temporary employment by gender. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys 4. 1. 2 Profile of Non-Standard Employment The next objective of this work is to investigate possible changes in the profile of non-standard employment, usually associated with lower productivity and greater vulnerability. The analysis of the employment profile was made based on access to social security benefits, education level, labor income and the task content performed by the workers. From the point of view of social security, we find that NSE shows a higher prevalence of informality compared with SE. For instance, the average prevalence of informality for our set of countries among NSE in the ending point of the study is 40 % while the prevalence among SE is 20 %. In this sense, a rise in the prevalence of NSE could be associated to a big set of workers without access to social security benefits. From a dynamic perspective, there is no a common trend across countries regarding the prevalence of informality among NSE workers (Figure 9). Indeed, several countries (Uruguay, Brazil, Chile, and Peru) registered a small decrease in the prevalence of informality among NSE but there is another set of countries for which the opposite is observed (Mexico, El Salvador, Bolivia and Argentina). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It is important to note that the changes in the prevalence of informality among SE workers between the starting and ending point of the study show a very similar dynamic compared with the observed dynamic for NSE (the prevalence of informality among SE workers is presented in the Annex II of the paper). Then, the trend in the prevalence of informality among NSE mainly reflects the overall trend of informality in the labor market instead of a specific characteristic of NSE. Figure 9: Prevalence of informality among NSE workers. (Mid-1990s / Mid-2010s) 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Brazil Chile Mexico El Salvador Female Male Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 14 of 51 Source: Own calculations based on Household surveys From the education perspective, an improvement in the profile of workers employed as NSE can be observed in the countries analyzed, even though there are some exceptions. In fact, in most of the countries of our sample, the prevalence of workers with secondary and tertiary education increases in detriment of workers with a lower educational level. However, there are some differences by type of non-standard employment. In the case of part-time employment (Figure 10), several countries show an obvious rise in the prevalence of workers with secondary and tertiary education (Argentina, Brazil, Peru, and Mexico). There is a second group of countries that presents a decrease in the prevalence of workers at the secondary level, but, this decrease is more than compensated by the greater incidence of tertiary workers, so that, taken together, workers with secondary or higher education increased their share within part-time employees (Uruguay and Chile). Therefore, we can also conclude from this group that part-time employment presents a better educational profile today than two decades ago. In the Dominican Republic and El Salvador, we find a rise in the prevalence of workers with secondary education but a decrease in the share of workers at the tertiary level. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 15 of 51 Figure 10: Education profile of Part-time employees. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys On the other hand, in the case of temporary employment, the improvement of the educational profile is more evident and generalized than in part-time employment (Figure 10). In fact, in all the cases in which temporary employment was identified, taken together, the share of workers at the secondary or higher education increased in proportion in the last two decades. The countries in which the improvements in the education profile are less deep are Chile, where the participation of workers with secondary education decreased in the last two decades, even though this decrease is more than compensated by the greater proportion of tertiary workers, and El Salvador, where the share of workers with tertiary education remains almost constant. 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % Argentina Uruguay Brazil Chile Peru Bolivia Dominican Republic Mexico El Salvador Incomplete primary (starting point) Incomplete primary (ending point) Primary (starting point) Primary (ending point) Secondary (starting point) Secondary (ending point) Tertiary (starting point) Tertiary (ending point) Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The only two exceptions are Chile and Peru in which the variance of the wage distribution of NSE shows a small decrease. 6As mentioned above, standard employment is calculated as the total number of employees who do not identify with any of the non-standard categories (temporary or part-time). However, since there are non-standard forms of employment not identified in the database, our standard employment category could, in fact, incorporate non-standard workers. 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % Argentina Brazil Chile Mexico El Salvador Incomplete primary (starting point) Incomplete primary (ending point) Primary (starting point) Primary (ending point) Secondary (starting point) Secondary (ending point) Tertiary (starting point) Tertiary (ending point) Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 25 of 51 higher followed by the “ Professionals ” and “ Skilled agricultural, forestry and fishery workers ”. On the contrary, “ Managers ”, “ Technicians ”, “ Craft and related trades workers ” and “ Plant and machine operators and assemblers ” are the types of occupations with a lower incidence of NSE in the region. Figure 16: Prevalence of NSE among salaried employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Figure 17: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) Source: Own calculations based on Household surveys. As in the case of Latin American countries, the observed prevalence of NSE across types of occupations suggests a strong heterogeneity across non-standard employees. Actually, as we will analyze in detail in next sections, the productivity and task profile of workers in the categories of “ Professionals ” and “ Elementary workers ” is very different, even though both types of occupations are characterized by a higher prevalence of non-standard employment arrangements. 0 % 5 % 10 % 15 % 20 % 25 % 30 % Russia Georgia Kyrgyz Republic Turkey Armenia Albania Moldova starting point ending point 0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % 40 % 45 % 50 % Managers Professionals Technicians Clerical Support Workers Services and Sales Workers Skilled Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 28 of 51 Figure 20: Part-time employment by age groups. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Figure 21: Temporary employment by age groups. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys A less clear picture is observed in the case of temporary employment. Indeed, in the four countries where temporary employees are identified, we observe different dynamics in the age profile. On the one hand, Kyrgyzstan and Turkey do not evidence significant changes in the age profile of temporary workers in the last 10 / 15 years. On the other hand, Georgia and Armenia present changes in the age profile but in opposite directions. Armenia shows today a higher share of the older groups within temporary employees while Georgia evidences a younger profile of temporary workers. Finally, analyzing the composition of non-standard employment by gender, we have a heterogeneous picture by types of non-standard employment in the levels but with a similar trend (Figure 22 and 23). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 32 of 51 Figure 25: Education profile of Temporary employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Note: SP indicates the starting point of the analysis and EP states de ending point. It should be noted, however, that, like in the case of Latin American countries, the improvement in the educational profile of workers is a generalized trend in the countries considered and cannot be considered a specific characteristic of non-standard employment since it is also observed in standard wage employment and self-employed workers. Statistics regarding the educational profile of standard employees are included in the Annex of the paper. When we analyze what has happened at the salary level in the period of consideration (Figure 26), two important conclusions emerge. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. This growth in wages is simply a consequence of the economic growth experienced by the economies. Note that this average wage increase is also observed in the counterpart of standard employment in all cases. 10 10As mentioned above, standard employment is calculated as the total number of employees who do not identify with any of the non-standard categories (temporary or part-time). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Page 36 of 51 In fact, part-time employment shows a more generalized trend towards non-routine cognitive tasks, where Russia and Georgia are the only exceptions (Figure 29). There is also observed a generalized growth in the intensity of routine cognitive tasks, Russia being the only exception. Finally, a less intense profile in manual tasks (both routine and non-routine) is clearly observed only in the cases of Albania and Moldova. The analysis of the task profile of temporary employees is much more limited since it only includes three countries. In these three cases, there is no observed a change towards a more intense profile in non-routine cognitive tasks. Indeed, only Kyrgyzstan shows a rise in the intensity of non-routine cognitive interpersonal tasks. Additionally, a lower intensity is observed in routine cognitive tasks in Georgia and Kyrgyzstan. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 MPI, constructed in Admasu et al. (2021), to capture the deprivations of forcibly displaced individuals and their gendered lives. The paper proceeds as follows. Section 2 reviews the existing literature to provide the background and motivation for the analysis, including a summary of the different country contexts covered by the case studies in this paper. Section 3 outlines the measurement strategy for deconstructing the MPI used for analysis and its limitations, followed by Section 4, which introduces the data. Section 5 presents the findings, first for deprivation results at the individual level and then results evaluating intrahousehold inequalities. Concluding remarks are discussed in Section 6. 2 Background and Literature Review 2. 1 Individual-level measures of gender and multidimensional poverty Individual-level analyses of multidimensional poverty have mostly centered around children, with various studies analyzing the relevance of indicators for children (aged 0- 17 years), 2 as well as other age ranges. The MPI has also been used to better understand gender issues, for example, Batana (2008) implemented a women ’ s MPI in Sub-Saharan Africa. Bhutan ’ s Gross National Happiness measures (2010, 2015), Vijaya et al. (2014), and Klasen and Lahoti (2016) are implemented at the individual level. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Women ’ s Empowerment in Agriculture Index (WEAI) (Alkire et al. 2012) uses individual-level data, and the linked Gender Parity Index reflects inequalities across women and men ’ s deprivation scores within the same household. Alkire, Apablaza and Jung (2014) design and implement an exploratory individual-level MPI for 31 European countries over six waves of data using EU-SILC data sets, finding no cases in which are women significantly less poor than men, and in many cases, they are significantly poorer. Espinoza-Delgado and Klasen (2018) create an individual-level MPI to understand differences in poverty between women and men in Nicaragua, finding similar overall incidence, but much higher intensity of poverty among women. Bessell (2015) and Pogge and Wisor (2016) explore deeply contextual gendered poverty measures and elucidate the ways that participatory consultations can inform the design and uses of gendered measures. Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa. Alkire, Ul Haq, and Alim (2019) use individual-level data alongside MPI data to expose gendered and intrahousehold differences among MPI poor and non-poor children. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["EU-SILC data sets"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 In this study, we apply their techniques to understand gender differences among adults as well. 2. 2 Intrahousehold analyses of multidimensional poverty The literature on multidimensional poverty measurement and intrahousehold analysis is limited. Espinoza-Delgado and Klasen (2018) propose an individual-based multidimensional poverty measure for Nicaragua and estimate gender gaps in headline statistics. Klasen and Lahoti (2016) question the neglect of intrahousehold inequality in multidimensional poverty indices by comparing a standard household-level MPI and an individual-level MPI to the MPIs proposed by Alkire and Santos (2014) and UNDP (2014), finding that females recorded a far higher poverty rate when using the individual measure and that age differentials in poverty were also larger. We follow their work of investigating poverty in the indicators for which individual data is available and compare the achievements of men and women and boys and girls living together. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This allows us to avoid relying on gender of the head to derive conclusions about gender gaps, while also recognizing the high prevalence of female-headed households among the displaced and large differences across countries between households based on the gender of the head (Admasu et al. 2021). 2. 3 Country contexts Sub-Saharan Africa is one of the most conflict-affected regions in the world, with one- third of the total number of conflicts taking place in the region (Pape et al. 2018). At the end of 2018, IDMC estimated that 16. 8 million people in Africa were internally displaced because of conflicts and violence, which amounts to 40 % of the global number. All five countries included in this study are among the most conflict-affected in the region and rank in the top 12 countries with the highest number of conflict-induced displaced population (Pape et al. 2018). East Africa (which includes four of the five countries considered in this study) is the sub-region with the largest number of internally displaced people and refugees, accounting for 22 % of the global number. Ethiopia, Somalia, and South Sudan are three of the top five countries in the region with the greatest number of new displacements since 2019 (IDMC 2020). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 3 Methodology 3. 1 The A-F method and individual deprivations The Multidimensional Poverty Index (MPI) used in this paper was first presented, with a full methodological discussion, in Admasu et al. (2021). Here we present a general overview of the measure for the individual-level and intrahousehold analyses. The MPI is constructed based on the Alkire-Foster (AF) method of multidimensional poverty measurement (Alkire and Foster 2011). Three key statistics characterize any MPI: incidence or headcount ratio (H), which is the proportion of the population who are multidimensionally poor; intensity (A), which is the average share of weighted indicators in which multidimensionally poor people are deprived; and adjusted headcount ratio (M0 or MPI), which is the product of the incidence and intensity (MPI = H × A). The AF method uses a dual-cutoff counting approach to poverty measurement. Having fixed relative weights across indicators that sum to 100 %, it first identifies who is deprived in each indicator, then sums up the weighted deprivations each person experiences into a deprivation score. A person is identified as poor if their deprivation score meets or exceeds a cross-dimensional poverty cutoff that is greater than 0 and less than or equal to 100 %. It then aggregates this information to compute society-level MPI, incidence, and intensity. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The MPI can be decomposed by any groups for which the data are representative and broken down by indicator to show the composition of multidimensional poverty, adding to the policy relevance of the analysis. To tackle individual-level and intrahousehold analyses, we build on the work of Alkire, Ul Haq and Alim (2019). The focus is on individual deprivations, and we call the persons with individual-level data in each indicator the eligible household members. For example, children aged 6-16 years might be eligible for deprivations in terms of school attendance, but not those older or younger. For individual-level indicators, we identify who and how many household members are deprived: their gender and their age, and what proportion of eligible household members are deprived. This is a powerful and potentially informative steppingstone for analysis. Consider two households, each of which has five eligible members with data on nutrition. The aggregation rule in this example is that if any household member is undernourished then the household is undernourished. So, both households are deprived in terms of Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The pregnancy care indicator is also excluded from the intrahousehold inequalities analysis as the reference populations for the analysis did not permit rigorous statistical testing. 3. 3 Limitations With a few exceptions, gendered MPIs have been designed using indicators that are present in standard survey instruments, which themselves struggle with normative challenges (Alkire 2018). To create improved gendered MPIs, in which people ’ s poverty can be compared across gender and age or the life cycle, research must develop “ comparable ” definitions of capability deprivation that matter to people in different age cohorts or different life situations. Reliable indicators comparing men and women ’ s income, ownership of assets, and decision-making powers in the same household are difficult, as are those measuring decent work. Health indicators also differ by gender, change across the life cycle, and vary across family structures and disability status. The MPI constructed in Admasu et al. (2021) has the same weaknesses as these measurement paradoxes, but it remains a step in the right direction. We aim to mitigate the limitations of this household-level measure by unpacking the deprivations of indicators available at the individual level, disaggregating those deprivations by gender Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "24 According to Table 12, all countries but Sudan show a significant relationship for school- age children ’ s experience of intrahousehold inequality and their displacement status, although sample sizes mean that only Nigeria and Somalia ’ s results are robust. In Nigeria, most children experiencing intrahousehold inequality reside in non-displaced households – although it is crucial to note that these children constitute most of the school-age children (71. 1 %), and the levels of intrahousehold inequality are nonetheless far higher than anticipated if displacement status had no effect. In Somalia, displaced children are significantly more likely to experience intrahousehold inequality in school attendance, as they constitute 63. 5 % of school-age children experiencing intrahousehold inequality even though they only make up 35. 3 % of the school-age children population in the sample. For the years of schooling indicator, the overall lack of intrahousehold inequality among the MPI poor in years of schooling obscures meaningful or robust differences by displacement status. Gender and displacement status appear to jointly have significant impacts in school attendance in Ethiopia, Somalia, and South Sudan. In Northeast Nigeria, it appears that displacement status has larger effects than gender. In Somalia, forcibly displaced school children experience intrahousehold inequality more often than non-displaced children, to the disadvantage of girls. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Refugees and Host Communities Household Survey expanded the national Household Consumption and Informal Sector Survey to include a representative sample of refugees and host communities, including Sudanese and host communities located in the east of the country. The remainder of this note is organized as follows. Section 2 presents a short discussion of the literature on the economic participation of refugees. Section 3 compares the characteristics of newly arrived refugees from Sudan with previous arrivals for whom survey data is available, to find that both groups are highly comparable. Section 4 uses the existing data to explore how the basic needs refugees are covered from own-income. Sections 5 and 6 dig deeper by exploring econometrically the correlates of higher incomes of refugees. A discussion of the results and their policy implications follows in section 7, after which section 8 concludes. 2. Benefits of economic participation of refugees Whether or not the arrival of Sudanese refugees in Chad contributes to economic growth is of limited immediate relevance as concerns about the safety of fellow humans drive the response. Nor does any decision maker suggest that hosting refugees is a development strategy Chad should pursue. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Refugees and Host Communities Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "But, given the presence of thousands of refugees in the country, the constrained fiscal space the authorities face, and the likelihood that international assistance for refugees will taper in the future, an imminent policy question is how to ensure that refugees can be hosted in a sustainable manner, without becoming a fiscal burden in the future. The fear that refugees are (or might become) a fiscal burden is driven by a broadly held perspective about forcibly displaced persons in general, and refugees in particular, namely that they are humanitarian subjects, vulnerable and worthy of public assistance (Betts and Collier 2017). This perspective is not universal, however. The economic contributions of refugees have been extolled for years, from posters 1 https: / / www. ecoi. net / en / file / local / 2091861 / 645b938a4. pdf. 2 Enquête sur la Consommation des ménages et le Secteur Informel au Tchad. The survey was carried out jointly with the National Statistics Office (Institut national de la statistique, des études économiques et démographiques, INSEED) and the UNHCR in Chad. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. Introduction Violent conflict is one of the most important development challenges facing the world today. The incidence of wars has decreased in recent years (Harbom and Wallensteen 2009). However, the legacy of violence persists in many regions, affecting millions of men, women and children (Geneva Declaration Secretariat 2008, UNHCR 2008). The economic, political and social consequences of violence are far-reaching. Violent forms of conflict kill, injure and displace people, destroy physical capital and infrastructure and change the ways in which societies are organized. These effects will have considerable consequences for the long-term human capital accumulation of populations exposed to violence. This is well visible in the fact that no conflict-affected country will reach the Millennium Development Goals by 2015 (DFID 2009): conflict-affected countries contain one-third of those living in extreme poverty, and are responsible for almost one-half of child mortality in the world (Collier, 2007, DFID 2009). They also account for 42 % of all out-of-school children (28 million children), even though only 18 % of all children in the world of primary school age live in conflict-affected countries (UNESCO 2011). The objective of this paper is to examine one important channel linking violent conflict and development outcomes: the level and access to education of children living in contexts of conflict and violence. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 household displacement and the destruction of household dwellings during the violent events. We are also able to measure conflict intensity across time – including peaks of violence at various stages of the conflict – at the district level from event data on violence intensity during the conflict in Timor Leste. We focus on primary school effects because only a small percentage of the Timorese population attended secondary school. Our results show mixed evidence for the impact of violent conflict on educational outcomes. Mirroring some of the findings of Bellows and Miguel (2006) and others, we find evidence for a rapid recovery of the education sector in Timor Leste, and of educational outcomes, particularly for girls. However, in line with emerging results in the micro-level literature, we find that the 1999 wave of violence in Timor Leste – as well as peaks of violence in the 1970s and 1980s – resulted in negative effects on primary school attendance and attainment. This effect is particularly strong for boys. We attribute the first result to a process of educational catch-up among girls in Timor Leste that started before the conflict and continued despite the conflict. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["event data on violence intensity during the conflict"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The second result is likely to be due to large rates of grade repetition and of delay entry that were exacerbated by the need to remove boys from school due to the negative economic effects of the conflict on households more exposed to the violence. The paper is structured as follows. In section 2, we present a literature review on the impact of violent conflict on development outcomes in general and education in particular. Section 3 provides a descriptive background of the conflict in Timor Leste and the country ‘ s education sector. In section 4, we describe the datasets, discuss our identification strategy and present some descriptive results. Section 5 discusses our empirical results, as well as a range of robustness checks. Section 6 concludes the paper. 2. Literature review An emerging body of literature has provided valuable empirical evidence on the effects of violent conflict on income and consumption levels, and more generally on the welfare of populations living in areas of violence (Ibáñez and Moya 2009, Justino and Verwimp 2006, Verwimp and Bundervoet 2008). A significant number of studies have also examined the health impact of violent conflict, finding that violence results in negative health effects in terms of lower height-for-age and lower nutritional outcomes among children that will generate long-term consequences on future outcomes. 2 2 See Alderman et al. (2006) for Zimbabwe, Bundervoet et al. (2009) for Burundi, Akresh and Verwimp (2006), Akresh, Verwimp and Bundervoet (2007) and Akresh and de Walque (2008) for Rwanda and Guerrero-Serdán (2009) for Iraq. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 The evolution of education in Timor Leste has been characterized by three distinct periods, coinciding with (i) the Portuguese colonial rule (from early 1500s to 1975), (ii) the Indonesian occupation (from 1975 to 1999) and (iii) the UNTAET administration (from October 1999 until independence in May 2002). Under Portuguese colonial rule, education was administered via the Catholic Church. Churches were the major providers of education and schooling was mostly available to the elite in urban areas. When, in 1975, Indonesia invaded the country, literacy rates were extremely low, at around five percent (UNDP 2002). Gender disparities were also very large. The Indonesian government planned to expand education access to the whole population of Timor Leste. Education was used as a means to control the population, and the Portuguese and Tetum languages were abolished. Under the Indonesian education system, children had to enroll in primary school by the age of 7, and were supposed to finish primary school at 12 years old (grade 6). In 1994, basic education was made compulsory up to low secondary school (nine grades of education up to age 15). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The data shows that cohorts that were of school age during the Indonesian occupation achieved higher level of education than older cohorts (i. e. those born in the 1970s compared to those born in the 1960s or before) testifying for an increasing trend as expected. The figure shows that despite the increasing trend a large fraction of individuals have low education levels. Interestingly, all curves start to drop after the 1987 cohort. This decreasing trend is observed among individuals aged 20 or younger in 2007 and provides evidence of a mismatch between the grade attended and the grade that they should have achieved at their age. 4 This is caused by a persistent sluggishness in grade achievement due to the high level of delayed entry to school and high rates of repetition. 5 The impact of the conflict in its different phases and the subsequent reconstruction efforts on schooling levels of children in Timor Leste is therefore unclear. The early years of violence coincided with an education for all policy in which quantity was preferred to quality. In addition, the 1999 violence that followed the withdrawal of Indonesian troops led to the destruction of schools and the removal of children from school. The reconstruction program implemented after 1999 tried to counteract this destruction, and achieved fast progress. However, the education sector was still in very poor shape. In the next section, we investigate in more detail the effects of the conflict on educational outcomes of boys and girls in Timor Leste. 4. Identification strategy and data description 4 Those born in 1992 are 15 in 2007. So they might have at most completed grade 9 and this justifies part of the drop in the curves as the grade completed is right censored. 5 The high levels of school delay are also confirmed by the figures on gross and net enrolment ratios calculated using the TLSS 2001 and 2007: primary gross enrolment ratio was 105 percent in 2001 and 128 percent in 2007, while net enrolment ratios were 74 and 94 percent, respectively, in 2001 and 2007. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["TLSS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Commission for Reception, Truth and Reconciliation (CAVR). 7 This information has been collected from deponents to the Commission ‘ s statement-taking process. 8 We make use of data on the number of killings that occurred during the war in order to derive patterns and variation of violence in Timor Leste over time and across space. We use this data to identify districts and years that experienced high and low violence-intensity, both at the start of the occupation and following the withdrawal of Indonesian troops in 1999. This allows us to estimate both the impact of the first years of the conflict and the impact of the last wave of violence in 1999. 4. 1. Identification strategy: The impact of violence on school attendance in 2001 We first investigate the short-term impact of the 1999 violence. The empirical questions being addressed are: (i) whether the violence in 1999 imperiled school attendance9 and school grade deficit, and (ii) whether different channels of exposure to conflict – displacement and house destruction – affected boys and girls and different age groups differently. 4. 1. 1. Primary school attendance and grade deficit rates in 2001 We make use of information in TLSS 2001 collected at the individual and household levels on displacement and house destruction to identify conflict-affected individuals. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["TLSS 2001"], "descriptive_data": ["data on the number of killings that occurred during the war"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In order to address these potential sample biases, the CAVR supplemented its documentation with reports produced by Amnesty International and Fokupers (a local NGO). The information contained in these reports was then included into the HRVD database. 9 Note that we do not analyse school completion in 2001 because most children that were of school age in 1999 were still in school in 2001. 10 The questions we used are ― Was [NAME] displaced outside E. Timor in 1999? ‖, and ― Was the [BUILDING] damaged in the violence of 1999? ‖. 14 % of the whole sample surveyed in 2001 report having been displaced, while 26 % report that their house was destroyed. Within our sample of school age children, these figures are 16 % and 25 %, respectively. We have made sure that buildings that are reported to having been destroyed were used for living purposes only. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["HRVD database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 4. 1. 2. Empirical strategy In order to make use of the retrospective information on school attendance provided in the dataset, we have constructed what we have called an ‘ ad hoc panel ’, whereby we exploit the time-variation of the variables of interests (age, attendance status and grade attained by the respondents) by reshaping the cross-sectional structure of the TLSS 2001 dataset. In this way, we are able to obtain observations for each individual over three academic years. All key education variables are time- variant, while other individuals and households characteristics are time-invariant. Within these three years, we focus our analysis on individuals that were of primary school age (between 7 and 12 years old) in each year. In practice, we keep all children aged at minimum 7 years old in 1998 and at maximum 12 years old in 2000. As a consequence, our panel data contains children aged 8-11 years in 1999, the year of the violence. 11 Since we are interested in looking at different effects across groups of individuals, we have split the sample between boys and girls and between younger children (aged 8-9 in 1999) and older children (aged 10-11 in 1999). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["TLSS 2001 dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We estimate the following equation: [2] where are our outcome variables, i. e. attendance rate (which is a binary variable12) or grade deficit. and are year dummies respectively for year 2, the year of the 1999 violence, and for year 3, the first year of the post-conflict period. The model includes individual fixed effects,. The variable is the random error. All standard errors are clustered at the village level. As discussed above, we identify violence-affected individuals using two different measures,, with j = 1, 2 depending on which measure is included in the specification. The first one is whether the individual was displaced with the whole household. The second is whether the individual reports that her house was completely destroyed during the 1999 violence. We allow the violence measure to interact with both year dummies. The estimation of our specification above is 11 We have also tried to keep a larger sample that includes those children in primary school age in the year of the violence (i. e. between 7 and 12 in 1999). This means including individuals aged 6 in year 1 and aged 13 in year 3. The inclusion of these latter individuals may generate ‗ spurious ‘ results as they are not of primary school age. We have estimated the model using both samples. We find that the estimates using the larger sample are similar to those obtained with the sample of children aged between 8 and 11 in 1999. The larger sample generates more statistically significant results but we have decided to opt for the most restrictive sample to avoid inclusion of ‗ tails ‘ of the age distribution that are not of primary school age. 12 We estimate our model with a linear probability model. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 of intense conflict. We examine also the overall impact of the 25 years of conflict on educational outcomes. This analysis allows us to consider the full long-term impact of the conflict on educational outcomes of different generations of children in Timor Leste. 4. 2. 1. The educational impact of the 1999 wave of violence We exploit variation in the number of killings over time and across districts to identify conflict affected individuals. Our intention here is to analyze whether individuals exposed to the violence during their primary school age show different primary school completion rates eight years after the end of the war, relative to those not affected by the conflict. The outcome variable in which we are interested is whether individuals completed primary school in 2007. Figure 7 shows average primary school attainment for all individuals in our sample. The graph shows an increasing trend in primary school completion across cohorts and a progressive reduction of the gender gap. The gap among the younger cohort (those born after 1987) is almost zero. The drop in the curve for the younger cohort confirms the presence of significant delays in school attendance. For the purpose of this analysis, we use the TLSS 2007 dataset and the HRVD dataset contained in the CAVR data publication. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["TLSS 2007 dataset", "HRVD dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As explained in section 4, the HRVD dataset contains data on the number of human rights violations occurred since the start of the conflict in 1975 until its end in 1999 for each district. The types of violations recorded are killings, deaths due to deprivation and disappearances. We use only the number of killings to identify years and districts affected by the conflict. We exclude the deaths due to deprivation because the districts in which this occurred may very likely not be those where the conflict was most intense, but were simply places were the victims were hiding as a consequence of escaping from the troops, and died for starvation. In addition, since killings are less likely to affect entire families than deaths due to deprivation, there is a lower underreporting bias attached to the former measure relative to the latter one (Silva and Ball 2006). We also exclude disappearances as, according to HRVD data, they do not show enough time and geographical variation in order to identify individuals more or less exposed to the conflict. We believe that the number of killings proxies quite well the intensity of the conflict across time and space as their occurrence largely tracked the movements of the Indonesian military operations. The other two types of violations do not seem to show the same pattern (Silva and Ball 2006). For the same reason, we believe that it proxies quite well the destruction of houses and infrastructure and the displacement of people given the way in which the last wave of violence occurred (i. e. the scorch-earth technique employed by Indonesian troops as they moved towards West Timor). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["HRVD dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, boys can get caught by adverse impacts of violence in ways that still remain under-researched. The evidence for Timor Leste suggests that boys were very vulnerable to the educational effects of violence. This result implies that much more attention must be paid to understanding how children are affected by violent conflict and the different roles girls and boys assume during and after the conflict. The third implication is that conflict is not a uniform phenomenon. Violent conflict affects different people and different aspects of their welfare through different channels. In the case of Timor Leste, and in line to evidence from other conflicts such as Colombia (see Ibáñez and Moya 2009 for instance), displacement has particularly adverse effects on educational outcomes. The recently released Education for All Global Monitoring report by UNESCO (2011) portrays displaced populations as the hidden victims of conflict. A significantly disproportional number of displaced children are out of school (even in comparison to conflict-affected populations in the same country), while enrolment rates among displaced populations across the world average around 69 % for primary school and 30 % for secondary school. The analysis of Timor Leste confirms the extreme disadvantage that displaced populations face in terms of lost educational opportunities. This is likely to affect generations of boys in Timor Leste, possibly perpetuating the risks associated with renewed conflict in the future. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 implemented the program in Irbid and Mafraq Governorates, which host over 47 % of the Syrian refugees in the country. In Lebanon, the programs were implemented in Zahle, West Bekaa, Chouf, Jezzine and Saida where one-third of the population are Syrian refugees live. Participants enrolled in courses that aligned with their private interests as well as market demand and sectors in which refugees could legally work. Courses were implemented by local training providers and lasted two to eight weeks. Topics included aluminum fabrication and installation, woodworking and carpentry, food and dairy processing, electrical repair, beautician, light construction rehabilitation, mechanical repair, artisanal manufacturing, greenhouse maintenance, and drip irrigation installation and repair. Although a small number of sessions trained only members of one nationality — partially due to employment restrictions-- a majority mixed host-refugee groups. On average, each group contained an approximate mix of 65 % hosts and 35 % refugees. Theoretical Motivation: Literature Review: Jobs programs are often utilized to not only promote economic outcomes, but also social cohesion goals. As delineated in the World Development Report 2013, there are two main pathways for jobs to promote social cohesion. One pathway is indirect. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 this oversubscribed list. Selection into the training group was based on a “ vulnerability score ” that gave priority to younger, female and unemployed individuals. Despite this approach, intake was “ fuzzy ”- participants were ordered by their vulnerability score, with the most vulnerable entering up until capacity. In some intakes, individuals with comparatively high scores were not taken into the program. In others, individuals with comparatively low scores were included. We construct our treatment and control groups from these intake decisions. Data were collected from members of the host and refugee communities in each country. 4 In both Jordan and Lebanon, the intervention was implemented on a rolling basis. As soon as one training cycle was completed, another would begin. Data were collected in three waves during each training cycle. First, during an “ outreach ” phase, where data were collected in order to assign treatment status. Second, at “ baseline ”, which occurred before the training had begun but after treatment assignment was known. Third, data were collected at “ endline ”, immediately following the end of the training. Data collection for those assigned to the treatment and control followed the same pattern. 5 Outreach and baseline data collection took place less than a week apart and were collected between July 2018 and September 2019. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Between outreach and baseline, one full survey round was collected due to potential survey fatigue and on the understanding that nothing of importance would likely change in such a short period. Basic demographic information, such as age, gender, marital status and employment status were collected at outreach. At baseline, additional indicators were collected, relating to the behavior, attitudes, opinions and personalities of the participants. The only exception to this is data on optimism, which were collected at both outreach and endline. This allowed us to test whether or not the intake decision had effects, even before the training began. Endline data were collected between July 2018 and November 2019 and repeated the combined outreach and baseline surveys and experiments. Variables: We collected a range of survey and experimental indicators in order to assess our key research questions and associated hypotheses: 6 Economic and life optimism: We collected two survey questions about optimism at outreach, baseline and endline. These questions ask individuals to rank their expectation that their life and economic situation will be better in one year than it is now. Answers are 4 In addition, data were collected from Palestinian Refugees in Lebanon (PRL). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["data on optimism"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Due to our research design and the complexity of introducing a third grouping who would play outgroup in the behavioral experiments regardless of partner identity, we exclude PRL from the main analyses presented here. 5 Data were also collected six months after the end of the training but this was heavily disrupted due to the outbreak of the COVID-19 pandemic. In Lebanon, this resulted in a change to the method of data collection (from in-person to telephone) and in Jordan, an end to data collection entirely. In Jordan, this had a more pronounced effect on the control group, due to the scheduling of data collection and implementation of restrictions in Jordan. Given these complexities, we do not present results from these analyses. 6 In addition, we attempted to collect information on the extent of social and economic interactions between hosts and refugees. At baseline, almost 95 % of respondents in both the treatment and control group reported such interactions. For this reason, we do not include this information in these analyses. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Intergroup behaviors: We collect data from two one-shot incentivized behavioral games: the dictator game (a division game where players choose how to split a prize) and stag hunt (which gives players the chance to cooperate). 7 In each wave of data collection, players were randomized, at the session-level, to play either with a partner from the host or refugee community in that country. For example, a Lebanese player could be paired with a Lebanese or a Syrian resident in Lebanon but not with a Jordanian or a Syrian resident in Jordan. Partner identities were re-randomized between the waves so that not all players played with a partner of the same identity in both rounds. We made clear that partners were not individuals in the same room and, at endline, that the partner was not the same partner from baseline. A hint was given about the partner ’ s identity based on dialectic differences in the words for common foods, along with a small amount of innocuous information (approximate age, favorite hobby and marital status). 8 Sample intakes and partner assignments by data collection wave are shown in Table 1. This prime relies on a minor, and subtle, difference in dialects in settings with an otherwise high degree of cultural similarity. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 the region. Third, again due to cultural closeness, the nature of these variations is known across the region. 9 Additional measures: We collected usual socio-economic and demographic information, including: age, gender, marital status and education. In addition, we collected data on self- reported risk preference and a short-form personality survey to ascertain GRIT among participants. Noting that a small number of participants did not answer all survey questions, we undertake a regression-based data interpolation process to complete the dataset. 10 Table 1: Partner Assignment and Sample Sizes by Treatment and Community Status Host Refugee Ingroup Outgroup Ingroup Outgroup T C T C T C T C Outreach / Baseline 219 48 203 48 147 72 147 49 Endline 179 34 222 37 148 45 133 51 We present summary statistics of demographic data and other covariates for the baseline (Top) and endline (Bottom) for Jordan in Table 2 and for Lebanon in Table 3. [TABLES 2 AND 3 ABOUT HERE] Identification: The “ fuzzy ”, treatment intake is not random. As can be seen in Table 1, there are some elements of attrition from the sample. The sample decreases by about 10 % from baseline to endline. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 employment status and education level also significant at 10 %. Marital status and education are important predictors of attrition. As we might expect, these imbalances suggest some threats that, if left uncorrected, could undermine the parallel trends assumption of difference-in-difference estimators. That said, we see no sign of differences between treatment and control, or attritors and non-attritors, over the key GRIT personality features. This suggests that members of the treatment group are not, for example, more motivated to succeed than members of the control group. To account for these biases, we generate a series of inverse probability weights to balance the data. These weights define the probability of an individual with particular characteristics (e. g. host or refugee status) being in each of the treatment and control groups at baseline and endline and are used to rebalance the data in order to closer support the parallel trends assumption. Results are shown in Column 3 of Table 4. Following weighting, data balances on all key factors, including nationality. This suggests that the parallel trends assumption is more reasonable under the weighted dataset than in the raw treatment / control data. 11 Based on these analyses, we conclude that it is safe to use weighted OLS-based approaches. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10099 Situations of forced displacement create unique challenges for social cohesion because of the major disruption of social dynamics among both displaced persons and host communi­ties. This paper uses a sequential mixed method approach to analyze the relationship between hosting displaced persons and perceptions of social cohesion in eastern Democratic Republic of Congo. First, participatory research methods in focus groups empowered participants to pro-duce a locally driven definition of social cohesion. The results from these exercises inform the quantitative assessment by dictating measurement strategies when analyzing original surveys. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Combining almost 50, 000 responses to 11 cross-sectional surveys between 2017 and 2021, displacement is neg­atively associated with perceptions of social cohesion in aggregate. But at the individual level, those who report hosting displaced populations in their communities often have higher perceptions of social cohesion. These results are strongest among respondents who self-report hosting IDPs as opposed to refugees, but important heterogeneity across indicators, local context, and gender should guide policy meant to promote social cohesion in forced displacement. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at ppham @ hsph. harvard. edu. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["cross-sectional surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "itative exercise empowered focus group participants to guide the research and conceptualization. The results from these exercises then dictating the definition and measurement strategy for social cohesion in the quantitative assessment. The participatory research strategy was based mainly on structured focus group discussions. The project conducted consultations in seven territoires with the objective to develop localized understandings of what elements were important to social cohesion in eastern DRC. Participants were selected from civil society and the public sector. 96 individuals participated in these exercises (Table 1), 55 % of whom were men and 45 % of whom were women. 6 Location (Groupement) Date Number Participants Goma City 06 Oct 2017 11 Bukavu City 13 Oct 2017 13 Nyabibwe (Kalehe) 12 Oct 2017 14 Ishungu & Lughendo (Kabare) 12 Oct 2017 15 Kamisimbi (Walungu) 16 Oct 2017 18 Wassa (Walikale) 20 Oct 2017 12 Biiri (Masisi) 03 Nov 2017 13 Table 1: Descriptive Information on Focus Groups The focus group discussions began with an open discussion on social cohesion designed to ascertain participants familiarity with the concept. The facilitator further asked participants to write down words or concepts participants related to social cohesion. These words were written on individual post-its, which were then posted on a wall. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Participants then grouped words, building a concept map to visually represent their common understanding of social cohesion. Following the development of the concept map, the facilitator broke participants into smaller groups and were asked to conceive of a fictional yet realistic person that exists in their contexts. 6Focus groups were conducted in Swahili and French. Focus groups were transcribed to French for analysis. 11 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In aggregate, we expect to observe a negative relationship between the percentage of the population that reports displacement, but at the individual level we expect that experience with hosting may, in certain circumstances, be positively associated with perceptions of social cohesion, regarding perceptions of relationships and solidarity. 4 Displacement and Social Cohesion: Survey Evidence To empirically evaluate the relationships between hosting displaced populations and social cohe- sion, this paper analyzes a series of surveys of civilian adults conducted in eastern DRC. 8 Each survey uses a multi-stage cluster sampling strategy capturing all territoires9 in North Kivu, South Kivu and Ituri provinces. The final sampling units are randomly selected adults above the age of 18 to avoid bias toward men and / or heads of households. Multiple attempts are made over the course of one day to contact selected respondents and if necessary, appointments are made for in- terview. Surveys are enumerated by Congolese college students or professionals and interviews are conducted by members of the same gender and ethnicity as respondents to minimize enumerator- induced response bias. Further methodological details have been published (Vinck, Pham, Bindu, Bedford & Nilles 2019) elsewhere and additional details and sample size calculation are detailed in Appendix. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["surveys of civilian adults"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "groupement level data cannot measure the character of the displacement in any meaningful way, but analysis at the individual level can. 5. 2 Individual Level Relationships Two survey waves posed additional questions on local displacement dynamics. While these ad- ditional questions restrict comparison with other survey waves, they provide the opportunity to unpack the mixed results found in the aggregate analysis. Poll 14 is a special survey that only sam- ples cities (Ville de Goma, Ville de Beni, Ville de Butembo, Ville de Bukavu, Ville d ’ Uvira, Ville de Bunia and Irumu in particular) while Poll 15 is a representative sample of all territoires in the three provinces. 11 These survey waves are labeled as “ Cities ” and “ General ” samples in the indi- vidual analysis. Analyzing these two surveys together enables the comparison of relationships by the local context, which may distort the impact that hosting has on perceptions of social cohesion. Figure 5 plots the coefficients from series of logistic regressions to account for the binary na- ture of the dependent variables. The regressions include Province fixed effects and groupement clustered standard errors to account for unmeasured context-specific dynamics. Responses are weighted by the inverse proportion of selection at the territoire in each regression. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The relation- ships between displacement dynamics and perceptions of each manifestation of social cohesion are analyzed separately by running 12 models for each independent variable (6 regressions for each sample). The regressions are correlations and should not be interpreted causally. Hosting status and displacement flows are likely related to perceptions of social cohesion in indirect ways and the structure of the survey data limit the ability to specify the channels through which these relation- ships run. Each regression controls for characteristics that may influence respondents ’ perceptions of social cohesion outside of the presence of IDPs or refugees in the local community such as province, gender, age, marital status, level of education, employment, and exposure to violence. 11Poll numbers correspond to the number wave in our larger project, as described and shown in Table 2. 28 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The paper extends the GTAP 8 database by separating Lebanon, Jordan, Syria, Iraq, and West Bank and Gaza from the rest of the Western Asia aggregate and Algeria and Libya from the rest of North Africa. Kuwait, Qatar, Bahrain, Saudi Arabia, UAE, and Oman are aggregated into a GCC composite group. In addition, the 57 sectors in the GTAP 8 database are aggregated into 22 sectors based on their importance for the countries in the MENA region (Table 1). The resulting MENA-specific database contains 26 countries, among which are the six Levant economies of interest in this paper (Turkey, Lebanon, Syria, Iraq, Jordan, and Egypt) and the rest of the developing MENA countries (Table 1). The procedure used to construct the individual country information employs data from several sources. The UN Statistics Division data for 2007 is the source for the six components of GDP – agriculture, hunting, forestry, and fishing (ISIC A-B); mining, manufacturing, and utilities (ISIC C-E); construction (ISIC-F); transport, storage, and communication (ISIC I); wholesale, retail trade, restaurants and hotels (ISIC G-H); and other activities (ISIC J-P). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["GTAP 8 database"], "descriptive_data": ["MENA-specific database"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The updated database from the pre-simulation analysis, which represents an integrated Levant in a peaceful alternative world, is the starting point for the simulation analysis of the Syrian conflict and the spread of ISIS as well as the disintegration of the deep regional trade ties. The design of the war and disintegration scenarios are presented in section 3. 2. 11 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the degree to which the global response should include a development element. We find that the average stood at around 10. 3 years at the end of 2015, with a median duration of 4 years, and significant sensitivity to a few situations. Such numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete protracted situations. To produce these numbers, we rely on the Population Statistics Database compiled and main- tained by UNHCR. The database records the number of “ persons of interest ” to UNHCR in each year since 1951 and for each situation, where a situation consists of a pair host-origin countries. The calculation of duration of exile is obtained under a no-turnover assumption, whereby a de- crease in the number of refugees for any given situation is fully attributed to exits from refugee status, while increases are assumed to be fully accounted for by new cases. Although such ap- proach tends to over-estimate the true duration of exile, the lack of individual-level data on regis- tration precludes refining the estimate further. Attempts to estimate similar statistics have been limited. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Population Statistics Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In a 2004 note to its Executive Com- mittee, UNHCR established the average at 17 years at the end of 2003 (Executive Committee of the High Commissioner ’ s Programme 2004). This number has been widely quoted by media, ac- tivists, humanitarian agencies, and development institutions (Milner 2014; United Nations 2016; UNHCR 2015). The rest of the paper is organized as follows. Section 1 gives some definitions and background information on the refugee population. In section 2, we provide some summary statistics from our main source of data, the UNHCR Population Statistics Database. Section 3 describes the method followed to construct duration statistics and presents a few stylized facts. The results of our anal- ysis are presented in section 4. Section 5 concludes. 1 Background: Definitions and Data Under the terms of the 1951 Convention Relating to the Status of Refugees – henceforth the Convention – later amended by the 1967 Protocol, a refugee is a person, who “ owing to a well-founded fear of being persecuted for reasons of race, religion, nationality, membership of a particular social group or political opinion, is outside the country of his nationality, and is unable to, or owing to such fear, is unwilling to avail himself of the protection of that country. ” Data on refugees and asylum seekers are collected by individual countries, international orga- 3 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["UNHCR Population Statistics Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "nizations such as UNHCR, and national and international non-governmental organizations. Data is compiled from a number of sources, including but not restricted to individual registration of refugees and asylum seekers (information typically includes name, gender, date of birth, country of origin, marital status, and place of displacement), tracking of population movement in situa- tions where the movement is fluid or continuous, standardized surveys such as Living Standards Measurement Study (LSMS) surveys, Labor Force Surveys (LFS), Demographic and Health Sur- veys (DHS), and Multiple Indicator Cluster Surveys (MICS), administrative records and registries. Yet, data collection is a difficult exercise, due to both methodological issues (UNHCR 2014) and practical challenges, especially in situations of heightened insecurity or mass refugee situations. To date, UNHCR maintains the most comprehensive statistical database under a uniform methodology. UNHCR publishes annual data on refugee flows and stocks by countries of resi- dence and origin dating back to 1951, shortly after the Office was established. UNHCR publishes annual statistical reports ranging from “ Global Trends ”, “ Mid-year trends ”, “ Asylum trends ”, to a “ Statistical Yearbook ”. There is a consensus that these data provide the most reliable source of information (Sarzin 2016). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Living Standards Measurement Study", "Labor Force Surveys", "Multiple Indicator Cluster Surveys"], "descriptive_data": ["individual registration of refugees and asylum seekers"], "vague_data": ["administrative records"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our analysis exclusively uses data from the 1951-2015 UNHCR Population Statistics Reference database (extracted September 18 2015). Data were provided for 173 countries: 77 percent of these data were based on individual refugee registration, 13 percent on estimates, 5 percent on combined estimation and registration, and 5 percent on other sources. The data are structured as follows: for each situation, the database records annual numbers of persons of concern, which comprise “ Refugees (including refugee-like situations) ”, “ Asylum seekers ”, “ Internally Displaced Persons ”, “ Returnees ”, “ Stateless ” persons, and “ Others of concern ”. A situation is a pair country of origin / country of destination. For example, Somali refugees in Kenya account for one situation, Somali refugees in Ethiopia for another, and South Sudanese refugees in Kenya for yet another. Furthermore, a situation is considered major if it involves more than 25, 000 people. It is referred to as protracted if it is major for at least 5 continuous years. The database, and therefore our analysis, is limited to refugees under UNHCR protection. It does not include asylum seekers, i. e. individuals who have sought international protection under the 1951 Convention but whose claims for refugee status have not yet been determined, and persons in “ refugee-like situations ”, i. e. individuals outside their country or territory of origin who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained (e. g., undocumented Rohingya originating from Myanmar 4 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["UNHCR Population Statistics Reference database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "in Bangladesh). The data set we use does not cover the 5. 1 million Palestinian refugees who are under the man- date of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UN- RWA). The definition of Palestinian refugees in international law is distinct from other refugees. Palestinian refugees are people “ whose normal place of residence was Mandatory Palestine be- tween June 1946 and May 1948, who lost both their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict ”. Importantly, their patrilineal descendants are also considered refugees regardless of citizenship (UNRWA 2009). For the purpose of the analysis, we do not include Internally Displaced Persons (IDPs), who are defined as “ persons who have been forced or obliged to flee or leave their home or place of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disas- ters, and who have not crossed an internationally recognized border ” (United Nations 2004). The categories “ returnees ”, “ Stateless ”, and “ Others of concern ” are also not included. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The countries that are the largest source of refugees include: Afghanistan, Syria, Somalia, South Sudan, Sudan, Democratic Republic of Congo, Central African Republic, Iraq, Myanmar, and Eritrea (UNHCR, 2017). These countries account for the vast majority of refugees worldwide. We will also consider three countries in Europe that are an important source of asylum seekers in Europe, and whose recognition rate is low, and account for many returnees – these are Kosovo, Serbia and Albania. Is return possible and safe, and if so, does the economic situation bode well to provide essentials, namely a job, housing, education and reliable public services? It is helpful to identify three groups of countries: ones that are still mired in war and / or high ‐ intensity civil conflicts, countries where medium ‐ intensity conflict persist and are already in the process of rebuilding, and countries which are not in conflict. According to the Armed Conflict Survey (IISS 2017), high ‐ intensity conflict is defined by frequent (daily) armed clashes between governments, government forces and insurgents, or among non ‐ state armed groups that control territory. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Armed Conflict Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Medium ‐ intensity, on the other hand refers to “ regular armed clashes between governments, government forces and insurgents. ” (IISS 2017) Within the first category, according to the Armed Conflict Survey in 2016 are Afghanistan, Syria, Somalia, South Sudan, and Iraq, while Myanmar, Central African Republic, Democratic Republic of the Congo, and Sudan are in medium ‐ intensity conflict. (Eritrea is not included in the Armed Conflict Survey in 2017, discussed in Annex). Minorities in Albania, Kosovo and Serbia are often the objects of discrimination but countries are not in conflict. A capsule summary of the security and social situation in each of the other countries included in this survey can be found in Annex 2. The survey illustrates how impractical return is today in the countries in conflict, such as Syria, Afghanistan, and Iraq, and voluntary return in large 36 Best practice and new methods in return policy, July 2017, http: / / www. bamf. de / SharedDocs / Meldungen / EN / 2017 / EMN / 20170504 ‐ emnjahrestagung ‐ rueckkehr. html Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Armed Conflict Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Internally displaced women are extremely vulnerable to rape by armed men, “ including government soldiers and militia. ” Protection for them basically does not exist58. According to UNHCR, Somalia has over 1. 4 million internally displaced people. In the year 2016, some 24, 500 refugees and asylum ‐ seekers were registered in Somalia. Refugee return from Kenya began in 2014. Voluntary repatriation number is close to 40, 000 Somali nationals from 2014 to December 201659. Economic Opportunity According to Berlin ‐ based Transparency International, Somalia is one of the world ’ s most corrupt countries. Improved governance could enable Somalia ’ s economy to grow on the basis of its oil and gas reserves. Ongoing droughts continue to drive hungry and thirsty refugees to surrounding countries, and large parts of the population are in need of humanitarian aid. The agriculture sector contributes to over two ‐ thirds of its GDP while industry only makes up for 7 % in 201360. According to the IMF, Somalia has a very high youth 57 EIU Syria economy: Quick View ‐ Wheat harvest set to fall short of government forecast, July 2017 58 Human Rights Watch, Somalia Events of 2016, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / somalia 59 UNHCR, Somalia, http: / / reporting. unhcr. org / node / 2550? y = 2016 # year 60 CIA The World FactBook, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / so. html Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "48 unemployment rate, contributing to irregular migration and participation in extremist activities, including Al ‐ Shabaab. Joining militant jihadist group is viewed as another form of employment61. Social Services The lack of infrastructure and basic service hinders IDP settlements. On top of that “ urban areas are being overwhelmed with new arrivals62 ”. Access to basic needs, such as health and education, are unmet. South Sudan Security South Sudan ’ s civil war began in December 2013 and continues with serious abuses against civilians. A peace agreement was signed in August 2015 but the ceasefire was not achieved63. On May 25th, 2017, South Sudan President declared a ceasefire. According to the World Report by Human Rights Watch, South Sudanese “ government soldiers killed, raped and tortured civilians as well as destroying and pillaging civilian property during counterinsurgency operations in the southern and western parts of the country, and both sides committed abuses against civilians in and around Juba and other areas. UN Special Advisor on the Prevention of Genocide Adma Dieng said the ongoing violence had transformed into an “ ethnic war ” and warned of a “ potential for genocide64 ” On top of the precarious living situation, security and logistical challenges posed constraints to the delivery of much ‐ needed humanitarian assistance 65. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "According to Council on Foreign Relations, the estimated number of people killed since December 2013 is over 50, 000, and over 1. 6 million people are internally displaced66. Economic Opportunity South Sudan has abundant natural resources. Before its oil production fell sharply, the government relies on oil for its revenue. It also has very fertile soils and abundant water supplies. South Sudan has struggled with economic development since its independence and its economic conditions have deteriorated since January 2012 when the government decided to 61 IMF, Six Things to Know about Somalia ’ s Economy, April 11, 2017, http: / / www. imf. org / en / News / Articles / 2017 / 04 / 11 / NA041117 ‐ Six ‐ Things ‐ to ‐ Know ‐ About ‐ Somalia ‐ Economy 62 UNHCR, Somalia, http: / / reporting. unhcr. org / node / 2550? y = 2016 # year 63 Human Rights Watch, World Report, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / south ‐ sudan 64 Human Rights Watch, World Report, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / south ‐ sudan 65 UNHCR, South Sudan http: / / reporting. unhcr. org / node / 2553 66 Council on Foreign Relations (CFR) https: / / www. cfr. org / global / global ‐ conflict ‐ tracker / p32137 #! / conflict / civil ‐ war ‐ in ‐ south ‐ sudan Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "49 shut down its oil production67. UNHCR reported that in 2016, South Sudan ’ s economic situation deteriorated further and the cost of living rose exponentially68. Social Service South Sudan has very high mortality caused by AIDS and high risk of infection of diseases. Education expenditure is low and the literacy rate is also very low (27 % in 2009). South Sudan has little infrastructure. According to the CIA Factbook, there are approximately 200 kilometers of paved roads. Electricity is produced mostly by costly diesel generators. Goods and services are mostly imported from surrounding countries. 69 Sudan Security Similar to South Sudan, Sudan ’ s government forces have raped, killed civilians, and destroyed hundreds of villages. In September 2016 UN found that violence has displaced up to 190, 000 people and many of them are not accessible to humanitarian agencies 70. Government forces and armed rebels in Southern Kordofan and the Blue Nile continue to be engaged in armed conflict for the fifth year in 2016. Civilians in populated areas were subject to indiscriminate bombing, especially during March through June in 2016. Citizens also face arbitrary detentions, ill ‐ treatment, and torture. Sudan ’ s National Intelligence and Security Service is known for detaining activists, students, lawyers, doctors and those who are perceived to be needed in some capacity by the government71. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Many detainees are facing ill ‐ treatment. Females are subjected to sexual harassment by security officers. According to UNHCR, there are 2. 7 million people of concern in Sudan (includes refugees, asylum ‐ seekers, IDPs, returned refugees, returned IDPs, stateless persons, and other concern), a lower number than 2015. About 37, 000 refugees returned to Sudan in 2016. At the same time, there were over 2 million IDPs, over 420, 000 refugees and over 16, 000 asylum ‐ seekers in other countries. Economic Opportunity Oil output in Sudan has been low due to civil war, poor infrastructure, and low productivity. Compared to South Sudan, Sudan also has fewer oil resources; nevertheless, it still has abundant resources72. Sudan ranks 186th of 190 states in the World Bank ’ s Doing Business 67 CIA Factbook, South Sudan, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / od. html 68 UNHCR, South Sudan http: / / reporting. unhcr. org / node / 2553 69 CIA Factbook, South Sudan, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / od. html 70 UNHCR, Sudan, http: / / reporting. unhcr. org / node / 2535 71 Human Rights Watch, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / sudan 72 EIU Country Outlook, Sudan, June 19th 2017 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "50 Rankings, with particularly poor scores for starting a business, getting electricity and registering property. Agriculture slowed in 2016 and is expected to do better in 2017. Lack of rain affects the outcome of the agricultural sector. CIA ’ s Factbook73 reports that 46. 5 % of Sudan ’ s population is living below the poverty line. Only 35 % of its population has access to electricity. Social Service It is difficult for the staff of international humanitarian aid agencies to obtain travel permits in Sudan, severely hampering the relief effort. The Democratic Republic of the Congo (DRC) Security The security situation in the Democratic Republic of Congo has been poor since 2012. An attempt to integrate a Tutsi rebel group into the Congolese military failed and prompted the defection and formation of the M23 armed group. The renewed conflict led to large population displacement and human rights abuses. Furthermore, the President of the DRC, Joseph Kabila is barred from running for a third term, but the DRC government has delayed national election originally slated for November 2016. The failure to hold election fueled sporadic street protests by Kabila ’ s opponents74. Although a deal signed by representatives of Kabila ’ s ruling party said the presidential election would be held before the end of 2017, the President himself has not endorsed the deal. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Government officials repeatedly banned opposition demonstrations, fired teargas and live bullets at peaceful protesters, shut media outlets, and prevent opposition leaders from moving freely75. According to UNHCR, there are over 2 million IDPs in the DRC, and over 450, 000 refugees. In year 2016, there were about 13, 000 returned refugees and 619, 000 returned IDPs. “ Dozens of armed groups remained active in eastern Congo, many of their commanders have been implicated in war crimes, including ethnic massacres, killing of civilians, rape, forced recruitment of children and pillage. 76 ” Economic Opportunity 73 CIA the World Factbook, Sudan, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / su. html 74 CIA, the World Factbook, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / cg. html 75 Human Rights Watch, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / democratic ‐ republic ‐ congo 76 Human Rights Watch, DRC, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / democratic ‐ republic ‐ congo Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9826 Despite the many simultaneous deprivations faced by forcibly displaced communities, such as food insecurity, inadequate housing, or lack of access to education, there is little research on the level and composition of multi­dimensional poverty among them, and how it might differ from that of host communities. Relying on house­hold survey data from selected areas of Ethiopia, Nigeria, Somalia, South Sudan, and Sudan, this paper proposes a Multidimensional Poverty Index (MPI) that captures the overlapping deprivations experienced by poor individuals in contexts of displacement. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using the MPI, the paper presents multi-country descriptive analysis to explore the relationships between multidimensional poverty, displace­ment status, and gender of the household head. The results reveal significant differences across displaced and host com­munities in all countries except Nigeria. In Ethiopia, South Sudan, and Sudan, female-headed households have higher MPIs, while in Somalia, those living in male-headed house­holds are more likely to be identified as multidimensionally poor. Lastly, the paper examines mismatches and overlaps in the identification of the poor by the MPI and the $ 1. 90 / day poverty line, confirming the need for complementary measures when assessing deprivations among people in con­texts of displacement. This paper is a product of the Gender Global Theme. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at atybogale @ worldbank. org, sabina. alkire @ qeh. ox. ac. uk, uekhator @ worldbank. org, fanni. kovesdi @ qeh. ox. ac. uk, juliethsa @ iadb. org, and sophie. scharlin-pettee @ qeh. ox. ac. uk. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 regards to who is poor, how poor they are, and the composition of their poverty. Based on the Alkire-Foster (AF) method, the index provides a summary measure of poverty for the population that can be disaggregated by displacement status and gender of the household head to analyze the variation in deprivations. The MPI can be further broken down by indicator to show the proportion of the population who are poor and deprived in each area. These features of the MPI can inform better policy responses, with interventions and programs targeting the most deprived communities and indicators with the highest headcount ratios. The paper proceeds as follows. Section 2 of the paper reviews some of the existing literature to provide the background and motivation for the analysis, including a summary of the different country contexts covered by the data analyzed in this paper. Section 3 outlines the Alkire- Foster method and the selected dimensions and indicators used to construct the MPI, followed by Section 4, which introduces the data. Section 5 presents the findings, first for results at the national level and then results disaggregated by displacement status. Section 6 analyzes differences in multidimensional poverty by gender of the household head to improve understanding of the gendered aspects of multidimensional poverty in these contexts. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For refugees, the situation is more varied, with most staying close to their country of origin while a smaller minority fled to countries further away. UNHCR (2020) estimates that three-quarters of all refugees were hosted by neighboring countries. To reflect the increase in forced displacement over the last decade and enable sustainable and long-term solutions to refugee situations, the UN Statistical Commission approved a new indicator, SDG Indicator 10. 7. 4, in early 2020 to measure and track the “ proportion of population who are refugees, by country of origin ” (UNHCR 2020). While the specific challenges for displaced communities depend on the country or host community context, often, in new locations, key challenges confronting IDPs and refugees include food insecurity, lack of livelihood opportunities, and tensions and competition over resources with host communities. The multiplicity of deprivations faced by displaced Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["SDG Indicator 10. 7. 4"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Children and adolescents ’ long-term exposure to households with a more equalized division of domestic labor, as a result of violent or political conflict, warrants further investigation. 2. 3 Country contexts The countries with subnational regions covered in this study, using data from 2017 or 2018, are Ethiopia, Nigeria, Somalia, South Sudan, and Sudan. All are located in Sub-Saharan Africa, have undergone or are currently involved in armed conflict, and are affected by environmental issues such as drought, famine or flooding. Despite some commonalities, each faces a unique set of social, political and economic challenges, which cannot be accurately covered in this study. However, to contextualize the findings, a brief introduction of the country context is presented alongside the poverty estimates by the $ 1. 90 / day measure and the global Multidimensional Poverty Index (MPI). 3 3 An international measure of acute multidimensional poverty, aligined with the 2030 Agenda, that captures deprivations in health, education, and living standards for more than 100 countries (Alkire and Jahan 2018; Alkire, Kanagaratnam and Suppa 2020). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Pregnancy Care A woman who gave birth in the last 2 years did not visit a clinic while pregnant or have a trained assistant during delivery 1 / 16 Physical Safety Any member feels unsafe at home or walking alone14 1 / 16 Early Marriage A member was married before age 19 1 / 16 Living Standards Garbage Disposal Main method of solid waste disposal is dumping, burying in own compound, burning, or other 1 / 24 Drinking Water Main source of drinking water is unsafe, or it takes more than 20 minutes (round-trip) to get water15 1 / 24 Electricity It does not have electricity 1 / 24 Cooking Fuel Main energy source for cooking is solid fuels 1 / 24 Housing It is an unimproved housing type 1 / 24 Sanitation Main toilet facility is unimproved, or shared with other households16 1 / 24 Financial Security Unemployment Any member 15 or older is unemployed and looking for work17 1 / 12 Legal Identification No member has a form of legal identification 1 / 12 Bank Account No member has a bank or mobile money account 1 / 12 The MPI presented here uses equal nested weights with all four dimensions considered to be equally important, and all indicators within a dimension receiving an equal share of the total weight. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The cross-dimensional poverty cut-off is defined as k = 50 %, with those deprived in half or more of the weighted indicators identified as multidimensionally poor. 4. Data Data on forcibly displaced populations are scarce, with many household surveys excluding refugees and IDPs from the sample framework. To ensure that MPI results are representative of these communities and that they can be disaggregated for comparative analysis, an initial review of possible data sets was conducted. Feasibility was determined based on the availability of sufficient sample sizes for forcibly displaced persons for quantitative analyses, as well as inclusion of many of the indicators (on health, education, living standards, etc.) 14 A household is deprived if the respondent reports feeling moderately or very unsafe when alone at home, walking alone after dark, or walking around during the day. In Sudan, the indicator on the ‘ feeling safe from crime and violence when at home ’ was not available, and the indicator only considers answers to the questions on safety when walking alone. 15 Unprotected dug well, unprotected spring, carts with tank, tanker-truck, surface water, or other are considered as unsafe water sources according to international guidelines. See https: / / washdata. org / monitoring / drinking-water. 16 Pit latrine without slab, bucket, hanging toilet, and no facility (open defecation) are considered as unimproved sanitation facilities according to international guidelines. See https: / / washdata. org / monitoring / sanitation. 17 According to the ILO definition, those who did not participate in employment in the last four weeks (and have no work to return to) are actively looking for work and are available to start, or those currently waiting to start work are classed as unemployed. See https: / / www. ilo. org / ilostat-files / Documents / description_UR_EN. pdf. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 5. 2 Composition of poverty Unpacking the headline numbers further, important patterns emerge about the composition of multidimensional poverty among forcibly displaced and host communities in these countries. Overall, the censored headcount ratios (proportion of people who are poor and deprived in a given indicator) are lower among non-displaced communities than among refugees and IDPs, but there are large differences in which indicators are the most salient in different countries. The indicators with the largest difference between the two populations are bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria. These findings reinforce the need for policies and programming that take into account the measured experiences of IDPs and refugees. In this way, the MPI can function both as tool to monitor, track, and bear witness to the lived experiences of forcibly displaced communities, as well as advise on evidence-based interventions that address the needs of the local population. Figure 1 shows the censored headcounts of each indicator in Sudan ’ s MPI, with large differences appearing by displacement. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "MPI of IDP communities is considerably higher than that of the host communities, so it follows that the censored headcount ratios display a similar gap. The difference by indicator is particularly noticeable in the living standards dimension, where the electricity, cooking fuel, housing, and bank account indicators show over 34 percentage point difference between the censored headcount ratios for the IDP and host communities. Figure 1. Censored headcounts of each indicator in the MPI, by displacement status in Sudan (2018) Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). As the Sudanese sample surveys IDPs living in the Abu Shouk and El Salam camps, we must understand these deprivations with the background that these settlements were created as emergency and crisis responses rather than durable, long-term solutions (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019). Although Sudan does have a national electric grid that supplies electricity to the urban and peri-urban areas of the nearby city El Fasher, IDP communities living in the camps report limited connection to the city ’ s electricity supply, reflected in the high deprivations in the electricity and cooking fuel indicators. The ad-hoc construction of dwellings in the two camps explains why 71 % of the IDP households in Abu Shouk and 65 % in El Salam live in tukuls or other permanent mud or wood structures (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019: p. 50), both of which register as unimproved housing types. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017)"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Bank branches in El Fasher have limited capital for small businesses as their headquarters in Khartoum regard the area as too great a risk and IDPs themselves as riskier investments than their host community peers (UN- HABITAT 2009: p. 8). Exposure to unclean cooking fuels and inadequate housing can lead to poor health outcomes, while lack of access to electricity and a bank account further excludes individuals from labor market integration and livelihood opportunities that would empower forcibly displaced persons to overcome their multiple, overlapping deprivations. Clearly, displacement status puts individuals at a greater risk of poverty than their host community neighbors, and we can unpack those risks in greater detail using the MPI. Results can also be broken down to show the percentage contribution of each indicator to multidimensional poverty (see Figure 2). Among refugees in Ethiopia, lack of a bank account is the largest contributor to poverty, while among host communities, the largest contributor is years of schooling. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 6. Gender differences in multidimensional poverty Next we examine differences in multidimensional poverty outcomes by the gender of the household head. Existing literature points out the limitation of household level MPI analysis in masking the intrahousehold distribution of deprivations, and thus being less sensitive to gender based differences in individual outcomes within the family unit, which might lead to underestimation of inequality and gender gaps (Espinoza-Delgado and Klasen 2018; Franco 2017; Klasen and Lahoti 2020, Rodriguez, 2016). However, as the MPI identifies poverty at the household level, our initial analysis focuses on disaggregated results by the gender of the household head. 19 We acknowledge that this approach has several limitations since most women reside in male-headed households, and the composition of households can change after displacement due to separation of family members, and widowhood. Regardless, the analysis at the household level remains relevant given the high prevalence of female-headed households that emerge after displacement, with the analysis showing large differences across countries between households based on the gender of the head. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Table 6: Percentage of displaced individuals deprived in selected indicators by gender Ethiopia South Sudan Sudan Male Female Male Female Male Female Years of schooling 55 78 * * * 36 63 * * * 32 46 * * * School attendance 16 19 * * 21 29 23 23 Early marriage 3 13 * * * 8 75 * * * 6 50 * * * Unemployment 7 5 * * * 2 0 * 3 3 Legal id 45 46 48 74 * * * 10 10 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Asterisks indicate statistical significance of mean differences between male and female at 1 % * * *, 5 % * * and 10 % * levels. Returning to patterns of household headship, Figure 5 breaks down the variation in censored headcount ratios among refugee households in Ethiopia, depending on the gender of the household head. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["High Frequency Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "22 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). To uncover the main drivers of the observed gender based differences in multidimensional poverty at country level, we study the absolute contribution of the gender difference in each indicator to the overall household gender gap (see Figure 6), calculated as the difference between the censored headcount ratio for males versus females. We find that in Ethiopia, the gender gap that disadvantages female-headed households is mostly driven by the difference in financial insecurity measures (lack of legal ID and bank account) and health measures (early marriage, physical safety, and food insecurity), which is further reinforced by the differences in the living standard and education measures. Female-headed refugee households are more food insecure, live in unimproved housing, have lower access to electricity, are more likely to be married at an early age, and have lower access to legal identification and a bank account. In South Sudan, gender gap that disadvantages female-headed households is mainly explained by the differential in the financial insecurity and health measures, but cumulative gaps in the living standard and education indicators also contribute to the overall gap at the household level. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "E Nigeria Somalia Sudan Host community Refugees Host community IDP Host community IDP Host community IDP No earners 40 % 51 % 62 % 50 % 32 % 64 % 7 % 45 % Remittance recipients only 7 % 8 % 76 % 69 % 16 % 37 %-- Female single earner 12 % 52 % 23 % 37 % 35 % 70 % 24 % 55 % Male single earner 8 % 23 % 4 % 14 % 24 % 50 % 8 % 39 % Majority female earners 3 % 57 % 9 % 28 % 9 % 28 % 15 % 45 % Equal contribution 10 % 24 % 17 % 16 % 23 % 63 % 8 % 41 % Majority male earners 5 % 16 % 15 % 19 % 20 % 52 % 9 % 39 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Overall, the results show that besides gender, displacement status and the number of household contributors plays a key role in the identification and level of poverty. In comparison with female-headed non-displaced households, more female-headed displaced households are classified as multidimensionally poor. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The lack of overlap might be explained by the relatively recent start of the displacement situation in 2014, when Boko Haram appeared in the north-eastern part of the country. Pape et al (2018) identify two groups of IDPs in this situation: one group representing 74 % of the IDP population that was more engaged in wages and non-farm business before displacement, and another group representing about 26 % of the population, that had significantly more unemployed women. Most of the displaced populations from the first group live in host communities with good access to basic services such as sanitation and water, and safety nets. However, they are disproportionally more likely to be female-headed households and lack access to education, health services, and may face more stringent labor-market barriers. In other words, this group has relatively better housing conditions, but may lack short-term resources that reduce their consumption expenditure. 22 In summary, expenditure in these three categories is computed based on the quantities and prices of a selected list of items in each category. See more details about the computation of the consumption aggregate in Appendix A of the Somali Poverty Profile (Pape et al, 2017). A similar procedure was followed in the other countries of analysis. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 1 ‐ Number of Forcibly Displaced Persons (1951 ‐ 2015) Source: Constructed from UNHCR population data (http: / / popstats. unhcr. org / en / time_series). Note: 2015 data are mid ‐ year and lower than end of year data. 5 http: / / www. unhcr. org / en ‐ us / figures ‐ at ‐ a ‐ glance. html. 0 10000000 20000000 30000000 40000000 50000000 60000000 70000000 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Figure 4 ‐ Research on Migration, Refugees and IDPs (% of total hits) Source: Authors ’ estimations based on Econpapers, SSRN and Google Scholars searches. This phenomenon can be explained by essentially two factors. The first relates to the humanitarian ‐ development nexus. For the longest time, refugees and IDPs remained the quasi monopoly of humanitarian organizations whose mandate is essentially the humanitarian protection of refugees and IDPs. These organizations are not typically staffed by economists and analysists but by field workers and lawyers. There was, therefore, little demand for hard economics on forced displacement for a very long time. This is changing as development organizations typically staffed by economists have started to work on forced displacement situations. The second factor relates to lack of good data. As we will see in the data section, data collection of mobile populations is complex and the main organizations in charge of data collection of refugee and IDPs data are humanitarian organizations that do not necessarily have the complex skills required for issues like sampling, questionnaire design and data analysis and have a duty to protect data by mandate. This, in turn, has resulted in very few micro data that would be both of good quality and accessible to researchers. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 outcome because of preferences for known odds to unknown odds. This ambiguity aversion violates the postulates of the subjective expected utility theory and offers a possible insight into why some people may choose to live under a conflict where risks are known rather than escaping elsewhere where risks and conditions are unknown. A more recent critique to the EU model includes Khaneman and Tversky (1979) prospect theory. The central theme of the critique is that people underweight outcomes that have very low probability of occurring and overweight outcomes that have a very high probability (this is called the certainty effect). Considering equal weighting as in EU theory can lead to the Allais paradox (Allais, 1953) where different choice frameworks can lead to opposite conclusions about dominance of alternative choices. Khaneman and Tversky (1979) also showed that, for negative prospects, preferences are reversed as compared to positive prospects (this is called the reflection effect). Therefore, “ (…) certainty increases the aversiveness of losses as well as the desirability of gains ” (Khaneman and Tversky, 1979, p. 269). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Hence, the authors introduce the notion of decision weight ߨ to weigh the importance that people give to different probabilities so that the expected utility function becomes ܧሺ ܷ ሻ ൌ ෍ ߨ ൣ ݌ ൫ܧ ௝ ൯൧ ܷ ሺݔ ௝ ሻ ௡ ௝ ୀ ଵ One can also study decision making in a game theory setting. Expected utility can be looked at as a one ‐ person game but the value added of game theory in the context of forced displacement relates to multiple ‐ person games. Suppose that actions taken by individuals under conflict situations affect the actions of others and ultimately one ’ s own action (the classic prisoner ’ s dilemma for example). This is what game theory is good in modeling and it could provide valuable contributions to the study of collective behavior under forced displacement situations (see for example Zeager and Bascom, 1996). New branches of economics such as neuroeconomics and behavioral economics, which combine elements of psychology and neuroscience with elements of economics, offer alternative new avenues to the construction of utility models in a forced displacement context. For example, neuroeconomics developed a hierarchical module oriented approach (Sanfey et al., 2006) whereby individuals take decisions in a hierarchical manner where multiple systems of specialized processing modules transform specific inputs into outputs in organized decision stages. This process can be observed in people ’ s brains with scans and can be modeled empirically using specifically designed questionnaires. This literature shows how the short ‐ term decision process is different from the long ‐ term process in terms of how we value potential Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Deciding on whether to flee or not to flee a conflict (the migration choice) can be an individual or household choice and risk coping strategies may include temporary migration, shuttling between places, migration of only selected members of the households or migration of the whole household. This implies that individuals may stay put throughout the period observed, join or leave the household during the period, or have several episodes of out and immigration. Households may decide to leave and come back several times. In econometric terms, this means that longitudinal data may be left and right censored and have spells within. They are therefore the most complex set of panel data possible and require particular treatment of data and modeling. Survival or duration models can usually accommodate many of these complexities but it is very rare to find similar data sets used in published articles. Collecting such type of data is also not obvious, particularly if conflict is intense and survey areas cannot be reached. This is an issue where empirical economics could provide a real contribution by defining the optimal data format and adapting panel models to this format. Macro models Macroeconomics has attempted to model forced migration using models borrowed from the trade and economic migration literature such as the gravitational model (Echevarria and Gardeazabal, 2016) or used other macro models to test the impact of refugees on trade (White and Tadess, 2010). A more recent body of work is adapting trade models to take into account stochastic shocks in a dynamic framework (Cameron et al., 2007; Artuc et al., 2008). These are rational expectations models that are able to model the unpredictability of shocks, and recent work has tried to adapt these models to the context of violent Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["longitudinal data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These people are then expected to return to their place of origin once the conflict is over and governments are typically over optimistic about the duration of civil conflicts and about return of IDPs. In some cases, governments also have an interest in denying the very existence of IDPs for political purposes. Therefore, little time is spent in surveying IDPs or trying to find durable solutions in the place where they migrated. Moreover, national censuses are usually conducted every ten years and statistical agencies have little incentives to revise censuses, master samples and sample survey structure for situations that are perceived as short ‐ term. In most cases, new surveys are suspended or carried out under the pre ‐ crisis frameworks and, in either case, information on IDPs is not collected or poorly collected. This leaves specialized government agencies or international organizations in charge of IDP statistics (and care). However, unlike refugees, the IDPs do not benefit from a specialized international agency such as the UNHCR. IDP assistance is currently provided by a multitude of organizations including ministries of interior, specialized government agencies, the UNHCR, the International Organization for Migration (IOM), the UN Office for Humanitarian Affairs (UN ‐ OCHA), specialized NGOs and others. Some of these organizations collect information on IDPs and make this information public while others collect information that is not published and others do not collect information and focus on providing assistance. Most data collected are for the simple purpose of counting IDPs and do not include individual or Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["national censuses"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 household socioeconomic information. In some cases, socioeconomic information is collected at the individual or household level but more often information is collected at the community level. It is extremely rare to have unit record data sets on IDPs, which explains why we found only 18 studies in the Econpaper repository as already documented. Recognizing the problem of scarcity and availability of information on IDPs, international organizations have set up in several countries coordination mechanisms to count IDPs usually coordinated by IOM, UN ‐ OCHA or the UNHCR. There are also global efforts to centralize this information on the part of organizations such as the UNHCR, UN ‐ OCHA, the international Displacement Monitoring Centre (iDMC) or the Joint IDP Profiling Services (JIPS). These efforts are making good progress on harmonizing counts of IDPs but remain short of establishing proper data collection systems that could deliver in the years to come unit data of quality for research. Hence, research on IDPs remains constrained by lack of data, lack of a blueprint on how to collect data and lack of an organization dedicated to IDP data collection. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some data challenges common to refugees and IDPs Irrespective of the specific questions related to refugee and IDP data, there are also general questions that refer to the forcibly displaced in general and that are distinct from data collection of regular populations or even migrant populations. We explore here selected issues including sampling, unit of analysis, welfare measurement, multidimensional aspects, and the measurement of risks and vulnerabilities. Sampling. As mentioned, the UNHCR is really the only statistical agency for refugees and the UNHCR registry the only population census. As for any other populations, sampling requires the preparation of a master sample that derives from the population census. With various degrees of knowledge and accuracy, this is also what happens with refugees. However, the master sample is more difficult to construct than for regular populations because refugees live in camps and outside camps and are diluted in a host population with different types of arrangements. Some households rent, others stay at relatives ’ places, other live in makeshift shacks and others stay in camps. The information available in the UNHCR registry (the census) can also be quite inaccurate, as already discussed, and the degree of accuracy changes for different groups of refugees. Stratification by urban and rural areas, a typical approach in sampling, may mean little for a population that is mostly in urban areas whether in camps or outside camps. Refugees and IDPs are also mobile and more difficult to track over time than other populations. Several statistical institutes worldwide have developed methodologies to track and measure mobile populations such as herders, nomads or homeless people. However, tracking refugees from other countries has been in the Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR registry"], "descriptive_data": [], "vague_data": ["population census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 capacity to offer these services. Many may be at risk of violence or trafficking. These are very important aspects from the perspective of welfare economists interested in measuring well ‐ being but these measurements are complex and not usually included in multidimensional indicators of deprivation or poverty. The dimensions of deprivations to consider are more numerous and more complex to measure. Again, there is very little research in welfare economics dedicated to the special needs of these populations. Risks and vulnerabilities. The analysis of risk and vulnerability is also much more complex in the context of the forcibly displaced. Welfare economics has only approached these topics recently, in the past decade or so. Essentially, the idea is to measure the risk of being poor or falling poor in the future using cross ‐ section or panel data studying spells of poverty over time. This is work that requires accurate and complex data sets that would be rarely available in a refugee or IDP context. More importantly, the nature of the problem changes. Refugees and IDPs are by definition more at risk and more vulnerable than regular populations and these vulnerabilities are not only linked to skills and efforts but to legal status, discrimination, limited mobility and other factors that are unique or much more acute with refugees and IDPs. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["cross ‐ section or panel data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Lebanon. It was the obvious step forward in overcoming these problems and the answer to the increasing demand for evidence around the Syrian crisis. The study benefitted from a unique and unprecedented set of data. The UNHCR registry data in Jordan and Lebanon were among the better quality registry data available worldwide and the UNHCR also conducted home visits in Jordan that, at the time of the study, covered over a third of all refugees. There were also sample surveys in both Jordan and Lebanon that were small in size but representative of the population present in the registry. The home visits and the surveys included questions on income and expenditure that could be used for the welfare assessment. Using these data, the study addressed ten questions defined as follows: 1) Who are the refugees?; 2) How different are refugees from “ regular ” populations?; 3) How poor are refugees?; 4) What are the main predictors of refugees ’ welfare and poverty?; 5) How vulnerable are refugees from a monetary and non ‐ monetary perspective?; 6) Do poverty and vulnerability statuses overlap?; 7) How effective are refugee assistance programs?; 8) What is the potential for alternative policies?; 9) How does welfare compare across countries and data sets?; 10) How transferable are Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR registry data"], "vague_data": ["sample surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These findings were also echoed in the context of other developing countries such as India (Allard et al., 2022) and Zimbabwe (Mabugu, Maisonnave, Henseler, Chitiga-Mabugu, & Makochekanwa, 2023). In the MENA economies ’ context, the evidence is considerably sparcer. Early work on the impact of the COVID-19 pandemic on labor markets in the MENA region re- lied on high-frequency phone surveys and highlight important job losses among wage workers and an uneven impact across industries (Krafft, Assaad, & Marouani, 2021, 2022). Providing evidence from Labor Force Surveys in the Islamic Republic of Iran, Dang and Salehi-Isfahani (2023) find that the pandemic exacerbated the pre-existing low participation of females in the labor force. Wahby and Assaad (2023), on the other hand, focus on the impact of the pandemic on Syrian refugees in Jordan and find a divergence in job finding and separation rates of Syrian refugees relative to their Jorda- nian hosts after the onset of the pandemic. Focusing on cross-border commuters in the West Bank and Gaza, Adnan and Etkes (2022) find that undocumented commuters benefited relative to their documented peers after the pandemic, as Israeli policies inadvertently created incentives for employers to favor the former. This sharply con- trasts the results by Borjas and Cassidy (2020) on the impact of the pandemic on immigrants in the United States. The rest of this paper is organized as follows. Section 2 provides background information on labor markets in the West Bank and Gaza, as well as background in- formation on the COVID-19 pandemic and government responses. Section 3 describes the data. Section 4 discusses our methodology. Section 5 presents the main regression results and investigates heterogeneous effects. Section 6 provides robustness checks. Finally, we provide concluding remarks in Section 7. 4 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Labor Force Surveys", "high-frequency phone surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 Background Information 2. 1 Characteristics of West Bank and Gaza ’ s labor markets The labor markets of the West Bank and Gaza exhibit features typical of the broader Middle East and North Africa (MENA) region, but also have attributes that are highly unique. Additionally, important differences exist between the West Bank and Gaza. This section provides an overview of these characteristics. We use data from the Labor Force Surveys (LFS) of the West Bank and Gaza and we focus on 20-59 years old men. In Section 3. 1, we provide more information about the data sources and sample selection. We divide each labor market into five mutually exclusive and jointly exhaustive states: public sector employment, private formal sector employment, private informal sector employment, unemployment, and out of labor force. 1 We focus our discussion exclusively on men, as women ’ s labor force participation in both the West Bank and Gaza is very low, never reaching values above 25 %. This low participation rate is common in MENA countries and makes the role of the pandemic on women ’ s labor market outcomes relatively less important than other, more relevant structural factors. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Labor Force Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "prior to the pandemic, a period characterized by less distinct time trends in the labor market. Since the focus of the paper is on the impact of COVID on labor market stock and dynamics, we also restrict the sample of interest to include only prime-aged working adults (aged 20 to 59). The dataset contains standard variables expected of a labor force survey, including those denoting employment, unemployment, and inactivity. It also contains information on the intensive margin of the labor supply, including hours worked and full-time and part-time status. Information on employment sector, industry, contract status, health insurance coverage, and mode of work (distinguishing between employees and self-employed, for example) is also available, allowing us to construct indicators of formality and to differentiate different modes of employment. Information on occupation is also available, but only at the level of 2-digit ISCO-08 classification. This information is enough to distinguish between white- and blue-collar occupations but it is not enough to observe additional relevant pandemic-related job characteristics such as the degree of contact with the public. 3. 2 Descriptive statistics Figure (1) tracks the evolution of labor market stocks in the West Bank and Gaza respectively over time. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A significant increase in non-participation among men can be clearly seen in the West Bank in 2020Q2, mostly at the expense of the private infor- mal sector. The labor market then quickly bounces back. By 2020Q3, labor market stocks in the West Bank appear indistinguishable from pre-pandemic periods. Gaza, on the other hand, experienced three consecutive quarters of depressed employment from 2020Q2 to 2020Q4. Non-participation spiked twice, first in 2020Q2 and then in 2020Q4, corresponding respectively to the initial lockdown orders and the subsequent outbreak in Gaza. Recovery also appears to be slow and uneven. Figure (3) shows the labor market flows. We exploit the specific panel structure of the LFS dataset, described in Section 3. 1 by focusing on one cohort of the same respon- dents who were surveyed in 2019Q1, 2019Q2, 2020Q1, 2020Q2, and finally 2020Q4. This cohort of individuals allows us to observe labor market transitions into the pan- demic; to compare with a period over the same quarters in 2019; and, finally, to observe their recovery outcomes in 2020Q4. Overall, Figure (3) shows two labor markets with high levels of churning. On average, 29 % of individuals in the sample would change their labor market states after just one quarter. These churns are especially prominent between informal employment and unemployment, and in Gaza between unemployment and non-participation. The figure also illustrates the significant differences between the West Bank and Gaza in labor market dynamics, differences already observed in the labor market stocks presented in Figure (1). In addition to the significant flows between unemployment 11 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["LFS dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the West Bank and Gaza. Out of all the estimated coefficients for each period for both outcomes (job loss and job gain), we only find a negative effect on job gain in 2019Q4, which is very small in magnitude (1 percentage point). Taken altogether, the results presented in this section bolster our confidence that we are correctly identifying the effects of the pandemic shock on labor market outcomes. Figure 12: Placebo effect on labor market flows Notes: The figure shows the output of a placebo test with a set-up analogous to Figures 6 and 9. We perform the same regression as specified in Equation (2). Our sample includes data from 2018Q2 to 2020Q1 and assumes that the pandemic started in 2019Q2. Therefore, the post-pandemic period refers to the quarters between 2019Q2 to 2020Q1. The analysis is restricted to men aged 20-59. 7 Conclusion This paper examines the effect of the pandemic on labor markets in the West Bank and Gaza using quarterly labor market data provided by national labor force surveys. With a focus on men ’ s labor market outcomes, this paper sheds light on how labor markets in the West Bank and Gaza adjusted to the COVID-19 shock examining adjustments at the extensive (employment) and intensive (hours of work) margins. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["national labor force surveys", "quarterly labor market data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "One of the main contributions of this paper is the use of panel data which allows us to examine the effect of the pandemic on labor market transitions — job loss and job gain rates — in addition to the effect on labor market stocks. Studying both stocks and flows provides a comprehensive framework to analyze the impact of the pandemic on labor markets and allows for a better understanding of the underlying mechanisms 30 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1 POST-CONFLICT TRANSITIONS WORKING PAPER NO. 16 Population Size, Concentration, and Civil War. A Geographically Disaggregated Analysis * Håvard Hegre Centre for the Study of Civil War, PRIO (CSCW) Clionadh Raleigh CSCW, PRIO & University of Colorado at Boulder Abstract Why do larger countries have more armed conflict? This paper surveys three sets of hypotheses forwarded in the conflict literature regarding the relationship between the size and location of population groups: Hypotheses based on pure population mass, on distances, on population concentrations, and some residual state-level characteristics. The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events. The analysis covers 14 countries in Central Africa. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper develops a statistical method to analyze this type of data. The analysis confirms several of the hypotheses. World Bank Policy Research Working Paper 4243, June 2007 The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post- Conflict Transitions Project can be found at http: / / econ. worldbank. org / programs / conflict). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Armed Conflict Location and Events Dataset"], "descriptive_data": [], "vague_data": ["conflict event data", "geographically disaggregated data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in these papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http: / / econ. worldbank. org. * Contact author: Håvard Hegre; CSCW, PRIO, Hausmanns gate 7, N-0187 Oslo, Norway. Email: hhegre @ prio. no. Thanks to Joachim Carlsen for writing a program to create the dataset used in the analysis, to Siri Aas Rustad for research assistance, and to Kristian Gleditsch, Anke Hoeffler, Pat Regan, Mike Ward, Nils Weidmann and Jen Ziemke for valuable comments. The research has been funded by the Research Council of Norway, grant no. 163115 / V10. WPS4243 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 however, since they cannot distinguish between a country where the population is concentrated in one cluster covering 10 % of the territory and one where the population is concentrated in two clusters of 5 % each, but with a considerable geographical distance between them. Population and conflict geography in the Democratic Republic of Congo (DRC) corresponds to these arguments regarding population concentration and dispersion. Concentrations of language-based minorities are evident throughout eastern DRC. Due to the limited access of the government, the close proximity to international borders, and the dense population concentrations, these concentrated minorities have a higher potential of conflict than other, more accessible, sparsely populated areas of DRC. Figure 1 shows the population concentrations in 1990 (CIESIN data) for Central Africa. Heavily populated areas are shaded in deeper tones of red / grey. Civil conflict in DRC has overwhelmingly occurred in the eastern portion of the state, which is the most densely populated area and also geographically peripheral to the capital, Kinshasa. Of the eleven Congolese rebel groups accounted for in the dataset used in this paper, all have operated either exclusively or partially in the eastern and southern areas of DRC. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["CIESIN data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Proposition 6 Concentration and Dispersion: The risk of civil war events at a location increases more strongly in local population concentrations in locations distant from the capital of countries. 2. 5 Residual State-Level Mechanisms We have pointed out a set of location-specific factors, each of which imply a positive relationship between country size and national-level risk of armed conflict. But it is not certain that such location-specific factors are the only relevant ones. The size of a country itself may affect risk over and beyond what is implied by sheer population size, distance, and population distributions. If the economies of scale with respect to defense are sufficiently large, the risk of conflict events at a location at a given distance from the capital may be lower the larger is the country (Collier & Hoeffler, 2002). Moreover, large countries may rather be more conflictual than small ones for several reasons. Fearon & Laitin (2003: 81), for instance, note that insurgency will be favored when potential rebels Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Proposition 7 State-Level Effects of Population Size: The risk of civil war events at a location varies with the size of the population of the country to which the location belongs, controlling for the local effects. 3 Research Design 3. 1 Unit of Analysis To distinguish between the different theoretical statements regarding how population sizes, population concentrations and locations relate to risk of conflict, we need to investigate exactly where conflicts occur. We have created a dataset using a Geographic Information Systems (GIS) program which converted large territories into smaller portions of 8. 6 km x 8. 6 km, totaling 74 square kilometers. Each of these grid squares are our units of observation (we will refer to them as squares). This approach is similar to that of Buhaug & Rød (2006), with two important differences. First, their squares are much larger (100x100km). Second, they code the dependent variable considerably more crudely than is done in the ACLED dataset described below. Buhaug & Rød (2006) use the `scope'and `location'variables in the Uppsala / PRIO dataset. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["ACLED dataset", "Uppsala / PRIO dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Although much more suited to geographically disaggregated analysis than other datasets, this location dataset has some limititations, It does not record changes over time in the center location and extent of conflicts, and it reports the total extent of the conflict zone without distinguishing between areas that saw repeated and extensive fighting and those that only experienced scattered activities or individual events far from the center of the conflict. 3. 2 Disaggregated Dependent Variable: ACLED The ACLED dataset (Raleigh & Hegre, 2005) deals with these problems. The dataset takes the PRIO / Uppsala Armed Conflicts Dataset as its point of departure. The dataset is limited to events within conflicts that fall within the Uppsala conflict definition; conflicts involving two parties, one of which is a government, and fighting resulting in at least 25 battle deaths. 3 ACLED is designed to parse out both the temporal and spatial actions of rebels and governments within civil wars. 3See the PRIO / Uppsala Armed Conflict Data codebook for more information (Strand, Wilhelmsen & Gleditsch, 2004). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["PRIO / Uppsala Armed Conflicts Dataset", "ACLED dataset"], "descriptive_data": [], "vague_data": ["location dataset"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 The fundamental unit of observation in ACLED is the event. Figure 2 illustrates the ACLED data for Central Africa for the 1980s and the 1990s. Each location of a conflict event is represented by a symbol. In several of these locations, multiple events occured over the periods. Events always involve two actors – a rebel group and a government – and are coded to occur at a specific point location and on a specific day. Most of the events are battles, but the dataset also records other activities. The dataset includes information on and distinguishes between six types of events: battles resulting in no change of territory, battles resulting in a transfer of territory to the rebel actor, battles Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 resulting in government forces recapturing rebel held territory, establishment of a rebel base or headquarters, rebel activity that is not battle related (e. g. presence or the killing of civilians), and territorial transfers. The dataset consists of 4, 145 battle events for the 1960 – 2004 period. In the present analysis, we use 2, 530 of these. The remaining events were dropped as they either were in countries not included in the analysis, or because information was missing for one of the key variables. Each conflict event is associated with geographic coordinates and a date of occurrence. This information allows for spatial and temporal modeling of conflict events. The dataset used in this article covers 14 countries in Central Africa. 6 of them had a conflict in the 1960 – 2004 period according to the Uppsala / PRIO Armed Conflict Dataset (Gleditsch et al., 2002): Angola, Burundi, Republic of Congo (Brazzaville), Democratic Republic of Congo (Zaire), Rwanda, and Uganda. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Uppsala / PRIO Armed Conflict Dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The remaining 8 did not have a conflict: Cameroon, Central African Republic, Equatorial Guinea, Kenya, Malawi, Tanzania, and Zambia. 3. 3 Handling temporal and spatial dependence Both the squares and the conflicts events are obviously not fully independent – all events within one conflict are related to each other as an action in one location leads to a later retaliation by the opposing party or to further advances in proximate locations. Events in one conflict may also affect the likelihood of other conflicts, such as the spillover of the conflict in Rwanda into Eastern DRC. The statistical model employed to analyze these data must handle the dependence between observations. We will do this by explicitly modeling the probability of an event in a location as a function of preceding events in the same and in adjacent squares. We can do this since we know both the precise date and the precise geographic location of each event. We use an adaption of the calendar-time Cox regression model presented in Raknerud & Hegre (1997) for this purpose. In Cox regression, the dependent variable is the transition between `states of nature'-- the transition from peace to conflict in a square. A central concept is the hazard function, () t λ, which is closely related to the concept of transition probability: () t t Δ λ is approximately the probability of a transition in the Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The observations of positive events contain more information than the non-event observations We therefore sample asymmetrically: We sample all of the transition events and 1. 0 % of the non-transition events. 3. 4 Disaggregated Independent Variables Local level data on land, population, and elevation is available in the geospatial format of raster files with a resolution of 1km. Using Geographic Information systems (GIS), attributes from raster and point data are associated with the grid square in which they lie. In this way, spatial data is georeferenced to a location that is defined by the grid cell. This process results in a data structure in which each row has within it combined information on a square defined by the grid, the national level information in which is it located, and () () () ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ ∑ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ = = ∉ = t X t X t d d j j p j R i d j j p j w w t β β 1 1 exp exp at out breaks war a | square a in war Pr Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 the local data on physical geography and population from the raster data. These data can then be imported into statistical programs for analysis. We aggregate all data up to a grid of 8. 6x8. 6km squares. Each grid square is assigned attributes of the country it is in along with information from data disaggregated to the level of the individual squares. Figure 3 illustrates this grid as a fictive country somewhat smaller than the average size in our dataset (50x50 squares, or 430x430 km) with a fairly representative but stylized population distribution. The country has three major cities, one of which is the capital, and two smaller ones. A rebel group has its headquarters at the Eastern border. The ACLED data for the Central African conflicts were aggregated up to the 8. 6x8. 6km squares and merged with information on other explanatory variables aggregated to the same level. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["ACLED data"], "descriptive_data": [], "vague_data": ["raster data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Distance from Rebel Group Headquarters We coded the location of the headquarters of the rebel groups participating in the conflicts under study, and calculated the distance from each square to the most proximate rebel group headquarters (we do not know a priori which rebel group or government that will act in a particular square). As the `distance from capital'variable, it was coded as the distance in terms of squares and log-transformed. Border Square We coded squares as border squares if a national border runs through it. Such squares belong to more than one country and are not straightforward to code. We coded national- level information for border squares according to the following rule: A border square was considered to belong to the country that was most frequent among the eight neighboring squares. In tie cases, we assigned nationality randomly between the tied countries. Interaction country-square population This variable was created to test the population settlement pattern hypothesis. It is an interaction between population count at a location (square) as a portion of the country's total population. Road type Road type is a variable by ESRI that is available in the Digitial Chart of the World Data. It is a high resolution dataset at 1: 1, 000, 000 scale and consists of arcs which indicate road mass. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A number of different road indicators are available and we choose road line type to use in the analysis. Road type is defined by the following: The reference category (0) points out squares with dual lane / divided highways, other primary roads, or road connectors within urban areas (types 1 or 8 in the ESRI dataset). The second category include secondary roads (type 2), and the third combines squares with informal or tertiary roads (tracks, trails or footpaths) or no road registered at all (types 3 and 0, respectively, in the ESRI dataset). Figure 4 overlays the types of roads in the original dataset before our recategorization. The shaded area represents the portion of Africa for which we code Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["ESRI dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Religious School Enrollment in Pakistan A Look at the Data Tahir Andrabi1 Pomona College Jishnu Das The World Bank Asim Ijaz Khwaja Harvard University Tristan Zajonc Harvard University Abstract Bold assertions have been made in policy reports and popular articles on the high and increasing enrollment in Pakistani religious schools, commonly known as madrassas. Given the importance placed on the subject by policy makers in Pakistan and those internationally, it is troubling that none of the reports and articles reviewed based their analysis on publicly available data or established statistical methodologies. This paper uses published data sources and a census of schooling choice to show that existing estimates are inflated by an order of magnitude. Madrassas account for less than 1 percent of all enrollment in the country and there is no evidence of a dramatic increase in recent years. The educational landscape in Pakistan has changed substantially in the last decade, but this is due to an explosion of private schools, an important fact that has been left out of the debate on Pakistani education. Moreover, when we look at school choice, we find that no one explanation fits the data. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["census of schooling choice"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While most existing theories of madrassa enrollment are based on household attributes (for instance, a preference for religious schooling or the household ’ s access to other schooling options) the data show that among households with at least one child enrolled in a madrassa, 75 percent send their second (and / or third) child to a public or private school or both. Widely promoted theories simply do not explain this substantial variation within households. 1 Corresponding Author: Tahir Andrabi (tandrabi @ pomona. edu). This study would not have been possible without the enthusiasm and continuous support we received from Tara Vishwanath. Charles Griffin first encouraged us to look at the data. We thank Veena Das, Shehla Andrabi, Sehr Jalal, Ritva Reinikka and Carolina Sánchez for their encouragement and to Hedy Sladovich for her excellent editorial suggestions. The paper has also benefited from comments by Ismail Radwan, Naveeda Khan, Shahzad Sharjeel and Shanta Devarajan. The research department of the World Bank provided funding for this study through the Knowledge for Change trust fund. The findings, interpretations and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent. Working papers describe research in progress by the authors and are published to elicit comments and to further debate. Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized WPS3521 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 The only publicly available sources of data to document patterns of enrollment and available educational options for Pakistani families are household-based surveys. These are the official 1998 Census of Population (Government of Pakistan) 3, the 1991, 1998, and 2001 rounds of the Pakistan Integrated Household Survey4, and a 2003 census of schooling choice conducted by our research team. The fact that three sources use different definitions of madrassa enrollment, and were collected at different times by individuals with very different institutional affiliations provides independent verification of enrollment estimates and allows us to determine the sensitivity of our results. The household data tell us whether a child is enrolled full-time in a madrassa, but not whether a child goes for an hour on any given day to study the Quran. Therefore this data does not confound full-time with part-time attendees — a child who attends a public school during the day and a madrassa in the evening is recorded as enrolled in a public school. This is an important distinction since parents might use a modicum of madrassa or mosque based education to teach their children about religion. Consequently, if we contrast these household-based numbers with numbers from establishment-based reports, discrepancies can arise. From virtually any policy perspective, including evening quran classes in enrollment figures seems misguided. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["1998 Census of Population", "Pakistan Integrated Household Survey"], "descriptive_data": ["2003 census of schooling choice"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These data are only for three districts in the province of Punjab, but is very recent, was conducted by an independent team of academics, and is a complete census of all households in the selected villages. Consequently, it yields sufficient madrassa enrollment to examine correlations with household attributes in a meaningful manner (this data source provides information on four times as many children as the PIHS). Table A2 in the appendix shows how these different data sources are used in the paper. Each source asks about madrassa enrollment in a slightly different but comparable way. The population census (1998) asks about the field-of-education (“ What is name ’ s field of education? ”) with options that include (for instance) engineering, medicine, or religious education. This question is also asked of all literate adults irrespective of their current enrollment status, allowing for comparisons in the stock of religious education over time. The PIHS rounds ask, “ What type of school is name currently attending? ” with options that include government school, private school, or deeni-madrassa (religious schooling). Finally, the LEAPS census directly asks, “ Is the child enrolled in a madrassa or an Islamic education school? ” Fortunately these different questions all give rise to similar numbers. This is reassuring since it suggests that any one particular result is not driven by the specific question or definition that was used. 7 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 (www. statpak. gov. pk). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": ["census of private schools"], "vague_data": ["population census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 There may be grounds for skepticism about these estimates for madrassa enrollment. Since the data were collected prior to 2001, geopolitical changes after September 11 could have led to greater madrassa enrollment. In addition, the household-based survey faces the usual problems of accurately estimating a low-probability event — although enrollment is less than 1 percent in these surveys, the sampling error is large (see Bauman, 2001, for a description of similar problems in estimating home-schooling in the United States). Finally, while the census of populations does not face the problem of small samples, it is not that recent (1998) and some may have reservations regarding the quality of government data. 10 The LEAPS census of schooling choice conducted in 2003 provides a rough check on these numbers (see appendix for details). This census was conducted in three districts of Punjab and villages were chosen randomly based on the criterion that each village must have at least one private school. Typically, this means that the villages lies somewhere between fully urban and fully rural populations and are not representative of the districts that they are in. Estimates from the LEAPS census show that as a percentage of enrolled children, the numbers in two of the three districts are slightly higher than those of the population census. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": [], "vague_data": ["household-based survey", "census of populations"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the third (Rahim Yar Khan) there is a large difference, with the census reporting that 1 percent of all school-going children attended madrassas, and the LEAPS showing that the fraction is closer to 3. 7 percent (Table II). There are three potential explanations for this difference. First, the LEAPS data is not representative of the district and could be off the mark for districts with wide variation in madrassa enrollment across rural and urban samples. Second, the experience of the last five years could have varied dramatically across districts — in some, the enrollment fractions did not change and in others it increased substantially. Third, the data could point to systematic problems with the census estimates from certain districts, or the statistical problems that arise when we try to estimate low-probability events. 3. 3 Explaining the Differences A number of reasons could account for differences between the estimates presented here and those in the popular press. 1. Differences in the sampling unit. Our estimates are all based on household surveys — an interviewer goes to a household and asks about the enrollment status of every child. Some census estimates of home rather than religious schooling in the United States — the former ranges from 1 to 2 percent (Bauman 2001) while the latter is closer to 8 percent (National Center for Education Statistics, 2001). 10 In our own analysis, we find the quality of the data generated by the Federal Bureau of Statistics in Pakistan to be consistently high. We have used the FBS Census of Private Schools (PEIP, 2000) to guide our fieldwork and feasibility study for LEAPS and found it tallied with the situation on the ground quite well, even in remote villages. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["LEAPS", "FBS Census of Private Schools"], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 4. 1 Variation across Districts Pakistan is divided administratively into four provinces with 102 districts — Punjab, Balochistan, North-West Frontier Provinces (NWFP), and Sindh — plus the federal capital Islamabad, the Federally Administered Tribal Areas (FATA), the federally administered Northern Areas and Azad Jammu and Kashmir (AJK). The four provinces or Punjab, Balochistan, Sindh and NWFP, together with Islamabad, account for more than 97 percent of the population. Geographically, parts of Balochistan, the NWFP and FATA border Afghanistan. Sindh and Balochistan are sparsely populated provinces, with the exception of Karachi in Sindh, which is the single biggest metropolis in the country with a population approaching 10 million. We use data from the population census, 1998, as well as the census of private schooling, 2000, to provide estimates of madrassa, private, and government school enrollment in each district except for those in the province of FATA. The geographical dispersion of madrassa enrollment depends on how we define madrassa prevalence. There are three alternatives. We could present a geographical breakdown of the total number of children enrolled in madrassas. This number is related to the total population of the district, and may thus reflect only the size of the district relative to others. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["population census"], "descriptive_data": ["census of private schooling, 2000"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Another option is to use the equivalent of the gross enrollment ratio (GER), defined as the total enrollment divided by the number of “ eligible ” children — in this case, children between the ages of 5-19. This statistic provides an estimate of the “ penetration ” of madrassas, but it does not take into account the overall enrollment decision of the family. Thus, a district with two children enrolled in madrassas, and 20 children enrolled in private or public schools out of a total of 100 children will have exactly the same gross enrollment ratio (GER) as a district with two children enrolled in madrassas and 98 children enrolled in regular schools. To the extent that we want to distinguish between these two districts, a third statistic, the ratio of children enrolled in madrassas to total enrollment (the madrassa fraction of enrollment or MFOE), can also be used. The picture changes dramatically depending on whether we use the raw numbers or the ratio of children enrolled in madrassas to total enrollment. However, since enrollment in madrassas is highly correlated with total enrollment, there is little difference in the pattern of madrassa enrollment whether we use the GER or the fraction of enrolled children in madrassas. Figure 1a shows the number of children enrolled in madrassas for every district in the country. As expected, numbers are closely linked to population size — the three most populated districts account for one-quarter of the enrollment, with the bulk of enrollment in large urban Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 between 1948 and 1965, with increases in the percentage of adults with religious education in the cohorts born after this date. There is also wide geographical dispersion in the prevalence of madrassa education in Pakistan. Although all districts report that less than 2. 5 percent of children in the relevant age group (children between the ages of 5 and 19) are going to madrassas, the Pashto speaking belt that borders Afghanistan stands out in terms of the popularity of madrassas as an educational choice. The notion that the madrassa movement coincided with resistance to the Soviet invasion of Afghanistan is supported by the 1998 data from the population census. The increase in the stock of religiously educated individuals starts with the cohort that came of age in 1979 (the year of the Soviet invasion of Afghanistan) and the largest increase is for the cohort co-terminus with the rise of the Taliban. Combined with the fact that the largest enrollment percentage in Pakistan is in the Pashtun belt bordering Afghanistan, this suggests events in neighboring Afghanistan influence madrassa enrollment. Is there something intrinsic about Pashtun sensibility or tribal culture that leads to higher madrassa enrollment? The differentiation of the Pashtun and non-Pashtun districts does not extend to Pashtun and non-Pashtun households in the LEAPS data. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["LEAPS data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "No data on religiosity was collected as part of the census and a more recent and detailed household survey that includes information on time-use elicits little variation — everyone reports high mosque attendance and regular prayers. An alternative, suggested by David Evans at Harvard University, which we pursue here, is to use recent developments in the use of “ names. ” Research by Fryer and Leavitt (2004) demonstrates the increasing use of names to define race identity in the United States. We postulate that households who named (at least) one child “ Osama ” (also spelt Usamah, Usamma or Usama) are more likely to favor a radical brand of Islam. The use of the name Osama was minimal until 1998, and then peaks in 1998 and 2001, following disruptive events. Of course, the naming of the child may reflect name recognition rather Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "displaced due to armed conflict, situations of generalized violence and violations of human rights. 28 Data on IDPs monitored by IDMC are disaggregated and currently published separately for conflict-induced displacement and disaster-induced displacement. 29 At the country level the IOM ’ s Displacement Tracking Matrix (DTM) 30 provides data on IDPs in both conflict and natural disaster settings (activated in all major natural disaster contexts in recent years). Global data on conflict-induced internal displacement reflect variations in how IDPs are defined across situations. There is no consensus on how far a person must flee in order to be considered internally displaced. The definition of internal displacement for nomadic populations, which account for a significant share of IDPs in the Horn of Africa and increasingly in the Sahel, is open to controversy. 31 Moreover, while some countries register IDP children born in displacement (e. g. Azerbaijan, Cyprus and Georgia), other countries do not (IDMC 2015). The crafting of a definition for IDPs and its application in a particular context may be heavily influenced by local and national politics in conflict and post-conflict countries, as well as the direct link between estimates of displaced populations and humanitarian assistance, which can lead to both over- and under-reporting. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Displacement Tracking Matrix"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In other contexts, deregistration signifies not the achievement of a durable solution but rather the end of state or international support (IDMC 2015). 33 The absence of a clear and operational approach to defining the ‘ end ’ of internal displacement may be one of the factors behind the continued overall increase in the global numbers of IDPs. Lack of clarity around when displacement ends also leaves room for political manipulation. Governments may find it politically expedient to artificially prolong IDP status by deterring returns or local integration, for example in Azerbaijan and Georgia to promote claims over territory (Beau 2003). In other contexts, national 28 UNHCR ’ s IDP data focus only on internally displaced populations to which it extends protection or assistance. IDMC coverage of IDP data is more expansive and in 2015 included additional data on: (a) 26 countries accounting for 4. 5 million IDPs including some significant IDP hosting countries (Turkey, India, Ethiopia, Bangladesh and Kenya); and (b) IDPs in countries where UNHCR is active who are not protected or assisted by the agency. In 2015, IDMC ’ s aggregate figure for conflict-induced internal displacement was 3. 3 million higher than UNHCR ’ s aggregate figure for IDPs protected or assisted by the agency. 29 IDMC ’ s 2016 report presents both data sets alongside each other. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["IDP data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In certain contexts, there can be significant overlaps in these two groups; however data systems may be maintained separately for conflict-induced displacement and natural disasters (e. g. in Afghanistan) leading to possible gaps or double counting if these categories are combined. 30 The IOM Displacement Tracking Matrix (DTM) is a system to track and monitor displacement and population mobility. It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route. It has been active in over 40 countries since its inception in 2004. See http: / / www. globaldtm. info /. 31 This is typically defined as nomads not having access to their traditional routes, but routes can vary. 32 IDMC has recently adjusted their methodology to facilitate greater comparability across situations and improvements are reflected in IDMC ’ s end-2015 data. 33 This is not necessarily a problem if the purpose of the registration system is to delineate entitlements to assistance rather than to determine status. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["IOM Displacement Tracking Matrix"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": " The Sri Lanka: Emergency Northern Recovery Project aimed to support government efforts to resettle IDPs in the Northern Province by creating an enabling environment through: (i) emergency assistance to IDPs; (ii) a work-fare program; and (iii) rehabilitation and reconstruction of essential public and economic infrastructure. The project closed in December 2013 and was rated satisfactory.  The Mitigate the Impact of Syrian Displacement on Jordan Project assisted the government to maintain access to essential healthcare services and basic household needs for the Jordanian population affected by the influx of Syrian refugees. The project closed in July 2014 and implementation was rated satisfactory.  The ongoing Azerbaijan IDP Living Standards and Livelihoods Project aims to improve living conditions and increase economic self-reliance of targeted IDPs.  The ongoing Lebanon Municipal Services Emergency Project addresses urgent community priorities in selected municipal services, targeting areas most affected by the influx of Syrian refugees in order to mitigate the impact on host communities, including: (i) provision of high priority municipal services and initiatives that promote social interaction and collaboration; and (ii) larger works to rehabilitate / develop critical infrastructure in the areas of solid waste management, roads improvement, water and sanitation and community infrastructure.  The ongoing Jordan Emergency Services and Social Resilience Project aims to assist municipalities and host communities to address the immediate service delivery Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "impacts of Syrian refugees and strengthen municipal capacity to support local economic development.  The FATA Temporarily Displaced Persons Emergency Recovery Project in Pakistan will promote child health, and strengthen emergency response safety net delivery systems in the affected Federally Administered Tribal Areas (FATA) by promoting the early recovery of approximately 120, 000 displaced families through cash grants.  The Great Lakes Displaced Persons and Border Communities Program is a regional program under preparation to target IDPs, refugee and host populations in the Democratic Republic of Congo (DRC) and Zambia with investments in socio-economic services, livelihood support, land access and social cohesion.  The Development Response to Displacement Impacts Project in the Horn of Africa will help improve access to basic social services, expand economic opportunities, and enhance environmental management for communities hosting refugees in target areas of Ethiopia, Uganda and Djibouti. The project is the first phase of an expanded program to include other countries affected by forced displacement. C. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Historical trends and patterns of forced displacement: Insights from available global data This section provides an overview of the available global data on conflict-induced forced displacement, drawing largely on UNHCR ’ s published data on asylum-seekers, refugees and IDPs. 41 Data are presented visually in a series of figures to highlight the scope and character of the current global forced displacement crisis and identify historical trends and anomalies. These data largely focus on the scale and trends in conflict-induced displacement (i. e. the numbers of forced displaced) with some coverage of other elements such as demographics, location and accommodation. Globally, there has been an unprecedented increase in the numbers of displaced people over the last decade, largely explained by the expansion in the number of reported IDPs. Historical data show a substantial increase in the numbers of forced displaced (see Figure 3), however the expanding geographical scope and quality of displacement monitoring systems are likely to account for much of the increase in forced displacement figures. The numbers of refugees under UNHCR ’ s mandate have recorded a number of variations over time, peaking in the early 1990s (at a level 10 percent over 2015 numbers) with the conflict and displacement associated with the end of the Cold War. The number of Palestinian refugees steadily has increased steadily over time, largely as a result of natural growth. IDP numbers (for which the underlying data are the least robust) have recorded the largest progression as a consequence of: (a) the expanded scope of monitoring efforts (IDPs were not counted before 1989 and methodologies were 41 UNHCR ’ s data only include IDPs protected or assisted by the agency. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["global data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 4: Refugees and Asylum-Seekers by Migratory Path 1951 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 5: Significant Historical Crises as a Share of Total Forced Displaced 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes IDPs protected or assisted by UNHCR, asylum-seekers and refugees. Excludes IDPs not protected or assisted by UNHCR and Palestinian refugees under UNRWA ’ s mandate. Figure 6: Top 15 Countries of Origin end-2015 Source: IDMC Global Report on Internal Displacement 2016, UNHCR Global Trends 2015, UNRWA A small number of countries carry the burden of hosting the majority of refugees. Historically since 1991, 15 asylum countries, overwhelmingly in the developing world, have hosted more than 50 percent of refugees and asylum-seekers (see Figure 7). 44 By the end of 2015, while almost all countries in the world were hosting refugees, the burden was unevenly shared (see Figure 8). Only seven countries hosted more 44 Major host countries are identified based on the cumulative number of refugees and asylum-seekers over the period 1991-2015. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "than 500, 000 refugees, together accounting for half of all refugees and people in refugee-like situations (excluding Palestine refugees). Major refugee hosting countries are typically the neighbors of countries of origin. For example, Syria ’ s neighbors (Turkey, Lebanon, and Jordan) together accounted for 27 percent of total refugee numbers; Afghanistan ’ s neighbors (Pakistan and the Islamic Republic of Iran) together accounted for 16 percent; and Somalia ’ s and South Sudan ’ s neighbors (Ethiopia, Kenya and Uganda) together accounted for 11 percent. Some countries (Lebanon, Jordan and Turkey) are hosting a particularly large share of refugees relative to their population (see Figure 10). 45 However, in all other countries, the number of refugees as a percentage of the population is 3 percent or lower, and most often below 1 percent. Figure 7: Top 15 Host Countries as a Share of Total Refugees and Asylum-Seekers 1991 – 2015 Source: UNHCR Statistical Online Population Database Note: Includes refugees, people in refugee-like situations and asylum-seekers. Excludes Palestinian refugees under UNRWA ’ s mandate. 45 Nauru is a special case since the Australian government funds the offshore processing center where refugees and asylum-seekers intercepted at sea are detained pending determination of their status. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the case of refugees, host countries rarely facilitate naturalization, only a minority of refugees ever gets resettled in third countries and voluntary repatriation is frequently not a realistic option for several reasons. International law provides for three possible durable solutions for refugees, including integration within the area of displacement, repatriation to their home country or resettlement in a third country; refugee status can also cease when there are no longer compelling reasons for an individual to refuse to avail themselves of the protection of their country of origin. In 2015 only 119, 265 refugees under UNHCR ’ s mandate were either resettled, naturalized53 or ceased to be refugees; and there were only 201, 415 voluntary returns, mostly Afghanistan, Sudan, Somalia and CAR (see Figure 17). These statistics highlight the significant gap between the unprecedented numbers of refugees and the capacity of the international community to provide durable solutions. For the 85 percent of refugees hosted in developing countries, there are only minute prospects for resettlement. Figure 17: Durable Solutions Relative to Refugee Stock 2015 Source: UNHCR Global Trends 2015 Global statistics that show the low rate of refugee returns masks the variation in returns over historical periods and across displacement crises — with significant voluntary returns for some countries. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 18: Significant Refugee Returns by Country of Origin 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Countries selected based on their cumulative returns of refugees over the period 1991-2015. Return does not necessarily lead to the full reintegration of a person into their home country or area of origin. In the absence of global data on the success of reintegration following return, data on returns appear to be taken as indication of sustainable return. In reality, many returnees face impediments to reintegration and continue to have specific economic and social vulnerabilities linked to their displacement. They may not be able to reclaim land, access sufficient financial resources (e. g. accumulated during their displacement) or reestablish social networks in areas of origin, which are critical factors for successful reintegration (World Bank 2015). Sustainable refugee return is therefore not a one-off event but a process that provides returnees with adequate safety, housing, livelihoods and services that address their specific vulnerabilities and reduce the likelihood of secondary displacement (World Bank 2015). Figure 19: Voluntary Returns of Refugees 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of refugees and people in refugee- like situations protected or assisted by UNHCR. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 20: Returns of IDPs Protected or Assisted by UNHCR 1993 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of IDPs and people in IDP-like situations assisted and protected by UNHCR. Consequently, the average length of protracted refugee situations has increased over the past two decades according to UNHCR estimates (see Table 2). UNHCR estimates that the average length of ongoing Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes high-income (OECD and non-OECD) countries. Excludes Palestinian refugees under UNRWA ’ s mandate. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 22: Numbers of Refugees in Ongoing Refugee Situations end-2014 Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes Palestinian refugees under UNRWA ’ s mandate. D. Data on asylum-seekers, refugees and IDPs: sources, applications and credibility In this section, a distinction is made between: (a) the collection of source data; and (b) the compilation of data across sources (within a country or across countries). In general, there is a delineation of roles between data collectors and data compilers, however there are organizations, such as UNHCR, IOM and the Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat (OCHA), 59 that are involved in both data collection and compilation activities. Data collection: Sources for refugees, asylum-seekers and IDPs60 Collection of primary data on forcibly displaced persons is generally undertaken by national governments through their national statistical offices, line ministries or immigration agencies. However, where countries lack the capacity to undertake this work, they may rely on international organizations as well as international and local NGOs to collect data or undertake estimates. 61 In general, governments tend to collect data on refugees in developed countries, while UNHCR and NGOs tend to collect data on refugees in developing countries. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Humanitarian organizations such as UNHCR and OCHA as well as international organizations such as IOM are also involved in the collection of data on IDPs, often involving international and local 59 OCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. See http: / / www. unocha. org /. 60 This section draws heavily on the “ Report of Statistics Norway and the Office of the United Nations High Commissioner for Refugees on statistics on refugees and IDPs ” presented at the UNSD in March 2015. 61 The number of countries where UNHCR exclusively collects data on refugees declined from 76 in 2010 to 72 in 2014, while the proportion of countries where refugee data were exclusively provided by governments gradually increased over the same period from 33 to 38 percent. In 2014, the proportion of countries where data were provided through collection conducted jointly by governments and UNHCR was 15 percent, while in the remaining proportion (13 percent), refugee data were provided exclusively by NGOs and other organizations. In 2014, more than 173 countries and territories provided data on refugees. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "NGOs. Initiatives such as JIPS, a collaborative project of UN and NGO actors, have also been established to support governments and operational organizations to design and implement data collection processes. There are a variety of data sources for generating statistics on forced displacement, each of which has strengths and weaknesses. Despite the significant challenges, large amounts of data are collected and disseminated every year. The main data sources and methods for the generation of statistics on forcibly displaced populations include: (a) registration of refugees and asylum-seekers; (b) registration of IDPs; (c) profiling of IDPs; (d) population movement tracking systems; (e) national population censuses; (f) sample surveys; (g) border crossings; (h) administrative records and registers; (i) general population registers; and (j) a variety of estimation methods for producing statistics when adequate and reliable data on individuals are unavailable (UNSD 2014). Several of these data sources might be used together to triangulate estimates of stocks and flows for a particular displacement situation. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["sample surveys", "general population registers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some data sources (such as registration systems and population censuses) are more appropriate for estimating stocks of asylum-seekers, refugees and IDPs at a particular point in time, while other data sources (such as population tracking systems and border crossings) are more appropriate for estimating flows over a specific period. In general, there is a lack of comprehensive and up-to-date data available on all stocks and flows for a particular displacement situation (see Table 4). Consequently, data on flows might be used to estimate stocks, for example in the absence of government data, the stock of refugees in many industrialized countries is estimated by UNHCR based on 10 years of individual asylum-seeker recognition. And, especially in the case of IDPs, changes in the total population combined with some contextual analysis, may be used to deduce estimates of new internal displacement or returns. However, these approximations are flawed unless data on all other flows (births, deaths, repatriation etc.) are also available, which is not usually the case. Even a static figure for the stock of IDPs in a particular location might obscure substantial flows including new displacement and returns. Moreover, there are no common definitions of the various stocks and flows, and therefore the risk of double counting or gaps cannot be discounted. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population censuses", "population tracking systems"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For example, IDPs who subsequently cross international borders may be counted as both IDPs and refugees (e. g. in the case of the Syrian displacement crisis). Table 4: Stocks and Flows Stock Increases Decreases Asylum- seekers  New applications for asylum, separately identifying individuals who were previously IDPs  Positive decisions (convention status, complementary protection status)  Rejected  Otherwise closed Refugees  Spontaneous arrivals (group recognition, temporary protection, individual recognition), separately identifying individuals who were previously IDPs  Resettlement arrivals  Births  Administrative corrections  Repatriation  Resettlement  Cessation  Naturalization  Deaths  Administrative corrections IDPs  New internal displacement  Births  Administrative corrections  Cross border flight, becoming an asylum-seeker or refugee  Return  Settlement elsewhere in the country  Local integration  Administrative corrections Source: UNHCR Global Trends, IDMC Forced Displacement Data Model Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Moreover, some refugees may not register because they are unaware that they should, and others may be reluctant to do so because they are skeptical of the integrity of the registration process (e. g. fair access to entitlements or opportunities for durable solutions) or lack confidence in protection measures. Individuals in irregular migration flows may also choose not to apply for asylum due to fear of declaring themselves to the authorities. A significant challenge with refugee registers is keeping them up to date. Individual registration can provide a robust snapshot of the stock of refugees and asylum-seekers, but registers need to be updated regularly to reflect flows, i. e. increases in refugee and asylum-seeker numbers (births, new arrivals) and decreases (deaths, departures, durable solutions). In situations of sudden mass influxes, existing registration capacity may not be adequate and the scope of registration data is then rationalized. 65 Additionally, it may not be possible to capture all demographic changes in the case of highly mobile populations. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee registers"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In principle, individual registration of IDPs is not used to determine the ‘ status ’ of an IDP, since IDPs have the same rights and entitlements as other citizens and do not need to apply or be granted a special legal status. 66 Rather, registration of IDPs can provide a basis for: (a) establishing the number, location, and key demographic characteristics of displaced populations; (b) providing protection and assistance; (c) keeping track of family relationships; (d) preventing fraudulent access to scarce humanitarian assistance; (e) facilitating the issuance of temporary identity cards to replace lost personal documentation (Brookings 2008); and (f) providing social security benefits. 67 Full IDP registration by international organizations is not 62 By the end of 2014, individual refugee registration was the source of about 77 percent of the data on refugees; estimation accounted for 13 percent of data, combined estimation and registration for 5 percent and other sources for 5 percent (UNHCR 2016). 63 UNHCR may undertake registration activities when national governments do not have the capacity to do so. 64 Additional data can also be recorded such as education and occupation. 65 Insufficient budgetary resources, staff, training or materials. 66 Countries with national legislation that provides a legal status for IDPs are an exception to this international standard. 67 The scope of data collected depends on the objectives of the registration exercise, for example in Kenya, registration of individuals displaced by the 2007 and 2008 post-election violence excluded ‘ integrated ’ IDPs, i. e. those who had Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "When national or international actors provide assistance, there may be an incentive for people to register in camps even if they are staying elsewhere, or to register in multiple locations (Brookings 2011). 69 Furthermore, registration data provide only a snapshot of the stock of IDPs at a particular point in time and may be out of date if registers are not maintained regularly. Registration methodologies can vary across displacement situations. For example, families may be registered rather than individuals and the population estimated based on an assumption of average family size, which can differ among organizations (UNSD 2014). IDPs may be required to present documentation, meet specific criteria or re-register periodically to maintain their benefits, which affects aggregate numbers (IDMC 2015). For example, in Ukraine, pre-requisites for IDP registration (including valid documentation, arrival from a recognized conflict zone and permanent residence registration in recognized conflict zone) means that people displaced within a non-government controlled area, people displaced from a non- recognized conflict zone in a government controlled area, unaccompanied children or people without current / valid identification are not counted as IDPs (IDMC 2015). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["registration data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "this way, profiling of IDP situations aims to underpin advocacy, protection and assistance activities as well as support the achievement of durable solutions by informing joint strategies between government, humanitarian and development actors. Profiling provides an overview of displacement-affected populations through the collection and analysis of minimum core data (number of IDPs, disaggregated by location, age and sex) and where possible additional quantitative and qualitative data (causes of displacement, patterns of displacement, protection concerns, humanitarian needs, vulnerabilities, and aspirations and prospects for durable solutions). Profiling may utilize data collection techniques at individual, household and community levels, often combining population estimation methods, a review of secondary data, focus group discussions, household surveys and key informant interviews targeted specifically at forcibly displaced populations (UNSD 2014). 70 Profiling methods focus on displacement situations, rather than only on displaced populations, and therefore includes comparisons to conditions in the host population. IDMC estimates that humanitarian profiling data forms the basis for 18 of their 60 country estimates and around 63 percent of their annual estimates (IDMC 2015), with the largest volume of data on conflict-induced internal displacement provided by OCHA followed by IOM. There are several practical challenges associated with IDP profiling exercises in displacement situations. Insecurity or terrain may impede access to displaced populations in conflict-affected or hard to reach areas. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["humanitarian profiling data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For example, profiling exercises in Pakistan do not cover all IDPs or areas affected by displacement due to insecurity, and in Afghanistan profiling of IDPs by UNHCR underestimates the scale of the initial displacement as IDPs are only interviewed once displacement sites are accessible, if they are profiled at all (IDMC 2015). Additional challenges include unwillingness of IDPs to participate due to fear of persecution, and mobile populations (IDMC 2008). There may also be political pressures to inflate or reduce numbers. Population movement tracking systems In situations where the movement of displaced populations is fluid or continuous, a movement tracking system can be a useful tool for providing rough estimates of population flows, including recurrent displacements. Movement tracking systems are useful for monitoring fluid population movements (including spontaneous and organized, internal and cross-border, and returns and resettlement) in remote or inaccessible routes and locations (including displacement sites, places of origin, and places of return and resettlement). UNHCR, IOM and other organizations have developed methods for tracking and monitoring movements of IDPs in over 30 countries, particularly in cases of disaster-induced displacement, but also in some cases of conflict-induced displacement (UNSD 2014). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "migration, and reason for movement including asylum / refugee protection (or conflict-induced internal migration) (UNHCR 2016), or a specific question to identify IDPs or refugees. However, not all censuses cover refugees and asylum-seekers (if foreigners are considered outside the scope of the census or because they are considered a special category), 71 nor is it common practice for national censuses to include questions related to forced displacement. 72 Nevertheless, there are several examples of national censuses that have included relevant questions on forced displacement. 73, 74 In the case of protracted internal displacement situations, IDPs are likely to be included in national censuses; however, census instruments may be subject to manipulation for political purposes. There are several drawbacks of population censuses including their cost, the significant training required for enumerators to ensure consistent answers to questions on forced displacement, impediments to field operations and data processing (such as weather conditions and technical problems), the relative infrequency with which they are carried out, and the long processing time before data and statistics become available, which have consequences for the timeliness of data. Moreover, often censuses are not conducted in contested territory or conflict zones where many displaced persons reside, and this limits the completeness of the data. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population censuses"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Nevertheless, censuses are the only nation-wide source of population data for potentially providing estimates of displaced populations, especially those in non-camp settings, and often provide a basis for sampling frames for survey instruments (UNHCR 2016). Capturing displacement situations in official statistics also increases their visibility. Sample surveys Sample surveys can potentially provide a rich source of data on forcibly displaced populations. Compared with censuses and registers, sample surveys allow more detailed questions to be asked about the characteristics and situations of households. If survey instruments identify displaced populations based on individuals ’ and households ’ self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status (UNSD 2014). There are opportunities to mainstream forced displacement into international survey instruments, but this has only been done in a handful of cases. Several standardized international sample surveys have been designed for special purposes including the Living Standards Measurement Study (LSMS), 75 Labor Force 71 The UN census recommendations for the 2010 World Population and Housing Census Programme stipulate that refugees and IDPs living in camps should be counted and their numbers disaggregated in population statistics, however there is no requirement to separately distinguish displaced people living outside of camps (UNHCR 2016). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The United Nations Economic Commission for Europe ’ s (UNECE) guidelines include a question on reason for migration, population with a refugee-like background and IDPs as non-core topics / questions (UNHCR 2016). 72 While most countries include questions on country of birth and citizenship, only about 40 percent include a question on year of migration, less than a quarter include a question on reason for international migration, and about a fifth include a question on the reason for internal migration (UNHCR 2016). 73 E. g. Kyrgyz Republic 1999 (refugee status), West Bank and Gaza 2007 (refugee status), Zambia 2000 and 2010 (purpose of stay), Germany 1970 (federal refugee identity card), Greece 2001 (reason for settling in Greece), Sudan and South Sudan 2008 (type of household including IDP and refugee), Liberia 1990 (ever displaced by war since 1990), Uganda 2014 (refugees). 74 UNHCR is collaborating with the Statistics Norway on systematically embedding forcibly displaced peoples in national statistics exercises and collaborates with national authorities and with UNFPA in various countries on the design of census exercises that include refugees, IDPs, returnees and stateless persons. 75 LSMS is a household survey program housed in the Bank's Development Research Group that provides technical assistance to national statistical offices in the design and implementation of multi-topic household surveys covering household behavior, welfare and interactions with government policies. All data gathered through the LSMS is published online in the Bank ’ s Central Microdata Catalog. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["LSMS", "Central Microdata Catalog"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Border crossings The registration of people crossing internationals borders is conducted in many countries, and in some cases these data are used to estimate migration flows. Identifying refugees among people crossing borders is a significant challenge, particularly if individuals decide not to apply for asylum or refugee status (UNSD 2014). Additional problems associated with the collection of data on border crossings include: (a) difficulties distinguishing migrants from other people crossing a border, such as tourists, commuters, traders and truck drivers; (b) lack of capacity of many border posts and officials to handle large migration flows; (c) less scrutiny and diligence of emigration flows compared with immigration flow; and (d) lack of tight controls at most borders and the high incidence of undocumented or irregular crossings (UNSD 2014). Administrative records and registers Many countries have administrative records or registers of immigrants that could generate statistics on asylum-seekers and refugees. In particular, data on residence permits issued to refugees or asylum- seekers could be used to generate statistics on both flows and stocks of refugees. 79 For example, Eurostat collects and disseminates data on residence permits granted to those with refugee status and subsidiary protection (UNSD 2014). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data on residence permits"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "General population registers In a small but growing number of countries, information from the central population register is the main source of migration statistics. 80 While population registers may generate statistics on both internal and international migration (if they record changes of residence, and international arrivals and departures) they do not typically record reasons for movement. However, it may be possible to link data from the central population register to those from immigration or border authorities to identify refugees and asylum- 76 Using standard ILO definitions, Labor Force Surveys collect data on work-related issues and provide a basis for measuring employment and unemployment indicators. They are typically conducted monthly in developed countries and quarterly or annually in developing countries. 77 Supported by USAID and implemented by ICF International, the DHS Program has collected, analyzed and disseminated data on population, health, HIV and nutrition through more than 300 surveys in over 90 countries. 78 MICS is an initiative of UNICEF that assists countries in collecting and analyzing health and education data in order to fill data gaps for monitoring the situation of children and women. 79 Many refugee hosting countries issue a form of identification, either specific to refugees or based on national identification documents or those issued to non-national residents. In many cases where such documents are not issued, refugee identity cards are issued in collaboration with UNHCR. 80 A population register provides a mechanism for the continuous recording of selected data on the resident population including a unique identification number, date of birth, sex, marital status, place of birth, place of residence, citizenship and language and possibly also socio-economic data, such as occupation or education. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Labor Force Surveys", "DHS Program"], "descriptive_data": ["central population register"], "vague_data": ["national identification documents", "population register"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Significant among these are: (a) the lack of capacity of national statistical agencies in many developing countries to collect robust data on refugees; (b) weak or incomplete monitoring of refugees dispersed within host communities; (c) lack of capacity to maintain up to date information on refugees (reflecting new arrivals, 81 General population registers may also provide opportunities for more elaborate analysis of the integration of refugees in asylum countries, as the data could be linked to other administrative registers, for example on labor and education (UNSD 2014). 82 UNHCR collects, compiles and publishes data on asylum-seekers, refugees and IDPs protected or assisted by UNHCR, including populations in refugee-like or IDP-like situations. 83 Established in 1863, the ICRC ’ s mission is to ensure humanitarian protection and assistance for victims of armed conflict and other situations of violence. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["General population registers"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ICRC ’ s work is based on the Geneva Conventions of 1949, their Additional Protocols, its Statutes — and those of the International Red Cross and Red Crescent Movement — and the resolutions of the International Conferences of the Red Cross and Red Crescent. 84 WFP is the food assistance branch of the United Nations and the world's largest humanitarian organization addressing hunger and promoting food security. 85 See: popstats. unhcr. org. 86 IDP data are only included from 1998 onwards. 87 See: http: / / data. unhcr. org. Currently the Burundi situation, Yemen (regional refugee and migrant response plan), DRC regional refugee response, Mediterranean (refugees / migrants emergency response), CAR, Côte d ’ Ivoire, Syria Emergency, Sahel Emergency, South Sudan Situation, Horn of Africa Emergency, and the Liberia Portal. 88 IOM ’ s new Global Migration Data Analysis Centre provides limited data on global migration trends such as data on asylum application in Europe and selected countries (including demographics, country of origin, and country of asylum). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["IDP data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Consequently, data may be aggregated across situations and countries even though the source data were generated for different purposes and using different methodologies. (b) Very few national governments collect or report up-to-date data on IDPs. Political will as well as the resources and capacities to carry out effective and timely data collection vary across countries. In 2015, only five governments responded to IDMC requests for data. 90 Consequently, data on internal displacement is outdated in several countries and is at risk of becoming outdated in others, including countries like Afghanistan with large IDP populations (IDMC 2016). 91 Problems of outdated and ‘ decaying ’ data are especially problematic in protracted displacement situations — international organizations reallocate resources to more visible or pressing displacement crises (IDMC 2016). (c) Limited official standards and guidance on how to collect data on IDPs in the field. IDMC may rely on more than one source in some countries (each gathering data for different purposes and using different definitions and methodologies with little or no coordination) and so double counting and gaps cannot be excluded (IDMC 2015). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "IDMC reported that changes in the way their sources collect and analyze their data led to dramatic adjustments in 2014 figures, e. g. in Côte d ’ Ivoire a profiling exercise led to a four-fold increase in IDMC ’ s estimate, and in Nigeria improvements in the national capacity to collect information led to a 70 percent decrease in IDMC ’ s estimate (IDMC 2015). (d) The lack of complete data for most countries. The fluidity of population movements, insecurity and other access restrictions (lack of transport infrastructure, high logistical costs and government restrictions) make primary data collection almost impossible in many areas (IDMC 2015). Consequently, data collectors often focus on IDPs in relatively stable, secure and accessible places (e. g. camps) and estimations in the most difficult areas rely on ‘ local informers ’ (e. g. local authorities, NGOs etc.) who may or may not have the capacity to provide adequate numbers, or which leaves data collection and reporting subject to the influence of parties to the conflict (IDMC 2016). Figures do not always capture ‘ invisible ’ IDPs that are living in individual accommodation dispersed in host communities, and therefore figures are 89 The statistics of UNHCR on IDPs are limited to countries (numbering 24 in 2013) where the organization is engaged in assisting or protecting IDPs. 90 Azerbaijan, Bosnia and Herzegovina, Georgia, Ireland and Mexico (IDMC 2016) 91 IDMC notes that outdated or ‘ decaying ’ data are a problem in 12 of the 53 conflict- or violence-affected countries it monitors (Armenia, Bangladesh, Congo, Cyprus, Guatemala, Macedonia, Nepal, Papua New Guinea, Thailand, Togo, Turkey and Uganda), accounting for approximately 20 percent of IDPs worldwide (IDMC 2016). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "IDMC reports that of the 52 countries it monitored in 2015, it was only able to obtain data on new displacements in 20 countries, 92 on returns in 20 countries, on integration in one country, on resettlement in two countries, on children born in displacement in two countries and on deaths in one country; no data was obtained for any county on cross-border flight in 2015 (IDMC 2016). Moreover, existing systems for collecting data on refugees and asylum-seekers make it difficult to know how many were formerly IDPs, and it is possible for some people to be simultaneously counted in both categories, e. g. in the case of the Syrian displacement crisis (IDMC 2016). If no data on returns are available, IDMC risks overstating the number of IDPs (IDMC 2015). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Disaggregated data on IDPs who are not protected or assisted by UNHCR are collected by other agencies, including IOM, but data are not comprehensive and therefore not published in IDMC ’ s global reports. 93 The difficulties of collecting disaggregated data on locations of IDPs are compounded by the fluidity of IDP movements — IDPs might suffer multiple displacements or they might resort to changing locations as a coping strategy (e. g. moving between their homes and place of displacement or testing different locations before deciding where to stay) (Brookings 2011). In recent years, efforts have been made to improve data collection for IDPs living outside of camps by employing a range of techniques including: (a) profiling; (b) household surveys; (c) collecting information on IDPs who come to camps to visit family members or collect relief items; and (d) community outreach programs (Brookings 2013). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Disaggregated data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, there is no consensus or agreed best practices on the use of these methods in different contexts or stages of displacement (Brookings 2013). 92 The absence of data on new displacement may simply mean that no displacement has taken place (IDMC 2016). 93 IDMC reports that data, disaggregated by age and sex, were available for 15 of the 60 countries it monitored in 2014, however these data were not comprehensive and are not published. Additionally, in some countries there are data provided by IOM on IDP populations by location from which the urban or rural character of the population may be inferred (e. g. if the camp is located in the capital), but data are not comprehensive and not published. While the majority of humanitarian profile data does not typically cover IDPs living outside of camp or camp-like settings (the large majority of IDPs), IOM ’ s DTM in countries such as Nigeria, Iraq, Yemen and Libya do include information about those residing in host communities. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["humanitarian profile data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 5: Coverage of Published Data on Location, Accommodation and Demographics 2015 Population end- 2015 (millions) Urban or Rural Location Accommodatio n Age Sex Refugees and people in refugee-like situations 16. 1 85 % 83 % 58 % 68 % Other people of concern to UNHCR 47. 8 (including 37. 5 IDPs) 70 % 47 % 25 % 39 % UNHCR ’ s total population of concern 63. 994 73 % 56 % 33 % 46 % IDPs monitored by IDMC but not included in UNHCR's data 3. 3 0 % 0 % 0 % 0 % Source: UNHCR Global Trends 2015 Note: Other people of concern to UNHCR include asylum-seekers, IDPs and people in IDP-like situations protected or assisted by UNHCR, stateless persons, and ‘ other ’. Overall robustness of current data The robustness of data is difficult to estimate. A review of data collection and compilation methodologies shows broad variations in terms of the accuracy and reliability of the global estimates of forced displacement that are widely used. Headline figures on forced displacement are significant in shaping public opinion and are critical for sound decision making, both to inform the allocation of resources and to design effective humanitarian and development responses. However, the available estimates are potentially misleading and should not be referred to without appropriate caveats and qualifiers. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": " In Somalia, the Mogadishu Household Survey covered both residential areas and IDP camps, collecting data on expenditures, demographics and living conditions [survey completed; analysis forthcoming]. As part of the Somalia Knowledge for Operations and Political Economy (SKOPE) initiative, the Puntland Household Survey will also cover both residential and IDP populations [ongoing].  An IDP study in South Sudan [ongoing] aims to assess the economic needs of IDPs and host communities in urban areas, covering livelihoods, water and sanitation, infrastructure as well as intentions and conditions to return. Basic information about education, employment and general health variables will also be collected.  The Iraq Crisis Response Study [ongoing] will assess the impact of the Islamic State and oil price-related crises on IDPs and households left behind in IS controlled areas. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Mogadishu Household Survey", "Puntland Household Survey", "Iraq Crisis Response Study"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The study: (i) compares the socio- economic profile of refugees with that of the Syrian population before the crisis and with the hosting populations of Jordan and Lebanon; (ii) provides a welfare and vulnerability assessment of Syrian refugees including a poverty profile, the socio-economic characteristics of higher poverty and where pockets of deep poverty are located; (iii) analyzes key drivers of welfare and poverty; and (iv) models monetary and non- monetary vulnerability.  In Lebanon, Jordan and Iraq, the Bank is leading an initiative to evaluate the socio-economic impact of the regional crises on the welfare of Syrian refugees and host communities in neighboring countries [ongoing]. Data on living conditions, access to services and economic opportunities, coping strategies and economic status are to be collected via a specialized household survey and a sub-component of the survey will be carried out on a semi-annual basis to continue to monitor and adapt support as needed.  A recent Bank paper, “ Turkey ’ s Response to the Syrian Refugee Crisis and the Road Ahead ” [completed in 2015] assessed the impact of Syrian refugees on host areas in various sectors. It found that the presence of Syrian refugees is placing a strain on municipal services, housing rental markets, social relations, and education services for Turkish households. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["specialized household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Impacts on the labor market are unclear; initial research suggests that there has been a supply shock to informal labor markets. This has had a large-scale impact on the employment of natives in the informal sector. At the same time, research suggests there has been a boost to formal employment for the Turks, but this has been uneven: the low educated and women experience net displacement from the labor market and, together with those in the informal sector, declining earning opportunities.  The “ Socio-economic Assessment of the Impact of Syrians under Temporary Protection (SuTPs) on Turkish Hosting Communities ” [ongoing], to be undertaken in partnership with the Government of Turkey, will include a nationally representative household survey with SuTP and local Turkish households including camp and non-camp environments. The questionnaire will cover welfare (assets, income, expenditure), municipal services, labor and employment, education, social networks and quality of life. F. Options to improve forced displacement statistics Significant efforts are needed to enhance the reliability, comparability, quality and scope of the global data on forced displacement. In particular, more robust estimates are needed of the scale (stocks, flows and locations) and typology (demographics, location and accommodation) of forced displacement crises. This requires substantial improvements in the rigor of data collection and compilation methodologies including: (a) Harmonization of definitions and methodologies used in the collection and analysis of statistical data on forced displacement — covering stocks and flows of refugees, asylum- seekers and IDPs — to ensure comparability across regions and countries; Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(b) Disaggregation and geo-mapping of data by location (current location and location of habitual residence; urban, peri-urban or rural location), accommodation (organized camp versus non- camp), and demographics (age and sex); (c) Expanded coverage of data collection exercises to include all areas of affected countries (security permitting); (d) Improved coverage and detailed data on displaced populations living outside of organized camps; (e) Improved coverage of ‘ flows ’, i. e. new displacement, durable solutions (returns, integration, resettlement), births, deaths, and in the case of IDPs, the numbers that flee across international borders becoming refugees; (f) Systematic data collection beginning from the earliest moment following displacement, following up as populations disperse, and continuing until sustainable / durable solutions have been achieved; and (g) Better aggregation, analysis and presentation of forced displacement data currently compiled separately by UNHCR, IOM, IDMC and UNRWA. Additional efforts are required to address the gaps in the data required for development policy and planning. These data are critical for informing the design of development policies and assistance programs. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["forced displacement data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Survey instruments enable detailed questions to be asked about the characteristics and situations of households, and if they identify displaced populations based on self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status. Additionally, more innovative tools and technologies for data collection, analysis and compilation should be explored and leveraged. For example, new methodologies (such as high resolution satellite imagery and unmanned drones) may expand the coverage of data collection efforts in insecure or inaccessible areas. Additionally, new techniques could be explored to improve the collection of robust data on flows of refugees and IDPs. Organizations such as the World Bank, UNHCR, IOM and IDMC are already exploring and in some cases are beginning to use more innovative data collection tools. These techniques include: 99 Several standardized international sample surveys have been designed for special purposes including Living Standards Measurement Studies, Labor Force Surveys, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys. The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Living Standards Measurement Studies", "Labor Force Surveys", "Demographic and Health Surveys", "Multiple Indicator Cluster Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Geographic Information Systems (GIS) and geospatial analysis can be used to map, monitor and analyze data on forced displacement. Triangulation of this information with socio-economic and other indicators can provide a rich source of data and enable insights into underlying patterns and trends over time. (e) Use of big data (mobile phone data, news scraping, social media). IDMC is pursuing big data approaches to capturing displacement data in real time in order to report on displacement situations as they are happening and to provide updates on how they are evolving (IDMC 2015). These data are not necessarily representative but can be used in conjunction with other methods to triangulate trends. For example, the Swedish NGO, Flowminder, has pioneered the use of de-identified data from mobile operators to track population displacement caused by natural disasters such as earthquakes in Haiti in 2010 and Nepal in 2015, and these techniques may also have applications in conflict-induced displacement crises. 100 (f) High-resolution satellite imagery and unmanned drones. High resolutions satellite imagery can be used to map physical structures in refugee and IDP camps including changes to the number and type of these over time, support the remote detection of displaced populations in hard to reach or insecure settings; and conduct rapid assessments during or immediately after a mass displacement (Harvard Humanitarian Initiative 2014). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["de-identified data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In settings with clearly distinguishable individual structures, these methods can also be reasonably accurate for the purposes of rapid estimation of displaced population (Checchi, et al. 2013). 101 The use of unmanned drones is also becoming more popular as the cost of this technology falls. This technique has been used by UNHCR to update its estimates of IDPs in Somalia ’ s Afgooye corridor (IDMC 2015) and by IOM to monitor disaster-induced displacement in Haiti, including the use of Unmanned Aerial Vehicles (UAVs) in collaboration with UNOSAT. (g) Open data initiatives. There are several initiatives to provide free and open data that enable Internet users to independently mine and analyze data and generate customized summaries, charts and visualizations. For example, the Bank has provided free, open access to its development data since the launch of its Open Data Initiative in 2010, however there is little open data on asylum-seekers, refugees and IDPs. JIPS has developed a web-based platform that allows users to explore, analyze and visualize profiling data online, and IDMC has 100 See http: / / www. flowminder. org /. 101 These methods are not effective in settings with connected structures, a complex pattern of roofs or multi-level buildings, as are prevalent in urban areas. Additionally, cloud cover and dense foliage can also obscure structures. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "recently introduced the first version of their Global Internal Displacement Database (GIDD) that allows users to explore, filter and sort IDMC ’ s data to produce graphs and tables, and export underlying data. 102 Such platforms need to incorporate safeguards to protect the privacy and confidentiality of individuals ’ data. UNHCR and Statistics Norway are currently leading an initiative to improve forced displacement statistics with the participation of national statistical agencies. This process began with the presentation of the “ Report on Statistics on Refugees and IDPs ” at the 46th session of the UN Statistical Commission in March 2015, 103 followed by an international conference in Turkey in October 2015. 104 The conference set in motion a process for national statistical agencies to collaborate to develop a set of recommendations that both countries and international organizations can use to improve data collection, reporting, data disaggregation, and overall quality, including the preparation of International Recommendations for Refugee Statistics (IRRS). Progress on this agenda was discussed at the 47th session of UNSD held in New York in March 2016, where it was recommended that the expert group should also include IDPs in its scope of work (UNSD 2016). 105 The current initiative is focused on refugees, asylum-seekers and IDPs but would ideally be extended to host communities and returnees. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Global Internal Displacement Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Policy Research Working Paper 10099 Hosting New Neighbors Perspectives of Host Communities on Social Cohesion in Eastern DRC Phuong Pham Thomas O ’ Mealia Carol Wei Kennedy Kihangi Bindu Anupah Makoond Patrick Vinck Social Sustainability and Inclusion Global Practice June 2022 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Hosting new neighbors: Perspectives of host communities on social cohesion in eastern DRC * Phuong Pham, † Thomas O ’ Mealia, ‡ Carol Wei, § Kennedy Kihangi Bindu, ¶ Anupah Makoond, | | & Patrick Vinck * * * Pham and O ’ Mealia are co-first authors. Acquisition of the data used in this manuscript was supported by the United Nations Development Programme (UNDP). The funder played no role in the analysis, inter- pretation or writing of the results and decision to submit the manuscript. This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “ Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts. ” The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. This work is part of the program “ Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership ”. The program is funded by UK aid from the United Kingdom ’ s Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This work does not necessarily reflect the views of FCDO, the WBG or UNHCR. † Assistant Professor, Harvard TH Chan School of Public Health and Harvard Medical School, USA ‡ Postdoctoral Fellow, Harvard TH Chan School of Public Health, USA § Research Consultant, Department of Emergency Medicine, Brigham and Women ’ s Hospital, USA ¶ Professor, Universit ´ e Libre des Pays des Grands Lacs, DR Congo | | Research Manager, Harvard Humanitarian Initiative, USA * * Assistant Professor, Harvard TH Chan School of Public Health and Harvard Medical School, USA JEL Codes: D74, F22, C83, N47, O15, R23 Keywords: Displacement, Hosting, Social Cohesion, Surveys, Democratic Republic of Congo", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1 Introduction One of the most challenging steps toward building a peaceful and just society after violence is the mending of broken relationships and establishing new ones between people, communities, and institutions. The international community has recognized this challenge, adopting social cohesion as a core objective and tool of peacebuilding (UNDP 2020, UNICEF 2021). Forced displacement creates major disruption to social dynamics among both displaced persons and host communities. The influx of displaced people has the potential to put strain on the host community by creating inequalities in access to services, resources, and income. At the same time, social cohesion can facilitate collective action for example by allowing pop- ulations to preemptively evacuate and escape (Arnon, McAlexander & Rubin 2021). High levels of social cohesion can improve outcomes after traumatic events either providing individual or so- cial assets, such as resilience (¨ Ozc ¸ ¨ ur ¨ umez, Hoxha & ˙ Ic ¸ duygu 2020) or mental stability (Greene, Paranjothy & Palmer 2015). But understanding what dimensions of social cohesion are especially salient – and how those dimensions are related to changing dynamics such as forced displacement – requires understanding how communities perceive what constitutes social cohesion in their lived experiences.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Despite a growing body of work that analyzes the relationship between forced displacement and social cohesion, there remains a lack of clear definition of social cohesion in the context of forced displacement (De Berry & Roberts 2018). Most research focuses on refugee situations and the resulting relationships with host communities in the global north. But the salience of different elements of social cohesion may be contextually driven and differ across several dimensions, such as the type of forced displacement experienced locally (IDP versus refugee, for example) 1 and local 1We employ the following definitions for different types of displacement to ensure analytical consistency: refugees are “ someone who has been forced to flee his or her country because of persecution, war or violence. ” Internally displaced persons (IDPs) are “ someone who has been forced to flee their home but never cross an international border. ” Returnees are “ someone who was of concern to UNHCR when outside their country of origin and who remains so for a limited period (usually two years) after returning home to their country of origin. It also applies to internally displaced persons who return home to their prior place 3", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "context (urban and rural). It is commonly accepted that displacement negatively affects social cohesion, and that forced displacement occurs in contexts with low levels of social cohesion to begin with. Protracted displacement can lead to political tensions, as associations are formed along ethnic or political lines and new grievances emerge. In contexts of internal displacement, ethnic and social tensions (which may have been the drivers or consequences of the displacement) are exacerbated by the presence of IDPs. For refugees, deeper social and cultural divides may exist, hindering social cohesion (De Berry & Roberts 2018). Indeed, in some situations, protracted displacement and expectations of retaliation on return create greater politicization of the displaced along ethnic lines (Harild, Vinck, Vedsted & de Berry 2013). More generally, economic competition, poor governance, lack of rule of law, and limited access to scarce resources are features of the living conditions for most IDPs and refugees and oftentimes the host population, further hindering social cohesion (Munoz & Shanks 2019). This paper analyzes the relationship between displacement and social cohesion in the eastern provinces of the Democratic Republic of Congo (DRC).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In 2020 alone, the country recorded 2 million new conflict displacements according to UNHCR, the majority of whom are in the eastern provinces of Ituri, North Kivu, and South Kivu (UNHCR Global Focus N. d.). Large segments of the civilian population are displaced regularly and temporarily live with hosts in neighboring communities until the local security situation improves. Additionally, political violence and in- stability in neighboring states (Burundi, South Sudan, Rwanda, and Uganda especially) produce refugee flows into eastern DRC. 2 Eastern DRC is therefore host to large numbers of both IDPs and refugees. The dynamics of hosting displaced persons in eastern DRC thus represent a fundamen- of residence. ” These categories are in contrast to migrants, “ someone who leaves their country purely for economic reasons unrelated to the refugee definition, or in order to seek material improvements in their livelihood. ” 2The conflicts in eastern DRC also produce refugee flows out of the country and into neighboring states. These flows are beyond the empirical scope of our paper but are connected to regional displacement dy- namics. 4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "tally different set of challenges to social cohesion than the more commonly analyzed camp-based displacement or refugee flows into European countries. Due to contextual differences, the salient dimensions of social cohesion may not match aca- demic definitions derived mainly within western contexts. To address this challenge, this project employed participatory research methods to identify the elements of social cohesion considered relevant in eastern DRC. By adopting this design, the project iteratively built a set of research questions and methodological tools to ensure locally appropriate decisions to measure contextu- ally appropriate concepts. The insights from the focus groups dictated our measurement strategy of social cohesion when analyzing a series of surveys conducted in eastern DRC between 2017 and 2021. The findings contribute to a growing research agenda on how hosting forcibly displaced per- sons impacts perceptions of social cohesion. The results are consistent with findings from Zhou, Grossman & Ge (2021), who find that proximity to refugee settlements can improve goods provi- sion to host communities and that the presence of displaced persons does not necessarily negatively affect host community attitudes towards the displaced persons they host. Similarly, Aksoy & Ginn (2021) also find that the arrival of migrants do not necessarily have negative effects on host com- munity attitudes.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Consistent with the results in this paper, Betts, Stierna, Naohiko & Sterck (2021) find that local context – including urban versus rural settings – matters in how host community interactions with displaced populations impact social cohesion. 2 Context: Eastern DRCongo For the past five decades, eastern DRC has experienced varying levels of conflict. The violence has been fueled by complex and interlinked domestic and foreign competition over access to resources and political power, deepening long-standing inequities and conflicts along ethnic lines. 3 Mobutu ’ s 3For details on the roots of the conflicts in eastern DRC, see Reyntjens (2001), Vlassenroot & Raeymaekers (2004), and Vlassenroot & Raeymaekers (2009). 5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "thirty years of autocratic rule that followed the Congolese independence from Belgium colonial rule paved the way to a violent transition, culminating in two internationalized wars from 1996 to 1997, in the aftermath of the Rwandan genocide, and from 1998 to 2003. Since the end of the Second Congo War in 2003, eastern Congo has remained unstable and violent, with many domestic and foreign-backed armed groups – including elements of the state military, FARDC – using violence against civilians and each other (Autesserre 2010). The violence and instability have resulted in poor living conditions and regular forced displacement for Congolese civilians. This project focuses on three provinces of eastern DRC that are especially impacted by forced displacement and political violence: North Kivu, South Kivu, and Ituri. 4 These three provinces account for 4. 5 million out of an estimated 5. 268 million total (85 %) IDPs in DRC 2020 (UNHCR Operational Data Portal: Democratic Republic of Congo 2021). Other provinces not included in this study but hosting IDPs include southern and central provinces such as Kasai, Kasai-Central, Kasai-Oriental, Lomani, Sankuru, and Tanganyika. The analysis in this paper focuses exclusively on dynamics in eastern Congo where the authors collected data.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The large number of interconnected conflicts in these provinces involving non-state armed groups and state actors create a continuous ebb and flow of displacement in eastern DRC (Jacobs & Kyamusugulwa 2018). In June 2020, UNHCR estimated that over 4. 5 million persons were internally displaced in Ituri (1. 6M), North Kivu (1. 9M) and South Kivu (1M) provinces alone (UNHCR 2020). DRC hosts an additional 536, 000 refugees (UNHCR 2020) from neighboring countries with recent experiences of violence, especially Burundi, Uganda, CAR, and South Sudan. Figure 2 plots the trend in the new IDPs in the DRC between 2009 and 2020. 5 Most IDPs in DRC favor staying with host families as opposed to camp displacement (Haver 2008, Rohwerder 2013). In 2017, UNOCHA estimated that around 500, 000 IDPs were in camp- like settings, whereas 3. 3 million sought refuge in host communities (Jacobs & Kyamusugulwa 4The qualitative analysis covers only the Kivu provinces, but the quantitative analysis covers all three. 5Data provided by the Internal Displacement Monitoring Center DRC Page, accessed May 14, 2021. 6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 15 ° E 20 ° E 25 ° E 30 ° E 10 ° S 5 ° S 0 ° 5 ° N South Kivu North Kivu Ituri (a) DRC, with North Kivu, South Kivu, and Ituri Shaded 24 ° E 26 ° E 28 ° E 30 ° E 32 ° E 34 ° E 4 ° S 2 ° S 0 ° 2 ° N 4 ° N G G G G G G G (b) Kivu Provinces, Territoires, and Focus Group Locations Figure 1: Area of Interest: Kivu Provinces", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "500000 1000000 1500000 2000000 2012 2016 2020 Year Newly Displaced Persons Due to Conflict New Displacements, DRC Figure 2: Temporal Trends in IDP Flows, Democratic Republic of Congo 2018). Many are displaced multiple times in short bursts as the security situation in their com- munities fluctuates (Zeender & Rothing 2010). When fleeing violence, IDPs in DRC oftentimes attempt to stay close to their home communities so they can monitor their properties with the intention of returning once the security situation improves (White 2014). In areas near violence, host communities are frequently and repetitively asked to host IDPs: by one count, host families often host IDPs around three to four times, for around three months on average (Simpson 2010). Alternatively, many IDPs flee to urban areas, increasing slum areas in cities like Goma and Bukavu (Zeender & Rothing 2010). Urban IDPs are less reliant directly on host families and many have found work in urban areas (Jacobs, Lubala Kubiha & Katembera 2020).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In summary, the dynamics of displacement in eastern DRC are fluid, with vulnerable popula- 8", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "tions frequently coming and going as conflict dynamics evolve. IDPs primarily rely on informal networks when seeking refuge, leveraging their ethnic, religious, and other social networks to find safety. Communities host displaced persons informally and long-term camp-based displacement is relatively rare. In contrast to most research that focuses on the impacts of tightly concentrated pop- ulations, displacement in eastern DRC lacks the sort of static geographic concentration of displaced populations. Such dynamics may have fundamentally different implications for social cohesion and require different policy. 3 Defining Social Cohesion in Contexts of Forced Displacement Existing definitions of social cohesion motivate the participatory research strategies used define social cohesion in this project. Social cohesion is a conceptual construct for which there is no universally agreed upon measure. In a comprehensive study reviewing whether community driven development positively impacts social cohesion, (King, Samii & Snilstveit 2010) note that both the attitudinal and behavioral measures of social cohesion manifest in highly context specific ways, making it difficult to identify universal and cross-cutting measures of social cohesion. In general terms, social cohesion is defined as a set of societal characteristics or attributes that foster “ mutual moral support, which instead of throwing the individual on his own resources, leads him to share in the collective energy and supports his own when exhausted ” (Berkman, Kawachi & Glymour 2014).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Scholars and practitioners studying social cohesion in the last two decades generally agree that social cohesion consists of two intertwined features of society: 1) the absence of latent social conflict, including income inequality, racial / ethnic tensions, and disparities in political participa- tion and 2) the presence of strong social bonds such as high levels of trust, norms of reciprocity, presence of associations and the presence of institutions of conflict management (Jenson 2010). Increasingly, and especially in the literature that explores the role of social cohesion in economic 9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "growth and development, a third component, sometimes included in the second above-mentioned dimension is made explicit: the presence of effective institutions and governance. These under- standings are mainly based on research conducted in Europe or North America, which excludes contextual factors in other societies, “ such as focusing on the impact of minority groups on social majorities and the effect of integration (or lack of integration) on social cohesion or theoretical blind spots, such as risks to good governance ” (De Berry & Roberts 2018). The relevant set of relationships and institutions that matter to social cohesion vary according to context. Although some studies use social cohesion and social capital interchangeably, King, Samii & Snilstveit (2010) argue that social cohesion emphasizes the group and the patterns of cooperation rather than the assets that give rise to them. Yet the question remains as to whether there is a set of common thematic concepts that can be operationally measured. De Berry & Roberts (2018) note “ initiatives to improve the definition and measurement of social cohesion have involved the development of subjective and objective indicators across the horizontal (inter-group) and vertical axis (person-state).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For example, the horizontal could be evident in the levels of trust in other social groups and the vertical evident in the level of trust in the institutions of the state. ” Social cohesion thus represents a broader social fabric, not just a particular circumstance, issue, or event. To understand how dynamics such as displacement are related to social cohesion, it is crucial to first what elements of the social fabric are considered most salient by those who live in these communities. 3. 1 Towards a Locally-Led Definition of Social Cohesion: Focus Group Ev- idence To ensure that the conceptualization of social cohesion used in this paper is appropriately contex- tualized to local understanding, the project consulted with local communities using a sequential mixed method approach to produce a locally driven definition of social cohesion. First, a qual- 10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "They then developed a series of questions that they could ask this person to understand their per- ception of social cohesion. This participatory process based on “ personas ” developed a better understanding of the concepts and outlined key questions that participants felt were relevant to social cohesion in eastern DRC. The findings from the seven focus groups were combined to draw a single concept map (Figure 3). Together, the participants outlined three broad domains that can be subsequently divided into dimensions, sub-dimensions, and finally indicators. Some overlap between dimensions is unavoid- able because of the conceptual proximity of many of the topics discussed in the focus groups. As such, the three domains of social cohesion, as well as their respective dimensions and sub dimen- sions, are interrelated. These represent a subjective and synthesized conceptualization of social cohesion in eastern DRC according to Congolese participants. Three main domains emerged as particularly salient for focus group participants: solidarity, relationships and governance.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 3: Concept Map Produced from Focus Groups on Understandings of Social Cohesion in Eastern DRC 12", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3. 1. 1 Relationships The predominant understanding of social cohesion was centered around relationships between individuals and between groups. The defining characteristic of group relations was either shared geographic origin, shared ethnicity, or shared religion, with ethnicity and geographic belonging being much more common than religion. Three distinct but interrelated dimensions of relationships further emerged. Participants associated cohabitation with character traits and human values, notably ‘ respect ’ and ‘ tolerance ’. According to most participants, these traits are fostered through education, both at home, in the community, and in schools. Some participants spoke about the importance of openness to learning, and to be able to reflect as necessary to cohabitation with others. People also spoke about the importance of communication, whether through media or dialogue spaces, as a factor that can contribute to peaceful cohabitation in so far that the opportunity to exchange with the other enables knowing the other and promotes mutual understanding, the latter of which is considered necessary for cohabitation. Harmony was often discussed alongside peace and at times used interchangeably to describe a desirable overall quality of a community. Peace and harmony were also associated with the man- agement of conflicts, with some participants emphasizing land conflicts.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In some consultations, participants spoke more specifically to the importance of norms, and knowledge of laws and social conventions as necessary elements leading to harmony. Unity was the most frequently cited dimension of relationship, with one participant for ex- ample suggesting that “ there is social cohesion when there is a love among people, and when a community is united. ” In areas marked by ethnic diversity, participants pointed to the ability to “ love each other despite differences ” and to the capacity to compromise. Regardless of the degree of ethnic heterogeneity, unity was frequently described in terms of community members having shared objectives. In some consultations, participants noted that socialization and intermarriages across ethnic, linguistic, and religious divides constitute a demonstration of unity. Unity is visi- 13", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ble when there is friendship between people, there is affection between people, people get along (“ bonne entente ”), people work together, people think as one. More formal concepts of “ recon- ciliation ” and “ integration ” were not frequently evoked. Other factors that were cited, albeit less frequently, as contributing to unity include shared (ethnic) origins and religion, suggesting that many people believe that a certain degree of homogeneity in cultural and religious background may contribute to “ organic ” unity. 3. 1. 2 Solidarity A second theme that was echoed across all seven consultations was solidarity. While closely related to relationships, solidarity was emphasized as a separate domain. In some focus groups, participants noted that solidarity was the very raison-d ’ etre of social cohesion and its most valuable benefit. Solidarity was divided into three dimensions: collaboration, sharing, and support. In some rural areas, participants cited examples of community infrastructure which had been built by the entire community, or of jointly managed public goods (e. g. water) as strong exam- ples of collaboration. Such collaborative efforts represent the cornerstone of solidarity for some participants. Collaboration emanates from having common objectives and working towards them together. Community associations or organizations aimed at achieving common objectives were given as examples of collaboration.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Participants also discussed the organization of ceremonies cel- ebrating births, marriages, or deaths as examples of collaborations. In urban consultations, many participants suggested that there is less cohesion in the city compared to rural areas because people do not work together as one when a neighbor dies or a relative is getting married. In contrast, some participants suggested that in rural areas, an entire village would be mobilized if there is a need to organize a wedding or a funeral. A third instance of collaboration cited across the consultations was that of community service (e. g. salongo) or community participation. This is also tied to civic duty, and while acknowledged as an example of collaboration, participants noted that authorities – both customary and formal – sometimes co-opt community service in a predatory manner. 14", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Sharing (partage) was a strong component of social cohesion across all consultations. Sharing was expressed in different contexts and at different levels. On one hand, participants cited religious values and charity, while on the other hand, it was conceptualized in a more structural manner and associated with notions of equality. One participant in Kalehe noted that “ if my neighbor has less than me, and is in need, he might steal from me, and I might not trust him. ” Inequalities with regards to the distribution of resources were cited as an impediment to social cohesion and evoked that “ sharing resources ” was thus a necessary remedy. This discussion also touched on issues of governance as many participants felt that the root cause of the unequal distribution of resources was favoritism by leaders for members of their own group or otherwise a truncated economic system that favored some groups over others (see domain 3 on governance). People also specified the importance of sharing information and exchanges as a factor contributing to social cohesion. Additionally, the flow of information between leaders or authorities and members of the community was also cited as necessary to social cohesion.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In addition to the formal forms of collaboration and to the importance of equity between in- dividuals and groups, persons consulted also evoked the importance of basic, quotidian solidarity, which we label as support. Examples included the willingness to help a neighbor who is sick or to assist someone in need of credit. Support did not give rise to many sub dimensions during the consultations, but scales can be developed to measure the attitudes and behavior of individuals in relation to quotidian solidarity. 3. 1. 3 Governance Finally, some participants (in Goma especially) noted the accountability of leaders to the popula- tion as a factor contributing to social cohesion. Elsewhere, participants discussed governance but in an indirect, and often negative, manner. The governance factor can be broken down into three dimensions. In almost all consultations, participants expressed that access to basic needs and services is key 15", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "to social cohesion, broadly encapsulating the concept of development. The fact that people are in need or suffering makes them desperate and may force them to give in to behavior that destabilizes the community, with the example of armed group recruitment cited on several occasions. In most consultations, people talked of the importance of job opportunities as key. While in some instances, the government ’ s responsibility to ensure access to basic public goods was cited, this point was not heavily emphasized. In some cases, however, participants did note that development was the shared responsibility of the community and that ensuring the education of youth would endow individuals with the tools necessary to foster development. A second recurring theme describe how the inequitable distribution of resources (of land in particular) encroaches upon social cohesion and harmony within a community. While rarely pointing fingers directly at authorities, participants did implicitly evoke corruption and nepotism as factors that can create inequities. The distribution of land was cited in almost all consultations. Some participants pointed to laws and customs that favored some groups over others, and in par- ticular men over women. Although participants generally refrained from citing the responsibility of authority figures and leaders for development, they were more emphatic in attributing inequities to their behavior. In some consultations, participants openly spoke of political manipulation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Es- pecially in urban areas, participants cited the institutionalized disparity between socio-economic classes as problematic. Participants felt that inequalities existed along ethnic and gender lines, but also along socio-economic and geographic lines. Geographically, the issue of inequality was espe- cially prominent in the city of Goma, where participants noted the striking disparity between the commune of Goma and that of Karisimbi, with the latter under-served for amenities and services. The third dimension involved participation. Most participants felt that a community in which groups are excluded or marginalized does not have social cohesion. Nonetheless, they often recog- nized that the primacy / dominance of some groups was rooted in tradition and that trying to change an established social order can also destabilize the social cohesion of a community. This was raised in relation to the question of gender equality. In some cases, some participants cited ‘ acceptance ’, 16", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "‘ patience ’ and obedience as qualities that a person should have to live in harmony / cohesion with others because some things cannot change. In other instances, however, participants were more critical of opaque decision-making mechanisms. The question of participation was not limited to decision making processes but also raised in more general terms of participation in the community. In almost all focus groups, care was taken to also include groups that are traditionally marginal- ized such as pygmies. They were usually the most critical of discriminatory practices by both authorities and other members of the community. Stereotypes and prejudice against some groups, whether women or pygmies also emerged as a factor hindering their participation in social and decision making. On one occasion, a participant challenged the view that the exclusion of certain groups was solely the fault of dominant / majority groups but also partly the fault of the minorities themselves, who resorted to attitudes of auto-victimization. 3. 2 Hosting Displaced Populations and Perceptions of Social Cohesion Social cohesion, consistent with the locally led definition presented above, is not a static state. Evolving political and social dynamics continually challenge or reshape the social cohesion locally.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Forced displacement poses unique challenges to social cohesion by stressing local economies and demographics; on the other hand, hosting forcibly displaced populations can attract NGO resources and government service provision (De Berry & Roberts 2018), potentially improving social cohe- sion. Despite the common perception that hosting displaced populations increases tension and results in nativist political agendas, 7 several studies find that hosting does not necessarily have detrimen- tal impacts on local social cohesion. Communities have a higher willingness to host refugees than we might assume (Zorlu 2017). In Rwanda, Fajth, Bilgili, Loschmann & Siegel (2019) finds host 7Several studies that examine the consequences of hosting refugees in Europe find that large refugee inflows produced resentment and caused the rise of nativists political sentiments (Dinas, Matakos, Xefteris & Hangartner 2019, Hangartner, Dinas, Marbach, Matakos & Xefteris 2019). Refugees can become the target of violence from host populations as well (Fisk 2018, Savun & Gineste 2019). 17", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "populations and refugees overwhelmingly co-exist peacefully. Contact theory is often posited as an important mechanism for promoting social cohesion between displaced populations and host communities (Finseraas & Kotsadam 2017, Ghosn, Braithwaite & Chu 2019): those who are ex- posed to displaced populations are more likely to be sympathetic to them and support policies that support displaced persons. These perceptions are influenced by the impact that hosting displaced populations has on the local economy. Low labor market integration of refugees can make it more difficult for exposure and integration to occur (Bauer, Braun & Kvasnicka 2013, Del Carpio, Seker & Yener 2018). Increases in displaced populations have been shown to increase the price of non-aid food items and are associated with more modest price effects for aid-related food items (Alix-Garcia & Saah 2010). In these ways, hosting displaced populations can strain economic conditions, potentially straining social cohesion locally. Other research finds more mixed implications. The probability of having a negative outcome for host communities in the consumer and labor markets is relatively low (Verme & Schuettler 2021). Jordanians living in areas with a high concentration of refugees have had no worse labor market outcomes than Jordanians with less exposure to the refugee influx (Fallah, Krafft & Wahba 2019).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some even found economic benefits: In Kenya, refugee camps improve the health and nutrient intake of local communities directly surrounding camps (Gengo, Oka, Vemuru, Golitko & Gettler 2018). Ugandans living near refugee settlements benefit both in consumption and public service provision (Kreibaum 2016). Importantly, these economic implications may not match perceptions of economic benefits. Where assistance to refugees is perceived as above average living conditions in the host communi- ties, resentment may build (Agblorti 2011). The influx of money that can come with the influx of displaced persons can have uneven impact and reinforce the position of privilege or marginalization that individuals have in a community (Whitaker 2002). Economic interventions that displacement attracts can create uneven distributional effects (Paler, Strauss-Kahn & Kocak 2020), as the target 18", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "group will benefit more in communities where elites and the excluded groups seek to compete to capture aid. As such, the evidence on the consequences of forced displacement for perceptions of local social cohesion is mixed. Existing findings are based on research conducted mainly in western Europe, particularly those that analyze large refugee flows from the Middle East and show that hosting refugees can negatively impact social cohesion. But other research demonstrates that, especially among those who are most directly exposed to displaced populations, hosting does not necessarily result in a backlash effect and may even be associated with limited economic bene��ts. 3. 2. 1 Fluid Displacement Flows and Perceptions of Social Cohesion In scenarios where displacement is more fluid, frequent, and informal amidst ongoing conflicts, host communities may have incentives and experiences that may differ in fundamental ways than in refugee contexts or more permanent displacement. These differences may result in unexpected relationships between hosting displaced populations and perceptions of social cohesion. Aggregate relationships between levels of displacement and social cohesion are likely influ- enced by the fact that areas that experience higher levels of forced displacement are precisely the areas that experience the most violence. As a result, aggregate displacement levels and perceptions of social cohesion are likely negatively related, but this is not necessarily a function of hosting displaced persons.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Instead, displacement is a manifestation of a broader erosion of social cohe- sion at the vertical level. Hosting IDPs may still impact host community perceptions in several ways, but more dis-aggregated analysis is required to unpack the relationship between hosting and perceptions of social cohesion. At the individual level, the relationship between hosts and IDPs may be more positive than other host-displaced community relationships. First, hosting IDPs populations can improve per- ceptions of relationships both with in-groups and out-groups by increasing contact and reliance. Hosting displaced populations can force people to rely on their own communities to respond to the 19", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "difficulties that come with hosting. Civil society groups, such as religious organizations, can help fill the gap when and where needed. Moreover, broader conflict dynamics can make in-group rela- tionships more important (Fearon & Laitin 2000). Such experiences may increase the importance and perceptions of intra-ethnic or intra-religious relationships. Second, hosting displaced persons is itself an expression of solidarity and may increase oppor- tunities for acts of solidarity as well. Many host communities may have themselves been displaced at other stages of the conflict (Beytrison & Kalis 2013). This set of experiences can change per- ceptions of hosting displaced populations through empathy- by increasing appreciation for the difficulty that comes with hosting and by understanding the hardship that displaced persons are experiencing. One study in eastern DRC found that 80 percent of hosts said they would do it again and 60 percent reported a positive bond with their displaced guests (Rohwerder 2013). Many communities host displaced out-groups, not just co-ethnics. Contact can break down stereotypes and create bonds between members of out-groups. Mixing with members of other groups tends to make individuals more empathetic towards those groups (Boisjoly, Duncan, Kre- mer, Levy & Eccles 2006), and displacement can incentivize or force such mixing.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It can also create opportunities for them to reach and learn other parts of society other than their own- culti- vating human intrinsic curiosity (Kashdan & Silvia 2009). Finally, hosting displaced persons can change perceptions of governance. IDPs in communi- ties may receive preferential treatment from the government or from international humanitarian actors (Paler, Strauss-Kahn & Kocak 2020). This can have cross-cutting implications, as host communities may resent the attention / resources paid to the IDPs, but host communities may also benefit from the increased attention paid to local problems. In contexts of informal displacement, host communities can share more equally in resources that arrive to support displaced populations, mitigating the negative perceptions that may grow based on these distributional challenges. Based on these dynamics, we expect to observe a different set of associations between aggre- gate trends in displacement locally and individual experiences with hosting displaced communities. 20", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "are combined. The surveys follow a repeated cross-sectional design and are not panels (i. e. the same administrative units, not the same people, are re-sampled), so responses are aggregated to the groupement level, the lowest level at which the project consistently collect representative data. Table 2 provides a summary of the dates, sizes, and percent of respondents who report being displaced within each survey wave. This aggregated temporal analysis can show, associations between fluctuations in displacement and perceptions of social cohesion over space and time at the groupement level. Question coverage varies across survey waves, but a battery of core questions enables consistent observation of how many individual respondents self-report being displaced at the time of the survey and being involuntarily moved within the past year. Poll Date N % Currently Displaced % Displaced Last Yr % Hosting Displacees # 11 July 2017 5834 4. 35 7. 42 – # 12 September-October 2017 4013 1. 62 2. 62 – # 13 December 2017 4883 3. 50 7. 97 – # 14 March-April 2018 1933 4. 97 8. 85 31. 35 # 15 June-July 2018 5951 3. 70 8. 35 30. 33 # 16 October 2018 1112 6. 47 4. 68 – # 17 December 2018 5918 5. 86 11. 20 – # 19 July-August 2019 5961 5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": ". 12 10. 45 – # 20 December 2019 5752 4. 71 8. 14 – # 21 November 2020 2627 4. 19 5. 14 – # 22 February-March 2021 5847 6. 86 9. 30 – Overall July 2017-March 2021 49831 4. 64 8. 19 30. 58 Table 2: Details on Surveys and Displacement Trends Second, the paper conducts an individual-level analysis of two cross-sectional surveys of 1, 933 and 5, 951 individuals conducted in March-April 2018 and June- July 2018, respectively, to probe the relationship between hosting displacees and social cohesion in more detail. This survey wave 22", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["cross-sectional surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "included a specific battery of questions that provided respondents the opportunity to report their perceptions of whether IDPs or refugees were present in their communities and, if so, what impact hosting displaced persons had on social cohesion their communities. Additionally, respondents reported whether their communities hosted IDPs (displaced persons from within DRC) or refugees (displaced persons from Burundi, Rwanda, Uganda, South Sudan, or other countries). Given the lack of reliable census data and frequent population movements in eastern DRCongo, sampling and weighting procedures are necessarily conservative. All of the surveys randomly select groupements (or quartiers in cities) in each territoire. Within selected groupements, select villages are drawn (or avenues in cities), which are clusters per territoire. Enumerators carry out 8 interviews per cluster using a random walk procedure. Responses are weighted to adjust for differences in probability of selection at the territoire level and all samples are gender-balanced. Additional details on the survey design are provided in the Appendix, Section D. 4. 1 Measuring Displacement Context Because this paper is interested in a context of frequent, unregistered, and informal displacement, it relies on self-reported measures for all variables included in the analysis.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The aggregate analysis calculates the percentage of respondents in each groupement that re- port being displaced in response to either of the following two questions: “ Are you currently dis- placed? ” and “ In the past 12 months, have you been involuntarily moved? ” The aggregate analysis is thus a representation of the proportion of respondents within each groupement that self-reports being displaced currently or having recently been displaced (but not necessarily displaced any longer). In the individual level analysis, the independent variable measures whether respondents ’ com- munities host displaced persons. The 2018 survey asks “ Are there any displaced persons or refugees here in the city or the territory? ” At the individual level, respondents who respond yes are coded as hosting displaced persons. Additionally, if respondents reported that they did host 23", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "displacees in their communities, the survey asked an additional question in which respondents re- ported whether their communities hosted IDPs (displaced persons from within DRC) or refugees (displaced persons from Burundi, Rwanda, Uganda, South Sudan, or other countries). These re- sponses are used to create a categorical variable that measures whether respondents report hosting IDPs, hosting refugees, or not hosting displaced persons in their communities. Table 3 summarizes the measurement strategies for hosting status. It is possible that respondents may misreport whether they are displaced or whether displaced people are present for a number of reasons. First, they might not know that displaced persons are present, a risk that is especially acute for IDPs. Because the paper is primarily interested in how knowledge of hosting impacts perceptions of social cohesion, this measurement challenge is not as acute a problem as it may at first seem. Respondents must know that IDPs or refugees are present in their community for hosting to impact their perceptions of social cohesion. If they are not aware of the presence of IDPs or refugees, their presence is unlikely to systematically impact their perceptions. Second, there might be incentives to either hide or, alternatively, to over-claim the presence of IDPs or refugees.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Enumerators reminded respondents that the survey was part of an academic study and not connected to service provision decisions, which we hope alleviate some of these incentives. That said, it is important to note that this project analyzes self- reported perceptions of the presence of IDPs or refugees, not the confirmed presence of displaced populations. Hosting Status Aggregate Individual % Respondents Displaced Currently Self-Reported Hosting % Respondents Recently Displaced Self Reported Hosting IDPs Self Reported Hosting Refugees Table 3: Operationalizing Hosting Status 24", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4. 2 Perceptions of Social Cohesion The outcome of interest is how respondents perceive various dimensions of social cohesion in their communities. The surveys capture each of the locally directed dimensions of social cohesion described above. Table 4 groups these measurement strategies by the dimensions of social cohesion from the qualitative exercise. Social Cohesion Dimension Relationships Solidarity Governance Perception of In-Group Relationships Participation in socio-cultural activities with Other Ethnic Groups Access to Basic Needs Perception of Out-Group Relationships Contact with Other Ethnic Groups Access to Services Table 4: Operationalizing the Locally-Led Definition of Social Cohesion with Survey Responses First, to measure how respondents perceive the quality of their relationships, the survey asked respondents to report their perceptions of the quality of their relationships with their own ethnic group and with other ethnic groups. Two binary variables based on each respondents ’ answers to these questions are used to measure relationships, with respondents answering either “ Good ” or “ Very Good ” coded as perceiving high-quality relationships. Second, respondents report their willingness to participate in socio-cultural activities / ceremonies, attend the same place of worship, work together, marry members of other ethnic groups, and how often they have contact with members of other ethnic groups to measure solidarity.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These ques- tions are used to create measures of whether respondents participate in activities with other ethnic groups and whether they have contact with other ethnic groups. Third, respondents answer questions about their perceptions of their access to basic services including accommodation, water, finding work, land, primary school for children and health care to measure governance. Additionally, a battery of questions enables respondents to report their access to civil status services (such as for registration of marriages, births, etc.) and access to state services for obtaining title deeds and other documents relating to land. 25", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These concepts are treated as separate dependent variables. Each variable is meant to capture a component identified by the focus group respondents as important for social cohesion in eastern DRC. Because the survey was carried out separately from the focus groups, however, the sur- veys cannot measure each of the presented in the concept map, but many of the relevant concepts overlap. 5 Survey Results 5. 1 Aggregate Relationships A series of linear regressions examine the correlation between levels of displacement and percep- tions of social cohesion at the groupement level, alternating independent variables between the per- centage of respondents who self-report being currently displaced within a groupement in a given survey wave and the percentage of respondents who self-report moving involuntarily within the past year. Each point estimate and confidence interval represent results from a separate regression, with the Y-axis noting the dependent variable. 10 Figure 4 shows that there is no significant relationship between current levels of displacement at the groupement level at the time of enumeration and any dimension of social cohesion. In contrast, groupements with a higher proportion of respondents who report involuntary movements are associated with significantly lower perceptions of solidarity and governance. Self-reported instances of involuntary movements in the community are negatively associated with perceptions 10The results of the analysis are plotted in coefficient plots.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "All results in the main text are presented as odds ratio plots with 95 % confidence intervals (CIs). On each of the plots, the X-axis is the Odds Ratio (log scale), the dashed vertical line is the “ line of null effect, ” and the colored point is the estimate from each regression, with 95 % confidence intervals. Estimates to the right of the dotted line signify positive and statistically significant relationships, estimates that intersect with the dotted line indicate results that do not reach statistical significance (defined as p =. 05), and estimates to the left of the dotted line indicate negative and statistically significant relationships. Each regression includes relevant controls, but the graphs only plot the displacement or hosting status variables to ease interpretation. Full regression tables are available in the Appendix, Section F. 26", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Relationships Solidarity Governance − 1. 0 − 0. 5 0. 0 0. 5 1. 0 − 1. 0 − 0. 5 0. 0 0. 5 1. 0 − 1. 0 − 0. 5 0. 0 0. 5 1. 0 Perception of Out − Group Relationships Perception of In − Group Relationships Contact with Other Ethnic Groups Participation with Other Ethnic Groups Access to Services Access to Basic Needs Regression Coefficient Variable Displacement Currently Displaced (a) % Displaced Relationships Solidarity Governance − 1. 0 − 0. 5 0. 0 0. 5 1. 0 − 1. 0 − 0. 5 0. 0 0. 5 1. 0 − 1. 0 − 0. 5 0. 0 0. 5 1. 0 Perception of Out − Group Relationships Perception of In − Group Relationships Contact with Other Ethnic Groups Participation with Other Ethnic Groups Access to Services Access to Basic Needs Regression Coefficient Variable Displacement Involuntarily Moved (b) % Involuntarily Moved in the Past Year Figure 4: Groupement Aggregated Correlations Between Displacement and Perceptions of Social Cohesion of out-group relationships (OR:- 0. 22; CI:- 0. 37 –- 0. 08), participation with other ethnic groups (OR:- 0. 37; CI:- 0. 55 –", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "- 0. 20), contact with other ethnic groups (OR:- 0. 36; CI:- 0. 63 –- 0. 09), access to basic needs (- 0. 25; CI:- 0. 46 –- 0. 03) and access to services (- 0. 23; CI- 0. 40 –- 0. 06). Groupements with higher proportions of respondents who report being involuntarily moved in the past year are also negatively associated with perceptions of out-groups but are not significantly correlated with in-group relationships The discrepancy between the aggregate results for groupements experiencing higher levels of current displacement versus those with higher levels of respondents who were displaced in the last year begs several questions. Of course, the negative relationship between involuntary move- ments may be a manifestation of low levels of social cohesion that create displacement in the first place, but the same can be said of groupements with higher levels of current displacement. The 27", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "But hosting status is not randomly distributed in ways beyond these descriptive characteristics. The regressions additionally control for each respondent ’ s ethnic status in their community by cal- culating the percentage of respondents who self-identify as each ethnic group in each surveyed groupement and creating an indicator variable for those who are members of an ethnic minority locally. Figure 5 plots the coefficients of displacement status these logistic regressions, where the inde- pendent variable of interest is a binary indicator for self-reporting hosting either refugees or IDPs in your community, a binary indicator for self-reporting hosting IDPs in your community, a binary indicator for self-reporting hosting refugees in your community. Figure 5 shows that the relationship between hosting and perceptions of social cohesion vary by context, sub-dimension of social cohesion, and the type of hosting. In cities, hosting is only pos- itively and significantly associated with solidarity measures (participation with other ethnic groups (OR: 1. 39, CI: 1. 13-1. 72) and contact with other ethnic groups (OR: 1. 35, CI: 1. 05 – 1. 73)), but these results differ based on who is hosted.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In cities, hosting IDPs is not significantly associated with participation with other ethnic groups, but the correlation between hosting refugees and par- ticipation is positive and significant (OR: 3. 40, CI: 2. 05 – 5. 62). In contrast, contact with other ethnic groups is positively associated with hosting IDPs (OR: 1. 89, CI: 1. 41 – 2. 53) in the general sample, but negatively associated with hosting refugees in cities (OR: 0. 23, CI: 0. 14 – 0. 38). Hosting IDPs is more consistently associated with higher perceptions of social cohesion across dimensions in the general sample. Hosting IDPs is positively associated with each social cohesion sub-dimension other than participation with other ethnic groups. Hosting refugees is positively associated with perceptions of out-groups (OR: 1. 92, CI: 1. 41 – 2. 61) and contact with other ethnic groups (OR: 1. 89, CI: 1. 41 – 2. 53) in the general sample, but insignificant for the other sub- dimensions. These differences suggest that IDPs and refugees pose different challenges to social cohesion even within the same region and that hosting status has different consequences for social cohesion in cities than in rural communities. 29", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "30 G G G G G G G G G G G G Cities General Relationships Solidarity Governance 0. 5 1. 0 2. 0 0. 5 1. 0 2. 0 Perception of Out − Group Relationships Perception of In − Group Relationships Contact with Other Ethnic Groups Participation with Other Ethnic Groups Access to Services Access to Basic Needs Odds ratio (log scale) Variable Displacement G Hosting Displaced Persons (a) Host (Any) Cities General Relationships Solidarity Governance 0. 5 1. 0 2. 0 0. 5 1. 0 2. 0 Perception of Out − Group Relationships Perception of In − Group Relationships Contact with Other Ethnic Groups Participation with Other Ethnic Groups Access to Services Access to Basic Needs Odds ratio (log scale) Variable Displacement Hosting IDPs (b) Host IDPs Cities General Relationships Solidarity Governance 0. 1 0. 2 0. 5 1. 0 2. 0 5. 0 0. 1 0. 2 0. 5 1. 0 2. 0 5. 0 Perception of Out − Group Relationships Perception of In − Group Relationships Contact with Other Ethnic Groups Participation with Other Ethnic Groups Access to Services Access to Basic Needs Odds ratio (log scale) Variable Displacement Hosting Refugees (c) Host Refugees Figure 5: Summary Plots of Logistic Regressions", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Gender may influence how members of the host community experience and perceive their role as hosts. Since each of the samples are gender-balanced, Figure 6 re-reruns the logistic regres- sions above but after sub-setting the data by gender. Hosting IDPs is associated with improved perceptions of social cohesion among men for all sub dimensions other than access to basic needs in the general sample, but women ’ s perceptions of social cohesion are only positively associated with contact with other ethnic groups (OR: 1. 26, CI: 1. 01 – 1. 56) and access to services (OR: 1. 44, CI: 1. 15 – 1. 80). In cities, female respondents were more likely to report negative perceptions of in-group (OR: 0. 59, CI: 0. 41 – 0. 84) and out-group relationships (OR: 0. 63, CI: 0. 44 – 0. 90) when hosting IDPs. Women were less likely to participate with other ethnic groups (OR: 0. 57, CI: 0. 40 – 0. 82) if they had IDPs in their communities. But men had positive associations for IDPs with relationships and solidarity in cities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Combined, the results in Figure 6 indicate that gender is an important mediating factor in how hosting impacts perceptions of social cohesion. Women ’ s perceptions of social cohesion are generally negatively impacted by hosting. The presence of IDPs is positively associated with social cohesion for men, but not for women. Hosting refugees is similarly associated with improved perceptions of solidarity and relationships for men in the general sample. 6 Policy and Program Implications This study contributes to establishing forced displacement as fundamentally a development chal- lenge that requires addressing complex dynamics, including the social causes of conflict, instabil- ity, and fragility. Social dynamics and cohesion in situations of forced displacement have potential far-reaching consequences for individuals and communities alike. Displaced persons are at risk of increased vulnerability and exclusion, especially in protracted situations. Their presence can be a source of instability and fragility for the communities that host them, especially in situations of internal displacement that see most displaced persons stay within the societal and institutional 31", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "32 G G G G G G G G G G G G Cities General Relationships Solidarity Governance 0. 5 1. 0 2. 0 0. 5 1. 0 2. 0 Perception of Out − Group Relationships Perception of In − Group Relationships Contact with Other Ethnic Groups Participation with Other Ethnic Groups Access to Services Access to Basic Needs Odds ratio (log scale) Variable Gender G Female Male Hosting displaced persons (a) Host (Any) G G G G G G G G G G G G Cities General Relationships Solidarity Governance 0. 5 1. 0 2. 0 0. 5 1. 0 2. 0 Perception of Out − Group Relationships Perception of In − Group Relationships Contact with Other Ethnic Groups Participation with Other Ethnic Groups Access to Services Access to Basic Needs Odds ratio (log scale) Variable Gender G Female Male Hosting displaced Congolese (b) Host IDPs G G G G G G G G G G G G Cities General Relationships Solidarity Governance 0. 1 0. 2 0. 5 1. 0 2. 0 5. 0 0. 1 0. 2 0. 5 1. 0 2. 0 5. 0 Perception of Out − Group Relationships Perception of In − Group Relationships Contact with Other Ethnic Groups Participation with Other Ethnic Groups Access to Services Access to Basic", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Needs Odds ratio (log scale) Variable Gender G Female Male Hosting displaced refugees (c) Host Refugees Figure 6: Summary Plots of Logistic Regressions by Gender", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "context that caused their displacement in the first place. The qualitative focus groups provide some important insights for international actors. Inter- national definitions of social cohesion may not match local realities. International humanitarian actors should keep abreast of the dimensions of social cohesion beyond simply the limiting of vio- lence. The qualitative and participatory exercises described in this paper could be re-purposed for development aid, which may not fit local needs (Ferguson 1990). In addition, the findings show that the relationship between hosting displaced persons and per- ceptions of social cohesion is not necessarily altogether negative. Instead, refugees and IDPs are associated with host community perceptions of social cohesion differently; refugees in general cor- respond to lower levels of social cohesion than IDPs. We also find that hosting in cities and general population exhibit some important differences. The backlash frequently observed in refugee stud- ies may not be present in situations of internal displacements and such an effect can be mediated by contextual factors of displacement. The results suggest that programming that seeks to address the needs of host communities in contexts of forced displacement may require fundamentally different approaches than those that are used in refugee or camp-based displacement scenarios. Programmatic decisions should focus on supporting host communities even in informal contexts but remain cognizant and avoid uneven distributional consequences.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Programming that seeks to address the needs of displaced persons in such scenarios must consider how multiple displacement or hosting during protracted conflict presents different challenges than single, long-term displacements. Humanitarian actors must additionally remain cognizant of gender. The analysis suggests that hosting may disproportionately and negatively impact women, in situations with IDPs in cities. Female respondents in cities were more likely to have negative perceptions of in group and out group relationships and were less likely to participate with other ethnic groups if they had IDPs in their communities. In contrast, hosting IDPs was associated with improved perceptions of social cohesion among men for all sub dimensions other than access to basic needs. International hu- 33", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "manitarian actors responding to IDP flows in cities focus programming that encourages the active participation of women in the host communities. The results indicate that inter-ethic relationships are a particularly fruitful area to target with programming among women in cities. 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Although 86 % of the refugee sample was surveyed at least once, there was some differential attrition between the treatment and control refugee sam- ples, and therefore we estimate bounded effects following Lee (2009) (and the text highlights the results for which both estimated bounds take the same sign unless otherwise noted). These data are analyzed largely following the econometric models and primary outcomes specified in an AEA pre-registration (AEARCT # 0006141) and associated pre-analysis plan, while making note of ad- ditional and exploratory results. Another core contribution of this study is to examine the host community reaction to refugee assistance, utilizing a detailed survey of the attitudes and experiences collected among a repre- sentative sample of the Jordanian neighbors (N = 2, 146) of both treatment and control households. To our knowledge, this is among the first studies to experimentally examine how humanitarian assistance to refugees affects the views of the local communities who do not directly benefit. Specifically, we examine whether refugee-targeted transfers — in this case via rental payments 1Statistic calculated directly from data on the universe of Syrian refugees in Jordan registered with UNHCR. 2 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There is also qualitative evidence that low take-up was at least in part due to some landlords ’ reluctance to make signed legal commitments to refugees for the lease, and with the implementing partner for the necessary construction. This is a setting in which most rental contracts are informal and landlords have ample discretion over their terms and a largely free hand to evict tenants. Despite the appeal of guaranteed rent for a year plus funding for housing upgrades, some landlords preferred not to “ bind ” themselves to the program and the particular recipient refugee household currently residing in their property. 3 One of the study ’ s main empirical findings is that we detect no significant positive impacts of the housing assistance program for refugees along a range of pre-specified primary outcomes, with the exception of housing expenditures where there is the expected (and somewhat mechanical) drop in spending. Beyond housing expenditures, the other primary pre-specified outcomes in- 2We do this by surveyed neighbors but excluding landlords, who were often involved in the program. 3The data underlying these findings is from our implementing partner ’ s Integrated Assessment Shelter Analysis. 3 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "clude an aggregate housing quality index, total household consumption, respondent mental health, and child socio-emotional well-being, measured using the standardized Strengths and Difficulties Questionnaire (SDQ). In fact, the analysis unexpectedly shows that the socio-emotional well-being of children in treated households decreases substantially, by 0. 34 standard deviations on average; we return to interpretation of these patterns below. Due to the non-compliance noted above, esti- mated instrumental variable (IV) treatment on the treated (TOT) effects are relatively imprecise. But the primary analysis, which pools across multiple rounds of follow-up survey data, does allow us to reject the existence of some moderate positive treatment effects. A second main finding of the study is that the refugee-targeted housing subsidy program led to a meaningful and statistically significant deterioration in community perceptions towards Syrian refugees among the Jordanian neighbors of treatment households. We estimate a 0. 33 standard deviation unit decrease in an index of their social attitudes and perceptions of refugees. The analy- sis also shows that neighbors become better informed about how much assistance Syrian refugees receive. There is no evidence of effects on neighbors ’ own economic outcomes. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "evaluation results, a large majority (70 %) of refugee respondents in this study stated in the endline survey that they would have preferred cash assistance of equivalent value to the rental subsidy. At the same time, such cash assistance would not do much to improve the structural impediments to economic and social integration facing refugees in most settings. 2 Background and Context 2. 1 Syrian Refugees in Jordan As of 2023, the Syrian crisis was one of the largest displacement crises in the world, resulting in 6. 8 million internally displaced Syrians and another 6. 5 million Syrian refugee abroad (UNHCR 2023c). The war began in 2011 with pro-democracy demonstrations against President Bashar al- Assad, whose government responded with lethal force against the protesters, leading to escalating violence that drove the country into civil war. Considering Syria ’ s 2010 population was 21 million, more than half of its population was displaced during the study period (UNHCR 2023b). Jordan hosts roughly 650, 000 registered Syrian refugees, accounting for about 6 % of its popu- lation of 11. 1 million people (UNHCR 2023a). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Treatment Randomization: The housing subsidy program was randomized geographically at the community level. 8 In the first step, 158 communities in Irbid and Mafraq were randomized into treatment or control for HSP assistance, stratifying on governorate and district population quartile. One third of communities were randomly assigned to treatment, while the remaining two thirds were randomly assigned to control. All eligible applicants living in the treatment communities were assigned to treatment. In all analysis below, error terms are clustered at the community level. There were several reasons for the cluster randomized design, including the ability to streamline implementation (and thus lower program costs), to reduce conflict among refugee households as- signed to treatment versus control, and to improve the analysis of treatment spillovers onto host community members by boosting the local saturation of program assistance, thereby making it more salient to neighbors. Refugee Sample: The survey sample consists of all refugees assigned to treatment and an 7See AER RCT Registration Number AEARCTR-0006141. 8Communities correspond to the Jordanian government ’ s administrative unit of “ localities ”. Communities in Mafraq have 3, 866 people on average, while those in Irbid are somewhat larger at 14, 626 individuals on average. 10 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "described above, HSP households received not only full rental subsidies for 9 to 18 months but also physical upgrades to the home environment. These proximate outcome measures can be seen as capturing successful program implementation. To measure physical improvements to the shelter, we constructed an index of self-reported floor, roof, and wall quality; electricity and water access; and crowding. Housing expen- ditures were measured in two ways: First, in the midline phone survey, respondents re- ported their total monthly housing expenditures, including the payments provided by the implementing organization. Second, in the endline in-person survey, once assistance had ended, participants reported their monthly out-of-pocket housing expenditures (excluding the amount paid by the implementer). The latter is our preferred measure of the direct effect of the program on housing expenditures, because it allows us to test whether treated house- holds experienced rental savings. Because the endline measure was collected after assistance ended, any observed expenditure reductions would most likely be an underestimate of the benefits experienced during the program. Recall that HSP also required the landlord to not raise rent for an additional year after the assistance ended, which could help account for any persistent effects on housing expenditures in the treatment group. 2. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Primary well-being outcomes: This category includes three main outcomes that seek to cap- ture refugee household well-being, including household consumption, mental health (mea- sured through the Center for Epidemiological Studies Depression Scale, CES-D), and an index summarizing the 25-item child Strengths and Difficulties Questionnaire (SDQ) that measures emotional and conduct problems, inattention, peer relations, and prosocial behav- iors. Both the CES-D and SDQ are validated tools that have been utilized in a variety of international contexts (Park and Yu 2021, Woerner et al. 2004). In each of the three survey rounds, the primary respondent completed the CES-D depression screening. The SDQ was completed by an adult respondent regarding a randomly selected child aged 3 to 8 years old in the endline survey, as well as for the same child in the one and a half year follow-up. Total household consumption was measured only at endline (which was collected in person). 12 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3. Secondary outcomes among refugees: These include a broader set of variables that were grouped into 14 broad families in the PAP including: (1) dwelling characteristics and house- hold structure, (2) consumption and expenditure, (3) financial participation, (4) earnings, labor, and occupational choice, (5) migration, (6) physical, mental health, and sleep, (7) marriage and fertility, (8) child outcomes, (9) social capital, (10) political attitudes, (11) time use, (12) education and cognition, (13) behavioral games and preferences, and finally, (14) specific COVID-19 related outcomes. The three primary outcomes noted above are a subset of these measures. Certain outcomes were collected in each of the three survey rounds, for instance, the food security measures (the number of meals eaten and the frequency of go- ing to bed hungry). Respondents also reported information on child school attendance, and completion of learning activities when schools were closed due to the COVID-19 pandemic. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the midline survey collected in 2020 (during the pandemic), the physical health outcomes included additional questions focused specifically on COVID-19 symptoms and treatment but these were dropped in later rounds as the pandemic had eased. 3. 2 Measures of social cohesion among neighbors We pre-specified four primary outcomes regarding the attitudes of Jordanian neighbors: 1. Interpersonal social attitudes and perceptions: This was assessed using an index derived from questions about social ties between the Jordanian respondents and Syrian refugees, attitudes regarding social proximity, and opinions on refugees ’ contributions to society. 2. Economic attitudes and perceptions: Focused on Jordanians ’ perceptions of the impact of Syrian refugees on the Jordanian economy, based on survey questions. 3. Altruism: Measured through a dictator game, this outcome gauged altruistic behavior to- wards refugees, with real monetary incentives provided for a random subset of the sample to 13 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "promote genuine responses. 9 4. Policy preferences: This index summarized the respondents ’ stances on various policies re- lated to Syrian refugees, including their living arrangements (e. g., a hypothetical requirement to live in camps) and employment rights. In addition, the neighbor survey collected comparable measures of economic and psychological well-being to those measured among the refugee sample, including housing expenditures, total consumption, and mental health, to assess program spillovers. 3. 3 Data sources The study employs four different data sources, with the first obtained from partners at the imple- menting organization, and the other three original survey data sets collected by the research team. They include: 1. Baseline administrative data: The implementing organization collected baseline data from program applicants to establish their eligibility for the HSP. This assessment form collected information on household demographics, housing quality, health and disability, employment, and education, which were used to construct a housing vulnerability index employed as a baseline covariate in the empirical analysis. 2. Surveys collected among refugee households: The study collected surveys during program implementation (the midline survey), immediately after (endline survey), and 1. 5 years after all assistance was delivered (the follow-up survey) (see Figure 2). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the midline survey, 1, 619 participants were surveyed by phone in 2020, during the first year of the COVID- 19 pandemic. The endline survey in 2021 collected in-person data from 1, 534 participants shortly after HSP assistance had ended. Finally, in the 1. 5 year follow-up collected in late 9A random one third of respondents were offered financial incentives whereas the others were told it was hypothet- ical. We cannot reject that the same choices were made in both groups on average. 14 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "using the Washington Group Short Set on Disability. 11 Sharing a home is common, likely as an economic coping mechanism, with on average of 1. 3 families living in the same house. Housing quality is low, with most houses in some state of disrepair or incomplete construction. Table 1 also illustrates descriptive statistics for the treatment versus control households to as- sess balance across groups, where the third column presents a mean difference test. Respondents across the study arms are largely balanced by age (with an average age of 34 years), marital status (84 % married), and incidence of a disability (Panel A). There is a slight imbalance in the share of respondents who are female, with the proportion slightly higher in the control group. Moreover, average household size is just above five members, with nearly three children on average, and these are balanced between treatment and control, as is the number of families per residential unit (Panel B). Refugee households in the sample face challenging and precarious housing conditions, but these characteristics are generally balanced across groups (Panel C). For example, only 66 % of households have access to piped water, 22 % have functional windows, and 44 % completed floors. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While the majority of refugees plans to stay in the same housing unit (92 %), a large share have moved shelter in the last year (with an average of 0. 44 and 0. 47 moves in the control and treatment groups, respectively). Across all baseline characteristics presented in the table, the p-value on the hypothesis that all differences are zero is 0. 102 (using a Chi-squared test), not quite significant at traditional confi- dence levels. This finding together with the fact that there are few economically meaningful differ- ences across treatment arms in terms of respondent, household and shelter characteristics – out of the 18 covariates examined only two exhibit a statistically significant difference at 95 % confidence – indicates that the study randomization generated largely comparable groups of households. 11We use the recommended cut-off (level 3) to classify respondents as being disabled or not based on six domains: seeing, hearing, walking, cognition, self-care, and communication. 16 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3. 5 Forecasts of Program Impacts The forecasters were mainly researchers working on topics in migration and development eco- nomics, or those with expertise in humanitarian program implementation, and were asked to predict effects on the program ’ s primary outcomes. For round 1, a total of 61 individuals, comprising 37 researchers and 23 non-researchers, responded to surveys about the program ’ s effects on refugee well-being. For round 2, 63 respondents, including 53 researchers and 10 non-researchers, pro- vided predictions on the effects on neighbors ’ social cohesion responses. Gathering forecast data allows us to assess whether estimated impacts were in line with the prior beliefs of research and policy experts. Table 2 presents the mean predicted effects, as well as the interval from the 10th to 90th percentiles of predictions. Generally, forecasters anticipated modest improvements in refugee well-being, particularly regarding housing outcomes, while predicting no average impact on the social cohesion measures collected among neighbors. 4 Empirical Strategy 4. 1 First stage compliance with program assignment Table 3, Panel B presents the first stage analysis and the HSP take-up rate of 33 %. While sta- tistically significant, the compliance rate is lower than expected, especially given the substantial funding offered by the program. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4. 2 Estimating program impacts on refugee well-being The primary approach to estimate program treatment effects on the treated (TOT) is instrumental variables (IV) estimation, as represented by the following two equations: Tic = α0 + α1Zc + X ′ cΛ1 + W ′ icΓ1 + µict + ηic (1) yict = β0 + β1 bTic + X ′ cΛ2 + W ′ icΓ2 + µict + ϵict (2) Equation (1) estimates the first-stage effect of assignment to treatment on the household ’ s take- up status (with results presented above), and equation (2) estimates the TOT effects. To enhance statistical power, data from the midline, endline, and follow-up surveys are combined in the main analysis when possible, as pre-specified. The data includes multiple rounds t of survey data for each household, and the data are stacked. Tic denotes the treatment take-up for household i in community c. Zc is an indicator variable signifying whether community c was randomly assigned to HSP treatment, where all eligible individuals were then assigned to the treatment. The outcome variable of interest is yict, and the predicted treatment take-up, bTic, is derived from equation (1). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "nity level. While we focus on this pre-specified analysis of neighbor impacts, the PAP contained several additional secondary or more exploratory analyses, including the estimation of ITT effects, the evaluation of the effects of the program using a continuous treatment variable (measured in either months of treatment and the total cash value of the transfer15), the assessment of treatment effects on refugees ’ economic convergence with their neighbors, and estimation of heterogeneous effects (based on various demographics, see the full set of pre-specified results in the Appendix). 4. 5 Estimating the accuracy of forecasts To determine whether mean forecasts differed significantly from the estimated program impact, the analysis takes into account both the estimation error in the estimated program impact (bβ1 and bθ1 in the second-stage estimation equations above) as well as the observed range of forecasts, using 1000 simulated draws of each. For each draw, the difference between the two values is taken, yielding the distribution of the differences. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(q-value = 0. 03), which represents a 24 % decrease in food assistance relative to control. This is a nontrivial proportion of households ’ budgets: it is equivalent to over 10 % of households ’ total monthly labor income pre-pandemic (as control households reported 218 USD PPP in average monthly food assistance and pre-pandemic monthly labor income of 503 USD PPP). It is worth noting that this measure of assistance includes both formal assistance, such as from the UN High Commissioner for Refugees and the World Food Program, as well as informal assistance from local organizations and community members. Regardless of the channel, this substantial decrease in the household ’ s food budget seems likely to have contributed to worse household food security. This is in line with conversations that the research team had with several humanitarian aid organizations during the pandemic, in which some implementers sought to allocate emergency assistance to households that were not already benefiting from large forms of assistance, such as the HSP. The midline survey data also indicates that there were several other unexpected short-run changes in household outcomes. For instance, it appears that changes in household composi- tion may have further contributed to household hunger (Panel C, Table 5). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There are several potential drivers of these impacts, including the possibility that observing refugee neighbors receive generous assistance led them to feel worse about their own living situation and quality of life, which is perhaps related to the resentment and backlash effects hypothesized above. Second, neighbors of treated households state that they believe refugees typically receive sig- nificantly less aid than that stated by control group neighbors (effect- 347 USD PPP, q-value < 0. 01). Interestingly, the neighbors of treated households have more accurate beliefs about aid levels, with fewer of them believing that refugees receive extremely high levels of aid that are rarely observed. There thus appears to have been some learning of objectively true information regarding the real- ity of refugees ’ assistance levels among their Jordanian neighbors. This is consistent with greater information exchange and learning as a result of the highly observable HSP. 29 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "education and socioeconomic status (see Appendix). The final aspect of pre-specified heterogene- ity was social desirability, measured using a normalized continuous score from the widely used Marlowe-Crowne scale (Crowne and Marlowe, 1960). The large negative program impact on the neighbor social attitude and perceptions index are predominantly driven by individuals with lower scores on the social desirability scale, indicating that they are less likely to suffer from experi- menter demand effects. In our view, this lends additional credibility to the negative social cohesion result, and also implies that the study may underestimate the extent of the negative effect on social attitudes, if Jordanian neighbors with higher social desirability tendencies do not truthfully report their possibly even more negative views of Syrian refugees. 7 Comparing Forecasts to Estimated Program Impacts Comparing experts ’ forecasts to estimated the program effects allows us to highlight areas where the study results advanced learning relative to prior expectations. The forecasts focus on the mid- line and endline measures of the primary outcomes, namely: i) the well-being measures among refugees, and ii) the attitudes and perceptions of Jordanian neighbors towards refugees. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Experts also forecasted a marginally significant improvement in child socio-emotional well- being at endline, yet if anything there are reductions in this outcome at endline though they are not significant; as noted above, there are significant reductions in the child well-being measure at the 1. 5-year follow-up but forecasts were not collected for that round. There is thus a meaningful and significant (at 95 % confidence) difference between the predicted and actual effects on the child outcome, indicating that the study generated new insights that diverge from the experts ’ priors. There is also a meaningful and significant gap between experts ’ forecasts and the observed effects of the program on neighbors ’ social attitudes (Figure 3, Panel B). Experts had anticipated an average null effect of the program on Jordanian neighbors across all three primary pre-specified outcomes (namely, policy support, economic perceptions, and social attitudes), and for the first two the predictions and actual estimates are all close to zero. The program ’ s significant negative impact on neighbors ’ social attitudes is significantly different from the predicted null effect at 95 % confidence, once again a meaningful update relative to priors. 8 Conclusion This paper investigates the short- and medium-term effects of a substantial housing subsidy pro- gram on Syrian refugee households and their Jordanian neighbors. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Therefore, this study aims to answer the following questions: (i) How do levels of maternal mental health — specifically depression, anxiety and parenting stress — differ by education level, refugee status, and living in an urban vs. rural area? (ii) What is the compounding influence of multiple maternal mental health conditions, namely depression, anxiety and parenting stress, on child development for children 0-6 years old? (iii) How, if at all, is this association altered by controlling for stressors and other confounders? This study builds on a nascent literature in the following ways: (i) it examines multiple maternal mental health outcomes for caregivers at the same time; (ii) it differentiates between maternal mental health outcomes for hosts and refugees; (iii) it assesses the severity of various maternal mental health conditions, not just the onset of the condition itself; (iv) it documents differences in child outcomes – both from caregiver reports and from a direct assessment of children; (v) it examines outcomes separately for children between 0-35 months of age and those aged 36-72 months. To the best of our knowledge this is the first study to assess the compounding association of multiple maternal mental health conditions on child development for children 0-72 months in Khyber Pakhtunkhwa, Pakistan. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Methods Research Design and Data Collection Strategy This household survey was commissioned by The World Bank and carried out by the Center for Evaluation and Development between December 2023 and February 2024 in Khyber Pakhtunkhwa, Pakistan. Households were selected based on being located in the catchment area of one of 200 public schools across Khyber Pakhtunkhwa (excluding newly merged districts). 2 In most cases, the boundaries of a catchment area represented a maximum of a 30-minute walk from the proximate school. Households living in a given catchment area who had at least one child under the age of 72 months (for either of the 0-35-month or 36-72-month age groups) available to be 2 The sample of 200 public schools was drawn for a different World Bank survey in 2022. These 200 schools are a representative sample of public schools across all districts of Khyber Pakhtunkhwa (excluding newly merged districts). The households in question are thus representative of those in the catchment area of a representative sample of schools. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 interviewed (i. e., on the premises when the enumerators began their interviews) were included in this survey. Enumerators selected households within the catchment area randomly. Among households with an age-eligible child present (i. e., 0-35 months or 36-72 months), enumerators explained the purpose of the study and invited the target child ’ s primary caregiver to be interviewed. Respondents were supported to withdraw their consent anytime during or after the interview and such cases were dropped from analyses. For children 0-35 months of age, assuming a minimum power level of. 8 and an alpha of. 05, the study design predetermined that the minimum desired detectable difference in average ECD Z- score measured by the Caregiver Reported Early Development Instrument (CREDI) was a 0. 2 standard deviation (SD) difference, which has been shown to be the average difference between urban and rural Pakistani children assessed using the CREDI (Hentschel et al., 2024). For children between 36 and 72 months of age, assuming a minimum power level of. 8 and an alpha of. 05, the study design predetermined that the minimum detectable difference in average ECD score (measured by the AIM-ECD score) is a 0. 23-point difference (the difference between males and females in Hentschel et al., 2024). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 𝑀𝑀 = ൝ 1 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑠𝑠𝑠𝑠ℎ𝑜𝑜𝑜𝑜𝑜𝑜 2 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑠𝑠𝑠𝑠ℎ𝑜𝑜𝑜𝑜𝑜𝑜 3 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑠𝑠𝑠𝑠ℎ𝑜𝑜𝑜𝑜𝑜𝑜 Household weights are subsequently calibrated using census data from the 2017 census. Let 𝑤𝑤𝑗𝑗 ℎ (𝑚𝑚) represent these model-adjusted weights for households. Design-based weights for children are calculated as 𝑤𝑤𝑗𝑗 𝑐𝑐 = 1 𝑤𝑤𝑗𝑗 ℎ (𝑚𝑚) ∗ 1 min൫𝑛𝑛𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒, 3൯ Where 𝑛𝑛𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 is the number of eligible children in the household. Weights were adjusted via iterative proportional fitting to reflect population-level margins for Khyber Pakhtunkhwa on the categorical variables taken from the 2017 census described above (e. g., area of residence, roof material, etc.). Survey weights were applied to all analyses, with household weights for the descriptives and child weights for the regressions. Key Variables and Tools Basic demographic data were collected from each respondent for child gender, age, maternal educational attainment, birth registration (respondents confirming the child was registered were asked to provide proof of the document), and area of residence (i. e., urban, rural, semi-urban, inner-city). All instruments were administered in Urdu. In circumstances where children were unable to speak Urdu, enumerators effectively engaged children using various methods, such as teaching them Qaida in Madrasa. Outcome Variables Early Childhood Development Measures Children 0-35 months. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Caregiver-Reported Early Development Instrument (CREDI) long- form was used for children under 36 months of age. The CREDI is a globally-validated developmental assessment tool (Waldman et al., 2021), and the short-form version of the CREDI has previously been validated and used in Pakistan (Hentschel et al., 2024; McCoy et al., 2018). The tool consists of up to 100 caregiver-reported items developed according to typical abilities by age within the birth to age 3 range in 6-month increments (e. g., for children 0-6 months, 6-12 months, and so forth) and administration ends based on 5 consecutive incorrect answers so as not to distress the child. Items measured children ’ s developmental status across four primary domains: motor, language, cognition and social-emotional development. An age-standardized Z-score, created by uploading deidentified data to the CREDI application, allows comparison of a given sample to a global, advantaged, sample based on children from 15 low- and middle-income countries. 5 A child scoring more than 2 standard deviations below the mean is typically considered to be developmentally “ off-track.\"5 The reference sample contains 19, 165 children who all have a mother who completed secondary school or higher education and live in a household where least one adult had engaged in 4 or more of the 6 “ Play activities ” from the Family Care Indicators with the child. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Children 36-72 months The AIM-ECD Direct Assessment (DA) was used for children 36-72 months (Pushparatnam et al., 2021). The AIM-ECD measures ECD in four domains: early literacy, early numeracy, executive functioning, and social emotional development. The AIM-ECD DA is structured into 14 subtasks, with a total of 77 questions / tasks, administered in a series of activities directly with the child. The AIM-ECD has previously been used in Khyber Pakhtunkhwa as well as other parts of Pakistan (Hentschel et al., 2024; Seiden et al., 2024). Items were designed to be used in various cultural or linguistic contexts with minimal adaptation (i. e., inclusion of literacy in both English and Urdu). Items are scored based on percentage correct with enumerators giving credit if items were responded to correctly in Pashto. The AIM-ECD Caregiver Report (CR), made up of 29 core items across early literacy, early numeracy, executive functioning, and social emotional development, was also utilized. Given the high correlation between the AIM-ECD DA and the AIM-ECD CR (corr =. 71, p <. 05), the AIM-ECD DA results were used as the primary outcome and the AIM-ECD CR results are available upon request. Maternal Mental Health Measures Depression. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Maternal depression was measured by the Self-Reporting Questionnaire (SRQ-20), which has previously been used in Pakistan and has been found to be valid and reliable for screening for depressive disorders in rural Pakistan (Husain et al., 2006). The SRQ-20 contains 20 yes / no questions, with higher scores indicating higher levels of depression, and was found to have a high internal consistency reliability, with a Cronbach ’ s alpha of. 89 in this sample. Anxiety. Maternal anxiety was assessed using the General Anxiety Disorder 7-item questionnaire (GAD-7). This 7-item questionnaire has been found to be valid in the Gilgit province of Pakistan (Ahmad et al., 2017) and has subsequently been used in other assessments of anxiety in Pakistan (Yasmin et al., 2021). The GAD-7 was found to have a high internal consistency reliability, with a Cronbach ’ s alpha of. 80 in this sample. Parenting Stress. Parenting stress was measured using an adapted version of the Parental Stress Scale (PSS), which has previously been validated in Urdu in Punjab (Bilal et al., 2021). The adapted version used in this survey was designed by Ugarte et al., 2024 for Rohingya refugees in Bangladesh. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The 10-item parenting stress scale used in this sample was found to have a high internal-consistency reliability, with a Cronbach ’ s alpha of. 85 in this sample. Independent (Stressor and Other Covariate) Variables A variety of known stressors and correlates of maternal mental health and child development were also assessed. Stressors, including food insecurity, poverty, flooding impact, experiences of domestic violence, felling unsafe in one ’ s community, discrimination, living in a rural area, lack of enrollment in ECE, and low maternal educational attainment have consistently been associated with lower levels of maternal mental health conditions and child development across contexts (e. g., Hentschel et al., 2023; Iqbal & Ali, 2021; Reed et al., 2012: Sitwat et al., 2015; Yousuf et al., 2023) Other covariates, such as refugee status and child age, were also assessed. Refugee Status. At the time of the survey, sensitivities around “ refugee status ” were particularly heightened. As such enumerators were asked to keep notes on interviews to help identify whether Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 a respondent was likely a refugee – rather than to ask explicitly about citizenship status. Because many Afghan nationals living in Pakistan lack documentation and are not registered with UNCHR as either asylum seekers or confirmed refugees (UNCHR, 2024b), the study did not differentiate between verified refugees, asylum seekers and unregistered Afghan nationals, the group of which are collectively referred to as “ refugees ” within this paper. Enumerators determined refugee status of the household by confirmation of at least one of the following factors: the primary caregiver or her parents were born in Afghanistan; the use of Afghani Pashto, Persian and / or Dari as the primary language in the home; respondent declined to share identification, or the target child ’ s date of birth card or other documentation was valid for Afghanistan but not Pakistan; community leaders had identified the household as Afghani; or enumerator-reported knowledge of the household ’ s refugee / migrant status based on previous survey engagement. A “ yes ” response to any one of these indicators placed the respondent and household in the refugee category for analytical purposes. Food Insecurity. Food insecurity was assessed by the Food Insecurity Experience Scale (FIES), an 8-item questionnaire comprised of yes / no questions relating to experiences of food insecurity. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It has been previously used in Pakistan and found to be reliable (Afridi et al., 2021). The Cronbach ’ s alpha of the scale was. 91, indicating high internal consistency reliability. Asset Index. An asset index was calculated by a principal components analysis of a series of questions related to household materials, access to water, and home ownership, among other resource-related factors. For each household, based on respondent report, an asset quintile was created to categorize by quintile from lowest asset index values (quintile 1) to highest asset index values (quintile 5). Flooding Impact. Respondents were asked a series of questions about the impact of flooding on their day-to-day life in both 2022 and 2023. Households were categorized as being “ impacted by flooding ” if they answered “ yes ” to at least one question about whether flooding impacted their day-to-day life in 2022 or 2023. Domestic Violence. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Domestic violence experiences and attitudes were measured based on self- report using three questions: (a) In your opinion, is a husband justified in hitting or beating his wife? (b) Did your husband ever hit or beat you? And (c) Are (were) you afraid of your husband: most of the time, sometimes, or never? These questions have been used in the Government of Pakistan Demographic and Health Survey 2017-2018. For the regression analyses, fear of husband was controlled for as it was the only variable that was correlated with all mental health outcomes and child development. Fear of husband was categorized from 1 to 3, with 1 indicating never afraid, 2 indicating sometimes afraid, and 3 indicating afraid most of the time. Community Safety. Perceptions of and experiences of safety in the community (or lack thereof) were determined by exposure to crime (i. e., if anyone has taken or tried taking something from you by using force or threatening to use force in the last 12 months) and perceptions of safety in the community — specifically, feeling safe while home alone at night or while walking through the neighborhood alone after dark. Discrimination. Respondents were asked if, in the past 12 months, they felt discriminated against or harassed on the basis of ethnic origin, immigration or refugee status, sex, sexual orientation, Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 age, religion or belief, disability, or some other reason. Individuals were classified as experiencing discrimination in the past 12 months if they reported experiencing discrimination for any of the mentioned reasons. School / ECE Enrollment. ECE enrollment was determined using an open-ended item administered only if the selected child was between 3 and 6 years old. Response options included nursery, prep, ECE, Katchi, kindergarten or grade 1. Area of residence. Enumerators categorized households as rural or urban based on recorded household location. Maternal Educational Attainment. Mothers indicated their highest level of educational attainment. Birth Registration. Mothers were asked if their child has a birth certificate. If they said yes, they were asked to provide proof. As such, there were three potential options: not registered (answered no to if their child has a birth certificate), birth certificate (includes those with and without proof of registration), and proof of registration (mothers that answered yes and provided proof). Child Age. Age of the child was recorded by either checking the child's birth registration or vaccination card, or where neither a birth certificate nor a vaccination card was available the child's age was recorded based on the mother's recall. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 Figure 3 Sample Distribution for Refugee Children Compared to Global Distribution (n = 92) Figure 4 Sample Distribution for Children Living in Rural Areas Compared to Global Distribution (n = 902) AIM-ECD direct assessment scores were created by calculating the percent correct out of the 77 direct assessment tasks and questions asked. There were four early literacy tasks (listening comprehension, letter identification, initial sound discrimination and name writing), six numeracy Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 tasks (receptive spatial vocabulary, producing a set, simple addition, number identification, number comparison and shape identification), three executive functioning tasks (head, toes, knees, shoulders task, forward digit span, and backward digit span) and one social emotional learning tasks (perspective taking / empathy). The average child in KP scored a 33 % on the AIM-ECD (95 % CI: 40. 9 %, 34. 9 %). The distribution of AIM-ECD direct assessment scores is reported in Figure 5. Figure 5 Sample Distribution of AIM-ECD Direct Assessment Scores (n = 635) 8 The prevalence of stressors is presented in Table 2. For example, 26 % of the sample reported having to skip a meal, with 14 % of the sample reporting they were hungry but did not eat. Approximately 8 % of the sample was impacted by flooding in 2022, 6 % of the sample experienced flooding impacts in 2023, and 8 % of the sample were impacted by flooding in either 2022 or 2023. Of those impacted by flooding, the most common disruptions were to household members schooling, essential travel, and household members well-being. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The majority of the sample reported being afraid of their husband sometimes or most of the time, and 22 % of the sample reported ever being beaten by their husband. Over half of caregivers reported feeling safe or very safe at home after dark (63 %), and 53 % reported feeling safe or very safe walking in their neighborhood after dark. Approximately 5 % of caregivers reported experiencing some form of discrimination, with ethnic origin and sex as the most common forms of discrimination. 8 Only 635 individuals were sampled for the AIM-ECD initial sound discrimination sub-domain due to a coding error. As a result, for the overall AIM-ECD score and AIM-ECD literacy sub-domain we only have data from 635 children instead of 705. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Cross-sectoral programming designed to meet families ’ holistic needs would also increase the odds of maternal and child well-being, including increasing provision of cash transfers through BISP, expanding access to ECE programs, ensuring food availability and affordability for mothers, and implementing emergency-response programs for continuity of basic services in the context of floods and other disruptive disasters to protect vulnerable mothers and young children. Both direct interventions that provide mental health services and indirect interventions that reduce exposure to stressors within communities and households are approaches to alleviate mental health concerns among mothers of young children in the FCV setting of the current study. In addition to individual-level impacts, societal and economic impacts result from reduced maternal depression, anxiety and stress. For example, costing estimates from Pakistan suggest that there is a $ 16. 6 billion cost per cohort of unaddressed maternal mental health concerns (Bauer et al., 2024). Limitations The data collection coincided with a politically sensitive time in Khyber Pakhtunkhwa, inclusive of security concerns for enumerators, ongoing election campaigns, and occasional disruptions to data collection, which could have impacted the accuracy and consistency of data collected. Further, repatriation efforts being implemented during the data collection period led researchers to avoid self-report determination of refugee (documented and undocumented) status and rely on enumerator report using observable indicators as outlined above. This may have therefore impacted the accuracy of the data on identifying refugee households. There are also several measurement constraints to be considered. The tools selected to measure maternal mental health may not cover all relevant dimensions of symptoms and experiences specific to the region of Pakistan studied, even though tools were specifically selected based on previous adaptation, validation and use in the province. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "32 It is important to note that the cross-sectional approach to the survey prohibits any implications of causality between variables. Additionally, there is a wide range of stressors likely to associate with maternal mental health and ECD status, and the current study examined a sampling of stressors. Conclusion Few studies in Pakistan have used direct observation measures to gauge children ’ s ECD levels, making this study an important contribution to the body of knowledge on contributing factors for child development in Pakistan. The results overall fortify previous research showing the powerful connection between maternal mental health and ECD and highlight how exposure to stressors can jeopardize maternal and child outcomes. While these findings provide insight into maternal mental health in Khyber Pakhtunkhwa, research on maternal mental health concerns in Pakistan is somewhat limited, especially on the quality of available programming. Data on both the quality and accessibility of services for mothers and children that go beyond physical health and growth metrics to incorporate mental health and human development needs will fill gaps in the evidence base and inform public policy and programming. Research is needed on how to design sustainable, community-based service delivery to redress some of the unique challenges faced by both parents and young children in FCV contexts, as well as enhanced programming that is socially acceptable, physically accessible, and financially affordable can be designed and implemented at scale. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "They exclude other categories of displaced people who were not forced to move because of conflict or violence, such as economic migrants and victims of natural or environmental disasters. 4 Of course, many people cannot be simply categorized in these groups and this makes statistics on FDPs gross estimates, but the growth and relevance of these numbers are undisputed. The growth in the number of FDPs poses a challenge to the measurement of global and national poverty. Those who are forcibly displaced and in need of international protection tend to be persons who have lost their assets, financial resources, and social networks. They are typically very poor with no obvious path out of poverty. For refugees, their number vanishes from poverty statistics of their own country because they are no longer counted in the place of origin. Both IDPs and refugees are also not properly accounted for in the country in which they reside. Their numbers – even though high in absolute terms – are often low relative to the non-displaced population (with some exceptions like Lebanon and South Sudan). Hence, they do not explicitly show up in official statistics. Even if – as in some but not all countries – their locations are appropriately included in the sampling frame, they are unlikely to be sampled due to their small proportion relative to the population and high clustering in specific locations. 3 https: / / www. unhcr. org / refugee-statistics / 4 Note that the IASC definition of IDPs explicitly includes those fleeing from disasters. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These exercises often lacked the data sophistication and academic rigor that characterizes poverty measurement for regular populations in high-, middle-, or low-income countries, typically undertaken by development agencies such as the World Bank, UNDP or regional development banks. The UNHCR, for example, did not collect survey data on income, monetary consumption, or expenditure for refugees systematically as the focus and mandate of the agency is on humanitarian protection rather than poverty alleviation. It only started to collect data on income more systematically when budget restrictions forced the agency to start targeting cash programs. WFP did collect data on consumption regularly, but this effort was largely focused on food consumption for food security and nutritional assessments rather than poverty measurement. With little data and visibility, the economics profession and poverty specialists across the social sciences have also largely neglected FDPs (Verme, 2016; Dionigi and Tabasso, 2020). It is only in the past decade that development agencies have taken an interest in FDPs and, together with humanitarian agencies, started to consider how to gather data and measure poverty among FDPs continuously and rigorously. This has changed over the past decade. For example, the UNHCR has significantly invested in improving the measurement of income and employment using the ILO standard approach and has adopted the poverty headcount to measure progress towards the Global Compact for Refugees (https: / / www. unhcr. org / global- compact-refugees-indicator-report /). This organization also works on embedding refugees and IDPs into national statistics exercises via the International Expert Group on Refugee and IDP statistics (EGRIS) process and has established, together with the World Bank, a Joint Data Centre focused on improving the data infrastructure for refugees. The World Bank has also taken important steps towards FDPs with the adoption of a special window for IDA projects, joint data and research initiatives with the UNHCR, and the establishment of FD coordinators across its global practices. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 has shown, for example, that most studies find a positive or non-significant effect of FD on hosts ’ employment, wages and household well-being, a finding that disputes much of the popular perceptions on this question (Verme and Schuettler, 2021). One of the important factors that has limited research on FDPs in the past was the chronic shortage of quality microdata, something that is quickly changing. Today, microdata on these populations can be found in two main publicly available repositories: The World Bank microdata library and the UNHCR microdata library recently established in collaboration with the World Bank. An analysis of these data repositories as of October 2022 shows that the WB microdata library has 576 data sets on refugees (454 dated after 2011) and 206 on Internally Displaced (142 dated after 2011), whereas the UNHCR microdata library has 274 data sets related to refugees (263 dated after 2011) and 34 data sets on IDPs (all dated after 2010). 5 This new research area is also generating significant innovations with the potential to expand research methods in the poverty measurement field. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the area of targeting based on means-tests, for example, a study has shown that Receiving Operations Characteristics (ROC) curves can be an effective decision making tool for humanitarian assistance programs (Verme and Gigliarano, 2019) while another study found that poverty differences in prediction methods for targeting purposes among refugees are attributable to few data fields suggesting that refugee homogeneity can make poverty predictions and targeting easier as compared to regular populations (Altindag et Al., 2021, Beltramo et al., 2019). The existence of the UNHCR refugee registration system, which can be regarded as a live census of refugees, has encouraged others to use cross-survey imputation techniques to estimate poverty among refugees even in the absence of income or consumption data (Dang and Verme, 2021, Beltramo et al., 2021). The mobile nature of refugees and IDPs also lends itself to experimenting with new methodologies to measure poverty with alternative methods such as mobile phones (Blumenstock et al., 2015; Pape et al. 2020, Wieser et al., 2021), or satellite imagery and remote sensing data (Abelson et al., 2014; Neal et al., 2016). For example, night lights or types of infrastructures captured by satellite imagery can provide gross estimates of poverty. In essence, poverty measurement among FD populations benefits from decades of developments in the poverty measurement field, but also provides new opportunities to expand the field because of the atypical characteristics of these populations. 5 Note that many microdata sets are present in both the WB and the UNHCR microdata libraries. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Measurement Issues Sampling strategies There are some technical reasons that make surveying refugees and IDPs complex. Sampling is one of these reasons. Refugees and especially IDPs are mobile populations which can move into host areas, even if they are initially residing in camps, and might settle outside camps in previously uninhabited areas. These factors make the inclusion of these populations in national master samples for surveys difficult. For refugees, this problem is partly overcome by the UNHCR registration system (proGres) which requires all registered refugees to provide a set of basic information including location and socio-economic characteristics of the household ’ s members. However, using the proGres registration system to sample refugees has its own challenges. This system is available in some countries but not in others where a system may be missing or managed by the local authorities and not available to others. Where the system exists, many refugees are not registered, some are registered but their information is outdated or missing, while identifying the unit of observation (household, family, case) may be challenging although in other contexts such as Bangladesh, the registration system may be up to date and almost universal. In other words, extracting a representative sample of refugees in many countries, even when the proGres registration system is available, is not simple. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 areas are listed so that a given number of households can be randomly chosen from the list to be interviewed. The advantage of this approach is that it only requires knowledge of any FDP residing in an enumeration area, so that this person can be included in the sampling frame. The more accurate the number of FDPs in an enumeration area before listing households, the more efficient is the resulting sample. This approach requires appropriate enumeration area maps that can be easily defined through satellite images for camps, but might not be available across the country for FDPs living in host communities. Implementation can be expensive especially with limited knowledge about the number of FDPs in enumeration areas. However, area-based sampling has the big advantage of not requiring FDPs to register. In contrast, list-based sampling uses an existing list of all FDPs and randomly chooses a sample among them. If additional characteristics are available, like location or country of origin, the sample can be stratified. However, the sample will only be representative of registered FDPs. Hence, it is rarely used in the context of IDPs unless a proper registration system is in place. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To partly address sampling issues, the UNHCR and the World Bank have been cooperating to use the UNHCR or national refugee registration systems as initial master samples to conduct consumption surveys, similarly to what is done with censuses of regular populations. Initial experiments in this respect have been conducted in Jordan, Lebanon, Iraq, Uganda, Kenya, Ethiopia, and South Sudan, resulting in consumption surveys and poverty analyses of refugees and IDPs. Both the UNHCR and the World Bank are now also working with statistical agencies in multiple countries across the Middle East and North Africa and Sub- Saharan Africa regions to include FDPs in national sampling frames with initial efforts conducted in countries such as Chad, Jordan, Kenya, Niger and Uganda (e. g., World Bank 2021b). However, the political economy and data privacy can make these efforts challenging. For host countries, FDPs may be only one of the many marginalized groups they may be concerned with. With limited budgets, statistical agencies need to justify prioritizing one group over another. Furthermore, data privacy is particularly relevant for FDPs. National sampling frames are constructed based on census information, including personal information such as addresses, phone numbers, names as well as GPS locations in some cases. The UNHCR and national governments generally work together to collect registration data but there is some information that may not be shared between the two sides. For example, national statistical agencies protect the national sampling frame and cannot share the underlying cartography that would be required for UNCHR to amend the sampling frame with counts of FDPs. Even if both agencies would be able to create a trusting relationship allowing close collaboration, questions remain on whether FDPs would need to agree with sharing their information with a government agency. This becomes even more relevant in the context Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 where a government might be a real or perceived contributor to displacement or is harboring an overt or covert policy to reduce the number of refugees in the country. With limited results in including FDPs in national sampling frames, alternative approaches remain necessary. The UNHCR, the World Bank and numerous scholars and practitioners worldwide are experimenting with satellite images and phone surveys to try detecting refugee and IDP populations that may escape the UNHCR and national registers with some initial encouraging results. In Lebanon, Jordan and the Kurdistan region of Iraq, Aguilera et al. (2020) designed sampling strategies for Syrian refugees with known ex-ante selection probabilities. They used a variety of data sources, including data collected by humanitarian agencies, and also employed geospatial segmenting to create enumeration areas where they did not exist. Systematic field experiments are also underway to test different sampling approaches for IDPs living in camps. For example, Himelein, Pape and Wild (forthcoming) compare the performance of five alternative sampling approaches (satellite mapping, segmentation, grid squares, “ Qibla method, ” and random walk). Different indicators are assessed including household size, consumption, poverty and ownership of assets. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 consumption or expenditure is measured in per-capita terms and has implications for other indicators of well-being such as housing, rents, or crowding. The Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and Living Standards Measurement Surveys (LSMS) are the most common household surveys used to produce comparative statistics on well-being across time and countries. The DHS and MICSs define household members as (i) usual residents or people who slept in the dwelling the previous night and who (ii) share living arrangements and (iii) share food (ICF International, 2012; UNICEF, 2013). The LSMSs define household members as (i) people who slept in the dwelling three or more months of the last 12 months and (ii) share food (Grosh and Munoz, 1996). 7 While the definitions differ, they are defined on similar concepts and are unlikely to lead to major differences in key household characteristics. The UNHCR has definitions for household that resemble the definitions used by MICSs and LSMSs but uses the concept of “ case ” as unit of observation. The UNHCR defines a case as: “ A processing unit similar to a family headed by a Principal Applicant. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It comprises (biological and non-biological) sons and daughters up to the age 18 (or 21) years, but also includes first degree family members emotionally and / or economically dependent and for whom a living on their own and whose ability to function independently in society / in the community and / or to pursue an occupation is not granted, and / or who require assistance from a caregiver. ” (Verme et al., 2016; see also https: / / www. unhcr. org / 5ea81c114). This definition is different from the DHS, MICS and LSMS, may pose challenges when one compares cases with households in surveys, and lends itself to exploitation on the part of users, for example by spreading different household members across different cases to maximize benefits. Some socio-economic surveys for FDPs do not rely on the UNHCR family definition as the unit of socio- economic analysis – in addition to the individual. Instead, they employ a household definition either from DHS, MICS or LSMS, or from the established national household survey implemented by the national statistical agency, which often is similar to the traditional definitions. When using a list-based sampling approach with UNHCR ’ s registration data, this creates the challenge of translating the family (case) definition used as a sampling unit to a traditional household definition for the interviews and the analysis. In most cases, not all members of a proGres family are members of the household, while the household usually also has members from other proGres families. Different approaches can be used to overcome this challenge. 8 7 While the DHS and MICS define households as mutually exclusive, the LSMS definition suffers from individuals potentially belonging to multiple households leading to double-counting. 8 If not explicitly mentioned, we assume that the universe of FDPs is defined as FPDs registered in proGres. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, household-level information like assets or consumption will usually still be reported across the household. Hence, households will appear richer as consumption and rooms are shared by fewer reported household members. This is potentially an important issue that would be important to test empirically. Selection of larger households creates a bias by moving the average estimate towards larger households, e. g. increasing the average poverty rate if larger households tend to be poorer. However, a misreporting of the number of household members biases the characteristics of households, often making them appear richer than they are as resources are shared by fewer reported members. Finally, mixed households consist of FDPs and non-FDPs. All household members independent of their displacement status must be equally considered in the interview, to allow accurate estimates for household- level indicators that depend on household size. Mixed households are usually more prevalent outside camps and often exhibit distinctly different characteristics, e. g., in terms of deprivation and labor market access. Comparing camp and non-camp households must consider the presence of mixed households outside camps. Results indicating that non-camp households are less deprived and have better access to labor markets might simply be driven by the presence of non-FDPs in non-camp households. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 Survey design and administration Designing a socio-economic survey including consumption measures for non-displaced households is challenging as the interview time is limited and one has to be very selective to restrict the number of questions in surveys. For FDPs, the challenge is exacerbated given the need to understand not only their present socio-economic well-being, but also their displacement trajectory as well as their aid dependency. In this context, spending 90 or more minutes on an elaborate consumption module might not be a priority. Instead, detailed information on displacement and aid is crucial and something that is not properly assessed in non FDPs surveys. Fatigue from over-surveying is often cited as an anecdotal challenge in the context of FDPs, leading to survey non-response. Even though FDPs are often subject to intensive surveying by multiple agencies, survey non-response is not as high as for regular populations and the likelihood for a household to be interviewed multiple times is limited. Only censuses interview everyone in the population, they are often prohibitively expensive, and suffer from low data quality outstripping their size advantage. Thus, only few censuses – with very short questionnaires – are necessary for verification exercises. Hence, the chance of multiple interviews for the same households in a short period of time is low. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, refugees do complain about excessive surveying, possibly because contacts with interviewers are not just limited to sample surveys but also needed for a wide variety of purposes related to administering services to refugees. FDPs are not only particularly vulnerable and need specific protection, they also often originate from a different region in the case of IDPs or country in the case of refugees. This creates several challenges in interviewing FDPs. The respondent might not speak the official language of the country or may not trust enumerators that come from different communities. This can be overcome by assigning purposefully enumerators to specific households to ensure that they speak the same language and share the same culture. However, enumerators should also have a similar cultural background as respondents. Di Maio and Fiala (2020) use a large-scale experiment in Uganda in which enumerators and respondents are randomly paired to explore for which types of questions a significant enumerator effect may exist. While the enumerator effect is minimal for many questions, it is large for specific perception questions, for which it can account for over 30 percent of the variation in responses. Such a bias can also occur for sensitive questions to FDPs including questions on their displacement trajectory, current needs as well as perception questions. Specifically, in the context of a statistical agency conducting a survey including FDPs, enumerators need to be carefully selected. Refugees might see the statistical agency simply as government that might pass information to other government agencies, potentially threatening their livelihoods. IDPs in the context of an internal conflict involving the government might similarly be reluctant – or even feel personally Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 threatened – if interviewed by a government official. These risks can be mitigated if enumerators are hired among FDPs from the same background. However, this can create legal challenges if FDPs do not have work permits. In some cases, it might also be necessary to have UNHCR or IOM officials accompanying enumerators or even conducting the interviews themselves. The implementation of FDP-specific socio-economic surveys can be done in parallel with UNHCR ’ s verification exercises. The verification exercise is a census that visits all registered refugees to update their data. In Kenya, an additional socio-economic questionnaire was administered to a random subset of refugees participating in the verification exercise (Pape et al., 2019b). The parallel implementation reduced costs substantially but the implementation requires close coordination between the verification exercise and the socio-economic survey. Ideally, no redundant data would be collected but in reality most questions from the verification exercise are asked slightly differently than in the socio-economic survey, which usually aims to be consistent with the latest national survey, necessitating different instruments for the same concept to ensure comparability. Poverty measures Poverty measures the level of deprivation in a population. The most widely used concept defines as poverty headcount the number of individuals living below a threshold, usually called the poverty line (Foster, Greer and Thorbecke, 1984). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Since the poverty headcount only reflects the proportion of poor people, it is usually complemented by a measure of the depth of deprivation, the poverty gap, which estimates the average gap between poor individuals and the poverty line. All poverty measures constructed in such a way are based on an underlying welfare metric. In most cases, the metric is defined at the household level and needs to be transformed into an individual measure. The transformation either divides household welfare by household size, providing a per capita measure or, in a more sophisticated way, takes into account differences in household composition (usually age and gender), leading to a per-adult equivalent measure. To measure household welfare, different metrics have been proposed and can generally be classified either into monetary or non-monetary metrics. Monetary metrics equate levels of well-being to a monetary indicator of utility (Samuelson, 1974), usually income, consumption or expenditure. Non-monetary metrics define normatively dimensions of deprivations, e. g., access to education and clean water, and aggregate them either by using dimension-specific thresholds or an aggregated threshold. The most commonly used approach in this class is the Multidimensional Poverty Index by UNDP and Oxford University (MPI; OPHI, 2018). While it is not uncommon to find experts with a strong preference for one measure over the other, both classes of poverty measures are in most cases complementing each other. While the monetary metric has the advantage of its theory-grounded definition without normative choices, it is complex to measure Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 and does not capture acute deprivations in health or education, as the OPHI MPI does. For example, a third of those experiencing multi-dimensional poverty are not captured by the monetary headcount ratio (World Bank, 2020). Hence, it is not surprising that efforts are underway to combine the advantage of both measures like the World Bank ’ s Multi-Dimensional Poverty Measure (MPM; Nguyen et al., 2021). In the case of FDPs, monetary and multi-dimensional measures of poverty are both indicated and, as it is often the case in other settings, they complement each other. In this respect, there is not much difference with other types of populations. However, the money metrics used for monetary indicators may be more challenging to estimate for FDPs than regular populations, and the composition of multi-dimensional indexes should be adapted to FDPs ’ characteristics. Moreover, especially in a context of very high deprivation, a poverty headcount might not be able to reveal the actual gravity of the situation and qualitative information should be sought to complement standard questionnaires and possibly inform the development of future questionnaires. Collection of socio-economic data is a passive process where respondents are asked pre-formulated questions. This constrains the respondents in sharing their own narratives and emphasizing what they feel is important. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The implementation of household surveys can be seen as an opportunity to collect qualitative information and transform a one-sided narrative into one that provides voice to the poor. Pape (2020), for example, describes how short, voluntary video testimonials with informed consent from people living in South Sudan and Somalia9 can be used to empower the poor in voicing their own needs. While this is a practice that can be developed in any context, it is particularly promising in contexts where measuring poverty is still relatively new and the specific challenges associated with this measurement are still little known, as in FDP contexts. Poverty metrics Income, consumption or expenditure can be used as monetary metrics to estimate poverty. The correct measurement and classification of income can be a real problem in low-income countries with large informal economies (Deaton and Zaidi, 2002; Mancini and Vecchi, 2022) and, especially, for FDPs. FDPs rarely have regular labor income and tend to rely on occasional, informal income, or have no labor income at all. They have various forms of in-kind and cash assistance that vary from household items such as blankets and kitchen items to food vouchers and cash assistance. The combination of these income sources is not always well captured in surveys because many of these items are provided occasionally and by multitudes of donors. Food vouchers are often traded and can function more as cash than food. FDPs may also produce products that they exchange as they try to use their crafts to supplement incomes, and some 9 www. thepulseofsouthsudan. com and www. thesomalipulse. com. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "19 instruments for FDPs as they would allow to track the same individuals over time, understand their livelihood and residency trajectories, and understand how FDPs take decisions. However, classic panel surveys typically rely on home addresses and they are particularly difficult to administer when populations are highly mobile. This has encouraged scholars working on FDPs to develop new instruments to track people over time. Etang and Hoogeveen (2020) developed a survey known as the “ Listening to Displaced People Survey (LDPS) ”, a survey that tracked living conditions of displaced people over time in Mali with a face-to-face baseline survey complemented by monthly follow-up mobile phone interviews for a period of 12 months. These data have been used by Hoogeveen, Rossi and Sansone (2019) to study patterns of return of the displaced and understand the factors that contribute to return. Phone interviews have also increased in popularity with the COVID-19 pandemic, which made it necessary to conduct interviews without face-to-face contact. During this period, the UNHCR and World Bank have launched bi-monthly monitoring surveys of the impact of COVID-19 on the well-being of refugees in several countries across the MENA, SSA and Latin America regions using phone interviews. These resulted in panel surveys that now offer the possibility to assess the impact of COVID-19 on refugees over time and across countries in a comparable manner. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Vintar et al. (forthcoming), for example, provide an example of how to use these data to understand the differential labor impacts of COVID-19 on refugees and non- refugees (see also the report “ Answering the Call: Forcibly Displaced During the Pandemic ” i). UNCHR ’ s proGres database includes phone numbers for refugee family heads that can be utilized as a sampling frame. However, data privacy concerns need to be addressed if the phone survey is conducted by a firm. A possible solution is sending text messages to selected respondents asking for permission to share phone numbers with a contractor. Comparisons between FDPs and host populations are also an essential exercise to conduct in the context of FDP poverty measurement. These comparisons are important for FDPs, host countries and international organizations given that resentment against FDPs is often fueled by a perception that FDPs receive special assistance that is not available to locals. Several surveys have now been conducted in Jordan, Lebanon, Iraq and a few Sub-Saharan African countries to compare the well-being of FDPs and their hosts. These comparisons, while important, are complex because host populations have full access to the labor and consumer markets and government services that are often not available to FDPs, whereas FDPs rely on aid from the international community that is not available to local residents. It is difficult to compare health services in camps, for example, to those provided to the host population by the government, or other social protection services such as unemployment insurance or paid leave that do not exist for FDPs. Again, these are new and largely under-researched issues among poverty specialists. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 Future prospects There are now a wide variety of survey instruments that are being designed for or adapted to measure income or consumption among FDP populations. Home visits initially designed by the UNHCR to question FDPs for protection purposes are being revised to ask questions on income and consumption, becoming in this way viable instruments for poverty measurement. Large home visit exercises are conducted every year in countries such as Jordan and Lebanon and these data have been used to conduct poverty assessments of Syrian refugees (Verme et al., 2016). The WFP conducts vulnerability assessments that have been used by the WFP, UNHCR and World Bank to make gross poverty estimates using the consumption modules of these surveys, even if these modules are typically very short, with few items. The UNHCR conducts Multi- sector Needs Assessment such as the one conducted in Cox ’ s Bazar, Bangladesh, in 2018, Socio-economic assessments such as the one conducted in Zimbabwe in 2017, or nutrition surveys such as the one conducted in Tanzania in 2017. 12 All these surveys contain some information on income, consumption, or expenditure that is being used to assess the well-being of FDPs. The World Bank has conducted welfare assessments of Venezuelans in various Latin American countries including Colombia, Peru and Ecuador, with a forthcoming study expected for Chile. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The welfare of Afghan refugees has now been studied in their main host country, Pakistan, and in Afghanistan upon return, providing elements to compare the living conditions of this population in the two locations. Again, all these surveys and studies are either very recent or were not used before for poverty / welfare analyses. Whereas individual and household data on refugees are now more systematically collected, data collection for IDPs remains extremely scarce when compared to refugees, mostly limited to head counting. Refugees and IDPs are very different in that they have a different legal status, which leads to different access to public services, labor markets, government, and international assistance. Surveys for IDPs are often more difficult to conduct because the host country might be linked to the cause of displacement, and a registration system like UNHCR ’ s proGres is absent, making it more challenging to obtain a representative sample. Given the large number of IDPs (58 percent of all FDPs), the real challenge will be to collect microdata on IDPs systematically in all those countries that are home to large numbers of IDPs. So far, the only country that collects data on IDPs systematically is Colombia and this country has shown that, when quality microdata are available, research on IDPs flourishes. While many of the discussed issues need more attention and research, some processes are in place that should lead to the establishment of guidelines that address some of these questions. A recent process 12 All these surveys can be found in the World Bank microdata library at: https: / / microdata. worldbank. org / index. php / catalog? sort_by = rank & sort_order = desc & sk = refugees. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As already mentioned, the UNHCR has started to introduce standards for the measurement of well-being of FDPs while the new World Bank-UNHCR Joint Data Center established in Copenhagen is expected to develop and fund further methodological advancements as well as playing a pivotal role in the production of poverty data for displaced populations especially through National Statistics Offices. Also other organizations such as WFP and IOM are developing their own standards for surveying FDPs and measuring various well-being indicators. Conclusion The paper provided a first insight into the state of the literature on the measurement of poverty among FDPs. We argued that the economics profession and poverty specialists across the social sciences largely neglected these populations for a combination of factors including lack of interest and microdata. This changed with the beginning of the Syrian conflict in 2011 and the peak of the European migration crisis in 2015. These events have generated partnerships between development and humanitarian organizations that contributed to boost microdata collection among FDPs, poverty and welfare studies worldwide, and is also sparking a virtuous cycle of poverty measurement innovations. As FDPs require special solutions to questions such as sampling, consumption measurement, and targeting, this search is also producing innovative approaches that can serve the poverty measurement community at large. 13 Since 2022, the group is renamed the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10631 This paper aims to understand the existing gaps in micro- level data on forcibly displaced people — refugees and internally displaced persons. The paper undertakes a com­prehensive review of all existing micro-level data sets in the United Nations High Commissioner for Refugees Micro­data Library and the World Bank Microdata Library. It first identifies a corpus of micro-level data sets that are designed to have a representative sample of refugees and / or internally displaced persons and assesses gaps in geographical and the­matic coverage. The paper then evaluates whether the data sets contain a core set of questions that are essential for the proper identification of refugees and internally displaced persons. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The findings show that microdata on forcibly dis­placed people are comparatively rich in Sub-Saharan Africa in contrast to other regions. However, data scarcity is nota­bly pronounced in countries facing fragility and conflict. Scarcity is also evident among internally displaced persons and on topics such as labor and employment, finance (for instance, credit, debt, and banking), agriculture / livestock / fishery, and education. The paper also highlights that many of the existing micro-level data sets on forcibly displaced people do not contain the core set of questions needed for proper identification of refugees or internally displaced persons according to international statistical standards. This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at tmasaki @ worldbank. org. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. Introduction The current era is marked by the highest number of forcibly displaced people (FDP) in history. As a result of the war in Ukraine coupled with ongoing conflict and disaster in countries such as Afghanistan, Ethiopia, Somalia, the Syrian Arab Republic, the República Bolivariana de Venezuela, and the Republic of Yemen, the number of people forced to flee due to fears of persecution, conflict, violence, human rights violations, and other events seriously disturbing public order had reached more than 110 million. 2 Understanding the extent and nature of the challenges they face is crucial for developing effective policy responses to address their needs and support their successful integration into their host communities or return to their places of origin or previous residence. However, existing data gaps on FDP make it challenging to design and implement such responses. The aim of this paper lies in understanding existing gaps in microdata on FDP – refugees and internally displaced persons (IDPs). This paper does this by undertaking a review of all existing micro-level datasets in the UNHCR Microdata Library (MDL) 3 and World Bank (WB) 4 MDL from 53 low-income and middle- income countries with a significant presence of FDP. 5 The microdata in these databases are documented in compliance with international standards and practices. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "They contain a rich source of information on various attributes of datasets, including the country and dates of data collection, sampling strategy, survey modules, and other related aspects. While submission of datasets to the libraries is voluntary and thus does not guarantee an exhaustive list of all publicly available existing datasets on FDP, they are considered to be among the largest databases of microdata concerning development and forced displacement (Thompson 2010; EGRISS 2023). For the purpose of this study, microdata are defined as primary data collected from household surveys. Our focus lies in identifying micro-level datasets that are publicly available and designed to have a representative sample6 of FDP so that collected data can be disaggregated for refugees and / or IDPs specifically. By studying the geographical and thematic coverage of existing FDP microdata, we seek to also shed light on critical data gaps that remain to be filled with further data collection efforts. The paper identifies critical gaps in geographical and thematic coverage as well as compliance with international recommendations for proper identification of displacement status. The paper highlights that microdata is comparatively rich in Sub-Saharan Africa in contrast to other regions. However, data scarcity is notably pronounced in countries facing fragility and conflict and also among IDPs. There are also certain topics that are relatively lacking in the FDP microdata. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These include topics like labor, finance (e. g., credit, debt, banking), agriculture / livestock / fishery, and education whereas FDP datasets are relatively rich in health, food insecurity, and water and sanitation in addition to coping strategies and protection. Furthermore, a large majority of questionnaires for these existing micro-level datasets do not include a core set of questions needed for proper identification of displacement status. This calls for further efforts 2 See https: / / www. unhcr. org / news / stories / unhcr-s-grandi-110-million-displaced-indictment-our-world. 3 The UNHCR MDL is available at https: / / microdata. unhcr. org / index. php / about. 4 The WB MDL is available at https: / / microdata. worldbank. org / index. php / about. 5 We focus on low-income and middle-income countries because they account for roughly 95 percent of FDP (refugees + IDPs) in the world based on data from UNHCR Refugee Finder and IDMC. 6 A representative sample means that when analyzed, the observed characteristics of the sample reflect the true characteristics in the target population that is being researched (Baal and Ronkainen, 2017). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 to ensure that questionnaires are designed to comply with the international standards laid out by the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS). 7 Our findings are relevant to the literature on knowledge gaps on FDP. Though few, there are prior studies undertaken to review existing evidence and data gaps around migrants and displaced populations (e. g., Baal 2021; Rico and Camilo 2022; USAID 2021; Berretta et al. 2023; EGRISS 2023). Berretta et al. (2023) provide a systematic review of existing studies on the causes of migration. Most recently, the Compilers ’ Manual on Forced Displacement Statistics by the Expert Group on Refugee, Internally Displaced Persons and Statelessness Statistics (EGRISS 2023) features some of the recent attempts to collect micro-level data on refugees and IDPs through censuses and household surveys. This paper develops a systematic approach to identify publicly available micro-level datasets on FDP. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While there are several previous studies that attempt to assess data gaps on FDP, 8 they are limited in scope in terms of both geographical and temporal coverage and often lack a rigorous and systematic approach to guard against reviewer selection bias (Sida 2020; James et al. 2016; Campbell Collaboration 2015; Clapton et al. 2015; Gough et al. 2012; Rico and Camilo 2022). 9 This current analysis systematically reviews all datasets on FDP from the UNHCR and WB MDLs. While our paper has its limitations, the proposed methodology is the first attempt to develop a replicable and scalable approach to identify the universe of publicly available FDP microdata. Our approach is innovative in that we leverage the entire collection of datasets found in the UNHCR and WB MDLs with the aid of a natural language processing tool and text mining. This involves scraping metadata from thousands of publicly available datasets in those databases, evaluating whether they include a representative sample of refugees and IDPs, and assessing various attributes of each dataset (e. g., geographical coverage, thematic focus, kinds of questions available for identification of displacement status). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The innovation in our approach lays the groundwork for future data gap mapping exercises to be more objective, replicable, and scalable. 10 Additionally, this paper contributes to highlighting specific data gaps that have yet to be filled. To make further progress in filling data gaps, it is critical that we understand where those gaps exist today. This paper does exactly that by analyzing what and where data exists, so policy makers and development practitioners alike can gain a broad knowledge of the current data landscape on the forcibly displaced and aid their decision making based on available data. 2. Data For this analysis, we leverage a rich catalogue of micro-level datasets from the UNHCR and World Bank MDLs. The WB MDL includes datasets from the World Bank, other international organizations, statistical agencies and other agencies in low- and middle-income countries. These datasets may also originate from population, housing or agricultural censuses or through an administrative data collection processes. This 7 See more information on EGRISS at https: / / egrisstats. org /. 8 See, for instance, Baal 2021; S 2020; Rico and Camilo 2022; USAID 2021; Berretta et al. 2023. 9 For instance, Rico and Camilo (2022) assess the coverage of information on migrants by the censuses and regular household surveys, but their analysis is geographically confined to Latin America. 10 All analysis for this report is performed in R. The replication codes are all available from https: / / github. com / takaakimasaki / DisplacementDataGap. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 MDL contained 4, 539 datasets at the time of this research (December 2022), some of which were also cross-listed in the UNHCR MDL. The UNHCR MDL contains micro-level datasets that are of concern to UNHCR ’ s mission and mandates. These datasets often – if not always – include a sample of refugees, asylum seekers, IDPs, or stateless people. The datasets come from censuses, registration and administrative exercises, and surveys. This MDL contained 546 datasets at the time of our mapping exercise. One of the key advantages of using these databases is that they offer detailed metadata which help us understand the main characteristics of each dataset. The metadata contains a rich set of attributes pertaining to each micro-level dataset, including the country of data collection, the producer (s) of the dataset, brief description or abstract and thematic scope of a dataset, dates of data collection, unit of analysis (e. g., households, individuals), geographic coverage (e. g., national, regions, camps), data type (e. g., sample survey data, census, administrative), questionnaire modules, as well as sampling strategy. A complete list and explanation of each attribute in the metadata can be found in Annex B: Metadata. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In terms of geographical coverage, this study covers all low-income and middle-income countries with a significant presence of refugees, other Venezuelan refugees and migrants 11 in need of international protection, and / or IDPs. We apply 100, 000 (in terms of the number of FDP as of 2021) as an inclusion threshold for this analysis. This results in the final list of 53 low-income and middle-income countries, which altogether account for 23 million refugees and 58 million IDPs. 12 3. Methodology The methodology we apply to analyze micro-level datasets in the UNHCR and WB MDLs follows a procedure that is systematic, replicable, and scalable (Figure 1). First, we scrape the metadata from all the micro-level datasets found on the UNHCR and WB MDLs. 13 We find 412 datasets from UNHCR MDL and 1, 927 datasets from WB MDL that have been collected in the sample of countries under study. Second, we remove false positive datasets from the UNHCR MDL. While a large majority of datasets in the UNHCR MDL are individual / household-level household survey datasets sampled from refugees or IDPs, some are not. Furthermore, many datasets do not have a clearly defined sampling frame from which a representative sample can be drawn, thus failing to meet our inclusion criteria for this exercise. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A few datasets listed in the UNHCR MDL are indeed based on key informant interviews (instead of a 11 Venezuelan refugees and migrants are included in this analysis due to their status as Persons of Concern (PoC) under UNHCR ’ s mandate. The reason why migrants are included here is laid out by UNHCR: “ Venezuelan [migrants] refers to persons of Venezuelan origin who are likely to be in need of international protection under the criteria contained in the Cartagena Declaration, but who have not applied for asylum in the country in which they are present. Regardless of status, Venezuelan [migrants] require protection against forced returns and access to basic services. ” (UNHCR 2020). In short, since Venezuelan migrants are likely to be in refugee-like situations, they are treated like refugees and thus should be included in this analysis. 12 The number of refugees, Venezuelan refugees and migrants and IDPs (in 2021) is taken from World Bank World Development Indicators (WDI) (https: / / databank. worldbank. org / source / world-development-indicators), UNHCR Refugee Finder, and Global Internal Displacement Database by the International Displacement Monitoring Center (IDMC) (https: / / www. internal-displacement. org / database / displacement-data), respectively. See Annex A: List of countries included in this study. 13 The extraction of metadata from the UNHCR and WB MDL was performed in December 2022. This involved scraping the JSON files of all existing datasets from both sources and compiling them into a master list. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For refugees, the identification questions recommended by EGRISS include (EGRISS 2018): a) Country of birth b) Country of citizenship c) Acquisition of citizenship d) Year or period of arrival in the country e) Reason for migration For IDPs, these are (EGRISS 2020): a) Place of birth b) Date of first displacement c) Date of most recent displacement d) Main reason for initial displacement e) Main reason for most recent displacement f) Place of usual residence g) Place of habitual residence We download questionnaires for each dataset from the UNHCR / WB MDLs and review each question in the questionnaires to assess whether they include any of the core questions listed above. 19 See Annex C: Taxonomy of for details on the procedure taken to create a list of topic areas examined in this study. 20 EGRISS is a multi-stakeholder group that was established by the United Nations Statistical Commission (UNSC) in 2016 and now consists of members from 56 national authorities and 36 regional and international organizations. 21 The full document of IRRS is available at https: / / egrisstats. org / recommendations / international- recommendations-on-refugee-statistics-irrs /. 22 The full document of IRIS is available at https: / / egrisstats. org / recommendations / international-recommendations- on-idp-statistics-iris /. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 Finally, we also apply text mining to distinguish those datasets sampling from IDPs from those that only sample from refugees or Venezuelan refugees and migrants. We evaluate whether some keywords indicating the inclusion of IDPs in the sampling frame are mentioned in the sampling section of the metadata. These keywords included “ internally displaced ”, “ internal displacement ”, “ IDP ”, or “ IDPs. ” We then perform a manual verification to make sure that IDPs are indeed included in the sampling frame to generate representative data for this population. 4. Results Through the methodology described above, we find 375 publicly available micro-level datasets that have a representative sample of refugees and / or IDPs. 23 Figure 2 plots the number of datasets over time between 1995 and 2022. 24 There has been a rapid increase in the number of FDP micro-level datasets collected and published in the UNHCR / WB MDLs especially since 2015. It is worth noting that part of this upward trend may also be explained by the fact that older micro-level datasets are not stored or published in the UNHCR / WB MDLs. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For instance, the UNHCR MDL was launched in 2019 and despite efforts to retrospectively cover and publish older micro-level datasets collected before the inception of the UNHCR MDL, the extent to which older datasets are still missing from the database is unknown. 25 23 The results and codes for the analysis are all available from https: / / github. com / takaakimasaki / DisplacementDataGap. 24 The oldest dataset available in WB / UNHCR MDL was collected in 1995 at the time of this study. 25 See more recent developments in UNHCR MDL: https: / / www. unhcr. org / blogs / responsible-and-timely-sharing- data-on-unhcrs-microdata-library-in-2022 /. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 Figure 2: The number of FDP micro-level datasets over time Notes: this graph shows a trend in the number of FDP micro-level datasets in the UNHCR / WB MDLs between 1995 and 2022 based on the year of data collection. Note that at the time of this study, many micro-level datasets collected in 2022 were not yet incorporated into the UNHCR / WB MDLs and thus the number for that year should be treated with caution. Project specific datasets Of the 375 FDP datasets, a non-negligible share of them turns out to be project specific, meaning that despite their utility for evaluating and monitoring the impact of a certain project or program, its application for analyzing any broader population of interest is quite limited. We find that 39 percent of the identified datasets sample from a narrow base of beneficiaries from a certain program or project. It is important to draw a representative sample of a broader population of refugees and / or IDPs instead of sampling only from a narrow base. If the sample consists of only beneficiaries from a certain project or program, the generalizability of findings or conclusions drawn from that sample is very much limited and does not extend beyond the small confine of the target population that is relevant only to the project or program itself but not to broader communities of policy makers, development practitioners and scholars alike. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Geographical gaps We identify a key set of country-level characteristics that may be correlated with the number of publicly available FDP datasets. These characteristics include GDP per capita (log-transformed) (GDP pc (ln)), the number of FDP (log-transformed) (FDP (ln)), as well as battle-related deaths (log-transformed) as a measure of conflict and fragility (Battle-related deaths (ln)). 26 We also include regional dummies to capture those regions that are underrepresented after accounting for those baseline country characteristics. We apply a negative binomial regression for this analysis as our dependent variable is the count of FDP datasets in a given country. 26 Data on GDP pc and battle-related deaths are from WDI and FDP from WDI, UNHCR and IDCM. GDP pc is averaged for the period of 2010-2021 and the number of reported battle-related deaths is summed for the same period. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Indeed, the level of conflict intensity is negatively correlated with the number of publicly available FDP datasets, meaning that the more fragile and violent a country, the fewer datasets it has available in the MDLs. This pattern is certainly not unique to FDP microdata. For instance, comparable poverty measures over time are often lacking in FCS countries (World Bank 2021). A confluence of factors explains data deprivation in FCS countries. To inform discussions about policy responses towards FDP in a given country, the surveys must be representative at least for the target population of interest to policy makers. However, a reliable sampling frame is often missing in such conflict situations where population census rarely takes place, and registration data may also be obsolete, thereby complicating the implementation of sample household survey (Aguilera et al. 2020). Furthermore, collecting data in the fragile context also raises a concern for the safety of enumerators, inhibiting survey implementation in the unsafe regions (Corral et al. 2020). Furthermore, FCS countries also suffer from limited statistical capacity and a lack of resources to implement data collection (Cas et al. 2022). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In terms of the regional representation of publicly available FDP datasets, data is relatively rich in Sub- Saharan Africa in contrast to other regions. 28 In fact, Sub-Saharan Africa accounts for 53 percent of all FDP micro-level datasets (excluding project-specific datasets and those datasets collected before 2010). These patterns hold even after accounting for the above-mentioned baseline country characteristics such as GDP per capita, conflict intensity, and number of FDP (see Figure 3 Panel B). The average marginal effects of regional dummies are all negative and significant (except for South Asia) using Sub-Saharan Africa as a benchmark. Data is particularly rich in East Africa. This skew is primarily driven by three countries that have a higher number of datasets than expected given the number of FDPs they host: Uganda, Kenya, and Tanzania. This pattern is reflective of a broader data and evidence landscape in Africa. Indeed, these three countries are also among the top in the region with the largest number of general micro-level datasets in the WB MDL. Additionally, Kenya and Uganda are among the most extensively studied countries in impact 27 For this country-level analysis, we exclude those micro-level datasets that are collected prior to 2010 or project- specific datasets. Furthermore, some survey datasets are stored as separate entries in the WB and UNHCR MDL even though they are indeed part of the same survey (e. g., entries by camp or by wave). Those independent entries are collapsed as one when they are part of the same survey. 28 Throughout this paper, we adopt the regional classification of the World Bank: https: / / datahelpdesk. worldbank. org / knowledgebase / articles / 906519-world-bank-country-and-lending-groups. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 of the most fragile countries where due to security concerns, physical access to reach them is restricted. Furthermore, many IDPs do not live in camps but rather mix with other population groups particularly in urban areas, thereby making it even more difficult to identify them (Baal and Ronkainen 2017). Figure 4: Number of IDP datasets vis-à-vis size of IDPs hosted by country A) Number of IDP datasets and number of IDP (ln) B) Number of IDP datasets by country Notes: Panel A shows the bivariate relationship between the number of IDP datasets and number of IDPs (log-transformed). Panel B shows the ranking of countries in terms of the number of IDP datasets available in the MDLs. Note that some survey datasets are stored as separate entries in the UNHCR and WB MDLs even though they are indeed part of the same survey (e. g., entries by camp or by wave). Those independent entries are counted as one dataset when they are part of the same survey, thus resulting in a total number of 22 IDP datasets excluding those datasets that are project specific or collected before 2010. Topic coverage In terms of the coverage of topics, FDP datasets are relatively richer in topics such as coping mechanisms, protection, water and sanitation, food insecurity, and health. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 5 Panel A illustrates the percent of FDP datasets that cover various topics in their questionnaire modules and Panel B shows differences in the percent of datasets covering each topic between FDP datasets and all other non-FDP datasets stored in the WB MDL. 32 The topic of health is by far the most common with roughly 78 percent of datasets mentioning keywords related to health in their descriptions of the questionnaire modules. Other common topics include food insecurity (49 percent) and water sanitation (39 percent). Less frequently included are topic areas related to labor and employment (19 percent); security and conflict (18 percent); finance, credit and debt (14 percent); agriculture and livestock (10 percent), and shocks (9 percent). The fact that topics such as security, conflict and shocks are often not included in the survey modules is not unique to FDP datasets. In fact, those topics are also relatively uncommon in other micro-level household surveys available in the WB MDL. When compared to the topic coverage of all non-FDP micro-level datasets in the WB MDL, FDP datasets are scarcer in such topics as labor, finance, agriculture / livestock / fishery, and education. As shown in Figure 5 Panel B, these topics are much more likely to be picked up in the non-FDP micro-level datasets. When it comes to labor and employment in particular, the gap is over 50 percentage points illuminating 32 See Annex C for further details on the methodology used to analyze topic coverage. For this topic coverage analysis, we exclude those datasets that are project specific or collected before 2010. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Panel B shows the differences in the percent of datasets covering these topic areas between FDP datasets and all non-FDP datasets collected after 2010 in the WB MDL (N = 726). EGRISS International Standards IRRS and IRIS provide a set of specific recommendations that countries and international organizations can use to improve the collection, collation, disaggregation, reporting, and overall quality of statistics on FDP. These recommendations are also intended to help improve national statistics on the stocks and flows and characteristics of FDPs, and to help make such statistics comparable internationally. Core questions for proper identification of refugees and / or IDPs as recommended by EGRISS are often missing from FDP datasets. Figure 6 Panel A shows the percent of FDP datasets containing each of the core identification questions recommended for refugees34 whereas Panel B shows the IDP equivalent of that. As seen in Panel A, the question of country of birth or citizenship is often missing from a large majority of datasets. Less than 40 percent of the datasets ask questions on citizenship and less than 30 percent of the datasets ask a question about the country of birth. While one is often substituted for the 33 We find that there are a number of datasets whose survey modules contain keywords referring to child labor (e. g., “ child labor ” and “ travail des enfants ”) flagged as relevant to labor and employment in non-FDP datasets in the WB MDL. However, these cases should be distinguished from those that have more standard questions around labor and employment and thus are not included when we aggregate the number of datasets for labor and employment. 34 For this analysis, we only included those FDP datasets designed to sample from refugees that are not project specific and collected after 2010. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 other in many of these datasets, 35 the country of birth and citizenship are not the same and IRRS recommends that a survey captures both the country of birth and nationality (or nationalities) for both the legal and statistical identification of refugees or refugee-like persons. 36 Reasons for displacement are also not commonly asked in FDP datasets. Of those datasets that are specifically designed to sample from refugees, reasons for (cross-border) migration are only asked in less than 10 percent of the refugee datasets. As for IDP datasets, only half of them ask reasons for the most recent displacement whereas questions around reasons for initial displacement are largely missing (with only 20 percent asking such questions). For refugees, reasons for migration should be included to properly identify those who have crossed an internationally recognized border on international protection grounds. For IDPs, reasons for initial and most recent displacements should be asked to identify those who have fled their place of habitual residence due to displacement-related protection needs and vulnerabilities such as “ the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters ” (EGRISS 2023, p. 13). The adaptation of future surveys to EGRISS recommendations is yet to be determined. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Considering that these statistical standards and recommendations were established in recent years, it may not be surprising that the overall level of compliance with EGRISS recommendations is still evolving. Figure 6: Prevalence of Identifying Questions in FDP Datasets A) Refugee identification questions B) IDP identification questions Notes: Panel A shows the percent of FDP datasets containing each of the core identification questions recommended by EGRISS for refugees (in all publicly available datasets designed to sample from refugees that are not project specific and collected after 2010) whereas Panel B shows IDP equivalent of that. For refugees, we do not make a distinction between the question on the country of citizenship and the acquisition of citizenship because many datasets we study often collapse these questions into one by simply asking respondents to indicate their nationality (or nationalities). 5. Conclusion With an ever-growing number of people forced to flee their homes due to conflict, violence, fear of persecution, and human rights violations, data on FDP has become ever more important. A solid evidence base is crucial to inform effective policy responses to address their challenges and to establish such a base, 35 More than 95 percent of the FDP datasets asked questions on either the country of birth or citizenship. 36 While the country of birth does not change, nationality or citizenship can. And it is the country of citizenship or acquisition of citizenship that determines whether refugees and refugee related persons should still be regarded as refugees by the national authorities even though their country of birth may be different from that of their citizenship or nationality and how citizenship interplays with refugee status varies by country (EGRISS 2019, p. 28). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 data – particularly representative micro-level data – on FDP will be essential. Despite the growing need for such data, however, the supply of it may not be adequately catching up. This study leveraged a rich catalogue of micro-level datasets from the UNHCR and WB MDLs to undertake a stocktaking of all available micro-level datasets on FDP, thereby also shedding light on existing data gaps. By geography, the study finds that FDP datasets are relatively rich in Sub-Saharan Africa compared to other regions given that more than half of the publicly available FDP datasets identified through this study come from Sub-Saharan Africa. By topic, FDP datasets are relatively scarcer in topics such as labor and employment, finance, agriculture / livestock / fishery, and education. One of the surprising results revealed from this study is an overall lack of publicly available microdata on IDPs. Of the 375 FDP datasets identified through this exercise, only 31 of them have a representative sample of IDPs. Furthermore, some countries hosting at least 1 million IDPs have zero publicly available dataset from the UNHCR and WB MDLs, including Colombia, Democratic Republic of Congo, Ethiopia, the Syrian Arab Republic, Türkiye, and Yemen. The study also reveals how a large majority of the existing datasets did not contain a core set of questions for proper identification of refugees and IDPs. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some of the essential identification questions are often missing, such as the place or country of birth as well as reasons for and history of displacement. The omission of these core questions makes it difficult to properly identify the displacement status of sampled households or individuals. Since the international recommendations by EGRISS were established in recent years, it still remains to be seen to what extent future surveys will adapt to these recommendations. These findings come with some caveats. First, the metadata of the datasets was extracted in December 2022. This means that our data gap map may already be missing datasets that have been added since then, calling for periodic updates in the analysis of data gaps. Second, our datasets only capture publicly available microdata from the UNHCR and World Bank MDLs. While we believe these MDLs contain the most datasets pertinent to microdata on FDPs, there may be other datasets not contained in either of these databases. Finally, as with all automated processes, it is possible that some datasets are miscoded due to the fact that the underlying metadata we use for this study is sometimes incomplete. We attempt to mitigate this concern by manual verification and making corrections in the metadata itself but there still remains more work to be done to ensure that the metadata is thoroughly populated in the UNHCR and WB MDLs. Despite the limitations, this paper contributes to advancing our knowledge about existing data gaps on FDP and by developing a systematic process to identify them based on the information from the UNHCR and WB MDLs. Future data collection efforts may build upon the findings from this report and strategically target those geographical or thematic areas where data gaps still abound. Furthermore, the methodology developed in this study can be adopted, modified, and applied in other contexts, thereby making it easier to undertake this sort of a data gap mapping exercise. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10194 This paper examines the influence of gender inequality on poverty among Syrian refugees in Jordan between 2013 and 2018. Two waves of Home-Visit surveys, collected by the United Nations High Commissioner for Refugees, are analyzed to track the evolution of poverty among Syrian refugees in Jordan. To compare changes in poverty between female- and male-headed households, the paper uses rela­tive comparisons of deciles in the expenditure distribution and quantile regressions. The analysis adjusts the poverty measure for economies of scale as the cost per person of maintaining a given standard of living may fall as household size rises. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Evidence for Sub-Saharan Africa shows that female-headed households that have previously been highly dependent on male income – notably widow-headed households – are particularly prone to poverty, although other types of female-headed households may not be more likely to be poor than male-headed households (Brown & Van der Walle, 2020). For this reason, while it is important to analyze differences in poverty risk between households with female and male heads, it is also important to understand how different routes to female headship, and the changes in the household composition more broadly, impact poverty risks and expenditure levels. This paper explores level and distributional changes in expenditure among Syrian refugee households in Jordan between 2013 and 2018 using a gender lens. By the end of 2018, UNHCR had registered more than 671, 000 Syrians1 who had fled their homes and settled in Jordan. At the end of the time period covered by this research, UNHCR ’ s 2019 Vulnerability Assessment Framework (VAF) 1 UNHCR ’ s data portal records 671, 551 registered Syrian refugees residing in Jordan on January 13, 2019. See UNHCR (2022b) available at https: / / data. unhcr. org / en / situations / syria / location / 36. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 population study2 (Brown et al, 2019) revealed that 78 percent of the population of registered refugees lived below the Jordanian poverty line, and between 2017 and 2019, there was only a 2- percentage point reduction of highly vulnerable cases. 3 UNHCR (2018a) also reports that close to 37 % of the registered Syrian refugees in Jordan are separated from a family member. Household structures have often been fluid. Many Syrian families sent members ahead to Jordan to settle and sometimes, after the reunification of the rest of the family in Jordan, male family members traveled on to Türkiye or to Europe (UNHCR, 2018a). These separations likely affected families emotionally and economically. However, little is known about the changes in economic well-being over time or how gender inequality has shaped poverty and expenditure outcomes. We use two waves of data from a unique household survey, the United Nations High Commissioner for Refugees (UNHCR) Home-Visits survey for 2013-14 and 2017-18. First, we create comparable measures of expenditure per capita over time. Then, we assess whether certain types of households are more likely to be below the median of the per capita expenditure distribution in each time period. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The UNHCR registers households as having a male or female principal applicant (PA) and we use this classification as equivalent to male or female headship used in the poverty literature. We find that there is no significant difference in the share of female versus male- PA households below the median in either 2013-14 or 2017-18. However, there are important differences between male and female PA households that impacts poverty risk. As is evidenced in other countries (see for example Brown et al. ’ s study of 43 African countries (2020)), we find that Syrian refugee female PA households are smaller in size than male- PA households. Once economies of scale in consumption are considered we find female-PA households are more likely to be poor than male PA households and that this gap has widened over time. Compared to other household heads, single caregivers (the vast majority of whom are women) exhibit some of the highest risks of falling in poverty and this risk has increased over time. We also find that the distribution of expenditure changed over time and negatively affected female- headed households. The gap between male and female-headed households grew over time causing a 2 The Vulnerability Assessment Framework for 2019 is based on analysis of data collected from a random, representative sample of registered refugees in October and November 2018. The sample consisted of 2, 248 Syrian refugee households, which comprised 3, 712 cases and over 10, 400 individuals and was fielded in October and November 2018. 3 A case is roughly equivalent to a household unit. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 market are limited; for the most part they are limited to the informal economy, mainly carrying out activities such as tailoring, cleaning houses, and cooking for neighbors and family (UN Women, 2018). The limited availability of employment opportunities for women is strongly associated with social norms that bind women to specific roles in society (Kelberer, 2017; Felicio et al., 2018). Data from the World Development Indicators (WDI) show that in 2019 only about 13 percent of women over 15 participate in the labor market. Changing gender roles in the labor market can cause tensions and have been linked to increased rates of domestic violence in refugee households in Jordan (Hagen- Zanker et al. 2017; Culcasi 2019). The most common reason for low levels of female labor force participation is family responsibilities such as childcare and housework, reported by 44 percent of Syrian refugee women and Jordanian women of working age (Stave, Kebede, & Kattaa, 2021). This is tied to deeply engrained norms surrounding gender roles. A recent analysis of gender norms in Jordan found that women and girls hold more equitable gender role attitudes than their male counterparts and that there are no significant differences between Syrians and Jordanians (Krafft, Assaad, & Pastoor, 2021). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Restrictive norms perpetuated by men continue to severely restrict women ’ s access to the formal labor market. Fortunately, the Jordanian government has worked in recent years to address this problem. For example, in the amendment made to the Jordanian labor law in 2019, the provision of childcare services was made mandatory in every business that has 15 or more employees, male or female, with children under 5 (World Bank, 2019). However, implementation is not consistent and low-income households report less access to childcare services (Weldali, 2022). Home-based businesses can also provide important access to economic opportunities for women, albeit within a limited number of sectors and the Government of Jordan ’ s 2017 amendments of the regulations governing the licensing of home-based businesses is particularly important for female entrepreneurs in both host and refugee communities (Slimane et al, 2020; Turner, 2019). 3. Data and descriptive statistics We use two rounds of household-level data collected by UNHCR in 2013-14 and 2017-18. 6 These data come from two sources, namely, the Profile Global Registration System (ProGres) and Jordan Home 6 For simplicity, from here on we refer to the first wave as the 2013 wave, and to the second wave as the 2018 wave. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 Visits (JD-HV). ProGres records demographic information of each individual refugee and their household at the time of registration with UNHCR including the relationship of each household member with the principal applicant. In this paper, we refer to the self-identified principal applicant as the household head – the term used in the poverty research literature. Self-identification is commonly used as a means of identifying the household head in household surveys of income, consumption and expenditure surveys which are used to estimate poverty rates (see Hanmer et al, (2020)). Home visits began at the same time as the UNHCR cash assistance program was launched. The program aimed to assist vulnerable Syrian households to meet their basic needs. During the home visits, UNHCR determined whether a household met the criteria to qualify for assistance, considering economic poverty and the presence of members in the household requiring protection. Since 2013, these visits have been made to every registered Syrian refugee household in Jordan outside the camps (UNHCR, 2014). At registration, each household is assigned a unique registration number that serves to cross- reference the initial registration data with Home Visits data. Home Visits data are collected for about one-third of the registered households. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In both waves, the selected households to be interviewed are those UNHCR considers to be most vulnerable in terms of education, specific needs and the use of livelihood coping strategies, 7 thus, they are a nonrandom sample of all registered Syrian households in Jordan. During Home Visits, data on household expenditures, aid, income, schooling, and other demographics are collected. Each wave of data is a snapshot of a different nonrandom sample of registered refugees because the pool of registered households changed over time. However, because of data confidentiality, we could not link records across years. For this reason, we treat each wave as an independent sample of cross-sectional data. We compare 204, 941 individuals in 54, 900 cases (households) in the 2013 wave, with 195, 930 individuals in 43, 292 cases in 2018. We focus on comparisons between different types of households; however, we use individual characteristics to determine the household categories. A case is formed when a Principal Applicant (PA) registers at a UNHCR office seeking asylum. Initially the self- identified PA reports all members of the household and their demographic characteristics, which are recorded in ProGres. Subsequently UNHCR carries out individual interviews with each family 7 See UNHCR (2018b) available at https: / / data2. unhcr. org / en / documents / download / 65143. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 member and records their data in ProGres. The PA is also the person who receives assistance from UNHCR although he / she may not be the only recipient of humanitarian assistance. 8 Although income data are available, we use expenditure data from JD-HV. The existing literature leans toward computing poverty estimates based on expenditure rather than income because (i) income does not capture the possibility that there are people who can live off their savings or have assets to meet their immediate needs; (ii) consumption is smoother and less variable than income, especially in agricultural economies; and (iii) income reporting appears to be more misleading, especially for those with more resources who tend to underreport income more (Carletto et al, 2021; Cutler & Katz, 1991; Deaton & Zaidi, 2002; Meyer and Sullivan, 2003, 2011, 2012). Aggregations of expenditure categories were used to compute total expenditure; however, the main challenge was constructing comparable poverty estimates across waves given a change in the number of spending categories included in the questionnaire. In the 2013 wave, there were six expenditure categories, namely, rent, utilities, food, health treatment, education, and others. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In addition to these categories, the 2018 home visit survey reports expenditure data on water, transportation, infant needs, essential household items, hygiene items, debt payment, and telecommunications. This difference in expenditure categories could inflate reported expenditure in the second wave. This is because survey respondents tend to reveal more accurate information when asked item by item, than if asked for an expenditure or income aggregate. In part, this may be due to an intention to withhold information to obtain the monetary assistance, and in part, it has to do with the recall error that this type of questionnaire elicits. To aggregate expenditure, we restrict the number of categories to those in both waves which represent a sizeable portion of a household expenditure; that is, rent, utilities, food, water, and transportation. These five categories average 97. 5 percent and 77 percent of household expenditure in the first and the second waves, respectively. While spending on healthcare, education, and household items were also recorded in both waves, the amounts were not large and were excluded because health care and education expenditures may reflect a vulnerability (for example, caused by illness or disability in the case of health care), or a luxury for those who can access the service, and 8 The monthly per capita cash assistance Syrian refugees receive is roughly 60 %- 65 % of the Survival Minimum Expenditure Basket (SMEB) and is intended to cover rent, water and sanitation costs. Assistance levels are mainly determined by family size. Since 2014, PAs often make the withdraws or their allocated cash assistance using iris scan enabled ATM machines (UNHCR, 2018b). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Nevertheless, this approach allows us to determine whether, for example, female-PA households are more likely to be below the bottom tail of the expenditure distribution in each wave and whether this likelihood has changed over time. We use quantile regressions to evaluate how the expenditure distribution has changed over time and what role demographic variables play in the location of a household in that distribution (second objective). As is well known, the linear regression approach estimates the effects at the mean. However, in this case it is also important to know the distribution of those effects. For example, if we found that expenditure gender gaps have widened over time, one also would like to understand whether that effect is more prominent among households at the 10th percentile of the expenditure distribution or among those at the 90th percentile. For that reason, we estimate conditional quantile regressions as follows: 𝑄𝜏 (𝑦𝑖 𝑒 (𝜃) | 𝐻𝑖, 𝐼𝑖) = 𝐻𝑖 ′ 𝜇𝜏 + 𝐼𝑖 ′ 𝛾𝜏 + 𝜖𝑖 𝜏 Where 𝑄𝜏 (𝑦𝑖 𝑒 (𝜃) | 𝐻𝑖, 𝐼𝑖) denotes the value at the 𝜏th percentile of 𝑦𝑖 𝑒 (𝜃), conditional on household (𝐻𝑖) and PA characteristics (𝐼𝑖). Notice that from this regression method we obtain different 𝜇𝜏 and Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "19 Figure 3. Intensity of poverty, by wave and gender of the household head Panel A. Distribution of poor households using the unadjusted expenditure (𝜃 = 1) Panel B. Distribution of poor households using poverty adjusted by economies of scale (𝜃 = 0. 5) Note: Own calculations based on ProGres and JD-HV database. Panel A shows the intensity of poverty using the headcount measure, while Panel B displays the intensity of poverty using a measure that accounts for economies of scale. The horizontal axis indicates the percentage of households in each decile of the bottom 40, for each wave. 0 % 20 % 40 % 60 % 80 % 100 % Male PA Female PA Male PA Female PA 2017-18 2013-14 % of households, per decile Decile 1 Decile 2 Decile 3 Decile 4 0 % 20 % 40 % 60 % 80 % 100 % Male PA Female PA Male PA Female PA 2017-18 2013-14 % of households, per decile Decile 1 Decile 2 Decile 3 Decile 4 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "29 6. Final remarks Our analysis shows how using a gender lens that differentiates between types of households and considers economies of scale can enrich the understanding of poverty in situations of forced displacement. In the case of Syrian refugees, specific household structures, particularly those that result from disruptions caused by displacement, are more prone to experiencing poverty. However, while we have described how patterns of poverty have changed over time, lack of data on critical variables, most importantly the amount of social assistance received by households and labor force participation, employment and earnings data limits our analysis. While some of the changes in poverty over time experienced by male and female PA households are consistent with some of the changes in the circumstances of Syrian refugees, specifically their improved access to labor markets and better access to services, it is not possible to understand the extent to which these and potentially other factors have made a difference to their risk of poverty. Overall, our results show that the share of female PA households has increased substantially from from 26 to 38 percent of all Syrian refugee households between 2013 and 2018. There are differences between the characteristics of male and female PA households in terms of education, marital status, and family types. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Impacts of Extremism in Refugee ’ s Integration: Evidence from Afghan Refugees in Tajikistan * Laurent Bossavie † Sandra V. Rozo ‡ Mar ´ ıa Jos ´ e Urbina § Keywords: Refugees, Female Education, Extremism JEL Classification: F22, D74, J15, I21, I10 * The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All errors are our own. The team is grateful to the project “ Enhancing Government capacity in hosting temporarily Afghan refugees ”, funded under the Rapid Social Response Program (RSR) multi-donor Trust Fund of the World Bank, for funding data collection and allowing us to use the Afghan refugee survey as one of the multiple sources of data for the paper. The purpose of the project and the data it collected was to identify opportunities for refugee integration in Tajikistan. We acknowledge financial support from the Research Support Budget at the World Bank. We also thank Daniel Garrote for his initial support on instrument design and Merve Demirel for supporting data collection. † World Bank, e-mail: lbossavie @ worldbank. org ‡ World Bank, Dev. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Dur- ing this period, the Taliban ’ s territorial control abruptly shifted from 19 to 100 percent of the Afghan territory. Specifically, we compare the integration outcomes of refugees living in Tajikistan in 2023, that had similar characteristics before the migration episode, but who were born in provinces that experienced diverse shocks in Taliban territorial control between 2017 and 2021. To do this, our main specification controls for a rich 3The census was collected by the project Enhancing Government capacity in hosting temporarily Afghan refugees with focus on socio-economic resilience and gender issues, under the Rapid Social Response Pro- gram (RSR), multi-donor Trust Fund of the World Bank. The objective of that project was to identify oppor- tunities for refugee integration in Tajikistan. The census was collected with the objective of understanding the profile of Afghan refugees, their current socio-economic outcomes, as well as their needs and potential vulnerabilities to support their integration. 4After their arrival, refugees are entitled to primary and secondary education, employment on legal basis, healthcare, and residence in Tajikistan until the final consideration of their migration status case. 3 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "set of demographics using retrospective information for the pre-migration period, while refugees still lived in Afghanistan prior to the Taliban ’ s take over. They include individ- ual characteristics — such as age, gender, employment status, student status, and number of friends in Tajikistan before migrating; and household demographics, encompassing 34 variables characterizing household demographics, dwelling characteristics, income, and asset ownership. Our primary analysis explores the effects of exposure to extremism on refugee integration, including both social and economic integration, using the rich set of outcome variables collected in the census. We also complement this analysis examining the effects of extrem- ism on secondary outcomes including educational attainment, mental health, income and consumption, and labor market-related variables. For each outcome, we estimate effects on individual related variables and also combine them in an index outcome variable to gain precision. Our data on Taliban ’ s territorial control between 2017 and 2021 comes from the Foun- dation for Defense of Democracies ’ Long War Journal. It encompasses information on the districts controlled by the Taliban, U. S. allies, and contested territories between the two factions. The data set combines information from NATO, press reports, government agencies, and the Taliban itself. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "From these, we create district-yearly data that reflect the territorial control in Afghanistan from 2017 to 2021, classifying each district into three cat- egories: Taliban-controlled, U. S. allies-controlled, and contested territories between both groups. Using this categorization we estimate the share of the territory controlled by each of the factions by province and year in Afghanistan between 2017 and 2021. We then uti- lize the census data to determine the province of birth for each Afghan refugee living in Tajikistan in 2021 and merge this information to calculate our three treatment variables, which include: the average share of a province that was Taliban-controlled, U. S. allies- controlled, or contested between the two factions for each individual ’ s province of birth 4 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "attainment, an increase in the average share of exposure to Taliban presence by one unit (i. e., Taliban has 100 % territorial control in the province of birth of the refugee between 2017 and 2021), results in a decrease of 18. 5 percentage points in the likelihood that the refugee has received some level of education. Furthermore, an examination of the het- erogeneous effects of Taliban ’ s exposure by gender reveals that these negative impacts are observed solely among women. The findings align with reductions in the likelihood of being literate and having primary or secondary education, and is also reflected by an index that combines all of these education-related variables. Our analysis reveals, as ex- pected, that exposure to contested territories has negative effects on refugee educational attainment. However, the magnitude of these effects is approximately half the size of the impacts observed for exposure to the Taliban ’ s territorial control. On the other hand, the effects of exposure to U. S. allies on refugee educational attainment are distinctly positive across all the variables related to educational outcomes that we examined. Lastly, we examine the impacts of Taliban exposure on mental health outcomes. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "on behalf of refugee beneficiaries. III DATA III. A Data for Afghan Refugees in Tajikistan The primary dataset is a detailed census of Afghan refugees in Tajikistan carried out by the World Bank at the end of 2022, after the Taliban regained complete control of Afghanistan in 2021. The questionnaire administered to refugee families was compre- hensive; it collected detailed information on the socio-economic background, migration history, labor market outcomes and current socio-economic situation of Afghan refugee household and their members, and some measures of socio-economic integration. The approximate duration of the interviews was about two hours. The sampling frame for the census is the administrative database of Afghan refugees registered with the United Nations Refugee Agency (UNHCR) in Tajikistan. All households recorded in the registry were interviewed, leading to a total of 1, 958 Afghan refugee households and 9, 763 indi- viduals. Figure 2 displays the dates of arrival of the refugees in the census. As shown in the figure, the vast majority of migrants arrived to Tajikistan in 2021 when the Taliban gained complete control of Afghanistan. We first use these data to describe the main socioeconomic characteristics of the popula- tion of Afghan refugees in Tajikistan and illustrate general patterns, as described in Ap- pendix A. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "tegrated in Tajik labor markets relative to their previous situation in Afghanistan. This is a typical characteristic of forcibly displaced populations as it naturally take time for them to recover their livelihood. Moreover, females have lower labor market participa- tion rates, hours worked, and wages relative to men. We also see that student enrollment for individuals of schooling age is dramatically lower in Tajikistan relative to Afghanistan. Finally, not surprisingly, household size for refugees is smaller in Tajikistan, which likely originates from family separation due to forced displacement from Afghanistan. Outcome variables. Using the census data, we construct five index variables for each of the outcomes of interest: refugee integration, educational attainment, mental health, income and consumption, and labor market outcomes. Indexes for dichotomous vari- ables were constructed as the average of all outcomes. Indexes that include continuous variables were constructed standardizing each variable, averaging all variables, and stan- dardizing the average once again. The methodology to construct indexes was adopted from Kling, Liebman and Katz (2007). Appendix B. A describes in detail all the variables included in each of the indexes. In sum, each of the indexes comprises the following: Integration index: Includes four variables that measure the feelings of belonging of refugees in Tajikistan. These are measured using a Likert scale. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Income and consumption index: includes the logarithm of monthly household income and consumption. Because these variables are continuous the index is constructed following the methodology outlined in Kling, Liebman and Katz (2007). Labor market index: includes a dichotomous variable for employment, and the logarithm of monthly wages and weekly hours worked. Descriptive statistics for each of the outcomes and their components variables are de- scribed in Table 1. III. B Data for the Taliban ’ s Geographical Presence To obtain information on the Taliban ’ s geographical presence, we scraped online data from the Foundation for Defense of Democracies ’ Long War Journal on the districts con- trolled by Taliban, by U. S. Allies, and contested territories between the two groups. 9 The data includes a yearly evolution of the territorial control in Afghanistan. The classification is based on open-source information from NATO ’ s data in Afghanistan, press reports, in- formation provided by government agencies, and the Taliban. To categorize the districts, they identify who provides government services, ensures security within the district, ad- ministers the district openly, and oversees local courts (see Appendix B. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "B for a detail description of the variable ’ s construction). 10 Figures 3 to 5 report province-yearly changes in the share of districts controlled by the Taliban, the U. S. allies, and the contested districts in dispute between the Taliban and the U. S. allies. 11 As seen in the figures, Taliban control was relatively stable from 2017 through 2019, after which drastic shifts took place across regions, in parallel with the U. S. withdrawal from Afghanistan. 9The Foundation for Defense of Democracies is Washington, DC-based nonpartisan research institute focusing on national security and foreign policy. It aims to strengthen US national security. 10The LJW data aligns with the territorial control analyses conducted by SIGAR and U. S. Forces- Afghanistan, which effectively tracks Taliban-controlled and contested districts by evaluating 5 indicators of stability, including governance, security, infrastructure, economy, and communications (Roggio 2017). 11More granular data is illustrated in Figures A. 5 and A. 6, which depict the district-yearly geographical distribution of Taliban controlled, U. S. allies controlled, and contested districts in Afghanistan. 17 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "V. B Impacts of exposure to Taliban territorial control on educational attainment Table 4 reports estimates for the effects of refugees ’ exposure to Taliban, contested terri- tories, and U. S. allies on refugees ’ educational attainment. First, this reveals that the im- pact of Taliban exposure is negative across four variables we examined: literacy, formal schooling attendance, primary education completion, and secondary education comple- tion. However, it is worth noting that the estimated effects do not reach statistical signif- icance for the completion of primary education. Nevertheless, when we combine these four variables into an education index (computed as their average), we observe enhanced precision, allowing us to document the significant negative effects of Taliban exposure on the education index. These effects are both substantial and meaningful. For instance, the coefficients in column (3) and Panel A indicate that a one-unit increase in the aver- age share of exposure to Taliban presence (representing 100 % presence in the province of birth during the five years between 2017 and 2021) is associated with an 18. 5 percentage point decrease in the share of individuals with some level of education. The estimates further demonstrate strong and adverse effects of refugee exposure to con- tested territories on educational attainment. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 2. Arrival Data of Afghan Refugees in Tajikistan in 2023 0 200 400 600 800 1000 1200 Afghans Inflows to Tajikistan 2017m1 2017m7 2018m1 2018m7 2019m1 2019m7 2020m1 2020m7 2021m1 2021m7 2022m1 2022m7 2023m1 U. S Withdrawal Date Source: Afghanistan Welfare Monitoring Survey conducted for this study in the fall of 2021. 36 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "B Description of Variable ’ s Construction This section describes how we constructed each of the variables in our main analysis. B. A Outcome Variables Outcome variables were constructed using the data from the Afghanistan Welfare Moni- toring Survey conducted for this study in the fall of 2021. • Integration: we measured the integration dimension with three different types of questions. The first included the individual reported answer from a 1 to 5 scale, where 1 means strongly disagree, and 5 strongly agree of the following questions: (i) ” Would you feel comfortable if your child or grandchild were to socialize or be friends with children of Refugee community people? ”, (ii) ” Do you feel welcomed in this village / town / city? ”, (iii) ” Do you think that everyone living in this village / town / city feels like they are a part of this village? ”. The second, included the answer to the following question ” On a scale of 1 to 10, with 1 being not very much to 10 very much, how much do you feel that Tajikistan is your home? ”, and the third, the total number of refugee, and Tajik friends among all of their friends, respectively. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Integration Index is constructed using the methodology of Kling, Liebman and Katz (2007) with the variables explained above. It implies i) standardizing the vari- ables, ii) averaging, and iii) standardizing the final average again. • Educational Attainment: we measured the educational attainment dimension using the following questions: (i) ” Can ¡ name ¿ read and write in any language? ”, and (ii) ” What is the highest level of formal school ¡ name ¿ completed? ”. With the answer to the question (ii), we generate an indicator variable corresponding to each level of education: Some Education [= 1] if the refugee has completed any level under the primary education level, Primary Education [= 1] if the refugee has completed basic primary education level, and Secondary Education [= 1] if the refugee has completed secondary education level. The Education index is constructed by calculating the average of the four indicator variables explained above. • Mental Health: we measured the mental health dimension by constructing an in- dicator variable, equal to 1 if the respondent answers ” More than half of the days or Nearly every day ” or 0 if the answer is ” Several Days or Not at All ”, for each of the following questions: (i) ” Over the last 2 weeks, how often have you felt little interest or pleasure in doing things? ” (ii) ” Over the last 2 weeks, how often have 57 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "you felt down, depressed, or hopeless? ” (iii) ” Over the last 2 weeks, how often have you had poor appetite or overeating? ” (iv) ” Over the last 2 weeks, how often have you had thoughts that you would be better off dead or of hurting yourself in some way? ” and (v) ” Did you seek mental health assistance during the last three months? (1 = Yes or 0 = No) ” The Mental Health index is constructed by calculated the average of the five out- come variables explained above. The average is calculated just with the information available for each individual, it excluded from the average the missing answers. • Labor Market Access: we measured the labor market access dimension using the following variables: (i) Employed is an indicator equal to 1 if the respondent re- ported to have worked for remuneration for at least one hour in the last 7 days, (ii) Weekly Hours Worked: is the total number of reported hours the respondent spent working in the last 7 days, and (iii) Monthly Wage: is the total amount in somoni that the respondent earned from salary or self-employed profits after taxes in the last 30 days. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Labor Market Access Index is constructed using the methodology of Kling, Liebman and Katz (2007) with the variables explained above. It implies i) stan- dardizing the variables, ii) averaging, and iii) standardizing the final average again. • Income and Consumption: we measured the income and consumption dimension using the answer to the following questions: (i) ” Approximately, what was the to- tal household income in the last month? (amount in Somoni) ”, and (ii) ” Approxi- mately, how much did your household spend in the last month in total? (amount in Somoni) ”. With these two questions, we created the Income and Consumption Index using the methodology of Kling, Liebman and Katz (2007). It implies i) stan- dardizing the variables, ii) averaging, and iii) standardizing the final average again. B. B Treatment Variables Treatment variables were constructed using the online data from the Foundation for De- fense of Democracies ’ Long War Journal (LWJ). They map the districts controlled by Tal- iban, by U. S. allies, and contested territories between 2014 to 2021, when the Taliban ’ took control over the total territory. The classifications are based on open-source information, such as press reports and information provided by government agencies and the Taliban. With this information, they defined the following: • Taliban and U. S allies controlled districts: places where the Taliban or the U. S. allies 58 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "are openly administering the territory, providing services and security, and running the local courts, respectively. LWJ may assess a district controlled by the Taliban or by the U. S. government if the district center frequently exchanges hands, and the Taliban or the government only controls a few buildings or villages in the district. • Contested districts: places where the government is in control of the district center or buildings within the district center, or a base, but little else, while the Taliban controls large areas or all of the areas outside of the district center. Or, the Taliban may control several villages, mines and other resources, runs prisons in the district, or administers areas of the district. With the classification of the LWJ, we construct the share of districts controlled by the Tal- iban, the U. S-Allies and the Contested territories in each of 31 provinces in Afghanistan. B. C Social Desirability Bias Individuals usually tend to answer according to how their responses will be viewed by others instead of answering what they really believe, and this phenomenon is known as social desirability bias. For this purpose, we measure social desirability bias by us- ing four questions from Marlone and Crowe ’ s social desirability scale (see Crowne (1964) for details). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 restrictions on job search, but they do not benefit from the same humanitarian assistance refugees in camps benefit from (Betts et al., 2017b). We investigate the determinants of the vulnerability of Syrian refugees to the COVID-19 shock in Jordan. In particular, we examine whether Syrian disadvantage during the shock is completely explained by institutional barriers reflected in precarious jobs and residency in camps, or if they experience added vulnerability by virtue of their refugee status. We use retrospective data on the job histories of young Jordanian and Syrian men between the ages of 16 and 30 years from the Survey of Young People in Jordan (SYPJ) (Assaad et al., 2021a; OAMDI, 2022) to construct a synthetic semi-annual panel dataset that tracks the job finding and separation experiences of respondents. We compare the trends of job finding and separation across the two populations before and after the onset of the COVID-19 pandemic. We find that Syrians have generally experienced lower job finding rates and higher job separation rates compared to Jordanians, although the differences were generally not statistically significant prior to the pandemic. With the onset of the pandemic, Syrians became significantly disadvantaged on both measures. Controlling for the type of employment showed that workers whose last job was informal were disadvantaged on both measures compared to formal workers regardless of nationality. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 formal employment. 6 In fact, among the five MENA countries with available data on labor market outcomes during the pandemic, Jordan exhibited the greatest stability in employment over time (Krafft et al., 2022). Moreover, Jordan undertook strict closure policies especially for camps whereby entering and exiting refugee camps was prohibited, including for humanitarian workers providing assistance to refugees. Data and descriptive statistics To analyze the trends of employment across Jordanians and Syrians before and during the COVID- 19 pandemic we would ideally need panel data that covers this period. Such data does not exist. The Survey of Young People in Jordan (SYPJ) 2020 (Assaad et al., 2021a; OAMDI, 2022) offers a suitable alternative. Collected between August 2020 and October 20207 from a nationally representative sample of Jordanian and Syrian youth between 16 and 30 years old, the survey captures the labor market experience of this population before and during the pandemic through retrospective questions about their job history since they entered the labor market. We use data on the characteristics of the individual ’ s current job as well as data on all their previous jobs since they entered the labor market. Individuals can report as many jobs as they took with no upper limit. The maximum number of jobs reported in our sample was seven jobs. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The other source of micro data for Jordan during the pandemic is the COVID-19 MENA Monitor (CMM) data (OAMDI, 2021). This data was collected over three waves in February, June and August 2021 through a phone survey. It also has retrospective data on individuals ’ employment 6 Defence order number 6 stipulated, early April, that institutions subject to the labor law must allow their employees who were dismissed or whose services were terminated since the beginning of the pandemic to return to their work < https: / / www. jordantimes. com / news / local / pm-issues-defence-order-no-6-stipulating-labour-rights- under-defence-law >. 7 Additional data was collected between February and March 2021 from refugee camps. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 statuses in February 2020, right before the pandemic. We deemed the SYPJ data to be more suited to our question since it allows to look at the trend of employment outcomes before the pandemic as well as right after the shock in the summer of 2020 and therefore allows investigating the effect of the pandemic on the two populations. The CMM data would be better suited to capture the recovery from the pandemic (Krafft et al., Forthcoming). The overlapping portion of the CMM data with the SYPJ data (February 2020 to February 2021) did not allow for a comparable analysis to the one we did in this paper. The overlap is not perfect. CMM data was collected a few months after the bulk of the SYPJ data only allowing for a comparison between the first half of 2021 (CMM) to the second half of 2020 rates (SYPJ). This is not a reasonable comparison especially in a period with rapidly changing events like the pandemic time. In addition, the time intervals are different as CMM data allows for examining annual change (February 2020 to February 2021) as opposed to semi-annual, and that is only one period which does not allow to compare the trend. In our analysis we focus on young males aged 15 and above who ever worked. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Since those who never worked do not have job finding and job separation outcomes we believe including them in the sample will not add much information. Moreover, given low participation rates among women for both populations, the number of women who ever worked in the SYPJ sample is only 229 (75 of whom are Syrians). It is therefore difficult to conduct the analysis by sex. Given the difference in the experiences of women and men in the labor market, we limit the sample to male respondents for whom we have information on their first job. 8 Furthermore, since our analysis spans the period from 2016 to 2020, we limit the sample to those who were 15 or above in 2016. This results in a sample of 824 young males, among whom 314 are Syrians. 8 We show the results of the full sample, men and women, in the appendix. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 Bearing in mind that this data is then translated to semi-annual data, i. e. the specific month is not used, among those who reported the starting month of their different jobs, the probability of starting a job in a given half-year was approximately 50 percent. We, therefore, think that a random assignment that gives equal weight to all months (and to each of the two half years) is a sensible choice. We construct a semi-annual synthetic panel. In doing so, we assume that each individual in the sample is observed over the period from the first half year of 2016 to the second half year of 2020. Individuals who entered the job market later than the first half of 2016 are considered non- employed until entry. Using the starting and ending dates for all the jobs an individual has engaged in, we can identify the individual ’ s job finding and job separation events. In particular, we convert the starting and ending month of a job into the half year they belong to. For example, a person who started a job in May of 2017 is coded as found a job in the first half year of 2017. In Table 2, we present the transitions by half-year, nationality of individual, their camp residency if they are Syrian and the imputation status. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Overall, 27 % of the transitions that occurred had an imputed month. Two things to keep in mind when considering this table. This does not show all the respondents-time periods in our sample (8, 240 observations). Since imputation will only occur when there is a transition in or out of a job, we are only including in the table those transitions and not all the periods where the individual is staying in a certain job. The total number of transitions is 726 which is less than the total number of individuals in the sample. That is because for some individuals all transitions occur prior to 2016 so they are not captured in our synthetic panel. Table 2 Transitions in and out of jobs by half, nationality, camp residency and imputation status of the month where the transition occurred Half Jordanian Syrian camp = 0 camp = 1 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 Figure 3 Job finding, job separation and the change in employment for young men who ever worked between 2016 and 2020 by half year and nationality conditional on informal employment in their most recent job (Seasonally adjusted) Source: Authors ’ calculations using SYPJ data Figure 4 Job finding, job separation and the change in employment for Syrian young men who ever worked between 2016 and 2020 by half year and residency in camp (Seasonally adjusted) Source: Authors ’ calculations using SYPJ data Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using an instrumental variable strategy, the results document a causal relationship between the level of employ­ment in the informal sector — where most immigrants are employed — and reports of discrimination. The second part is focused on studying the impact of Venezuelan migra­tion on local ’ s labor market outcomes, reported crime rates and attitudes using a variety of data sources. The results provide evidence that inflows of Venezuelans to particu­lar locations in Peru lead to better labor market outcomes for locals, decreased reported crime, as well as improved reported quality of local services, greater trust in neighbors and higher community quality. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at steven. stillman @ unibz. it. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "arriving to Peru have taken up jobs in the informal sector, directly competing with rela- tively low skilled native workers. To causally identify the relationship, we use a shift-share instrumental variable strategy that exploits local exposure to exogenous national-level ex- port shocks (Jaeger et al., 2018). As informal employment and discrimination could both be related to other area characteristics, we also control for the local industrial structure, household expenditure, population size, distance from the capital and center of economic activity (Lima), and, importantly, the number of Venezuelans based in each location prior to the current immigration wave, which we show to be a significant pull factor for where Venezuelans settle. Our results show that weaker informal labor markets lead to significant increase in the dis- crimination reported by Venezuelans in Peru. Overall, a 10 % decrease in the informal em- ployment rate increases discrimination by 2. 3-3 %. This effect is twice as large for men as for women. The data we use also collects information on where discrimination occurs. We find that weaker informal labor markets lead to more discrimination for men in public places, as well as on public transit and, for women, on public transit exclusively. We do not find evidence of an impact on workplace discrimination for either gender. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "One interesting pattern is that We find that more educated Venezuelans are more likely to report being discriminated against. A potential explanation for this, consistent with the previous results, is that higher skilled Venezuelans are disappointed with their situation in Peru, especially when they settle in areas with strong labor markets, and this lack of opportunity is either caused by or perceived as discrimination (Guerrero-Ble et al., 2020). In the second part of our analysis, we examine the impact of immigration in terms of changes in the number of Venezuelans as a share of the local population in a province on a wide-variety of outcomes. We rely on administrative data to measure the number of Venezuelans newly registered in each district in Peru on a monthly basis between January 2015 and December 2020. We aggregate this information at the province level, which roughly corresponds to a labor market. Having a time-varying measure of the presence of Venezuelans in each of the 198 provinces allows us to use repeated cross-sectional data on outcomes for Peruvians and control for location and time fixed effects, as well as, location-specific time-trends. Hence, we identify the impact of the presence of Venezuelans by examining how outcomes for Peruvians change when more Venezuelans arrive in a province, conditional on the trend in that outcome. However, it is possible that local shocks impact both the destination choice of Venezuelans and outcomes for Peruvians, hence we also use an instrumental variable strategy where we in- strument for the number of Venezuelans in a location with the presence of Venezuelans in that 3 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "location in the past, interacted with the year of observation. This is a semi-parametric ver- sion of the traditional migrant network instrument as recommended by Goldsmith-Pinkham et al. (2020) and it allows the strength of the network effect to potentially vary in each year. An overidentification test can be used to examine whether the instrument has a consistent relationship over time. We find robust evidence that increased immigration from Venezuela has a positive impact on labor market outcomes for Peruvians, with increased employment rates, incomes and expenditure in locations that receive more Venezuelans. Additionally, locations that receive more immigrants have lower levels of reported non-violent crime, improved reported quality of local services, greater reported trust in neighbors and higher reported community quality. On the other hand, we find evidence that in locations with more Venezuelans, Peruvians report that their community likes diversity less. There are a number of potential explanations for these findings that we plan to explore in future work. 1 The arrival of Venezuelans may have expanded the economic opportunities for Peruvian because of their higher levels of potential productivity, due to higher human capital, and their concentration in low wage jobs. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Furthermore, most of these jobs are in the service sector which potentially could have freed up time, especially for Peruvian women, to be more engaged in the labor market, as well as lowered the costs for these types of goods and services. Venezuelans also might have expanded opportunities by increasing the demand for certain goods and services. The paper proceeds as follows. In section 2, we describe the context and institutional back- ground, then turn to present the related literature in section 3. Section 4 describes the data we use in our analysis as well as our empirical model and identification strategy. We then present the results in Section 5, and finally we discuss policy implications and conclude. 2 Context Venezuela is currently experiencing the biggest crisis in recent history. A deep economic and humanitarian crisis started ramping up in with the fall in oil prices and the death of former president Hugo Chavez in 2013 (Chaves-González and Echevarría Estrada, 2020). This has led to what some authors have called the great Venezuelan exodus (Hausmann et al., 2018; Rozo and Vargas, 2021). In mid-2016, large waves of migrants started to leave the country, with Colombia (1 ’ 700, 000), Peru (870, 000), Ecuador (385, 000) and Chile (371, 000) being their 1Additional data which we do not currently have access to on firms and consumer prices is needed to examine these explanations in more detail. 4 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "many in the years following 2015 and its effects on different proxies of social cohesion such as trust, perceived fairness, and attitudes towards immigrants. Similarly, they find no evidence of any effect, neither negative nor positive. In contrast, they identify an increased incidence of anti-immigrant violence in the short-term which was larger in areas with higher unem- ployment and greater support for right-wing parties. Such null effects of refugee migration on native attitudes in host communities are also identified by Zhou et al. (2021) for the case of Sudanese refugee immigration to Uganda. 4 Research Design and Data 4. 1 Data Our analysis relies on data from the following sources: Encuesta Dirigida a la Población Venezolana que Reside en El País (ENPOVE) is a special- ized survey of Venezuelans living in Peru conducted by the National Institute of Statistics (INEI) in December 2018. The sample covers five main urban areas in the country where Venezuelan immigrants were most likely to be present. The survey collects data on the immi- grant ’ s origin, migration date, and details on their current employment. Importantly, a full module asks about the immigrant ’ s experiences with locals, which includes questions about discrimination and hostile attitudes towards them. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The respondent ’ s current location is iden- tified down to the centro poblado level, which roughly corresponds to an urban neighborhood or a rural town. Encuesta Nacional de Hogares (ENAHO) is the Peruvian version of the Living Standards Measurement Survey, e. g. a nationally representative household survey collected monthly on a continuous basis. For our analysis, we use data from January 2007 to December 2020. The survey covers a wide variety of topics, including basic demographics, educational back- ground, labor market conditions, crime victimization, and a module on respondent ’ s percep- tions about the main problems in the country and trust on different local and national level institutions. Observations are also spatially identified at the municipality level, but here we focus on variation in the Venezuelan share of the population at the province level, of which there are 196, as these are best representative of local labor markets. Latin American Public Opinion Project (LAPOP) is a opinion survey conducted bi-annually in all countries in Latin America and designed to be representative of urban populations. This was fielded in Peru in 2010, 2012, 2014, 2017 and 2019 and consists of about 2, 000 observations from mostly urban areas. The survey questions are centered around politics, 8 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "governance and opinions on current events. Observations are also spatially identified at the municipality and again we focus on variation in the Venezuelan share of the population at the province level. Gallup World Poll (GWP) is a nationally representative opinion survey and has been con- ducted annually since 2006 in a wide range of countries around the world. The sample collected in Peru is a repeated cross-section of about approximately 1, 000 observations each year. For our analysis, we use data from 2013 to 2020. The survey questions are centered around politics, governance and opinions on current events. We make use of several opinion indices provided by Gallup that measure individual opinions on various domains. Observa- tions are spatially identified at the region level for Peru, which is our level of analysis in this case (there are 25 regions in Peru). PTP We measure the location of Venezuelan immigrants on a monthly basis from January 2015 to December 2020 using administrative data on the district Venezuelan immigrants register at with the Peruvian authorities to obtain access to social services. There are strong incentives to register as this is also a prerequisite for applying to obtain the PTP. This data only records monthly gross arrivals so we do not know the outflows of Venezuelans to other locations within Peru or out of the country entirely. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, in ENPOVE, 84 % of Venezuelan immigrants in Peru report having lived in the same district during their entire time since arriving in the country. The data shows the arrival of 511, 223 Venezuelans as of December 2020, which, while somewhat lower than estimates of the actual number of Venezuelans living in Peru, is quite substantial. We also use data from the National Census 2007 and 2017. We use the 2017 Census data to measure the share of workers in the formal and informal sector in each centro poblado as well as the total local population in each centro poblado, province and region. We use the 2007 data to construct both of our instruments discussed in more detail below as well as to create additional controls for the local economic environment. More specifically, in the first part of our analysis, we use information on the industrial distribution (using detailed four-digit codes) in each centro poblado, while in the second part, we use information on the total number of Venezuelans in each province in Peru. To construct the Trade shock instrument for the first part of our analysis, we also use trade data from the reports of TradeMap. From this website, we are able to identify export and import values for Peru on a monthly basis since 2006 at the HS 6-digit product revision. In addition, correspondence tables of HS 6-digit product revision to ISIC 3. 1 revision (United Nations) are used to harmonize products with their corresponding industry sector in order 9 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "problems. Previous literature has shown that immigrants are more likely to move to locations where they have a network of peers from the same country. We show below that this is true among Venezuelans in Peru as well. Our main interest is on β1, which represent the impact of the labor market conditions in centro poblado j on the discrimination experienced by Venezuelans. Venezuelans who arrive to the country clearly evaluate where to settle based on the labor market opportunities (among other reasons), and therefore to causally identify β1 we need a source of exogenous variation for the labor market at the local level. We use an instrumental variable strategy that exploits variation in the share of workers employed in different industries in 2007, along with national level shocks to trade in specific industries between Oct 2016 and Oct 2017 when the census was collected. More precisely, the first stage regression is given by: lnEmpj = α + νSharejk (2007) × ∆ lnExportk + ηXij + νZj + αo + ϵj (2) where Sharejk (t − 1) is the share of workers in centro poblado j employed in industry k in 2007, and ∆ Exportk represents the log change in national level exports in industry k between 2016 and 2017. The remaining control variables are similar to those in Equation 1. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "outcomes measured in ENAHO. εipt is an error term clustered at the province level as we measure the number of Venezuelan immigrants at this level and suspect there is strong serial correlation in many of our outcomes. β identifies the effect of the number of Venezuelan immigrants in year t in region p. Impor- tantly, we also control in all models for time (either year or month * year) fixed effect (αt) and province fixed effects (αp). Hence, we control for any time-invariant differences in outcomes across provinces and aggregate changes in outcomes, both of which may be related to the location choice decisions of Venezuelans. In our preferred specification, we also control for province-specific time-trends (time ∗ αp) which account for any local trends in the outcome variable. In this model, the impact of the presence of Venezuelans is identified by examining how outcomes for Peruvians change when more Venezuelans arrive in an area conditional on the trend in that outcome. 8 It is possible that local shocks impact both the destination choice of Venezuelans and out- comes for Peruvians, hence we also use an instrumental variable strategy where we exploit the intuition that immigrants are more likely to move to localities where immigrants from the same nationality are located. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We therefore instrument our measure of the number of Venezuelans in a province p with the presence of Venezuelans in that province as recorded in the 2007 census interacted with year dummy variables. This is a semi-parametric version of the traditional immigrant network instrument as recommended by Goldsmith-Pinkham et al. (2020) as it allows the strength of the network effect to potentially vary in each year. An overidentification test can be used to examine whether the instrument has a consistent relationship over time. 5 Results 5. 1 Labor Market Conditions and Discrimination Table 2 shows our main results on the effects of local labor market conditions on self reported discrimination. We first present the OLS results, and then turn to provide the estimates from our IV specification. Importantly, given that the types of jobs in which men and women work differ, in Table 2 we show the main results for the full sample of immigrants who responded the survey, and split the sample by gender. Columns (1)- (3) show the OLS relationship between the (log) local informal employment 8Our results are robust to controlling for district fixed effects and time-trends as well, but we believe this is over-fitting the model as many individuals commute across district boundaries for work. 14 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To explore further the mechanisms underlying these effects, in Table 4 we exploit the fact that ENPOVE collects detailed information on where discrimination episodes took place. We show the OLS and IV results for our preferred specification, the one that includes all controls and fixed effects, and for the IV, the specification that uses the linear instrument. Discrimination at work seems to respond the least to local employment, with a coefficient that implies that a 10 % increase in informal employment leads discrimination to decrease by 1. 4 %, although the relationship is not statistically significant. Interestingly, there is a clear gender split on whether discrimination in streets and public spaces. A 10 % increase in informal employment causes a decrease in discrimination against men in streets and public spaces of about 3. 7 %, with no significant change in discrimination against women in these spaces. Finally, discrimination in public transit responds similar regardless of the gender, with an effect of about 2 % for reductions in employment of 10 %. 5. 2 Immigration, Local ’ s Labor Market Outcomes and Perceptions In the previous section, we established that labor market conditions have a causal effect on the way Venezuelan immigrants perceive that are treated by locals: lower unemployment in the informal labor market leads to a decrease in discrimination. We now turn to study the flip-side, namely, the way in which the presence of Venezuelans affect Peruvians ’ labor 16 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The relationship between our instrument and the endogenous regressor is depicted in Figure 6, where is clear that immigrants are more likely to move to locations where there is an established network of compatriots and that this relationship is stable over time even though there has been a large increase in the Venezuelan immigrant share over time. This is true even though the number of Venezuelans in Peru in 2007 was quite small. The F-stat for the excluded instrument is 2300, showing the strong relationship in the first-stage robustness of the instrument. We also typically fail to reject that our model is over-identified which is an indication that the shift-share instrument is truly picking up the impact of increasing Venezuelans being pulled to locations where Venezuelans previously settled. Our IV results tell the same qualitative story. A doubling in the share of Venezuelans in a province increases the probability of a Peruvian being employed by 0. 6 %, increases household income raise by 2. 2 % and expenditures by 1. 4 %. The effects on income and expenditure are nearly twice as large for women as for men. These positive impacts on labor market outcomes are sizeable, given the large overall increase in the Venezuelan share of the population. 9 There are a number of potential explanations for these findings that we plan to explore in future work. The arrival of Venezuelans may have expanded the economic opportunities for Peruvian because of their higher levels of potential productivity, due to higher human 9Previous studies have shown that Venezuelan immigration caused either small but significant losses in the labor market for low education women (Morales and Pierola, 2020) or null effects (Boruchowicz et al., 2021). 17 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "capital, and concentration in low wage jobs. Furthermore, most of these jobs are in the service sector which potentially could have freed up time, especially for Peruvian women to be more engaged in the labor market, as well as lowered the costs for these types of goods and services. Venezuelans also might have expanded opportunities by increasing the demand for certain goods and services. One widespread claim mentioned in some media reports is that Venezuelan migration led to an increase in crime (Freier et al., 2021). We test whether this claim is supported by the data in Table 6, where we use administrative information on the number of non-violent and violent crimes reported in each municipality, the personal security index from Gallup, and reports on whether crime is perceived as a major problem in ENAHO. The structure of this table is the same as the previous with Panel C our preferred specification. Consistent with the idea that Venezuelan inflow lead to labor market conditions improving, we observe that locations that received a larger number of immigrants have lower number of reported non-violent crimes (columns 2). This effect is large with a double of Venezuelans in a province leading to a 42 % decline in reported non-violent crimes. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. Introduction The COVID-19 pandemic has caused unprecedented global employment losses of 114 million jobs worldwide in 2020 compared to the previous year (ILO, 2021). Kenya, as well as other parts of the world, has been strongly affected as restrictions on personal mobility have severely disrupted economic activities. 2 Although losses have been alleviated through welfare support programs, hard-hit groups remain: children, youth, women, low-paid and low-skilled workers (FAO, 2020; Hill & Narayan, 2020; Chetty, Friedman, Hendren, Stepner, & Opportunity Insights Team, 2020; ILO, 2021; Josephson, Kilic, & Michler, 2020). Displaced populations are particularly vulnerable as they arrive in host destinations without initial assets or established business connections and often lack access to official documentation to establish themselves in formal markets or access formal financing. Refugees living in Kenya face working and movement restrictions which severely impact their ability to participate in the labor market and limit their livelihood opportunities (Zetter & Ruaudel, 2016). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Refugees were experiencing high levels of poverty, food insecurity as well as low school attendance and labor market participation rates. 3 Yet despite their large numbers and contributions to the local economy, quality data on refugee communities is scarce. 4 In particular, while qualitative assessments or cross-sectional quantitative analyses have been conducted in the past (MacPherson & Sterck, 2021; Sterck & Delius, 2020; Alix-Garcia, Walker, Bartlett, Onder, & Sanghi, 2018), reliable longitudinal studies on refugee job market outcomes are not readily available, severely restricting the ability to assess the impact of a crisis like COVID-19 on refugees. This study uses the Kenya COVID-19 Rapid Response Phone Survey (RRPS) to investigate the socioeconomic implications of the COVID-19 pandemic on labor market outcomes of urban national and refugee communities in Kenya. Refugees in Kenya live in densely populated refugee camps inhabiting more than 200, 000 inhabitants and in urban areas, primarily in Nairobi. Available job-market opportunities and labor market dynamics are therefore comparable to urban nationals rather than rural nationals who predominantly engage in agriculture, an economic activity refugees cannot pursue as they do not have the legal right to own land. From May-June 2020 until April-June 2021 we interviewed 6, 343 households consisting of both refugees and Kenyans over five survey waves. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our data is unique in at least four dimensions: i) it leverages the high cell phone penetration and coverage throughout the country, including the refugee communities, to reach households during lockdowns when face-to-face interviews are impossible to conduct; 5 ii) its longitudinal nature allows not only to assess the first order impact of the COVID-19 shock but also its longer-term implications for recovery; iii) interviews cover refugees and nationals over the same period and are 2 Khamis et al. (2021) estimate that the work-stoppage rate in Kenya reached up to 62 percent compared to before the pandemic. 3 Results from socioeconomic surveys carried out by UNCHR and the World Bank in Kalobeyei settlement in 2018 and in Kakuma camp in 2019 show that 65 percent of Kalobeyei refugees and 68 percent of Kakuma refugees are poor, while at least 7 in 10 of them are highly food insecure (UNHCR & World Bank, 2020; UNHCR & World Bank, 2020). 4Kenya hosted around 530, 000 refugees in August 2021 which makes it the second largest hosting country in Africa after Ethiopia (UNHCR Kenya, 2021). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Alix-Garcia et al. (2018) estimate that the presence of Kakuma refugee camp has increased demand for low-skilled jobs and wage labor and led to favorable price changes for local producers which on aggregate contribute to a 25 percent increase in household consumption in areas in close proximity to the camp compared to surrounding areas. 5 Eighty-six percent of Kenyan households own a mobile phone (KNBS, 2016). Among refugees, 69 percent of Kakuma refugees own a mobile phone (IFC, 2018) and 99 percent report having access to a mobile phone (Hounsell & Owuor, 2018). Access is harder to estimate for urban refugees living in Nairobi, as no official survey numbers are available (Eppler, et al., 2020). However, given the high access to technology in the capital, the mobile phone ownership share is expected to be at least at the national average level (Eppler, et al., 2020). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This suggests there is a third, unobservable ‘ refugee factor ’ which inhibits recovery after a major demand-supply shock and therefore aggravates pre-existing vulnerabilities in times of need. In particular, components of the refugee factor which we cannot control for are the quality of education and training received, the special institutional framework which limits refugees ’ rights to move, settle and work freely in Kenya, the restricted access to formal permits, documentation, and social safety-nets or variation in the provision of aid-distributions. Our results indicate that these unobserved characteristics translate to observed effective differences between refugees and nationals, which opens avenues for policy programs to level the playing field through inclusive evidence-based measures. This study contributes to the emerging literature investigating socioeconomic impacts of the COVID-19 pandemic with special focus on labor-market impacts which remain under- investigated in the African context as post-COVID-19 data is often scarce (Khamis, et al., 2021; Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Second, refugees are not allowed to leave the encampment unless a travel-permit is acquired in advance which requires an application process that can be lengthy. Income generation strategies that rely on mobility are therefore limited. In addition, refugees forfeit their right to food and cash assistance if relocating to an urban area where assistance from NGOs is very limited. Third, refugees do not have the official right to work outside the camps, unless a work permit is obtained in advance for which a recommendation from a prospective employer must be accompanied by a letter from the RAS confirming refugee status leading to in practice permits being rarely issued (UNHCR & World Bank, 2020). This results in many refugees taking low- paying jobs in the informal sector that are more prone to short-term contracts and layoffs without much protection during times of crises (Betts, Sterck, & Omata, 2018). As many refugees flee their country without much preparation, they frequently lack official documents such as birth certificates, IDs or schooling certificates, which are necessary to access many Kenyan services or signal one ’ s skill level to a prospective employer. Refugee entrepreneurs are allowed to open their own business inside the camps by applying for a business license to the local county government. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, the license needs to be renewed annually with charges depending on business size, effectively making it a ‘ tax ’ on economic activity (Betts, Sterck, & Omata, 2018). Given the regulatory challenges, only a fraction of refugees in camps were employed before the pandemic. Results from socioeconomic surveys show that in 2019 and in 2018 only around 20 percent of refugees in Kakuma Camp and 39 percent of those in Kalobeyei Settlement were employed in any sort of economic activity. In contrast, more than 60 percent of Kenyans at the national level and in the county-level host community were employed over the same timeframe. Wage work is the most important type of employment for refugees in Kakuma with nearly 50 percent of the 18-64 year old population employed for wages, usually in the 6 While the refugee camps are jointly administered by the Refugees Affairs Secretariat (RAS) of the Government of Kenya and UNCHR, shelter, food and cash assistance, health care, and education services in Kakuma and Dadaab are primarily provided by the international community (UNHCR & World Bank, 2020). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 educational institutions, suspensions on in-person meetings and a dusk-to-dawn curfew. The lockdown was subsequently lifted in May 2021. Refugees living in Kenya were subject to the same containment measures as nationals, although refugee camps were in addition closed off from the outside in the very beginning of the pandemic. When the nation-wide lockdown was stepwise lifted in the fall of 2020, permissions to enter the camp for outsiders continued to be strongly limited to protect the refugees living in the densely populated camp settings. Figure 1: COVID-19 cases and RRPS timeline in Kenya Source: Ritchie, et al (2020). Data downloaded on June 14, 2021, here. 3. Data and methodology 3. 1 Survey design The Kenya COVID-19 Rapid-Response Phone Survey (RRPS) is structured as a five-waves bi- monthly panel survey that targets nationals, refugees, and the Shona community. 9 The first wave of interviews was administered in May-June 2020 and the last available wave in April- June 2021. The questionnaires capture extensive demographic and socioeconomic data with modules on employment, income, coping strategies, food security, access to education and health services, child labor, subjective well-being, knowledge of COVID-19, changes in behavior in response to the pandemic, and perceptions of the government ’ s response. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Interviews were administered via phone by trained enumerators in the local language of the respondents. The survey uses a multi-frame design consisting of three sampling frames, two for nationals and one for refugees. The first national sampling frame consists of 9, 009 households received from households providing a phone number in the 2015 / 16 KIHBS-CAPI pilot 9 The Shona are a community of 1, 670 formerly stateless persons living in Kenya who mostly originate from Zimbabwe. They were granted citizenship on December 12, 2020. In this paper we only investigate the outcomes of refugees relative to nationals in the wake of COVID-19. We do not investigate the Shona stateless sample. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In fact, there is no significant discernable difference between the distribution of jobs refugees are employed in and the distribution of jobs they are laid off from. 29 27 For example, work is restricted to within refugee camps. For any work outside the camps, a work permit needs to be acquired which is costly both in terms of time and money. Further, refugees are often not allowed to engage in agriculture, keep livestock or engage in certain industries due to fears of competition with the native pastoralist community (Betts, Sterck, & Omata, 2018). 28 Sector information is not available at the household-member level for agricultural work and work in household businesses for the baseline period, hence we use wave 1 data for the first available sector. 29 Except for ‘ other services ’ which was among the five most important layoff sectors over all waves but was not strongly represented among the employed in wave 1. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 expenditures – is an important aspect of the analysis, given that policy decisions should be guided by net fiscal incidence. Inchauste and Lustig (2017) point out that a particular measure that might be rejected in isolation based on equity considerations (such as the increase in the value added tax rates), may be reconsidered if the transfers that the resulting revenues are financing have a large equalizing impact. The marginal contributions of each fiscal intervention presented are therefore estimated in the context of all the other fiscal activity. The use of an internationally consistent methodology allows the comparison of Uganda ’ s results to those of other countries in the Sub-Saharan Africa region: Ethiopia, Tanzania, South Africa, Ghana and Kenya. 5 In addition to development targets summarized in the NDPII, Uganda has re-committed to increasing internal revenue mobilization. The country has one of the lowest ratios of revenue-to-GDP in the region. In 2015 / 16, the 13 percent ratio was lower than the average of the 21 African countries by 5. 1 percentage points according to the OECD (2018). 6 Conscious of the need to increase tax revenue, the Government of Uganda is currently preparing a new medium-term revenue mobilization strategy (World Bank, 2018b). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Firstly, we do not capture all items in Uganda ’ s public spending or revenue collections, such as the large investments in infrastructure, expenditure on national security (on the spending side) or the corporate income tax (on the revenue side), despite that these fiscal instruments provide indirect benefits (or detriments) for households. 7 Secondly, as Inchauste and Lustig (2017) point out, the approach does not provide information on the trade-off between long-term human capital spending- which creates higher future levels of human capital (for example, a better educated and healthier, more productive citizenry)- and short-term spending on programs that bring immediate poverty relief (such as conditional cash transfers). 5 The corresponding publications for these analyses are: Hill et al. (2017) for Ethiopia, Younger et al. (2017) for Ghana, Inchauste et al. (2017) for South Africa, Younger et al. (2016) for Tanzania, and World Bank (2018a) for Kenya. 6 http: / / www. oecd. org / tax / tax-policy / revenue-statistics-in-africa-2617653x. htm 7 Identifying the individual beneficiaries of general infrastructure spending as well as the individuals who might bear the burden of a corporate income tax is difficult with only a standard household budget survey. In addition, investment in infrastructure in the current fiscal year will also provide benefits in the future that are impossible to allocate to the households identified in the household survey. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 3. Methodology and Data 3. 1. Data The UNHS survey is used by the GoU to monitor the well-being of the population. The Uganda Bureau of Statistics (UBOS) has conducted the survey every three to four years since the 1990s, providing a series of comparable data stretching more than 20 years. The last UNHS survey was conducted between July 2016 and June 2017, collecting data from around 15, 000 households in all 112 existing districts of Uganda at the time. The UNHS 2016 / 17 is representative at the national, rural-urban, regional and sub-regional level, and gathers information on the socio-economic characteristics of the household (including income and consumption) and its members (including educational attainment, health status and use of services, etc.). Thus, it is possible to allocate all fiscal interventions for each household in the survey, as explained in detail in the next sections. 3. 2. Summary of CEQ Methodology14 The CEQ Assessment takes specific fiscal policy elements, programs, expenditures, or revenue collections and allocates them to individuals and households appearing in the UNHS 2016 / 17. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "22 Box 1. Gini Coefficient: an inequality measure While poverty measures absolute deprivation with respect to a given threshold, inequality is a relative measure of poverty indicating how little some parts of a population have relative the entire population. In the context of monetary poverty, equality can be defined as an equal distribution of consumption / income across the population. This means that each share of the population owns the same share of consumption / income. The Lorenz Curve compares graphically the cumulative share of the population with their cumulative share of consumption / income. A perfectly equal consumption / income distribution is indicated by a diagonal. The other extreme is complete inequality where one individual owns all the consumption / income. These two (theoretical) extremes define the boundaries for observed inequality. The Gini coefficient is the most commonly used measure for inequality. A Gini coefficient of 0 indicates perfect equality while 1 signifies complete inequality. In relation to the Lorenz Curve, the Gini coefficient measures the area between the Lorenz Curve and the diagonal. Source: World Bank ’ s Poverty Handbook, World Bank (2009). 4. 2. Poverty The fiscal system in Uganda is slightly poverty inducing. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "36 7. Conclusions To make progress in promoting equality and improving the living standards of the population in Uganda, it is necessary to understand the effect of Uganda ’ s fiscal policy on inequality and poverty. This report uses the internationally comparable CEQ methodology to assess the individual and combined effects of taxes and public social spending, based on the microdata from the UNHS 2016 / 17 household survey. The main objective is to provide considerations for policy makers in terms of which fiscal instruments or, more specifically, which mix of fiscal instruments can contribute to reduce poverty and inequality. Overall, our results show that Uganda ’ s fiscal system is modestly equalizing. As a whole, taxes and transfers in 2016 / 17 reduced inequality by approximately 3. 23 Gini points. This result is comparable to Ghana, moderately lower than in Kenya and Tanzania, and much lower than the result observed for South Africa. The largest contributive factor to this equalizing effect is direct taxation (personal income tax or PAYE), followed by the education in-kind transfers (net transfers). This is not surprising, given the size of these fiscal interventions as a proportion of pre-fiscal income and their progressiveness. Direct transfers and social protection programs more generally contribute to the redistributive effect and are simulated as highly concentrated among the poorest households. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This study reports two key findings. First, particularly after the 2014 arrival of over 1 million South Sudanese refugees, host com­munities with greater levels of refugee presence experienced substantial improvements in local development. Second, using public opinion data, we find no evidence that refu­gee presence is associated with more negative (or positive) attitudes towards migrants or migration policy. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at ggros @ upenn. edu. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Given the sheer scale of the refugee inflow and its welcoming asylum and refugee policy, our finding that Uganda has not ex- perienced a backlash against refugees among those most directly affected by the country ’ s asylum and refugee policies offers important lessons. To explore the effects of refugees ’ presence in Uganda, we combine publicly available geocoded Afrobarometer survey data with newly constructed fine-grained georeferenced panel data on service delivery in three domains: access to (primary and secondary) education, health care access and utilization, and road density. We focus on service delivery inputs rather than outcomes since service providers — governments, humanitarian organizations, and UN agencies — can directly affect access, but linking access to outcomes (such as infant mortality) is more complex, materializes with a long lag, and depends on several factors that are outside policy makers ’ control. We use ACLED data to explore possible conflict dynamics. We report two sets of findings. First, we find no evidence that a larger refugee presence increases support for restrictive migration policies (though in some years it is associated with a somewhat heightened sense of personal insecurity). Second, using a difference-in-differences (DiD) research design, we find robust evidence that access to education, health care, and roads significantly improved for those living near refugee settlements, and that these residents recognized 4 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We contribute to this literature by exploring the effect of refugees ’ proximity on the migration policy preferences of host community populations in a low-income country. 5 Focused almost exclusively on the Global South, the second strand of research explores whether the presence of refugees is associated with a greater risk of conflict (Jacobsen, 2002). Earlier studies highlighted possible tensions with local citizens that are exacerbated by resource competition or ethnic rivalry (Salehyan and Gleditsch, 2006; Rüegger, 2017). Much of this scholarship recognizes that refugees are often victims of conflict (Onoma, 2013; Fisk, 2018; Böhmelt, Bove and Gleditsch, 2019; Savun and Gineste, 2019). Recent research suggests that conflict between host communities and refugees may be avoided if refugees ’ presence attracts aid and economic activity that benefits both (Lehmann and Masterson, 2020). We complement this literature on the refugee – conflict nexus, which thus far has relied on cross-country analysis, by exploiting — following Zhou and Shaver (2021) — within-country variation in exposure to refugee settlements. The third strand explores the welfare consequences of refugees ’ presence on host communities in developing countries. However, without auxiliary data (such as survey data on policy preferences regarding refugee policies), these studies cannot tell us how the economic consequences of refugee hosting affect social cohesion (if at all). Moreover, almost all studies in this research domain strand of the literature concentrate on a single domain, such as labor market outcomes (Fallah, Krafft and 5See also Zhou (2018), which examines how the presence of refugees can change local citizens ’ opposition to citizenship inclusion in sub-Saharan Africa. 6 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The consistency of the findings across these domains increases our confidence in the direction of the effect of refugee presence. In a second contribution, unlike past studies, we link the economic consequences of refugees ’ presence and locals ’ policy preferences and behavior. Third, we use a continuous measure of refugees ’ presence that integrates information on both refugee settlements ’ distance from the host community and population size, and allows localities to be affected by more than one settlement. Building on this prior research, we test whether government and humanitarian aid agencies resource allocation decisions ensure that nearby communities do not carry a disproportionate burden of hosting refugees. We first explore whether localities that are geographically proximate to larger refugee settlements (i. e., those that have greater exposure to refugees) have better access to public goods and development outcomes because they benefit from the increased aid and resources flowing into these settlements. If this is the case, we contend, a backlash against refugees and refugee policies in host communities is much less likely. 3 Context As several of its neighbors — including South Sudan, Burundi, and the DRC — have experienced war and displacement, Uganda hosts one of the largest refugee populations in the world (see Figure 1). About 65 percent of the country ’ s refugee population is from South Sudan; the remainder come 7 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 2: Locations of refugee settlements, 2001 – 2020. Refugee settlements indicated in blue, and parishes (our unit of analysis) in orange. Darker orange parishes denote higher level of exposure to refugees. residing in the country (d ’ Errico et al., 2021). Thus with the exception of the small number of refugees who forgo assistance and reside in towns, refugees in Uganda do not self-select into specific settlements. Two major regulatory frameworks guide the settlement of refugees in Uganda — the 2006 Na- tional Refugee Act and the 2010 Refugee Regulations introduced to operationalize it. This legal framework provides refugees with the right to documentation (e. g., identity cards, birth certifi- cates, death certificates, etc.), the same rights as Ugandan nationals to access social services such as health, water and sanitation and education, the right to land for agricultural use and shelter, the right to start a business or seek employment, freedom of movement, the right to receive fair justice, the principle of family unity, the right to transfer assets within and outside the country, the right of association regarding non-political and non-pro��t associations and trade unions, and 9 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "thereby also benefiting local host communities. It is somewhat unclear how successful Uganda ’ s integration policy has been, because only a handful of studies has analyzed how the presence of refugees has affected welfare outcomes in host communities. With the exception of Kreibaum (2016), these studies have focused on general welfare (income and consumption) rather than service delivery. Other studies explore environ- mental outcomes. Based on focus group discussions in host communities near the Nakivale refugee settlement, Ronald (2020) reports that host communities are concerned about environmental degra- dation. Similarly, based on stakeholder interviews, IRRI (2019) also reports tensions over natural resources, especially around the allegation that refugees engage in illegal logging. 10 Zhu et al. (2016) assess the impacts of World Food Program aid within a 15 km radius of two refugee settlements in Uganda. They find that the average refugee household receiving cash food assistance increases the annual real income in the local economy. Here, locals around the settlements benefited from aid provided to refugees because on average, they are in a better position to increase their supply of goods and services as the local demand rises. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Kreibaum (2016) corroborate the finding that host communities near refugee settlements in Uganda have relatively higher consumption levels using data on three south-western districts, though the effect is small in magnitude. d ’ Errico et al. (2021) find that the presence of refugees has only modest effects on local households ’ consumption levels. The authors attribute this finding not to an increase in demand for local produce, but to greater participation by host households in paid employment in aid agencies, and to the resulting increase in wage incomes. However, the effects they observe are small, and concentrated in areas very near refugee settlements. 11 Most relevant to our study, Kreibaum (2016) reports that access to private primary schools (but not public schools or health services) has increased at a greater rate as a function of refugee presence. They measure refugee presence at the district level (refugee share of the district popu- lation) and focus on the south-western districts between 2002 and 2010. Thus, by including all of Uganda at the parish-year level as well as the post-2014 influx, we extend their analysis. In sum, our study is the first to measure the effects of refugee presence on service delivery outcomes in Ugandan host communities. 10See Gianvenuti, Jalal and Kirule (2020) for more details. 11Note that both Zhu et al. (2016) and d ’ Errico et al. (2021) use original cross-sectional surveys. Without pre-treatment data, it is harder for them to make causal claims. 12 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Following Maystadt and Verwimp (2014) and Maystadt and Duranton (2019), our measures of refugee presence are continuous, consider both the size of and distance to the proximate settlements, allow a locality to be affected by multiple settlements, and are more directly related to theory. 4. 3 Outcomes: Public Goods Access and Utilization, Attitudes toward Migrants, and Insecurity The main outcomes of interest for public goods and development include newly constructed geocoded data on primary schools, secondary schools, health clinics and hospitals, health utiliza- tion, and road density. First, our primary school data on over 19, 500 schools comes from the Uganda Education Management Information Systems, and is supplemented by about 2, 700 addi- tional schools manually collected by a Ugandan education consultant we hired to do manual checks. For each primary school, we have their geographic coordinates, founding year, and whether they 14 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "many people it serves, and (c) the type of facility, since ‘ higher-level ’ facility types offer a wider range of health services and have more and better trained staff. The intuition is that parishes have better health access if their residents can travel shorter distances to facilities that serve fewer people and offer more (and better) services. To combine these three factors, for each parish, we calculated the distance and population served to the nearest facility of each type. 14 We then rescaled and transformed these measures (since shorter distances and fewer people indicate better access), averaged across the five facility types, and standardized it. Fourth, since health access is based only on the number of new facilities, it does not capture improvements to existing facilities (e. g., more providers, equipment, and medications). Although we do not have data on these improvements during our study period, we proxy for healthcare quality by including a measure of Health Utilization. Unlike the previous measures, which are constructed at the parish-year level, health utilization is an individual-level measure. It is derived from Demographic and Health Surveys (DHS) of over 30, 000 Ugandan children and their households in 2006, 2011, and 2016. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our standardized utilization measure averages child health services (e. g., vaccinations, deworming, iron supplements), their mothers ’ maternal health services (e. g., tetanus injections before and during pregnancy, antenatal visits, delivery by health professionals), and household measures (e. g., insecticide-treated mosquito nets). Reassuringly, Health Utilization is positively correlated with Health Access. For more details on how both variables are constructed and validated, see SI Section S1. 5. Fifth, to create a measure of Road Density for 2011, 2016, and 2020, we use two data sources: the Global Roads Open Access Data Set gathered by the NASA Socioeconomic Data and Appli- cations Center from 2010, and the World Food Programmes road networks shapefile from Open- StreetMap for 2017 and 2020. For each parish polygon, we extract the total length of roads (km), weighted by the speed limit of each type of road. There are six types of roads in Uganda, ranging in speed from trail to highway. See SI Section S1. 6 for more details on road density data construction. Lastly, to assess public opinion, we use the Afrobarometer surveys Rounds 3 – 8, which roughly correspond to our study years. The Afrobarometer is an in-person, nationally representative survey. For Uganda, each round has roughly 2, 400 adult citizen respondents, so for this analysis our units are respondent-years (as opposed to parish-years). Note that this survey data is repeated cross- sections, not panel, and not all questions are asked every round. We evaluate the following questions 14For HC-I, we use a binary indicator of whether a parish has an HC-I rather than the distance, because many parishes have one or more. 16 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We capture possible support for restrictive migration policies by exploring preferences for more expansive or limited naturalization criteria using a two-part question: In your opinion, which of the following people have a right to be a citizen of Uganda? A citizen would have the right to get a Ugandan passport and to vote in Ugandan elections if they are at least 18 years old: • Born Non-Ugandan (Round 5): A person born in Uganda with two non-Ugandan parents? Yes (1) / No (0). • Able to Naturalize (Round 5): A person who came from another country, but who has lived and worked in Uganda for many years, and wishes to make Uganda his or her home? Yes (1) / No (0). To assess feelings of insecurity, we use the following two questions: • Feel Unsafe in Community (Rounds 5 and 6): Over the past year, how often, if ever, have you or anyone in your family: Felt unsafe walking in your neighbourhood? Never (0)... Always (4). • Feared Crime (Rounds 3 to 6): Over the past year, how often, if ever, have you or anyone in your family: Feared crime in your own home? Never (0)... Always (4). Since the possible responses to all of these questions are on different scales, we standardize all Afrobarometer measures to have a mean of 0 and SD of 1 for ease of comparison and interpretation. 17 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To examine whether actual levels of insecurity changed as a function of refugee presence, we use ACLED data, which geocodes violent events. For each parish-year, we construct a binary variable, Any Violent Event, which equals 1 if any of the following events occurred: violence against civilians, riot, attack, mob violence, or violent event. 4. 4 Control Variables We (flexibly) control for the following covariates in our parish-year analyses. From the 2002 census, we include measures of each parish ’ s population, average age, proportion male, literacy rate, unemployment rate, agriculture share, share of the parish population that is coethnic with the president, and average household wealth (based on a composite index of household items). We also include a binary indicator from ACLED for any violent events from 2002, as well as each parish ’ s distance to the nearest oil well, distance to the nearest border, distance to a major road, and distance to Kampala, Uganda ’ s capital. To prevent post-treatment bias, we only use the 2002 measures of these variables. We interact these time-invariant variables with year to allow their effects to change over time. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For the individual-level analyses — the Health Utilization outcome based on the DHS and Afro- barometer survey items — we control for respondents ’ gender, age, urban or rural residency, house- hold wealth, and education attainment, and for location-based controls (distance to the nearest border, to the nearest major road, and to the capital). We further interact these demographic and location-based controls by year. 4. 5 Empirical Strategy Our main analysis uses a time-varying DiD research design. We run the following OLS model interacting refugee presence (time varying) with year, interacting time-invariant controls with year, and including parish, year, and region fixed effects, with standard errors clustered at the parish level: yit = ηi + ηt + ηr + β1exposureit + β2exposureit × 1 { yearit = 2006} + β3exposureit × 1 { yearit = 2011} + β4exposureit × 1 { yearit = 2016} + β5exposureit × 1 { yearit = 2020} + λ1xi × 1 { yearit = 2006} + λ2xi × 1 { yearit = 2011} + λ3xi × 1 { yearit = 2016} + λ4xi × 1 { yearit = 2020} + ϵit When we only have 1 year of observations for an outcome (certain Afrobarometer questions were only asked in a single round), we run a cross-sectional analysis with region fixed effects. 18 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Compared to the baseline year of 2001, residents were more fearful of crime by 0. 17 sd. These fears are not necessarily unfounded, as we find some weak evidence of changes in levels of violence in their parishes. Compared to 2001, a 1-SD increase in refugee presence in 2011 is associated with a 1. 26 percentage point increase in likelihood of a violent event, and in 2016, a 1. 11 percentage point increase. These findings are in line with our predictions for Uganda. As in Global North contexts, we recognize that out-group members can elicit fears and insecurity. It is unclear from the ACLED dataset, however, whether these violent events involve refugees. It is also possible that ACLED underreports violent incidences that are not reported by the media. Recent scholarship on the relationship between refugees and violent conflict finds that hosting generally has null effects on conflict (Zhou and Shaver, 2021); when conflicts do occur, refugees tend to be the victims (Savun and Gineste, 2019). Nevertheless, by 2020, like with Afrobarometer respondents reporting feeling more safe, these fears of crime reverse with a negative effect of- 0. 13 sd. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As we theorize, the positive externalities for local communities (in the form of improved service delivery), generated by Uganda ’ s integrative approach to hosting refugees, generally balance out these fears, which do not generate a backlash against refugees and inclusive migration policies. Improving basic services is of the utmost importance to host communities in developing contexts regardless of who is providing them (Sacks, 2012). All the results reported here use our main specification of the Nearest + 20km exposure measure (which takes into account not only the nearest settlements, but also all settlements within 20km) and the subset of parishes that are within 150km of a settlement. In SI Section S4, we present the regression tables for all of the results shown in Figures 5 and 6, along with their robustness specifications. These tables display the effects across all three measures of exposure (Nearest, Nearest + 20km, Nearest + 50km) and across the various radius cutoffs for parishes (within 100km, 150km, 200km, and all parishes). These results demonstrate that our main results are robust across specifications. In additional robustness checks, we also conduct formal sensitivity analyses and address concerns about multiple hypothesis testing by adjusting for the false discovery rate and showing Benjamini-Hochberg-adjusted p-values in SI Sections S5 and S6. An alternative explanation is that our results are driven by a change in the composition of host citizens. It is possible that the positive effects we observe are due to the internal migration of Ugandans: those who move to refugee settlement areas may be positively disposed toward refugees, while those who leave are more likely to be anti-migrants. While there is no data to test 23 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "this possibility directly, in SI Section S7 we use DHS survey data to demonstrate that rates of both in- and out-migration are no different in refugee-hosting districts vs. neighboring districts that do not host refugees. 6 Policy and Program Implications In this section, we summarize the paper ’ s findings and discuss the implications for policies and programs. Using a DiD research design and a newly constructed geocoded panel dataset of core service provision — the locations of health centers and schools as well as the quality of roads — we find that host communities near refugee settlements in Uganda experience positive externalities. Our findings with respect to service provision are consistent across three key domains and are robust to alternative measures of proximity, and to different samples based on distance to settlements, which increases the confidence in our results. Using individual-level surveys, we find little evidence that proximity to refugees causes a back- lash within host communities against them or related policies. These results are consistent with findings from other contexts such as Jordan (Ferguson et al., 2021) and the DRC (Pham et al., 2021). While we cannot directly assess this argument, we maintain that positive spillovers in the form of service delivery improvements likely help reduce tensions between refugees and host com- munities, and thereby contribute to social cohesion. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9821 Rates of intimate partner violence vary widely across regions. Evidence suggests that some of this variation can be attributed to exposure to armed conflict. This study exploits variation in the timing and location of conflict events related to the war in Mali to examine the effect of conflict on intimate partner violence and some women ’ s empowerment outcomes. The study used data from the Demographic and Health Survey spatially linked to con­flict data from the Armed Conflict Location and Events Database. Wartime conflict increases the prevalence of women ’ s experiences of intimate partner violence. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It also increases women ’ s household decision making autonomy but decreases women ’ s ability to decide how their earnings are deployed. The results imply that to be successful, pro­grams to mitigate these adverse effects of conflict on women need to be context specific and rely on data-driven evidence from situations of conflict whenever possible. Policy makers are called to design programs that address harmful gender norms and intimate partner violence at the individual / household and community levels, especially for women residing in areas with high-intensity conflict. Measurement of women ’ s empowerment should consistently include sev­eral domains of women ’ s lives to gauge progress in voice and agency, financial autonomy, and violence reduction. This paper is a product of the Gender Global Theme. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at uekhator @ worldbank. org, jkelly @ hsph. harvard. edu, Amalia. h. rubin @ gmail. com, and darango @ worldbank. org. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 This research contributes to the emerging literature examining the relationship between armed conflict and IPV, analyzing the effect of conflict in Mali on women ’ s experience of various forms of IPV and their agency within the household. We employ a quasi-experimental method as it is possible that unobserved characteristics affect both conflict and socioeconomic factors, making it difficult to isolate the effect of armed conflict using other approaches. The effect of armed conflict on IPV may be context specific. Therefore, evidence using quasi experimental methods where the data makes it possible to isolate the effect of armed conflict on IPV outcomes is important to strengthen the emerging empirical literature, and can inform evidence-based long and short-term policies to prioritize assistance in conflict affected settings. Since 2012, Mali has been faced with a complex political, security, and humanitarian crisis which began when groups of armed Tuaregs — a semi-nomadic ethnic minority in northern Mali — joined forces with extremist groups to declare the establishment of the independent state of “ Azawad. ” As the conflict progressed, jihadist groups took control of the rebellion and, after a successful coup d ’ état by national soldiers, international armed forces from France, neighboring countries, and the United Nations intervened. Despite an initial peace deal in June 2013 and a ceasefire in 2015, the conflict has continued (Lamarche, 2019). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Before this unrest, Mali was recognized as a country with a peaceful democracy. Data gathered for conflict data sets support this reality. The Armed Conflict Location and Events Database (ACLED) shows an average of 16 conflict events reported between 2006 and 2010 compared to 278 events reported in 2011 alone (figure 1). The conflict also led to increased displacement, with over 227, 000 newly forcibly displaced people in 2012 (Internal Displacement Monitoring Center, n. d). High levels of gender inequality pre-date the conflict in Mali. Mali ranked 158 of 162 countries in the most recent Gender Inequality Index (GII), little different from its rank of 143 of 146 countries in 2012 when the conflict began. According to the World Bank Gender Data portal, women ’ s economic participation has declined since 2000 in all industries except services, and women ’ s unemployment has increased. In contrast, women ’ s involvement in politics and decision making has increased since the war began. During the war (2012-2015), the number of seats held by women in parliament decreased. In 2020, political participation of women surpassed pre-war levels (World Bank, n. d). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We use available data from the Demographic and Health Survey (DHS) in a period before and during Mali ’ s conflict and spatially link the DHS data to conflict data from ACLED. Trends in the ACLED data allow for the clear distinction between periods and locations of high conflict and relatively little or no conflict. This provides an opportunity to exploit the variation in the timing and location of wartime conflict in Mali and isolate the effect of wartime conflict on the study outcomes. This study adds to existing quantitative studies using quasi experimental methods to examine the effect of armed conflict on various human development outcomes (Ekhator- Mobayode and Asfaw 2019; Chukwuma and Ekhator-Mobayode 2019). It builds on Ekhator- Mobayode et al. (2020), which uses a similar method to isolate the effect of the Boko Haram (BH) insurgency in Nigeria on women ’ s experiences of IPV, which finds increased experiences of IPV in BH affected areas after controlling for individual, partner, household, and country specific characteristics. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 Figure 1: ACLED Conflict Events in Mali, 2007 – 2018 Source: Authors own calculation using data from ACLED and Mali shape files from the Database of Global Administrative Areas – GADM. 2. Methodology 2. 1 Data and sample construction The study sample is drawn from the 2006 and 2018 Mali Demographic and Health Surveys (DHS)- both of which are nationally representative household surveys that provides data on population, health, and nutrition for women aged 15-49 in Mali. Both surveys include information on the location of the interview and its GPS coordinates as well as a Domestic Violence (DV) module asking women about their experiences of IPV. The 2006 Mali DHS provides data for the period before protracted conflict in Mali (referred to as “ peacetime ” hereafter). The 2018 Mali DHS provides data for the period during protracted conflict in Mali (referred to as “ wartime ” hereafter). Exposure to wartime conflict is measured using conflict events reported by ACLED. ACLED collects real-time data on the locations, dates, actors, fatalities, and types of all reported political violence and protest events across various countries. These events are recorded by date and type whether they generate fatalities or otherwise. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We spatially link observations from the 2006 and 2018 Mali DHS to events recorded in the ACLED for Mali using the GPS coordinates provided in both the DHS and ACLED. Although anecdotal reports suggest that the current crisis in Mali began in 2012, we consider all events after the period of peace in our study, i. e., we consider all events between 2007 to 2018. We identify 1, 926 events during this period with over 95 percent of these occurring between 2012 and 2018. 2. 2 Measuring Exposure to Conflict Studies on micro level outcomes exploiting the heterogeneity within country have measured conflict exposure as residence in an administrative area with high intensity of conflict or residence within a target distance of conflict events, i. e., a conflict buffer zone. Ekhator-Mobayode et al. (2020) measure exposure to conflict as residence within 10km of any conflict event in the period of interest. We refine this definition and posit that events within the conflict radius do not have the Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 same weights – closer events are likely to have greater impacts than events further away and so should have greater weights. Hence, we geographically discount the events using an exponential decay function as in Bozolli (2011). This strategy applies a lesser weight the further away a conflict event is from an individual / household. The sum of events within the 10km buffer is thus given as follows2: 𝑆𝑆𝑆𝑆𝑆𝑆 𝑜𝑜𝑜𝑜 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔ℎ𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 = ∑ 𝑒𝑒 − 𝜋𝜋 (𝑑𝑑 (𝐴𝐴𝑖𝑖, ℎ)) 𝑋𝑋 𝑥𝑥 = 1 (1) Where 𝑑𝑑 (𝐴𝐴𝑖𝑖, ℎ) is defined as the square of the distance (d) in kilometers between the household (h) and each of the ACLED events (𝐴𝐴𝑖𝑖) and 𝜋𝜋 is the distance-discount factor. We estimate the exponential function in equation (1) using nonlinear least squares and calculate the predicted values for events within 10km for each woman in the DHS. These predicted values are then summed to determine the sum of geographically discounted events. Exposure to wartime conflict in Mali is then defined as residence within at least 1 geographically discounted ACLED event within 10km during wartime. Applying this definition of exposure, we find that about 36 percent of the women in our study sample were exposed to wartime conflict. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These are the women living in the DHS clusters affected by wartime conflict in Mali and make up the treatment group in our study (see figure 2). Figure 2: Wartime conflict occurring within 10km of communities surveyed by the Mali Demographic and Health Survey 2 We check that the results are robustness to changes in the definition of the distance away from conflict events by exploiting other distance definitions- 20km to 50km. This is especially important since the GPS coordinates for randomly selected respondents in the DHS are displaced to protect the confidentiality of respondents. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 Table 2: Rates of Intimate Partner Violence (IPV) in Mali in peace and wartime, percent Affected Area Non-Affected Area Variable Peacetime (A) Wartime (B) Difference (C = B-A) Peacetime (D) Wartime (E) Difference (F = E-D) N (Total) Past year physical IPV 13 17 4 * * 23 18- 5 * * * 9 N 1503 556 2835 875 5769 Past year sexual IPV 3 8 5 * * * 5 10 5 * * * 6 N 1502 556 2835 875 5768 Past year emotional IPV 9 32 23 * * * 13 29 16 * * * 16 N 1503 556 2835 875 5769 Past year physical or sexual IPV 15 21 6 * * * 24 22- 2 21 N 1503 556 2835 875 5769 Past year physical, sexual or emotional IPV 18 37 19 * * * 28 35 7 * * * 28 N 1503 556 2835 875 5769 Source: Authors own calculation using data from the 2006 and 2018 Mali Demographic and Health Survey Note: Affected Area defined as DHS clusters exposed to any geographically discounted event within 10km between 2007 and 2018. * * * Difference in means is significant at the 1 % level. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 Figure 3: Peace vs. wartime trends- IPV Figure 4: Peace vs. wartime trends – controlling behavior, and decision making Second, we estimate equation (1) for IPV, decision making and controlling behavior using the sample of women in peacetime only, i. e., using only the 2006 DHS for which eligible women were interviewed between April and December 2006. The results are presented in tables 8 and 9. We assume that women who were interviewed in the first half of the survey period (between April and July) make up the placebo sample in peacetime, while those interviewed in the second half of the survey period (between August and December) make up the placebo sample in wartime. We find no spurious significant coefficient. Although some of coefficients of women ’ s experience of Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "22 5. Discussion and Conclusion This study exploits variation in timing and location of conflict events during the ongoing civil war in Mali to identify the impact of conflict on women ’ s experience of IPV and decision-making autonomy. Two DHS surveys- one conducted before the conflict and one conducted during the ongoing violence- were spatially linked to conflict data. The difference-in-difference approach allows for matching of women in the two areas to isolate conflict as a driver of increased violence using a rigorous quasi-experimental research design. This approach represents one of the first quantitative, population-based assessments of how the ongoing instability may affect Malian women in key domains of violence and personal decision-making autonomy. These findings highlight the fact that women living in conflict-affected areas experienced notable and significant increases in all forms of IPV, and combined measures of IPV. In the non-conflict- affected areas, women faced lower levels of physical IPV and no change in the combined measure of physical and sexual IPV. Women in non-conflict affected areas in Mali still experienced increased emotional and sexual IPV. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The fact that men may be less present or engaged within the home also highlights the stark findings related to IPV; even while some households may experience changes in composition and certain power dynamics, domestic violence has still increased in conflict-affected areas. Given this, it is important to consider that improvements in autonomy at the household level alone may not indicate an increase overall gender equality in conflict-affected areas – as starkly highlighted by the results related to IPV. This is particularly salient given the post-conflict context in Mali. The country continues to rank at the bottom of the Gender Equality Index, 158th of 162 countries. Malian women continue to lack legal protections around mobility, employment, pay, GBV and marriage (Trumbic et al, 2020). Mali also continues to show high prevalence rates of IPV and failure to pass a draft GBV law drafted sin 2017 that has received strong opposition from the High Islamic Council. Lack of equality in these other areas, coupled with the findings of this paper speak to the need to undertake research that examines women ’ s experiences holistically when examining their safety and empowerment. Further research could support the understanding Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9914 The paper examines the patterns of economic integration of refugees in Switzerland, a country with a long tradition of hosting refugees, a top-receiving host in Europe, and a prominent example of a multicultural society. It relies on a unique longitudinal dataset consisting of administrative records and social security data for the universe of refu­gees in Switzerland over 1998 – 2018. This data is used to reconstruct the individual-level trajectories of refugees and to follow them since arrival over the life-cycle. The study documents the patterns of labor-market integration, and highlights the heterogeneity by gender and age at arrival. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "An open empirical question remains on the relative importance of the three initial conditions (i. e., unemployment, co-ethnic enclaves and attitudes) when examined together. Moreover, we expect the effect of the individual factors to vary in the short, medium and long run over the 20 years examined. In the next section we empirically investigate these issues, and examine whether these assumptions are supported by the empirical evidence. 4 Research Design 4. 1 Data and Descriptive Statistics In our analysis we construct a unique longitudinal dataset which covers the universe of refugees and migrants in Switzerland over 1998-2018. It allows us to follow refugees over the life-cycle for 20 years, and allows us to follow them even after they change residence permit and status. To construct this longitudinal dataset we are combining three administrative datasets. The first dataset, that covers all asylum seekers is AUPER (Automatisierte Personen Registratursystem) which is provided by the State Secretariat for Migration. The data includes information about the residence permit, year of arrival, country of origin, canton of allocation and socio-demographic characteristics. Once an asylum seeker has obtained a residence permit other than permit N (asylum seekers) or F (provisionally admitted foreigners), he or she is registered in ZAR (Zentrales Ausländerregister). This longitudinal dataset is also coming from the SEM. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The ZAR dataset includes all foreigners residing in Switzerland and contains similar information than AUPER. We are combining these two datasets with the yearly population census data that includes ZAR and AUPER starting from 2010. By combining these three datasets we can follow refugees and migrants over 1997-2018 and natives over 2010-2018. We define as refugees, all foreign-born individuals that went through an asylum process, and as migrants all other foreign-born individuals. Natives are defined as individuals born in Switzerland. In the final sample, we are drawing a random sample of 6 % of the native population. For the main outcome variables used in the descriptive analysis, we are first adding Swiss social security data provided by the Federal Compensation Office. This data collects information about every Swiss resident that contributed to old age provision (i. e., the old-age and survivor ’ s insurance OASI or AVS in French). We know the size and nature of the contribution made by individuals (from paid work, independent work, voluntary contribution or other kinds). This data is available from 1998-2018. Our main outcome variables measuring economic integration include employment, earnings and self- employment, and are constructed from the social security data. An individual is defined as employed if he or she contributed to old age provision from salaried or independent work. Earnings are defined as the sum of all positive contributions made from salaried and independent work in a year. Lastly, we 10 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For our indicator of attitudes toward migration and asylum, we use the share of votes in each canton that express a preference for a more restrictive regulation of migration and asylum. A first challenge is that each vote concerns a different project. Therefore, it is difficult to carry out comparisons between votes over time. We solve this problem by standardizing each vote outcome: what matters for our indicator is each canton ’ s relative position at each moment in time. An important implication of this standardization is that our indicator cannot measure the change over time in average attitudes (at the country level). This is, however, not a problem for our empirical analysis, since we use year fixed effects in all our regressions. 20 We use the voting data to create our yearly indicator in the following way. When there is more than one vote within a year, we take the average of these vote outcomes. Then we use a simple method to fill in the missing values: for each missing value, we take the simple average of the two observations that are closest in time. 21 Figure 4 shows the resulting data for all 26 cantons over the period 1998 – 2018. Standardized vote outcomes are shown as black dots, whereas the filled-in data appears in red. Several patterns are clearly visible. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Research on long-term migration has established positive effects on host countries and shown that these effects are larger or faster if migrants are permitted to integrate eco- nomically (Abramitzky and Boustan 2022). The features of PEP facilitate causal identification of its effects. First, the program was in- troduced unexpectedly, thereby isolating anticipatory decisions or ex-ante behavioral re- sponses. Unknown to both migrants and government officials, ex-post eligibility for the program was based solely on prior registration in a nationwide census of irregular forced mi- grants, the Registro Administrativo de Migrantes Venezolanos (RAMV for its Spanish acronym), that was administered between April and June of 2018. According to the government of- ficials who designed RAMV, the census was implemented to count the number of irregu- lar Venezuelan forced migrants in Colombia and was not intended to precede or lead to a regularization program. However, in August 2018, Colombia ’ s president unexpectedly 3 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Since forced migrants are a hard- to-reach population, we constructed the sampling frame for the survey using the RAMV census, referrals from other forced migrants, and databases of local migrant organizations. 2 The survey data enabled us to examine the impact of PEP on three groups of outcomes: socioeconomic and health well-being, access to rights and services, and labor market con- sequences. This paper concentrates on the first dimension — socioeconomic and health well- being — while the latter two allow us to discern possible mechanisms. Each dimension in- cludes a series of individual outcomes and a summary index. The survey took place between October 2020 and February 2021. This analysis thus provides a picture of PEP ’ s short-term effects two years after its enactment. Despite the advantages for causal identification produced by the circumstances of PEP ’ s rollout, registration in RAMV and PEP was voluntary, so self-selection could potentially confound the identification of effects. For this reason, our empirical analysis follows a fuzzy regression discontinuity methodology that compares forced migrants who arrived before 2Importantly, as shown in the analysis, migrants in these three data sources were otherwise similar across socioeconomic characteristics in Venezuela and in Colombia before the program was launched. 4 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "criteria related to education, sector of occupation, or job sponsorship. To be eligible to ap- ply for PEP, Venezuelan forced migrants only needed to: (i) have previously registered in RAMV; (ii) reside in Colombia by August 2018, when the PEP decree was issued; (iii) have a valid Venezuelan ID or other proof of Venezuelan citizenship; and (iv) have no criminal record or deportation order. PEP processing was free and migrants had to submit applica- tions online. According to official records, 442, 462 Venezuelan forced migrants registered in RAMV, and 64 percent of them (281, 307 individuals) applied for PEP. The RAMV registry was implemented in 441 of the 1, 122 municipalities in Colombia, including those with the highest number of Venezuelan migrants. The RAMV census was advertised on social media, in local newspapers, and through local organizations to support forced migrants. III DATA We estimated PEP ’ s impacts using data from the first wave of the Venezuelan Refugees Panel Survey (VenRePS) that was administered to 2, 232 households of forced migrants in Colombia. This section describes the sampling frame, data collection process, and outcomes measured by VenRePS. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "conomic characteristics. The data suggests that both groups were comparable and that those referred by forced migrant organizations were not more vulnerable before migration. Of 15 characteristics analyzed, only the time of settlement in Colombia was statistically different between groups. While this difference is mechanical (because RAMV migrants migrated earlier in general and likely referred other forced migrants who migrated around the same time), it is also small (less than one month). Moreover, in Figure C. 1, we show that date of arrival was uncorrelated with an index constructed with baseline socioeconomic character- istics of migrants in our sample during our period of analysis. We address concerns related to biases introduced by the characteristics of migrants sam- pled through different sources by estimating the local effects for RAMV and non-RAMV migrants who migrated around the RAMV cutoff date. First, we checked the internal valid- ity of this empirical strategy by showing that RAMV and non-RAMV migrants who arrived around the cutoff date were comparable based on a rich set of baseline observables (Table 1). Second, we checked for the comparability of RAMV and non-RAMV referrals from or- ganizations and the comparability of RAMV and non-RAMV referrals from other migrants (Tables B. 2 – B. 3). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "All the exercises confirm the internal validity of the empirical exercise as the vast majority of tests point to no statistical significant differences between groups. III. B Survey and data collection The survey was administered over the telephone between October 2020 and January 2021. Originally, we planned in-person data collection but shifted to a telephone mode because of the Covid-19 pandemic. To ensure the quality of the responses during phone interviews, the overall survey and some specific modules were shortened, and key modules (including labor and health ones) were administered only to the household head and partner. Absent a partner, these modules were administered to another adult member randomly selected from the household roster. The questionnaire had five main modules. The first posed standard sociodemographic ques- tions to all household members. The second module elicited information on the registration 13 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "process for the RAMV census and PEP, including whether each member had PEP (in any version), its issue date, perceived benefits, and reasons why they had registered in RAMV and PEP or had not. Next, the questionnaire included a labor module following the design of the Colombian Labor Force Survey (Gran Encuesta Integrada de Hogares) to make it com- parable to existing administrative data on monthly and weekly income; this module also collected data on labor history in Venezuela and Colombia. Fourth, the survey included a module on health and access to healthcare that included the EQ-5D-3L, a standardized scale used to assess health across different dimensions, including physical and mental health, via a Likert scale. 9 The final module offered information at household level on these dimen- sions: (i) migration, (ii) integration into Colombian society and connections with migrant networks, (iii) prosocial preferences, (iv) housing, and (v) expenditure and remittances. The qualitative findings informed the survey design and data collection protocols. First, dur- ing the focus groups, forced migrants reported that although Venezuelans and Colombians both speak Spanish, there are important differences in everyday words and terms that make it difficult for Venezuelans to understand information from local authorities and NGOs. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For this reason, Venezuelans reviewed the survey to ensure appropriate language usage. Sec- ond, forced migrants also reported high levels of mistrust because they fear deportation and are often targeted by scams and misinformation via text and social media. To build trust and enhance participation, all surveys were administered by Venezuelan enumerators, many of them forced migrants themselves. Furthermore, Venezuelan migrant organizations dissem- inated information on the objectives and scope of the survey. On average, the survey was administered over an average of one hour and 40 minutes, and respondents received an incentive of 27, 000 Colombian pesos (about $ USD 9) for partici- pating. As most forced migrants are excluded from the financial system, it was hard to deliver the incentives during data collection. For this reason, different delivery options ex- 9The questionnaire has been adapted to different settings including Colombia and Venezuela, and it has demonstrated appropriate psychometric properties and validity. The Spanish-language version adapted to the Venezuelan population was administered to elicit severe symptoms of anxiety and depression. 14 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "isted, including cellphone credit, supermarket vouchers, and electronic transfers. Appendix D discusses VenRePS and the data collection procedures in more detail. III. C Outcomes The analysis of PEP ’ s impact focuses on three groups of outcomes: the socioeconomic well- being of forced migrants (herein “ migrants ”), their access to rights and services, and their labor market outcomes. The first dimension, well-being, is the focus of this article, while the latter two delve into potential mechanisms. Each dimension includes the individual outcomes described below and an index estimated following Kling et al. (2007) to summarize each dimension. Specifically, the three dimensions of outcomes are: (i) socioeconomic well-being encom- passes consumption, income, and employment; (ii) access to rights and services captures effective access to PEP ’ s direct benefits and services that are not available to migrants with- out it; 10 and (iii) labor market outcomes include holding a formal job, hours worked, reser- vation wage, job satisfaction (measured as the inverse of the desire to find a different job), and self-employment. We defined these outcomes and dimensions of interest following a preanalysis plan reg- istered before data collection (see Ib ´ a ˜ nez et al. 2020). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These were revised from the original version to make the analysis more intuitive concerning PEP ’ s impacts on migrant well-being and whether PEP improved access to rights and services and labor market outcomes, which are two potential mechanisms. 11 10These include registration in Sisb ´ en, the proxy means-testing system, and access to subsidized healthcare, financial products, and government transfers. 11In the preanalysis plan, families of outcomes included: (i) mechanical outcomes, which correspond to the set of outcomes on rights and services and formal employment; (ii) main outcomes, which correspond to socioeconomic well-being; and (iii) secondary outcomes, which correspond to the larger set of labor market outcomes. The preanalysis plan also included a set of outcomes that captured integration, social preferences, and resilience to the Covid-19 pandemic. The analysis on the impacts of the PEP program on integration and social preferences are not reported as we do not identify changes in any of these outcomes. Finally, the impacts of the PEP program on Covid-19 resilience are analyzed separately in Urbina et al. (2023). 15 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "III. D Descriptive statistics Table 2 reports descriptive statistics on the summary indices and individual outcomes. The data in the table is stratified between RAMV and non-RAMV migrants to describe the differ- ences in well-being, access to rights and services, and labor market outcomes between these two groups, the latter being ineligible for PEP. This table indicates that RAMV migrants were better off at the time of data collection across several dimensions of interest, with statistically significant and meaningful differences in all summary indices and in 11 out of 12 individual outcomes. First, RAMV migrants had higher levels of socioeconomic well-being — including higher income and consumption — and a higher likelihood of being employed. Second, RAMV migrants also had more access to rights and services, with large differences across all outcomes. While this points to the ef- fectiveness of PEP, access to rights and services is far from complete. For instance, at the time of the survey, 50 percent of RAMV migrants did not have access to Sisb ´ en, 77 percent did not have access to subsidized healthcare, and 76 percent had been unable to access the financial system. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The data thus suggests the existence of other barriers, including weak institutional capacities; lack of information among migrants, civil servants, and service providers; and discriminatory practices, all of which accord with our qualitative findings. Finally, the data also substantiates more positive labor market outcomes including a higher reservation wage, higher job satisfaction, and a lower likelihood of self-employment. To summarize, Table 2 highlights meaningful and statistically significant differences of 0. 57 sd in the socioeconomic well-being index; 3. 45 sd in the access to rights and services index; and 1. 20 sd in the labor market outcomes index. IV EMPIRICAL STRATEGY IV. A Threats to validity Despite the meaningful differences between RAMV and non-RAMV migrants, the descrip- tive analysis of the previous section cannot be taken to portray PEP ’ s causal effects on mi- 16 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "istry was open between April 6 and June 8 of 2018, meaning that migrants who arrived in Colombia after June 8 could not register in RAMV and thus were ineligible for PEP. Further- more, the RDD takes advantage of the fact that PEP was enacted unexpectedly, was available to all migrants registered in RAMV, and was not paired with other eligibility requirements or policies, which enabled us to rule out behavioral and anticipatory effects as well as simul- taneous treatments that have precluded the analysis of similar programs. Specifically, the fuzzy RDD compares eligible and ineligible migrants on each side of the RAMV cutoff date under the following two-stage specification: 1 [PEPi = 1] = β1 + β21 [Ti < ¯ T] + β3f (di) + θ ′ Xij + γ ′ Zj + φ + ϵij (1) Yij = α0 + α1 \\1 [PEPi = 1] + α3f (di) + ω ′ Xij + Ψ ′ Zj + φ + µij (2) Equation 1 models the likelihood of receiving PEP based on whether Venezuelans migrated to Colombia before the RAMV registry closed, while equation 2 models the effects on the outcomes of interest Yij as a function of the predicted likelihood of having PEP. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "official PEP decree declared otherwise. This pattern is likely due to administrative and bu- reaucratic loopholes that may have let non-RAMV migrants apply for PEP. Importantly, we know these discrepancies were not due to recall error regarding the migration date because we compared the reported arrival dates in our survey with those reported on PEP applica- tions to migration authorities; in 98. 2 percent of cases, they were the same. Moreover, results from the qualitative survey suggest that the arrival date was extremely salient for migrants, marking as it did the end of one life and the start of another. Finally, these discrepancies are also not due to misinformation or misreporting by migrants without PEP since we requested proof of PEP registration for anyone who reported applying for PEP. For completeness, our main results include the full sample depicted in Figure 3. Robustness tests show the results are remarkably robust (both in magnitude and statistical significance) when the observations of these “ defiers ” are dropped (see Figure F. 1 and Tables F. 1 – F. 3). Figure 3 also plots gray bars that illustrate the density of migrants who arrived in Colom- bia each week. Visual inspection of the figure indicates no discontinuity in the number of individuals who arrived in Colombia before or after June 8, 2018. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Further, the McCrary test rejects the existence of any discontinuity in the density of the sample or manipulation by in- dividuals (p-value = 0. 96). This is expected because when RAMV opened, it was not intended to regularize migrants, and there were no public discussions, announcements, or expecta- tions in this regard. Moreover, the data from the survey indicates that only 0. 5 percent of respondents reported migrating to register in RAMV. Panel B in the figure illustrates the discontinuity in the probability of treatment, estimated as the average treatment take-up in each bin. This figure illustrates the discontinuity using a linear polynomial to confirm the existence of a large, robust discontinuity in the probability of treatment around June 8, 2018. At each point, the figure illustrates the mean probability of treatment in each bin and its 95 percent confidence intervals. Figure G illustrates the discontinuity fitting a quadratic polynomial. Both figures illustrate the existence of a large discontinuity in the likelihood of applying for PEP around June 8, 2018. 20 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "IV. D Validity of the local continuity assumption Table 1 examines whether migrants who migrated just before and after the RAMV cutoff date were similar across a range of individual and household characteristics. For this pur- pose, a sharp RDD model was estimated with a set of pre-migration and pre-RAMV controls used in the RDD as the outcome variables. Only one out of 22 estimated coefficients is sta- tistically significant for the robust RDD estimator. The conventional, bias-corrected, and robust estimators, illustrated in Figure G. 2, further confirm the validity of the local continu- ity assumption. Moreover, Tables B. 2 – B. 3 report the same exercise but restrict the sample of non-RAMV migrants obtained through referrals or refugee organizations. The data in both tables confirms that the local continuity assumption holds regardless of the sample of non-RAMV migrants. Finally, we present robust evidence that the socioeconomic characteristics of migrants are uncorrelated with their arrival date during our period of analysis. For this purpose, we first regress the arrival date on a rich set of baseline socioeconomic characteristics before the pro- gram onset (and the RAMV registration). The results show that the covariates are not jointly statistically significant (Table C. 1). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A visual inspection of the four figures highlights sizeable differences in the indices of socioe- conomic well-being and access to rights and services between migrants who arrived before June 8, 2018 and could register in RAMV and be eligible for PEP, and those who arrived later and could not. It also illustrates positive effects of the program on labor market outcomes, although the evidence is noisier for this index relative to the other two. The sections below detail the main results and multiple robustness tests. V. A Socioeconomic well-being Table 4 reports estimates of PEP ’ s impact on migrants ’ socioeconomic well-being. Column (1) reports the estimated coefficient for the summary index, while Columns (2) – (4) report coefficients for the individual outcomes in this dimension: consumption per capita, labor income, and employment. 13 For each estimated coefficient, the table includes the estimated standard error and the FDR q-value that adjusts for multiple hypothesis testing. The results in Table 4 indicate PEP had positive and substantial effects on migrants ’ socioe- conomic well-being, represented by a positive impact of 1. 2 sd on the summary index. When the index is unpacked, the results point to statistically significant and economically mean- ingful effects across the three individual outcomes. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "or another financial product. Moreover, Column (5) indicates that the likelihood of receiving government transfers is 0. 22 higher for migrants who arrived before the RAMV closed and were therefore eligible for PEP. To provide a “ visual ” confirmation of the results, Figure H. 2 includes the RD plots for individual outcomes in this dimension. The figures highlight the sizeable discontinuities for all outcomes except for government transfers, which follows a downward-sloping linear trend according to the arrival date in Colombia. All the above effects are substantial considering that access across all outcomes is close to zero for ineligible migrants (as reported in the second-to-last row of Table 5) and that these are short-run effects that emerged less than two years after PEP ’ s introduction. This means the Colombian government was able to expand social protection services in a short period of time to serve Venezuelan migrants, although this occurred with some limitations from both the supply and demand sides as discussed during the descriptive analysis. The qualitative findings enable us to understand the different ways in which improved ac- cess to rights and services helps to explain PEP ’ s positive effect on migrants ’ well-being in addition to the direct effects on income and employment. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ity of employment, including an increase in the rate of labor formality and a reduction in self-employment, rather than effects on the intensive margin such as fewer hours worked, job satisfaction, and reservation wages. V. C. 1 Are the results driven by higher formalization rates? The following analysis provides descriptive evidence to discern whether job formalization is associated with more positive labor market outcomes and if it can explain PEP ’ s positive effect on migrants ’ socioeconomic well-being. Table J. 1 reports mean differences in outcomes between RAMV migrants with PEP who have formal and informal jobs. Overall, migrants with formal jobs have higher socioeco- nomic well-being (measured by the summary index), consumption per capita, and income, as observed in Panel A. Likewise, the data in Panel B indicates lower access to subsidized healthcare and to government transfers, which is consistent with job formalization and less socioeconomic vulnerability. Finally, the data in Panel C highlights that migrants with for- mal jobs have jobs of better quality (as summarized by the index), are more satisfied with their jobs, and are less likely to be self-employed. These results are suggestive of the way in which PEP improved migrants ’ well-being and so- cioeconomic prospects by enabling access to formal and quality jobs. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "VII Figures Figure 1. Registry and Program Rollout: RAMV Census Registration, PEP Application, and Data Collection 2017 2018 2019 2020 2021 SAMPLE April 6, 2018 RAMV registry starts June 8, 2018 RAMV registry ends July 25, 2018 Residency permit (Permiso Especial de Permanencia – PEP) for irregular refugees registered in the RAMV is announced August 2, 2018 PEP program starts December 21, 2018 PEP program ends October 2020 Survey collection starts February 2021 Survey collection ends 442, 462 refugees registered in 395 municipalities in Colombia (35 % of the territory) Around 281, 307 people received the PEP document 38 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 2. Sample and Distribution of Venezuelans in Colombia Notes: The left-hand panel of the figure illustrates in shades the number of Venezuelans registered in the RAMV census; the red circles depict the surveys carried out per municipality. The right-hand panel illustrates the number of Venezuelans per municipality reported in the 2018 Colombian census, a proxy of the overall distribution of migrants in the country. The correlation between the sample and the 2018 Colombian census registry is 0. 93. 39 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "D VenRePS Survey The survey was administered in two steps to ensure targeting of the forced migrants who fulfilled the characteristics laid out in the sampling frame, and to define (with respondents) the best time to call and administer the questionnaire. First, forced migrants received a text that introduced the survey team from IPA, described the broad objectives of the research project and the monetary incentive for participation, and mentioned that the team would call them in the next few days to conduct the survey. The text also included a link to the project ’ s website that had more detailed information. A few days later, the survey was administered by phone. 16 First, a short screening mod- ule was administered to verify the respondent ’ s eligibility and to obtain informed consent. RAMV migrants were asked if they or other family members had registered in the RAMV census and whether they had PEP. Non-RAMV migrants were asked for their migration date because the targeted migrants had arrived in Colombia between January 2017 and Decem- ber 2018, were older than 18 years, did not have a different PEP, and did not have a valid passport. 17 Following the screening, the survey was administered with a focus on the family head, part- ner, or another adult member of the family. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The original questionnaire was adjusted and trimmed because of the challenges posed by phone surveys. We decided not to collect data for the entire household roster, and collected the labor module only for the respondent and one other member of the nuclear family. 18 In total, we collected information from 3, 455 Venezuelan families living in Colombia. This sample included families with some members who were Colombian, either from birth or 16The call was rescheduled when the respondent was not available. When ineligible respondents were called, the team included them in a raffle for 50, 000 COP (approximately $ USD 18). 17As discussed above, PEP was also awarded in previous waves to forced migrants who entered Colombia using a passport and therefore had regular migratory status. By asking if respondents had a Venezuelan passport, the team ensured the exclusion of other PEP holders who were typically wealthier. 18The nuclear family includes the household head, partner, children, parents, parents-in-law, daughters-in- law, and sons-in-law. 57 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "because of nationalization. 19 To guarantee that the results were not confounded by access to the labor market and other services by these Colombians, the sample was stratified to exclude families with a Colombian citizen 10 years of age or older, and with a member who held PEP from a different wave. The structure of the survey is explained below: 1. Screening Module: the screening was designed to be done in a first call to determine the family ’ s eligibility for the survey. The screening and the survey were to be an- swered by any adult in the nuclear family of the person who was originally contacted. The person who answered the survey became the main respondent and would be the only one to provide information for themselves. In turn, they had to answer the survey, from a third-person perspective, for all other family members. In the screening, the main respondent was asked for their age, place of birth, Venezue- lan ID number, current city of residence, whether they had a Venezuelan passport, and if they had registered in the RAMV census in 2018. If the contact came from the sam- pling frame of irregular migrants, they were asked if they had PEP and for the date of their arrival in Colombia. For the census sample, this information was available. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Only families whose main respondent arrived in Colombia from January 2017 to December 2018 were eligible. 2. Household Roster Module: the main respondent had to answer sociodemographic, educational, and PEP-related questions for every member of the nuclear family: • Sociodemographic: age, relation to household head, citizenship (Colombian and / or Venezuelan) and proof of citizenship, gender, civil status, date of arrival in Colom- bia, date when they became part of the family, and cities of birth and residence in Venezuela. • Education: maximum level of education before migration, current level of educa- tion and enrollment, degree validation in Colombia, reasons why their degree is invalid, and whether they have lost a job because the degree is invalid in Colom- bia. • PEP: whether they have PEP, date of PEP issue, reason why they do not have PEP, perceived benefits of having PEP, renewal information on PEP (the PEP had to be renewed every two years), and whether they registered in the RAMV cen- sus. They were also asked about last week ’ s and last month ’ s income, healthcare regime, and expected length of stay in Colombia. 3. Labor Module: the main respondent and a second household member of working age 19In the 1980s and 1990s, large numbers of Colombians migrated to Venezuela to escape the socioeconomic crisis, conflict, and drug-related violence. Many of them have since returned to Colombia. Although they too could be considered forced migrants, they still hold Colombian nationality and thus can access the labor market and public services without PEP. 58 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We also asked them if they knew of people who had returned to Venezuela and why they did so. • Health and healthcare: general health, children ’ s immunization schedule, fertility and pregnancy-related questions, mental health (the EQ-5D-3L, a mental health scale that has been validated for Colombia, was collected), and Covid-19 related questions. • Food insecurity: if the family had ever been without food in Colombia, how many days of the previous week they had protein in at least one meal, and with what frequency a family member had to skip a meal before migrating, before the Covid- 19 crisis began, and in the previous month. • Integration into society: how much they felt part of Colombian society and their neighborhood, if they had Colombian friends, if they were part of a migrants ’ organization, and if they had ever felt discriminated against, in what context, and how frequently. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We also asked if they had access to official services such as SISBEN (the vulnerability assessment system), cash transfer programs, and if they had ever filed a police report, for what reason, and if not, what kept them from doing so. • Prosocial behavior: how much they agreed or disagreed with the following state- ments: (i) you can trust Colombians / Venezuelans, (ii) you can count on Colom- bians / Venezuelans even if you don ’ t know them, (iii) Colombians / Venezuelans want to help me, (iv) you can trust the Colombian government, and (v) the Colom- bian government wants to help me. Half the sample was asked for their opinions on Colombians first and the other half about Venezuelans first to see if the order 59 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 Figure 1 Annual household income of returning refugees and total amount of assistance received from UNHCR, by month of return Source: World Bank estimates based on Voluntary Repatriation Form and Cash Assistance Database (UNHCR). Note: Month zero is May 2017. Observations to the left of 0 show results for households that received $ 350 per returning household member. Observations to the right show side shows results for households that received $ 150 per returning household member. This paper studies the effect of the increased cash assistance on returning Afghan migrants. It studies whether and how, in a context of challenging security, economic, and labor market conditions, increasing the cash assistance affected households ’ consumption patterns, investment in long-term assets, and welfare. The main questions are (a) whether Afghan refugees who returned between July 2016 and March 2017 and received the reintegration allowance of $ 350 per returnee are better off than those who received the reintegration allowance of $ 150 per returnee and (b) if so, whether the higher cash allowance had a measurable impact on beneficiary livelihoods in the medium term (after about one and a half years after return). Literature Review Humanitarian agencies have increasingly switched from in-kind to cash assistance. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In a second paper evaluating the same UCT program, the authors find significant impacts (7. 8 percentage points) on enrollment of children 12 and older but no impacts on younger children. The finding that cash transfers had no impact on the enrollment of children under 12 could be related to the fact that primary (but not secondary) education is free in Kenya. The literature on the effectiveness of assisting return migrants to Afghanistan includes an evaluation of a shelter assistance program implemented by UNHCR. UNHCR provided post-return shelter assistance to Afghans between 2009 and 2011. Loschmann, Parsons, and Siegel (2005) use a multidimensional poverty index comprising four dimensions: economic welfare, health, education, and basic services. To address selection bias, they use a propensity score matching approach. They estimate that the shelter assistance 2 The cash transfer programs studied were implemented in Honduras, Indonesia, Morocco, Mexico (two programs), Nicaragua, and the Philippines. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 Ownership of Legal Documentation Post-Return Ownership of tazkira is an important measure of reintegration, as it is needed to engage in civil life, such as enrolling in schools or owning property. We thus examine the trends in tazkira ownership post-return and the relationship between UNHCR reintegration assistance and the likelihood that returning households obtained it upon return. Monitoring data from 19, 600 returnees between 2016 and 2017 show a significant correlation between tazkira ownership and enrollment in school. About half (49 percent) of 2016 returnees who had tazkira for their children enrolled their children in school. Among the 7 out of 10 children without tazkira, only a third were enrolled in school 16 months after return to Afghanistan. Households in the treatment group were more likely to have tazkira for all household members than households in the control group (76 percent versus 60 percent) (figure 10). Figure 10 Relationship between amount of reintegration assistance and likelihood of receiving tazkira (legal document), by date of return Note: Month zero is May 2017. Observations to the left of 0 show results for households that received $ 350 per returning household member. Observations to the right show side shows results for households that received $ 150 per returning household member. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For example, in her typology of political blame management strategies, McGraw (1991) features ‘ horizontal diffusion of responsibility ’, whereby political leaders avoid conflict in the face of a decision gone awry by arguing that their decision was a joint product of a group of individuals. Thompson (1980) discusses the blame-mitigation strategy of emphasizing ‘ collective responsibility ’. Shared decision making between doctors and patients is also highlighted as a tool for reducing decisional conflict — and hence, malpractice lawsuits — in the medical literature (Hoffmann et al. 2014, Kremer et al. 2007). Our paper builds on existing work by examining the relationship between joint decision making and physical, sexual and emotional IPV across 12 Sub-Saharan African countries, using nationally representative Demographic and Health Surveys (DHS) for 31, 243 couples. We find a strong and significant relationship between joint decision making and all three types of IPV. Compared to joint decision making, sole decision making by the husband is associated with a 3. 3 percentage point (20. 6 percent) increase in Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 avoid electoral consequences. Conflict resolution theory has similarly highlighted the importance of joint decision making, typically analyzed as a process of negotiation (Filley 1975, Zartman 1977). Management and organizational science has stressed the importance of managers employing joint decision making, typically to prevent workplace frustration boiling over into conflict (Greenhalgh and Chapman 1995). Lastly, the medical literature has explicitly advanced ‘ Shared Decision Making ’ (SDM) as a risk management tool for doctors seeking to avoid potential malpractice lawsuits. This literature highlights how a lack of shared decision making leads to decisional conflict, with risk managers encouraging joint decision making between doctors and patients to enhance a practice ’ s legal protection (Hoffmann et al. 2014, Kremer et al. 2007). In what follows, we explore the empirical relevance of this multidisciplinary concept — joint decision making as a conflict-reducing strategy — for women ’ s experience of intimate-partner violence in Sub- Saharan Africa. 3. Data and Methodology 3. 1 Data The DHS is a nationally representative population-based household survey that has been conducted since 1984. The DHS includes data on family planning, maternal and childcare, gender, fertility, and nutrition and has been collected in over 90 countries. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Our sample consists of married couples in 12 Sub-Saharan African countries covered by the DHS for which both husband and wife answered the question “ who usually makes decisions about making major household purchases? ” The question is asked about large purchases overall and is not obtained by aggregating different survey questions on decision making over individual asset purchases. It was introduced in the questionnaire administered to husbands in 2004. The last two phases of the DHS thus include a consistent question on decision making for both women and men in married couples over making large household purchases. Response options were (a) respondent, (b) husband / wife, (c) respondent and husband / wife jointly, (d) someone else, (e) other. In these 12 countries, women were also asked questions about their experience with different forms of violence through the Domestic Violence Module. This module asks eligible women whether they ever experienced emotional, physical, or sexual violence perpetrated by their husband. 1 The answer options are (a) never, (b) often (c) sometimes (d) yes, but not in the last 12 months or (e) yes, but currently widowed / divorced / separated. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 households where both they and their husbands report that decisions are made jointly also underreporting incidences of IPV. 2 We use the last available survey round for each of the 12 countries where the decision making question was administered to couples and the domestic violence module was administered to married women. Hence, our data set covers the years 2010-2016. Restricting to non-missing observations results in a sample of 31, 243. 3 3. 2 Empirical Strategy The objective of this paper is to understand the importance of joint decision making and agreement between couples over decision making on the incidence of IPV. Thus, the empirical strategy developed throughout this paper consists of two main parts. In the first part, we use the wife ’ s response on who makes the decisions regarding major household purchases to see how it is correlated with incidence of violence. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "26 Buller et al. (2016) conducted a mixed methods study in Ecuador, combining secondary analysis from a field experiment on the impact of a transfer program on IPV with in-depth interviews and focus group discussions with male and female beneficiaries. The qualitative component aimed to better understand the mechanisms underlying the quantitative results, which showed substantial reductions in physical and sexual violence among beneficiaries of the cash and in-kind food transfer program. These qualitative interviews revealed a similar finding to Arugay et al. (forthcoming). Specifically, a core feature of women ’ s reported joint decision making was asking their spouse or partner for input into a decision, so that they would not be blamed if something went wrong. Similarly, an analysis of qualitative data from eight projects in Africa and Asia focused on understanding women ’ s empowerment finds that joint decision making can be empowering for women. In particular, in focus groups in Ghana, women stressed the importance of family harmony and in the individual interviews, “ women indicated that they want more input on decisions, but do not want full responsibility for decisions in case they go wrong (Meinzen-Dick et al, 2019, p. 20). The qualitative evidence provides one interpretation of our empirical results. Couples who agree that they jointly make decisions may also be sharing responsibility for these decisions. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1 Introduction The political and economic crisis in Venezuela has given rise to a massive exodus, esti- mated at 4. 3 million people (as of the end of 2019). Many Venezuelans sought refuge in neighboring countries. It is estimated that 1. 5 million Venezuelans had settled in Colombia by the end of 2019, and over 2 million elsewhere in Latin America. Naturally, Venezuelan migrants have sought employment in the host countries but, because of their recent arrival and their transient status, our knowledge of their working conditions is scant. Our paper aims to fill this gap focusing on Ecuador, where almost 400, 000 Venezuelans have settled and many more have transited through the country on the way to other destinations. More specifically, we provide the first analysis of the labor-market conditions of Venezuelan migrants in Ecuador, based on a new nationally representative survey of this population that also includes information on the Ecuadorans living in the same localities. The survey (known by its Spanish acronym EPEC) was promoted by the World Bank and the government of Ecuador and implemented during the summer of 2019. The design and scope of the survey are unique in the context of the countries across Latin America hosting Venezuelan migrants. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "high informality. Some of these studies also find that women and young workers were disproportionately affected. More recently, researchers have shifted the focus to other dimensions of the effects of Syrian refugee inflows into Turkey. Altindag et al. (2020) argue that refugee inflows had a positive impact on firm production, both in terms of the volume of production and the introduction of new product varieties. Akgunduz and Torun (2020) examine other channels by which Turkish workers and firms accommodated the inflows of Syrian refugees. The authors find that skilled native workers increased their specialization in complex tasks, moving away from manual tasks, and domestic companies took advantage of the increased abundance of labor by reducing capital intensity. As shown in earlier studies, both mechanisms contribute to mitigate the effects of immigration on the wages of the receiving country (Lewis (2005), Peri and Sparber (2009), Gonzalez and Ortega (2011) and Dustmann and Glitz (2015)). In the last few years, some researchers have begun to analyze the economic effects of the exodus of Venezuelans on the surrounding countries but progress has been slow due to the difficulty of analyzing Venezuelan migrants equipped solely with government- provided data or the standard labor force surveys. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The structure of the paper is as follows. Section 2 describes the survey. Section 3 presents summary statistics. Section 4 describes the main characteristics of Venezuelan migrants, using natives in the same local area as benchmark. Section 5 analyzes the occupations of Venezuelans in Ecuador and in the home country. Section 6 collects our policy analysis and Section 7 concludes. 2 Data: The EPEC Survey The goal of the EPEC survey was to collect household-level information on the popula- tion of recent Venezuelan migrants to Ecuador along with the native population in the receiving communities. The survey focuses on Venezuela-born individuals that arrived in Ecuador after January 2016 and over-samples this population. 2 In June and July 2019, almost 1, 900 households were interviewed (in person) and provided information on 6, 425 individuals. The survey is representative of the population of recent Venezuelan migrants in Ecuador and of the native population residing in the areas where Venezuelan migrants are found. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Besides the typical socio-demographic and labor market information of labor force surveys, the questionnaire also includes questions regarding pre-migration employment, the gateways of entry into the country, health, children ’ s schooling, access to internet and remittances, among other topics. 3 In the final sample (of 6, 425 individuals), 10 % of respondents resided in sectors with low density of Venezuelan migrants (below 5 % of the sector ’ s population), 43 % in medium-density sectors (with a density ranging between 5 % and 15 %), and 47 % from high-density sectors (with a density of Venezuelan migrants above 15 % of the sector ’ s population). The final sample contained 6, 425 individuals. Among these, 1, 715 were born in Venezuela and considered recent migrants. 2EPEC stands for Encuesta a Personas en Movilidad Humana y en Comunidades Receptoras en Ecuador, which can be translated as Survey of Migrants and Receiving Communities in Ecuador. 3To be specific, Ecuador is administratively organized into 221 cantons. To implement the survey, the cantons were subdivided into sectors classified according to the density of Venezuelans estimated using mobile phone data (described in detail in Olivieri et al. (2020)). About 190 sectors were randomly selected and, within those, households were also selected for the interview at random. By virtue of the sampling strategy, the survey is nationally representative of the target populations and the three strata of sectors defined by low, medium and high density of Venezuelan migrants. 4 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It will also be useful to compare the main labor market outcomes (employment, informality, temporality, work hours, earnings and hourly wages) between Venezuelan migrants and Ecuadoran workers with the same (nominal) education levels. This com- parison also allows us to examine whether the outcomes of highly educated Venezuelans are relatively better or worse than the outcomes for the less educated ones. 13 Table 4 reports this information. As before, we restrict the sample to the working- age population (age 15-70). The first column summarizes the values for Ecuadoran natives, column 2 reports the data for the Venezuelan migrants, and column 3 presents the ratio of the value in column 2 relative to column 1. Several points are worth noting. As noted earlier, Venezuelan workers are much less likely to have low education levels (26 percentage points) and much more likely to have a college degree (25 percentage points) than the average native. In terms of employment rates, we observe that among 13 We have not succeeded in obtaining a systematic comparison of the quality of education in Venezuela and Ecuador. The best assessment is based on an analysis by Juan Maragall (Inter-American Development Bank) based on a 2009 PISA study conducted in the state of Miranda in Venezuela. The data show that students in Venezuela have lower reading and math levels than the average for Latin America. More specifically, the gap is estimated to be 13 percentage points for public schools and 6 percentage points for private schools. Given that Ecuador ’ s scores are in line with the average for Latin America, these data suggest that the quality of the Venezuelan education system is somewhat below the Ecuadoran counterpart. However, we also note that many Venezuelan migrants were schooled prior to the recent deterioration of educational institutions in Venezuela and that migrants are typically pos- itively selected in regards to their origin populations. As a result, it seems reasonable to assume that the educational credentials of Venezuelans are comparable to those of Ecuadoran workers. 8 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As a result, migration may entail negative effects on the productivity and wages of migrants, often reflected in terms of occupa- tional downgrading (Jasso et al. (2000)). For instance, our survey indicates that 72 % of the Venezuelans who migrated to Ecuador report that their skills were used more productively in their jobs back in Venezuela. On the other hand, international migration typically entails moving from low to high productivity countries, which can lead to increases in wages and productivity (Clemens (2011)). Migration driven by natural or man-made disasters, such as the Venezuelan exodus, is much more likely to be of the South-South type and, as a result, this type of productivity gain may be less relevant. An important feature of the EPEC survey is that it contains retrospective information on the last job held by migrants prior to leaving Venezuela. The goal in the remainder of this section is to compare the changes in occupation experienced by Venezuelan migrants 14 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We focus on two policy actions: legalization (in the sense of providing legal work and residence permits to all migrants) and measures aimed at improving the quality of employment for highly educated Venezuelan migrants. 6. 1 The Effects of Legalization Based on the EPEC survey, approximately 90 % of Venezuelan migrants in Ecuador lack legal status, in the sense that they are not authorized to work. This has important consequences for Venezuelan workers. As shown in Section 4. 3, lack of legal status is associated with a higher likelihood of informal employment (column 5 in Table 7). Inspired by Clemens et al. (2018), the goal of this section is to use the estimates of these effects to simulate the consequences of providing legal work permits to Venezuelans on the quality of their employment (measured by informality) and, through this channel, on their productivity (measured by wages). The relevant information is collected in Table 10. We classify Venezuelan workers that lack legal status on the basis of their education level (primary, secondary or tertiary) and the quality of their employment (formal or informal). Based on the data in column 3 15 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6. 2 Employment Upgrading for Skilled Migrants Our empirical analysis has shown that over-qualification is a pervasive problem among Venezuelan migrants in Ecuador. As a result, their productivity and wage levels are well below those of Ecuadorans in the same region of residence and with the same education level. This is a waste of productive skills that lowers GDP and shifts the burden of the labor-market adjustment toward low-skill natives (as documented in Olivieri et al. (2020)). A natural policy to consider is to facilitate the access of highly educated Venezuelans to skilled jobs. The main goal of this section is to conduct a simulation of the potential effects of such a policy. As a first step, we begin by analyzing some relevant informa- tion in the EPEC survey. In practical terms, a barrier that may be preventing skilled Venezuelan workers from applying to skilled jobs may be the lack of transferability of education credentials or even the lack of credentials altogether. We estimate that around 51, 000 Venezuelan migrants in Ecuador (completed) college education prior to migration. Among these, 76 % report having received a college degree certification. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "count of existing contributions and the differing capacities and resources among States. ” It formally “ intends to provide a basis for predictable and equitable burden- and responsibility-sharing among all United Nations Member States, together with other rel- evant stakeholders as appropriate. ” Underpinning the global debate on responsibility- sharing is the assumption that “ the grant of asylum may place unduly heavy burdens on certain countries ” (UN General Assembly 2018), typically countries neighboring a conflict area. In this perspective, the number of refugees a country is to host is simply a function of its geography. This paper examines empirically the proposition that the hosting of refugees falls disproportionately on neighboring countries, which in most cases are in the developing world. To do so, we use data on worldwide bilateral refugee stocks compiled by UNHCR to examine the spatial distribution of refugees and its evolution over time. Our period of analysis is 1987-2017. Our main findings can be summarized as follows. While refugees still remain over- whelmingly in a country neighboring their country of origin, the past decades have seen a trend towards greater geographic diffusion. We begin by showing that the global pop- ulation of refugees has been increasingly dispersed across host countries, as captured by a falling Herfindahl index of host-country shares. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 Data Our analysis is primarily based on data on refugee stocks compiled by the UNHCR. UNHCR annually publishes the data on refugee stocks by source and destination coun- try pair. The term “ refugee ” includes both refugees and asylum seekers. Under the 1951 Convention Relating to the Status of Refugees and the 1967 Protocol, a refugee is defined as “ a person who has been forced to flee his or her country because of per- secution for reasons of race, religion, nationality, political opinion or membership in a particular social group ” (Art 1. A. 2.). The UNHCR Population Statistics Reference database contains data for the period 1951 – 2017 (released on June 19, 2019). The data set compiles annual stocks of refugees and asylum seekers at the source-destination level for 197 destination and 223 source countries. The ultimate source of the data is the authorities of each receiving country. While in principle there are observations going back to 1951, coverage prior to the late 1980s is too sparse to be usable. Thus, our analysis covers the period 1987-2017. Overall, we have 112, 522 non-zero observations for bilateral stocks over the period 1987-2017. Since the data are not recorded at the individual level, we cannot reliably calculate refugee flows. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "due to armed conflict but for more idiosyncratic reasons. We check robustness of this approach in two ways: (i) using all of the refugee stock observations available in the data set, and (ii) computing refugee flows as the positive time differences in refugee stocks from year to year (setting negative time differences to zero). The results are robust to these two alternatives. 5 10 15 20 Number of refugees (million) 1987 1992 1997 2002 2007 2012 2017 Year Figure 1: Global refugee population, 1987-2017 Note: This figure plots the global stock of refugees. The data on bilateral distance and contiguity come from CEPII. The distance vari- able refers to the great circle distance between the most populated cities of each country in the pair. The contiguity indicator is equal to one if the two countries share a land border. Figure 1 charts the global refugee population over time. The sharp increase in the number of refugees over the past decade is evident. Such refugee movements can have significant impacts on the destination countries. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Panel (b) plots the share of refugees that find themselves in a country that shares a land border with their country of origin. At the beginning of the sample 95 percent of refugees were in a country contiguous with their home country. That share fell to 77 percent in the the period 2012-2017. Another manifestation of the increasing geographical reach of refugees is the greater number of destination countries to which they go. Similar to the global destination Herfindahl index above, we construct a source-specific Herfindahl index that captures whether refugees from a given source country diversify their destinations over time. That is, for a specific source country s and year t, the source Herfindahl index is 9 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Impact vs. diffusion over time We next address the question of whether the trends documented in Figures 3-4 are due to the initial decision of refugees of where to flee from their homeland, or subsequent movements to third countries. Note that we cannot answer this question definitively without individual-level panel data. In our data, we do not observe the country from which a refugee entered their current host country, and thus cannot tell whether a given refugee in a given host country came from their homeland, or from yet another host country. Nonetheless, we perform the following exercise. We are working with a set of refugee events defined in Section 2. An event is combination of a source country, a year of onset, and an end year. Thus, we can compute the evolution of all of our outcome variables – distance, contiguous share, Herfindahl, and share in high-income OECD – for each specific event and each year following its onset. We then plot these outcome variables in event time, with year 0 indicating the initial year of the event, up to year 10 of the event. Figure 5 plots the four outcome variables in event time, for events starting in 4 different sub-periods. The main conclusion from this figure is that the differences across 14 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Appendix Figure A3 replicates the analysis using all refugee stocks available in the data, without constraining the sample to refugee events. The results are very similar to the baseline. Taking another approach, Appendix Figure A4 instead uses refugee flows. As argued above, without individual-level data, flows cannot be computed precisely. We build flows by taking annual time differences in stocks by source-destination pair. In some instances, stocks fall over time. Since we do not have confidence that a reduction in stocks represents a return to the home country – as opposed to transition to another host country – we set flows to zero whenever the difference in stocks is negative. As evidenced in the figure, the point estimates of the time effects and their statistical significance are quite similar for flows to the baseline. Fourth, it may be that the destination-specific conditions (such as the global finan- cial crisis) also affect the distance traveled by refugees, or the probability of not going to a contiguous country. To account for this possibility, we net out the time variation in the destination country conditions as follows. In step 1, we project the refugee stocks at the 16 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "source-destination-year level on source-time, destination-time, and source-destination fixed effects in a gravity-like specification: Refugeessdt = δst + δdt + δsd + εsdt. We estimate this equation by Poisson Pseudo-Maximum Likelihood (Eaton, Kortum and Sotelo 2012), pooling countries and years (and thus including observations with zero bilateral stocks). We then construct a destination-adjusted refugee stock by subtracting the destination-time effect from the actual stock: AdjustedRefugeessdt = Refugeessdt − δdt. Then, we compute the average distance traveled, share of refugees going to a contiguous country, the Herfindahl index of destinations, and share in wealthy OECD countries using this adjusted refugee data set instead of the actual data. Appendix Figure A5 re- ports the results. Netting out destination-time effects prior to carrying out the analysis leaves the main results virtually unchanged. 4 Conclusion Our analysis suggests that the assumption underpinning the debate on responsibility- sharing may need to be partly revisited. Countries neighboring a conflict do host a majority of refugees and are hence bearing a disproportionate portion of the respon- sibility for providing asylum to those who are fleeing from violence and oppression. Yet, the share of refugees who move to further-away destinations, including OECD countries, has been growing over time. In other words, responsibilities are increasingly shared across countries. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9533 Refugee camps are believed to represent safe havens for forcibly displaced persons, but studies looking at refugees ’ quality of life in camps are few. This paper explores how Syrian refugees ’ quality of life in camps in Jordan differs from that of Syrian refugees residing outside camps. Using data from the Syrian Refugee and Host Community Survey, the study measures life quality through indicators of subjective life experience and material living conditions. Data are analyzed using advanced statistical methods (difference-in-difference and propensity score matching) to control for selection bias that could skew estimates of causal effects. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 policy relevance, which is that it could show how differences in camp location and administration affect refugees. This paper aims to analyze how the living arrangement (camp vs. out-of-camp) affects Syrian refugees'QOL by studying the case of Syrian refugees hosted in Jordan. Besides its policy relevance, another reason for looking at Syrian refugees in Jordan is availability of data. The Syrian Refugee and Host Community Survey (SRHCS) implemented in 2015 in Jordan allows comparing living conditions of separate samples of out-of-camp and in-camp refugees, as well as a sample of a host population. While Jordan hosts Syrian refugees in two refugee camps, Zaatari and Al Azraq, most refugees live outside camps in urban, peri-urban, and rural areas of the country. How has the decision to live out-of-camp improved their QOL? Does living out- of-camp reduce deprivations and vulnerability of female-headed refugee households? Moreover, does living in either of the two camps affect the refugees'QOL? This paper investigates these issues. Data are analyzed using the difference-in-difference and propensity score matching methods. Combining these methods helps control selection bias and other unobserved variables that could bias estimates of causal effects. Multidimensional indicators are used to measure QOL to capture deprivations that cannot be measured by income indicators alone. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These indicators include life satisfaction, satisfaction with access to services, household assets, risk of overcrowding, income and poverty levels. The results show that living in a camp reduces QOL for refugees. On average, refugees in camps are 36 percent more likely to live below the national abject poverty line, meaning that they find it difficult to meet daily basic needs. They are 37 percent more likely to live in overcrowded shelters. They own fewer household assets than refugees living outside camps (- 2. 85 assets) and are less satisfied with water, electricity, and sewerage access. They also report lower life satisfaction by 0. 76 step in a 10-step Cantril Ladder measurement of life satisfaction. The QOL indicators differ by gender. Female-headed refugee households living in camps were more deprived of material living conditions and subjective quality of life than male-headed households. However, moving out-of-camps tends to benefit them more in terms of poverty reduction. There are also noticeable differences in quality of life between refugees in different camps; indicators imply that refugees living in camps situated closer to the city enjoy higher quality of life. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 The rest of this paper is organized as follows. Section 2 offers some background to the paper, providing context for Syrian refugees and their QOL in Jordan. Section 3 introduces a multidimensional quality-of-life indicator. Section 4 explains the methodology used in this paper, describing the data, identification strategy, and analysis method. Section 5 presents results of the statistical analysis, while section 6 presents conclusions and policy implications. 2. Syrian Refugees in Jordan The war in the Syrian Arab Republic has turned into a decade-long crisis, and refugees from Syria form the largest share of international displaced persons. About 5. 5 million Syrians are registered as refugees in Turkey (65 %), Lebanon (16 %), Jordan (12 %), Iraq (4 %), and other countries (3 %). Many Syrian refugees settle in urban and peri-urban regions in these host countries (World Bank 2020). The number of refugees living in camps has declined since 2017. Many refugees seem to prefer living outside the crowded camps and to escape the precarious living conditions and enjoy the freedom to live with relatives and friends and to find work. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 Most previous studies have measured QOL narrowly by focusing on income, an approach that researchers have often criticized. Diener & Suh (1997) elaborate on the limitations of the income-only QOL approach, include the failure of increased income to guarantee happiness or to reduce several deprivations experienced by the poor. These criticisms paved the way for broader multidimensional QOL assessments (Alkire & Foster, 2011a; Nussbaum & Sen, 1993). Multidimensional QOL assessment is critical in a situation of forced displacement. Refugees may be economically engaged yet have low life satisfaction due to exploitation or multidimensional deprivations in nutrition, health, education, employment, and shelter (Becchetti & Rossetti, 2009; Sand & Gruber, 2018). These deprivations disproportionally affect refugees ’ QOL compared to the host population. The level at which these issues affect refugees living in camps may also differ from how they affect refugees living out of camps. Another issue is that the income indicator alone may not provide reliable information about refugee welfare. For example, refugees may not be truthful about their earnings if they conceive that the purpose of the survey is to plan for refugee assistance or resettlement. Therefore, for policy consideration and proper targeting, the multidimensional QOL indicator is appropriate in understanding refugees'deprivations, whether they live in or out of camps. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Generally, multidimensional measures encompass several indicators, such as income, health, education, living standards, empowerment, quality of work, threat of violence, and housing conditions (OPHI, 2016). However, as a framework for poverty measurement, researchers can measure a person's or a group's QOL using any combination of the indicators that reflect policy needs and priorities (Alkire & Foster, 2011; Robeyns, 2005). As such, different research groups have adopted different sets of indicators. For instance, the Oxford Poverty and Human Development Initiative (OPHI) and the United Nations Development Programme (UNDP) adopted a measure of 10 indicators categorized under the three dimensions of poverty: health, education, and living standards (Alkire et al., 2020). Stiglitz, Sen, & Fitoussi (2009) also provided nine QOL dimensions including material living condition; productive or other main activity; health; education; leisure and social interactions; economic security and physical safety; governance and basic rights; natural and living environments; and overall experience of life. Following the recommendation of Stiglitz, Sen, & Fitoussi (2009) that QOL should be measured comprehensively, linking both subjective and objective conditions, I adopted two dimensions Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 to measure refugee QOL. The first dimension is the overall experience of life, or\"life satisfaction\", and the second is the material living conditions. 3 The\"life satisfaction\"indicator assesses people's subjective well-being. It is popular for use in studies of refugees because it requires respondents to reflect on, and make an overall assessment of, their life happiness, including wealth, security, and hopes for the future.\"Life satisfaction\"tends to be an overall reflective evaluation that uses a Likert response scale of between 0 to 10, where 0 means not satisfied and 10 completely satisfied. However, it is left for the respondent to define what\"satisfaction\"means and the scaling of his / her satisfaction; thus, it is a subjective indicator of well-being.\"Material living condition\"captures households'objective living conditions and opportunities, including material deprivations and housing conditions that directly affect their QOL. Material deprivations refer to the level at which households are able to have the consumption goods and services needed in a society at a given time. Several indicators could measure material deprivations. One typical indicator is the ability or inability for households to meet basic food needs at above the national abject poverty level. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Other measures of deprivations may include counting the number of household assets, such as beds, air conditioners, and cooking utensils. The availability of housing and housing conditions can also be captured by calculating overcrowding and satisfaction with accommodation services, such as water and electricity. 4. Data and Identification Methodology 4. 1. The survey The World Bank Development Economics Data group conducted the Syrian Refugees and Host Communities Survey (SRHCS) in 2015 by surveying registered and unregistered refugees (Krishnan, Munoz, Riva, Sharma, & Vishwanath, 2019). The survey was designed to produce comparable findings on living conditions and quality of life of Syrian refugees and host communities in Jordan, Lebanon, and Kurdistan. The Jordanian survey is designed from the adjusted sample frame of the 2005 Jordanian population. The SRHCS assesses the socio- 3 Note that 3 dimensions — productive or other main activity, health, and education — were the subjects of the work of Ginn (2020). More so, the remaining 4 dimensions — leisure and social interactions, economic security and physical safety, governance and basic rights, and natural and living environments — will be discussed in a follow-up study. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 economic and living conditions of a sample of Syrian refugees living in the Al Azraq and Zaatari camps and refugees and Jordanian citizens living in the surrounding governorates. I retrieved the following data from the survey: characteristics of the refugees'household head, household income per capita, household dwelling, access to services such as electricity and water, assets accumulation, and overall life experience. The survey also has some retrospective information on pre-crisis characteristics, such as the household head's economic status in Syria, household earnings in 2010 before the crisis, household assets in Syria, and the number of years the household has been living in Jordan. The estimations in this paper use this information as control variables. 4. 2. Identification strategy Using an immigration survey to evaluate causal impacts often faces identification threat (Borjas, 2018). In other words, refugees who are surveyed in camps may be different from refugees who are surveyed in the cities, leading to selection bias. I identify three reasons selection bias can arise in this research. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 then the average treatment effect of the associated treatment outcome on the treated (ATT), can be estimated, provided that certain assumptions are not violated. Some of these assumptions include the Conditional Independence Assumption (CIA), which implies that selection is based on pre-treatment characteristics not affected by the treatment. The other assumption is the common support or overlap condition, which implies that individuals with similar pre-treatment characteristics have a positive probability of being both in the treated or control group (Rosenbaum & Rubin, 2006). Becker & Ichino (2002) specified that the average treatment effect on the treated (ATT) of a potential outcome 𝑌𝑌1𝑖𝑖 could be estimated from a population 𝑖𝑖 using the propensity score 𝑝𝑝 (χ𝑖𝑖) 𝐴𝐴𝐴𝐴𝐴𝐴 = 𝐸𝐸 { 𝑌𝑌1𝑖𝑖 − 𝑌𝑌0𝑖𝑖 | 𝐷𝐷𝑖𝑖 = 1} 𝐴𝐴𝐴𝐴𝐴𝐴 = 𝐸𝐸 [𝐸𝐸 { 𝑌𝑌1𝑖𝑖 − 𝑌𝑌0𝑖𝑖 | 𝐷𝐷𝑖𝑖 = 1, 𝑝𝑝 (χ𝑖𝑖)} 𝐴𝐴𝐴𝐴𝐴𝐴 = 𝐸𝐸 [𝐸𝐸 { 𝑌𝑌1𝑖𝑖 | 𝐷𝐷𝑖𝑖 = 1, 𝑝𝑝 (χ𝑖𝑖)} − 𝐸𝐸 { 𝑌𝑌0𝑖𝑖 | 𝐷𝐷𝑖𝑖 = 0, 𝑝𝑝 (χ𝑖𝑖)} | 𝐷𝐷𝑖𝑖 = 1] (3) I followed the recommendation of Caliendo & Kopeinig (2008) and Heckman, Lalonde, & Smith (1999) to ensure that the above assumptions are not violated. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Most refugees started arriving in Jordan in 2010, and the mean year of stay is 3. 5 years. About 50 percent of the surveyed households lived outside of camps. Among the camp residents, 832 households lived in the Zaatari refugee camp, and 359 lived in the Azraq refugee camp. The main dependent variables (the outcomes of interest) are\"life satisfaction\"and the refugees'\"material living conditions\". The life satisfaction question is:\"Think about your overall satisfaction with your life over the last few years: your happiness, wealth, security, hope for the future, etc. For each year (from 2005 to 2020), how do you rate your life on a scale of 1-10, with 1 meaning,'my life could not be worse,'and 10 meaning'my life could not be better'?\"The question, therefore, is a combination of retrospective, present feelings, and projection for the future. The descriptive result (2005 to 2020) is described in the Figures 1- 3; however, I 4 An alternative specification could be to use a fixed-effect model. For consistency, I discuss only the results of the DiD in this main paper but placed the results of the fixed-effect model in the Appendix. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 The current study aims to build on this literature by comparing the links between conflict, forced displacement and IPV in two different conflict-affected settings: Colombia and Liberia. Both countries have faced long-running civil conflict and high levels of societal violence. This paper draws on the availability of Demographic and Health Survey (DHS) data, which provides population-based data on health outcomes in countries around the globe. Unique to Colombia and Liberia, however, is the fact that the DHS collected data on internal displacement in addition to information about exposure to IPV. The 2007 data from Liberia was collected four years post- conflict and can provide insight into the long-term impact of displacement on women. Similarly, 2010 data from Colombia gives insight into displacement and IPV during the ongoing Colombian conflict. The availability of this data for two conflict-affected countries allows for a unique comparative analysis. The analysis of experiences of IPV after conflict exposes the long- term impacts conflict has on the lives of women and calls us to include these experiences in our development programming to support peace and state building. In both countries, conflict has led to high levels of forced displacement. During Liberia ’ s two consecutive civil wars between 1998 and 2003, around 1. 4 million people were forced to flee their homes (Global IDP Project, 2003). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Colombia, a more populous country, has seen roughly 15 percent of its population displaced between 1995 and 2018 (IDMC, 2019). At the same time, both countries have also experienced some success in establishing peace agreements and undertaking subsequent social programs to aid the implementation of the agreements and demobilization efforts. Both Liberia and Colombia also present a unique opportunity to examine the links between IPV and displacement because each contains unique data from the DHS not readily available from other countries- namely, information about displacement as a result of conflict. Below, we examine the literature on the drivers of IPV in conflict and displacement settings, followed by a detailed examination of the drivers of IPV within each country context. Following this, a description of each country context is provided. Background A growing literature examines how violence can spread across time and space, as well as across the population and individual levels (Hudson, 2020; Catani, 2010; IOM, 2012; Kelly, 2017; Otsby, 2016; Saile, 2013). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Browne et al. (2019) found that men who had been excluded from employment or politics due to displacement in Colombia were more likely to perpetrate IPV as a negative coping mechanism for stress and a way to and reassert their masculinity. This marks an important contextual difference between Colombia and Liberia, where, as noted above, women ’ s access to work was seen as a pathway to decrease household stress and, as a result, risk of IPV. While the literature on drivers of IPV in Colombia is larger, the research available in these two contexts has largely been qualitative and relies on relatively limited sample sizes, with few population-based studies on GBV available. Collecting systematic quantitative data on displaced populations can be challenging, particularly in countries currently or recently affected by conflict. In particular, few studies have undertaken multi-country comparisons. A notable exception is Otsby ’ s (2016) paper examining the link between sexual IPV and conflict experiences in 17 countries in Sub-Saharan Africa between 2006 and 2011. Otsby finds that conflict exposure heightens the risk of sexual intimate partner violence across the conflict-affected countries examined in the analysis. The paper calls for future studies to explore how conflict impacts other types of partner violence (Otsby, 2016). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The current study builds on the literature above by drawing on nationally representative, population-based surveys to look at how displacement experience is associated with different forms of IPV (lifetime, past-year and injury-causing IPV) in Colombia and Liberia. Additionally, independent data on the level and location of conflict was merged with individual level data in each country. These unique data sets provide insight into how both types of adversity- conflict and displacement- may impact a woman ’ s risk of IPV. The goal of this work is to highlight the link between IPV and forced displacement and conflict, and to explore the policy implications for state and peace building efforts. This understanding will help direct scarce funding appropriately to create more effective and targeted programs. Colombia Background Colombia has endured civil war, violent conflict, and displacement for over 60 years. Levels and intensity of violence have fluctuated throughout this period between a multitude of groups, including guerrilla groups such as the Revolutionary Armed Forces of Colombia (FARC), the National Liberation Army (ELN) and the Popular Liberation Army (EPL), the government, and paramilitary forces (Palacios, 2006). The historical root cause of the conflict rests in the unequal Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In 2010, the same year the DHS data which we will analyze was published, the government of Juan Manuel Santos took office in Colombia with over 3, 672, 000 internally displaced citizens (Meertens, 2010). Of these IDPs, 75 percent had moved from rural areas, 25 percent from urban areas, and 49 percent were women (UNHCR, 2010). While most IDPs have been displaced from rural to urban areas, violence in larger urban centers has led to substantial intra-urban displacement due to the conflict or violent disputes over territorial control, employment pressures, and gang-related violence (Internal Displacement Monitoring Center, 2013). As of July 2010, the Colombian NGO Instituto de Estudios para el Desarrollo y la Paz estimated 6, 000 armed combatants were active in operations in 29 out of 32 departments. The first year of the Santos presidency experienced both increases in violence and a commitment to beginning peace negotiations (Human Rights Watch, 2011). The economic and social structures in Colombia improved despite the conflict. In 2010, Colombian average income had been steadily increasing since 2002. Between 2000-2010, the government increased investments in citizens as seen through spending on health and education. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Gender Development Index has consistently been above the global average in the last 15 years and the Gender Inequality Index has been on a steady decline since 2000 (UNDP, N. d). However, Colombia data from 2010 indicates high numbers of women working in the informal sector, where more than 50 percent of women are protected by minimal regulations; have few or no benefits; lack voice, social security and decent work conditions; and are vulnerable to low salaries and possible job loss (UNDP, 2019). In addition, and in concert with issues of displacement, Colombia continued to report high levels of violence against women and gaps in gender equality in nationally representative surveys such as the Demographic Health Survey (DHS Statcompiler, 2010; Profamilia, 2010, UNDP, n. d.). In 2010, over one-third of women in Colombia reported ever having experienced physical violence by a partner, and 9 % reported partner sexual violence. However, tolerance for abuse remained low. Only 2 percent of women believed that a husband is justified in beating his wife for at least one reason and 44 percent of ever-married women who experienced any physical or sexual Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 markets, destroyed infrastructure, and an absence of state-provided electricity and piped water until 2006 (International Crisis Group, 2003). Three years after the last peace agreement, only about half of those who had been forcibly displaced returned home. While many former combatants had entered reintegration programs, demobilized army officers and former security personnel continued to stage protests, some violent, throughout the years (Amnesty International, 2007). By the end of 2006, approximately 9 % of Liberians still resided in other countries such as Guinea, Sierra Leone, Côte d ’ Ivoire, Ghana and Nigeria (Amnesty International, 2008). Violence against women, including rape and IPV, was pervasive in post-conflict Liberia, as reported in the DHS conducted five years after the war. In a 2007 survey, over one-third of women in Liberia reported ever having experienced physical violence by a partner, and 11 % reported partner sexual violence (Liberia Institute of Statistics, 2008). Just under half of all women (49 %) reported having experienced some kind of violence (physical, sexual or emotional) from a partner. Sixty percent of women believed that a husband is justified in beating his wife for at least one reason. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Almost half of women surveyed (45 %) indicated that they experienced some kind of physical violence from the age of 15, and one-third of women experienced this abuse in the past 12 months. During this time, other incidents of violent crime such as armed robbery and murder increased as well (Human Rights Watch, 2008). The Human Development Index (HDI) provides a rare insight into Liberia ’ s development pre-, during, and post-conflict. The scale aggregates information about lifespan, education and gross domestic product (GDP) to create a global ranking of countries (Anand & Sen, 1994). In 1970, Liberia scored in the lowest quartile of development. The country ’ s progress was reflected in its HDI scores, which climbed steadily in the 1970s, only to plunge to near the global bottom in 1989 (Klugman, 2010). In addition to high levels of intimate partner violence, data from Liberia indicates that large gaps in gender equality remained post conflict, despite Ellen Johnson Sirleaf ’ s election in 2006 and the appointment of several women to high-level positions in the administration. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A UNESCO report written during the height of Liberia ’ s second civil war reported that Liberia had one of the lowest gender parity scores globally, and girls were disproportionately represented in the number of out-of-school children in Liberia. This disruption in education presaged lifelong consequences for this cohort of women (Kirk, 2003). In 2007, the literacy rate for adult men was 55 % and women was 41 %-- the number lowering to 26 % for rural women (OECD et al., 2009). In 2001 an education law was enacted making primary education free and compulsory, however, as of 2006, primary school enrollment in urban areas was 63. 7 % for girls and 33. 1 % for girls in urban areas (Woodon, 2012). Liberia also scores in the bottom eight countries that reported women having fewer than half of the years of education as men (Klugman, 2010). Five years after the war ended, women ’ s labor force participant was high, with women accounting for 54 % of the labor force. However, women were disproportionally represented in the informal sector (CWIQ, 2007). According to data from the 2017 Women, Peace, and Security Index, Liberia still has a number of gaps to fill particularly related to education, financial inclusion, legal discrimination and intimate partner violence (WPS, 2017). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 Methods Data sources Demographic and Health Surveys (DHS): Individual-Level Data The DHS Program has been collecting data since 1984 in over 90 countries. The surveys examine fertility, family planning, maternal and child health, gender dynamics, HIV / AIDS, malaria, and nutrition. A core standard questionnaire is administered in all countries, with some variation to ensure that questions are culturally appropriate and relevant. This project uses DHS data from 2010 in Colombia and 2007 in Liberia. These surveys were chosen because they represent data collection that has occurred after or during a period of active conflict, and have Geographic Information System (GIS) information about the cluster where the women were sampled. 2 The DHS surveys use a two-stage cluster sampling design that first randomly selects clusters and then randomly selects households within the cluster. Only one woman per household is eligible to take the domestic violence module for privacy concerns. The DHS Women ’ s Questionnaire collects data on women aged 15 to 49 years. Data on interpersonal and partner violence is collected as part of the DHS Domestic Violence (DV) Module, now applied in conjunction with the Women ’ s Individual Questionnaire. 3 Dependent Variables This study examines three outcomes: lifetime IPV, past-year IPV and injury resulting from IPV, each is described below. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Lifetime Intimate Partner Violence The DHS Domestic Violence module uses a modified Conflict Tactics Scale (CTS) to measure IPV, one of the most widely used and reliable measurement tools for IPV (Straus et al, 1990). Strengths of this assessment include the number of opportunities to disclose violent events; detailed information about a range of behaviors; and its widespread use (Hindin et al., 2008). The CTS provides comparable estimates of violence across different settings (Hindin, 2008). Ever-partnered women were asked about a list of eight specific behaviors they may have experienced that would classify as physical or sexual violence. Women answering “ yes ” to any of the items from a to g were classified as having experienced partner physical violence ever by 2 In the DHS data, Global Positioning System (GPS) data for clusters are randomly offset in order to safeguard respondent privacy and confidentiality (Burgert et al., 2013). In urban areas, clusters are displaced by 0 to 2 kilometers, and in rural areas locations are displaced between 0 to 5 kilometers (Perez-Heydrich et al., 2013). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The displacement is checked to ensure that displaced clusters do not leave national or administrative boundaries (thus, a cluster in Colombia would not be displaced in a way that it would move to Peru, for example). Since this displacement does not move clusters across administrative boundaries, it does not impact the current analysis. 3 The number of women pre-selected to take the domestic violence module in each household is established using a matrix known as the Kish grid technique that matches the number of eligible women with a random number generated as part of the household identifier (Kish, 1965). When the interviewer arrives at the first question of the domestic violence module, he or she establishes whether the woman has been pre-selected; if so, the module is administered. Only one woman per household is eligible to take the domestic violence module in order to ensure that others in the house do not know what types of questions were asked. The module is administered only to individuals in a private setting, and an additional consent script is read to the respondent. If privacy cannot be ensured, the domestic violence module is not administered. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Injury from Intimate Partner Violence The 2010 Colombia DHS and the 2007 Liberia DHS asked women “ Did the following ever happen as a result of what your (last) husband / partner did to you: 1) cuts, bruises, or aches; 2) burns, eye injuries, sprains, or dislocations; and 3) deep wounds, broken bones, broken teeth, or any other serious injury. Women who reported “ yes ” to any of the 3 categories were classified as having injury from IPV. Independent Conflict-Intensity Data: District-Level Data Within countries experiencing conflict, there is notable heterogeneity among districts that experience violence. This conflict-affectedness information at the district level can be combined with DHS data at the individual level to examine the links between interpersonal violence and conflict. Women ’ s experiences are nested in districts, which are classified according to whether or not the district has experienced conflict-related events. The multi-level modeling approach described below accounts for the natural clustering of women into these administrative units and acknowledges the hierarchical structure of the data (Kreft and Leeuw, 1998). The district-level covariate acts as a proxy for other important cluster-level characteristics that are not measured. Counting the number of fatalities in administrative units has been used successfully in similar efforts in the past, including in Peru (Gallegos and Gutierrez, 2011) and Liberia (Kelly et al, 2017; Kelly et al, 2018). Independent data sets exist to measure conflict-fatalities in both Colombia and Liberia. For Liberia, Armed Conflict Location and Event Data Project (ACLED) data provides a measure of the extent to which a community has been affected by conflict at the sub-national district level. While ACLED data is not available for Colombia in the years prior to the 2010 DHS survey, Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 there is another data source that provides independent information on conflict events. The Uppsala Conflict Data Program, Conflict Encyclopedia Database (UCDP) provides similar information that spans the time period of interest. In contrast, UCDP data is not available for Liberia- so each country draws on similar but distinct data set to incorporate independent information about conflict intensity. The study data set contains one observation for every woman in the DHS individual recode, i. e., the woman level data and variables showing the annual number of ACLED or UCDP events and fatalities beginning in the year the woman was interviewed up to 10 years preceding the survey. Providing conflict for the 10 years preceding the DHS survey is key to the methodology and helps establish temporality in how conflict may affect IPV outcomes. A 10-year period was chosen because this captured hostilities from both Liberian civil conflicts and allowed the data set to reflect the long-standing impact of the conflict in each country. Each conflict data set used is described below. Uppsala Conflict Data Program (UCDP) Data- Colombia The Uppsala Conflict Data Program (UCDP) Georeferenced Event Dataset is an event data set that disaggregates three types of organized violence: state-based conflict, non-state conflict, and one-sided violence. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The database compiles articles containing information about individuals killed or injured, and triangulates information with reports from non-governmental organizations and the UN, as well as truth commission reports and other local sources of information. For Colombia, UCDP includes conflict data from as far back as 1946, the beginning of La Violencia period as noted above. The total number of conflict related deaths reported in Colombia since 1946 equals 27, 743 as of January 2021. This includes deaths from state-based actors, non-state actors and one-sided violence. None of the 32 departments of Colombia has been spared from conflict related deaths, however the number of deaths varies across Colombia ’ s different regions. Armed Conflict Location and Event Data Project (ACLED)- Liberia ACLED data provide the dates and locations of all political events related to conflict and unrest in over 50 countries. ACLED data provide information on the implicated actors of political events that may occur in the course of civil and communal conflicts, violence against civilians, rioting and protesting. Armed actors may include governments, rebels, militia, organized political groups, ethnic groups, and civilians. ACLED geocodes event data at the first and second administrative boundary levels and provides latitude and longitude coordinates for each event. The database draws on three different types of sources in order to achieve comprehensive reporting: local, regional, national and continental media are reviewed daily; NGO reports are used to ensure reporting occurs in remote or hard-to-access locations; and Africa-focused news reports and analyses are used to supplement previous sources. ACLED states that this methodology achieves the most comprehensive source material currently available for digital conflict event coding (Raleigh et al., 2010). For this project, the ACLED Version 5 database was used to determine the numbers of fatal conflict events. Linking DHS and Conflict Data Using GPS points provided in ACLED and UCDP, and for each cluster of the Demographic and Health Survey (DHS), we temporally and spatially link information on conflict events to Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 The 2007 Liberia DHS asks respondents “ During the war, did you leave your house? ”- a question intended to explore whether respondents moved to a camp, lived in the bush or faced another form of displacement because of conflict. Those respondents who answered “ yes ” to this question were classified as forcibly displaced. It is noteworthy that this question was followed by a question about where the respondent was displaced. Answers included: stayed with relatives or friends inside Liberia; went to a camp; living in the bush; went outside Liberia. However, respondents could choose multiple of these options. For this reason, the first question was chosen as the most simple and accurate measure of displacement. Conflict at the District-Level Previous studies have used number of conflict fatalities as an effective proxy for levels of political instability (Kelly, 2018), since fatalities are the most definitive and violent measure of armed conflict. In addition, since the UCDP and ACLED data sets code conflict events differently, fatality measures were the most comparable between the data sets. For both UCDP and ACLED, the conflict was coded as 1 if women lived in a district with conflict fatalities and 0 if she did not. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Independent Variables The variables that are included in this analysis (Table 2) have been chosen based on the ecological framework for addressing drivers of conflict and post conflict VAWG, as well on those variables that have been found to be significantly associated with IPV in previous analyses (Heise, 2011; Feseha et al., 2012, Swaine et al., 2019). Data on religion is not available from the 2010 Colombia DHS and so this variable was not included in the analysis. Broadly, the covariates of interest include demographic information; household characteristics; risk factors associated with IPV; and women ’ s partners characteristics. Table 2. Model Covariates Women ’ s demographics Age married Education level Marital status (currently versus formerly in a union) Currently working Household Characteristics Wealth quintile Number of children under 5 Urban or rural Women is household head IPV risk factors Number of control issues husband exhibits Wife-beating justified Father beat respondent ’ s mother Woman has decision making autonomy on at least one major decision Partner characteristics Partner education level Partner drinks alcohol or uses drugs Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 Data Analysis All analyses were conducted with Stata / SE 14. 0 (StataCorp LP, College Station, TX). A bivariate model was also used to examine the relationship between the main predictor (fatalities) and each outcome. For the final model, a multilevel approach was used to account for the nested structure of the data, with clustering of women within districts. Model Specification Multilevel logistic regression models were used to quantify the effect of district level conflict on the odds of IPV after sequentially adding blocks of independent variables as described in Table 2. The models included a random intercept for district, to account for the geographic clustering of the sample and systematic differences between districts that would not otherwise be captured in a simple logistic regression. This approach has been used in similar analysis in past research (Kelly et al, 2018; Kelly, 2019). Multilevel Model- Dichotomous Exposure: In the regression equation above, i indexes the district and j indexes the individual. 𝑌𝑌𝑖𝑖𝑖𝑖 is the indicator for whether a woman (j) in district (i) has reported experiencing violence in the last 12 months. β_0 + b0i defines the district level odds of a woman experiencing violence in district i given no conflict holding the individual-level covariates fixed. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This equation expresses the model in the case of a dichotomous measure of conflict. Here, it gives the odds ratio of IPV if the district experienced any conflict compared to the odds of IPV if the district had experienced no conflict. X_ij contains individual, household and partner characteristics summarized in Table 1. As noted before, the independent variables are added to the model in blocks to examine for possible confounding or effect modification with the main association. For all analyses, significance was assessed using an alpha of 0. 05. For each country, a multilevel model assessed the association between IPV and forced displacement. Stepwise model fitting was undertaken to assess how the main association was affected by sequential blocks of variables. The final model results for each country are given in the tables below. Sample Weights In order to account for the complex survey design of the DHS, the survey weights for the DV module were included in all analyses, using the probability weight or pweight option within the gllamm survey command. The probability weight is defined as the inverse probability of the respondent ’ s being included in the sample. The pweight command assumes weights are specified at least two levels in the data. Since the data were not weighted at the district level, the level-1 weight within Stata was specified as 1. The level-2 weights were calculated using the Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Ghana is experiencing its third gold rush, and this paper sheds light on the socioeconomic impacts of this rapid expansion in industrial production. Using a rich data­set consisting of geocoded household data combined with detailed information on gold mining activities, the authors conduct two types of difference-in-differences estimations that provide complementary evidence. The first is a local-level analysis that identifies an economic footprint area very close to a mine, and the second is a district-level analysis that captures the fiscal channel. The results indicate that men are more likely to benefit from direct employment as miners compared to men further away, and that women in mining communities may more likely gain from indirect employment opportunities and earn cash for work. Authors also find that infant mortal­ity rates decrease significantly in mining communities, compared to the evolution in communities further away. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1 Introduction The mining sector in Africa is growing rapidly and is the main recipient of foreign direct investment (World Bank 2011). The welfare effects of this sector are not well understood, although a literature has recently developed around this question. The main contribution of this paper is to shed light on the welfare effects of gold mining in a detailed, in-depth country study of Ghana, a country with a long tradition of gold mining and a recent, large expansion in capital- intensive and industrial-scale production. A second contribution of this paper is to show the importance of decomposing the effects with respect to distance from the mines. Given the spatial heterogeneity of the results, we explore the effects in an individual-level, difference-in-differences analysis by using spatial lag models to allow for nonlinear effects with distance from mine. We also allow for spillovers across districts, in a district-level analysis. We use two complementary geocoded household data sets to analyze outcomes in Ghana: the Demographic and Health Survey (DHS) and the Ghana Living Standard Survey (GLSS), which provide information on a wide range of welfare outcomes. The paper contributes to the growing literature on the local effects of mining. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 only in conjunction with policies for local procurement. Moreover, some of the mining-related papers have focused on mining in an African context, exploring a range of outcomes, including HIV-transmission and sexual risk taking (Corno and de Walque 2012; Wilson 2012), women ’ s empowerment (Benshaul-Tolonen 2018), infant mortality (Benshaul-Tolonen, 2019) and labor market outcomes (Kotsadam and Tolonen 2016). Mining is also associated with more economic activity measured by nightlights (Benshaul-Tolonen, 2019; Mamo et al, 2019). Kotsadam and Tolonen (2016) use DHS data from Africa, and find that mine openings cause women to shift from agriculture to service production and that women become more likely to work for cash and year-round as opposed to seasonally. Continuing this analysis, Benshaul- Tolonen (2018) explores the links between mining and female empowerment in eight gold- producing countries in East and West Africa, including Ghana. Women in gold mining communities have more diversified labor markets opportunities, better access to health care, and are less likely to accept domestic violence. In addition, infant mortality rates decrease with up to 50 % in mining communities, from very high initial levels (Benshaul-Tolonen, 2019). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In a study that focuses exclusively on Ghana, Aragón and Rud (2013) explore the link between pollution from mining and agricultural productivity. The results point toward decreasing agricultural productivity because of environmental pollution and soil degradation, which could have negative welfare effects on households that do not engage in mining activities or in indirectly stimulated sectors. Lower productivity in agriculture could potentially push households to engage in mining-related sectors, in addition to pull factors such as higher wage earnings in the stimulated sectors. We explore the effects of mining activity on employment, earnings, expenditure, and children ’ s health outcomes in local communities and in districts with gold mining. We combine the DHS and GLSS with production data for 17 large-scale gold mines in Ghana. We find that a new large-scale gold mine changes economic outcomes, such as access to employment and cash earnings. In addition, it raises local wages and expenditure on housing and energy. An important welfare indicator in developing countries is infant mortality, and we note a large and significant decrease in mortality rates among young children, at both the local and district levels. 1 We hypothesize that increased access to prenatal care is one of the mechanisms behind the increased survival rate. 1 In the 2010 Ghana population census average district size is 112, 000 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, should ASM respond to large-scale activities, either by increasing or decreasing activity in the close geographic area, we will end up estimating the impact of these sectors jointly. In a later stage, should the opportunity arise, we encourage researchers to try to disentangle the effects of small-scale and large-scale mining. 3 Data To conduct this analysis, we combine different data sources using spatial analysis. The main mining data is a dataset from InterraRMG covering all large-scale mines in Ghana, explained in more detail in section 3. 1. This dataset is linked to survey data from the DHS and GLSS, using spatial information. Geographical coordinates of enumeration areas in GLSS are from Ghana Statistical Services (GSS). 2 Point coordinates (global positioning system [GPS]) for the surveyed DHS clusters3 allow us to match all individuals to one or several mineral mines. We do this in two ways. First, we calculate distance spans from an exact mine location given by its GPS coordinates, and match surveyed individuals to mines. These are concentric circles with radiuses of 10, 20, and 30 kilometers (km), and so on, up to 100 km and beyond. In the baseline analysis where 2 The data was shared by Aragón and Rud (2013) 3 Both the DHS and GLSS enumeration area coordinates have a 1-5 km offset. The DHS clusters have up to 10km displacement in 1 % of the cases. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 we use a cutoff distance of 20 km, we assume there is little economic footprint beyond that distance. Of course, any such distance is arbitrarily chosen, which is why we try different specifications to explore the spatial heterogeneity by varying this distance (using 10 km, 20 km, through 50 km) as well as a spatial lag structure (using 0 to 10 km, 10 to 20 km, through 40 to 50 km distance bins). 4 Second, we collapse the DHS mining data at the district level. 5 The number of districts has changed over time in Ghana, because districts with high population growth have been split into smaller districts. To avoid endogeneity concerns, we use the baseline number of districts that existed at the start of our analysis period, which are 137. Eleven of these districts have industrial mining. Because some mines are close to district boundaries, we additionally test whether there is an effect in neighboring districts. 3. 1 Resource data The Raw Materials Data are from InterraRMG (2013). The data set contains information on past or current industrial mines. All mines have information on annual production volumes, ownership structure, and GPS coordinates on location. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We complete this data with exact geographic location data from MineAtlas (2013), where satellite imagery shows the actual mine boundaries, which allows us to identify and update the center point of each mine. The production data and ownership information are double-checked against the companies ’ annual reports. For Ghana, this exercise results in 17 industrial mines tracked over time. We have annual production levels from 1990 until 2012. As mentioned, Table 1 shows the mining companies active in Ghana during recent decades, with opening and closing years (although some were closed in between, and are not presented in the table). Figure 2 shows the geographic distribution of these mines. Figure 2 Gold mines and DHS clusters in Ghana Panel A Gold mines and 20 km buffer zones Panel B Gold mines, DHS clusters, and 100 km buffer zones 4 The distances are radii from mine center point, and form concentric circles around the mine. 5 The DHS and the GLSS data are representative at the regional level, and not at the district level. Since the regional level is too aggregated, we do the analysis at the district level, but note that the sample may not be representative. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 have a stronger focus on all households ’ members, rather than focusing only on women and young children. In addition, they provide more detailed information on labor market participation, such as exact profession (where, for example, being a miner is a possible outcome), hours worked, and a wage indicator. The data estimate household expenditure and household income. Wages, income, and expenditure can, however, be difficult to measure in economies where nonmonetary compensation for labor and subsistence farming are common practices. 4 Empirical Strategies 4. 1 Individual-level difference-in-differences Time-varying data on production and repeated survey data allow us to use a difference-in- differences approach. 7 However, due to the spatial nature of our data and the fact that some mines are spatially clustered, we use a strategy developed by Benshaul-Tolonen (2018). The difference-in-difference model compares the treatment group (close to mines) before and after the mine opening, while removing the change that happens in the control group (far away from mines) over time under the assumption that such changes reflect underlying temporal variation common to both treatment and control areas. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 The choice of district – rather than cluster – fixed effect is informed by the understanding that meaningful time-invariant factors- such as mining laws, level of development, local political institutions, norms regarding environment, women ’ s participation in the labor market, etc.- that influence exploitation of the mine happens at the district level. Including district fixed effects, we control for various institutional and cultural factors at the district level that are stable over time. Including district fixed effects also ensures that we are not only capturing effects from transfers or the fiscal system as we compare individuals within the same districts. With this method we capture the geographic spillover effects in the vicinity of the mine. Moreover, cluster fixed effects are not possible because of clusters are not repeatedly sampled over time. However, since the estimation is at individual level, all standard errors are clustered at the DHS cluster level. The sample is restricted to individuals living within 100 km of a deposit location (mine), so many parts of Northern Ghana where there are few gold mines are not included in the analysis. The sample restriction is created by using the time-stable continuous distance measure that we calculate from each mine location to each DHS cluster. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This is also the distance measure that we use to create the “ mine ” dummy, which captures whether the cluster lies within 20 km of a known gold deposit. Note that we only consider deposits that have been in production at some point until December 2012. All households are thus within 100 km of one, or several, gold deposits. To ascertain whether there is any gold production in these potential mining sites, we construct an indicator variable active, which takes a value of 1 if there is at least one mine within 100 km that was extracting gold in the year the household was surveyed, and 0 otherwise. While the mine dummy captures some of the special characteristics of mining areas (for example, whether mines tend to open in less urban areas), the active dummy captures long-range spillovers of mining. The treatment effect that we are mostly interested in is captured with the active * mine coefficient. The coefficient for β3 tells us what the effect of being close to an actively producing mine is. Since the inclusion of the three dummies (active, mine, and active * mine) captures the difference between close and far, and before and after mine opening, we have created a difference-in-differences estimator. Panel B of figure 2 shows this strategy in a map, where the small blue circles show the treatment areas, and the 100-km-radius green circles show the geographic areas that constitute the control group. As is common in difference-in-differences analysis, the estimation relies on treatment Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "attempt at distinguishing the currently available methods in statistics and economics as well as incorporating the advances from the former into the latter. This is consistent with similar ongoing efforts in other disciplines that build on the multiple imputation method in statistics to better address their own disciplinary needs. 4 Empirically, we illustrate our method with an application to Jordan, a particularly interesting case for analysis. Not much is known about poverty trends since Jordan ’ s Department of Statistics (DOS) last conducted its Household Expenditure and Income Survey (HEIS) in 2010. In the meantime, this country ’ s economy has experienced several major events such as the introduction of new poverty-reduction policies by the government (e. g., in accordance with its recent Poverty Reduction Strategy), economic reforms (e. g., reducing its petroleum subsidies and implementing a targeted cash transfer), and shocks due to higher energy prices. Socio-political change and unrest in neighboring Syria and Egypt also add further uncertainty to the economy. Given this fast evolving context, policy makers are keenly interested in tracking poverty trends on a more frequent and timely basis. In contrast with the HEIS survey which was last conducted in 2010, DOS administers the Employment-Unemployment Survey, a labor force survey (LFS) with wide geographical coverage, on a quarterly basis. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We exploit the LFS, which does not collect consumption data and has a different design from the HEIS, to fill the missing poverty data problem in Jordan for the years the HEIS is absent. We validate our imputation-based estimates of poverty against those obtained from the actual consumption data (or design-based estimates) for the two years 2008 and 2010 when consumption data are available, before imputing estimates for other years when consumption 4 See, for example, King et al. (2001) and Honaker and King (2010) for examples of adaptation of multiple imputation methods in the field of political science. 6 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "consumption data, its limited sample size means the survey is only representative at highly aggregated administrative levels; conversely, the population census has exactly the opposite strength and weakness, being nationally representative at a far more disaggregated administrative level but offering no consumption data. Applying the estimated model parameters of consumption from a household expenditure survey onto overlapping variables with the census, ELL can predict consumption data into the latter. These data can then be disaggregated to estimate poverty at lower administrative levels than are possible using the household survey alone. This method is sometimes referred to as the “ poverty-mapping ” approach owing to its extensive presentation of poverty estimates in a cartographic format. Kijima and Lanjouw (2003) then apply this method to provide survey-to-survey imputation-based poverty estimates for India. Building on this approach, Stifel and Christiaensen (2007) combine household expenditure survey data with more recent rounds of the Demographic and Health Survey (DHS) in Kenya to impute household consumption into the latter. A more recent paper by Christiaensen et al. (2012) predicts consumption in the second round of a consumption survey using the estimated model parameters from the first round of the same survey for several countries. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "By generating consumption data in the second round that are more consistent with those in the first round, this study indicates that imputation methods can help obviate the need of updating expenditure data with problematic deflators over time. Using seven rounds of household survey data from household wealth based on household assets (Sahn and Stifel, 2000). This method ’ s greatest strength is perhaps that it is straightforward to implement in most contexts where information on household assets is available; however, the non-monetary nature of asset indices renders poverty estimates more difficult to interpret. Another branch of the (statistics and economics) literatures constructs weights to adjust estimates in the presence of missing data instead; for studies that follow this approach, see, e. g., Tarozzi (2007) and Bethlehem, Cobben, and Schouten (2011). 8 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Let xj be a vector of characteristics that are commonly observed between the two surveys, where j indicates the type of survey that can either be the same household expenditure survey or another survey. 9 Subject to data availability, these characteristics can include household variables such as the household head ’ s age, sex, education, ethnicity, religion, language, occupation, household assets or incomes, and other community or regional variables. Occupation-related characteristics can generally include whether household heads work, the share of household members that work, the type of work that household members participate in, as well as context-specific variables such as the share of female household members that participate in the labor force. Regional characteristics related to macroeconomic trends such as (un) employment rates or commodity prices can also be included if such data are available. As discussed below, these variables would play a critical role in capturing the changes in estimated poverty rates. Household consumption (or income) data exist in one survey but are missing in the other survey, thus without loss of generality, let survey 1 and survey 2 respectively represent the survey with and without household consumption data, and y1 represent household consumption in survey 1. More generally, these two surveys can be either in the same period or in different periods. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We focus in this section on the latter case, before discussing the more complicated cases of combining surveys of different designs in the same period and in different periods in the next section. 10 9 More generally, j can indicate any type of relevant surveys that collect household data sufficiently relevant for imputation purposes such as labor force surveys, demographic and health or youth surveys. To make notation less cluttered, we suppress the subscript for each household in the following equations. 10 Theoretically, it is trivial to consider the change in poverty estimates when we impute from one survey to another in the same time period; this change is zero by construction. But practically, this imputation exercise is relevant for validation purposes when imputation is done using two surveys with different design. We will come back to discuss this later. 12 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "using official CPI deflators to obtain a comparable poverty line in 2008 and its associated poverty rate of 19. 5 percent. Macroeconomic trends shown in Figure 1 appear to corroborate the poverty decline as shown by the household consumption data, since the downward sloping poverty trend is consistent with that of growth in real GDP per capita. The period between 2002 and 2007 sees rapid growth, which, however, slows down in the subsequent period between 2008 and 2010. Real GDP per capita grew by 3 percent and poverty was estimated to fall by about 5 percentage points in this latter period. While poverty could be tracked between 2008 and 2010 with the consumption data from the HEIS, no consumption data exists after 2010 that can be used to monitor poverty trends. Projections show per capita GDP growth to be weak, but this alone does not say much about poverty trends. The recent subsidy reforms and the associated cash transfer could well impact poverty, as could the various economic stresses including a continued weak labor market, increased energy prices, and a large influx of war refugees from Syria. 18 Against the background of infrequent collection of consumption data, the country ’ s economically uncertain atmosphere provides an even stronger impetus for policy makers to track poverty with alternative methods like imputation-based estimates. III. 2. 2. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Data Description for the HEIS We use the most recent two rounds of Jordan ’ s Household Expenditure and Income Survey (HEIS) in 2008 and 2010, which are the nationally representative surveys used to produce official poverty statistics. The HEIS has been implemented nine times since 1966, and every other year between 2006 and 2010. In addition to household expenditures, it collects data on 18 According to the UNHCR (http: / / data. unhcr. org / syrianrefugees / country. php? id = 107), in July 2012 there were about 29, 000 registered Syrian refugees in Jordan; a year later the number of refugees rose to about 115, 000, and by August 2014 the number further increased to slightly more than 600, 000, which is roughly a tenth of Jordan ’ s population. 20 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "other household characteristics including demographics, employment, assets, and incomes. This survey ’ s sampling frame comes from the 2004 Population and Housing Census and is divided into 89 strata (or sub-districts). The survey is typically administered over a 12 month period and follows a two-stage sampling design where census enumeration areas serve as primary sampling units (PSUs). For the 2010 survey round, 1, 736 PSUs were selected in the first stage out of a total of 13, 027 PSUs for the whole country using a systematic probability proportionate to size (PPS) sampling method. Within each selected PSU or cluster, 8 households were randomly selected at the second stage. The 2008 and 2010 rounds of the HEIS collected consumption data respectively for 10, 961 and 11, 223 households. The questionnaire design of these two survey rounds remains essentially the same. III. 2. 3. Estimation Results We start first with checking on Assumptions 1 and 2 before discussing estimation results. Since the 2008 and 2010 rounds of the HEIS share the same sampling frame based on the 2004 Population and Housing Census, and their questionnaire design remains almost identical, Assumption 1 for a similar survey design is satisfied. Assumption 2 is usually assumed and can only be checked if data for both survey rounds are available. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Estimation Procedures We thus propose the following estimation procedures to predict the poverty rate in period 2, where consumption data are missing but the relevant characteristics x are available. Step 1: Check that Assumption 1 is satisfied, which involves verifying that key features of the two surveys such as the sampling frames and the questionnaires are (essentially) the same. If data from earlier survey rounds are available, check that the regression model that is used for imputation satisfies Assumption 2 on these data. 23 The difference is that we use a random effects probit model to estimate equations (1) and (2) instead of the linear random effects model, that is, the estimating equation is)'() (j j j x j j y P ε µ β + + Φ =, with j = 1, 2, where (.) Φ is the cumulative normal distribution. 26 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For better estimation results, it may also be useful to transform (some) variables in both surveys to normality before standardizing them. We come back to discuss this more in the next section. IV. 2. Updating Poverty Estimates with Different Survey Sources IV. 2. 1. Data Description for the LFS The Employment Unemployment Survey (LFS) is the official source of employment and unemployment data in Jordan. While it shares certain similarities with the HEIS such as a two- stage cluster stratified sampling design and a common sample frame based on the Population and Housing Census of 2004, its design is different. In particular, between 660 and 680 PSUs (depending on the year) were selected in the first stage out of a total of 1, 336 PSUs for the whole country, and within each selected PSU, 10 households were randomly selected at the second stage. Twelve governorates are divided into 24 rural and urban strata and the six major cities across the country with more than 100, 000 people are strata on their own, which together form 30 strata in total. The LFS collects data on employment status, occupation, and economic activities for between 11, 000 and 12, 500 households on a quarterly basis, and these data are representative of the population for each quarter. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The LFS questionnaire practically remains the same during the period under study. We analyze all 24 quarterly rounds of the LFSs from 2008 to 2013 in this paper. 25 The LFS does not collect data on assets but collects demographic and employment variables, as does the HEIS (those variables are used in Model 3, Table 2). The LFS also collects data on wage income in the past month for each worker, which is categorized in five income groups: less than 100 JD, 100 to 199 JD, 200 to 299 JD, 300 to 499 JD, and 500 JD or more. Since a considerable number (around 38 percent) of household heads did not work and thus had no 25 Half the sample households in the LFS are designed to be renewed across two consecutive years and for two straight quarters within a year. However, DOS does not maintain any identifying information that allows the construction of panel households or individuals over time, and the data provided to us have no non-Jordanians in three quarters in 2011 and 2012. For these reasons, we analyze each quarter of the LFS separately, and average four quarters within each year to obtain the yearly estimates later. 30 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "income in the past month, we assign zero to the wage income for these individuals to make use of all the data. To match this categorical income variable in the LFS, we convert the continuous per capita income variable in the HEIS into a categorical variable with the same income categories. We provide in Tables 5 and 6 a comparison of the distributions of the common variables across the two surveys for their overlapping years in 2008 and 2010, and test for their differences taking into account the complex survey design. 26 Given the different survey design, it is unsurprising that the means of the variables in the LFS are mostly statistically different from those in the HEIS. For example, households in the LFS generally have younger but more educated heads, a smaller share of young household members (ages 0-14 and 15-24), but higher shares of both younger and working age members (ages 25-59), are less likely to have self- employed members, and more likely to live in urban areas. These differences help emphasize the need to benchmark the variables in the LFS against those in the HEIS. IV. 2. 2. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Estimation Results Using Proposition 3 (i), we start first with transforming some positive variables including household size and age in the HEIS and LFS to normality using the Box-Cox method, then standardizing the variables in the LFS according to the distributions of the corresponding variables in the HEIS respectively for 2008 and 2010. As a result, t-tests (not shown) indicate that the distributions of the standardized LFS variables are not statistically different from those in the HEIS, which satisfies Assumption 1. To ensure that Assumption 2 is satisfied, we use the closest version of Model 6 in all the following estimation, where the income variable is in a categorical format as earlier discussed. 26 We implement this test by pooling data from the two surveys, setting the data to incorporate the complex sampling design, and running a (complex survey adjusted) regression of the variable of interest on a dummy variable indicating the survey round. 31 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We offer less restrictive assumptions and formal tests for these assumptions where data are available, provide more insights into the selection of control variables for model building, and offer simpler variance formulae. Our framework can be generally applied to imputation either from one survey 27 We also experimented with imputation from the HEIS into the DHS. However, one major issue is the latter survey ’ s most recent two rounds are in 2009 and 2012, which do not overlap with the HEIS, thus making it difficult to benchmark the DHS. We tried benchmarking both rounds of the DHS using the HEIS in 2010, and found a qualitatively similar decreasing trend in poverty across these two survey rounds. 28 It is also more demanding to make the distributions of the explanatory variables comparable for smaller population groups (e. g., as disaggregated by regional characteristics or other distributional characteristics such as quintiles) in surveys of different designs. We leave this extension for further research. 33 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Given the importance of relative population shares of various ethnicities in conflict-prone societies and given the importance of fertility for individual and household well-being in very poor economies, understanding the relationship between conflict and fertility is also important from a policy perspective. We study the specific case of the 1994 Rwandan genocide, using three waves of Rwanda Demographic and Health Surveys (RDHS) collected in 1992, 2000 and 2005. Our research strategy includes cross-sectional analyses of the post-genocide data as well as analyses of pooled data from before and after the genocide. This allows us to compare the determinants of fertility for sub-samples of women of the same age groups across the survey years. The most challenging identification of an effect of conflict is at the micro-level – especially if the survey does not ask about an individual ’ s exposure to conflict explicitly (Brück et al. 2010). Two alternative categories of proxies for conflict exposure are employed to distinguish women who were and were not likely to be affected directly by violence. Each category represents a transmission mechanism of conflict: (1) replacement effects and (2) marriage market effects. We interpret the coefficients for these variables as the ‘ pure ’ conflict effects on fertility, bearing in mind that conflict may also shape fertility indirectly through the other variables included in the regressions. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 2005). On the other hand, about 700, 000 Tutsi people returned to Rwanda from exile in Uganda shortly after the genocide (Newbury 2005). This group of old caseload refugees either fled Rwanda during waves of ethnic violence against Tutsi since independence or were the offspring of Rwandan exiles. Grasping the demographic imbalance in numbers, Fig. 1 depicts sex ratios for five-year age groups calculated from the (pre-genocide) 1991 Census and the (post-genocide) 2002 Census for Rwanda. The graph allows comparing the relative distribution of men and women across age at the two points in time. Clearly, in 2002 there are shortages of men that may be attributable to genocide-related excess male deaths (i. e. shortages of men even larger than prior to the genocide for some age groups). The shortage of men is most pronounced in the groups of 20-45 year olds and the elderly older than 55 years. An immediate implication that follows from the unbalanced sex ratios is the reduced chance of women to get married to men of similar age for women in the age group most affected by genocide or to remarry after being divorced or widowed. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This is hence a topic that we will investigate in more detail below. 4 Data 4. 1 Rwanda Demographic and Health Surveys The analysis builds on three cross-sectional Rwanda Demographic and Health Surveys (RDHS) collected in 1992 (before the genocide) (ONAPO and Macro International 1994), 2000 (after the genocide) (ONAPO and ORC Macro 2001) and 2005 (INSR and ORC Macro 2006). The data in each survey is representative of households at the national and in 1992 and 2005 at the provincial level, based on a stratified survey design. In the 2005 RDHS, each of Rwanda ’ s twelve provinces was divided into an urban and a rural stratum, resulting in 23 strata (the province of Kigali City only consists of urban areas). In a first stage, primary sampling units were drawn from a listing of enumeration areas prepared for the 2002 Census. Primary sampling units were selected with probability proportional to size regarding the number of households in each enumeration area. This exercise was conducted separately in every stratum. In a second stage, 20 and 24 households within each urban and rural primary Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 sampling unit were drawn, respectively. 5 In the following, all analyses account for the survey design and population weights are used as recommended by the data providers. In every selected household, all women of age 15-49 years who were either usual household members or who were present in the household on the night before the interview were eligible for interviewing. In half of all selected households, an additional questionnaire was administered to survey all men aged 15-59 years about their health status. The questionnaire design remained broadly similar across the survey waves. Still, both the number of variables and the sample size increased over time with about 6, 500, 10, 600 and 11, 300 prime age women included in the 1992, 2000 and 2005 survey, respectively. The RDHS include detailed information on women ’ s birth histories (permitting the calculation of a fertility indicator to be used as the dependent variable below), maternal and child health, marital history, access to health services, domestic violence, sibling mortality, and women ’ s socio-economic characteristics, including schooling and main occupation. In contrast to LSMS-type household-surveys, the information captured on the characteristics of other household members and respondents ’ partners is limited to age, schooling, and occupation (the latter is only available for current partners). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Data on community characteristics were not collected. Due to confidentiality policies we only know the province in which a respondent currently resides, but not the administrative unit below the province level. This prevents us from merging the RDHS data with secondary data on geographical conflict intensity. Moreover, no information on income or consumption expenditure is recorded and households ’ physical asset endowments, such as owning a radio or the quality of roofing materials, is the only implicit measure available on household wealth. Instead, we construct a wealth index based on recorded household assets. Components of the index include durables, such as radio and bicycle, source of drinking water, characteristics of floor materials, and type of toilet facility. While the 2000 and 2005 surveys record a larger number of assets than the 1992 5 This description of the sample design refers to the 2005 RDHS, with slightly different designs used in the two previous RDHS waves. The 1992 RDHS builds on the 1991 Census as a sampling frame. At the time of the 1992 survey collection, a civil war was ongoing, with most actions of warfare taking place along the Ugandan-Rwandan border. Due to security concerns, 44 rural sectors in the provinces of Byumba and Ruhengeri in northern Rwanda were excluded from the sample frame at the outset. The 2000 RDHS builds on the listing of enumeration areas outlined for another household survey, the Enquête Intégrale sur les Conditions de Vie des Ménages (EICV) collected in 2000, as no other population records were available at the time. The sampling frame of the EICV itself is based on the pre-genocide Census of 1991. Three strata were used – Kigali, other urban areas, rural areas – and rural areas were further stratified into provinces, resulting in 13 strata (Ministère des Finances et la Planification Economique 2003). The sample design of the 2000 RDHS is only representative of rural areas of each province and Kigali City. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 survey, we construct the asset index based on the same categories of assets captured in every survey wave, ensuring full comparability over time. Most of these assets are recorded as dichotomous variables, taking 0 / 1 values, while the few categorical variables with multiple categories are manually reorganized along an ordinal scale according to costs. The asset variables are first normalized and then transformed into a single wealth index through principal component analysis, following an approach proposed by Kolenikov and Angeles (2009). Scree plots indicate that the first principal component is highly significant in every wave, while further components carry little information, as desired. The wealth-index is likely to indicate the long-term economic well-being, as many durables captured are typically held by households for many years and are not frequently replaced (Sahn and Stifel 2000). In between the first and second survey waves an administrative reform took place in which the definition of urban areas was revised (Megill 2004), among other things. Some communities previously considered rural were now coded as towns, which, to some extent, explains the sharp increase in the proportion of urban population. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As a consequence, results for urban areas are not comparable across the three RDHS waves in a strict sense. 4. 2 Conflict proxies One obvious predictor of being a genocide victim in 1994 is ethnicity (that is, being Tutsi). Yet, only the 1992 pre-genocide RDHS wave records ethnicity, as self-reported by respondents. In an effort to suppress further ethnic tensions, the post-genocide government of Rwanda forbids the usage and identification of ethnic categories. Hence, RDHS collected after 1994 does not record respondents ’ ethnicity. In response to this challenge, we construct several ‘ conflict proxies ’ measuring likely exposure to conflict. These proxies allow us to differentiate two channels through which exposure to mass violence may influence fertility: replacement effects (where women choose to have children in the post-conflict period to compensate their lost children from during the conflict period) and marriage market effects (where a relative shortage of men to women creates a ‘ bottleneck ’ for women to get married). It is important to note that the conflict proxies do not necessarily identify victims of targeted genocidal violence. Rather, these proxies indicate individuals and age groups that were likely to be exposed to conflict-related violence. Given that the 1994 genocide occurred within a time span of just about 100 days, the duration or timing of conflict exposure is of less importance in the Rwandan genocide. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 Table 1 provides on overview of the definition and data source of all conflict exposure proxies discussed in the following. a) Child and sibling mortality A first proxy that captures replacement effects is whether or not a woman lost a child during the genocide (CHILDDEATH). The RDHS questionnaires record child mortality in great detail and even ask for the month of death. This allows us to precisely code CHILDDEATH to take the value one if a woman lost one or more children between April and July 1994. Moreover, we differentiate child death by gender in two further conflict proxies: SONDEATH indicates the death of at least one son during the genocide; DAUGHTERDEATH the death of at least one daughter. Fig. 2 displays the occurrence of child deaths over time as calculated from the 2000 and 2005 RDHS. Child deaths peak during the 1994 genocide, although child mortality remains relatively high in the immediate post-war period. Given that many mothers would have been killed in the genocide at the same time as their (young) children, this proxy somewhat underestimates the effects of genocide on child mortality. We interpret this proxy as capturing also the negative effects of conflict on health, sanitation and nutrition, leading to (even) higher child mortality. A second proxy indicating replacement effects uses sibling mortality during the genocide (SIBLINGDEATH). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Sibling mortality was recorded in the 2000 and 2005 surveys, but not in the 1992 RDHS. Every prime age woman was asked about all of her siblings born to the same mother. Of every sibling, information is available on the sex, date of birth, whether the sibling is still living, year of death, and whether the death was related to pregnancy or childbirth. This information is recorded irrespective of whether or not the sibling lives in the same household as the respondent and thus provides a good geographical coverage of deaths occurring across the whole country. However, no information is available on the sibling ’ s place of living or the place of death. The occurrence of sibling deaths over time calculated from the 2000 and 2005 RDHS is displayed in Fig. 3. The graphs from both years exhibit one single and outstanding peak which coincides with the timing of the 1994 genocide. The peak of sibling deaths is somewhat less pronounced and spread over a slightly longer time period in the 2005 survey. We suggest that this is due to the imprecise way the time of death was captured Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 in the questionnaire. 6 Accuracy tests on the sibling mortality data collected in the RDHS are discussed in more detail elsewhere. 7 Moreover, we disaggregate sibling deaths by gender, differentiating between women who lost a brother (BROTHERDEATH) and a sister (SISTERDEATH) in 1994. As discussed below in more detail, these variables are likely to capture the direct effects of genocide at the household most accurately. Women still living with their parents who recorded a sibling death during the genocide have a very high probability of having experienced the sibling death very closely. This may shape fertility negatively, for example through the stigma attached to having been part of a conflict victim household. b) Marital status and sex ratios The third proxy is a specific form of marital status, namely whether or not a woman is a widow (WIDOW). This proxy is available in both the pre-genocide and post-genocide RDHS. However, the RDHS questionnaire does not record the husband ’ s cause of death or date of death. It is hence impossible to distinguish conflict widows from HIV / AIDS widows or other widows. Still, the majority of widows in the 2000 and 2005 waves are very likely to be genocide widows (Brück and Schindler 2009) – that is, formerly wives of Tutsi husbands or moderate Hutu husbands. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Given the gender-unbalanced mortality during the genocide, widowhood can be assumed to be exogenous. This point is underlined in Fig. 4, which depicts the distribution of current marital status (single, married, divorced, widowed) for women of various age groups in 1992, 2000, and 2005. The proportion of widows relative to married and unmarried women increased considerably after the genocide, particularly for younger women. For instance, the proportion of widows among women of age 25-34 years doubled between 1992 and 2000. Moreover, the proportion of widows rises steadily with older birth cohorts; a similar pattern is apparent for divorced women. This may indicate that once a 6 The original question was ‘ How many years ago did [name of sibling] die? ’ The 2005 RDHS was collected between February and July 2005, while the genocide occurred between April and July 1994. Hence, a woman interviewed in February 2005 whose sibling died in May 1994 lost her sibling ten (discrete) years before the interview, which translates into 1995. In contrast, the 2000 RDHS was collected between June and November 2000, so most respondents who lost a sibling during the genocide period would have reported the death occurring six years ago, which translates into 1994. 7 Verwimp and de Walque (2010) use the same RDHS data from 2000 to analyze excess mortality patterns related to the 1994 genocide. They find that the mean and median of both the siblings ’ sex and date of birth are similar and conclude that there is no evidence for a systematic bias in the reporting of sibling deaths. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 woman becomes a widow, it is likely that she does not marry again given a lack of suitable partners of similar age. 8 Finally, we calculate a demographic conflict proxy that captures the extent of deaths across age groups (SEXRATIO). The data on sex ratio comes from two secondary sources: the 1991 Census (which is matched with the 1992 RDHS) and the 2002 Census (which is matched with the 2000 RDHS and the 2005 RDHS). Women are assigned the average sex ratio (defined as the ratio of males to females) in the cohort of their potential partners in a given province, taking into account the typical age difference between spouses in Rwanda. More precisely, sex ratios in a woman ’ s five-year age group, one younger age group and two older age groups were averaged. These provincial, age group-specific sex ratios are the closest approximation to the local marriage market possible with publicly available data. Still, sex ratios derived from census data overestimate the number of men potentially available on the marriage market, as tens of thousands of male perpetrators of genocide were in jail (Ministry of Finance and Economic Planning et al. 2003). It is important to note that our analysis is based on a sample of survivors. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Households in which all (female) members died during the genocide are by definition not accounted for. In other words, the impact of the genocide is underestimated in the present analysis. However, given that the focus of this paper is on the impact of the genocide on the fertility of the survivors in the post-genocide period (and not on estimating excess mortality), this does not bias our analysis. 5 Estimation strategy We employ two slightly different estimation strategies to explore the impact of the two channels – replacement and marriage market – on fertility. This is due to the fact that proxies for conflict exposure measuring replacement effects are only available for post-genocide RDHS data, while conflict proxies measuring marriage market effects are available for both pre-genocide and post-genocide RDHS data. Table 2 provides a schematic summary of the estimation strategy. 8 Unfortunately, most information on marital history available in the RDHS refers to the current partnership. Hence, we do not know whether a woman has ever been widowed and then remarried. Such a woman would simply appear as ‘ married ’ in the data. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 is the number of children born in the five years prior to the data collection. In the 2000 RDHS, this corresponds to the period between May 1995 and June 2000. This time span will be compared to the period between May 1987 and June 1992, when data collection for the 1992 RDHS began. Similarly, in the pooled 1992-2005 data, the time span of interest is the ten years prior to the starting date of the 2005 RDHS, which is the period between May 1995 and February 2005. This corresponds to the period from September 1982 to June 1992 in the 1992 RDHS. To explore the marriage market effects of conflict on fertility, the following equation is estimated: Ki = α0 + β1Xi + β2D + β3R + β4Year + β5Conflicti + β6Conflicti x Year + μi (1b) which additionally includes a time dummy Year for the survey year and an interaction term between the proxy for conflict exposure Conflicti and the time dummy. The estimated coefficient β6 captures the impact of actual conflict exposure on fertility after the genocide relative to women with similar risk of exposure to conflict before the genocide. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Given that we control for the effects of conflict in addition to the usual socio-economic determinants of fertility and given that conflict may also affect these other variables, the estimated coefficient of the conflict variable can be interpreted as the ‘ pure conflict ’ effect. This makes our calculations conservative estimates of the total effects of conflict on fertility. For example, conflict is likely to reduce the educational attainment of children exposed to violence during their school-age (Akresh and de Walque 2008) and of girls in particular (Shemyakina 2006), hence inter alia raising their fertility. In both (1a) and (1b), measures of each woman ’ s socio-economic characteristics include three age categories (young: 15-24 years, middle: 25-34 years, and old: 35-49 years), the number of sons and daughters born before the time span of interest, her education (no education, some primary education, and some secondary or higher education), a dummy variable indicating whether she is currently in a union, 9 a dummy variable indicating whether she has had more than one union, a continuous household wealth index (see Section 4. 1), a dummy variable indicating whether she has always lived in the same community, a dummy variable indicating whether the current location is urban, mortality rates of children under five years at the district 9 This variable is not included in the estimation of equation (1b), given that widowhood is strongly correlated with being in union. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "26 1. 69 years later than Hutu women (all three figures are significantly different in means across Hutu and Tutsi). Other socio-economic characteristics differ significantly as well across Hutu and Tutsi, including education and wealth. However, it seems that these differences in fertility behavior and socio-economic status are driven by the place of residence: 14 percent of Tutsi women live in urban areas, compared to 5 percent of Hutu women. A regression analysis confirms this: When regressing the total number of children on multiple characteristics (including ethnicity, age, education, place of residence, and partnership characteristics), ethnicity is no longer statistically significant. Fifth, we run all estimates based on the full sample of women in order to enhance the comparability and precision of the estimates. In both short-term and long-term analyses, the impact of conflict exposure is only slightly smaller in terms of the magnitude of effects and the level of significance compared to the original results derived from restricted samples. 7 Conclusions The paper analyzes the effects of mass violent conflict on fertility for conflict-survivors in the case of the genocide in Rwanda in 1994 using individual-level data. To enable an identification of the genocide at the micro-level, two types of proxies for conflict exposure are constructed using three waves of RDHS data from before and after the genocide. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "With this estimation strategy, we identify the ‘ pure ’ effects of genocide on fertility over and above conflict-related effects like urbanization, destroyed infrastructure, and weaker health infrastructure – which in turn also affect fertility. We study the short- and long-term effects of genocide and differentiate the analysis by cohort, kinship relation and gender of the deceased persons. Our approach is unique in that we estimate the effects of genocide on fertility using multiple measures of conflict exposure and disaggregating in more dimensions than done previously. In Rwanda, this is important as by law collecting a key conflict proxy, ethnicity, is not possible. In other contexts using conflict proxies may be important if data-sets, which do not contain any direct identification of conflict at the micro-level, are to be analyzed retrospectively. The paper has five major findings. First, we find a significant direct (or ‘ pure ’) effect of mass violent conflict in determining fertility. Of all conflict proxies studied, the loss of a child Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 rule. The post-colonial period in addition to these works draws on available datasets and quantitative studies. II. Pre-colonial Africa: An Era of Gender Parity? The distinguished historian of the Akan and Asante, Ivor Wilks, describes the 16th century in Akan history as the ― era of great ancestresses ‖ (Wilks (1993: 66-68)). Akan oral traditions place women at the leadership of migrant groups and nascent communities that would later form the nucleus of Asante. According to Asante oral traditions, the very land on which the capital city of Kumase stands was bought from a woman with the incoming Oyoko clan group led by a matriarch (Akyeampong and Obeng (1995)). The first government anthropologist appointed to the Gold Coast in the 1910s, R. S. Rattray, authored a trilogy on Asante religion, art, law and constitution that have become foundational texts for the study of Asante. In this matrilineal society with a paired male (chief) and female (queen mother) leadership, represented by stools as symbols of office, Rattray was informed that: In fact, but for two causes, the stool occupied by the male would possibly not be in existence at all. 1. The natural inferiority of women from a physical standpoint. 2. Menstruation (with its resultant avoidances). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9688 This paper provides an empirical analysis of refugee returns to the Syrian Arab Republic. Since 2011, about 5. 6 million Syrians — more than a quarter of the country ’ s pre-conflict population — have been registered as refugees. By mid- 2018, only about 1. 8 percent of them had returned to Syria voluntarily. This paper compiles a novel data set with administrative data for 2. 16 million refugees, existing and new household surveys, a new conflict-events database, and nightlights data for Syria to analyze the correlates of these returns. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "stayed. Our objective is to identify the factors that facilitated or hampered the return of these refugees. However, an inherent challenge in the literature on conflict and forced migration is the absence of a complete longitudinal data set for conditions in countries of asylum and origin that can be mapped onto refugee characteristics. Establishing causality is even more challenging. We make progress on the first part by combin- ing different sources and types of data. For demographic characteristics of refugees and their arrival and return information, we use administrative data from the Profile Global Registration System (ProGres) database of UNHCR. For the conditions faced by refugees in exile, we use vulnerability surveys conducted by UN agencies in Jordan and Lebanon, and complement these with a new household survey comprising similar demographic and socioeconomic modules but also including vignettes about the drivers of return. Finally, for conditions in Syria, we have compiled a novel monthly conflict events data set to use along with nighttime light emissions data that proxies access to utilities. These sources are utilized in two different but complementary ways. First, we exploit the temporal and spatial variation of the nightlights and conflict events series to build a sub-district-month panel for conditions inside Syria. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This is used to analyze the impact of changes in conflict and luminosity patterns on return in an aggregate manner using ordinary least squares (OLS) and Poisson quasi maximum likelihood (PQML) count models. Second, we use the detailed information on refugee characteristics provided by ProGres together with conditions in countries of asylum, 4 to analyze individual return decisions. Given that we have arrival and- where applicable- return dates for each refugee, we can study their likelihood of return for a given month using both discrete 4Since the conditions in countries of asylum are only captured for a small sample of registered refugees in Lebanon and Jordan, we approximate host country conditions with district averages for the full sample. 3 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "countries covered in this study. We next turn to analyzing the factors that helped or hindered the return of Syrian refugees until early 2018. 3 Data and Empirical Strategy Return migration decisions are potentially influenced by expected payoffs in both the country of origin and country of asylum, as well as the individual characteristics of refugees. To analyze these factors, we need a comprehensive data set, which is often not available especially in active conflict situations. In what follows, we describe the strategy we followed in exploiting the available information. 3. 1 Data With an active conflict situation in Syria, a complete longitudinal data set for conditions in countries of asylum and origin was not available. Thus, we adopt a pragmatic approach that combines different sources and types of data. Refugee attributes: We use the Profile Global Registration System (ProGres) database, which is compiled by UNHCR to record each person of concern who ap- proaches it. 7 Our version comprised about 2. 16 million Syrian refugees in the Middle East and North Africa region (Turkey and European countries are not included), with a cutoffdate of March 2018. The ProGres database is a limited administrative database, which functions like a civil register. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "education. In addition, information on their registration status is recorded, including refugee status, arrival and, where applicable, return date, and sub-district-level location information for last residence in Syria and current residence in the country of asylum. It also identifies kinship of individuals within each “ case\"(e. g., familial relationships of everyone within a case to the principal applicant, ranging from members of the nuclear family to extended family, such as in-laws and aunts). Following the initial registration, entries are updated in subsequent contacts. Up- date frequencies vary from one operation to another, with at least 5 percent of all observations being updated in a given month. 8 Therefore, although information on single-shot events like arrival and return dates is fixed, other information like occupa- tion, education and marital status may change over time. In the case of education and marital status, these changes are largely driven by the aging of the refugee. However, the occupation variable is more problematic, since it could refer to current employ- ment or past employment (including in Syria) depending on when it was last updated. Therefore, while we are able to use all of the demographic and registration information of the ProGres database, we exclude the occupation variable from the main analysis. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Conditions in countries of asylum: We use vulnerability surveys conducted by UN agencies in Jordan and Lebanon. These surveys assess living conditions of registered refugees at the case, household, and individual levelS, and monitor protection, shelter, education, health, water and sanitation, as well as poverty and food coping strategies. Our data from the Vulnerability Assessment Framework (VAF) in Jordan comprises two years: 2015 and 2017 (sampled cases: 2, 163 and 2, 001), which are comparable. Samples are weighted by the share of refugees in each governorate, and representa- tive at the 95 % confidence interval. Data from the Vulnerability Assessment of Syrian 8For example, in Tunisia where UNHCR has fewer than 600 persons of concern, the information is updated every 3 months, while in the Arab Republic of Egypt the cycle is every 18 months, which is the maximum in the region. For the overall data set, we can assume that the information is up-to-date on average for 2017. 12 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Refugees (VASyR) in Lebanon covers three years: 2015, 2016, and 2017 (sampled house- holds: 4, 105, 4, 596, and 4966, respectively). VASyR surveys employ a two-stage cluster sampling approach: first, to ensure geographical representativeness, 30 clusters are ran- domly selected in proportion to refugee population size and, then, 5 to 6 randomly selected households in each selected cluster are visited. There are a number of challenges when using the VAF and VASyr surveys. The first is the limited comparability of questions across surveys. This limits the number of variables we can use to measure conditions in Lebanon and Jordan consistently. Nonetheless, we are able to proxy for living conditions and access to employment by computing a composite food security index using principal components analysis (PCA) of normalized food consumption variables. The latter include the average number of meals per day, and the average number of days a week a case did not have to borrow food, restrict portion sizes, limit the number of meals or restrict consumption of adults. A PCA index is also computed for housing conditions, using normalized dummies for whether the case has an acceptable roof and windows, and access to a (private) latrine. The second problem is the limited sample size of the two surveys. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To take advan- tage of the much larger ProGres database of nearly 2. 2. million refugees, we compute area averages for the above-mentioned case-level host community conditions, aggregat- ing to the smallest possible geographic unit available (district level for Lebanon and governorate level for Jordan). This information is then matched with all refugees in the ProGres database that have location information in Lebanon and Jordan, yielding a sample of 1. 85 million refugees. Finally, we worry about reporting bias. Respondents may have felt they were more likely to receive assistance if they reported worse living conditions. The bias could also go the other way if refugees want to signal their gratitude for the assistance they receive. The problem is even more acute given our research question. Those who intend 13 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "to return may have systematically different tendencies in reporting. It is possible that those who plan to return no longer feel the need to mis-report their income, and this would generate a bias in the correlation between the return decision and asylum country conditions. We attempt to solve this problem in two ways. First, we exploit the fact that we observe whether refugees return to Syria for 1-3 years after the survey data was collected. We can therefore remove the responses from cases who ultimately return when aggregating the data to the district and governorate levels. This should at least keep the reporting bias constant across geographic areas. Second, we employ a fixed effect specification which looks at changes in conditions in Lebanon and Jordan, which will remove differences in reporting bias which are time invariant. Conditions in Syria: To capture conflict dynamics, we compiled a novel conflict events data set, covering all districts in Syria between January 2011 and August 2018 at a monthly frequency. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This data set provides a record of verified conflict-driven casualties, changes in area control, and key conflict events (light skirmishes, airstrikes, artillery strikes, and chemical attacks) using more than 7, 000 news items and multiple databases. 9 Whereas casualties are recorded as a count variable in this case, other conflict events are defined categorically with two or more values, e. g., yes, no for presence of combat activity and low, medium, high for the intensity of it. This allows us to differentiate between different types of conflict events while assessing the impact on the return decisions of refugees: a priori, some conflict events, like chemical attacks, are expected to pose a greater deterrent to return than others. Such decomposition of conflict events also helps us to reduce potential endogeneity concerns between return and the proxies of conflict intensity, e. g., casualties. Finally, we also computed a Conflict 9These include the following: ACLED, Carter Center Syria Conflict Resolution Database, Institute of War Syria Events Database, University of Maryland Global Terrorism Database, Syrian Observatory for Human Rights Database, Syrian Shuhada Database, The Uppsala Conflict Data Program and The Violations Documentation Center among others. In addition, activity-specific databases have been consulted, including airwars. org and Arms Control Organization timeline of confirmed chemical weapon use in Syria. 14 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Events Index (CEI) for each district-month using PCA of normalized conflict activity. 10 This index is used where a more complete picture of the conflict conditions is considered. For non-security-related conditions in Syria, it was not possible to acquire a com- parable and geographically comprehensive time series. Instead, we use nighttime lights measurements from the Suomi National Polar Partnership (SNPP) satellite, which was launched by NASA and NOAA in 2011. The satellite uses a Visible Infrared Imag- ing Radiometer Suite (VIIRS) instrument to collect low light imaging data in spectral bands covering emissions generated by electric lights, excluding stray light, lightning, lunar illumination, and cloud-cover. Temporal averaging is done on a monthly and annual basis starting from April 2012. For the purposes of this study, we used the monthly data set with zonal statistics up to ADM3-level aggregation, comprising governorate, district, and sub-district divi- sions. The nightlights in this scheme can be interpreted narrowly as the availability of electricity (grid or generator) or more generally as a proxy measure for the existence of utilities, economic activity or the conflict-driven isolation of a given location. Figure 2 maps the evolution of visual nightlights and the CEI onto each other. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "mean CEI over our sample period. Since return decisions are likely impacted by the persistence of conflict, we classify districts into high and low conflict districts, using the top 10th percentile of the mean CEI as a cut-offpoint. 11 We will use this classification to explore the extent to which return decisions of key social and demographic groups are impacted by the persistence of conflict. Survey of refugees in Lebanon and Jordan: In a survey of 900 Syrian refugees in Jordan and Lebanon, we randomly varied the details of the scenario or vignette presented to a given individual respondent. Some refugee families are certainly more predisposed to wanting to return than others. By describing hypothetical scenarios, but ones which hit fairly close to home, and varying key factors within those scenarios should help us identify what factors are important to many refugee families when deciding whether to return. For all respondents in all vignettes, we asked “ How likely is this family to return to Syria in the next 2 months? ” where the respondent could answer using a Likert scale, ranging from “ Very likely ” to “ Very unlikely ”. For the analysis below, we use an indicator which is equal to one if a respondent says the family is either very likely or likely to return, and 0 if the respondent says neutral, unlikely or very unlikely. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Each respondent was presented with three vignettes, where key aspects of the sce- narios were randomly varied across respondents. These three vignettes were designed to probe the impact of different pull and push factors on the refugees ’ return deci- sion, allowing us to go beyond the data limitations of the above analysis. That is, the vignettes not only explore the impact of security on return decisions, but also of employment prospects in both the country of asylum and Syria, the status of property in the home community, and the availability of financial assistance. In particular, the first vignette probes three questions: first, whether the ability to 11According to this classification, Jebel Saman, Deir-ez-Zor, Homs, Al Ma ’ ra, and Duma are high- conflict districts. 16 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The information was provided to the family either by a resident of the village or from family members who remained in their village in Syria. As part of this survey, we also collected information on the vulnerability of the sur- veyed refugees using the subset of common questions from the VAF and VASyr surveys. Since the data for this survey was collected through a third party unaffiliated with the UNHCR- and, thus, any decision to allocate assistance- we would expect answers on income, food security, and poverty coping strategies to be more truthful. These data provide important contextual information on the correlations between income, food security and employment status. Overall, our ability to put together a comprehensive data set with key dimensions 17 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(micro-characteristics of refugees, conflict dynamics, and the conditions in the countries of asylum and origin) has made the analysis of return decisions possible. The next section will discuss how we leverage the different dimensions of this data set for our purposes. 3. 2 Empirical Strategy The analysis proceeds in two parts: we first analyze a panel data set, constructed at the sub-district level within Syria, 12 to understand the relationship between returns and se- curity and access to utilities in Syria. Formally, we estimate the following specification: ln (returnssmt) = α + β1 ∆ CEIdmt + β2 ∆ luminositydmt + β3AoCdmt (1) + δd + τt + εdmt, where returnssmt is the number of refugees originally from sub-district s in Syria who returned to Syria in month m of year t. Also included are district fixed effects δd and year fixed effects, τt. 13 Since refugees will make the decision to return home based on past conflict events and recent changes in standards of living like electricity reliability, we look at a lag of both the conflict events index (CEI) and luminosity. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In particular we construct ∆ CEIdmt as the change in the conflict events index for district d between the quarter immediately prior to month m in year t from the previous quarter. 14 The second term on the right hand side ∆ luminositysmt is analogously constructed using the same lagged time periods. The third term AoCsmt is a series of Area of Control dummy variables, which capture who is in control of sub-district s in month m in year 12Syria is a unitary state, but for administrative purposes it is divided into 14 governorates, which are further divided into 65 districts and 281 sub-districts. 13As a robustness check, we also include month of the year fixed effects to pick up seasonal changes in migration patterns. 14Note that the conflict data is only available at the district level. 18 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "month for which we have ProGres data. However, this is a right-censored panel (i. e., the majority of individuals did not return by the end of our records). Thus, we use survival analysis. By estimating the hazard rate of return for a given point (month) in time, conditional on refugee i not having returned yet, survival models account for the right-censoring and time-varying explanatory variables that are problematic when using OLS or a binary dependent variable model, such as logit or probit to estimate transition probabilities (Cameron and Trivedi (2005) and Jenkins (2005)). Following Jenkins (2005), we estimate a proportional hazard model. Given that our data is grouped monthly, we first estimate the complementary loglog (cloglog) model with robust standard errors clustered at the origin district level as follows: h (t, X) i = 1 − exp [− exp (c (t) + β1chari + β2regi (3) + β3foodseclt + β4housinglt + + δd + τt] where h (t, X) i is the proportional hazard function of refugee i and c (t) represents the generic baseline probability to return to Syria after a refugee spell t (duration) condi- tional on not having yet returned. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Following Constant and Massey (2002), we assume that t enters with a quadratic term in the baseline hazard, such that c (t) = α1t + α2t2. In addition, we control for refugee i ’ s social and demographic characteristics chari, such as her sex, age, marital status, education level, and family relationship, her registration status regi as well as livelihood opportunities foodseclt and living conditions housinglt in refugee i ’ s location l in the country of asylum. 15 Lastly, we include fixed effects for the country of asylum coa, the origin district δd and year τt. However, since misspecification of the parametric model could lead to inconsistent 15The level of disaggregation of location l is determined by the UNHCR vulnerability surveys. For Jordan the VAF data was aggregated to the Governorate, whereas for Lebanon the VaSyr data is also available at the district level. 20 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "estimates, we also estimate the continuous time, semi-parametric Cox proportional hazard (Cox) model. The main advantage of the Cox model is that it allows us to estimate the relationship between the hazard rate and the determinants of refugee returns without having to make any assumptions about the shape of the baseline hazard function. A continuous time model might also be appropriate in this context, given that we have a long panel of 75 months. Using the same variable definition as in 3, we estimate h (t) = h0 (t) exp [β1chari + β2regi + β3foodsect + β4housingt + µl + δd + τt] (4) We restrict survival analysis to the Syrian refugees based in Jordan and Lebanon as their host country conditions are proxied by the geographical aggregates computed from the VAF and VASyr surveys. Despite this restriction, more than 85 percent of all refugees in our data set is included in the analysis. It is important to note that the conditions in the country of asylum may be the result of a refugee ’ s anticipated length of stay in the host country. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As such, we discuss these results, which are reported in section 4, as correlations and not necessarily causal relationships. 4 Results 4. 1 Conditions in Syria The evolution of the security situation and overall quality of life must be important factors for refugees to consider returning home. We therefore start this analysis by looking at how the return decision varies as a function of our composite measure of security (the CEI) and the luminosity measure using nightlights. Table 2 shows an overall robust relationship between security and returns. In our 21 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The magnitudes are similar as we saw with the overall conflict index. A one standard deviation decline in quarterly casualties, which is about 90, is associated with a 2 % higher return rate. The use of chemical weapons and incidents of skirmishes and fighting all have a significant negative effect on returns to Syria. In results not shown, we find less robust relationships between the number of air strikes and incidents of artillery and carpet bombing and refugees ’ decision to return. 4. 1. 1 Vignette Analysis We complement the study of returns that have already happened with data from hy- pothetical vignettes. There are advantages and disadvantages of both of these data sources. The returns observed to date are still a very small percentage of the overall refugee population, and therefore the factors affecting their return decision may not be representative of the larger Syrian refugee population. The hypothetical vignettes, while clearly weaker in that they represent hypothetical scenarios, seek to provide in- sights into the external validity of the earlier results. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Whether the wife is working or not in the country of asylum does not have a significant effect on the reported likelihood of return. However, the vignette highlights how schools in Syria affect the return decision: respondents are 19 percentage points less likely to expect the hypothetical household to return when the schools are under-resourced. This is more than a 40 % reduction in the likelihood of expected return. Overall, these two vignettes signal that conditions back home in Syria have a large and economically meaningful impact on the return decisions. 4. 1. 2 Does Conflict Affect Who Returns? The conflict pattern in Syria may not only affect the total number of individuals who choose to return, but also the profile of the refugees who return. In this section, we show 24 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "that different types of refugees, as can be measured using observable characteristics, return to high versus low conflict areas. We designated districts that were in the top 10th percentile of the conflict events index (CEI) averaged over the time period January 2012-2018 as “ high conflict ”. As was seen in Figure 4, these districts experienced significantly more conflict over our study period. Note we do not observe where refugees return to, so we instead use their origin district as a proxy. We then look at the percentage of refugees from a given district in Syria in that demographic category who returned. This helps account for the fact that the profile of refugees from a given origin district may have been altered when there is high versus low conflict- i. e. there may be more widows among the refugee population who hail from high conflict districts. Figure 5 has five panels. The regression results behind these figures are available in appendix tables A1-A4. 17 Note that the standard error bars show differences within each conflict grouping (e. g. the difference between male and female refugees among low conflict districts; and analogously the difference between male and female refugees within high conflict districts). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "likelihood of return. Moreover, the sensitivity of this return decision to conditions in Syria (i. e., conflict intensity) is also markedly different along such characteristics. 4. 3 Conditions in Host Countries A refugee ’ s livelihood opportunities and housing conditions in the host country may also be important determinants of her return decision. In fact, this point often appears in the popular media in different forms like\"good conditions make refugees stay\"and, by extension,\"bad conditions make refugees return\". 19 In this section, we show that our results do not necessarily support this view. First, we explore how individual return decisions are affected by living conditions in the host countries by estimating equation 4. These results are reported in Table 8, where individual controls from ProGres are suppressed for ease of exposition. In column (1), we find that more food secure households have a higher hazard rate of return, a result that is highly significant. However, the magnitude of the effect is small: a one standard deviation increase in the food security PCA index increasing the hazard rate by 0. 27 %. Moreover, Column (2) shows that better housing conditions- proxied for by the housing PCA index- also increase the hazard rate by a similar magnitude. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Second, we analyze how conditions in the host countries affect aggregate returns by using the district-month panel. Unlike the panel data set used in Tables 2 and 3, we aggregate returns by their location l in the country of asylum not their home district in Syria. 20 However, because the country of asylum indicators are reported for only 2 years for Jordan and 3 years for Lebanon, the time variation in this panel is severely 19See Berry et al. for a review of press coverage about Syrian refugees until 2015. 20The regression specification is E (returnslmt) = δlexp (β1 + β2foodseclt + β3housinglt + τt) where deltal is the location specific fixed effect and all other variables are defined as in equation 3. Since the VAF and VASyr surveys were only conducted during the 2015-2017 period, we only estimate the regression for this subset of years. 31 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "constrained. Nevertheless, we pursue the panel estimates since they remove time invari- ant characteristics which we do not observe and are correlated with the return decision. This includes reporting biases, since UNHCR provides assistance to the respondents, that are constant over time. Results are shown in Table 9. Better livelihood opportu- nities- proxied for by the food security PCA index- increase the number of returnees. In particular, a one standard deviation increase in the food security index increases the number of returns by 24 % when using the PQML specification in column (2), a result that is statistically significant at the 5 % level. We view this as strong evidence. How- ever, the corresponding OLS result in column (1) is insignificant. Housing conditions, on the other hand, do not seem to affect the aggregate return numbers, as shown in Columns (3) and (4). This may be because we do not have enough variation in the data. These results do not support the view that poor living conditions in host countries push refugees to spontaneously return to their origin country. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 errors, and possible survival bias when using ex post measures to approximate baseline information and, especially in the case of sexual and gender-based violence measures, the possibility that increased incidence may simply be an artifact of improved reporting over time. In addition to these hurdles, there is a general lack of empirical information on gender variables at the individual and household levels, difficulty in measuring intrahousehold issues when investigating gender inequalities, and logistical difficulties and risks involved in both conducting research and acting as a research subject in conflict and postconflict situations. Safety and ethical issues arise, especially in investigations of sexual and gender-based violence and interviews with combatants. Finally, conflict research generally conceptualizes conflict as a discrete event or shock, although conflict is a process that evolves over time and recurs in repeated cycles of violence (Brück and others 2010). Despite these limitations, recent research on the consequences of conflict has advanced and has benefitted from more and better micro-level data, increased use of innovative approaches, and quasi-experimental variation. A growing number of longitudinal household-level data sets and follow-up household surveys in postconflict settings that integrate prewar data are facilitating new microstudies on the impacts of war (Blattman and Miguel 2010). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Confronted with a lack of data from household surveys on violence, researchers have merged household data sets with secondary sources that register violent events and death tolls at the village or district level. An example is the work of Shemyakina (2011) in Tajikistan, which used variation in the number of incidents reported in newspapers at the “ raion ” (district) level to differentiate violent districts from nonviolent ones. Seeking a more satisfactory solution, Brück and others (2010) proposed adding a generic violence module to standard household surveys. The inclusion of such a module would remediate or offset the inability of survey-based research to infer the effects of violent conflict on schooling, health, and labor market outcomes disaggregated by gender. To address the issue of attribution of causality, researchers have sought to control for unobserved heterogeneity correlated with both conflict and outcomes of interest as much as possible (e. g., using area or household fixed effects). Researchers have also used instrumental variables to control for endogeneity. Examples of instrumental variables include proxy measures for the intensity of conflict, such as Shemyakina (2011) used for Tajikistan. The difficulty in identifying Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 convincing instruments and the impossibility of implementing a randomized controlled trial forces conflict researchers to confront the limits of any identification strategy. Panel data, when available, minimize problems related to recall and ex post measures and enable researchers to trace the dynamics of conflict over time (e. g., Guerrero-Serdan 2009). Some studies have constructed panel data by resurveying households that had been surveyed before the conflict, such as Bundervoet, Verwimp, and Akresh ’ s (2009) study in Burundi and Andre and Platteau ’ s (1998) study in Rwanda. Researchers have often empirically addressed the issue of attribution of causality using difference-in-differences, a nonexperimental technique that compares a conflict-affected group (or region) with its preconflict situation and with a control group (or region) that did not experience the conflict. The validity of this method is contingent on no other changes between the two groups (regions) at the same time as the exposure to conflict. Even if there are no other changes, there may be spillover effects from the conflict-affected group to the nonconflict control group, leading to an underestimation of the effects of conflict. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 Mortality and morbidity Young adult men typically suffer the highest mortality in conflicts, creating a shortage of working-age males and a high share of females and widows in the population (figure 2 shows missing men in postwar age pyramids for Cambodia, Germany, and Russia). The World Bank (2011) estimated that men constitute 90 percent of the missing, whereas women and children constitute 80 percent of refugees and those internally displaced by violence. According to reports by the Truth and Reconciliation Commission, males accounted for 74 percent of reported fatalities in Sierra Leone, 87 percent of reported fatalities in East Timor and 84 percent of reported killings and disappearances in Guatemala (Cohen 2011). Similarly, Obermeyer and others (2008) reported that males accounted for 81 percent of violent war deaths in 13 countries over the period from 1955 to 2002. De Walque and Verwimp (2009) used age at marriage to partially control for being a Tutsi and estimated that adult males and educated people were most likely to die in 1994, the year of the Rwandan genocide that killed at least 500, 000 people. In Kosovo, Spiegel and Salama (2000) found that men were 8. 9 times more likely than women to die from war-related trauma. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The World Health Organization estimated that in the year 2000, there were 310, 000 deaths due to wars. These deaths were concentrated primarily among men aged 15 to 44. Nearly half of these deaths occurred in Africa, where there were an estimated 32 male deaths per 100, 000 population due to war-related injuries compared with 15 female war deaths per 100, 000 population. The disability burden from war was similarly skewed toward men and toward Sub- Saharan Africa (Krug and others 2002). The extent to which males suffer higher mortality than females varies somewhat with the nature of the conflict. Foreign armies sent to participate in a conflict tend to consist largely of young men, so excess deaths are highly concentrated among this group (Buzzell and Preston 2007). When the conflict is on home territory, women may also suffer elevated mortality, and they do so particularly as an indirect consequence of war. Using census and DHS survey data, Neupert and Prum (2005) estimated that 65 percent of the approximately 2 million deaths during the Khmer Rouge occupation in Cambodia were men. As many as 45 percent of all deaths occurred among people younger than 14 years or older than 60, suggesting that most of these deaths did not occur Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 genocide in 1994, with estimates that approximately 300, 000 to 400, 000 women suffered rape; Somalia in the early 1990s; the conflict in Kashmir; the 15-year-long civil war in Peru; and the recent civil war in Sudan (See, for example, McGinn 2000; El Jack 2003; Human Rights Watch 1995, 1996; McGinn 2000; Swiss and Giller 1993). A global review of 50 countries found significant increases in gender-based violence following major wars (World Bank 2011). Estimates of sexual and gender-based violence can suffer in both wartime and peacetime from serious underreporting (i. e., because people are unwilling or afraid to report gender-based violence, especially when the perpetrator is a family member) or overreporting, when incidence statistics are inflated because reporting improves with time (Nordås and Cohen 2011). This situation also occurs in peacetime, making it very difficult to accurately assess the increases in sexual and gender-based violence that are associated with conflict. Recent evidence highlights variations in the prevalence of sexual and gender-based violence in war situations and relates this variation to combatant norms and group cohesion. This evidence shows that Bosnia and Rwanda are anomalous cases of wartime rape being used as a war weapon for ethnic cleansing. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In most other cases, sexual and gender-based violence is a crime of opportunity that is often committed by relatives rather than strangers (Wood 2009, 2006). A study of sexual violence in a pediatric ward in Goma, Democratic Republic of the Congo, showed the predominance of domestic sexual violence over militarized rape. Of 500 pediatric cases treated for sexual violence at the hospital (2006 – 2008), nearly all were females between the ages of 10 and 18 (Kalisya and others 2011). Also in the Democratic Republic of the Congo, the results of a population-based household survey with a randomly assigned module on sexual violence (a subsample of 3, 436 women) yielded a very high prevalence of rape — an estimate of more than 400, 000 women were raped in the 12 months prior to the 2007 survey — and showed that the most pervasive form of sexual violence was from intimate partners and that the most conflict-affected provinces were at higher risk of sexual violence (Peterman, Palermo, and Bredenkamp 2011). A population-based random cluster survey of adults in Liberia (conducted in 2008) is one of the few quantitative studies on the legacy of sexual violence in conflict situations. The study showed Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 These findings extend to ex-combatants. Employing survey data from northern Uganda, where rebel recruitment generated quasi-experimental variation in people who were conscripted, Blattman (2009) found that abduction leads to greater postwar political participation, with a 27 percent increase in the likelihood of voting and a doubling of the likelihood of being a community leader among former abductees. Ex-combatants were also nearly twice as likely to participate in youth peace clubs. Many of the abductions were brief, especially among those younger than 11 or older than 20 years. However, the average duration of abduction was 15. 3 months, and the average abductee reported receiving, witnessing, or perpetrating 11 violent acts (Blattman 2009, 233). Abductees who witnessed the most violence were also most likely to participate politically later in life. However, abduction does not generally affect nonpolitical forms of social activity, suggesting that the effects of war on participation may be uniquely political. Another positive outcome of peace processes and political transitions has been women ’ s increased participation in civil and political life. As survivors of conflict, the expansion of women ��� s roles in postconflict reconstruction often leads to the emergence of women ’ s organizations and networks. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The notion that volunteering may affect positively youth ’ s sense of social cohesion has gained policy relevance since the publication of the World Development Report 2013: On Jobs, which stresses that in countries affected by conflict situations, creating the types of productive opportunities that strengthen social cohesion can help reduce the volatility of economic growth and achieve international development goals by defusing tensions and building trust among the different communities involved. This paper provides novel empirical evidence on the impact of volunteering on enhancing social cohesion values in Lebanon, a country with a fragile and highly complex political, religious and social landscape, as well as high degrees of social and economic exclusion among its young population. To our knowledge, this is the first impact evaluation that rigorously addresses this research question in Lebanon and in the Middle East and North Africa (MENA) region. The main results show that youth who were selected to participate in a volunteering program that consisted of 80 hours of inter-community volunteering activities and 20 hours of soft skills training were more likely to report higher and improved values of social cohesion in the short term. In specific, they were more likely to report higher tolerance values as well as a stronger sense of belonging to Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 SECTION 1: CONTEXT & INTERVENTION Lebanon ’ s political development system since Independence has been heavily influenced by its confessional system. While originally established to balance the competing interests of Lebanon ’ s diverse religious communities, it is seen as an impediment to inclusive growth and effective governance (World Bank, 2016), and has been closely tied to the economic and social inclusion challenges facing Lebanese youth today. The confessional system of governance has heavily impeded the equitable and efficient distribution of investments and public services. Provision and targeting of public services tend to be guided by considerations of confessional quotas and electoral geography rather than needs- based service delivery that favors the poor. In the absence of effective state institutions, sectarian organizations have played a key role in the provision of social services such as education, health, and welfare support to the most vulnerable groups linked to their electorates, thus deepening a sense of discriminatory and inequitable system (World Bank, 2016; Kraft et al., 2008). Regional disparities are stark, with the bulk of the poor living in peripheral areas (particularly the North and the South), with visible inequality in access to and quality of social services. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "According to the 2012 Institutional Profiles report, the quality of public services and its territorial coverage, which was weak to being with, have significantly deteriorated since 2006. 1 A combination of rising poverty, rising insecurity, and deteriorating public services have further strained inter-communal relations and contributed to deteriorations in social cohesion. Many Lebanese youth do not trust their state and become disillusioned as they are not able to affect their own life or contribute productively to society at large. 2 Political and civic engagement is reported to be low (Status of Women in the Middle East and North Africa Survey Project, 2010). 3 In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and large numbers of refugees, many Lebanese 1 On the quality of public services indicator, Lebanon ’ s score declined from 2. 5 in 2006 to 0. 8 in 2012 on a 4-point scale. On the territorial coverage indicator, its score went down from 2. 7 in 2006 to 1. 5 in 2012. 2In a Gallup World Poll, Lebanese reported low confidence in (a) their national government (37 percent) and the judiciary, (b) the honesty of elections (15 percent), and (c) the honesty of government (4 percent) (World Bank, 2016). 3 According to the SWMENA survey, only 18 percent of Lebanese women are members of an organization, compared to 34 percent of men. Men are more likely to be members of a political organization than women (21 percent of men vs. 7 percent of women), whereas women are more likely to be active in religious groups and charity organizations than men. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Challenges around skill mismatch exacerbate exclusion, as many young Lebanese lack the skills and competencies demanded by private sector employers, particularly ‘ soft skills ’. To address some of these challenges, the Lebanese government (GOL) identified volunteerism as a mechanism to enable diverse youth to work together for improved community assets and service delivery as well as increased employability. In September 2012, the GOL issued a Decree (Number 8924 / 2012) that created a new extra curriculum program that requires secondary school students to complete 60 hours of civil work. In addition, the Ministry of Social Affairs (MOSA), through its Volunteering Department, launched annual action plans for the implementation of youth volunteer summer camps across Lebanon. 4 Father ’ s education and residence (region and location of school) are the two largest contributors to inequality of opportunity in students ’ math test scores, accounting for 44 and 23 percent of total inequality, respectively (World Bank, 2016). 5 According to the 2013 Gallup Poll, 90 percent of respondents in Lebanon agreed with the statement that knowing people in high positions is critical to getting a job. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 as self-confidence, responsibility, and respect. Additionally, the curriculum includes workplace readiness skills, such as interviewing and time management. SECTION 2: STUDY TIMELINE & DATA A quasi experimental impact evaluation design was embedded into the NVSP. As mentioned before, the NVSP received 38 applications from eligible NGOs. Per well-developed selection criteria, 8 the highest 22 ranked proposals were selected to receive funding. Each of the 38 proposals included a list of 50 youth (the minimum number of youth set by the NVSP) who would benefit from the project if selected for funding. However, as mentioned before, the 22 selected projects benefited a total of 1, 296 youth, exceeding the set target of 1, 100 volunteers. Of the 50 volunteers included in each of the 38 proposals, 22 youth per proposal were randomly selected to participate in the impact evaluation study. Therefore, the initial sample size of the study comprised a total of 825 youth: 473 youth who served as the treatment group (representing the 22 selected NGOs that received NVSP funding) and 352 youth who served as the comparison group (representing the 16 non-selected NGOs). However, two NGOs refused to participate in the study once informed that their proposals had not been selected for funding. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 outcomes between selected and non-selected youth at baseline. 𝛿 is the DiD estimator. 𝜀 ௜ ௧ is a mean-zero error term. Standard errors are robust and allow for intra-cluster correlation at the NGO level. 10 The DiD estimator can be derived from the above regression as follows: E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 1ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽. 1 + 𝛾. 1 + 𝛿 (1. 1) + E (𝜀 ௜ ଵ | 𝐷 ௜ ൌ 1ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽 + 𝛾 ൅ 𝛿 E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 1ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽. 0 + 𝛾. 1 + 𝛿 (0. 1) + E (𝜀 ௜ ଴ | 𝐷 ௜ ൌ 1ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛾 E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 0ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽. 1 + 𝛾. 0 + 𝛿 (1. 0) + E (𝜀 ௜ ଵ | 𝐷 ௜ ൌ 0ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽 E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 0ሻ = 𝛼 + 𝜇 ௡ ൅ 𝛽. 0 + 𝛾. 0 + 𝛿 (0. 0) + E (𝜀 ௜ ଴ | 𝐷 ௜ ൌ 0ሻ = 𝛼 ൅ 𝜇 ௡ Hence, the Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "DiD estimate is (E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 1ሻ- E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 1ሻ) – (E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 0ሻ- E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 0ሻሻ ൌ ሺ 𝛽 ൅ 𝛿ሻ െ 𝛽 ൌ 𝛿. The DiD estimator relies on the “ Equal Trend Assumption ” that does not require both selected and non-selected youth to be on average balanced at baseline on key observable & unobservable characteristics. Table 1 shows that both groups differ on some key characteristics. Non-selected youth are more likely to be older, more educated (hold more academic degrees), come from Beqaa and Nabatiye, and have parents with intermediate education (grade 7 to 9). Selected youth are more likely to be males, younger, students, come from Mount Lebanon and the North, and have mothers with university education. Both groups appear balanced on key outcomes related to soft skills, tolerance values, and labor market outcomes. The exception is that non-selected youth exhibited a better sense of belonging to the Lebanese community and selected youth were more likely to have been unpaid employees (interns) at the time of baseline data collection. In addition to comparing means of observable characteristics, the study also tested for the differences in the statistical distributions of key outcomes using two sample Kolmogorov-Smirnov tests of the equality of distributions. Results indicate that the only key outcome for which there is a statistically significant difference in its distribution between the treatment and comparison groups at baseline is the sense of belonging to the Lebanese community. The largest difference between the distribution functions in the direction that the comparison group contains larger values 10 Standard errors are clustered at the NGO level because that was the unit of allocation into treatment and comparison groups. Abadie et al. 2017 argue that clustering is generally needed even if NGO fixed effects are included in the regression. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "FE remove the effect of individual-specific time-invariant characteristics so as the net effect of the predictor variable on outcome variables can be assessed, per the following equation: 𝑌 ௜ ௧ ൌ 𝛼 + 𝜇 ௡ + 𝛽𝑇௧ + 𝛾𝐷 ௜ + 𝛿ሺ𝑇 ∗ 𝐷ሻ ௜ ௧ + 𝜃ଶ𝐸ଶ ൅.. ൅ 𝜃 ௡ 𝐸 ௡ ൅ 𝜀 ௜ ௧ (3) where 𝐸 ௡ is entity n (i. e. the individual volunteer). Since they are binary (dummies), there are n-1 included in the model (i. e. 758 individual volunteers). 𝜃ଶ is the coefficient for the binary regressors (the 758 volunteers). Additionally, we propose dealing with attrition in two ways. First, we utilize the standard “ Manski Bounds ” approach (Horowitz and Manski, 2000) by imputing upper and lower bound estimates for missing data on estimated outcomes of interest at follow-up, where lower bound estimates take the lowest possible value and upper bound estimates take the highest possible value for individuals who could not be tracked over time. This allows us to provide the two extreme possible scenarios for estimated impacts had data been successfully collected for attritors. Second, we use the Inverse Probability Weighting (IPW) procedure to establish narrower bounds that might provide a better sense of whether there is a robust treatment effect. This entails first estimating a probit model that predicts the probability of data being observed (i. e. not attrition) using a set of covariates at Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 baseline that were found to be uncorrelated with the treatment in Table 1. 12 Observations are then weighted by the inverse of their probability of having data observed. Therefore, those who had a small chance of being observed are given increased weight, to compensate for those similar observations who are missing. The pseudo R-squared from the probit model suggests that those baseline covariates explain about 8 percent of the probability of data being observed. A Wald test confirmed that those variables are jointly statistically different from zero (the P-value is 0. 000). However, this still leaves a large percentage of attrition (around 92 percent) unexplained. 13 Therefore, we note that the results in the following section should be interpreted with caution. We present results in the next section for four specifications. Specification 1 presents OLS estimates from equation 1. Specification 2 presents results that control for individual fixed effects from equation 3. Specification 3 presents OLS estimates for the full sample by imputing missing observations for attritors at follow-up using lower and upper bound estimates. Specification 4 presents OLS estimates with the estimated constructed weights. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 do selected volunteers increase their teamwork / leadership and their communication skills? Do they increase their self-esteem / self-satisfaction? iii) As a result of their assignment to NVSP volunteering experience, are selected youth more likely to find a job than non-selected ones? (a) IMPACTS ON SOCIAL COHESION VALUES The two main indicators that measure improvements in social cohesion values are tolerance and a sense of belonging to the Lebanese community. Measuring social cohesion values in large-scale surveys is challenging. We are unable to use extensive measures, but rely instead on brief measures adapted from Harb (2010). The tolerance measure relies on a series of 12 questions, each of which is ranked on a four-point scale, which makes the total possible score range between 12 and 48 points. The sense of belonging to the Lebanese community measure consists of 18 questions, each of which is ranked on a seven-point scale, which makes the total possible score range between 18 and 126. Thus, higher scale values indicate higher tolerance values and a stronger sense of belonging to the Lebanese community. Both values are internally standardized so that they have a mean of 0 and a standard deviation (S. D.) of 1 in the comparison group at baseline. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "They have also been piloted ahead of data collection to test their validity and reliability. Results in table 4 indicate that assignment to NVSP has no impact on the three indicators for soft skills among selected youth for specifications 1 & 2 (see the 𝛿 estimate for columns 1, 2, & 3). The leadership skills measure appears to have worsened for both selected and non-selected youth over time, which is somewhat puzzling given that both groups are active volunteers and members in their communities (see the 𝛽 ൅ 𝛿 estimate and the 𝛽 estimate for column 1). Any changes for the communication and confidence scores one year following NVSP were not statistically 15 The selection of the indicator for this study was based on its extensive utilization (to maximize the chance for the scale to be reliable when calculating Cronbach ’ s Alpha with the data of the pilot), on the availability of detailed information regarding how the indicator was designed, and of how the scales should be interpreted once data have been collected. 16 This scale had been tested with youth aged 12-18 showing high levels of internal consistency. Additionally, it was a relatively simple scale with no need for special training to administer it or to analyze the results of the scale. 17 These skills include: awareness of one ’ s own styles of communication; understanding and valuing different styles of communication; practicing empathy; adjusting one ’ s own styles of communication to match others'styles. (communicative adaptability); and communication of essential information; Interaction management. 18 This scale has been used extensively in the psycho-social / soft skills literature, ensuring possible comparability with other studies of the soft skills literature. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 significant for both selected and non-selected youth (see the 𝛽 ൅ 𝛿 estimate and the 𝛽 estimate for columns 2 & 3). Lack of results also holds for specification 3, where imputing missing data with upper and lower bound estimates to account for the potential bias introduced by attrition did not alter the lack of impact of the program, as well as for specification 4 (see the 𝛿 estimate). Figures 1, 2, and 3 plot the distribution of soft skills scores at baseline for both selected and non- selected youth. The figures indicate that scores across the three skills are concentrated towards the end of the scale, suggesting that soft skills training offered by NVSP might have been ineffective or too basic for this pool of volunteers. 19 Indeed, as table 1 shows, a high percentage of selected youth (71 percent) and non-selected youth (63 percent) had taken previous training in soft skills prior to NVSP. Results from a process evaluation conducted separately support this explanation. The majority of NVSP volunteers in focus group discussions and interviews mentioned that they would have welcomed more advanced trainings on soft skills, as well as on technical topics and job-relevant skills that can support their employability. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These may include skills on how to write a CV, prepare for job interviews, business and entrepreneurial skills to start a business, etc. In this regard, the mechanism for improving social cohesion values appears to have come from inter-community volunteering activities, rather than improvements in soft skills. (c) IMPACTS ON LABOR MARKET OUTCOMES While the NVSP was designed primarily to improve social cohesion values among participating Lebanese youth, it was also hoped that engaging them in volunteering activities, coupled with soft skills training, would enhance their employability and thus increase their chances of employment. At baseline, half of the selected and non-selected volunteers were active and searching for a job. Among them, 49 percent reported being unemployed, 31 percent wage employed, 13 percent employed in unpaid jobs, and 7 percent self-employed (see table 1). Those active volunteers were older in age than the rest of volunteers who reported being inactive in the study ’ s sample (with an average age of 21 and closer to labor market insertion). One year later, it appears that many of 19 Our interpretation that offered soft skills are likely too basic for this pool of volunteers is provided given the scale that we used in the questionnaire to test their knowledge on soft skills. We cannot rule out the possibility that had we used a different scale, we might have found an impact, either negative or positive. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Following the approach used for the Global Costing of Refugee Inclusion, successful inclusion is defined as earning sufficient income to be no longer poor and to consume more than the (international) poverty line. This definition opens two tracks for investigation: first, how much aid would be needed if the policy objective were to bring refugee consumption up to the poverty line. The answer to this question is found by identifying the poverty gap for refugees. This opens the second track which explores the factors that determine, or at least that are associated with, the size of the poverty gap. The note is organized as follows. The next section discusses data and presents some key descriptive statistics on refugees and host communities in Uganda. This is followed by a methodological section discussing how own income and aid are complements and how an analysis of poverty gaps informs about the need for assistance. This is followed by two analytical sections. The first identifies refugee poverty gaps, and assistance needs for refugees with distinct characteristics. The following section estimates how much has been saved by including refugees in the economy and explores how more could be saved. Conclusions follow. 2 The poverty numbers in World Bank (2019) are based on the official poverty line adopted in Uganda in 1997. There was a need to update this line as it was too old and producing a very low poverty rate. For example, using this line produced a national poverty rate of about 21 percent in 2019 / 20 compared to more the than 40 percent international poverty rate using the USD 2. 15 2017 PPP daily poverty line. In order to address this criticism, the poverty line was revised by the Uganda Bureau of Statistics in 2021, but it is not available for the 2018 Refugee and Host Communities Household Survey used in this note. Instead, we are using the international poverty line throughout. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 Figure 10. Selected characteristics across Ugandan and refugee households, % Source: RHCS 2018, WB staff calculations. A simple comparison between refugees and Ugandan households demonstrates that refugees lag with regards to selected characteristics found to narrow the poverty gap. For example, refugees are less likely to have access to land than Ugandans. If refugees have access to land, the majority do not own it, but have user rights. The size of land also differs a lot among Ugandan and refugee households. Most Ugandans have at least 0. 05 hectare per capita, while the majority of refugees have less than 0. 05 hectare per capita. Refugee heads of household are also less likely to work and less likely to be literate compared to their Ugandan counterparts. Refugees have higher shares of children and elderly in household size compared to Ugandans. For example, among almost 60 percent of refugee households, more than half of the household members are children and elderly compared to 42 percent of households among Ugandans. Economic inclusion dividend When a development approach to hosting refugees is followed and refugees earn incomes, there are two key beneficiaries. Refugees themselves, who gain dignity, financial autonomy and pathways to self-reliance. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 if they are built to code. 2 These spillovers or externalities are absent in more sparsely populated rural areas where damages to smaller sized and dispersed dwellings will cause less or no collateral damage. Exposure The main reason why urban risk is large and increasing is the rise in exposure. Although urbanization statistics suffer from a lack of standard definitions of what should be considered ‘ urban ’, the assumption of half the world ’ s population living in cities seems realistic. Urban populations are growing in practically all developing countries. About 40-60 percent of this growth can be attributed to natural growth, i. e., fertility of urban dwellers (Montgomery 2009). The remaining growth is due to urban expansion and migration, reducing the share of rural residents except where rural fertility is vastly larger. The latest UN urban population estimates suggest that, globally, urban population exceeded rural population for the first time in 2008 (UN 2008). In less developed regions, this threshold is expected to be reached by 2019. This continuing urbanization process will lead to an increase of exposure of people and economic activity in hazard prone urban areas. Although we can only speculate about the global distribution of disaster damage in cities today and in the future, newly available geographically referenced data yield some estimates of urban exposure to natural hazards. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A recent global hazard analysis generated a comprehensive database of hazard events during 1975- 2007 from observed data and of event probabilities from geo-physical models (ISDR 2009). We combined this hazard information with city-specific population projections 2 It should be noted that well-documented evidence for such externalities is quite scarce. Their importance is usually taken as given: “ A building collapse may create externalities in the form of economic dislocations and other social costs in addition to the economic loss suffered by the owner. The owners may not have taken these consequences into account when evaluating specific mitigation measures. Consider the following example. A building toppling off its foundation after an earthquake could break a pipeline and cause a major fire, which would damage other homes that had not been affected by the earthquake in the first place. “ Kuenreuther and Roth (1998). See also www. quakesmart. org / index. php? option = com_content & view = article & id = 92 & Itemid = 209. But some experiences have been documented: “ As shown by research on the Great Hanshin-Awaji Earthquake, including that conducted by the Architectural Institute of Japan, Architectural Institute of Japan (1997), houses with inferior earthquake-resistant quality triggered large negative externalities in the neighborhood. For example, broken fragile houses blocked transportation networks, thereby preventing effective fire fighting and, by severing lifelines, they made recovery more difficult. ” (Nakagawaa et al. 2007). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 Investors actively trade-off disaster risk with gains from economic density. In addition to the city or location specific analysis, we examine how investors value risk from natural disasters. Data from a recently compiled dataset of a sample of global cities provides some insights. Gomez-Ibañez and Ruiz Nuñez (2006) constructed a dataset of central business district office rents for 155 cities around the world in 2005 to identify cities where rents seem elevated or depressed by poor land use or infrastructure policies. Their dataset also includes information on many factors that determine the supply and demand for central office space such as construction wage rates, steel and cement prices, geographic constraints, metropolitan populations and incomes. We link this information to the natural disasters hotspot dataset (Dilley et al. 2005), and examine if city demand – as reflected in office rents, is sensitive to risk from natural disasters. Gomez-Ibañez and Ruiz Nuñez (2006) focus on offices in the primary business district, which they define as the district having the highest density of employment; a very large, if not the largest, concentration of offices; and the highest rents in the metropolitan area. As we are interested in the tradeoff between economic density and disaster risk, using the central business district works well for our analysis. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "19 As property prices in the worst affected areas reduced the most, low income households responded by moving into low-rent housing being offered in these locations. On the other hand, middle income households moved away to avoid risk, and the wealthy, for whom insurance and self-protection was the most affordable, did not change where they lived. Poor people “ sort ” into low rent locations – which are often at higher risk to natural hazards. The problem is particularly acute in developing countries where there is a divide between the formal and informal markets for land. While formal developments may respect land use regulations, informal settlements are often located in hazard prone locations, such as on hill slopes, close to river banks, or near open drains and sewers. In Dhaka for example, informal settlements are developing across the metropolitan area, with many residents lacking basic public services and in locations at risk from flooding. In fact, most informal settlements do not have access to a public toilet within 100 meters, and 7, 600 households in 44 slums live within 50m of the river (World Bank 2005, Dhaka Urban Poverty Assessment). For the city of Bogotá, we use the same database discussed earlier to examine if poor people are at greater risk from natural hazards – particularly earthquakes. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In our employment arm, we offer gainful employment in the form of a surveying assignment for an average of three days per week for two months. 1 The surveying task requires workers to walk through their blocks four times per day tallying the various activities their neighbors are engaged with and consumes approximately 2. 5 hours per workday, resulting in a form of part-time employment. The job is designed to embody the key features inherent to ‘ work ’. Drawing from the economics literature, workers must exert real effort and their task occupies a meaningful portion of their work day. Drawing from the sociology literature, the work involves some degree of sociability and purpose in the completion of a productive task. Employment lasts for eight weeks, a long duration given the scarce daily labor opportunities that arise in our setting. Relative to this employment arm, our control arm receives no work and a small fee for weekly survey participation. A comparison of the control to the employment arm therefore yields the psychosocial benefits of the employment intervention. In order to estimate the non-pecuniary psychosocial value of employment, we include a cash treatment arm, in which no work is offered, but a large fee (equivalent to that received by those in the employment arm) for weekly survey participation is provided. We work in the Rohingya refugee camps, situated upon the southern tip of Bangladesh. 1We obtained formal permissions from camp administration to engage our study participants in this manner through our NGO partner, Pulse Bangladesh. 1 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "An additional 33 blocks were assigned to the cash group, where participants earned 450 taka (USD $ 5. 30) per week as compensation for survey participation. Finally, 83 blocks were assigned to a work group, where we offered participants gainful employment. We compensated participants in this treatment arm with 150 taka (USD $ 1. 77) per day of work. Households were assigned an average of three days of work per week, resulting in 450 taka per week on average over the course of the eight weeks and thereby equivalent to that received by the cash group. All participants were aware of the randomization process: enumerators described the three arms and displayed the random number to the participant as it appeared on their tablet, assigning the participant to his or her treatment group. Employment intervention details We now turn to the nature of the employment we offer. Employees were asked to engage in a data collection exercise in which they completed time-use sheets describing the activities of fifteen unnamed, same-sex neighbors of their choosing four times per day. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We asked that households complete their work on specific days to which they were as- signed: work schedules varied week to week, averaging three days weekly. To ensure com- pliance with the work schedule, we stationed a tamper-proof box in a preselected household within each block (the facilitator household) and informed participants that they should submit their tasks into the box at the end of each assigned workday. The facilitator would slip an additional piece of paper into the box at the end of the day to bookend that day ’ s set of submissions, and the respondent ’ s submission was marked late if it was inserted after the bookend. Facilitators were compensated with an additional 50 taka per week for their services, and had no access to the materials inside the box. Along with dropping offtheir submissions at the end of each workday, participants were instructed to visit the facilitator ’ s home on their designated ‘ collection day ’ each week. The facilitator made their home available for a few hours on this day so the enumerator could complete the check-ins with the block ’ s five respondents and pay the participants their respective amounts in a relatively private setting. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "required to purchase other basic staple foods such as salt and vegetables. Given that the WFP provisions are the only reliable rations that refugees receive, we approximate a cash transfer of 450 taka per week to at least double potential weekly consumption. Relative to the wealth refugees possess, 450 taka per week is likewise sizeable: average baseline savings is 195 taka, with the median refugee reporting zero taka in savings. Average baseline borrowing (typically in the form of store credit) is 1, 600 taka, with a median of 600 taka. Refugees have no economically meaningful assets that may be more common among the rural poor, such as land or cattle, given the unanticipated displacement which forced them from their homes. Relative to other employment opportunities, average reported pay is 300 taka per day for less than three days. The monthly cash transfer is therefore more than double what a refugee might expect from alternative employment if he or she is fortunate enough to secure a job. 4 Data Collection and Survey Instruments Timeline and survey instruments We collected data via a baseline, commencing in November 2019, and endline survey, commencing in February 2020, as well as seven midline surveys conducted prior to payment disbursal each week. These weekly surveys collected a small subset of well-being outcomes. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In an effort to ensure that our temporary interventions had no unintended negative mental health consequences on our participants, we also con- ducted a final short followup survey six weeks after the interventions concluded (Appendix Figure A1. We had 2 % attrition at endline and followup, neither differential by treatment arm (Appendix Table A1). Main outcome variables Our primary outcome of interest is psychosocial well-being, which we assess through an index of seven mental and social health measures, henceforth re- ferred to as the psychosocial (PS) index: depression, stress, life satisfaction, locus of control, sociability, self-worth, and stability. Our measures of depression, stress, life satisfaction, and locus of control are drawn from standard screening tools (PHQ-9, Cohen ’ s Perceived Stress Scale, Diener ’ s Satisfaction With Life Scale, and the Levenson Multidimensional Internal Locus of Control Scales, respectively) adapted for sensitivity to the Rohingya camp context. The PHQ, our depression screening tool, has been validated against antidepressant medica- tion (L ¨ owe et al., 2006) and employed in the cross-section among refugee populations (Poole et al., 2018) as well as in experimental evaluations of psychotherapy programs in South Asia (Patel et al., 2017; Bhat et al., 2021). For sociability, we inquire about the number of interactions that participants have had 11 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Having experienced the work task and therefore able to realistically value the work, we offer individuals in the employment arm an additional [surprise] week of work at a series of wages following the incentivized Becker-DeGroot-Marschak (BDM) method. We inform participants that we have a limited amount of funds remaining and are therefore unable to pay everyone their previous wage. This strategy realistically motivates the reservation wage elicitation exercise and makes clear that there will be no further opportunities for work. We piloted this exercise extensively. To maximize comprehension, we employ a multiple price list strategy, embed repeated confirmations, and conduct a trial run of the exercise for each respondent before the real exercise; this mimics the procedure employed in Burchardi et al. (2021) for which participants in another low-income country field context exhibited high comprehension. For those individuals who express willingness to work at a wage of zero, we offer an alternative option of answering a brief survey at the end of the week for a small, randomized fee; we then use the fraction of respondents who are willing to forego this paid option and instead work for free as an estimate of the proportion of respondents who have a negative reservation wage of at least the foregone magnitude. 16 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Second, we complement our psychosocial index with measures that are not vulnerable to experimenter demand. Demand effects are unlikely to alter one ’ s cognitive ability as measured through the arithmetic questions and memory tests of our cognitive index. Our risk and time preference games are incentivized with meaningful stakes (respondents gamble with a minimum of 1. 20 USD in the risk preference game and trade off3. 50 USD today with higher amounts tomorrow in the time preference game), stake sizes that de Quidt, Haushofer, and Roth (2018) have found effectively eliminate demand effects. Perhaps employed individuals feel a need to impress the enumerator, as their proximate employer, in a way cash recipients do not. This may lead to reporting better mental and physical health and investing greater effort in the cognitive tasks. However, we find that life satisfaction increases substantially for both groups, inconsistent with a differential desire to impress among the employed. We also observe patterns of treatment effects within our validated PHQ-9 module that are inconsistent with experimenter demand (Appendix Table 12The signaling value of the certificate may have been diminished if other employers learned about the nature of the certificate distribution. Our time in the field suggests this is unlikely: we randomized certificate distribution at the block level to limit spillovers, only five people in each block of ˜ 200 adults was involved in the experiment, and job opportunities were scarce. 13The certificate read “ I engaged with Pulse Bangladesh to do data collection ”. It was written this way in order to be generic enough to apply to all the individuals in the experiment, all of whom were providing us data from the weekly surveys. 18 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Such a channel would be consistent with psychology literature on behavioral activation, or the act of scheduling structured activities as a means of combatting depression (Cuijpers, van Straten, and Warmerdam (2007)). To explore this question, we supply a random subset of the employed with a calendar marking every date of work (Appendix Figure A5). The re- mainder receive a blank calendar and are instead informed weekly about their schedule. We find no impact of a schedule on respondent well-being or decision-making (Appendix Table A7). Despite this exercise, we cannot causally estimate the role of the structure alone on well-being, as the structure imposed by regular employment is coextensive with employment itself. Indeed, our measure of stability, which asks respondents how secure they feel at the moment and expect to feel in the future, increases substantially among the employed relative to both control and cash arms. Time use Does employment improve well-being by allowing participants to substitute time away from unsavory or psychosocially costly activities? Appendix Table A8 presents how cash and work arms use their time. We document no significant difference between the two arms in the number of hours that respondents report spending across a variety of activities. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Notably, while we cannot rule out that time outside due to employment may play a role (e. g., fresh air may boost one ’ s mood), our time-use data indicates that the average refugee already spends at least three hours outside per day, with no measurable difference between employed and cash arms. As we are powered to detect changes of at least twenty minutes for each activity, our results suggest that large substitutions away from unsavory activities are unlikely to be driving the improvements in psychosocial well-being, insofar as the respondent recalls. 1718 We also investigate whether those who were more idle prior to being employed benefit more from employment. We find no impact along this margin, suggesting that the elimination of boredom per se is not the driving force behind the psychosocial value of employment (Appendix Table A10). 17Most respondents do not track their day by time, making collection of reliable time use data challenging (though recent literature documents the broader unreliability of such data). We piloted a variety of strategies, and settled on asking respondents how much time they spent on a set of activities in the previous day. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "If that person is a 10, where would you put yourself? ” Locus of Control The standardized total score from responses to four locus of control questions. “ In the last 7 days, how many days did you feel that to a great extent your life is controlled by accidental / chance happenings... ” Allocation Decision Game Indicator (yes / no) for response to an offer to participate an allocation committee to decide how money is spent. Participants are offered the opportunity to make a resource allocation decision for their community or have another individual (an NGO worker, an “ expert ”, or another refugee) make the decision. Stability Index The standardized total score from responses to two stability questions using a Cantril ladder. “ How secure [do you feel / think you will feel] [at present / five years from now] ” Physiological Index An inverse-covariance weighted average of PHQ, Stress, Life Satisfac- tion, Sociability (Total), Self-Worth, Locus of Control, and Stability indices. Gender Dynamics Gender Perceptions- Work The standardized total score of two questions regarding women ’ s work, “ How often would you agree that women should be allowed to work for a living [inside / outside] the block? ” 64 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Gender Perceptions- Violence (IPV) The standardized total score of five questions regarding norms for inti- mate partner violence (IPV) from the Demographic and Health Survey (DHS) (The important decisions in the family should be made only by the men of the family. How often would you agree? The wife has the right to express her opinion even when she disagrees with what her husband is saying. How often would you agree? A wife should tolerate being beaten by her husband in order to keep the family together. How often would you agree? A husband has the right to beat his wife. How often would you agree? It is more important to send a son to school than it is to send a daughter. How often would you agree?). Financial Well-being Savings Response to the question “ How much money do you currently have in savings? ” During the collection surveys (midlines) this question instead asked “ How much money did you save in the past week? ” Borrowing Total amount of money the household has borrowed. Economic Decision Making Risk Preference Measured using incentivized responses to the multiple price list deci- sions adapted from Holt-Laury and Sprenger (2002). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Outcome Variable Collection Periods Basline Midline Weekly Endline Psychological Well-being PHQ9 X X Life Satisfaction Index X X Stress Index X X X Sociability (Total) X X X Sociability (Positive) X X X Self-Worth Index X X Locus of Control X X Allocation Decision Game X X Stability Index X X Physiological Well-being Index X X Gender Dynamics Gender Perceptions- Work X X Gender Perceptions- Violence (IPV) X X Financial Well-being Savings X X ∗ X Borrowing X X Economic Decision Making Risk Preference X X Time Preference X X Other Outcomes Cognitive Ability X X ∗ X Physical Health X X ∗ X Notes: The “ Baseline ” survey was conducted with respondents before treatment assignment was revealed. The “ Midline ” survey were questions asked immediately after treatment assignments were disclosed after the baseline survey, but before the work task had begun. “ Weekly ” surveys were conducted after each week of work (if any). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Has this hap- pened during the recent growth period in the CIS-7? Explaining labor market flows The key to understanding output and employment growth in the CIS-7 is evidently the relation- ship between self-employment which is largely informal and employees in the formal sector. And the transition from non wage (informal) to wage (formal) labor can occur thanks to 1) A migra- tion of workers from self-employment to wage labor or 2) Endogenous growth of self- employment turning into SMEs and generating formal employment. We have in fact introduced one further dimension of labor market segmentation, the wage / non-wage labor divide. Labor flows between these different states may contribute to explain the employment puzzle. For this purpose, we turn to Moldova, a country that in many respects could be considered as the average scenario in our CIS-7 sample. Moldova is also the only country that disposes of a consis- tent longitudinal panel survey between 1997 and 2002 which can be used to assess labor market flows during the growth period and test some hypotheses on the evolution of the labor market. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We use the whole longitudinal sample and the restricted panel sample to compute the statistics, the transitional probabilities and the probit regressions presented below. 19 In figure 4. 1, we show the distribution of the population across working categories and between 1998 ad 2002. The employed population has marginally increased in percentage of the total popu- lation. A migration of workers has also occurred from wage labor to non wage labor and this mi- gration has taken place mostly within agriculture. The most significant change in fact occurred among rural workers with farmers growing very significantly at the expenses of agricultural em- ployees. Non agricultural labor has remained practically unchanged during the period while agri- cultural employment has increased marginally. This phenomenon occurred during the post-1998 recession (1998-1999) and during the subsequent growth period (2000-2002). In table 4. 4, we report the population structure by category20. It is visible the constant growth of private agriculture and the constant decline of employment in agricultural enterprises in both the public and private sectors. Among non-agricultural enterprises, there is a growth in the private sector and a decline in the pubic sector suggesting a migration of workers between the two sectors 19 See http: / / www. statistica. md / for details on the survey. 20 Categories are identified on the basis of the main source of income of respondents. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 as the Demographic and Health Surveys (DHS), though researchers have reduced under-reporting by providing special training to interviewers, placing greater emphasis on respondents ’ privacy and safety, and allowing women multiple opportunities to disclose their experiences (Ellsberg et al., 2001a; Garcia Moreno et al., 2003; Ellsberg et al., forthcoming). Estimates of the magnitude of the problem Population-based surveys have found that between 10-70 % of women report being physically assaulted by an intimate male partner at some point in their lives (Heise, Ellsberg and Gottemoeller, 1999). See Annex A for estimates from many recent population based studies (Ellsberg et al., forthcoming). Findings from a multi-country study on domestic violence and women ’ s health carried out by the World Health Organization in fifteen sites and ten countries found that between 13-62 % of women had experienced physical violence by a partner over the course of their lifetime, and between 3-29 % of women reported violence within the past year (Figure 1. 1). Figure 1. 1. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Percentage of women who reported sexual violence by an intimate partner (ever), physical violence by an intimate partner (ever), and physical violence by an intimate partner in the past 12 months. 50 % 59 % 30 % 27 % 34 % 13 % 31 % 50 % 62 % 34 % 33 % 47 % 23 % 41 % 37 % 20 % 10 % 14 % 6 % 17 % 23 % 47 % 23 % 29 % 31 % 6 % 23 % 40 % 42 % 49 % 3 % 18 % 19 % 16 % 8 % 19 % 16 % 8 % 13 % 29 % 3 % 17 % 25 % 13 % 15 % Bangladesh (Urban) Bangladesh (Province) Brazil (Urban) Brazil (Province) Ethiopia (Province) Japan (Urban) Namibia (Urban) Peru (Urban) Peru (Province) Thailand (Urban) Thailand (Province) Tanzania (Urban) Tanzania (Province) Serbia Samoa sexual violence ever physical violence ever physical violence past 12 months Source: Unpublished data from the WHO Multi-Country Study on Women ’ s Health and Domestic Violence Against Women. The final published comparative report is forthcoming. Cited with permission. Prevalence data on sexual violence is even more limited than physical violence. However, evidence suggests that a substantial proportion of girls and women have experienced child sexual abuse, forced sex and other forms of sexual coercion in virtually every setting of the world. For example, population-based studies have asked about “ forced ” sexual debut among sexually experienced young people and found rates from 7 % (New Zealand), to 46 % (in the Caribbean) (Heise and Garcia Moreno, 2002). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A growing number of low and middle-income countries have tried to improve the law enforcement response to gender-based violence by training professionals, reorganizing police and courts, and trying to provide a more comprehensive response to survivors. Evidence of effectiveness is relatively limited; most well-evaluated initiatives come from high-income countries, and the lessons learned may not be applicable to developing countries. Evaluations of law enforcement reforms in low and middle income countries have typically been limited to case study approaches drawing from police records (notorious for under-reporting), qualitative perspectives from key informant interviews, intermediate outcomes such as changes in attitudes and knowledge among police and judges, and interviews with small numbers of women who have sought legal redress. Population-based data collection, control groups, or follow-up among more than a handful of survivors are rare. Nonetheless, the following initiatives illustrate the types of efforts that have produced important lessons learned. Training personnel in the police and judiciary and other parts of the justice system Throughout the world, organizations have launched efforts to improve the knowledge, attitudes, and practices of justice sector personnel regarding gender-based violence. Some law enforcement institutions organize training internally, as did South Africa following passage of the 1998 Domestic Violence Act (Usdin et al., 2000). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In other settings, NGOs such as Rozan in Pakistan (Rashid, 2001), Profamilia in the Dominican Republic (Guedes et al., 2002), and the Musasa Project in Zimbabwe have trained law enforcement personnel on issues related to gender-based violence. Elsewhere, governments have collaborated with the United Nations to provide training and support for the police and judiciary. For example, ILANUD is a joint institute of the government of Costa Rica and the United Nations that works with governmental agencies throughout Latin America to improve the work of prosecutors, judges, lawyers, police and other professionals in criminal justice generally, and gender-based violence specifically (Villanueva, 1999; ILANUD, n. d.). Most of these initiatives have been evaluated using key informant interviews and pre and post questionnaires before and after training-if they have been evaluated at all. Nonetheless, training appears to be both constructive and urgently needed (Rashid, 2001; Villanueva, 1999). Other lessons learned include the finding that changing attitudes of law enforcement is a challenging, long-term process. The quality of the trainings ’ content and the skills of the trainer are essential. Training appears to be most effective when all levels of personnel (especially high-level officials) participate, and when training is backed up with changes throughout the institution, such as policies, procedures, adequate resources, and continual monitoring and evaluation. Special police stations or cells for crimes against women All-women police stations began in Brazil and were later tried in other countries in Latin America and Asia. As of 2003, for example, Nicaragua had 17 police stations for women and children (called Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Unfortunately, evidence suggests that without system-wide reforms and support, single training sessions or even routine screening policies rarely produce long-term changes in the quality of care for survivors (McLeer et al., 1989; Heise, Ellsberg, and Gottemoeller, 1999). Instead, Heise and colleagues argue that the most effective way to improve the health care response is to use a “ systems approach ” involving reforms throughout the organization. Typically, these initiatives include changes in norms, policies and protocols, infrastructure upgrades to ensure private consultations, training all staff (including managers), ensuring that providers have adequate resources such as referral networks and directories, and strengthening the ability of staff to provide emergency services such as danger assessment, safety planning, emotional support, STI prophylaxis, and emergency contraception. In settings where adequate referral services do not exist, health programs sometimes offer specialized services such as counseling, legal aid and women ’ s support groups. The International Planned Parenthood Federation, Western Hemisphere Region (IPPF / WHR) carried out an initiative illustrating the “ systems approach ” in four member associations in Latin America, namely: Profamilia (the Dominican Republic), INPPARES (Peru), and PLAFAM (Venezuela), with some participation from BEMFAM (Brazil). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Quantitative and qualitative baseline, midterm and follow-up studies concluded that the initiative improved provider attitudes and practices; strengthened patient privacy and confidentiality; increased detection of women who experienced physical and sexual abuse; improved the overall quality of women ’ s health care; and benefited survivors through the provision of specialized services such as legal aid, counseling and support groups (Guedes, Bott, and Cuca, 2002; Guedes et al., 2002; Bott, Guedes, and Guezmes, forthcoming). This initiative benefited from generous funding from international donors, and it might be difficult for other organizations to replicate the project in its entirety; however, IPPF / WHR has disseminated a large body of recommendations and tools designed to help organizations in low-income settings build on their experiences. Routine screening (also called routine enquiry) Research indicates that without routine screening, providers typically identify only a fraction of women requiring assistance with physical or sexual abuse. Routine screening for violence has increasingly been considered the standard of care within women ’ s health services in the United States and other industrialized countries (American Medical Association, 1992; Buel, 2001). However, a vigorous debate has erupted over the benefits and risks of routine screening, particularly in resource-poor settings (Ramsay et al., 2002; Garcia Moreno, 2002). Some argue that routine screening may harm women in settings where providers are unprepared to respond appropriately, where privacy and confidentiality cannot be ensured, and where adequate referral services do not exist. In many settings, providers blame victims of gender-based violence-without an appreciation of gender issues or human rights-and may Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "29 evaluations have been conducted (Renton et al., 2000; Shaw, 2000; Shaw, 2002a; Shaw, 2002b; Paine et al., 2002; White, Greene and Murphy, 2003; Interagency working Group, 2003). For example, the Medical Research Council (from the United Kingdom) conducted an external evaluation of program effectiveness in the Gambia using key informant interviews, focus groups, and a KAP (knowledge, attitudes and practices) survey in four villages, including two program sites and two controls. That study found that the Gambia program improved self-reported attitudes and behaviors related to violence against women. Specifically, the program reduced the social acceptability of wife-beating at the community level and appeared to produce a corresponding drop in that behavior. Qualitative findings from other Stepping Stones sites suggest similar benefits. Program H (Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru) is being carried out by four NGOs. It aims to change gender norms and sexual behaviors in Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru (Barker, 2003; White, Green and Murphy, 2003; Guedes, 2004). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "By 2002, ReproSalud had reached over 123, 000 women and 66, 000 men. Qualitative and quantitative evaluation data suggest that the community-based PLA approach had a positive impact on attitudes and behaviors related to gender based violence (Rogow and Bruce 2000; Ferrando, Serrano, and Pure, 2002, cited in Boender et al., 2004). The quantitative evaluation (using community based surveys) was complicated by the fact that the project coincided with a period of strong investment by the Ministry of Health, which made it difficult to isolate the project ’ s impact. Gender-equitable attitudes and practices increased significantly in both intervention and control communities, though improvements in intervention sites were slightly higher. The qualitative data suggested a much greater difference in intervention and control sites and gathered evidence of dramatic changes in social relations and men's behavior. Respondents spoke at length about decreased alcohol consumption, domestic violence, and forced sex in all intervention villages studied. In the words of one 35 year-old woman,\"Before, they brutally forced sex. They hit, especially when they were drunk. Now, no more\"(Rogow and Bruce, 2000, page 20). Individual behavior change strategies Many other programs have attempted to produce individual (rather than community-level) behavior change by working with individual men and boys. White, Greene and Murphy (2003) reviewed the literature on such programs aimed at men. That review suggests that less information is available on the effectiveness of individual behavior change strategies compared to community-level approaches. Some Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "33 recommendations (Jewkes, 2000). Their efforts contributed to the Employment of Educators Act and new Department of Education guidelines, both of which were introduced in 2000. These regulations mandate dismissal of educators found guilty of sexual or physical assault, or of having a sexual relationship with a student. They also define penalties for failing to report abuse. It remains to be seen whether these measures will have the intended impact. After the act was passed, Human Rights Watch (2001) suggested that the South African government needed to do more to increase awareness of the law among school principals and to strengthen enforcement. Institutional reform Efforts to improve the institutional response to gender-based violence range from sensitization and training of staff, sexual harassment policies, curriculum reform, school-wide anti-violence awareness campaigns, counseling and referrals, and broader efforts to reduce discrimination against girls and improve school safety. Initiatives to increase female enrolment by improving girls ’ safety at and on the way to school As mentioned earlier, parental concerns about girls ’ safety in school appears to lower female school enrolment in settings such as South Asia, Africa and the Middle East. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some initiatives have addressed these concerns by establishing single sex schools, hiring more female teachers, building separate latrines or canteens for girls, reducing the distance that girls must travel in order to receive an education, and / or providing in-service gender sensitivity training to teachers, principals and inspectors (UNICEF, 2004). For example, the UNICEF African Girls Education Initiative (AGEI) used a combination of these approaches, (along with other strategies) to boost girls ’ enrolment in 34 African countries (UNICEF, 2003a). Evidence of this project ’ s effectiveness was limited in many sites, largely due to limitations in the evaluation design. While some demonstrated significant enrolment increases (for example, 15 % in Guinea, 12 % in Senegal, and 9 % in Benin) in relatively short periods of time, the extent to which this was due to the project impact was not clear. Overall, however, the experience of this project suggests that addressing concerns about girls ’ safety and reducing the risk of sexual harassment and violence in schools is not only a high priority for parents, but also a potentially promising way to improve girls ’ access to education in selected settings (UNICEF, 2003b). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Improving attitudes, knowledge, skills and practices of educators Many initiatives have aimed to improve educators ’ attitudes, knowledge and practices in regards to gender discrimination, sexual violence and sexual harassment. Few have been well documented or evaluated. A comparative study of HIV / AIDS education in three African countries found evidence that awareness and responses to sexual harassment in the Uganda sites was markedly better than those in Botswana and Malawi; researchers attributed this to the Ugandan government ’ s efforts to curb sexual harassment in schools (Bennell, Hyde, and Swainson, 2002). The South African National Department of Education (in collaboration with international organizations) has developed a training module for educators (South African National Department of Education, 2001). Composed of eight interactive workshops and other materials, the module aims to increase educators ’ awareness of sexual harassment and gender violence, highlight the links between violence and HIV / AIDS and increase the safety of the school environment. The module is a professional development tool, rather than a part of the national curriculum. It has been field tested in some sites, and according to some reports is being rolled out nationwide. In other settings, schools have trained educators to teach courses promoting gender-equitable norms and nonviolence among students. For example, a consortium of researchers and advocates field-tested the\"Gender and conflict\"Model Curriculum in South Africa (Dreyer et al., 2001; Guedes, 2004) to compare a\"whole school\"approach (which trained the entire primary school staff, including principals and auxiliary staff) with a “ trainer of trainers ” approach (which trained two teachers from each school and Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "38 Expanding social services for women and children In many countries, public and private institutions have worked to improve social services for women and children who experience violence. In some settings, NGOs and coalitions such as the Nicaraguan Network of Women against Violence have spearheaded these initiatives (Velzeboer et al., 2003). In other settings, governments have promoted institutional reforms by establishing ministries, departments or agencies devoted to the advancement of women, including Mexico, Jamaica, Guatemala, Bolivia, Peru (Center for Reproductive Laws and Policy, 2000); these agencies often work to strengthen comprehensive services for survivors of gender-based violence. For example, in El Salvador, the Salvadoran Institute for the Development of Women is a government agency that coordinates the “ Program to Strengthen the Family ” (Programa de Saneamiento de la Relación Familiar), a multi-sectoral effort among public and private institutions (Valdez, 1999). In some settings, such as Nicaragua and Costa Rica, coalitions of government agencies and NGOs develop National Plans to improve the network of services for women and children affected by violence (Velzeboer et al., 2003). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "At a community level, social service initiatives accompanied by substantial outreach efforts have sometimes increased the proportion of women who know what services exist and where, as well as the numbers of women who seek help, but little scientific research has explored the impact of expanded social services on violence prevention. Batterer programs Increasingly, NGOs and governments have attempted to reduce violence against women by organizing treatment programs for batterers, aimed at changing their attitudes and behaviors. Most are run by NGOs, but they often depend on court- mandated attendance as an alternative to criminal sanctions. Most batterer programs have been carried out in high-income countries, but increasingly they have been implemented by developing country NGOs, such as the Instituto Noos in Brazil (White, Greene, and Murphy, 2003) and CORIAC in Mexico (Morrison and Biehl, 1999). Many studies have evaluated these programs ’ effectiveness in high-income countries, but most evaluations have been methodologically flawed. The only randomized controlled trial to date was carried out by the United States Navy, which found no reduction in abuse compared with controls (Dunford, 2000). Battered women often identify treatment or counseling for their husbands as a high priority (e. g. Ellsberg, 2001b), but it remains to be seen whether cost-effective strategies for changing batterer behavior can be found. Shelters Many researchers and advocates have called on governments and donors to invest in shelters for women who experience gender-based violence. Typically, these facilities offer emergency refuge as well as counseling, medical and legal assistance, job training, telephone hotlines, and other services. Most rigorous evaluation studies on the effectiveness and quality of shelters come from settings such as the Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, subsequent multivariate analysis and qualitative research found that the link between micro-credit and violence was more complex (Hashemi, Schuler and Riley, 1996; Schuler, Hashemi and Badal, 1998). While participation in microcredit programs appeared to increase women ’ s empowerment over time, levels of violence did not decline, and in some cases even rose. Ultimately, researchers concluded that-- similar to other types of empowerment initiatives-- micro-credit programs appear to work in two directions at once. On the one hand, they reduce women's vulnerability to violence by strengthening their access to resources and making women's lives more public; on the other hand, they may increase the risk of violence by challenging patriarchal norms and escalating conflict in the household. Some micro-credit programs are trying to reduce the potential risks of exacerbating violence associated with micro-credit. For example, RADAR (South Africa) has integrated HIV / AIDS and gender-based violence prevention into an existing microcredit program for women in poor rural communities. Since RADAR is designed as a prospective, randomized community intervention trial, it may-- in the future-- contribute to a richer understanding of how to provide the benefits of micro-credit while mitigating the risks (RADAR, n. d.). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "presented by equation 3 where Rit is instrumented by the instrument defined in equation 2. Since we use all provinces in the main IV estimations, i is defined as i = (1,..., 81) while t is defined as t = (2011, 2014). Following Del Carpio and Wagner (2015), we use the year-specific natural logarithm of the distance to the closest border crossing, LDit as a control variable. The year-specific distance control is defined as LDi2011 = 0 and LDi2014 = LDi. Yit = a + ρRit + Pi + Tt + βLDit + eit (3) Our second strategy is to estimate a linear difference-in-differences model with province level fixed effects for all outcomes, which is the method used by Ceritoglu et al. (2017). Their approach defines the years 2012 and 2013 as treatment years and the previous years as pre-treatment. We do exclude 2014 in the DD model since Syrian refugees have been spreading across Turkey from 2014 onwards, while they were more concentrated near the border areas that we define as the treatment region in 2012 and 2013. 4 In effect, the DD estimates use treatment years that are completely excluded from the IV model: 2012 and 2013. A second issue in the DD specification is the definition of the control area. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The weight given to each province in constructing the synthetic control is based on pre-treatment outcomes. We use the pre-treatment average of the outcome dimension, Y, the unemployment rate, employment rate and the import and export per capita of the province to determine the degree of similarity between control group provinces and the treated provinces, which in turn determines the weight assigned to control provinces. The unemployment and employment rates are included to control for the general economic performance while trade values are added to control for the degree of ’ openness ’ of the province. 6 The treated unit i = 1 is constructed by taking the mean of the outcome variables in the provinces hosting refugees in 2012 or 2013. 5 Data We use several data sources for the analysis. The IV estimations use data from years 2011 and 2014 while the DD estimations use data from 2009 to 2014. The numbers of refugees up to 2012 are treated as 0. The refugee data for 2012 and 2013 are obtained from UNHCR ’ s official weekly statements in December. Data on the number of refugees in 2014 is from Erdo ˘ gan (2014), who uses statements released by the Ministry of the Interior to compile his data. All refugee data we use in the analysis is provided at the level of 81 provinces. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The data on total gross profits and net sales acquired from the Turkish Ministry of Science, Industry and Technology are compiled from administrative taxation data and was provided upon request by the ministry. The key difference from the Chamber of Commerce data is that the sales and profits data include all businesses including self- proprietorships. 9 Data were provided for the years between 2010 and 2014 and are re- ported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by definition and they are likely to be less accurate and complete for smaller firms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets which includes sales and profit figures. 10 We scale the variables according to province size by dividing sales and profits by the pop- ulation of the provinces. If we use sales and profits in absolute terms, we get qualitatively similar results. The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we 12 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There is a clear increase in the entry of new foreign firms which may be driven by refugees ’ enterpreneurship. Cheaper low-skilled labor may have helped all businesses cutting costs. Balkan and Tumen (2016) had also found a decline in prices and attribute their finding to lower labor costs, which may also be one of the mechanisms driving our results. Gross profits and sales also appear to have gone up, which would be consistent with an increase in demand. As noted by Maystadt and Verwimp (2014), heterogeneous effects on specific subgroups of the native population should be expected from refugee crises. In case of the Syrian refugee crisis in Turkey, the business activity in hosting region appears to have benefited. For a complete picture of the effects of the Syrian refugee crisis on local economies in Turkey, further research will be needed on market activity, health and longer term effects. More specifically for the line of research this study focused on, further analysis using micro-level firm data would be needed to understand how firms adjust their activity, 24 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The instrument becomes even weaker if we use refugee to population ratios rather than the number of refugees, therefore we use the absolute number of refugees throughout. 4Including 2014 data in the DD estimations generally results in more statistically significant and larger coefficients but does not change the direction of the results. 5The weights are calculated using the Stata package synth provided by the authors. We use the option nested to calculate the weights through the nested optimization procedure described in Abadie et al. (2011). 6Tunceli ’ s 2011 export and import values are missing for 2011, therefore only 2009 and 2010 values could be used in calculating Tunceli ’ s average. 7The Chamber of Commerce also provides information on the number of firms that shut down. However, reporting exits is not mandatory and the indicator is therefore less reliable. We found no significant effects in both the IV and DD estimates on the number of firms that shut down. 8Since the number of new foreign firms is 0 in several observations, we add 1 to the value. As an alternative, we used the hyperbolic inverse sine transformation which does not have the same problem with 0s as log transformation and found similar results (Burbidge et al., 1988). 9Publicly available data from the Ministry of Science, Industry and Technology indicate that less than 10 % of total revenue is from micro-establishments. Most of the net sales and gross profits reported stem from larger firms that should be included in the Chamber of Commerce data. 10All firms exceeding 200, 000 Turkish Liras (ca. $ 85, 000) in sales are obligated to report detailed balance sheets. Smaller firms may still report their balance sheets but would be doing so on a voluntary 25 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "If many people migrate to a specific location, such as the capital city, this is likely to affect wages, incomes, and access to amenities in that location. 7 This would generate a potential endogeneity bias due to the fact that incomes and amenities in that location result in part from the decision of many migrants to locate there. To eliminate this bias, we use past data to estimate the income regression. More precisely, let T be the period for which we have income information and T + t the period at which we 5The dropped observation corresponds to the location of origin M h ii which, as explained earlier, we do not include in the analysis since including M h ii would mean de facto including the decision of whether to migrate or not. 6McFadden (1974) has shown that, in multiple choice problems of the kind studied here, the application of logit estimation is justified if (1) the errors in each latent choice equation follow the extreme value distribution and (2) errors are independent across choices. See Train (2003), Chapter 3 for a detailed discussion. The estimation of models with correlated errors across choices requires either multiple integration or the use of Bayesian estimation techniques relying on Gibbs sampling. With a choice of over 70 possible destinations, multiple integration is out of the question. Gibbs sampling remains a possibility but would require extensive programming. We choose instead to keep the logit approach but to correct the standard errors for possible correlation in errors across choices. In our case the possible efficiency gain achieved by Bayesian methods does not appear to justify the programming cost. 7The effect could be negative — e. g., congestion — or positive — e. g., agglomeration externalities. 10 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To this effect, we replace yh i with yh i / yi in equation (1) and proceed as outlined above. If migration decisions are based on relative rather than absolute income, then the coefficients of eδs − eδi and (eηs − eηi) zh should be positive and significant only when they are computed using yh i / yi. In addition to relative and absolute income differences, the analysis also examines the re- spective roles of various location characteristics such as housing and food prices, availability of public services, and density of human settlement. 8An alternative strategy for the estimation of pre-migration income distribution in cross-section data is sug- gested by Bayer, Khan and Timmins (2008). 11 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In contrast, districts of origin are distributed widely across the country. This reflects the fact that much work migration is from remote rural areas to towns and cities. The main characteristics of work migrants are reported in Table 1, together with those of non- migrant adult males. We see that work migrants are on average younger and better educated. The census contains detailed information about ethnicity, language, and religion. In the Nepal census, the term ‘ ethnicity ’ is used to capture a hodgepodge of caste and tribal distinctions. The census distinguishes up to 103 ethnic categories. Most of these categories only account for a tiny proportion of the total population. In terms of the total adult population, the most common ethnic categories are Chhetri, Brahmin, and Newar who, together, account for 35 % of 13 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "standard fixed or random effect approach is not sufficient to ensure correct inference; clustering standard errors by individual is necessary. This is what we do. Having described how the dependent variable is constructed, we turn to regressors. We begin by describing how we construct an estimate of g E [yhs | zh], the level of income (or consumption) yh s that a migrant with characteristics zh can expect to earn in district s. To construct such estimate, we use the 1995 / 96 NLSS data. The reason for using the 1995 / 96 data instead of the 2002 / 3 NLSS survey is to avoid reverse causation, i. e., migration causing a change in income patterns. Migrants are unlikely to be able to accurately predict the evolution of incomes in each district over time. Income and consumption levels observable before migration are thus a reasonable starting point. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "not included because they are possibly affected by migration. 9 In contrast, age, education, and caste status can be regarded as exogenous to the migration decisions of adult males. Equation (4) is estimated using correct sampling weights. 10 Regression estimates for equation (4) are summarized in Table 2 where we show α as well as the average and standard error of δs, βs and χs. The coefficients eδi and eηi are large and jointly significant. There is considerable variation across districts not only in average log income and consumption but also in the income or consumption premia associated with education and high caste. These results are used to construct, for each of the 16, 000 or so work migrants in the census, a measure of the income or consumption they can expect to achieve in each of the possible destination districts. Formally, this measure is calculated as: g E [yhs | zh] = eδs + eβs (Eh s − Es) + eχs (Hh s − Hs) (5) where Eh s and Hh s are the education and high caste dummy for migrant h. Age is ignored from the calculation since work migrants typically migrate around the same age, i. e., in early adulthood. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Formula (5) can be decomposed into two parts: eδs, which measures the average income level in district s, and eηszh ≡ eβs (Eh s − Es) + eχs (Hh s − Hs) which captures individual-specific variation in income. Migration models predict that, other things being equal, the choice of migration destination should depend on g E [yhs | zh]. This means that if we regress the choice of destination separately on eδs and eηszh, they should have the same coefficient. The same methodology is used to construct other variables that may affect the choice of 9The literature has often emphasized that migrations often serve an important role in household formation. For migrants, the prospect of forming a large, successful household is likely to be one of the purposes of migration. 10The 1995 / 96 NLSS survey adopted the following sampling strategy. Within each district a small number of wards were selected at random. Within each ward, 12 randomly selected households were interviewed. Because the wards differ widely in terms of population, applying sampling weights is essential in order to obtain consistent estimates of δs. 16 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The univariate analysis showed that migrants on average move to destinations where they are on average less likely to find people like them. The results presented in Table 4 present a different picture. Conditional on the other regressors, the ethnicity and language proximity indices are significant with the anticipated sign: social proximity between the migrant and the population of the destination district is higher than in alternative destinations. The religion proximity index is not significant. Taken together, these results suggest that, conditional on material benefits from migration, migrants prefer to move to a destination where they integrate more easily — and possibly enjoy network benefits in terms of access to jobs and housing (Munshi 2003, Beaman 2006). 24 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These calculations confirm our earlier assessment. 5 Conclusion Combining data from a household survey and an 11 % census of the population, we have estimated destination choice regressions for Nepalese internal migrants. Results show that population density, social proximity, and access to amenities exert a strong influence on migrants ’ choice of destination. These results confirm earlier work on the factors affecting the subjective welfare cost of isolation (Fafchamps and Shilpi, 2008). 29 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It is difficult to draw causal inference from observational data. This study is no exception. The results presented here are nevertheless sufficiently suggestive to cast doubt on the theory that the choice of migration destination is driven primarily by income differentials. Other factors seem to play a strong — and probably more important — role. References 1. Adams, Richard, Remittances, Investment, and Rural Asset Accumulation in Pakistan, 30 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 11055 The paper examines the early integration of Ukrainian ref­ugee students into Italy ’ s education system following the Russia ’ s invasion of Ukraine in 2022. Using administrative and survey data, the study presents enrollment trends, aca­demic performance, and barriers to educational integration. Findings from the analysis indicate that Ukrainian refugees face lower enrollment rates, higher absenteeism, and lower test scores than other students, particularly in subjects requiring language proficiency. Despite these challenges, teachers often recommend Ukrainian refugee students for advanced educational tracks, thus revealing their optimism about the potential of these students. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Language barriers, mental health challenges, and uncertain futures are identi­fied as major obstacles to integration. The study highlights the importance of tailored interventions, such as psycho­logical support and more dedicated teaching time, to foster refugee students ’ academic and social inclusion. This paper is a product of the Development Data Group, Development Economics and the Social Protection and Labor Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at michela_carlana @ hks. harvard. edu; pcastaing @ worldbank. org; mtestaverde @ worldbank. org; and mtiberti @ worldbank. org. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This distinction is also applied to foreign students, with those entering the system after February 2022 referred to as “ Migrant post-Feb 2022 ” and others referred as “ Migrant pre-Feb 2022 ”. 7 Administrative data from MoE. The Ministry data includes information for all students who enrolled at any point during the academic year. 8 This information covers academic year, grade, gender, birth date, birthplace, citizenship. They also include school-speciϐic information such as the name and identifying code of the institution where the student is enrolled. Furthermore, the dataset includes a variety of school outcome variables, including grades in English, Italian, Mathematics, overall GPA calculated as the average across all subjects, behavior scores from grade 9 to grade 12, guidance council evaluations from lower secondary school, records of absences, late entries, and early exits. Given the timing of this study, data for academic year 2022-23 are the most complete. For academic year 2022-2023, school enrollment data at the provincial level was provided for 4, 269, 348 enrolled students across the 8 years of Italian lower and upper secondary school, encompassing both public and private institutions. The dataset includes nearly all students in the country irrespective of their citizenship. 9 Table 1 shows the distribution of the different groups of students by grade. In the 7 For ease of reference, we refer to non-Italian and non-Ukrainian students as migrants. However, we acknowledge that some of these students may be refugees or displaced students. 8 At the time of writing this paper, both MoE and INVALSI data were not fully available and, as such, only the information on enrollment was used for the academic year 2023-24. 9These numbers do not include students enrolled in Provincial centers for adult education (CPIA). See footnote 5. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 measured by the number of days missed during the academic year, can function as an indicator of school attachment or integration for refugee students. To address potential biases from teacher subjectivity, the analysis of score disparities relies on INVALSI test results. The standardized and anonymized nature of these tests helps mitigate subjectivity in assessment. First, the results section presents some summary statistics of the main outcomes across the different categories of students. Second, we use the administrative data to analyze empirically how Ukrainian refugees and newly arrived foreigners compared to other students as regards their education performance. This estimation is based on an OLS model with the following econometric speciϐication: 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 + 𝛽𝛽1 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 + 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 + 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟����𝑖 + 𝑓𝑓𝑔𝑔 + 𝑓𝑓𝑠𝑠 + 𝑓𝑓𝑙𝑙 + ϵigs (1) where 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 represents the outcome of interest (such as test scores, absenteeism, or high-track recommendation) for student i in school s, in grade g, and with language l. The variable 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 is a dummy indicating whether the student is a Ukrainian refugee, and 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 indicates if the student is a newly arrived foreigner. 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 represents the Economic, Social, and Cultural Status of the student, and 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 indicates the student ’ s gender. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The model includes ϐixed effects for grade, school, and language spoken. 14 The results are presented in Table 3. We then narrow our focus to foreign students who joined Italian schools after February 2022, speciϐically comparing Ukrainian refugees to other newly arrived foreign students. This approach allows us to examine how Ukrainian refugees compare to other foreign students who entered the education system around the same time. By restricting the sample to these two categories of students, we estimate the following regression: 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 + 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 + 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 + 𝑓𝑓𝑔𝑔 + 𝑓𝑓𝑠𝑠 + 𝑓𝑓𝑙𝑙 + 𝜖𝜖𝑖𝑖𝑖𝑖𝑖𝑖 (2), with variables as deϐined in (1), and results presented in Table 4. Our analysis also aims to explore potential mechanisms that could explain results derived from equations (1) and (2). Using the administrative data, we investigate whether being placed in a smaller class inϐluences school achievement in the sample of Ukrainian refugees. The results are presented in Table 5. We then draw on ϐindings from the survey data to unpack and analyze how Ukrainian refugees feel in Italy, the challenges they face, and their aspirations. 4. Results 4. 1. Integration challenges faced by Ukrainian refugees in Italy Low enrollment and substantial dropout rates At the end of the 2021-2022 school year, the enrollment rate of Ukrainian refugee children in Italian schools was low. In the months following Russia ’ s full-scale invasion of Ukraine in 2022, 3, 320 Ukrainian refugees were enrolled into Italian secondary schools. This ϐigure constitutes 24 % of the 14, 106 Ukrainian refugees aged between 11 and 18 years who sought temporary protection as of 14 This variable is included to account for the potentially greater ease of learning experienced by students who speak languages that are considered closer to Italian. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 coefϐicients from this estimation. The results indicate that Ukrainian refugees are consistently more absent than other foreign students who entered the Italian education system around the same time. Lower test score performance The evidence suggests that Ukrainian refugees in Italy face important learning gaps across all subjects. Figure 4 reports the INVALSI test scores by topic and category of students. 23 The educational disparity is particularly pronounced between Ukrainian refugees and Italian students, but signiϐicant gaps also exist between refugees and both Ukrainian nationals and foreign students who were enrolled in Italian schools before February 2022. However, Ukrainian refugees tend to have INVALSI scores comparable to migrant students who joined the educational system after February 2022. Notably, Figure 4 shows that Ukrainian refugees perform better in mathematics than recent migrants but score lower in Italian. Figure 4-INVALSI scores in Grades 8, 10, and 13 (Source: INVALSI, a. y. 2022-23) Table 3 presents the regression estimates that control for various potential confounding factors. The results indicate that both Ukrainian refugees and recent migrants score lower across all subjects. In mathematics, both groups score 16 points less than the rest of the sample. As expected, given their relatively short time in Italy, their performance in Italian is notably weaker. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "19 children and 61 % of caregivers prefer to remain in Italy. Adolescents between the ages of 15 and 19 express a greater desire to continue living in Italy compared to children aged between 9 and 14. Another survey conducted across Europe from June to December 2022 shows that only 8 % of Ukrainian refugees planned to settle outside Ukraine (Adema et al., 2024). Compared to other foreign children in Italy, the aspirations of Ukrainian refugees to return to Ukraine seems signiϐicantly higher: indeed, a recent study from ISTAT on children 11 to 19 years old shows that only 11 % of foreign children wish to return to their home country (ISTAT, 2024). The relatively strong desire to return to Ukraine can have negative effects in refugee parents'educational decisions, particularly in encouraging their children to learn the language of the host country and in enrolling in school (Dryden-Peterson et al., 2019; Zengin and Atas-Akdemir, 2020). Figure 6- Aspirations and identity of refugee caregivers and students (Source: World Bank Survey on Ukrainian refugees in Italy) Many students facing uncertain futures try to stay connected to both educational systems. Findings from the World Bank survey indicate that 25 % of children are engaging in online Ukrainian schooling while being enrolled and attending Italian schools. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The lack of certainty means children are trying to prepare for further studies (e. g., in universities) in two separate systems with varying requirements. The survey results indicate that students enrolled in both systems spend as much time in Italian schools as those attending only Italian schools, averaging 31 hours per week. However, students participating in both systems spend an additional 8 hours per week on online Ukrainian classes. This puts an extra burden on these children. Connectedness to Italy is correlated with demographic characteristics and social environment of refugee children. Additionally, Table 6 shows that making new friends in the country of destination and speaking Italian are strongly associated with higher connection to Italy. The mental distress resulting from displacement is a key barrier to educational integration for many Ukrainian refugees in Italy. The link between poor mental health and low school attendance and performance is widely acknowledged in the literature (see Fiining et al., 2019 for a systematic review). In the World Bank survey data, children and caregivers reported signs of mental distress, with 16 % of children and 24 % of refugee caregivers reported experiencing psychological distress 61 % 35 % 31 % 68 % 50 % 36 % 23 % 68 % 59 % 26 % 26 % 59 % Would like to continue living in Italy Would like to move back to Ukraine Feel strongly connected to Italy Feel strongly connected to Ukraine Caregivers (N = 283) Children between 9 and 14 years old (N = 141) Children between 15 and 20 years old (N = 96) Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Consequently, they may fear detection by authorities when asked to respond to surveys or participate in public initiatives. 1 Furthermore, collective repre- sentative and longitudinal data on forcibly displaced migrants, a population with high mobility rates, is difficult and costly (Ib ´ a ˜ nez et al. 2024). This complexity is compounded when focusing on children and adolescents, given the need for enumerators to receive specific training to interact with such a vulnerable demographic and for migrant parents to authorize their children ’ s involvement despite prevailing distrust issues. To address this knowledge gap, we launched the Venezuelan Refugee Panel Study for Kids (VenRePs-Kids) in Medell ´ ın, Colombia. VenRePs-Kids is a longitudinal study repre- sentative of forcibly displaced Venezuelan and Colombian children and adolescents aged 5 to 17. To our knowledge, it is the first study to gather panel data specifically on forcibly 1This concern is also prevalent among undocumented migrants in the United States, as highlighted by Amuedo-Dorantes and Lopez (2015). 2 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "displaced children and adolescents residing in host communities, rather than in refugee camps. Particularly, VenRePs-Kids collects data on 2, 556 households including 1, 338 Colombian and 918 Venezuelan households, respectively. The study collects rich and comprehensive data on children ’ s and adolescent ’ s development including anthropometric measures, vo- cabulary ability tests, and socio-emotional and mental health assessments. It also collects data on risk behaviors, time use, social integration measures, prosocial behaviors, and parents and caregivers sociodemographics, among other dimensions. 2 The study includes Colombian children and adolescents as the comparison group to high- light the developmental differences of Venezuelan forcibly displaced children. This choice stems from the fact that comparing Venezuelan children in Colombia with their counter- parts remaining in Venezuela is impractical due to the latter ’ s exposure to a severe eco- nomic and humanitarian crisis, marked by limited access to services and food. This envi- ronment severely hampers their potential for normal human development. Additionally, many Venezuelan children and adolescents have spent more of their lives in Colombia than in Venezuela. Therefore, Colombian children and adolescents serve as the most ap- propriate benchmark for assessing the developmental gaps of their Venezuelan peers. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This paper analyzes data from the initial wave of VenRePS-Kids, conducted from Oc- tober to December 2022, to outline key demographics and stylized facts about forcibly displaced children and adolescents. Initially, we examine the human development dis- parities of forcibly displaced Venezuelan children and adolescents in comparison to their Colombian counterparts. Our approach to human development is broad, covering physi- cal, cognitive, socio-emotional, and mental health aspects. Additionally, we complement our analysis by exploring differences in food security, social cohesion, and the economic status of parents. Although our analysis is descriptive, it represents a crucial initial step 2Venezuelan households are defined as those where both parents and their children have a Venezuelan nationality. Colombian households are composed of Colombian citizens only. 3 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "vices. Our findings reveal significant delays in the physical and cognitive development of Venezuelan minors compared to their Colombian peers. Specifically, we observe a 0. 3 standard deviation difference in Body Mass Index (BMI), indicative of nutritional status, and a 12-percentage point difference in the Peabody Vocabulary Test scores, which as- sesses receptive vocabulary and verbal ability. Surprisingly, our analysis does not identify any disparities in socio-emotional and mental health between the two groups. This out- come is unexpected, given the high incidence of socio-emotional and mental health chal- lenges among forcibly displaced populations. The absence of discernible gaps in these areas could be attributed to the non-exposure of Venezuelan migrants to warfare, or it may reflect the vulnerabilities of the Colombian population, which has its own extensive history of internal forced displacement and violence. When examining the role of time of settlement, regularization status, and service access on the developmental disparities between Venezuelan and Colombian minors, we un- cover two significant facts. On the one hand, the gaps in both cognitive and physical development are diminishing over time. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The upcoming section provides an overview of the situation facing Venezuelan migrants in Colombia, setting the stage for understanding the context within which our study is situated. Section three offers a comprehensive description of the VenRePS-Kids study, covering aspects such as the sampling frame, the instrument used for data collection, the representativeness of the study, its implementation process, and an overview of descriptive statistics. Section four delves into the human development disparities observed among forcibly displaced chil- dren and adolescents, providing detailed insights into the nature of these gaps. In section 9 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "III THE VENREPS-KIDS STUDY In this section, we offer a detailed overview of the VenRePs-kids Study, covering its de- sign, implementation, the questionnaire utilized, and the primary outcomes that will be employed to evaluate the human development disparities between forcibly displaced children and adolescents and their peers in host communities. III. A Design Location. Our study is conducted in Medell ´ ın, Colombia ’ s second-largest city, following Bogot ´ a. Medell ´ ın was chosen for this study because it hosts the third-largest Venezue- lan migrant population in the country, trailing only Bogot ´ a and C ´ ucuta, as indicated by the 2018 population census data. Additionally, previous research has demonstrated that survey response rates among migrants in Medell ´ ın are notably high. For instance, a na- tionally representative survey of Venezuelan migrants conducted in 2018 — which was representative across Colombia — revealed that Medell ´ ın had the highest response rates among migrants, whereas Bogot ´ a recorded the lowest (Ib ´ a ˜ nez et al. 2022). This finding supports the decision to focus our study exclusively on Medell ´ ın, also considering the challenges and high costs associated with tracking a highly mobile population longitu- dinally in previous research efforts. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A map highlighting Medell ´ ın ’ s geographic position and the locations of the households interviewed for this study is provided in Figure 2, offering visual context to our research setting. Representativeness and stratification. VenRepS-Kids is designed to be representative of two groups of youth. The first group consists of Colombian children and adolescents, aged 5 to 17, born to Colombian parents. The second group encompasses Venezuelan migrant children and adolescents of the same age range, born to Venezuelan parents, who mi- grated to Colombia between 2016 and 2020. The sample was further stratified by gender and socioeconomic levels, using Colombia ’ s neighborhood income-based classification system that ranges from 1 to 6, where six indicates the wealthiest neighborhoods. Our 13 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 2. Location of Households in the VenReP-Kids Sample Notes: The figure depicts the exact geographic location of all the households in the VenRePs-Kids study. Green and blue dots depict the location of Colombian and Venezuelan households, respectively. The map in the upper right corner illustrates the location of Medell ´ ın (blue pin) with the department of Antioquia (highlighted in red). survey focuses on strata 1 through 4, intentionally omitting strata 5 and 6 to avoid bias toward higher-income groups which are less likely to include migrants in need of sup- port. 14 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In households with more than two children, one child from each age group was chosen through a random selection process to participate, ensuring a broad representation of experiences within the study ’ s scope. Sampling frame. We use the 2018 population census data as a sampling frame for the Colombian sample. It allowed the identification of residential blocks and households with children in the desired age range. With this source of information, it was possible to identify the number of households and residential blocks with children and adoles- 7Although Colombia only grants nationality to children of Colombian nationals, it follows a jus sanguini principle, the Colombian government has introduced reforms, such as the the program Primero la Ni ˜ nez to give Colombian nationality to children of Venezuelan parents born at times when diplomatic relations between Colombia and Venezuela were cut and hence, it was not possible to apply for a Venezuelan nation- ality for this minors in Colombia. 15 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "cents. The universe of blocks (“ manzanas ”) was stratified by socioeconomic strata and a representative sample of blocks was selected at random without replacement. To en- sure a sample representative of Colombian children by age group, stratum, and sex, we followed a multi-stage random sampling process. 8 As mentioned earlier, one of the biggest constraints in characterizing the role of forced migration in children ’ s human development within developing countries is the difficulty of finding a representative sample of those migrants. This is specially true in contexts where migrants are not hosted in refugee camps, but are integrated in local communi- ties, which account for 80 % of refugees worldwide (Climate Center 2022). We address these difficulties, leveraging all available information on Venezuelan settlements across the country to construct the largest possible comprehensive listing. The listing included data on Venezuelan settlements from all available sources, such as the 2018 population census, migrant organizations, and settlements identified by iMMAP, a non-profit orga- nization. iMMAP uses multiple sources, including OIM, United Nations, local migrant organizations, and satellite images, to identify Venezuelan settlements geographically. 9 Hence, to create our sampling frame, our field team verified the geographic location of all the Venezuelan settlements in-person and implemented a snowball sampling procedure in all the settlements found. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5. Time Use and Child Labor: The fifth module examined how children spend their leisure time, their involvement in child labor, and interactions with peers. 6. Pro-social Preferences and Migration Outlook: The sixth module concentrated on adolescents ’ pro-social behaviors, such as altruism and trust, and explored their expectations and intentions regarding migration. 7. Socio-emotional and Mental Health: The final module involved the administration of various scales to assess socio-emotional well-being and mental health, includ- ing trauma, behavioral problems, anxiety, and depression. The scales include the Trauma Symptom Checklist for Young Children (TSCYC), Strengths and Difficulties Questionnaire (SDQ), General Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire (PHQ-9). All these scales and the corresponding outcomes that we evaluated are described in the next subsection. The survey also employed the Peabody vocabulary test to evaluate the cognitive devel- opment of all participating children and adolescents. A summary of the survey modules is depicted in Table A. 1. III. C Sample comparability While Medell ´ ın ranks as the third city with the highest migration in Colombia, it is crucial to recognize the degree to which migrants arriving in the city differ from those migrating to other regions in Colombia. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Ideally, we would conduct a comparative analysis of the characteristics of migrants residing in Medellin versus those in other parts of the coun- try to discern the extent of these differences. However, the lack of comprehensive data regarding the living conditions of this population makes such analysis unfeasible. To explore how this population compares with other migrant groups in the country, we turn to the only two available data sources on migrants. First, we use the Venezuelan Refugees Panel Survey (VenRePS), conducted by Ib ´ a ˜ nez et al. (2022), which captures a 19 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "to the Venezuelan migratory crisis, whereas the VenRepPS survey was conducted during the pandemic in 2020. These temporal inconsistencies result in varying sample composi- tions across surveys, diverging from the landscape we observe in 2022. Notably, forced migrants in the VenReps Kids survey migrated during the crisis but have since remained in the country for several years, potentially leading to disparities in household integration outcomes. IV GENERAL DESCRIPTIVE STATISTICS IV. A Key characteristics of adults Table 2 provides descriptive statistics for the adults in our study, encompassing the pri- mary caregiver, mother and father (if residing with the child), and the individual finan- cially responsible for the child (should they be different from the aforementioned per- sons). Typically, the roles of primary caregiver and financial provider are fulfilled by either the mother or the father. The table is organized into three panels for clarity: Panel A details key individual characteristics, Panel B outlines adults ’ access to services, and Panel C focuses on labor market characteristics. Within the table, columns (1) and (2) present average values for adults from Colombia and Venezuela, respectively, while the final column displays the results of mean difference tests between these two groups, with standard errors noted in brackets. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "by the migration episode. These include the minors age and sex, the parent ’ s and grand- parent ’ s education pre-migration, and a wealth index constructed with retrospective in- formation on the household conditions pre-migration. 14 Grandparent ’ s education is used a proxy for living standards that is unaffected by the migration episode from Venezuela to Colombia and the Venezuelan crisis, which intensified in 2016. ϵij depict the standard errors clustered at the household level to correct for intra-household correlation. For robustness, we will present the estimates of equation 1 with and without controls. As further robustness, we use propensity-score weights (Hirano and Imbens 2001, Hirano, Imbens and Ridder 2003). 15 V. A Physical development: Body Mass Index and health status In our study, we examine disparities in nutritional and health status among Colombian and Venezuelan children aged 5 to 10 years, focusing on standardized body mass index (BMI), instances of overweight and underweight, and overall health status. The BMI serves as an indicator of nutritional status for both adults and children, calculated as an individual ’ s weight in kilograms divided by their height in meters squared, according to World Health Organization (WHO) guidelines. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Due to the variations in weight and height ratios among children and adolescents according to gender and age, the benchmarks for determining nutritional status are specifically tailored to these factors. We adhere to WHO guidelines to calculate the standardized BMI values for minors. A BMI exceeding one standard deviation (SD) from the mean suggests overweight, while a BMI less than minus one SD indicates underweight. 16 Health status is assessed through a binary variable, assigned a value of one if the caregiver has reported any health issues such as disease or chronic pain, accidents, dental pain, surgical interventions, or preg- 14For the Colombian households the wealth index is measured with contemporaneous data. 15This procedure restricts the sample to the common support of the propensity score for being a forced migrant and weights observations for Colombian kids by a non-parametric function of the propensity score. This procedure has been shown to increase the estimate ’ s efficiency. 16Furthermore, a BMI greater than 2SD is indicative of obesity risk, and less than- 2SD signals a risk of severe thinness. 33 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of difficulty, ranging from those suitable for children as young as 2 and a half years old to more complex items for individuals over 18 years old. 19 The Peabody Vocabulary Test is calibrated with a mean standard score of 100 and a standard deviation of 15, placing scores between 85 and 115 within the average range. This test, in its Spanish edition, has been validated for use in Colombia. Additionally, to ensure relevance and accuracy for Venezuelan nationals, we conducted a preliminary validation by administering the test to a sample of Venezuelan mothers in our study. This step confirmed that the words used in the test held consistent meanings for participants from Venezuela. Figure B. 1 depicts the distribution of PVTS scores for Venezuelan and Colombian chil- dren and adolescents in our sample. This visualization indicates that Venezuelan minors consistently score lower on the PVTS compared to their Colombian peers across the en- tire score distribution. In Table 6, we present the average disparities in percentile rank on the Peabody scale, revealing that Venezuelan children and adolescents, who are forcibly displaced, score approximately 12 p. p. lower than their Colombian counterparts. The difference is meaningful and in turn translated into Venezuelan minors falling into the a higher likelihood of having extremely low, moderately low, and low score categories. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Additionally, these findings remain consistent even when adjusting for a range of control variables pertaining to the individual, their parents, and grandparents, underlining the robustness of these observations. V. C Socioemotional and mental health development To explore the differences in mental health and socioemotional development across Colom- bian and Venezuelan minors we use multiple scales. For children aged 5-10 years, the Trauma Symptoms Checklist for Young Children is employed. This 90-item question- 19Children only respond to items within their “ critical range ”, determined by a lower limit called the “ base item ” and an upper limit called the “ ceiling item ”. The base item, marking the starting point, is determined by the individual ’ s chronological age in years (date of test administration- date of birth). Once the child correctly answers 8 consecutive questions, they reach the ” Base ”. Subsequently, upon making 6 mistakes within 8 consecutive questions, the ceiling is established. The direct score is calculated as the item number where the test ends (ceiling item) minus the number of errors from the highest base to the end of the ceiling. 36 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "to severe based on the score. Moreover, depression is screened with the Patient Health Questionnaire, a 9-item tool administered directly to adolescents. These instruments collectively gauge a broad spectrum of psychological and emotional states, including post-traumatic stress, emotional disturbances, behavioral issues, inat- tention, peer relationships, prosocial behavior, anxiety, and depression, offering a com- prehensive view of the mental health and socioemotional development of the minors in our study. Figures B. 3 and B. 4 depict the distribution of the raw scores for Venezuelan and Colom- bian minors for each of the four scales. Surprinsingly, we do not observe any stinking differences on the distribution of any of these scores across groups. We are also not able to distinguish statistical differences between Colombian and Venezuelan children in any of the scales, when we estimate the specification highlighted in equation (1) as illustrated in Table 7. This is an unexpected result considering that typically, forcibly displaced pop- ulations have a high prevalence of socioemotional and mental health issues, but might be related to the fact that Venezuelan migrants have not faced war (as many forced migrants have in other contexts) directly and as such, these issues are less prevalent. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Another ex- planation of these results could be the vulnerability of the Colombian population who in many cases has also a long history of internal forced displacement and violence. V. D Social Cohesion We also delve into the differences in secondary outcomes among Colombian and Venezue- lan adolescents concerning social cohesion. We focus on assessing altruism, trust, iden- tity towards specific domains, networks, and experiences of discrimination. To measure altruism and trust, we employ the questions from the Global Preference Survey, a tool developed by Falk et al. (2022) to elicit risk, time, and social preferences. Specifically, to measure altruism we ask the adolescents how much of a fictional endowment would they be willing to donate to a good cause. To measure trust, we include the 7-itme ques- 38 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Colombian and Venezuelan migrant children and adolescents. VII Discussion In this study, we analyze novel and unique data on forcibly displaced migrants and hosts, focusing on children and adolescents, to highlight the disparities in human development between them. The structure our analysis in two parts. In the first part, we characterize the main trends in the data. We show that forcibly displaced households have a wealth distribution skewed towards lower values relative to Colombian households. This is likely explained by the assets ownership loss that forcibly displaced households expe- rienced after the migration episode. We also identify meaningful lags in human capital accumulation between Colombian and Venezuelan children and adolescents of approxi- mately 1 year. We further note that the Colombian government ’ s supportive policies for Venezuelan forced migrants are evident through high levels of service access and pro- gram participation for migrants. Nevertheless, it remains surprising that participation is not higher, suggesting significant potential for improvement in increasing sisb ´ en and health insurance enrollments. In a second part of our analysis, we document sizeable lags in physical and cognitive de- velopment of Venezuelan children and adolescents, relative to their Colombian counter- parts. However, we were not able to identify any gaps in the socioemotional and mental health between the two groups. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 As displacement crises are largely unpredictable, all the studies surveyed in this paper are evaluations conducted ex-post. In theory, a few of the crises studied could have been predicted but it would not be possible to allocate individuals to treated and non-treated groups randomly given that, by the definition of forced displacement we provided, people are fleeing violence, persecution or high levels of insecurity or uncertainty. Consequently, none of the papers reviewed is based on a Randomized Controlled Trial (RCT). Due to the randomness of the decision to leave (because of conflict, violence, insecurity or major political events) and / or the random allocation of displaced people in the country of destination (by policy or by default), some authors argue that they are in the presence of natural experiments. All authors do, however, address the question of endogeneity and, if one searches for a common thread, these evaluations would be better described as quasi-natural experiments. The basic model used by the literature is a model of the following form: 𝑦 ௜ ൌ 𝛼 ൅ 𝛽𝐹𝐷 ௜ ൅ 𝛾𝐹𝐸 ௜ ൅ 𝜀 ௜ Where i is the unit of observation, y is one of the four outcomes described, FD is the forced displacement shock and FE are fixed effects. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Most papers with few exceptions use standard OLS estimators or some of its variants (Table 2). Two papers use general equilibrium models (Bodvarsson, Van den Berg, and Lewer 2008; Hercowitz and Yashiv 2002) and two papers simply compare means between treated and non-treated groups resulting in simple difference estimations (Card, 1990 and Alix-Garcia and Bartlett, 2015). [Table 2] The unit of observation varies depending on the data at hand. Most studies rely on household survey data where individuals or households are the unit of observations and most studies include some regional dimension (more frequently administrative areas). Where longitudinal or panel data are available time is also included. Other choices for unit of observations include skills or education level, various types of population groups (based on gender, age etc.), and, in a few cases, economic sectors, industry or labor market segments. The use of fixed effects varies. Some papers use the full set of parameters depicting units of observation (for example, household, region and time fixed effects in equations where the unit of observation is constructed using household, region and time). Other papers use subsets of these parameters whereas other papers introduce variables that are not used to identify the unit of observation. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Very few papers provide explanations for these choices and there is no clear common approach to this choice. There are also only a handful of papers that discuss estimations of the error term and choices made in this regard. The two prevalent evaluation methods used by these studies are Differences-in Difference (DD) methods and linear elasticities models. In the first case, the variable of interest (FD) is a discrete status variable (generally a pre / post- treated / non-treated interaction term) and the coefficient of interest measures the impact on outcomes in the presence or absence of displaced people. In the second case, the model is typically in log form and is based on a shock variable that measures the intensity of the shock such as the number or share of refugees per geographical unit. In this case, the coefficient measures the elasticity of outcomes to the intensity of displacement. A few papers conduct simple differences illustrating results graphically or in tabular form. A few papers use ordinary matching methods (Alix-Garcia and Bartlett 2015, Aydemir and Kirdar 2018, Murard and Sakalli 2017; Mayda et al. 2017) and three papers use Synthetic Matching Methods (Peri and Yasenov 2017; Borjas 2017; Makela 2017). We could not find any paper using a discontinuity design. 12 The essential ingredients used to measure the population shock are the number or presence of forcibly displaced persons, the size of the host population and the distance of the displaced from host communities if the displaced are clustered in camps or other forms of independent settlements. The literature covering high-income countries tends to focus on labor markets and the host population is often defined in terms of 12 Schumann (2014) is an exception, but only looks at the impacts on municipality size. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Sarvimaki (2011) uses the elements of the government ’ s placement policy as instruments (i. e. the proportion of a municipality ’ s population speaking Swedish and the hectares of potential agricultural land). Other authors focus instead on the counterfactual group testing alternative designs of the control group, sometimes including placebo groups and other times recurring to matching methods. The choice of matching methods varies from ordinary methods such as nearest neighbor to more recent advances such as Synthetic Control Methods (Abadie and Gardeazabal, 2003). The inclusion of fixed effects is common to almost all papers although the choice of fixed effects can be very different, as described above. Only one paper uses Fixed Effects (FE) and Random Effects (RE) formal models in conjunction and tests for differences (Esen and Binatli 2017). Cross-section econometrics is, by far, the method of choice even if time is included into the equations but we also found three papers employing time-series models (Carrington and de Lima 1996, Makela 2017, Fakih and Ibrahim 2015). Only few papers are able to exploit panel data (Foged and Peri 2015, Depetris-Chauvin and Santos 2017) and several of them use the same data set (Maystadt and Duranton 2018, Maystadt and Verwimp 2014; Ruiz and Vargas-Silva 2015, 2016, 2017). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Not all cross-sectional studies have multiple rounds of comparable data, covering the period before and after the crisis. When comparing impacts between locations within a country, cross-sectional data also usually does not allow to capture impacts on those who moved out and to differentiate impacts between those who were already there before the shock and those who moved in afterwards. Some of the models based on administrative areas qualify as spatial econometrics models in that they use estimation methods that derive from this literature and are published in spatial econometrics journals. Studies that compare different areas within a country are not only confronted with the potential endogeneity of the size and skill composition of the inflow and the choice of destination, but also with the endogenous reactions of the host community. Local workers might respond to the labor supply shock by dropping out of the labor force, investing in education, occupational upgrading or moving to other areas and diffusing the impact of the inflow. Even if local workers do not respond to wage variations, capital flows may equalize capital / labor ratios within the country, labor-intensive industries might move towards the regions with a high refugee or IDP influx or firms might use more labor-intensive production technologies. The reactions of the host country workers, investors and firms are medium-to long-term in nature and will play less of a role in the short-term if there are large, sudden and geographically concentrated inflows. Some of the papers explicitly analyze these potential channels, notably migration of local workers, and, to a lesser extent, occupational upgrading. Outmigration of hosts is a critical complement to the labor market analysis and excluding this outcome can lead to an underestimation of the impacts of forced displacement on the labor market outcomes of natives. The papers we reviewed that looked at tasks complexities and the question of substitution vs complementarities between refugees and natives found occupational upgrading among Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In order to address this issue, one has to consider a labor supply model that is able to measure both effects separately whereas most papers confound these two effects into one. Foged and Peri (2015) is one of the exceptions, as their paper looks at the intensive margin (fraction of year worked). Rozo and Sviastchi (2018) include the number of hours worked, and Ruiz and Vargas-Silva (2017) look at the changes in number of hours dedicated to a task (including employment outside the household). The second question relates to possible spurious correlations generated by how variables are combined in models. Linear models that use ratios of two variables as dependent variable (think of average prices or wages, employment rates or consumption per capita) and the denominator of this ratio as independent variables (think of the share of refugees on host communities or household size) can produce spurious correlations (Kronmal 1993). This is noted and addressed in Clemens and Hunt (2017) who show how addressing this issue change results for several studies in the literature covered here. Indeed, almost all models reviewed use the same population or household size on both sides of the equations. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Finally, the expenditure shock which we discussed in the theory section (international aid or an increase in public spending associated with the forced displacement crisis) is considered by only a handful of papers. This is a possible confounding factor of the impact of forced displacement on host communities and one that is not easily addressed with the use of fixed effects. This is clearly a shortcoming of this literature that will require increased attention in the future. 4. Meta-analysis of empirical results 4. 1 Data The literature review covers 49 papers spanning over a period of 29 years. We were not able to find published papers prior to the work by Card in 1990, which effectively started this literature, and there is a relatively low interest in this topic between 1990 and 2011 with only one or two papers published per year. With the Syrian crisis starting in 2011 and the EU crisis in 2015 the number of papers per year increased by several fold. Most of the papers and results considered in this review are therefore very recent (Figure 2). We used academic databases and search engines (EconLit, Social Science Research Network, JSTOR, Google Scholar) and searched websites of institutions with relevant working paper series (NBER, IZA, ERF and others). Relevant unpublished papers were included by searching agendas of workshops and conferences organized during the past few years. From the papers reviewed, we selected a total of 762 results summarized in Table 3. The results database was compiled as follows. For each paper we focused on the results that the authors considered the main and Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Looking also at the impact of IDPs in Colombia on wages, Morales (2017) uses a labor force survey, census data and registry data to study short and long-term effects as follows: 𝑆ℎ𝑜𝑟𝑡 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ௧ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ௧ ି ଵ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ௧ ൅ 𝜆 ௠ 𝑋 ௠ ௧ ൅ 𝛾௧ ൅ 𝛿 ௠ ൅ 𝛿 ௠ 𝑇 ൅ 𝜀 ௜ ௠ ௧ 𝐿𝑜𝑛𝑔 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ൅ 𝜆 ௠ 𝑋 ௠ ൅ 𝛿 ஽ ௠ ൅ 𝜀 ௜ ௠ where y is the log of wages, i, m, and i are individuals, municipalities and time respectively, 𝑋 ௜ ௠ ௧ are individual controls, 𝑋 ௠ ௧ is the log of total population or other municipality controls, 𝛾௧ and 𝛿 ௠ are time and municipality fixed effects, 𝛿 ௠ 𝑇 are municipality time trends, 𝛿 ஽ ௠ are department fixed effects and d is the inflow of IDPs defined as 𝑑 ௠ ௧ ൌ 100 𝑝𝑜𝑝 ௠ ௧ 𝑓 ௠ ௧ where 𝑓 ௠ ௧ is the total number of IDPs arriving in municipality m at time t. The same variable without the t subscript is used for the long-run effects equation. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The third proxy for economic activity is given by per capita growth of night light, computed using satellite data from the National Oceanic and Atmospheric Administration (NOAA). 6 Night light data has the benefit 6Satellite data is available for a shorter time period, 1992-2013. 9 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, the cross-country literature has not found that negative, contemporaneous shocks to growth systematically lead to violence. 8 Second, we have run a large number of robustness checks by adding time trends or lagged growth to our specification and controlling for rainfall shocks directly. 9 The upshot from this is not only that results remain significant but also that the estimated coefficients barely change. This does not mean that a causal link from falling growth to conflict can be ruled out. But it is unlikely to drive the macro relationship we see in the data. In order to further explore the relationship between violence and country-level out- put we run two specifications of the model described above. In the first model conflict in country i at time t is defined by any violence, i. e. if at least one battle related deaths occurs. In the second specification, conflict is defined by a higher threshold, by 0. 008 deaths per 1000 population. 10 We expect to get different results from the two specifications. From the analysis of Figure 1 we know that economic damage of civil war increases with the severity of conflict. The estimated impact from the second model should therefore be more acute. Table 1, panel A and B, reports the results. Each column contains one of our measures for economic growth. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This database lists seven categories: refugees, asylum-seekers, returned refugees, internally displaced persons (IDPs), returned IDPs, stateless persons and others of concern. For each group the database provides yearly information about their composition by loca- tion of residence and origin. We exploit only the data on “ refugees ”. 22 In particular, we are interested in the annual stock of refugees for each country of residence, i. e. how many people with refugees status have left their home country each year. We focus on these numbers as they appear to be the most comparable across time and countries. However, this is likely to capture only the tip of the iceberg in some cases. The number of IDPs is extremely high in some instances but cannot be captured with the same level of confidence as refugees generally. 23 Cross-country data about conflict is provided by the UCDP / PRIO. As for the index of country-level economic activity, we use again information provided by the Penn World Table and World Bank databases. As mentioned above, our aim is to explore the dynamics of refugees during conflicts. In other words, we attempt to answer several questions. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Do people run away from their home country when a conflict breaks out? Does the seriousness of the conflict matter in this decision? In which phase of the conflict do they leave? When do refugees come back to their home country? In order to answer these questions, we look at the impact of conflict incidence on 21We will return to these issues in the policy section. 22According to the UNHCR definition, this category includes “ individuals recognized under the 1951 Convention relating to the Status of Refugees; its 1967 Protocol; the 1969 OAU Convention Governing the Specific Aspects of Refugee Problems in Africa; those recognized in accordance with the UNHCR Statute; individuals granted complementary forms of protection; or those enjoying temporary protection; and people in a refugee-like situation ”. 23The UNHCR Global Trends Report 2014 provides evidence that confirms this hypothesis. About 59. 5 million people were forcibly displaced worldwide by the end of year 2014. Among them, 19. 5 million were refugees and 38. 2 million were IDPs. 28 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The third graph represents the kernel density of our productivity loss measure for observations with positive loss, which represents around 11 percent of country-year observations in our dataset. It gives an idea of the distribution of this variable across country-years. The distribution shows a long and thin upper tail driven by countries with repetitive and highly intense conflict history like Afghanistan or the Lebanon. The average productivity loss is 15 percent in this sample and one fourth of all country-year observations are associated with losses of more than 20 percent of productivity. Even if these estimates were drastically overestimated they indicate that the long run impact of mass violence through this channel could be substantial. 5. 2 Macro Evidence In this subsection, we investigate the correlation between the aggregate loss measure and output. For this purpose, we use the height loss measure from the previous sub- section to estimate the marginal effect of an extra cm loss on log GDP. This serves two objectives. First, we explore whether the micro evidence can be used as a conduit for understanding the long-term damage to output from conflict. Second, we check whether the aggregate loss in output that we get is consistent with the micro estimates of marginal economic return to health. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We follow Besley and Mueller (2015a) and analyze international investment flows in a fixed effects Pseudo Poisson framework. The investment flow at the country level is given by E { xit} = exp (αc + γ0 ∗ peaceit + log Xt) (9) where xit is the inflow of investment in country i in year t. We use the exposure model which controls for global investment flows through Xt. The regression with total inflows as an exposure variable can be thought of as modeling the annual rate of investment inflows into a country in each year. 40 The variable peaceit is a dummy that takes a value of 1 in all years with peace. We lag this variable by one year to allow for the fact that investment needs some planning and will not react immediately to changes in the host country. We expect γ0 > 0 if inflows increase after the end of conflict. It is likely that effects of violence will be most visible if the conflict has been intense in terms of battle related deaths per capita. Yet, the right cut-offfor the peace dummy is a priori not clear. India, for example, is coded as in conflict throughout the period if we choose a very low threshold. Choosing a higher cut-offmeans we treat low intensities as experiencing no conflict. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In all these cases violence probably did not affect the entire economy notably. In what follows we focus on positive net flows, i. e. we subtract outflows from inflows and code negative numbers as 0s. Our results are robust to using gross inflows but as these are not provided by all sources. 40See Frome (1983) for a discussion of using the Poisson model to study rates. For a general discus- sion of count data models, see Cameron and Trivedi (2013). Our results are also robust to using year fixed effects instead of exposure. 41The reason is that the OECD data, the Dutch Central Bank data and the UN data allows us to distinguish between net flows and gross flows. 42We also distinguish two different ways of calculating the cut-offof intensity using contemporaneous and average population in a country. In total we therefore have 14 different estimates per cut-off. 43Each coefficient is also estimated quite precisely at this cut-off. 50 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 17: Peace and Foreign Inflow Across Cut-offs and Data Sources The basic results are in Table 8 which shows results for equation (9) using the threshold of 0. 008 battle-related deaths per 1000 population. According to this the investment from OECD countries was almost 70 percent larger in peacetime than during conflict. The flows from other data sources shows an increase of between 35 and 50 percent. The consistency of this result across very different datasets is striking. Note also that the average change in inflows implied by these rates is very large. In 2012, average inflows in the World Bank dataset were over 9. 5 billion USD and over 3 billion USD in the OECD data. Our estimates therefore imply a gain of between 2 billion and 4 billion USD in yearly inflows for countries which emerge from conflict. In order to understand the dynamics of recovery it is useful to understand the dynamics of this change around the end of conflict. For this purpose we add a set of dummies to the equation above. We construct a dummy that indicates the start of recovery and add three forward and lag dummies to trace average investment around this date. As before we always lag the explanatory variables by one year. Results for the OECD data are shown in Figure 18. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the original variables used to predict fragility as controls without changing results. It is only in the post-war period that exclusion and refugees become a factor that influences foreign investment flows. Finally, the results we find are robust across all datasets of foreign investment we use. These results make it at least plausible that political exclusion and refugees matter because they predict a relapse to more intense violence. As final piece of evidence for this idea we use data on political short and mid-term credit risk from the Belgian insurer Delcredere Ducroire (ONDD). We collected data on political risk evaluations from ONDD who, according to their annual report, insured transactions worth about 7 billion EUR in 2011. The variable we use measures the risk of a credit default for rea- sons beyond the control of the debtor, i. e. due to political or financial macroeconomic events. We choose this variable because it provides the most consistent time-series in the ONDD data. ONDD measures both short- and mid-term risk on a scale from 1 (low risk) to 7 (high risk). Table 12, columns (1) and (4) show that risk ratings are decreasing in peacetime. Note that, as before, we control for country fixed effects which implies that we look at changes within country. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As in previous sections we follow Henderson et al. (2012) who argue that the relationship between GDP and night light at the country level can be expressed fairly well in a constant elasticity model in which an increase of night light by 1 percent implies an increase of GDP of about 0. 25 percent. Hodler and Raschky (2014) also look at the relationship between log nighttime light intensity and log GDP at the regional level using the panel data of regional GDP per capita assembled by Gennaioli et al. (2013) 48 and they confirm that the relationship is linear and also find an elasticity of around 0. 3. Access to political power is ranked on a scale from 1 to 7 in the GROWup dataset. Ethnic groups are\"powerful\"(monopoly of power or dominant group in power), have access to central power through a formal system of power sharing (as\"Senior\"or\"Ju- nior\"partner) or are “ excluded ” from power (self excluded, powerless or discriminated). Strong executive constraint is measured as a dummy indicating whether or not we have executive parity or subordination of the executive at the country level, a value 7 for “ xconst ” variable in Polity IV dataset. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 19: Trust and Conflict in the Cross Section The problem with a lack of trust and strong group identities is that they penetrate and pervert formal institutions. The ethnic politics analyzed by Burgess et al. (2015) is just one example. Shayo and Zussman (2011), for example, use data from Israeli small claims courts to show that Arab and Jewish judges displayed significant judicial ingroup bias. Furthermore, this bias is strongly associated with terrorism intensity in the vicinity of the court in the year preceding the ruling. Confidence-building is also a crucial ingredient for the establishment of a fertile investment climate, which in turn is a trigger of economic development post conflict. This is the core message of the World Bank Report by Mills and Fan (2006). An important role of increasing trust doubtlessly goes to the media. It has been shown, for example, that hate radio in Rwanda played a critical role in the extent of ethnic violence during the genocide. 59 Other research has shown that media coverage can have strong effects on political preferences more generally. 60 Perhaps the most direct proof of the crucial role played by the media in the post-conflict situation comes from DellaVigna et al. (2014). The authors exploit variation in radio reception of na- tionalistic Serbian radio in border regions in Croatia. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The authors argue that Croats listened to Serbian radio for its consumption value but reacted negatively to national- istic messages intended for Serbian ears. Election results and street surveys are used to elicit preference for extremist nationalist parties among Croats who are able to listen to Serbian radio and those that do not. The authors find that 3 to 4 percent of those 59See Yanagizawa-Drott (2014). 60See, for example, Enikolopov et al. (2011) and DellaVigna and Kaplan (2007) who find large effects on voting shares. 70 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Appel and Loyle (2012) analyze the role of Post Conflict Justice (PCJ) institutions in attracting FDI in post-conflict countries. They show that post- conflict states that adopt PCJ are more likely to receive higher levels of FDI compared with post-conflict states that refrain from implementing these institutions. 71 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 7253 This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at jlendorfer @ worldbank. org and jhoogeveen @ worldbank. org. This paper analyzes the impact of the 2012 crisis in Mali on internally displaced people, refugees and returnees. It uses information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 trust in the government and its institutions and perspectives on conflict resolution. By analyzing the impact of the crisis on welfare, the consequences of returning home versus remaining in displacement and by comparing immediate with longer term impacts, this paper contributes to the literature on refugee, IDP and returnee populations. The paper combines data from a face-to-face baseline survey with information collected via mobile phone interviews from respondents identified during the baseline. This innovative approach to data collection makes it possible to collect welfare data with high frequency (monthly) – important in a volatile crisis situation – and allows measuring changes over time. It also permits following displaced and refugee households once they return, even if they return to areas that are inaccessible to enumerators. The remainder of this paper is organized as follows. Section 2 provides a brief overview of the methodology, the sample and sample selection. Section 3 discusses the characteristics of the displaced and returnees, looking specifically at ethnic composition, place of origin, household size, education, asset ownership and employment status. Section 4 considers how the crisis affected food consumption, employment, assets and school attendance. Section 5 is devoted to the specificities of returnees who turn out to be, on aggregate, less affected by the crisis and better off than IDPs or refugees. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 al. 2014), mobile phone surveys also turn out to be remarkably flexible and adaptive. New questions can be introduced on a needs basis and in-depth qualitative interviews can be carried out at a moment ’ s notice. These qualities make mobile phone surveys well suited for monitoring welfare in volatile environments: they have been used for welfare monitoring during the ebola crisis in Liberia (Himelein 2014) and for welfare monitoring in conflict-affected areas such as South Sudan (Demombynes et al. 2013). Unique about using a mobile phone survey with a displaced, mobile population is that it allows tracking welfare during displacement, and upon return. 8 Three target populations were identified for the purpose of this survey: Internally Displaced Persons (IDPs) living in Bamako, refugees in refugee camps in Mauritania and Niger, and returnees in Gao, Timbuktu and Kidal, the capitals of regions that bear their names. The sample does not include those who were not displaced by the crisis nor those who returned to places other than the three regional capitals in the North. While the sub-sample of IDPs includes exclusively IDPs in Bamako, and the refugee sub-sample only refugees in Niger and Mauritania, the returnee group includes people who were displaced elsewhere (33 %). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 2: Ethnic composition of IDPs, refugees, returnees in the North Ethnicity IDPs in Bamako (%) Refugees Niger (%) Refugees Mauritania (%) Returnees (%) Total I + R + R (%) Ethnic composition of the North (%) Songhai 75 21- 71 43 45 Kel Tamasheq 12 56 69 12 38 32 Arab 3- 28 4 11 3 Peulh 4 21- 6 4 7 Other 6 11 3 7 4 12 Total (%) 100 100 100 100 100 100 Total (n) 100 81 100 220 501 1, 268, 009 Source: Listening to Displaced People Survey, 2014 and 2009 Population and Housing Census. The ethnic composition of IDPs and returnees is almost identical. This is a reflection of the fact that 94 % of returnees were displaced within Mali. Only 6 % returned from outside the country. The reason why few returned refugees are in the returnee sub-sample is explained by their place of residence prior to the crisis: only 5 % of the refugees in Mauritania and Niger lived in Timbuktu town before their displacement; 2 % lived in Gao town and 1 % in Kidal town. The remaining 92 % lived in 27 different towns and villages in northern Mali, locations not covered by the survey. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Figure 3: Level of education age 25 +; compared with regional average (%) (*) INSTAT refers to census 2009 Source: Listening to Displaced People Survey, 2014. With regards to ownership of consumer durables, IDPs, refugees and returnees were better endowed than the average citizen of the North (see Figure 4). As was the case for education, they are more comparable to the average citizen in Bamako than to the average citizen in the regions of Gao, Timbuktu and Kidal. Figure 4: Asset ownership compared with regional average Source: Listening to Displaced People Survey, 2014 and EMOP 2011 (INSTAT). The main occupation of IDPs, refugees and returnees before the crisis was commerce (Table 5). This held for over half of the IDPs, 37 % of refugees and 34 % of returnees. 18 % of the refugees 51 85 60 47 85 89 87 15 6 18 29 11 8 8 34 9 22 25 5 3 5 IDPs Refugees Returnees Bamako (INSTAT) Gao (INSTAT) Timbuktu (INSTAT) Kidal (INSTAT) Secondary or Higher Primary None 0 100 200 300 400 500 600 IDPs Refugees Returnees Bamako (Instat) Gao (Instat) Timbuktu (Instat) Kidal (Instat) Percentage Mobile Phone Car / Motorized Vehicle Motorbike / scooter Bicycle Refridgerator TV CD Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 displacement. 55 % of the returnees reported to have been employed before the crisis and 36 % in June 2014. Over time the employment situation among the displaced has improved steadily and by December 2014 more people reported being employed than prior to the crisis. All the returnees were able to regain employment after returning. The employment situation of IDPs, returnees, and refugees in Niger is steadily improving; only for refugees in Mauritania does one notice a steady decrease, with 100 % reporting no employment during January and February. Source: Listening to Displaced People Survey, 2014 and 2015. The ownership of livestock and consumer durables was reduced significantly as a consequence of the crisis. Table 7 demonstrates this by showing the Tropical Livestock Units (TLU) 12 owned prior to the crisis and in June 2014 as well as the percentage of ‘ yes ’ responses on a question whether a given asset was owned by the household. 13 The loss on livestock has been enormous particularly amongst IDPs and refugees who lost respectively more than 90 % and 75 % of their animals. 12 TLU is a common unit to describe livestock numbers of various species as a single figure that expresses the total amount of livestock present – irrespective of the specific composition. 13 This was a ‘ yes / no ’ question meaning that if 56 % of the Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 Figure 11: Timing of Return (%) Source: Listening to Displaced People Survey, 2014. 94 % of the returnees were displaced inside Mali and 6 % outside the country. 67 % went to Bamako, 11 % in Koulikoro, 9 % to Ségou, 5 % to Mopti and 3 % went elsewhere (Kidal, Gao and Sikasso). The majority returned between June and October 2013 a period that followed the signing of a peace deal between the interim government and rebel factions to allow presidential elections to be held in July (first round) and August (second round) 2013. In October security in the North worsened again and ever since the number of people returning has been very limited. The main challenges reported by returnees in June 2014 were (i) poverty and food insecurity; (ii) lack of infrastructure (including lack of safe drinking water) and (iii) unemployment. 11 % of the returnees stated not to be facing any challenges (Figure 12). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Results suggest that 66 % of the returnees trust the Malian police and army most when it comes to providing security in the North. Almost half believe that the Malian army is brave and well trained. The vast majority of returnees believe that the government ’ s policies regarding reconciliation, security and social cohesion are good or very good. They also support the government ’ s approach towards decentralization and providing infrastructure such as access to potable water and electricity. As the next section will illustrate this differs strongly with the opinions of refugees. 6. Prospects for Peace IDPs, refugees and returnees have comparable opinions with regard to the requirements for peace: (i) addressing the ongoing crisis, (ii) improving security and (iii) reconciliation. Although there is agreement on what needs to be done, there is little consensus on what happened during the crisis, who the culprits are and who the main victims. Figure 15: What is the most important problem the Government needs to resolve today? (%) Source: Listening to Displaced People Survey, 2014. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "23 86 % of the IDPs, 91 % of the refugees and 88 % of the returnees are confident or fully confident that a coalition like this would be capable of providing security. Source: Listening to Displaced People Survey, 2014. In an open-ended question on who can be trusted most when it comes to ensuring security in the North, survey results suggest that the majority of refugees in Mauritania (86 %) trust the armed rebel groups as opposed to the army or police. This does not hold for refugees in Niger of whom 75 % trust the army and police. Similar results hold for IDPs and returnees, who put much more confidence in state authorities when it comes to securing the North: most trust is placed in the army and police (72 % of the IDPs and 66 % of the returnees) while little to no trust is placed in armed rebel groups (3 % of IDPs, 1 % of returnees). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "25 less cohesion. The increase in solidarity is less apparent among refugees where the majority (53 %) stated that the crisis had no effect on solidarity. Figure 18: Levels of trust, by group (June) (%) Source: Listening to Displaced People Survey, 2014. Perceptions that different groups have of others are important elements of peace. When asking for the degree to which neighbors, other villagers and people from other ethnic groups can be trusted the survey finds positive outcomes. Although all groups trust people from other ethnic groups slightly less, the general level of trust is high and it remains stable over time. Finally, consider how IDPs, refugees and returnees envision the future of Mali. The majority of refugees in Mauritania vie for an independent or autonomous North, while the majority of IDPs, returnees and refugees in Niger wish to see full government control over the North. 20 20This contradicts, in part, findings of an Afrobarometer perception survey on causes and consequences of the conflict in Mali conducted in December 2013. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "27 Source: Listening to Displaced People Survey, 2014. 7. Conclusion The 2012 crisis in northern Mali led to widespread displacement. The welfare of the displaced – and those who returned – is monitored by combining a baseline survey with structured monthly follow-up interviews carried out by mobile phone. This innovative approach allows tracking changes in welfare with high frequency – even for those who returned to areas that are insecure and inaccessible to enumerators. After 6 rounds of follow-up interviews attrition rates are very low (more than 99 % response rate), demonstrating that it is possible to collect robust and representative data from hard-to-reach, conflict-affected populations. The results show that those who fled were better educated, better off and less affected by violence than the average population in the North. Those who fled lost significant amounts of durable goods (20-60 %) and livestock (50-90 %); many of their children ended up being taken out of school and their welfare (measured subjectively and by the number of meals consumed) declined considerably. Over time, the impact of the crisis on welfare has lessened and by February 2015 the majority of eligible children of the displaced were going to school and levels of employment and number of meals consumed were at pre-crisis levels. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 International migration — the movement of people across national borders — has important economic, social, and political implications. Despite the recent emergence of a dynamic literature, empirical analysis of migration flows and their impact lags behind the policy debate and the theoretical literature. The main reason is the absence of comprehensive and reliable data on international migration patterns and migrant characteristics at either the aggregate or the household level. The objective of this article is to use data from more than one thousand national censuses and population registers to estimate a complete global origin – destination migration matrix for each decade over 1960 – 2000. These 226 * 226 matrices, comprising every country, major territory, and dependency around the world, are divided into periods corresponding to the last five completed census rounds. The gender dimension of international migration over this period is also presented. The primary source of the raw data is the United Nations Population Division ‘ s Global Migration Database, created through the collaboration of the United Nations Population Division, the United Nations Statistics Division, the World Bank, and the University of Sussex (United Nations [2008]). This unique data repository comprises 3, 500 individual census and population register records1 for more than 230 destination countries and territories over the last five decades. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The database provides information on international bilateral migrant stocks (by citizenship2 or place of birth), sex, and age. There is considerable variation, however, in how destination countries collect, record, and disseminate immigration data. Meaningful comparison of destination country records over time is thus often confounded. In constructing global bilateral migration matrices, several challenges arise. First, destination countries typically classify migrants in different ways — by place of birth, citizenship, duration of stay, or type of visa. Using different criteria for a global dataset generates discrepancies in the data. Second, many geopolitical changes occurred between 1960 and 2000, with many international borders redrawn as new countries emerged and others disappeared. In addition to creating millions of migrants overnight — as when the Soviet Union collapsed — these events complicate the tracking of migrants over time. Third, even when national censuses of destination countries include data on international migrant stocks, the data are presented along aggregate geographic categories rather than by country of origin. Data therefore need to be disaggregated to the country level. Finally, the greatest hurdle is dealing with omitted or missing census data. Very few destination countries — especially developing countries — have conducted rigorous censuses or population registers during every census round over the second half of the twentieth century. Wars, civil strife, lack of funding, and political intransigence are but a few reasons why records may be discontinuous. 1 Of the 3, 500 sources detailed in the overarching UN Global Migration Database, 1, 107 were suitable for analysis, once repeated censuses had been removed or combined. Global Migration Database should not be confused with the Trends in International Migrant Stock Database, which lists aggregate migrant stocks for each destination country in the world at five year intervals (United Nations 2006) 2 The article treats the concepts of nationality and citizenship as analogous and uses the terms interchangeably. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 The main contributions of this article lie in identifying and overcoming these challenges in order to construct a consistent and complete set of origin – destination matrices of international migrant stocks for 1960 – 2000, disaggregated by gender. The starting point is a master set of 226 origin or destination countries and regions. Despite border changes, all migrants are assigned to this master set so that migrations can be meaningfully tracked over time. These assignments, especially in cases where only aggregate data are available, are made using several alternative propensity measures based either on a destination country ‘ s propensity to accept international migrants or on an origin country ‘ s propensity to send migrants abroad. Cases of omitted data occur when destination countries do not collect or publicly disseminate the information on migrants. When data from census rounds are missing altogether, the approach taken depends on the extent of the omission (see appendices 3 and 4). When sufficient data are available for other decades, interpolation is used. When not enough data are available, propensity measures are used to generate bilateral data. When a gender breakdown is missing, gender splits are calculated based on supplementary statistics or other data in the matrices (see appendix 5). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The resulting migration matrices should be viewed as work in progress, but they are an important step in an ongoing global effort to improve migration data. The matrices can be readily updated as additional or superior information surfaces, and they can easily be extended to include future census rounds. Bilateral datasets of international migration are rare. Attempts to create them have focused almost exclusively on industrialized countries as destinations because these countries have more accurate and more frequently produced data. Harrison and others (2003) calculate bilateral remittances for the countries of the Organisation for Economic Co-operation and Development (OECD) together with the 27 largest nonmembers. These estimates are based on international bilateral migrant stock data that the authors also provide, although many of the data are derived from the Trends in International Migration (OECD 2002). This report, published annually since 1973, was arguably the most comprehensive guide to international migration for many years and has been the basis for many studies (see, for example, Mayda 2007). More recently, the OECD has developed a database that provides a comprehensive overview of migration to OECD countries in 2000 (OECD 2008). These data are disaggregated by a number of covariates including age, gender, educational attainment, and place of birth. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Database. Where both censuses and population registers are available, censuses receive priority. Censuses, generally conducted decennially, are retrospective tools for surveying an entire population (or in some cases, a representative sample) at a single point in time. In addition to their universal coverage, their greatest strength is the inclusion of questions on place of birth and nationality. Censuses also typically aim to enumerate the resident population, whether documented or undocumented (Bilsborrow and others 1997). So although some migrants have a strong incentive to provide false information to enumerators, many undocumented migrants will be captured in these matrices. 7 The size and scope of the census questionnaires vary enormously, both over time and in different destination countries. And there is potential variation in the quality of censuses both across countries and over time. Richer countries have many resources at their disposal to design questionnaires, train interviewers, employ statisticians, and disseminate results. Researchers have little choice but to accept the data at face value. However, where the underlying census is clearly substandard (when there are errors that are obviously not coding errors or not easily corrected), these data are omitted from the analysis. Popular in many parts of Europe, population registers are continuous reporting systems providing up-to-date demographic and socioeconomic information for everyone surveyed. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Last, specific adjustments are made in the case of Germany and the Republic of Korea. For Germany, bilateral data are available only by nationality. However, these data fail to take adequate account of the large number of ethnic Germans who arrived from other countries between 1944 and 1950 (mainly expellees) and those who arrived after1950 (mainly resettlers). Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3). In the case of Korea, data by nationality are readily available for each census round. However, these data fail to account for the large numbers of migrants from the People ‘ s Democratic Republic of Korea living in the Republic of Korea. Since the United Nations Trends in International Migrant Stock details the total migrant stock in the Republic of Korea by the country of birth definition and because citizenship is rarely granted to people from outside, it is simply assumed that the nationality data were comparable to the foreign-born definition. The nationality total was then subtracted from the UN total and the remaining migrants were assigned to the People ‘ s Democratic Republic of Korea. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Recording and Recoding There is little standardization in the recording and dissemination practices for censuses across destination countries. 14 The level of detail with which destination countries record and disseminate migration data depends on the design of the original questionnaire. Some census questionnaires ask for a specific country of birth and others simply ask for a general geographic region, such as Africa. Even if the original questionnaire asked detailed questions, some countries disseminate data only on how many residents were born abroad or have foreign citizenship. In general, three types of migrant origin are observed in the disseminated census data:  Specific geographic regions: Some of these correspond to exactly one of the 226 countries and territories in the master list. Others pertain to localities that tend to be obscure territories, islands, or regions, such as the Isle of Man or Ceuta.  Aggregate geographic regions: These correspond to two or more countries or territories in the master list. They can be continents (such as Africa), parts of continents (such as South Asia), political alliances (European Union), or other classifications (such as Other Ex-French Africa; Algeria, Tunisia, and Morocco; and Melanesia). The data for these aggregate regions need to be allocated to the 226 countries in the master list. The details of the procedures are discussed below.  Miscellaneous categories: These include refugees, stateless, and born at sea. There are generally no geographic correspondences for these. Thousands of geographic regions and categories emerged from the more than one thousand individual destination country sources chosen for the analysis. The vast majority of these are repetitions that refer to identical geographic locations using different 14 The United Nations (1998) has developed recommendations aimed at promoting standardized recording practices across countries. Until such practices are followed uniformly, harmonization will remain a key issue in understanding and comparing migration statistics. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 { Tables 4 and 5 here} Among the first six categories that are based on raw census data, three categories (raw scaled, R & R not scaled, and R & R scaled) are constructed through the summation of bilateral raw numbers and disaggregations of some aggregate categories in the original censuses. Since these categories together constitute around 45 percent of migrants in each census round, the original bilateral portion of each cell was compared with the final number assigned to them after the various calculations as a check on accuracy. For each decade, therefore, the overall percentage contribution of the raw bilateral data to the total is calculated (table 6). 23 In each census round, at least 92 percent of all those categories are derived from the raw data. { Table 6 here} Simulating Missing Data Finally, to examine the reliability of the estimated missing census data and test the methodologies, several scenarios are assumed. All bilateral observations for a single year for four countries (Australia, United States, Switzerland, and Chile) in different parts of the world are deleted and the missing cells are filled using one of five methods. 24 The first simulation assumes that all bilateral data for 2000 are missing but that the total number of migrants is available. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Second, there are cases where the current study reports data by nationality, but the corresponding figure in the Trends in International Migrant Stock refers to the foreign born. This situation generally arises when a census does not report the number of foreign-born migrants on a bilateral basis. Examples include Austria and Côte d ‘ Ivoire. Third, differences in the years to which the data refer can generate large disparities. For example, this study uses the 1966 data for Australia, whereas Trends in International Migrant Stock reports data for 1970. Overall, however, the fact that the totals are remarkably close in every decade adds credence to the estimates here. IV. THE EVOLUTION OF GLOBAL BILATERAL MIGRATION The greatest strengths of the global migration matrices are their bilateral coverage, the number of decades covered, and the disaggregation by gender. These data are too rich for a full analysis of all movements between all pairs of countries. Instead, this section summarizes the major trends in the evolution of bilateral migrant stocks, based primarily on World Bank regions. 25 Global Trends The migration matrix for the 1960 census round reflects a realigning world in the postcolonial era. Over the 1960-2000 period, the composition of world migration 25 Appendix 1 details the World Bank regions: South Asia, East Asia and Pacific, Sub-Saharan Africa, Latin America and the Caribbean, Europe and Central Asia, and Middle East and North Africa. High-income Middle East and North Africa refers to the predominantly oil producing countries in the Persian Gulf (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) and to Israel. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "33 Ratha, D., and W. Shaw. 2007. ― South-South Migration and Remittances. ‖ World Bank Working Paper 102, World Bank, Washington, DC. United Nations Statistics Division. 1998. Recommendations on Statistics of International Migration Revision 1. New York: United Nations. United Nations, Department of Economic and Social Affairs, Population Division. [2008]. United Nations Global Migration Database. New York: United Nations. http: / / esa. un. org / unmigration — — —. 2006. Trends in Total Migrant Stock 1960 – 2000, 2005 Revision. Database. POP / DB / MIG / Rev. 2005 / Doc. New York: United Nations. — — —. 2009. Trends in International Migrant Stock: The 2008 Revision. Database. POP / DB / MIG / Stock / Rev. 2008. New York: United Nations. http: / / www. un. org / esa / population /. — — —. 2010. ― World Population Prospects: The 2009 Revision, Highlights ‖, Working Paper No. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the standard ethnic diversity indices to include the annual variation in refugee ethnicities. 8 We then construct a measure of proximity between the clusters in the host country and refugees in surrounding camps by defining an 80-km buffer around each cluster. 9 To control for unobserved heterogeneity and changes within a given cluster, we introduce cluster and year fixed effects, αj and δt. To minimize the risk of confounding the refugee-induced changes in diversity with the annual changes in refugee numbers, we also control for the presence of refugees based on the same buffer as the one used to construct the refugee-induced change in diversity. More specifically, the variable Refugeesjt − 1 counts the number of refugees present in cluster j at year t − 1 within the predefined buffer. The variable is also transformed into an inverse hyperbolic sine to ease interpretation. Finally, Qjt controls for yearly shocks at the cluster level, such as weather shocks. In particular, we control for rain and temperature anomalies. Standard errors are clustered at the Afrobarometer cluster level. 4. 2 Data and descriptive statistics Our analysis combines various sources of data: Afrobarometer, UNHCR refugee camp data, Armed Conflict Location and Event Data (ACLED), Uppsala Conflict Data (UCDP), and the Ethnic Power Relations- Ethnicity of Refugees (EPR-ER) 2019 dataset. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using Afrobarometer ’ s geocoded surveys, we focus on clusters as our unit of observation. 10 Our sample consists of 7, 547 such locations and 76, 518 individuals in 23 countries in Sub-Saharan Africa. “ The sampling universe normally includes all citizens age 18 and older. As a standard practice, they [we] exclude people living in institutionalized settings, such as students in dormitories, patients in hos- pitals, and persons in prisons or nursing homes. ” (Afrobarometer, https: / / afrobarometer. org / surveys − and − methods / sampling − principles) Since the sampling frame is based on recent censuses, with the aim of representing all citizens of voting age in a given country, the Afrobarometer samples are unlikely to include refugees. Note also that “ the sample design is a clustered, stratified, multi-stage, 8We explain the construction of theses indices in Section 4. 2. 9We test the robustness of our results with a smaller (40 km) and a larger (120 km) radius in Section 5. 3. This choice of buffer size assures us that between 75 percent and virtually all refugee camps fall within a cluster buffer. Other studies relying on Afrobarometer data construct buffers ranging from 25 km (e. g., Michaelopoulos and Papaioannou (2011), investigating ethnic-specific pre-colonial institutional structures) to 100 km (e. g., McGuirk and Burke (2020a), analyzing the impact of food-price shocks on conflict). 10Afrobarometer is a pan-African research network conducting public attitude surveys on democracy, governance, the economy, and society in African countries that are repeated on a regular basis (Afrobarometer, 2020). 10 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location. Area stratifi- cation reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analyzed. ” Afrobarometer provides geocoded data for 6 rounds, which correspond to the 1991 – 2016 period, with the information on an individual ’ s ethnicity available from round 3 (corresponding to 2005 – 2006). We therefore restrict our analysis to the 2005 – 2016 period. The selection of countries is driven by data availability. Among the 33 countries with available Afrobarometer data, we exclude Botswana, Cape Verde, Lesotho, Madagascar, Mauritius, Sao Tome and Principe, South Africa, and Swaziland, for which no data is available on refugee camps or from the EPR-ER. We also exclude Sudan since the question on individual ethnicity is not asked in this country ’ s survey. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 1: Data and Descriptive Statistics: Clusters, Refugee Camps, and Conflicts Revised refugee diversity indices. We first use Afrobarometer data to construct standard indices of diversity, namely the EF and the EP indices (Bazzi et al., 2019; Esteban and Ray, 1994). The EF index describes the probability that two randomly selected individuals from a given location belong to two different ethnic groups (Alesina et al., 2003, 2016; Gomes, 2020b). The EF index can be defined as EFjt = Njt X e = 1 get (1 − get), (2) where Nj is the number of ethnic groups in cluster j at time t and get is the population share of 14 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ethnic group e at time t. It can also be expressed as one minus the Herfindahl index (Alesina et al., 2016). The EP index gives more weight to intergroup differences at the expense of within group homo- geneity. It can be defined as (Esteban and Ray, 1994, 1999; Montalvo and Reynal-Querol, 2005) 14 EPjt = Nrt X e = 1 (g2 et) (1 − get). (3) We compute this index for each cluster at the time of each Afrobarometer survey to assess how refugee-induced changes in diversity differ from standard indices of diversity. In order to construct the revised refugee diversity indices according to ethnicity e, we first combine information about the country of origin of refugees hosted in refugee camps c in year t with the data from the EPR-ER 2019 dataset. The EPR-ER records the ethnic composition of refugee stocks originating from neighboring countries and countries in proximity to each other (maximal distance between country borders ≤ 950 km) with at least 2, 000 refugees and provides the ethnic composition of refugees (Vogt and Girardin, 2015). More specifically, the EPR-ER dataset gives us the share of refugees from ethnic group e moving from country o to country d at year t. The EPR-ER data gives us the three main ethnic groups. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There is one limit to our approximation in Equation 4. The ethnic composition of refugees in each year t for a given origin – destination pair of countries obtained from the EPR-ER database is assumed to be homogeneous across camps of the same origin – destination pair of countries for the refugees at year t. This may seem to be a strong assumption; however, the risk of misallocating refugees is reduced as the annual variation in the EPR-ER is generated by just a few dominant groups for a given origin – destination pair and the geographical distribution of refugees by country of origin is highly influenced by the proximity to their countries of origin. 16 As can be seen from panel A of Table B. 2, in refugee-hosting areas, on average, both EF and the EP seem to increase quite significantly when they are revised by incorporating the number of refugees in an 80-km buffer: the mean value of the standard EF index is 25. 58 %, while the mean value of the revised refugee EF index is 37. 90 %. The mean value of the standard EP index is 10. 11 %, while the mean value of the revised refugee EP index is 14. 07 %. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 3 also shows that there is considerable variation in both indexes within our sample when averaging these indexes at the regional level over the period of investigation. 16It is possible that our approximation is noisy and could potentially induce non-random measurement errors. In Section 4. 3, we propose an instrumental variable approach and estimate Shareodet from the EPR-ER data using a gravity model. Our findings concerning the number of ethnic groups across time for a given origin – destination pair are in line with the EPR-ER data. It seems that refugees of a given origin – destination pair mainly belong to two major ethnic groups. This also means that the variation in diversity in refugee-hosting areas is coming from the refugee composition at the camp level. Figure B. 5 shows the movements of refugees from origin to destination countries under scrutiny. Somalia, the Democratic Republic of Congo, Liberia, South Sudan, and Sudan are major source countries for refugees, while Kenya, Tanzania, Uganda, Zambia, and Ghana appear to be countries hosting most refugees. Representing refugees in camps per ethnic group for the top 5 asylum countries over the sample period, Figure B. 9 shows that there is considerable variation in ethnic composition across camps. 17 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "at the same level with a similar categorization process; instead, the information can relate to an individual ’ s linguistic ethnicity, dialect, or an ethnic group encompassing several languages. In our main analysis, we use LEDA ’ s binary linking at the “ dialect ” level, based on the minimum linguistic distance to link these ethnic groups. 17 This involves computing a value corresponding to the shortest path (see Equation A. 1) between ethnic groups using a language tree. In our case, “ dialect ” is the level defined to match the two groups (see Figure A. 1 from M ¨ uller-Crepon et al. (2020) for a Ghanaian case). 18 We further describe the use of the LEDA software package in Section Appendix A. 1. 4. 3 An instrumental variable approach In Section 4. 1, we acknowledged that non-random measurement errors might be a concern. Another major identification challenge is the risk that our revised measures of diversity are biased due to the selection of hosting areas by refugees. We should first acknowledge that the ability of refugees to select their places of residence is much more limited than economic migrants. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, we can- not exclude the possibility that refugees would sort non-randomly into areas with particular ethnic characteristics. 19 In order to address this potential endogeneity, we implement an instrumental variable (IV) ap- proach. We are particularly concerned about certain ethnic groups from certain countries of origin moving to destination countries with similar ethnic characteristics. Such endogenous selection would be reflected in the EPR-ER data. To deal with the plausibly endogenous nature of the resulting refugee EF and EP indices, we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data. The predicted (and plausibly exogenous) number of refugees by ethnic group e is then used to create other (plausibly exogenous) diversity indices to be used as instrumental variables. More specifically, we estimate the following gravity model: 17We also use this method to link data from EPR-ER on the ethnicities of refugees with data from the Murdock Atlas on their historical homeland (Section 4. 3). 18As a robustness check (Section 5. 3), we use an alternative linkage based on the relations between sets of language nodes associated with two groups. 19Another source of selection may come from the fact ethnic groups are more likely to be displaced when they share territory with regime supporters in their countries of origin (Lacina et al., 2017). Since similar ethnic groups are likely to share common borders (Michaelopoulos and Papaioannou, 2016), it is not impossible to think conflict might spill over through this channel. 19 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "REFodet = αod + γe + τt + β1Conflictot − 1 + β2Conflictet − 1 + β3Distanceed + ϵodet, (5) where REFodet is the stock of refugees of ethnic group e from country o in country d at year t. As we have data on yearly refugee stocks and would like to estimate the changes in these stocks over time using a gravity model, we include origin – destination fixed effects αod so that identification is based only on changes in stock over time (Zylkin, 2019). 20 We also include time τt and ethnic group fixed effects γe. Here we obtain data on the ethnicity of refugees from Murdock ’ s Atlas, which provides a map of ethnographic regions for Africa and the historical homelands of refugees (Murdock, 1967). To match ethnic groups across datasets, we again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity from the EPR-ER dataset and, later, with data from Afrobarometer. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We use the sum of conflict events occurring in the historic homeland of ethnic group e in the previous year t − 1, denoted as Conflictet − 1, and we use the mean distance between the historic homeland of ethnic group e and the border of country d to predict the number of refugees of a certain ethnic group e moving from country o to d at time t. 22 In order to be consistent with EPR-ER data construction, we restrict our analysis to all origin – destination country pairs that are at a maximum distance ≤ 950 km from each other. Predicted numbers of refugees are then transformed into predicted shares for the three largest groups to follow the logic used by the EPR-ER dataset. We then plug in these predicted shares in the following way: X \\PredictedRefcet = Refocdt ∗ \\Shareodet. (6) The predicted shares of refugees per camp c are then used to compute (as documented above) refugee diversity indices to be used as instrumental variables. The first-stage equations corresponding to the 2SLS-equivalent of Equation 1 can be expressed as 20We conduct a robustness check on Equation 5, replacing the dyadic origin – destination fixed effects with separate origin and destination fixed effects (Section 5. 4). 21More information on LEDA can be found in Appendix A. 1. 22The construction of the IV follows a long tradition in using the gravity model to predict bilateral migration flows (Ravenstein, 1985, 1989; Crozet, 2004; Mayda, 2010; Garcia et al., 2015; Beine et al., 2016). In our analysis, a major difference is that we have an additional dimension: the ethnic group e. 20 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Indeed, despite the recent literature rejecting the conflictive impact of refugees in hosting areas (Zhou and Shaver, 2021), the magnitude of our coefficients might be explained by the confounding presence of refugees. Columns (5) and (6) further introduce climatic controls. Column (6) corresponds to Equation 1 and refers to our benchmark specification. Columns (1) and (2) show that without incorporating the changes in ethnic diversity induced by refugees we would not be able to identify a relationship between diversity and violent conflicts. In column (3), the revised refugee fractionalization index has a negative and significant coefficient, while the revised refugee polarization index has a positive and significant effect on the incidence of violent conflicts. In columns (2), (4), and (6), our coefficients of interest are of the same order of magnitude when the number of refugees is controlled for. Our results are not altered by incorporating rainfall and temperature anomalies (columns (5) and (6)), but the estimates become slightly more precise. 21 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5. 5 Ethnic diversity at different levels Despite the use of sampling weights in the construction of the diversity indices, we have no guarantee that our diversity indices are representative at the local level. Although similar ethnic diversity indices have been used at the local level (Nunn and Wantchekon, 2011; Rohner et al., 2013; Robinson, 2017; Desmet et al., 2020; Gomes, 2020b, a; Hodler et al., 2020), we cannot exclude the possibility that a lack of representativeness at the local level introduces some noise into our estimates. Ideally, we would have liked to construct our local diversity indices based on census data. However, such data are not available on an annual basis and only a minority of African countries include ethnicity questions on their censuses (Robinson, 2017). Robinson (2017) highlights other benefits but also warns against the risk of using non-random samples or of the size of samples introducing significant errors. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As summarized by Robinson (2017), “ fortunately, Afrobarometer respondents comprise stratified random samples at all levels, making population estimates based on them unbiased: thus, the major concern with using Afrobarometer sample data to construct demographic measures is unbiased measurement error. ” Based on a comparison of census-based and survey-based diversity indexes across five African countries, Robinson (2017, 224) found that a “ sample-based measure tends to underestimate the overall degree of diversity compared to census data ”. In theory, this should make it more difficult to observe the true relationship between ethnic diversity and some outcomes at the local level. Diversity indices are more likely to be measured with noise in highly diverse communities at the local level. We nonetheless argue that such a concern should not be overestimated, for three reasons. First, such noise cannot easily explain the contrast between the coefficients corresponding to the pre-revised and revised indices and the opposite results found for the revised refugee fractionalization and the revised polarization. This set of results can be explained by the fact that our identification comes from the annual changes in refugees flows. Second, the IV approach is likely to deal with the measurement errors if they are correlated with our main variables of interest. Our IV estimates therefore capture a local average treatment effect coming from the plausibly exogenous increase in annual refugee flows of particular ethnic groups. The similarity of the IV results to the OLS results supports this interpretation. Third, at the cost of introducing attenuation bias30, we also aggregate the number of conflict events at the regional level. Lines B and C of Table 7 confirm the negative and positive effects found for the revised fractionalization and polarization indexes, respectively, whether or not 30Another risk highlighted by Robinson (2017) is the fact that ethnic diversity may also capture different theoretical mechanisms at aggregated levels. 34 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of the groups and the distances between them. For instance, (Bazzi et al., 2019) shows that polariza- tion increases ethnic attachment. Others have highlighted the reduction in trust, either interpersonal trust or institutional trust Alesina and Ferrara (2002); Beugelsdijk and Klasing (2016). To assess the importance of alternative explanations, we first replicate our analysis using individual data on violence. In addition to participation in protests, we follow McGuirk and Burke (2020b) in using the Afrobarometer survey data on interpersonal crime and physical assault. We then assess the relationship between the revised refugee diversity indices and alternative individual outcomes such as ethnic vs. national identity, generalized trust, trust in neighbors, and institutional trust (trust in government). The questions from the Afrobarometer mentioned below are used as a proxy for these outcomes: 32 1 Attack: Over the past year, how often (if ever) have you or anyone in your family: Been physically attacked? 2 Crime: Over the past year, how often (if ever) have you or anyone in your family: Feared crime in your own home? 3 National identity: Let us suppose that you had to choose between being a [Ghanaian / Kenyan / etc.] and being a [respondent ’ s identity group]. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research. 1989-2019. 50 (4). Taylor, J., M. Filipski, and M. Alloush (2016). Economic impact of refugees. Proceedings of the National Academy of Sciences 113 (27), 7449 – 53. United Nations High Commissioner for Refugees (2020). Global Trends: Forced Displacement in 2019. Geneva. Verme, P. and K. Schuettler (2021). The impact of forced displacement on host communities a review of the empirical literature in economics. Journal of Development Economics 102606. Verwimp, P. and J. Maystadt (2015, December). Forced Displacement and Refugees in Sub-Saharan Africa. An Economic Inquiry. World Bank Policy Research Working paper 7517. Vogt, Manuel, N.- C. B. S. R. L.- E. C. P. H. and L. Girardin (2015). Integrating Data on Ethnicity, Geography, and Conflict: The Ethnic Power Relations Data Set Family. 51 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "LEDA, which computes the minimum linguistic distance between two ethnic groups and, therefore, provides the closest linguistic neighbor for each given ethnic group (see Figure A. 1). This function computes a variable called distance, which measures the linguistic distance between two ethnic groups. Mathematically, these distances are calculated as DL1L2 = 1 − \u0012 2d (ω (L1,..., O) ∩ ω (L2,..., O)) d (ω (L1,..., O)) + d (ω (L2,..., O)) \u0013 δ, (A. 1) where d (ω (L1,..., O) is the length of the path from the first language to the tree ’ s origin and d (ω (L1,..., O) ∩ ω (L2,..., O) is the length of the intersection of the paths from the first and second language to the origin. δ is an exponent to discount distances further away from the root of the tree; it is typically set to 0. 5. Figure A. 1: Linking Ethnic Data from Africa Source: M ¨ uller-Crepon et al., 2020. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure A. 2: Injective relations We also isolate many-to-one (bijective) relations. In this case, we have to aggregate the Afro- barometer ethnicities with their unique and more aggregated correspondence in the UNHCR refugee camps data (See Figure A. 3). Figure A. 3: Bijective relations The remaining correspondences are either (i) one-to-many (bijective) but opposite to Figure A. 3 (i. e., many ethnicities from the UNHCR refugee camps data correspond to one ethnicity from the Afrobarometer) or (ii) many-to-many relations. For both cases, we apply a more pragmatic approach: a. In both cases, we disregard ethnicities that do not appear either in the Afrobarometer or in the UNHCR refugee camps data. This means that for the remaining ethnicity that has no counterpart in either the Afrobarometer or the UNHCR refugee camps data, we simply keep the name of the ethnicity as such, i. e., this information is not dropped. b. Then, after ignoring ethnicities that have no occurrence in our datasets, we check whether the one-to-many or the many-to-many relation has not boiled down to a one-to-one resp. many-to- one relation again. If so, we can treat them as above. c. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For the remaining one-to-many relations, we keep these ethnicities in the Afrobarometer as such and consider them as a single ethnic group. Some manual treatment can even further improve 4 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 INTRODUCTION In 2015, an estimated 2. 2 million Syrians Under Temporary Protection (SUTPs) were residing in Turkey, the majority arriving in the country over the last 4 years. 2 Turkey ’ s national population is roughly 75 million; recent refugees account for approximately 3 percent of the population. For a country that has never experienced such a large-scale, sudden inflow of foreigners, demographic changes in the composition of the population and labor force will yield unprecedented implications. This paper examines, as data allows, the relationship between the size of the foreign-born population and host community poverty rates in Turkey. First, this paper finds the poverty rates of ‘ recent migrants ’ near the Syrian border (NSB) significantly increased from 2009 to 2013. Second, the number of foreign-born households being captured by the Labor Force Survey (LFS) is expanding, which suggests a growing number of foreign households that are likely to be Syrians. Third, with respect to poverty, the results show no negative impacts on the host community as a result of the increasing size of the foreign-born population. The impact of SUTPs has been both positive and negative. Overall, a significant negative impact on host communities ’ welfare is not observed in the data. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While there is a large literature on the role of immigrants on the native-born population in terms of labor market competition, there is a limited amount of studies that examine the effect from displaced populations. Many conclusions from the traditional literature on the study of immigrants ’ impact on natives cannot be applied to the case of Syrians in Turkey. There are many differences between the inflow of Syrians and other flows of extended family and economic immigrants. First, the sheer volume of Syrian refugees and the short time- frame in which they entered Turkey is unprecedented. For the case of Syrians in Turkey, or displaced populations in general, large movements of refugees are not restricted due to humanitarian reasons. Second, formal immigration processes are controlled, limited, and regulated by destination countries. Therefore, results from literature on “ immigrants ” are very different than a focus on displaced or refugee populations. Recent literature on the labor market effects of SUTPs estimates negative impacts on host community employment rates. The negative displacement results are largest for the young, women, informal workers, 2 United Nations High Commissioner for Refugees (UNHCR) – Syrian Regional Refugee Response, Inter-agency Information Sharing Portal 3 (UNHCR) – Syrian Regional Refugee Response, Inter-agency Information Sharing Portal Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 and the less educated (Ceritoglu, Tunculer, Torun, and Tumen, 2015; Del Carpio and Wagner, 2015). The economic effects of SUTPs not only vary across different segments of the labor market, there are also strong regional differences in their economic effects. Using synthetic modelling methods, Ozturkler and Goksel (2015) estimate the impact of Syrian refugees on local prices, wages, inflation, and services in 10 cities with large refugee populations. Some of the salient negative effects have been increases in rental prices, increases in inflation at border cities, illegal hiring by small business, and decreases in wages. However, in some cities (Gaziantep, Adana, Kahramanmaras, and Mardin), the presence of refugees has improved the trade balance, and economic activity in these areas are projected to increase as economic integration with MENA deepens. Orhan and Gundogar (2015) also note both positive and negative aspects of SUTPs. A primary contribution of this paper is the estimation of poverty at the sub-national level and among population groups of interest. Since migration, geographic, and welfare variables do not exist in a single data set, imputation techniques are required to overcome these limitations and to compute household level poverty. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The imputation of income poverty is done using the Turkish Labor Force Survey (LFS), and with information and modeling parameters determined from the Survey of Income and Living Conditions (SILC). More details and validation of this methodology is discussed throughout this paper. While explicit identification of Syrians in available surveys is not feasible, there is evidence of an increase in the amount of foreign-born individuals that is being captured in the LFS. The arrival year of foreign-born migrants is available in the data which allows for identification of “ Settled Migrants ” and “ Recent Migrants ”. The latter is used as a proxy for Syrian refugees for the purposes of this paper. National official surveys that are conducted under-report the refugee population. Yet, since about 10 percent of Syrian refugees are in camps and the remaining are residing throughout the country, it is not surprising that they are accessible to interviews by the LFS. Despite data limitations, there are strong and significant trends in the poverty rates for the recent foreign- born, especially for those near the Syrian border. In 2013, recent migrants near the Syrian border were the poorest group4 in Turkey. While this statistic in itself is not initially surprising, fluctuating welfare trends of recent migrants over time is noteworthy. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In previous years, migrant households in Turkey tend to have much lower poverty on average than even the host community. Across comparison groups and time, the poverty rates of recent migrants is higher than among the host community in only one instance: in 2013 near the Syrian border. This sudden change in the historically stable pattern implies that the LFS is able to capture at least a part of the incoming SUTPs who have significantly different socioeconomic profiles in comparison the previous economic migrants. Throughout history, immigration to Turkey has been relatively limited and consisted mostly of those of Turkish heritage. In the early 20th century, immigration was encouraged by the government as a method to increase the population. Since 1970, immigration has slowed down and has been even discouraged at times. Many immigrants to Turkey are of Muslim Turkish background, since the government prioritized preserving a national identity. This is likely why “ migrants ” had very similar or even lower poverty rates than the host community. The sharp degradation of welfare among recent migrants in 2013 illustrates the severity of poverty that is arising very likely from a growing population of Syrian refugees. The Syrian refugee inflow to Turkey 4 Based on grouping by host community, established migrant, recent migrant. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 began in April 2011 and has been continuing at an increasing pace as the conflict in Syria expands. 5 The Turkish government has provided a tremendous amount of support in the form of shelter and essential items to help sustain the livelihood of large numbers of refugees. However, aid funds are not limitless and refugees face hardships that will persist over the long-term. The refugee camp population in Turkey has been stable since March of 2013 as the physical capacity of the camps have been exhausted. 6 This saturation has resulted in a steep increase in the number of Syrians living outside camps across Turkey. The proportion of Syrian refugees living outside camps increased from 53 percent to 87 percent between March 2013 and November 2014. 7 In addition, even though refugees living outside camps continue to be concentrated near the Syrian border (64 percent), the dispersion of Syrians across the country has expanded, especially in major urban centers such as Istanbul and Ankara. The results in this paper are limited to 2013 due to changes in the 2014 LFS that make poverty estimations incomparable to previous years. 8 Therefore our results may provide only a partial insight into the impact of SUTPs, since the dispersion of Syrians across Turkey has increased greatly in 2014 and 2015. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The descriptive characteristics of foreign-born and host community households are still comparable, and therefore allow for comparisons between 2013 and 2014. It is possible to make some inferences on welfare changes by looking at changes in host community employment rates and labor market characteristics. Overall, this paper finds no negative effects on host community welfare from an increasing population of SUTPs. As other authors have stated, the influx of SUTPs has had both positive and negative impacts. It seems on average, the host community has been strong and adaptive, and not negatively impacted. This is not to disregard that real strains do exist in some regions where the SUTP population is very large. Nor do these results undermine findings of displacement effects in the labor market that certain types of workers are experiencing. However, on average nationally, we do not see a systematic decline in the welfare of the host community between 2011 and 2013. The remainder of the paper is organized as follows. Section 2 outlines the data availability and technical issues. Section 3 discusses descriptive statistics of the foreign-born and host community. Section 4 explores the impact of the foreign-born population on host community welfare. 1. DATA AND TECHNICAL ISSUES Data Sets Data availability limits which data set can be used to identify the foreign-born population and geographic location while measuring poverty. The Turkish Statistical Institute (TUIK) has been conducting three nationally representative surveys annually since 2005; the Household Income and Consumption Expenditure Survey (HICES), the Survey on Income and Living Conditions (SILC) and the Labor Force Survey (LFS). However the HICES, which is the national survey that is used to measure official poverty, 5 UNCHR, 2013pg 13 6 UNHCR (22 November 2013), UNHCR (15 September 2014) 7 UNHCR (22 March 2013), Erdogan (2014), pg 14 8 This technical issue will be discussed further later in the paper. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As a result, imputed poverty is measured as income poverty. Another advantage of using the LFS is the availability of CPI at the NUTS2 level in Turkey which allows for spatial deflation of different price levels across the country. Table 1. Survey Comparison and Data Availability Years Available Migration Variables Income or Consumption Geographic Identifier Spatial Deflation HICES 2003-2012 No Consumption, income National, urban / rural No SILC 2009-2012 No Income NUTS1 No LFS 2009-2013 Yes Imputed Income NUTS2 Yes However, there are other issues for consideration when using the LFS. Principally, there is a low number of sample points that are migrant households. Moreover, the study cannot identify migrant households and individuals that are specifically Syrian refugees. Foreign migrants are defined as those who were born abroad and have lived abroad for at least more than 12 months. Some Turkish-born households have also lived abroad for over a year, and these individuals are not considered to be migrants. Amongst foreign-born individuals, only the ones who have been in the country for more than 12 months are included in the sample which underrepresents the actual number of foreign migrants in the region. In addition, no specific procedure is adopted by the enumerators if the household does not speak Turkish. Given that a majority of Syrian refugees do not speak Turkish, the language barrier might result in the removal of Syrian households from the sample. Finally, refugee camps are not included in the sample frame, which limits the study to only examining recent migrants who do not live in refugee camps. 9 Wage income is only available for regular and casual employees in the LFS which accounts for around 60 % of total employment. There is no other monetary income value for the rest of the working population. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 Comparison Groups Six population groups are constructed based on their migrant status and geographic location (Table 2). Five out of 26 regions are defined as Near Syrian Border (NSB) regions based on their proximity to Syria as well as their popularity as a destination for migrants (see Map 1 for details). These regions are Mardin (TRC3), Sanliurfa (TCR2), Gaziantep (TCR1), Hatay (TR63), and Adana (TR62). 10 TRC3-Mardin, TCR2- Sanliurfa, TCR1-Gaziantep and TR63-Hatay are Southeastern regions of Turkey that border Syria. TR63- Adana does not border Syria but is a southern Mediterranean region that is a common destination for migrants due to abundant labor opportunities. The rest of the country includes the remaining 21 NUTS2 regions. Map 1. Near Syrian Border Regions 10 NUTS2 regions are referred with name of the largest province in each regions. The full list of provinces in each region are; Mardin-Batman-Sirnak-Siirt (TRC3), Sanliurfa-Diyarbakir (TCR2), Gaziantep-Adiyaman-Kilis (TCR1), Hatay-Kahramanmaras- Osmaniye (TR63), and Adana-Mersin (TR62). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Host community households are those whose head of household were born in Turkey, or born outside Turkey but did not live abroad for more than a year. Conversely, migrant households are defined as those whose head of household was born abroad and has lived abroad for more than 12 months. The duration of a migrant household ’ s stay in Turkey is also based on when the head of household arrived in Turkey. Three thresholds are tested: 2, 3, and 4 years. The 4 year cut-off is preferred to maximize the sample size of recent migrant households. Table 2. Population Groups for Comparison Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Geographic Location Near the Syrian Border The Rest of the Country Status Host Community Settled Migrant Households Recent Migrant Households Host Community Settled Migrant Households Recent Migrant Households Years in Turkey Head of Household born in Turkey Arrived in Turkey more than: 2, 3, or 4 years Arrived in Turkey less than: 2, 3, or 4 years Head of Household born in Turkey Arrived in Turkey more than: 2, 3, or 4 years Arrived in Turkey less than: 2, 3, or 4 years Are Syrian Refugees being captured using the LFS? While variables covering all topics of interest (migration, welfare, and geography) are available or can Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 3. ANALYSIS Has the increase in Syrian refugees impacted the welfare and socioeconomic conditions of the host community? Summary statistics in the previous section showed clear trends of increasing poverty among recent migrants throughout the country, both near the Syrian border and across rest of the country. While the poverty rates of recent migrant households spiked in 2013, poverty of host community households maintained a relatively constant level in the whole country. From these trends, it appears that there at least has not been an increasing trend in poverty among the host community over the latest years. The empirical model is shown in Equation 1. Regressions are estimated at the NUTS2-year level and using data from only the years 2011 and 2013. The dependent variable of interest is the host community poverty rate by region and year, where the poverty rate is based on spatially deflated imputed household income. Unlike the computation of the poverty rates, “ recent migrant ” information is not used for the analysis. Only the host community poverty rates are calculated using the LFS and the number of Syrians are taken from government sources. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Moreover, descriptive characteristics suggest that their conditions might have worsened in 2014. As this unprecedented event continues, the integration of Syrians into the Turkish labor market, access to public services, changing demographics, and socioeconomic impacts should be monitored closely. Especially with an increasing rate of SUTP migration to Turkey during 2014 and 2015 and the continued conflict in the region, the inflow of Syrians will be one of the most critical short, medium, and possibly long term policy issues in the country. In addition, Turkey ’ s role as a pathway to Europe for those escaping conflict in the Middle East makes the issue an international phenomenon. In this respect, the healthy incorporation of SUTPs that will protect the wellbeing of host communities while satisfying the humanitarian necessity of helping Syrians will be among the more important development issues of today and the foreseeable future. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "34 Any statistics on the imputed welfare will based on the set of imputed welfares for each household. The estimator takes the form, with R denotes the number of simulation: ܪ ෡ ൌ 1 ܴ ෍ ݄ ሺݕ ෤ ௥ ሻ ோ ௥ ୀ ଵ where ݄ ሺݕሻ is a function that converts the vector y with (log) incomes for all households into a poverty measure (such as the head-count rate or bottom 40 %), and where ݕ ෤ ௥ denotes the r-th simulated imputed welfare. Figure 6. Survey-to-Survey Imputation Methodology, an illustration For the case of Turkey, we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey. Income is used instead of consumption for this paper ’ s analysis. The model included variables related to: household demographics (age, gender, age composition, etc.), household characteristics (education, labor activity, etc.), household head ’ s characteristics (age, gender, labor, education, marital status, etc.) and household assets holding (both livestock and durables). Based on that model the simulated values of consumption (at household level) were imputed for the households in the corruption survey. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This allowed for consistent ranking the households into welfare quintiles and cross- tabulation of welfare status with household characteristics and indicators derived from the survey data. The imputation was carried out using s2sc algorithm in STATA. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "35 Inputs: 1. Household Survey with consumption or income welfare aggregates 2. Project data / Other survey data without welfare aggregates 3. Set of harmonized common variables in both surveys Outputs: 1. Set of imputed welfare variables for project data / other survey for each household in the data 2. Imputed welfare variables can be used for poverty, distributional analysis (quintiles or more), profiling of the poor or group of interest Models: 1. Ordinary Least Squares (OLS) 2. Probit 3. Multiple Imputation (MI) Table 18. Model Specification Variables Demographic Share of children, share of adults, share of adults squared and share of old (omitted) Characteristics of head Age, gender, and level of education Interactions with urban dummy variable Level of education of the head, age of the head Geography Dummies for regions at NUTS 1 level (12 regions) Interactions with Geography Level of education of the head, age of the head interacted with regions at NUTS 1 level (12 regions) and urban-rural division 1. Validation and Robustness Check Figure 7. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The author may be contacted at asacks @ worldbank. org. and managing these resources. The author assesses these competing hypotheses using multi-level analyses of Afrobarometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows for analysis of associations between donor and non-state actor service provision and the sense of obligation to comply with the tax authorities, the police and courts. The findings yield support for the hypothesis that the provision of services by donors and non-state actors is strengthening, rather than undermining, the relationship between citizens and the state. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "reason to believe that the provision of services by donors and non-state actors could either strengthen or weaken citizens ’ deference to government. I will identify the conditions under which these two scenarios are likely to occur. On the one hand, citizens may be less likely to support the government with deference to its laws and regulations when they credit non-state actors or donors for service provision. The provision of services by donors and non- state actors is likely to prompt citizens to question why they should pay taxes to a government that is not providing them with anything in exchange. On the other hand, the provision of goods and services by donors and non-state actors might strengthen citizens ’ legitimating beliefs and their willingness to defer to governmental laws and regulations if citizens view their government as essential to leveraging and managing these external resources. I assess these competing hypotheses using multi-level analyses of Afro- barometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows us to analyze associations between donor and non- state actor service provision and the sense of obligation to comply with the tax authorities. Third, I assess the relationship between the provision of ser- vices by donors and non-state actors and citizens ’ willingness to defer to two additional authorities, the police and courts. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 1). 2 For example, there is also some evidence that when tax increases are linked to improvements in public goods provision, citizens are less likely to resist the tax increases. In Ghana, the government linked increases in the VAT rate explicitly to the new public spending programs, such as the Ghana Education Trust (GET) Fund in 2002 and the National Health Insurance Scheme (NHIS) in 2003 which enjoyed broad public support. The government used strategic communication to make this link in order to avoid major public protests, such as the Kume Preko protests that greeted the introduction of the VAT in 1995 and left several people dead (Osei, 2000; Prichard, July 2009). Similarly, Ghana ’ s government linked the introduction of a talk tax on mobile phone calls to efforts to combat youth unemployment, which helped to curb public opposition (Prichard, July 2009). 3. 1 Is donor and non-state actor service provision likely to undermine the fiscal contract? We are beginning to accumulate knowledge about what government can do to influence the perception of the relationship between citizens and political authorities. We know very little about what happens once non-state actors mediate that relationship. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "impression of the state ’ s role in development and give credit to the state for helping to leverage external resources. Citizens are also likely to give the state credit where mechanisms to voice complaints about non-state actors exist and where bureaucrats are able to effectively respond to complaints. Under these conditions, non-state service provision is likely to strengthen the fiscal contract. 5 Data and Methods I explore the relationship between external service provision and deference to government using Afrobarometer survey data from 19 Sub-Saharan African countries (see Table 1). Africa is an especially good place to examine these issues because of the large amount of variation both within and across African countries in the extent to which non-state actors, donors and other states are active in service provision and the extent to which governments are relatively effective and fair. Government responsiveness, corruption and reliance on non-public resources vary considerably among localities with consequences for citizen understanding of and relationship to government (Gibson and Hoffman, 2005). This project relies on the fourth round of Afrobarometer data that surveys Africans ’ views towards democracy, economics, and civil society with random, stratified, nationally representative samples. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In 2008, trained enumerators conducted face-to-face interviews in local languages with 26, 513 respondents across 19 countries. 5 The sample is designed as a representative cross-section of all citizens of voting age in a given country. The dataset used for this paper has a multilevel structure; individuals are nested within primary sampling units (PSUs), which are nested within countries. The PSUs are the smallest, well-defined geographic units for which reliable population data are available and they tend to be socially homoge- nous, thereby producing highly clustered data. In most countries, these will be Census Enumeration Areas (Afrobarometer, 2005, 37-38). Although re- spondents were not sampled based on their ethnic affiliation, there is likely to be a high level of clustering in the dataset around ethnicity. In other work, I discuss the advantages of multilevel modeling (Levi and Sacks, 2009). Treating the dependent variable as a binary outcome and taking into account the multilevel nature of our data, I estimate random intercepts for 5I excluded Zimbabwe from the analysis because of missing data on key variables. 9 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "obtained from the multilevel logistic model. Thus, I am confident that I did not lose substantial statistical information by dichotomizing this variable. 6. 2 Independent Variables 6. 2. 1 Socio-Demographic Variables I control for standard socio-demographic variables that can affect citizens ’ acceptance of government ’ s right to make people pay taxes. A question prob- ing respondents on their household income was not included in the fourth round of Afrobarometer surveys. Asking respondents to quantify their in- come can be problematic in the context of developing economies, where in- dividuals are often embedded in barter or commodity exchange, rather than, market economies. There are, however, reasonably good proxies including whether respondents own a television, radio, car, and mobile phone, and use the internet. Age, education, employment, and urban or rural residence are demographic factors that also affect household resources. 6. 2. 2 Experience with Paying Taxes or Fees It is difficult to assess just how ubiquitous taxes are in ordinary Africans ’ lives. There has not been any systematic effort to take stock of the types and amount of taxes citizens pay across Africa. Similar to pre-modern European states, African states ’ revenue raising capacity is generally low. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "On average, the tax-to-GDP ratio in Sub-Saharan Africa is around 21 percent, compared with the OECD average of about 32 percent. In Tanzania and Uganda, the total tax share drops to about 10 percent. Historical data suggests that the tax share of many European countries did not reach 15 percent of GDP until World War II when incomes were substantially higher than they are in many African countries (Fjeldstad and Rakner, 2003, 3). The types and amount of taxes citizens pay varies both within and be- tween countries. We do know there are taxes on agricultural crops, but the rates and processes of collection vary within countries (Kasara, 2007). User fees from electricity, water, sanitation, and other services comprise the major- ity of local revenue in South Africa (Hoffman, 2007). In Tanzania, Fjeldstad and Semboja (2001) count ten major categories of taxes, eighteen major categories of licenses, forty groups of charges and fees, and seventeen items listed as other revenue sources. In some countries including Kenya, Malawi, 11 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, I include a measure of whether citizens believe that a large portion of tax administrators is corrupt. Second, I in- clude a variable indicating whether citizens approve of how well their local government is handling the collection of license fees on bicycles, carts and barrows. 8 Third, both the size of a country and the size of the government may affect a government ’ s ability to detect and punish evaders. I include the 7I also include a country-level indicator of government performance, the World Bank Governance indicator of government effectiveness, in the model. This indicator measures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implemen- tation, and the credibility of the government ’ s commitment to such policies (Kaufmann, Kraay and Mastruzzi, 2006, 4). This variable is not significant at the p <. 05 level. 8I included two additional measures in the model neither of which were significant at the p < 0. 05 level. One is a measure of citizens ’ approval of how well their local government council provides citizens with the information about the councils budget (i. e. revenues and expenditures). The other, the World Bank governance indicator, control of corruption, measures the extent to which public power is exercised for private gain, as well as capture of the state by elites and private interest (Kaufmann, Kraay and Mastruzzi, 2006, 4). 13 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "natural log of a country ’ s population size and government consumption as a share of GDP; the latter is the most widespread measure of government size (Adsera and Boix, 2002; Alesina and Wacziarg, 1998; Rodrik, 1998). Tax compliance is also related to the government ’ s ability to effectively detect and punish tax avoiders, tax evaders, and tax arrears. Although an imperfect measure of states ’ deterrent capacity, Afrobarometer includes two survey questions on perceptions of government enforcement and monitoring capacities. One question probes respondents on how often ordinary people who break the law go unpunished. The other probes respondents on how often officials who commit crimes go unpunished. This latter question is also a measure of perceived government fairness- the extent to which a government implements the law evenly across all social groups. 6. 2. 5 Procedural Justice I include two indicators of procedural justice. The first probes respondents on how often people are treated unequally under the law. The next taps citizens ’ perceptions of the government ’ s treatment of their ethnic group. Specifically, respondents were asked how often their ethnic group is treated unfairly by their government. 9 6. 2. 6 Donor and Non-State Actor Provision of Services I include a measure of who citizens believe is responsible for collecting income taxes. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, there is reason to believe that better service delivery may affect citizens ’ willingness to defer to the tax department only through its effect on improved outcomes that matter for citizens ’ livelihoods. Unless improved services and infrastructure have a positive impact on citizens ’ welfare, indi- viduals are unlikely to credit the government for these outputs (Sacks and Levi, 2010). The Afrobarometer ’ s objective measures of service delivery only denote the presence or absence of infrastructure and services. The data do not indicate the condition of the services and infrastructure. Citizens may perceive and reward relative improvements or sanction de- teriorations in services, rather than the absolute level of service quality they receive. If services deteriorate or improve, taxpayers may alter their beliefs about governments ’ performance and should attempt to adjust their terms 10I also tested whether there is a relationship between the presence of a concrete road, health clinic, post office and electricity grid in the enumeration areas and respondents ’ willingness to pay taxes. None of these objective indicators except for the presence of an electricity grid were significant at the p < 0. 05 level. The presence of an electricity grid is negatively associated with the willingness to defer to the tax department. 17 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of trade with government. Because the dataset used for this study is cross- sectional rather than longitudinal, I am unable to examine whether citizens adjust their beliefs and behavior to relative changes in service delivery. I was only able to test whether there are associations between the absolute service quality across neighborhoods, countries and ethnic groups and deference to the tax department, police and courts. Further, citizens may not be attributing goods and services to the gov- ernment. Rather, citizens may be attributing goods and services, such as roads, electricity grids, sewage systems, health care and education to vari- ous non-state actors including the following: the private sector; NGOs and community-based groups; churches, mosques and other religious institutions; traditional leaders; and, bilateral and multilateral donors. Survey questions on the Afrobarometer only indicate the presence or absence of services and infrastructure, and the quality of these services, but these questions do not probe respondents on who they believe are providing these services. Each of the indicators of perceptions of government performance is sig- nificant at the p < 0. 05 level. Food security is positively associated with a willingness to defer to the tax department. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "appear to be a relationship between perceptions of the helpfulness of donors and non-state actors and the willingness to defer to the police and to the courts. Individuals who believe that donors and non-state actors exert too much, rather than, too little influence over one ’ s government is associated with the willingness to defer to the court and to the police. Findings also suggest that citizens who believe non-state actors are responsible for provid- ing law and order are less likely to be willing to defer to the police and to the courts than respondents who believe the state is responsible for providing law and order. 7. 4 Conclusion This paper demonstrates that the logic of the fiscal contract is relevant to a wide variety of contemporary African states. Findings from a cross-national analysis of survey data from Africa link citizens ’ legitimating beliefs — in- dicated by a willingness to defer to the tax department, the police and the courts — to a government ’ s fulfillment of a fiscal contract. Citizens who are satisfied with their government ’ s provision of services and goods are more likely to be willing to defer to the tax department, courts and police than citizens who disapprove of government service provision. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. Introduction The most common sampling approach for cross-sectional household surveys in the developing world is a stratified two-stage design (Grosh and Munoz, 1996). Following stratification based on administrative boundaries, clusters are selected in the first stage with probability proportional to size from a national census-based frame. In the second stage, a canvassing operation is conducted in the selected clusters to compile an updated list from which households are randomly selected. While this methodology is straight forward to implement in the field and reliably produces unbiased estimates, there are several downsides. The first downside is cost. The World Bank ’ s Living Standards Measurement Study team, which provides technical assistance on large-scale household surveys around the world, estimates the field listing operation increases the overall budget for data collection by 25 percent. Due to confidentiality concerns, the data collected during a field listing operation, typically the name of the household head and address or location description of dwellings, does not have any analytical applications beyond as a component of the weight calculations. 2 At a time when typical survey costs are in the USD millions, reducing a significant cost component will increase the financial sustainability of data collection. The second drawback to the traditional design relates to timeliness. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Grais et al (2007) used a methodology in which the closest household to a randomly selected point is selected for a study of vaccination rates in urban Niger, though did not attempt to calculate probabilistic sampling weights. Similar approaches were used by Kondo et al (2014) in a study of the city of Sanitiago Atitlán, Kumar (2007) in urban India, and Kolbe and Hutson (2006) in Port ‐ au ‐ Prince, Haiti. Shannon et al (2012) also used such a method to select points in a study of violence in Southern Lebanon in 2008 but used the radius of a circle to define an area to be field listed, and from which buildings and then households were selected for enumeration. The circle area and building density were used to calculate probability weights. The main difference between most random point selection methods and the North Method described here is that the North Method attempts to accurately estimate the probabilities of selection. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The assumption of perfect implementation, however, is quite strong as interviewers have shown a preference for selecting respondents willing to participate in the survey (Alt, 1991), and a number of other studies found that data collected with random walk designs exhibit differences from known population statistics on gender, age, education, household size, and marital status (Bien et al. 1997, Hoffmeyer ‐ Zlotnick 2003, Blohm 2006, Eckman & Koch 2016). Probabilities of selection inherently cannot be calculated in a random walk sample design as no information is collected on how many structures are in the camp, or how likely it was that a given structure was the xth structure along any path. Random walk must then assume all structures have the same selection probability, implying constant sampling weights. Therefore, the only component of the weights for the random walk is the sub-sampling of households within a selected structure: 𝑤𝑤𝑖𝑖 ′ = 𝑁𝑁𝑗𝑗𝑗𝑗 𝑖𝑖. 2. 6. Comparison of Methods As mentioned above, stratified cluster samples with the canvassing of selected clusters is the most common sample design used to collect official socioeconomic statistics in the developing world, but in Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 other disciplines it is relatively rare. A review of published public health literature by Chen et al. (2018) found most surveys use probabilistic designs in the first stage, but random walk or similar methods in the second stage. Lupu and Michelitch (2018) suggest that the combination of random walk and quota sampling is the common approach for political science-themed surveys conducted in the developing world, with 77 percent of respondents to their expert survey using a variation on this design. Diaz de Rada and Martínez (2014) compare a combination of random walk and quota sampling (based on age and gender) to probability designs and find a more accurate estimation of age and educational attainment in the combined method than in the probability methods, but that the probability methods perform better for measuring unemployment. The authors cite the replacement protocols for the probability methods as a reason for the bias and attribute the use of quota sampling for the success in estimating age and education, compared to the gold standard of a high-quality probability sample design. There are also a limited number of papers which directly compare two or three of the methods, but none that consider this wide range of alternatives. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Chew et al. (2018) use a baseline convolutional neural network model on a gridded population sampling frame to select a sample of households in Nigeria and Guatemala. The authors found this technique to be on par with human canvassing in terms of accuracy, and to outperform other machine learning models based on crowdsource or remote sensing data. Grais et al (2007) compared an unweighted random point selection methodology to a random walk in their study of vaccination rates in urban Niger. The authors do not find statistically significant differences between the methods, though the sample size was limited and both methods were non-probabilistic. 3. Design and Field Protocols 3. 1. Experiment Design This paper makes use of a dataset from the purposefully designed methodology experiment conducted in one section of the Protection of Civilians site 1 (PoC1, Figure 1), one of the largest IDP camps in Juba, South Sudan. To generate a gold standard as the basis of comparison, a household census was conducted between August and September 2017. During this exercise, 2, 655 households were interviewed using a questionnaire designed to collect demographic information, dwelling characteristics, household consumption, and perception data. At the end of each census interview, households received a unique barcode that could be used to identify them later in the experiment. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To avoid changes in camp composition, immediately following the completion of the census fieldwork, the interviewers returned to the field to implement the experiment. Teams used each of the sample selection methods to identify which households would have been selected had that method been used for a survey. To avoid respondent fatigue, instead of re-asking the questionnaire, the interviewers simply scanned the unique bar code of the selected household. Once scanned, the barcodes created an observation in the method-specific dataset with the information captured in the census. Each sampling technique targeted about 322 interviews so that comparisons could be made between the methods using an identical sample size. There was, however, some non-response for each method if interviewers were not able to contact a household member who could provide access to the barcode, if the barcode had not been retained by the household, or if the barcode was not scanned correctly. Protocols for each individual method are listed below. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The weights would be over-estimated if new structures had been built in the shadow since the imagery was taken. 3. 6. Random Walk Random Walk obtains a sample by randomly selecting starting points for enumerators with generic but unambiguous instructions to select households at regular intervals on their path. For this experiment, enumerators conducted random walks using 21 RSPs (Figure 8). Starting as near as possible to the RSP, the supervisor chose any random point (like a street corner or a school). From this point, four enumerators walked each in one of the four cardinal directions. Walking in their designated direction away from the RSP, they counted structures on both the right and the left and each selected the fifth structure for interview. Enumerators were instructed to start with the buildings on the right if two buildings were opposite to each other. To select the next structure, enumerators continued along the cardinal path, and selected the next fifth structure. If the enumerator could not proceed on its cardinal path because she had reached the boundary of the PoC camp, enumerators were instructed to turn right at a 90-degree angle and continue counting until finding the fifth dwelling. Enumerators had to conduct six interviews along their paths. 4. Implementation Issues 4. 1. Failure to Follow Survey Protocols As noted above, even if field protocols are perfectly implemented, the estimates generated from Random Walk designs are likely to be biased. Enumerators furthermore often were unable or unwilling to follow the protocols. Streets and paths were not necessarily aligned with cardinal directions and obstacles further impeded the ability to follow a straight path. Additionally, since the selection method requires enumerator judgment, it is not replicable and therefore allows enumerators greater discretion to choose which households are “ selected. ” Figure 9 shows the paths taken by two teams of enumerators from Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 implementation matters. Pooling the analysis across indicators and using satellite mapping as reference, the North Method is unbiased, while the Segmenting and Grid Square methods show minimal bias (0. 1 percent and 0. 2 percent, respectively). The Random Walk method shows 1. 2 percent bias on average across the 14 questions. In conclusion and in line with the literature, most probability-based methods perform better than non-probability methods like random walk. In addition, implementation of adherence with the survey protocol is extremely important and using appropriate methods and tools to cope with this challenge is absolutely mandatory for coming as close as possible to the theoretical results derived by the simulation for the probability-based methods. In practice – in a fragile setting like South Sudan – deviations from the survey protocol, measured as differences between the experiments and the simulations, have large influence on the actual bias of estimates. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 7. Appendix 7. 1. Simulation and Frame To compare the efficiency of the different sampling frames and designs, we will apply an empirical sampling simulation. In this type of (Monte-Carlo style) simulation, either a true or synthetic population is used as the target population. By applying a specific sampling design, and repeated sampling (usually 1, 000 repetitions) under this design, we can compare the resulting population estimates with the known true population values for each run of the simulation. The resulting distribution of these estimates is called the sampling distribution, and the average squared deviation from the underlying population value is the Mean Squared Error (MSE) or when taking its square root, the Root MSE (RMSE). To facilitate the comparison, we use the relative version expressed in percentage deviation. Empirical sampling simulations can be considered as the “ […] ultimate tool for investigators who want to know if one sampling strategy will work better than another for their population. ” (Thompson, 2012). However, this requires the underlying simulation population to replicate as realistically as possible the target population. 7. 2. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Quality Metrics A standard Measure in the assessment of a sampling designs is the Root Mean Squared Error (RMSE) and calculated as: 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = ∑ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠𝑠𝑠𝑠𝑠 1000 𝑠𝑠𝑠𝑠𝑠𝑠 = 1 1000 = ൥ 1 1000 × ඥ (𝑌𝑌 ෠ − 𝑌𝑌) 2 𝑌𝑌 ൩ × 100 Expressed here as percentage deviation from the population mean Y and calculated for each parameter of interest. Equation.. is only the empirical representation though and a result of rearranging the definition of the Mean squared Error, 𝑀𝑀𝑀𝑀𝑀𝑀൫𝑌𝑌 ෠ ൯ = 𝐸𝐸൫𝑌𝑌 ෠ − 𝑌𝑌൯ 2 = 𝐸𝐸 ൣ ൫𝑌𝑌 ෠ − 𝑌𝑌෨൯ + ൫𝑌𝑌෨ − 𝑌𝑌൯൧ 2 = 𝐸𝐸 (𝑌𝑌 ෠ − 𝑌𝑌෨) 2 + 2𝐸𝐸൫𝑌𝑌 ෠ − 𝑌𝑌෨൯൫𝑌𝑌෨ − 𝑌𝑌൯ + ൫𝑌𝑌෨ − 𝑌𝑌൯ 2 And decomposing it into 𝑀𝑀𝑀𝑀𝑀𝑀൫𝑌𝑌 ෠ ൯ = 𝑉𝑉𝑉𝑉𝑉𝑉൫𝑌𝑌 ෠ ൯ + 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 (𝑌𝑌 ෠) with 𝑌𝑌 ෠, 𝑌𝑌෨ and 𝑌𝑌 being the estimate from the sample, the mean of this estimate and the true value in the population respectively. Var is the corresponding variance, and Bias the resulting bias component, which is defined as: 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 ൫𝑌𝑌෨൯ = 𝑌𝑌෨ − 𝑌𝑌 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10044 Most refugee hosting communities are characterized by high levels of poverty with precarious livelihood conditions, low access to public services, and underdeveloped infra­structure. While the unexpected inflow of refugees might bring both constraints and opportunities for improving and maintaining local livelihoods in these communities, the understanding of these effects remains limited. Using a household level micro data set from a 2018 baseline survey of the Ethiopia Development Response to Displacement Impacts Project, this paper assesses the impact of refugee inflow on the livelihood strategies of host communities with respect to diversification and agricultural commer­cialization. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The endogeneity of refugee inflow is addressed by exploiting differences in factors that influence refugee arrival in the host communities. Specifically, the analysis uses potential refugee inflow as an instrument, which is the product of population density and intensity of con­flicts (number of fatalities per event) in the closest region of the origin country to the refugee camp weighted by the distance of the refugee camp to the closest region. The paper also constructs an aggregate index to proxy house­holds ’ livelihood diversification strategies. The findings show that refugee inflow brings substantial benefits to host communities by creating significant jobs, in which people engage as secondary occupations, and triggers an increasing demand for livestock products. Specifically, while no effect was found on diversification of activities such as a primary occupation and crop product sales, a 1 percent increase in refugee inflow leads to a 2. 7 percent rise in diversifica­tion of livelihood activities as a secondary occupation and a 15. 9 percent increase in the value of livestock product sales. These effects tend to be heterogeneous across refu­gee hosting regions and the gender of the household head: negative effects were mainly observed in Gambella region, which hosts the largest refugee population in the country, and male-headed households were more likely to benefit from the refugee presence for the whole sample. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. Introduction We are in the midst of protracted refugee crises. According to the latest UNHCR trends report, at the end of 2020, 76 percent of refugees globally (15. 7 million) were in a protracted situation (UNHCR 2021). 2 Most refugees reside in low-income countries, and more than eight of every 10 refugees (86 percent) live in countries within territories affected by acute food insecurity and malnutrition (UNHCR 2021a). Refugee receiving host communities also tend to be poor, experience precarious livelihood conditions and face many socio-economic challenges, such as low economic status, poor access to public services, and infrastructural development. For these communities, refugees might bring both challenges and benefits. On the one hand, refugees increase competition for natural resources (e. g., wood for energy, construction, land), public services and infrastructure (e. g., education, health, water supply), and economic opportunities (e. g., traditional livelihoods, labor employment). Refugee inflow may also affect the local market by mainly depressing wages and raising product prices (Vemuru et al. 2020). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Specifically, a 1 percent increase of the refugees ’ presence5 leads to a 2. 7 and 15. 9 percent increase in the diversification of livelihood activities as a secondary occupation and value of livestock product sale, respectively. It should be noted that this analysis is taking place during a period where refugees in Ethiopia were prohibited by law from seeking work outside designated camps. This has changed after 2019 because of the revised Ethiopian Refugee Law. These effects tend to be heterogeneous across regions and to a limited extent, vary depending on the gender of the household head. The negative effects tend to be concentrated in Gambella, a region that hosts most of the refugee population in Ethiopia and where the refugee population is as large as the population of the region. Overall, compared to women-headed households, households with a male head seem to benefit through increased diversification of activities as a secondary 4 Region refers to the administration level 1 from the Database of Global Administrative Areas (GADM). The nearest region to the refugee camp is identified as the one that has the shortest straight distance to the refugee camp among all neighboring regions in the major refugee source countries. 5 As explained above, refugee presence is the number of refugees (population) in the nearest refugee camp to the household location weighted by the household ’ s inverted distance to the camp. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "First, most of the studies only consider a subset of occupation or livelihood activities (mainly employee-based) and do not provide a full picture of the livelihood impacts of refugee presence on host communities. We consider an exhaustive set of livelihood activities in which households (individuals) in the impacted communities may engage. 7 Second, prior studies tend to focus on the different livelihood activities separately (i. e., whether the individual adult members or the household engage in each of the livelihood activities). 8 Therefore, they are unable to infer whether households are diversifying or specializing their livelihoods or are engaging more on the commercialization of activities. 9 The current paper goes beyond the allocation of labor to individual (specific) 7 As the data we used does not have a good welfare indicator (e. g., income, consumption, and assets), we could not explore the welfare impact of refugee inflow. 8 We examined households ’ engagement in individual livelihood activities as a mechanism for household livelihood strategies. 9 Generally, households tend to diversify their livelihood when facing negative shocks (e. g., conflicts, droughts) to minimize risk (Ellis 2000a, b). In the case of refugee inflow, households may either diversify or specialize as refugee inflow could be both a negative shock (through increase competition for resources, services, and employment) and a positive shock (through creating opportunities, such as high demand agricultural products, provision of cheap labor). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 inflow). We then use the composite variable as an instrument to identify the causal impact of refugee presence on livelihood diversification and commercialization in host communities. Figure 2: Household livelihood strategy framework under refugee inflow Source: Adapted from Nielsen et al. (2013) and Walelign and Jiao (2017) 4. Data sources The major data source of the current study is the World Bank ’ s Development Response to Displacement Impacts Project (DRDIP) 12 baseline survey from Ethiopia. The Ethiopia DRDIP survey was administered between September 2017 and August 2018. The survey covers 113 Kebeles (wards) in 16 Woredas (districts) from the top five refugee-hosting regions in Ethiopia. The selection of the sample households follows stratified random sampling with proportion to size (the number of households) using Woredas as a geographic stratum. The sample originally comprised a total of 3, 390 households, who were selected using systematic random sampling within each Woreda. We used data from 3, 375 households, as 15 of them were excluded due to missing location information (GPS). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The sample households are located within varying distance from the nearest refugee camp (approx. 67 to 76, 665 meters) (see Figure 3). 12DRDIP aims to improve access to basic social services, expand economic opportunities, and enhance environmental management for communities hosting refugees through providing funding for community driven projects. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 Figure 3: Location of refugee camps in Ethiopia and the Ethiopia Development Response to Displacement Impacts Project (DRDIP) sample households Source: Authors ’ compilation using the database of the Global Administrative Areas (GADM) (https: / / gadm. org / data. html, accessed November 20, 2020) and the Humanitarian Data Exchange (HDX) database for the refugee location (https: / / data. humdata. org / dataset / ethiopia- refugee-camp-locations, accessed November 20, 2020). From the Ethiopia DRDIP data set, we derive two measures of livelihood diversification and two measures of agricultural commercialization (all at household level). The measures of diversification include: (i) the degree of labor diversification in different productive livelihood activities (e. g., farming, wage employment) as a primary activity (occupation), and (ii) the degree of labor diversification in different livelihood activities as a secondary activity (occupation). 13 These two outcomes were constructed using the inverse Simpson diversity index as 1 ∑ 𝑛𝑛 𝑖𝑖 𝑆𝑆𝑖𝑖 2, where 𝑆𝑆𝑖𝑖 is the share of the number of adult labor engages in 𝑖𝑖𝑡𝑡ℎ livelihood activity to total active adult household labor and 𝑖𝑖 ranges from 1 to the number of livelihood activities that a household engages in (Valdivia et al. 1996). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 crop products (e. g., wheat, potatoes) and the value of five livestock products (i. e., milk, egg, butter, hides, and honey) sold in the market. 14 Several other data sources were utilized. First, the Ethiopian refugee camps location data set from the Humanitarian Data Exchange (HDX) 15 and the total number of refugees by camps from the United Nations High Commissioner for Refugees (UNHCR), Addis Ababa office. We use data from 26 official UNCHR refugee camps in Ethiopia that were operational in 2018 (see Figure 1; 3). Second, we use administrative data sets for Ethiopia and refugee source countries from the database of Global Administrative Areas (GADM). 16 We also use the conflict data set from the Armed Conflict Location and Event Data Project (ACLED) 17 and the population data from the Gridded Population of the World (GPW) data set. 18 On the basis of these data sets and the location of sample households from Ethiopia DRDIP data set, we generated the following variables: i) distance of sample households to the nearest refugee camp, the nearest region (administration level 1 in GADM) to the refugee camps, ii) distance of the refugee camps to the nearest border of the refugee source country, iii) Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Further, as Jordan and the international community develop new approaches that respond holistically to the specific needs of refugee and host communities, more evidence is needed about how gender ‐ based constraints and vulnerability affect refugee women ’ s ability to take up economic opportunities and to access the services and resources they need to enable their families to move out of poverty. Our analysis applies a gender lens to a rich set of microdata on Syrian refugees in Jordan collected by UNHCR between 2011 and 2014. As these data do not capture how the changes in policies affect refugees and the constant evolution of their situation since 2014, the analysis is not intended to directly inform current policy choices and decisions. Instead, our aim is to devise an approach that can provide greater insights into gender ‐ specific barriers, based on the premise that the experiences and potential vulnerabilities of women, men, and children are significantly different in refugee settings. We use household ‐ level data to examine the relationship between poverty and gender for Syrian refugees. Our approach is informed by a body of work in the academic literature that has used household survey data to examine the relationship between the gender of the household head and household 2 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 3 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 4 https: / / data2. unhcr. org / en / documents / download / 64568, accessed August 2, 2018. 5 https: / / reliefweb. int / sites / reliefweb. int / files / resources / 64114. pdf, accessed August 2, 2018. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The UNHCR data identify principal applicants for each household and our analysis examines differences in household poverty between households with a female rather than male principal applicant. 6 The principal applicant is the person who receives assistance from UNHCR for the family and is self ‐ selected or selected by the family. This definition of female headship has advantages over the way that household headship is commonly identified in household surveys. An often ‐ noted drawback of the headship variable is that female headship may reflect the enumerators ’ perception about who should be considered a family head rather than who has the most responsibility for the family ’ s welfare in practice. 7 Social norms can also affect whether female respondents self ‐ identify as household heads. For example, some Eritrean returnees who would in other cultural settings be regarded as de jure female headed (single mothers, widows, divorcees, separated women) reported being male ‐ headed. Other Eritrean female returnees who would be considered de facto heads reported headship by absent husbands or male relatives (Kibreab, 2003). Our approach is therefore to distinguish between different types of female and male principal applicant households, using a typology that reflects some of the indicators of vulnerability used by UNHCR. We find that distinguishing between different types of female principal applicant households is important in the setting of Syrian refugees in Jordan. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Most male principal applicants are one of a married couple with children whereas most female principal applicants are single care ‐ givers, single persons or living in non ‐ traditional family groups. While on average female principal applicant households are no more likely to be poor than male principal applicant ones, poverty rates for some types of households are higher when these households have a female principal applicant. Households that have formed because of the unpredictable dynamics of forced displacement, such as sibling households, unaccompanied children, and 6 Identification of the head of the case (as family groupings are referred to in the UNHCR ProGres database) is determined by who best represents the family for case management purposes. It is not assumed that the household will be best represented by a man; a woman or even a child can be a head of a case, depending on standard operating procedures. 7 Even when traditional household survey data are gathered at the individual level, the information is often collected from a single respondent. The respondent is usually the self ‐ identified ‘ most knowledgeable ’ household member, which overwhelmingly corresponds to the ‘ head ’ of the household. In the case of a household survey that solicits information on ‘ headship ’, this information is gathered often through the question: “ Who is the head of this household? ” Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 single caregivers, are an extremely vulnerable group and especially so if principal applicant is a woman or girl. Moreover, poverty gaps between male and female principal applicant ’ s for these households remain after humanitarian assistance is received. To understand how gender differentiates the poverty experienced by the Syrian refugees, we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD ‐ HV). The ProGres database for Jordan includes information on refugees ’ registration since 1935. The registration process assigns refugees a unique registration number that serves as a reference for recording data at the initial registration and in all subsequent activities, including decisions on refugee status and right of return or resettlement in a third country, as applicable. UNHCR issues refugees residing in camps a ‘ proof of registration ’ document, which they hold while they remain there. For those who live outside the camp, UNHCR provides an asylum seeker certificate stating that those on the certificate are persons of concern. The asylum seeker certificate allows Syrians to access United Nations (UN) services and assistance provided outside the camps, such as monthly cash support, nonfood goods, and healthcare (NRC and IHRC 2016). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This assistance is crucial and has been reported to support a meal a day, a better roof, and dignity for Syrians who have fled to Jordan. 8 The JD ‐ HV database has detailed information on expenditure, sources of income, and indicators of household ‐ level welfare, for example, as reflected by recourse to coping mechanisms, standard of accommodation, or access to water, sanitation, and hygiene (WASH). JD ‐ HV data collected between October 2013 and December 2014 were first analyzed in Verme et al. (2016) who produced welfare aggregates and poverty measures to help target benefits and assistance to those most in need. Verme et al. (2016) draw attention to the precarious circumstances of Syrian refugees in Jordan and Lebanon. Around 55 percent of refugees in Jordan are vulnerable to monetary poverty and more than half are vulnerable to food shocks. Family size increases the probability of being poor, with the poverty rate almost doubling if the size of the family goes from one to two members and increasing by 17 percent when the number of children increases from one to two. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To work legally in Jordan, refugees must have a work permit (Verme et al 2016). However, there is a list of professional jobs, including physicians, engineers, teachers, and workers in the services sector, that can only be done by Jordanian nationals (ILO 2015). Since the agreement of the Jordan Compact in 2016, the Government of Jordan has taken steps to open formal employment opportunities for Syrians. It has waived the fees required to obtain a work permit for Syrian refugees in a number of occupations open to foreign workers and simplified the documentation requirements. These measures have encouraged employers to regularize their workers; 10 Nonformal education services include catch ‐ up courses, dropout and basic literacy programs, and learning support services offered in Makani Centers of the United Nations Children's Fund (UNICEF) (UNICEF 2017). The Makani Centers are multifunctional spaces providing learning support, psychosocial support, and a safe environment with opportunities for play. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "32 Appendix 1. Variable Definitions Table A1. 1. Variable definitions Variable Definition Age of PA Age of the principal applicant (PA) Children under 5 1 if there are one or more children below the age of 5 (inclusive) in the household Disable 1 if there are one or more disabled persons in the household Education Categorical variable. We classified education of the PA in three groups: below years, 6-11 years, and more than 12 years of education Elderly 1 if there is one or more persons above the age of 65 (inclusive) in the household Entry status Categorical variable. ProGres reports 5 entry statuses of which we selected the three categories with the largest number of PAs: Informal, formal, and smuggled. Expenditure Raw addition of all expenditure categories, which include rent, bills, food, healthcare, education, and others. Family Type Categorical variable. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For a detailed definition see section 4 Household size Number of people included in the case records of each PA in Individual ProGress dataset Wage Income 1 if the household receives income from employment and / or daily or irregular work Income from remittances 1 if the household receives income from remittances Income per capita Raw sum of household income from all sources; work, pension, assets in Syria transfers, donations, other organizations'humanitarian aid, and other divided by household size Male Adults Number of males above 18 (inclusive) in the household Marital Status Categorical variable. The classification includes married PAs with spouse in the household, married PAs without spouse in the household, widowed, single or engaged, and divorced or separated. Proportion of female Number of female divided by the household size Location Categorical variable for 11 Governorates / cities. Ajloun City, Aqaba, Balqa, Irbid Jerash, Karak, Maan, Madaba, Mafraq, Tafilah, Zarqa. In Camp 1 if the household is located in a refugee camp Poverty before UNHCR and WFP assistance 1 if household expenditure before UNHCR plus WFP assistance is below the poverty line (JD50) Poverty before UNHCR assistance 1 if household expenditure after WFP assistance but before UNHCR assistanc is below the poverty line (JD50) Source: Authors ’ elaboration. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 (UNHCR, 2014), developing countries hosted 10. 1 million refugees or 86 percent of the world ’ s refugees. Contrary to what has been sometimes claimed in popular media, refugees are not “ invading ” the higher ‐ income countries. Actually, SSA has been hosting more refugees than sending them since 1990. The divergence of trends occurring in 2005 is certainly related to large inflows of refugees from North Africa and the Middle East. The second peak in 2011 corresponds to the uprisings that spread across several Arab countries (Egypt, Libya, Syria, Tunisia and Yemen), and the recent one in 2013 to the large outflows of refugees from Iraq, Syria and Yemen. Figure 2. Refugees and Internally Displaced People in SSA, 1990 ‐ 2013 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). Due to changes in classification and estimation methodology in a number of countries, 2007 figures are not fully comparable with pre ‐ 2007 figures (see also footnote 1). Gathering data on internally displaced people is much more challenging since most existing data on IDPs are incomplete or unreliable. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We only found reliable approximations from the International Displacement Monitoring Center (IDMC) between 2003 and 2013. According to IDMC, there were about 12. 5 million internally displaced people in SSA at the end of 2013 (IDMC 2014), more than one third of the total number of IDPs and more than tripling the number of refugees in SSA. Although the number of IDPs in SSA is the highest since 2007, the share of IDPs in SSA has been decreasing from 53 % in 2003. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The vulnerability of the Internally Displaced People in SSA has certainly been overlooked for too long, but the increased support provided by UNHCR is an encouraging but challenging sign in that respect. Figure 3. Refugees and Internally Displaced People in SSA, 2003 ‐ 2013 Source: Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). The annual number of IDPs is collected from the IDMC (2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2012, 2013, 2014) annual reviews. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 2. 3. The Challenges of Protracted Refugee Situations in Sub ‐ Saharan Africa At the global level, about 54 % (i. e. about 6. 3 million) refugees were in protracted refugee situation by the end of 2013 (UNHCR 2014). 2 As reported by Kreibaum (2016), the number of protracted refugee situations has increased from 22 in 1990 to 30 in 2008. These protracted situations in Africa have been characterized by Crisp (2003) as in most of the cases: i) peripherally located with poor security, unfavorable climatic conditions, and economical and political marginalized; ii) concentrating people with special needs like e. g. children and women (see Section 3); and iii) lacking basic human rights, including those covered by the provision of the 1951 refugee convention. Another distinct feature of refugees in SSA is that they are mostly hosted in organized camps. While in developing countries around one third of refugees are hosted in camps, the share raises to about 40 percent in Sub ‐ Saharan Africa (Figure 5). 3 The percentage of 76 percent in Eastern Africa and the Horn of Africa stresses again the pressing situation in this part of the world. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While camps have been recognized as posing serious challenges (Jacobsen and Crisp 1998), it is quite striking to observe that this organizational feature is not as spread in other regions of the world as in SSA. At best, only 28, 25 and 15 percent refugees are hosted in planned / managed camps in Asia, Americas, and the MENA region, respectively. Such figures are based on the most recent year available (2013) and may change significantly following the large inflows of Syrian refugees into Egypt, Lebanon, Iraq, Jordan and Turkey. Nonetheless, the differences are sufficiently striking to believe that this is a distinct feature of refugee hosting in SSA. 2 UNHCR defines a protracted refugee situation as “ one in which 25, 000 or more refugees of the same nationality have been in exile for five years or longer in a given asylum country ” (2012: 23). 3 The figures are based on refugees (including those in refugee ‐ like situation). Persons in refugee ‐ like situations include “ groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained ” (UNHCR 2014: 39). The number of refugees and people in refugee ‐ like situation for which demographic data is available does not necessarily equal the total number of refugees. However, for SSA, there is little difference between the two. We also restrict the number of refugees to those whose accommodation is known by the UNHCR (approximately 19 % in the world and 8 % for SSA). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 3. Refugees as Agents of Their Own Destiny 3. 1 The Composition of Africa ’ s Refugee Population and Its Consequences One of the first elements that catch the eye in Figure 6 is the difference in the composition of the refugee population in Africa compared to the rest of the world. The share of children and women among refugees is higher in Africa than elsewhere, in particular East and West Africa stand out here. This is, at least partly, a consequence of Africa ’ s younger, general population, but other forces could be at work as well, e. g. higher mortality of adult males in Africa or adult males staying behind or being separated from the rest of the household. It does mean however that, relative to other areas, more attention should be going to the needs and capacities of women and children in Africa. This means, for example, adaption of and increased supply of schooling and health services. Figure 6. The composition of refugees by age and gender, 2013 Source: Note: UNHCR statistics (UNHCR 2014). Asia excludes Australia, Japan and New Zealand. Americas exclude Canada and the United States. These percentages have been calculated by country when demographic data are available for at least 30 % of the total. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "When general living conditions in one ’ s residence or home area are worse compared to a camp environment, e. g. because health services are available in the latter, mortality may also be lower in the camp. The strong presence of children in Africa ’ s refugee population implies that we should also look at the potential long ‐ term effects of forced displacement on survivors. Given the composition of the refugee population, such long ‐ term effects will be more important in Africa compared to elsewhere. Few studies have followed children exposed to forced displacement over a long time to directly infer the long ‐ term effects of forced displacement, in particular on health, education and labor market participation. Most studies of the long term effects of conflict use an indicator of exposure to violent conflict, but few of them have forced displacement as one of the indicators. There is however a very well established literature (see Currie and Vogl, 2013 for an overview) on the long ‐ term consequences of deprivation in early childhood which can be applied to the situation of refugees. If young children between the ages of 0 to 3 years old are exposed to malnutrition, disease, stress and violence during episodes of forced displacement, then, this literature shows that this deprivation will have negative long ‐ term effects. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "17 Revealingly, in Betts et al. ’ s (2013) survey of refugees in Uganda, 96 % of all interviewed households in the capital and 70 % outside the capital said they owned and used a mobile phone. They use this mobile phone to communicate with customers and suppliers, to get market information and to transfer money. Half of the urban refugees and 11 % of rural refugees also have access to the Internet. 4. Refugees As a Burden? As pointed in Section 2, most refugees in SSA are hosted in neighboring countries. Most of these hosting countries are likely among the least developed countries. It has been argued that these refugees may constitute an additional burden in terms of economic development in hosting countries (Mabiso et al. 2014). UNHCR (2014: 17) implicitly recognizes that potential burden by suggesting that the ratio of the size of the country ’ s hosted refugee population to its average income level can provide a proxy measure of the burden of hosting refugees. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "SSA counts 13 out of the 20 countries hosting the larger number of refugees per 1 USD GDP (PPP) in the world. 4 While such figures stress that SSA hosts a fair share of the refugees in the world and underline that refugee flows are mainly a South ‐ South phenomenon, we shed doubt on this view of refugees described as a burden. We even argue in Section 6 that such representation is not conducive to the right policy framework in refugee ‐ hosting areas. 4 The other major host countries per USD GDP are all developing countries, with Pakistan (1st), Jordan (8th), Bangladesh (9th), Yemen (10th), Iran (14th), Lebanon (17th), and India (20th). Figure A2 provides the top 10 ranking in the world. At a global level, we should note that in 2013 “ the 40 countries with the largest number of refugees per 1 USD GDP (PPP) per capita were all members of developing regions, and included 22 Least Developed Countries ” (UNHCR 2014: 17). It should be noted that the way UNHCR computes that “ potential burden ” gives more weight to countries with very large population since is equivalent to. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "At least, at the time of the closure of the last camp in the region of Kagera, the aid workers from UNHCR and other organizations were aware of the challenge of the transition for the hosting population and seeks to coordinate with development actors such as the United National Develop Programs or local NGOs to support the hosting population in the district of Ngara. Nevertheless, the limited resources remained a major constraint on that effort and sheds light on the institutional constraints existing to scale up such positive efforts of coordination. Case Study # 2: The protracted refugee situations in Kenya Dealing with refugees remains a relatively novel phenomenon in Kenya. It was not until the early 1990s that Kenya witnessed massive refugee influxes from Somalia, Sudan, and Ethiopia (Banki 2004). Prior to that period however, Kenya had a reputation for having generous refugee policies, which allowed the successful integration of a number of refugees from Mozambique, Uganda and Rwanda (Banki 2004). However, with the arrival of hundreds of thousands of new refugees from neighboring countries during the 1990s, the responsibility for the care of the refugees shifted from the Government of Kenya ’ s (GoK) to the international community, leaving the more inclusive policies that were prevailing before 1991. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The total economic benefits, including through savings on food purchases (including through purchases from refugees), income accruing to local contractors from assignments for the United Nations or Non ‐ Governmental Organizations or support for host communities, “ using 2010 as a reference year, are [estimated to be] around USD 14 million annually. On a per capita basis this equates to around 25 % of average annual per capita income in North Eastern province ”. This estimation corresponds to a back ‐ on ‐ the ‐ envelope approximation but it gives a sense of the major benefits to the local population. Similar to the Tanzanian case, the presence of the Dadaab refugee camps is reported to have improved the provision of local public goods such as the frequency and reach of transport services and the availability of health and social services. NORDECO also observed environmental degradation around the Dadaab camps5 but spatially restricted in an area of inherently low resource value. It seems that environmental support programs have helped limiting the collection of firewood by refugees and providing alternative fuel sources (Milner and Loescher 2004). Compared to the Tanzanian case, two main differences emerge. Less emphasis is given to the distributional effect of the refugee inflows on the hosting communities, while less pressure on prices is observed around the Dadaab refugee camps. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Both differences may actually be related to the dominance of pastoralist livelihoods. First, on the distributional dimension, NORDECO (2010) did not point to a similar substitution effect between unskilled labor or refugees. In a pastoralist environment, the low ‐ middle ‐ income group and the poor are those primarily engaged in selling their products to refugee camps. Second, contrary to Alix ‐ Garcia and Saah (2010) for Tanzania, “ the price of basic commodities such as maize, rice, wheat, sugar and cooking oil is [reported to be] at least 20 % lower in camps than in other towns in arid and semi ‐ arid parts of Kenya. The main reasons are the re ‐ sale of WFP [World Food Program] rations, access to free food by locals registered as refugees and illegal imports via Somalia ” (NORDECO 2010: 9). Another possible explanation reported by Maystadt and Duranton (2014) in the Tanzanian case, is the importance of transport services in pushing the price of traded goods down. Although focusing more on the urban function of the refugee camps and the social transformation underpinned in the hosting society, Jansen (2011) also reports similar trading activities and wealth 5 Nonetheless, such a degradation is acknowledged by NORDECO (2010) to be difficult to distinguish from general trend prevailing the region. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some Key Findings from Case Studies The above case studies point to an emerging body of the literature that seeks to quantify the impact of refugees in protracted situation on the hosting economies (Alix ‐ Garcia and Saah 2010; Baez, 2011; Betts et al. 2014; Maystadt and Verwimp, 2014; Maystadt and Duranton 2014; NORDECO 2010; Kreibaum 2016). Although that literature is still in its infancy, we can seek to draw a few lessons, even if these lessons can also serve as further hypotheses to be tested. First, the three case studies underline the importance of market mechanisms. Previous literature was very much focused on the health, environmental, and security consequences of hosting refugees. These concerns still rank as first priorities when refugees cross borders. But the understanding of protracted refugee situations requires paying much more attention to the interactions between refugees and their 6 As pointed by Dryden ‐ Peterson and Hovil (2003), de facto local integration has been a common occurrence, well before 1999. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "40 Sub ‐ Saharan Africa 8604000 5895000 7055000 5406100 5068000 N. A. MENA 6230000 8000000 6675000 8592900 10892000 N. A. Asia and Pacific 4325000 2405000 3392000 2128800 5490000 N. A. (excl. Australia, Japan, New Zealand) Americas 1126000 1280000 2176000 2900000 3661000 N. A. (excl. North America) Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). As indicated in Figure A1, these data are much lower compared to those provided from 2003 by IDMC but provide a longer time series. UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "41 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Since some adjustments could take place over time, we always use the figures from the last available report. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure A2: Refugees a burden for SSA? Panel A: Not weighted by economic capacity Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Migration and Economic Mobility in Tanzania: Evidence from a Tracking Survey Kathleen Beegle The World Bank Joachim De Weerdt EDI, Tanzania Stefan Dercon Oxford University, UK We thank Karen Macours, David McKenzie, and seminar participants at the Massachusetts Avenue Development Seminar, Oxford University and the World Bank for very useful comments. All views are those of the authors and do not reflect the views of the World Bank or its member countries. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 quest for imaginative and convincing instruments for migration (see the review of the migration and poverty literature by McKenzie and Sasin, 2007, and the references therein). An additional hurdle is the need for panel data to study migration and economic mobility. The costs and difficulties in re-surveying migrants mean that attrition may be relatively high for this group and may also result in the loss of some of the most relevant households to study this process (Beegle, 2000; Rosenzweig, 2003). This paper uses unique data from a region in Tanzania to address this key question: What is the impact of physical movement out of the original community on poverty and wealth? Although we do not have experimental data, the nature of our data allows us to limit the potential sources of unobserved heterogeneity considerably. Building on a detailed panel survey conducted in the early 1990s, we re- interviewed individuals in 2004, making a notable effort to track individuals who had moved. The tracking of individuals to new locations proves crucially important for assessing welfare changes among the baseline sample. The average consumption change of individuals who migrated was more than four times higher than that of individuals who did not moved. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Those who had moved out of the Kagera Region by 2004 experienced consumption growth that was 10 times higher compared to those who remained in their original community. These averages translate into very different poverty dynamics patterns for the physically mobile and immobile. For those who stayed in the community, poverty rates drop by about 4 percentage points over these 13 years. For those who moved elsewhere within the region, poverty rates drop by about 12 percentage points, and for those who moved out of the region, they drop by 23 percentage points. Had we not tracked and interviewed people who moved out of the community – a practice found in many panel surveys – we would have seriously underestimated the extent to which poverty has gone down over the past 13 years in the Kagera Region; we would have reported poverty reduction at about half of its true value. Clemens and Pritchett (2007) raise similar concerns in the context of income growth and international migration. In addition, the data would omit the part of the population with a high information content on pathways out of poverty. Still, these statistics are not evidence that moving out of the community leads to higher income growth. As noted above, we cannot observe the counterfactual: What would income growth have been for migrants had they not migrated? We exploit some unique features of these data to address concerns about unobserved heterogeneity. First, individual fixed effects regressions for movers and stayers produce a difference-in-difference estimation of the impact of physical movement, controlling for any fixed individual factors that affect consumption. Second, we can control for initial household fixed effects in the growth rate of consumption since we observe baseline households in which some individuals migrate Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 3. The Data The Kagera Health and Development Survey (KHDS) was originally conducted by the World Bank and Muhimbili University College of Health Sciences (MUCHS), and consisted of about 915 households interviewed up to four times from fall 1991 to January 1994 (at 6-7 month intervals) (see World Bank, 2004, and http: / / www. worldbank. org / lsms /). The KHDS 1991-1994 serves as the baseline data for this paper. Initially designed to assess the impact of the health crisis linked to the HIV-AIDS epidemic in the area, it used a stratified design to ensure relative appropriate sampling families with adult mortality. Comparisons with the 1991 HBS suggest that in terms of basic welfare and other indicators, it can be used as a representative sample for this period for Kagera (results not shown but available upon request). The objective of the KHDS 2004 survey was to re-interview all individuals who were household members in any round of the KHDS 1991-1994 and who were alive at the last interview (Beegle, De Weerdt and Dercon, 2006). This effectively meant turning the original household survey into an individual longitudinal survey. Each household in which any of the panel individuals live would be administered the full household questionnaire. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Respondents who were untraced were much more likely to be residing outside Kagera (43 %) compared to their counterparts who were re-interviewed (8 %). The consumption data come from an extensive consumption module administered in 1991 and again in 2004. The consumption aggregate includes home produced and purchased food and non-food expenditure. The non-food component includes a range of non-food purchases, as well as utilities, expenditure on clothing / personal items, transfers out and health expenditures. Funeral expenses and health expenses prior to the death of an ill person were excluded. Monetary levels were adjusted to account for spatial and temporal price differences, using price data collected in the Kagera survey in 1991 and 2004, and, for households outside Kagera, data from the National Household Budget Survey. Consumption is expressed in per capita, per annum terms. The poverty line is set at TZS 109, 663, calibrated to yield for our sample of respondents who remained in Kagera the same poverty rate as the 2000 / 1 National Household Budget Survey estimate for Kagera (29 %). 4. Growth, Poverty and Physical Mobility in Kagera In this section, we discuss changes in living standards overall, and the changes for four mutually exclusive groups based on residence in 2004: (i) still residing in the baseline community, (ii) residing in a Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Excluding respondents who have relocated would omit those with the higher rates of income growth and poverty reduction. Table 5 reports confidence intervals for the incremental samples (which are not mutually exclusive); it gives a more detailed picture of how inference on consumption growth and poverty reduction would have changed if we had not tracked movers. It is apparent that inference from a ‘ simple ’ panel survey of respondents continuing to reside within the original communities would have produced underestimates of actual consumption growth and poverty reduction in this population. These conclusions are robust across the distribution of consumption, as well as at the mean and poverty line. Panel A in Figure 2 depicts the cumulative density function for consumption per capita for those people who remained living in the same community. Panels B, C and D make the same graph for respondents found residing in neighboring communities, elswehere in Kagera Region and outside Kagera Region. As respondents were located further from their location in 1991, so the difference between the 1991 and 2004 graphs becomes more pronounced. Note how, for people who remained in the baseline community, the 1991 and 2004 distributions lie close to each other under the poverty line and diverge above it, while for other mobility categories there is more divergence at the bottom of the graph. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While both sons and daughters of the head may be expected to be more likely to stay in the community than other initial household members, patri-locality would make this probability higher for boys than for girls. In sum, this means we are using a set of six instruments. Although we can show that statistically convincing and close to identical results can be obtained by only using a subset of these instruments, we use the full set of instruments in the reported results. While our main measure of migration (Mi) is an indicator for having moved, we also substitute this for the log of the distance moved (kilometers from the original community of the location in which the individual was found in 2004, ‘ as the crow flies ’, set to 0 for non-movers). We will also extend the multivariate analysis to explore the role of moving to more urbanized areas and the role of sector movement in raising consumption growth. 6. Regression Results Table 9 presents the basic results for the initial household fixed effects (IHHFE) and 2SLS estimates (means for covariates are in Appendix Table 1). For each we estimate using an indicator for having moved and a measure of distance of the move. The 2SLS estimates in column (3) and (4) use the six instruments defined above. In Table 10, we present the first stage results of regressions explaining migration or the distance traveled in migration. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of dependability (Heller and Kessler, 2022). In a recent study, Karpowitz et al. (2023) show how these soft skills interact with discriminatory practices. They find that assigning leadership roles to women in a classroom setting reduces gender discrimination. Similarly, Kaas and Manger (2012) use a correspondence study to show that presenting soft information, such as reference letters with information on conscientiousness and agreeableness, seems to mitigate discrimination. Second, unlike most correspondence studies, we exploit data on firm characteristics and decisions at multiple stages of the hiring process to conduct a rich heterogeneity analysis. Most studies are only able to observe if the candidate receives an interview offer. Hangartner et al. (2021) is an interesting exception, which tracks online employers ’ actions and collects information that allows them to study hiring decisions. We instead observe five stages in the hiring process: 1) if employers reject an application, 2) if they visit an applicant ’ s profile, 3) the number of visits to each profile, 4) if employers contact the candidate, and 5) if they offer an interview. These five outcomes provide a rich preview into the hiring decision. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3. 2 Job Search and Classification Every Friday from May 12 to July 21, 2023 we scraped all job ads posted within the previous seven days from the job site. Then we filtered and kept job ads that met the criteria: full time, entry level position or requiring at most 1 year of experience. Relevant jobs were then classified into one of the five degree-based specializations. Each job was assigned a degree-based specialization using the area of specialization reported in the ad. For the accounting degree we used job ads classified as “ Accounting / Finance ”. For the business administration degree we used job ads with special- izations: ‘ Admin / Human Resources ’, ‘ Sales / Marketing ’, ‘ Customer Service ’ or ‘ Logistics / Supply Chain ’. For the computer science degree we used the specializations: ‘ Tech & Helpdesk Support ’ or ‘ Computer / Information Technology ’. For electrical engineering we used specializations that are related to ‘ Electronical ’, ‘ Electronics ’ or ‘ Other engineering ’. If the position had the word ‘ engineer ’ and the industry of the company was related to Electronical or Electronics, we also classified the ad into the electrical engineering degree. Finally, for mechanical engineering we used specializations related to mechanical, industrial or chemical engineering and specializations related to oil and gas. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the power calculations performed to determine the sample size. 3. 3 Randomized Assignment and Job Applications Each week we randomly selected 300 job ads from the sample of ads meeting our inclusion criteria. Among these 300 ads, each job ad was randomly assigned to a single applicant profile. Job ads are stratified to ensure that our treatment and comparison units are balanced on key variables. We use two variables for our strata: company size and company location. Company size is a dummy variable that takes the value of 1 if the company has up to 50 employees, and takes the value of 0 if companies have 51 or more employees. Company location is a dummy variable that takes the value of 1 if the company is located in greater Kuala Lumpur, the capital and largest metropolitan area in Malaysia, and 0 otherwise. 6 Our stratified randomization procedure guarantees balance in the assignment of job profiles to specific characteristics of companies. The application process was carried out manually from May 17 to July 28, 2023. At the beginning of each week, a research assistant was given a list of randomly assigned jobs for each applicant profile. Applications were completed on Mondays, Wednesdays, and Fridays of every week (with day of the week randomly assigned). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Applications were submitted during the same time span each of these days (8am-12pm CT). 7 The order of applications (grouped by profile) each day was also randomized. The application process is straightforward, it consists of submitting the application and completing an optional pitch. However, some jobs have a mandatory pre-scan questionnaire. For these type of ads we standardized answers and recorded the job ads that implemented these questionnaires. 3. 4 Monitoring Job Applications Job applications were monitored using a web scraping algorithm. For each job application, the following data was scraped from the website every Tuesday, Thursday, and Saturday (between 8am- 12pm CT): 1. Number of times the profile was viewed by the employer 6The locations we classify as Greater Kuala Lumpur are: Kuala Lumpur, Putrajaya, Petaling Jaya, Klang / Port Klang, Kajang / Bangi / Serdang, Subang Jaya, Ampang, Cyberjaya, Seremban, Selangor, Selangor- Others, Selayang, Semenyih, Shah Alam / Subang, and Central. 7If the website is under maintenance, which is common, applications will be delayed until the website is available. 10 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ethnicity: For Chinese name candidates is 4 days, for Malay name candidates 5 days and for Indian name candidates 7 days. We find this evidence consistent with a hypothesis of the existence of statis- tical discrimination since employers can be taking more time to collect information of discriminated groups, or sorting applications. Companies that conduct pre-scan questionnaires in the application process are less likely to discriminate against Indian-sounding name candidates in the profile visit outcome. We do not find heterogeneous effects for location or for engineering jobs. Heterogeneity results for gender discrimination are presented in tables 21 to 26. Companies located in Kuala Lumpur and small companies are less likely to visit female profiles than male profiles. There is no effect in any of the other outcomes of the hiring process. We do not find heterogeneous effects for high-paying jobs, for companies with low processing time, for companies with pre-scan questionnaires or jobs in engineering. 6 Do Soft Skills Matter? In this section we explore if soft skills are relevant in the labor market and how soft skill signals affect ethnic and gender discrimination. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The role of soft information has been previously raised by Kaas and Manger (2012) that find that soft information on conscientiousness and agreeableness mitigate the discrimination practices. 6. 1 Response to Soft Skills Does the labor market respond to signals of soft skills (leadership / teamwork / neither)? If so, what is the extent of the response? To answer these questions, we use the soft skill signal that we randomly assigned to each profile. We can test if soft skills are differentially relevant in the labor market using specification 6: (6) yi = θ0 + 2 X k = 1 θkSik + εi Again, the outcome yi and error term εi are defined as in specification 1. The soft skills we want to test are leadership and teamwork, in comparison to a control soft skill that we call ‘ neither ’. Sk is the soft skills variable, where k = { 0, 1, 2}. That is, Sk is a dummy variable that takes the value of 1 for soft skill k (e. g. leadership) and 0 for other soft skills (e. g. teamwork and our counterfactual soft 25 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 Conclusions We conducted a correspondence study using an online job platform in Malaysia. We tested for ethnic discrimination, gender discrimination and the value of signaling soft skills in the labor market. Unlike many correspondence studies, the data allow us to observe different stages in the hiring process. We observe if the employer rejects an application, visits the profile of a candidate, number of times the profile is visited, if they contact them and if they offer an interview. Uniquely, we observe competition in the labor market on both the demand and supply sides. We do not find evidence of gender discrimination in the hiring process. Malaysia ’ s observed differential wages and labor force participation rates by gender do not seem to be associated with discrimination or human capital accumulation. More research is needed to determine why women in the Malaysian labor market have lower employment rates and wages. We find that Indian and Malay sounding name profiles are discriminated against in comparison to Chinese-sounding name profiles. There is discrimination along all the hiring process variables we observe. Malay and Indian candidates are 8 and 9 percentage points less likely to receive an interview offer relative to a Chinese candidate. Discrimination for both ethnicities is also present in other outcomes. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "29 should be entered immediately into the accounts; and (c) Payment must be recorded as soon as they are made. Budget implementation should be reviewed periodically to ensure that programs are implemented effectively and to identify any financial or policy derailment. The review of budget execution should cover financial, physical and other performance indicators. Development budgets are often beset by implementation problems because of insufficient implementation capacities and other factors such as delays in mobilizing external financing, overoptimistic implementation schedules or difficulties in importing supplies. It is thus important to have in place mechanisms for reviewing the most significant or problematic projects. These could consist of a regular monthly or quarterly review of projects within the line ministries and a midyear review involving line ministries and central agencies29. The government has taken steps to improve the tracking of budget expenditure until the intended destination, particularly investments spending, for which a tracking survey was entrusted in 2005 with the Ministry of infrastructures. In addition, the ministries took themselves certain internal initiatives, in particular in health and education sectors, but the action plans of these ministries were not updated as envisaged in 2005, and there are neither reliable benchmark, nor quantitative targets as regards improvement of the arrival of the expenditure at intended destination in these sectors. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A survey was carried out in the health sector (health centers) with the support of the World Bank in 2005, but has not been yet validated. In the rural development sector, where the first expenditure tracking survey between the decentralized center and services was to be carried out in 2005 on PEFA funds, the survey is yet to be carried out. It is critical that adequate management measures are gradually put in place to stop the current waste of resources. These measures include, inter alia, the accounting improvement of material, improvement of the inventory and delivery control, and especially a transparent planning of the deliveries, including, the posting of the received deliveries, their comparison with the planned deliveries and their certification by users within each service. It would also be desirable that in each ministry, an action plan is prepared for the implementation of these measures, on the basis of existing ones, and with target indicators as regards improvement of the arrival of the expenditure at intended destination. The social ministries could usefully open the way in this field, on the basis of some projection already carried out. Without a quantitative and qualitative improvement of the arrival of the expenditure at their final recipient, the increase in the budgetary appropriations to the priority sectors will hardly be translated into substantial concrete results on the ground. 29 It is difficult to make accurate forecasts for the implementation of certain programs or developments in economic parameters such as inflation or interest rate. Some immediate needs that were not foreseen during budget execution may appear during budget execution. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The estimated impacts on measures of reconciliation, post-con­flict justice, trust and participation in community groups are mostly statistically insignificant. The paper also explores how these effects differ across different sub-samples based on ethnic composition, land scarcity and attitudes towards return. The results highlight the possible role of new migra­tion-related societal divisions (i. e. returnees versus stayees) in affecting post-return social cohesion. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at carlos. vargas-silva @ compas. ox. ac. uk. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 agreements that did not fully satisfied either party (Ndayirukiye and Takeuchi 2014). This tension related to land, and who has a claim to the land, can lead to social tensions in communities with higher levels of return. Figure 3 – Refugees in Tanzania in 2005 by province of origin in Burundi Note: The number in brackets is the number of refugees in Tanzania in 2005 which was originally from the given province in Burundi. This information comes from (UNHCR 2021b). The number in parenthesis is that number as a percentage share of the population of the provinces as estimated in 1990 Burundi Census. An important question for our hypotheses is the degree to which there is evidence of migration-related societal divisions in the country. There is no direct quantitative evidence on identity issues (i. e. returnees versus stayees), but we have data on attitudes towards emigration, remittances and return that can provide insights on these identities and even be a proxy for migration-related identity in some cases. Overall, attitudes towards emigration and return are mixed and show that there is scope for the existence of migration-related divisions. In Table 1 we report the share of respondents who agreed with different statements regarding emigration, remittances and return. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 Finally, we divide the communities based on land scarcity before the 1993 conflict in order to explore the possible role of posterior rules regarding land provision to returnees. Respondents in communities that had more and less pre-war land available have broadly similar attitudes towards return. 4. Research design 4. 1 The survey We collected the data for this project during January to March 2015 as part of a nationwide survey on issues related to migration for the Labour Market Impacts of Forced Migration (LAMFOR) project. The survey had two components. First, a household survey in which 15 households were interviewed in 100 communities (i. e. sous-collines) across the 17 provinces of the country. Second, a community survey in which a local leader was interviewed in each of the 100 communities. The number of communities selected in each province was based on information from the 2008 Census. Figure 4 indicates the location of the communities surveyed. Figure 4 – Location of communities surveyed in Burundi Note: Geolocation of the 100 communities (i. e. sous-collines) sampled in the survey. Each community corresponds to a dot. Fifteen households and a local leader were interviewed in each community. The number of communities selected in each province was based on information from the 2008 Census. In the analysis below we focus on rural areas. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "UNHCR (2021a) projects that the number of returnees will reach 141, 000 in 2021, up from 41, 000 in 2020. There are no datasets such as the one used in this study to explore the impact of post-2015 returnees on social cohesion and it is not possible to determine the degree to which our findings our applicable to this new context. However, it is possible to explore similarities and differences between the two contexts. Looking at a UNHCR report about the latest wave of returnees, it states that “ almost all returnee households rely on food obtained from their own gardens (93 %) and / or fields- households struggle to get food during the period they do not produce. 81 % of households declared that they are not satisfied with their level of food security because of the low dietary diversity ” (UNHCR 2021a). The report also suggests that “ 88 % of returnee heads of households are subsistence farmers, but most of them declared not having the adequate resources to produce their land. ” This high level of dependence on agriculture and prevalence of food insecurity are similar to the ones in our dataset for returnees and suggests that tensions related to access to agricultural land could also be present for post-2015 returnees. There are also signs of potential differences between current dynamics and the pre-2015 period. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 I. Introduction After two decades of multiparty democracy, Mali was viewed as a democratic success story. The fifth presidential elections were scheduled to take place in March 2012 and another peaceful and democratic transfer of power was widely anticipated. Reality was different, however. A secessionist movement sparked by a Kel Tamasheq1 rebellion led to a political and constitutional crisis culminating in a coup d ’ état in March 2012 and an attempt to take over the country by force. The three northern regions of Gao, Timbuktu and Kidal became occupied by various rebel and Islamist factions until early 2013, when a coalition composed of the Malian Army, French troops and the ECOWAS-led African-led International Support Missions to Mali (AFISMA) recaptured the occupied areas. 2 After months of insecurity in the North and two violent attacks in Bamako, a Peace Accord was signed in May and June 2015 between the government and different actors involved in the rebellion. The Accord established a joint vision for peace and prosperity predicated on demobilization and disarmament, the devolution of authority to local governments, and the establishment of conditions for restoring stability and economic recovery in northern Mali. In spite of the Accord, the regions of Gao, Kidal and Timbuktu remain in a state of prolonged crisis, with high levels of insecurity and weak governance. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Without army protection most parts of the North, especially Kidal remain inaccessible to those working for the central and local government. 3 Armed bandits are active and IED explosions as well as violent attacks on the MINUSMA peacekeeping forces are regular occurrences. Under these circumstances, data collection is very difficult. INSTAT, the National Bureau of Statistics, has not been in a position to collect information from northern Mali since the beginning of the crisis. To our knowledge, our surveys implemented by a private survey entity, GISSE, are the only systematic and representative effort to collect data in north Mali since the crisis. They offer a unique database providing a crucial perspective that would otherwise not be reflected in academic analyses and policy level decision-making, and a perspective that is indispensable in any attempt at understanding the situation in Northern Mali. 4 Preceding the Accord on Peace and Reconciliation in Mali (Accord pour la paix et la réconciliation au Mali) (hereafter, the Peace Accord) of May and June 2015, four peace accords had been signed between the government and Toureg and Arab armed groups in 1 Kel Tamasheq (those who speak Tamasheq) is synonymous for Tuareq. 2 Francis David (2013): The regional impact of the armed conflict and French intervention in Mali. NOREF, Norwegian Peacebuilding Resource Centre. 3 Assessing Recovery and Development Priorities in Mali ’ s Conflict-Affected Regions. Draft Report of the Joint Assessment Mission for Northern Mali (January 2016), p. 15-16. 4 All data can be downloaded from www. gisse. org. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 households in each. To select a starting point the enumerator used a code of the day16 and chose every second house in rural areas and every fifth house in urban areas. The selection of individuals within the household to answer the questionnaire was conducted as follows: the head of household (male or female) was selected to answer the first part of the questionnaire dealing with general questions about the households. Using the roster of household members which was compiled during the first part of the interview, another member of the household aged 18 or above was selected randomly to answer the second part of the questionnaire in which perception questions were asked. Alternation between male and female was ensured. The survey thus generated data that are reflective of the opinions of those aged 18 and above in northern Mali. To assess the representativeness of the data, which were collected under rather challenging circumstances, the ethnic composition of the sample was compared with the ethnic composition in the North as reported by the 2009 Census. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 In addition to the 500 households and 180 refugees, 50 local authorities were interviewed. Of those, 18 are based in Goa, 22 in Timbuktu and 10 in Kidal. Included were 38 village chiefs, 11 mayors and 1 local notable. Table 3: Authorities interviewed by function and by region (%) Gao Kidal Timbuktu Total Village chief 13 8 17 38 Mayor 5 1 5 11 Local notability 0 1 0 1 Total 18 10 22 50 The authorities interviewed are all men aged between 30 and 86. Forty-four percent are either just literate or have no education at all. In Gao, authorities have a higher level of education compared with Kidal and Timbuktu. The majority (almost 39 %) of respondents in Gao have a high school education, 22 % are literate and 17 % have primary education. In Kidal, 80 % of respondents have no education at all. In Timbuktu, about 32 % of the surveyed authorities are literate, 27 % have a high school education and 23 % a primary education. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Worry about economic conditions after a recession can make companies more cautious about hiring, which generates temporary forms of recruitment. Finally, the process of technological change, especially the development of digital communications, is a key factor in justifying new forms of non-standard employment. The expansion of services and global supply chains is inextricably linked to technological advances. The new information technologies, the higher quality and the lower cost of infrastructure and the logistical and transport improvements, allow companies to compare, organize and manage production in a more diversified way in territorial terms. At the same time, new communication technologies have allowed the generation of new forms of work, such as work on internet platforms or work on demand through digital applications. In this sense, technological developments allow companies to assemble teams of workers who develop activities in any part of the world through a virtual network (Brews and Tucci, 2004). The most recent development of online recruitment services, such as\"eLance\"and\"oDesk\"enables the search for workers who can be subcontracted, performing their activities in virtual mode. 3. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Sources of information This work includes a descriptive analysis that allows us to identify how the prevalence of non- standard employment (NSE) has evolved in the last two decades in different regions of the planet, as well as the evolution of the profile of workers in those roles, in terms of their educational level, salary per hour, and type of tasks performed. This analysis was based mainly on periodic surveys of households that included information regarding the employment and educational situation of individuals. Although the denomination of this type of surveys varies from country to country, in all the cases analyzed there is usually a survey of annual or higher frequency that includes information required to identify the labor status of the individuals as well as to analyze the salary profile and education of the employed. However, it should be noted that the identification of the type of work relationship (standard or non- standard) is frequently limited in these data sources. Indeed, it is only possible to identify part-time employment and temporary employment (not in all cases) within the non-standard forms of employment mentioned in the previous section. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In this sense, it is necessary to take into consideration that all indicators of prevalence of NSE and its profile will be limited to a subset of this kind of workers. 4. 1 Latin America and the Caribbean This section focuses on the Latin America and the Caribbean region, where a set of 9 countries, that we consider representing the different realities of the region in an exhaustive way, was analyzed. Specifically, the analysis was conducted for Argentina, Brazil, Bolivia, Chile, El Salvador, Mexico, Peru, Dominican Republic and Uruguay. 5O * NET is the successor of DOT (Dictionary of Occupational Titles) which is no longer updated. O * NET was launched in 1998 on the basis of the BLS Occupational Employment Statistics codes. In 2003, it was changed to SOC which implies that the consistent measures of task content are calculated from 2003. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 5: Part-time employment by age group. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Uruguay Brazil Chile Peru Bolivia Dominican Republic Mexico El Salvador > 64 years old 50 ‐ 64 40 ‐ 49 25 ‐ 39 15 ‐ 24 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It is important to note that the changes in the prevalence of informality among SE workers between the starting and ending point of the study show a very similar dynamic compared with the observed dynamic for NSE (the prevalence of informality among SE workers is presented in the Annex II of the paper). Then, the trend in the prevalence of informality among NSE mainly reflects the overall trend of informality in the labor market instead of a specific characteristic of NSE. Figure 9: Prevalence of informality among NSE workers. (Mid-1990s / Mid-2010s) 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Brazil Chile Mexico El Salvador Female Male Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Therefore, although the available evidence is very limited, we do not observe a shift towards a more intense task profile in cognitive activities in the case of temporary workers. 11 Figure 27: Variation in the task content performed by NSE and SE. (Change from the late 90s) Non-Routine Cognitive Analytical 11 Evidence of a higher intensity of the cognitive tasks in the ECA countries is presented in Keister and Lewandowski (2016). ‐ 1 ‐ 0, 5 0 0, 5 1 1, 5 Russia Georgia Kyrgyzstan Armenia Albania Moldova NSE SE Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 MPI, constructed in Admasu et al. (2021), to capture the deprivations of forcibly displaced individuals and their gendered lives. The paper proceeds as follows. Section 2 reviews the existing literature to provide the background and motivation for the analysis, including a summary of the different country contexts covered by the case studies in this paper. Section 3 outlines the measurement strategy for deconstructing the MPI used for analysis and its limitations, followed by Section 4, which introduces the data. Section 5 presents the findings, first for deprivation results at the individual level and then results evaluating intrahousehold inequalities. Concluding remarks are discussed in Section 6. 2 Background and Literature Review 2. 1 Individual-level measures of gender and multidimensional poverty Individual-level analyses of multidimensional poverty have mostly centered around children, with various studies analyzing the relevance of indicators for children (aged 0- 17 years), 2 as well as other age ranges. The MPI has also been used to better understand gender issues, for example, Batana (2008) implemented a women ’ s MPI in Sub-Saharan Africa. Bhutan ’ s Gross National Happiness measures (2010, 2015), Vijaya et al. (2014), and Klasen and Lahoti (2016) are implemented at the individual level. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Women ’ s Empowerment in Agriculture Index (WEAI) (Alkire et al. 2012) uses individual-level data, and the linked Gender Parity Index reflects inequalities across women and men ’ s deprivation scores within the same household. Alkire, Apablaza and Jung (2014) design and implement an exploratory individual-level MPI for 31 European countries over six waves of data using EU-SILC data sets, finding no cases in which are women significantly less poor than men, and in many cases, they are significantly poorer. Espinoza-Delgado and Klasen (2018) create an individual-level MPI to understand differences in poverty between women and men in Nicaragua, finding similar overall incidence, but much higher intensity of poverty among women. Bessell (2015) and Pogge and Wisor (2016) explore deeply contextual gendered poverty measures and elucidate the ways that participatory consultations can inform the design and uses of gendered measures. Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa. Alkire, Ul Haq, and Alim (2019) use individual-level data alongside MPI data to expose gendered and intrahousehold differences among MPI poor and non-poor children. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 Bank Account No member has a bank or mobile money account. 1 / 12 Many of the indicators align with goals identified in the 2030 Agenda for Sustainable Development, such as no hunger, good health, access to quality education, clean water and sanitation, and decent work, as well as indicators that are especially relevant for displaced people, such as possession of legal identification, physical safety, and food security. The focus on gendered dynamics justifies health indicators related to pregnancy care, combining information on prenatal care, assisted delivery, and early marriage. A full discussion of the MPI ’ s indicator selection can be found in Admasu et al. (2021). We focus on six of these 15 indicators that use individual-level data – viz years of schooling, school attendance, pregnancy care, early marriage, legal identification, and unemployment. Our intrahousehold analysis drops the two health indicators due to data limitations; the question about age at marriage was only asked to the household head in Ethiopia, Nigeria, and South Sudan, whereas in Somalia and Sudan, it was applied to more members than the head. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "24 According to Table 12, all countries but Sudan show a significant relationship for school- age children ’ s experience of intrahousehold inequality and their displacement status, although sample sizes mean that only Nigeria and Somalia ’ s results are robust. In Nigeria, most children experiencing intrahousehold inequality reside in non-displaced households – although it is crucial to note that these children constitute most of the school-age children (71. 1 %), and the levels of intrahousehold inequality are nonetheless far higher than anticipated if displacement status had no effect. In Somalia, displaced children are significantly more likely to experience intrahousehold inequality in school attendance, as they constitute 63. 5 % of school-age children experiencing intrahousehold inequality even though they only make up 35. 3 % of the school-age children population in the sample. For the years of schooling indicator, the overall lack of intrahousehold inequality among the MPI poor in years of schooling obscures meaningful or robust differences by displacement status. Gender and displacement status appear to jointly have significant impacts in school attendance in Ethiopia, Somalia, and South Sudan. In Northeast Nigeria, it appears that displacement status has larger effects than gender. In Somalia, forcibly displaced school children experience intrahousehold inequality more often than non-displaced children, to the disadvantage of girls. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 The Government of Chad subscribes to this logic as evidenced by the application decree of Chad ’ s Asylum Law signed in 2023, 1 and the National Response Plan to the Impact of the Sudanese Crisis which is under preparation. The Law and the Plan promote the local integration of refugees, aim to avoid settling refugees in permanent camps and promote self-sufficiency. They offer refugees the right to own land, to engage in formal employment and commercial activities, to move freely, and to access to banking services. While participation is the stated policy objective, the reality is that previous arrivals are almost exclusively living in camps, and that the new arrivals live in “ organized sites ” (as humanitarians now call them), presumably to cope with massive arrivals but with little concrete evidence for the onward movement of refugees. This note explores the size of this four-way benefit (for refugees, hosts, the Chadian state and the international community) by estimating how much could be saved on aid for basic needs consumption by enabling refugees from Sudan to realize their economic potential. For its empirical work the note draws primary on data from the ECOSIT4 survey. 2 In 2018 – 19, Chad became one of the first countries in Africa to capture refugees and host communities in its national household survey. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Refugees and Host Communities Household Survey expanded the national Household Consumption and Informal Sector Survey to include a representative sample of refugees and host communities, including Sudanese and host communities located in the east of the country. The remainder of this note is organized as follows. Section 2 presents a short discussion of the literature on the economic participation of refugees. Section 3 compares the characteristics of newly arrived refugees from Sudan with previous arrivals for whom survey data is available, to find that both groups are highly comparable. Section 4 uses the existing data to explore how the basic needs refugees are covered from own-income. Sections 5 and 6 dig deeper by exploring econometrically the correlates of higher incomes of refugees. A discussion of the results and their policy implications follows in section 7, after which section 8 concludes. 2. Benefits of economic participation of refugees Whether or not the arrival of Sudanese refugees in Chad contributes to economic growth is of limited immediate relevance as concerns about the safety of fellow humans drive the response. Nor does any decision maker suggest that hosting refugees is a development strategy Chad should pursue. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "But, given the presence of thousands of refugees in the country, the constrained fiscal space the authorities face, and the likelihood that international assistance for refugees will taper in the future, an imminent policy question is how to ensure that refugees can be hosted in a sustainable manner, without becoming a fiscal burden in the future. The fear that refugees are (or might become) a fiscal burden is driven by a broadly held perspective about forcibly displaced persons in general, and refugees in particular, namely that they are humanitarian subjects, vulnerable and worthy of public assistance (Betts and Collier 2017). This perspective is not universal, however. The economic contributions of refugees have been extolled for years, from posters 1 https: / / www. ecoi. net / en / file / local / 2091861 / 645b938a4. pdf. 2 Enquête sur la Consommation des ménages et le Secteur Informel au Tchad. The survey was carried out jointly with the National Statistics Office (Institut national de la statistique, des études économiques et démographiques, INSEED) and the UNHCR in Chad. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 household displacement and the destruction of household dwellings during the violent events. We are also able to measure conflict intensity across time – including peaks of violence at various stages of the conflict – at the district level from event data on violence intensity during the conflict in Timor Leste. We focus on primary school effects because only a small percentage of the Timorese population attended secondary school. Our results show mixed evidence for the impact of violent conflict on educational outcomes. Mirroring some of the findings of Bellows and Miguel (2006) and others, we find evidence for a rapid recovery of the education sector in Timor Leste, and of educational outcomes, particularly for girls. However, in line with emerging results in the micro-level literature, we find that the 1999 wave of violence in Timor Leste – as well as peaks of violence in the 1970s and 1980s – resulted in negative effects on primary school attendance and attainment. This effect is particularly strong for boys. We attribute the first result to a process of educational catch-up among girls in Timor Leste that started before the conflict and continued despite the conflict. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The second result is likely to be due to large rates of grade repetition and of delay entry that were exacerbated by the need to remove boys from school due to the negative economic effects of the conflict on households more exposed to the violence. The paper is structured as follows. In section 2, we present a literature review on the impact of violent conflict on development outcomes in general and education in particular. Section 3 provides a descriptive background of the conflict in Timor Leste and the country ‘ s education sector. In section 4, we describe the datasets, discuss our identification strategy and present some descriptive results. Section 5 discusses our empirical results, as well as a range of robustness checks. Section 6 concludes the paper. 2. Literature review An emerging body of literature has provided valuable empirical evidence on the effects of violent conflict on income and consumption levels, and more generally on the welfare of populations living in areas of violence (Ibáñez and Moya 2009, Justino and Verwimp 2006, Verwimp and Bundervoet 2008). A significant number of studies have also examined the health impact of violent conflict, finding that violence results in negative health effects in terms of lower height-for-age and lower nutritional outcomes among children that will generate long-term consequences on future outcomes. 2 2 See Alderman et al. (2006) for Zimbabwe, Bundervoet et al. (2009) for Burundi, Akresh and Verwimp (2006), Akresh, Verwimp and Bundervoet (2007) and Akresh and de Walque (2008) for Rwanda and Guerrero-Serdán (2009) for Iraq. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The data shows that cohorts that were of school age during the Indonesian occupation achieved higher level of education than older cohorts (i. e. those born in the 1970s compared to those born in the 1960s or before) testifying for an increasing trend as expected. The figure shows that despite the increasing trend a large fraction of individuals have low education levels. Interestingly, all curves start to drop after the 1987 cohort. This decreasing trend is observed among individuals aged 20 or younger in 2007 and provides evidence of a mismatch between the grade attended and the grade that they should have achieved at their age. 4 This is caused by a persistent sluggishness in grade achievement due to the high level of delayed entry to school and high rates of repetition. 5 The impact of the conflict in its different phases and the subsequent reconstruction efforts on schooling levels of children in Timor Leste is therefore unclear. The early years of violence coincided with an education for all policy in which quantity was preferred to quality. In addition, the 1999 violence that followed the withdrawal of Indonesian troops led to the destruction of schools and the removal of children from school. The reconstruction program implemented after 1999 tried to counteract this destruction, and achieved fast progress. However, the education sector was still in very poor shape. In the next section, we investigate in more detail the effects of the conflict on educational outcomes of boys and girls in Timor Leste. 4. Identification strategy and data description 4 Those born in 1992 are 15 in 2007. So they might have at most completed grade 9 and this justifies part of the drop in the curves as the grade completed is right censored. 5 The high levels of school delay are also confirmed by the figures on gross and net enrolment ratios calculated using the TLSS 2001 and 2007: primary gross enrolment ratio was 105 percent in 2001 and 128 percent in 2007, while net enrolment ratios were 74 and 94 percent, respectively, in 2001 and 2007. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Commission for Reception, Truth and Reconciliation (CAVR). 7 This information has been collected from deponents to the Commission ‘ s statement-taking process. 8 We make use of data on the number of killings that occurred during the war in order to derive patterns and variation of violence in Timor Leste over time and across space. We use this data to identify districts and years that experienced high and low violence-intensity, both at the start of the occupation and following the withdrawal of Indonesian troops in 1999. This allows us to estimate both the impact of the first years of the conflict and the impact of the last wave of violence in 1999. 4. 1. Identification strategy: The impact of violence on school attendance in 2001 We first investigate the short-term impact of the 1999 violence. The empirical questions being addressed are: (i) whether the violence in 1999 imperiled school attendance9 and school grade deficit, and (ii) whether different channels of exposure to conflict – displacement and house destruction – affected boys and girls and different age groups differently. 4. 1. 1. Primary school attendance and grade deficit rates in 2001 We make use of information in TLSS 2001 collected at the individual and household levels on displacement and house destruction to identify conflict-affected individuals. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We have constructed two different variables that try to account for the degree of severity of the conflict. 10 The first variable identifies individuals belonging to households that were displaced due to the 1999 wave of violence (all members displaced). The second variable identifies individuals in households that report having their house completely destroyed by the violent attacks in 1999. The TLSS 2001 contains also useful retrospective information on school attendance and grade attained across three different academic years: 1998 / 99, 1999 / 00 and 2000 / 01. We are 7 Commission for Reception, Truth and Reconciliation & Benetech Human Rights Data Analysis Group. ― Human Rights Violations Database. ‖ 9 February, 2006. Website: http: / / www. hrdag. org / resources / timor-leste_data. shtml. 8 There may be potential sample biases in the statement taking procedure given the voluntary nature of the process. It is possible that those living in more remote or mountainous areas, those living far away from the areas where the statements were taken, the sick, old and disabled and those with no access to the media or means of mass communication have a lower probability of being part of the sample. By contrast, those more active in local communities are more likely to have provided a testimony. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In order to address these potential sample biases, the CAVR supplemented its documentation with reports produced by Amnesty International and Fokupers (a local NGO). The information contained in these reports was then included into the HRVD database. 9 Note that we do not analyse school completion in 2001 because most children that were of school age in 1999 were still in school in 2001. 10 The questions we used are ― Was [NAME] displaced outside E. Timor in 1999? ‖, and ― Was the [BUILDING] damaged in the violence of 1999? ‖. 14 % of the whole sample surveyed in 2001 report having been displaced, while 26 % report that their house was destroyed. Within our sample of school age children, these figures are 16 % and 25 %, respectively. We have made sure that buildings that are reported to having been destroyed were used for living purposes only. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We estimate the following equation: [2] where are our outcome variables, i. e. attendance rate (which is a binary variable12) or grade deficit. and are year dummies respectively for year 2, the year of the 1999 violence, and for year 3, the first year of the post-conflict period. The model includes individual fixed effects,. The variable is the random error. All standard errors are clustered at the village level. As discussed above, we identify violence-affected individuals using two different measures,, with j = 1, 2 depending on which measure is included in the specification. The first one is whether the individual was displaced with the whole household. The second is whether the individual reports that her house was completely destroyed during the 1999 violence. We allow the violence measure to interact with both year dummies. The estimation of our specification above is 11 We have also tried to keep a larger sample that includes those children in primary school age in the year of the violence (i. e. between 7 and 12 in 1999). This means including individuals aged 6 in year 1 and aged 13 in year 3. The inclusion of these latter individuals may generate ‗ spurious ‘ results as they are not of primary school age. We have estimated the model using both samples. We find that the estimates using the larger sample are similar to those obtained with the sample of children aged between 8 and 11 in 1999. The larger sample generates more statistically significant results but we have decided to opt for the most restrictive sample to avoid inclusion of ‗ tails ‘ of the age distribution that are not of primary school age. 12 We estimate our model with a linear probability model. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 of intense conflict. We examine also the overall impact of the 25 years of conflict on educational outcomes. This analysis allows us to consider the full long-term impact of the conflict on educational outcomes of different generations of children in Timor Leste. 4. 2. 1. The educational impact of the 1999 wave of violence We exploit variation in the number of killings over time and across districts to identify conflict affected individuals. Our intention here is to analyze whether individuals exposed to the violence during their primary school age show different primary school completion rates eight years after the end of the war, relative to those not affected by the conflict. The outcome variable in which we are interested is whether individuals completed primary school in 2007. Figure 7 shows average primary school attainment for all individuals in our sample. The graph shows an increasing trend in primary school completion across cohorts and a progressive reduction of the gender gap. The gap among the younger cohort (those born after 1987) is almost zero. The drop in the curve for the younger cohort confirms the presence of significant delays in school attendance. For the purpose of this analysis, we use the TLSS 2007 dataset and the HRVD dataset contained in the CAVR data publication. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Due to our research design and the complexity of introducing a third grouping who would play outgroup in the behavioral experiments regardless of partner identity, we exclude PRL from the main analyses presented here. 5 Data were also collected six months after the end of the training but this was heavily disrupted due to the outbreak of the COVID-19 pandemic. In Lebanon, this resulted in a change to the method of data collection (from in-person to telephone) and in Jordan, an end to data collection entirely. In Jordan, this had a more pronounced effect on the control group, due to the scheduling of data collection and implementation of restrictions in Jordan. Given these complexities, we do not present results from these analyses. 6 In addition, we attempted to collect information on the extent of social and economic interactions between hosts and refugees. At baseline, almost 95 % of respondents in both the treatment and control group reported such interactions. For this reason, we do not include this information in these analyses. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 scored on a Likert scale running from 0 (significantly worse) – 10 (significantly better). The survey questions on optimism were collected at outreach, baseline and endline. Employment status: Due to slight differences in access to labor markets for refugees in Jordan and Lebanon and differences in how we were able to ask about employment status, we tabulate employment status as whether or not an individual is employed. Participants were asked at outreach about their employment status, and, in subsequent rounds, whether or not this had changed. This variable is coded 0 for not currently employed and 1 for employed. Economic scarcity: We collect survey questions on individual perceptions on: ability to meet current needs; ability to meet future needs; expectation that access to jobs is fair; expectation that salaries are fair; and belief that unfair access to labor markets fuels tensions. Ability to meet current and future needs are coded on a Likert scale running from 1 (completely unable) to 5 (fully able). The “ fairness ” indicators are coded: 0 (unfair) or 1 (fair). Whether or not competition around employment contributes to tensions is captured on a 1 (not at all) to 5 (absolutely) Likert scale. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Intergroup behaviors: We collect data from two one-shot incentivized behavioral games: the dictator game (a division game where players choose how to split a prize) and stag hunt (which gives players the chance to cooperate). 7 In each wave of data collection, players were randomized, at the session-level, to play either with a partner from the host or refugee community in that country. For example, a Lebanese player could be paired with a Lebanese or a Syrian resident in Lebanon but not with a Jordanian or a Syrian resident in Jordan. Partner identities were re-randomized between the waves so that not all players played with a partner of the same identity in both rounds. We made clear that partners were not individuals in the same room and, at endline, that the partner was not the same partner from baseline. A hint was given about the partner ’ s identity based on dialectic differences in the words for common foods, along with a small amount of innocuous information (approximate age, favorite hobby and marital status). 8 Sample intakes and partner assignments by data collection wave are shown in Table 1. This prime relies on a minor, and subtle, difference in dialects in settings with an otherwise high degree of cultural similarity. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 employment status and education level also significant at 10 %. Marital status and education are important predictors of attrition. As we might expect, these imbalances suggest some threats that, if left uncorrected, could undermine the parallel trends assumption of difference-in-difference estimators. That said, we see no sign of differences between treatment and control, or attritors and non-attritors, over the key GRIT personality features. This suggests that members of the treatment group are not, for example, more motivated to succeed than members of the control group. To account for these biases, we generate a series of inverse probability weights to balance the data. These weights define the probability of an individual with particular characteristics (e. g. host or refugee status) being in each of the treatment and control groups at baseline and endline and are used to rebalance the data in order to closer support the parallel trends assumption. Results are shown in Column 3 of Table 4. Following weighting, data balances on all key factors, including nationality. This suggests that the parallel trends assumption is more reasonable under the weighted dataset than in the raw treatment / control data. 11 Based on these analyses, we conclude that it is safe to use weighted OLS-based approaches. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 degree of group bias. Thus, should the program reduce bias, 𝜓𝜓7 < 0. 𝑋𝑋is the same 𝑛𝑛 × 𝑘𝑘 matrix of control variables. 𝜓𝜓 is a 𝑘𝑘 × 1 vector of regression coefficients; and 𝜔𝜔 is the idiosyncratic error. We employ two small deviations from these approaches to produce the full set of results. First of all, as we do not have two sets of control variables from outreach to baseline, we run a fixed effects analysis to understand the impact of assignment to treatment status on life and economic optimism. Second, due to a data collection error in the field, indicators of economic scarcity were not collected from all of the control group at baseline. Instead, we seek to approximate the effect of treatment on these indicators by triangulating comparisons in two dimensions. First, we test whether or not these indicators improved for the treatment group from baseline to endline. Second, we test whether or not there are differences between the treatment and control groups at endline. This stops short of causality but still reveals interesting information about the dynamics at play. We produce five outputs for each analysis, with the exception of the economic scarcity indicators. First, we use uncontrolled OLS. Second, we introduce control variables. Third, we remove the controls but add inverse probability weights. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Combining almost 50, 000 responses to 11 cross-sectional surveys between 2017 and 2021, displacement is neg­atively associated with perceptions of social cohesion in aggregate. But at the individual level, those who report hosting displaced populations in their communities often have higher perceptions of social cohesion. These results are strongest among respondents who self-report hosting IDPs as opposed to refugees, but important heterogeneity across indicators, local context, and gender should guide policy meant to promote social cohesion in forced displacement. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at ppham @ hsph. harvard. edu. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "itative exercise empowered focus group participants to guide the research and conceptualization. The results from these exercises then dictating the definition and measurement strategy for social cohesion in the quantitative assessment. The participatory research strategy was based mainly on structured focus group discussions. The project conducted consultations in seven territoires with the objective to develop localized understandings of what elements were important to social cohesion in eastern DRC. Participants were selected from civil society and the public sector. 96 individuals participated in these exercises (Table 1), 55 % of whom were men and 45 % of whom were women. 6 Location (Groupement) Date Number Participants Goma City 06 Oct 2017 11 Bukavu City 13 Oct 2017 13 Nyabibwe (Kalehe) 12 Oct 2017 14 Ishungu & Lughendo (Kabare) 12 Oct 2017 15 Kamisimbi (Walungu) 16 Oct 2017 18 Wassa (Walikale) 20 Oct 2017 12 Biiri (Masisi) 03 Nov 2017 13 Table 1: Descriptive Information on Focus Groups The focus group discussions began with an open discussion on social cohesion designed to ascertain participants familiarity with the concept. The facilitator further asked participants to write down words or concepts participants related to social cohesion. These words were written on individual post-its, which were then posted on a wall. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Participants then grouped words, building a concept map to visually represent their common understanding of social cohesion. Following the development of the concept map, the facilitator broke participants into smaller groups and were asked to conceive of a fictional yet realistic person that exists in their contexts. 6Focus groups were conducted in Swahili and French. Focus groups were transcribed to French for analysis. 11 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These surveys are part of a long-term data collection effort by the research team (Vinck & Pham 2014) and were collected separately from the focus group discussions. The focus group discussions thus did not directly influence the design of the surveys, but rather directed the analysis strategy of the surveys that our team has collected at regular intervals in eastern DRC. The survey data are analyzed in two ways. First, 11 surveys collected between 2017 and 2021 8Eastern DRC is a site of ongoing violence, raising a number of ethical, methodological, and practical concerns about collecting data. We discuss the ethical protections we implemented when collecting this survey data in the Appendix, Section B. 9Territoires are sub-provincial administrative units. Additional details on the structure of administrative units are available in the Appendix, Section A. 1. 21 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We sourced bilateral trade value data from WITS and bilateral tariff data from a medley of sources, presented in Appendix Table A1. As part of this procedure, all entries in the two composite regions (rest of Western Asia and rest of Northern Africa) were split and assigned the split values to the newly created economies, while all entries for the two composite regions from the GTAP database were removed from the database. Each entry was split using the most thematically relevant external source. Sectoral GDP shares were used to split consumption and production values, trade data were used to split export and import values, and tariff information was used to assign tariff values. Export shares were used to split further production and consumption information into the final set of industries presented in Table 1. For internal consistency purposes, the required accounting relationships were imposed on the split database 8 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These tariff rate modifications are essential for this analysis as suggested by the substantial differences between the tariff rates available in the GTAP 8 database, especially those implied for Jordan, Iraq, Lebanon, and Syria (Figure 1), and the updated tariff rates, presented by country, product, and source in Appendix Tables B1-B6. Since the GTAP tariffs attributed to Jordan, Iraq, Lebanon, and Syria are composite rates, they do not correspond to the actual trade profile of these countries. Therefore, the new tariff rates differ from the GTAP ones both because of differences in the tariff lines and trade composition. By contrast, the tariff information on Egypt and Turkey in the GTAP 8 database represents relatively accurately existing preferences (Figure 1). 3. Simulation design The pre-war efforts for deeper trade integration in the Levant are reflected in the pre-simulation analysis. Starting from the newly constructed database, the pre-simulation analysis implements the deep trade initiatives discussed by the Levant countries prior to the onset of the Syrian war in 2011. The context for these reforms and the shocks associated with each of these reforms are presented in section 3. 1. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The updated database from the pre-simulation analysis, which represents an integrated Levant in a peaceful alternative world, is the starting point for the simulation analysis of the Syrian conflict and the spread of ISIS as well as the disintegration of the deep regional trade ties. The design of the war and disintegration scenarios are presented in section 3. 2. 11 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the degree to which the global response should include a development element. We find that the average stood at around 10. 3 years at the end of 2015, with a median duration of 4 years, and significant sensitivity to a few situations. Such numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete protracted situations. To produce these numbers, we rely on the Population Statistics Database compiled and main- tained by UNHCR. The database records the number of “ persons of interest ” to UNHCR in each year since 1951 and for each situation, where a situation consists of a pair host-origin countries. The calculation of duration of exile is obtained under a no-turnover assumption, whereby a de- crease in the number of refugees for any given situation is fully attributed to exits from refugee status, while increases are assumed to be fully accounted for by new cases. Although such ap- proach tends to over-estimate the true duration of exile, the lack of individual-level data on regis- tration precludes refining the estimate further. Attempts to estimate similar statistics have been limited. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our analysis exclusively uses data from the 1951-2015 UNHCR Population Statistics Reference database (extracted September 18 2015). Data were provided for 173 countries: 77 percent of these data were based on individual refugee registration, 13 percent on estimates, 5 percent on combined estimation and registration, and 5 percent on other sources. The data are structured as follows: for each situation, the database records annual numbers of persons of concern, which comprise “ Refugees (including refugee-like situations) ”, “ Asylum seekers ”, “ Internally Displaced Persons ”, “ Returnees ”, “ Stateless ” persons, and “ Others of concern ”. A situation is a pair country of origin / country of destination. For example, Somali refugees in Kenya account for one situation, Somali refugees in Ethiopia for another, and South Sudanese refugees in Kenya for yet another. Furthermore, a situation is considered major if it involves more than 25, 000 people. It is referred to as protracted if it is major for at least 5 continuous years. The database, and therefore our analysis, is limited to refugees under UNHCR protection. It does not include asylum seekers, i. e. individuals who have sought international protection under the 1951 Convention but whose claims for refugee status have not yet been determined, and persons in “ refugee-like situations ”, i. e. individuals outside their country or territory of origin who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained (e. g., undocumented Rohingya originating from Myanmar 4 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "in Bangladesh). The data set we use does not cover the 5. 1 million Palestinian refugees who are under the man- date of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UN- RWA). The definition of Palestinian refugees in international law is distinct from other refugees. Palestinian refugees are people “ whose normal place of residence was Mandatory Palestine be- tween June 1946 and May 1948, who lost both their homes and means of livelihood as a result of the 1948 Arab-Israeli conflict ”. Importantly, their patrilineal descendants are also considered refugees regardless of citizenship (UNRWA 2009). For the purpose of the analysis, we do not include Internally Displaced Persons (IDPs), who are defined as “ persons who have been forced or obliged to flee or leave their home or place of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disas- ters, and who have not crossed an internationally recognized border ” (United Nations 2004). The categories “ returnees ”, “ Stateless ”, and “ Others of concern ” are also not included. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This allows us to determine the earlier possible date of arrival for each cohort of current refugees. As an illustration, we go back to the Somalia-Kenya situation. In 2015, the number of Somali refugees in Kenya amounted to 418, 844 persons. The first year such number was reached was in 2011. The no-turnover assumption implies that all current refugees have been in exile for 3 years. But in 2010, the number of refugees was 353, 208 persons. We conclude that the difference between 2010 and 2011 represents 74, 104 new refugees in 2010, who have hence been in exile for four years. In 2009, the number was at 310, 458: the difference between 2009 and 2010 (42, 750) are therefore people who have been in exile for five years, etc. We repeat the exercise for each year until the beginning of the crisis in 1991. This gives us a num- ber of new arrivals for each year since 1991. On this basis we can calculate average and median durations for this situation. Next, we aggregate all situations and consider one single “ global refugee population ”. We have for each year a number of people who arrived in a variety of situations and make up a global flow. We can hence calculate global average and median durations. For each year, we can also break down the flow across countries of arrival. We can construct similar lower-bound envelopes starting from any “ current year ” which we choose as a reference point. For example, we can apply the same protocols to evaluate average and median durations as of 1994 (the lower-bound envelope is depicted by the dash-dot line in Figure 3). This allows us to follow the variation of aggregate mean and median averages over time. 10 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Medium ‐ intensity, on the other hand refers to “ regular armed clashes between governments, government forces and insurgents. ” (IISS 2017) Within the first category, according to the Armed Conflict Survey in 2016 are Afghanistan, Syria, Somalia, South Sudan, and Iraq, while Myanmar, Central African Republic, Democratic Republic of the Congo, and Sudan are in medium ‐ intensity conflict. (Eritrea is not included in the Armed Conflict Survey in 2017, discussed in Annex). Minorities in Albania, Kosovo and Serbia are often the objects of discrimination but countries are not in conflict. A capsule summary of the security and social situation in each of the other countries included in this survey can be found in Annex 2. The survey illustrates how impractical return is today in the countries in conflict, such as Syria, Afghanistan, and Iraq, and voluntary return in large 36 Best practice and new methods in return policy, July 2017, http: / / www. bamf. de / SharedDocs / Meldungen / EN / 2017 / EMN / 20170504 ‐ emnjahrestagung ‐ rueckkehr. html Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Internally displaced women are extremely vulnerable to rape by armed men, “ including government soldiers and militia. ” Protection for them basically does not exist58. According to UNHCR, Somalia has over 1. 4 million internally displaced people. In the year 2016, some 24, 500 refugees and asylum ‐ seekers were registered in Somalia. Refugee return from Kenya began in 2014. Voluntary repatriation number is close to 40, 000 Somali nationals from 2014 to December 201659. Economic Opportunity According to Berlin ‐ based Transparency International, Somalia is one of the world ’ s most corrupt countries. Improved governance could enable Somalia ’ s economy to grow on the basis of its oil and gas reserves. Ongoing droughts continue to drive hungry and thirsty refugees to surrounding countries, and large parts of the population are in need of humanitarian aid. The agriculture sector contributes to over two ‐ thirds of its GDP while industry only makes up for 7 % in 201360. According to the IMF, Somalia has a very high youth 57 EIU Syria economy: Quick View ‐ Wheat harvest set to fall short of government forecast, July 2017 58 Human Rights Watch, Somalia Events of 2016, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / somalia 59 UNHCR, Somalia, http: / / reporting. unhcr. org / node / 2550? y = 2016 # year 60 CIA The World FactBook, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / so. html Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "48 unemployment rate, contributing to irregular migration and participation in extremist activities, including Al ‐ Shabaab. Joining militant jihadist group is viewed as another form of employment61. Social Services The lack of infrastructure and basic service hinders IDP settlements. On top of that “ urban areas are being overwhelmed with new arrivals62 ”. Access to basic needs, such as health and education, are unmet. South Sudan Security South Sudan ’ s civil war began in December 2013 and continues with serious abuses against civilians. A peace agreement was signed in August 2015 but the ceasefire was not achieved63. On May 25th, 2017, South Sudan President declared a ceasefire. According to the World Report by Human Rights Watch, South Sudanese “ government soldiers killed, raped and tortured civilians as well as destroying and pillaging civilian property during counterinsurgency operations in the southern and western parts of the country, and both sides committed abuses against civilians in and around Juba and other areas. UN Special Advisor on the Prevention of Genocide Adma Dieng said the ongoing violence had transformed into an “ ethnic war ” and warned of a “ potential for genocide64 ” On top of the precarious living situation, security and logistical challenges posed constraints to the delivery of much ‐ needed humanitarian assistance 65. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "According to Council on Foreign Relations, the estimated number of people killed since December 2013 is over 50, 000, and over 1. 6 million people are internally displaced66. Economic Opportunity South Sudan has abundant natural resources. Before its oil production fell sharply, the government relies on oil for its revenue. It also has very fertile soils and abundant water supplies. South Sudan has struggled with economic development since its independence and its economic conditions have deteriorated since January 2012 when the government decided to 61 IMF, Six Things to Know about Somalia ’ s Economy, April 11, 2017, http: / / www. imf. org / en / News / Articles / 2017 / 04 / 11 / NA041117 ‐ Six ‐ Things ‐ to ‐ Know ‐ About ‐ Somalia ‐ Economy 62 UNHCR, Somalia, http: / / reporting. unhcr. org / node / 2550? y = 2016 # year 63 Human Rights Watch, World Report, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / south ‐ sudan 64 Human Rights Watch, World Report, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / south ‐ sudan 65 UNHCR, South Sudan http: / / reporting. unhcr. org / node / 2553 66 Council on Foreign Relations (CFR) https: / / www. cfr. org / global / global ‐ conflict ‐ tracker / p32137 #! / conflict / civil ‐ war ‐ in ‐ south ‐ sudan Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "49 shut down its oil production67. UNHCR reported that in 2016, South Sudan ’ s economic situation deteriorated further and the cost of living rose exponentially68. Social Service South Sudan has very high mortality caused by AIDS and high risk of infection of diseases. Education expenditure is low and the literacy rate is also very low (27 % in 2009). South Sudan has little infrastructure. According to the CIA Factbook, there are approximately 200 kilometers of paved roads. Electricity is produced mostly by costly diesel generators. Goods and services are mostly imported from surrounding countries. 69 Sudan Security Similar to South Sudan, Sudan ’ s government forces have raped, killed civilians, and destroyed hundreds of villages. In September 2016 UN found that violence has displaced up to 190, 000 people and many of them are not accessible to humanitarian agencies 70. Government forces and armed rebels in Southern Kordofan and the Blue Nile continue to be engaged in armed conflict for the fifth year in 2016. Civilians in populated areas were subject to indiscriminate bombing, especially during March through June in 2016. Citizens also face arbitrary detentions, ill ‐ treatment, and torture. Sudan ’ s National Intelligence and Security Service is known for detaining activists, students, lawyers, doctors and those who are perceived to be needed in some capacity by the government71. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Many detainees are facing ill ‐ treatment. Females are subjected to sexual harassment by security officers. According to UNHCR, there are 2. 7 million people of concern in Sudan (includes refugees, asylum ‐ seekers, IDPs, returned refugees, returned IDPs, stateless persons, and other concern), a lower number than 2015. About 37, 000 refugees returned to Sudan in 2016. At the same time, there were over 2 million IDPs, over 420, 000 refugees and over 16, 000 asylum ‐ seekers in other countries. Economic Opportunity Oil output in Sudan has been low due to civil war, poor infrastructure, and low productivity. Compared to South Sudan, Sudan also has fewer oil resources; nevertheless, it still has abundant resources72. Sudan ranks 186th of 190 states in the World Bank ’ s Doing Business 67 CIA Factbook, South Sudan, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / od. html 68 UNHCR, South Sudan http: / / reporting. unhcr. org / node / 2553 69 CIA Factbook, South Sudan, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / od. html 70 UNHCR, Sudan, http: / / reporting. unhcr. org / node / 2535 71 Human Rights Watch, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / sudan 72 EIU Country Outlook, Sudan, June 19th 2017 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Government officials repeatedly banned opposition demonstrations, fired teargas and live bullets at peaceful protesters, shut media outlets, and prevent opposition leaders from moving freely75. According to UNHCR, there are over 2 million IDPs in the DRC, and over 450, 000 refugees. In year 2016, there were about 13, 000 returned refugees and 619, 000 returned IDPs. “ Dozens of armed groups remained active in eastern Congo, many of their commanders have been implicated in war crimes, including ethnic massacres, killing of civilians, rape, forced recruitment of children and pillage. 76 ” Economic Opportunity 73 CIA the World Factbook, Sudan, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / su. html 74 CIA, the World Factbook, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / cg. html 75 Human Rights Watch, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / democratic ‐ republic ‐ congo 76 Human Rights Watch, DRC, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / democratic ‐ republic ‐ congo Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9826 Despite the many simultaneous deprivations faced by forcibly displaced communities, such as food insecurity, inadequate housing, or lack of access to education, there is little research on the level and composition of multi­dimensional poverty among them, and how it might differ from that of host communities. Relying on house­hold survey data from selected areas of Ethiopia, Nigeria, Somalia, South Sudan, and Sudan, this paper proposes a Multidimensional Poverty Index (MPI) that captures the overlapping deprivations experienced by poor individuals in contexts of displacement. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 regards to who is poor, how poor they are, and the composition of their poverty. Based on the Alkire-Foster (AF) method, the index provides a summary measure of poverty for the population that can be disaggregated by displacement status and gender of the household head to analyze the variation in deprivations. The MPI can be further broken down by indicator to show the proportion of the population who are poor and deprived in each area. These features of the MPI can inform better policy responses, with interventions and programs targeting the most deprived communities and indicators with the highest headcount ratios. The paper proceeds as follows. Section 2 of the paper reviews some of the existing literature to provide the background and motivation for the analysis, including a summary of the different country contexts covered by the data analyzed in this paper. Section 3 outlines the Alkire- Foster method and the selected dimensions and indicators used to construct the MPI, followed by Section 4, which introduces the data. Section 5 presents the findings, first for results at the national level and then results disaggregated by displacement status. Section 6 analyzes differences in multidimensional poverty by gender of the household head to improve understanding of the gendered aspects of multidimensional poverty in these contexts. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Section 7 compares the MPI results with monetary poverty, with some concluding remarks discussed in Section 8. 2. Background and literature review 2. 1 Poverty and forced displacement By 2019, there were about 51 million IDPs across the world, most of them – 46 million – displaced by conflict and violence, with around five million displaced due to natural disasters (IDMC 2020). According to estimates by UNHCR (2020), the number of refugees reached over 20 million as of the end of 2019. While in many cases, conflict and natural disasters have been temporary, resulting in fluctuations in the number of people fleeing their homes in any given country, the global number of IDPs and refugees has grown almost every year over the last two decades. UNHCR estimates that the number of refugees has doubled over the last ten years. Nearly all IDPs live in low- and middle-income countries, and many have experienced secondary displacement. Overall, about half live in urban areas, with one-fourth in major urban areas (i. e., populations exceeding 300, 000). Since almost all IDPs are in developing countries, governments are often resource-constrained in terms of providing assistance and access to services, and in some cases, government authorities may be a cause of displacement (World Bank Group 2020). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Children and adolescents ’ long-term exposure to households with a more equalized division of domestic labor, as a result of violent or political conflict, warrants further investigation. 2. 3 Country contexts The countries with subnational regions covered in this study, using data from 2017 or 2018, are Ethiopia, Nigeria, Somalia, South Sudan, and Sudan. All are located in Sub-Saharan Africa, have undergone or are currently involved in armed conflict, and are affected by environmental issues such as drought, famine or flooding. Despite some commonalities, each faces a unique set of social, political and economic challenges, which cannot be accurately covered in this study. However, to contextualize the findings, a brief introduction of the country context is presented alongside the poverty estimates by the $ 1. 90 / day measure and the global Multidimensional Poverty Index (MPI). 3 3 An international measure of acute multidimensional poverty, aligined with the 2030 Agenda, that captures deprivations in health, education, and living standards for more than 100 countries (Alkire and Jahan 2018; Alkire, Kanagaratnam and Suppa 2020). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The cross-dimensional poverty cut-off is defined as k = 50 %, with those deprived in half or more of the weighted indicators identified as multidimensionally poor. 4. Data Data on forcibly displaced populations are scarce, with many household surveys excluding refugees and IDPs from the sample framework. To ensure that MPI results are representative of these communities and that they can be disaggregated for comparative analysis, an initial review of possible data sets was conducted. Feasibility was determined based on the availability of sufficient sample sizes for forcibly displaced persons for quantitative analyses, as well as inclusion of many of the indicators (on health, education, living standards, etc.) 14 A household is deprived if the respondent reports feeling moderately or very unsafe when alone at home, walking alone after dark, or walking around during the day. In Sudan, the indicator on the ‘ feeling safe from crime and violence when at home ’ was not available, and the indicator only considers answers to the questions on safety when walking alone. 15 Unprotected dug well, unprotected spring, carts with tank, tanker-truck, surface water, or other are considered as unsafe water sources according to international guidelines. See https: / / washdata. org / monitoring / drinking-water. 16 Pit latrine without slab, bucket, hanging toilet, and no facility (open defecation) are considered as unimproved sanitation facilities according to international guidelines. See https: / / washdata. org / monitoring / sanitation. 17 According to the ILO definition, those who did not participate in employment in the last four weeks (and have no work to return to) are actively looking for work and are available to start, or those currently waiting to start work are classed as unemployed. See https: / / www. ilo. org / ilostat-files / Documents / description_UR_EN. pdf. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 5. 2 Composition of poverty Unpacking the headline numbers further, important patterns emerge about the composition of multidimensional poverty among forcibly displaced and host communities in these countries. Overall, the censored headcount ratios (proportion of people who are poor and deprived in a given indicator) are lower among non-displaced communities than among refugees and IDPs, but there are large differences in which indicators are the most salient in different countries. The indicators with the largest difference between the two populations are bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria. These findings reinforce the need for policies and programming that take into account the measured experiences of IDPs and refugees. In this way, the MPI can function both as tool to monitor, track, and bear witness to the lived experiences of forcibly displaced communities, as well as advise on evidence-based interventions that address the needs of the local population. Figure 1 shows the censored headcounts of each indicator in Sudan ’ s MPI, with large differences appearing by displacement. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "MPI of IDP communities is considerably higher than that of the host communities, so it follows that the censored headcount ratios display a similar gap. The difference by indicator is particularly noticeable in the living standards dimension, where the electricity, cooking fuel, housing, and bank account indicators show over 34 percentage point difference between the censored headcount ratios for the IDP and host communities. Figure 1. Censored headcounts of each indicator in the MPI, by displacement status in Sudan (2018) Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). As the Sudanese sample surveys IDPs living in the Abu Shouk and El Salam camps, we must understand these deprivations with the background that these settlements were created as emergency and crisis responses rather than durable, long-term solutions (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019). Although Sudan does have a national electric grid that supplies electricity to the urban and peri-urban areas of the nearby city El Fasher, IDP communities living in the camps report limited connection to the city ’ s electricity supply, reflected in the high deprivations in the electricity and cooking fuel indicators. The ad-hoc construction of dwellings in the two camps explains why 71 % of the IDP households in Abu Shouk and 65 % in El Salam live in tukuls or other permanent mud or wood structures (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019: p. 50), both of which register as unimproved housing types. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Bank branches in El Fasher have limited capital for small businesses as their headquarters in Khartoum regard the area as too great a risk and IDPs themselves as riskier investments than their host community peers (UN- HABITAT 2009: p. 8). Exposure to unclean cooking fuels and inadequate housing can lead to poor health outcomes, while lack of access to electricity and a bank account further excludes individuals from labor market integration and livelihood opportunities that would empower forcibly displaced persons to overcome their multiple, overlapping deprivations. Clearly, displacement status puts individuals at a greater risk of poverty than their host community neighbors, and we can unpack those risks in greater detail using the MPI. Results can also be broken down to show the percentage contribution of each indicator to multidimensional poverty (see Figure 2). Among refugees in Ethiopia, lack of a bank account is the largest contributor to poverty, while among host communities, the largest contributor is years of schooling. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "16 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 6. Gender differences in multidimensional poverty Next we examine differences in multidimensional poverty outcomes by the gender of the household head. Existing literature points out the limitation of household level MPI analysis in masking the intrahousehold distribution of deprivations, and thus being less sensitive to gender based differences in individual outcomes within the family unit, which might lead to underestimation of inequality and gender gaps (Espinoza-Delgado and Klasen 2018; Franco 2017; Klasen and Lahoti 2020, Rodriguez, 2016). However, as the MPI identifies poverty at the household level, our initial analysis focuses on disaggregated results by the gender of the household head. 19 We acknowledge that this approach has several limitations since most women reside in male-headed households, and the composition of households can change after displacement due to separation of family members, and widowhood. Regardless, the analysis at the household level remains relevant given the high prevalence of female-headed households that emerge after displacement, with the analysis showing large differences across countries between households based on the gender of the head. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "E Nigeria Somalia Sudan Host community Refugees Host community IDP Host community IDP Host community IDP No earners 40 % 51 % 62 % 50 % 32 % 64 % 7 % 45 % Remittance recipients only 7 % 8 % 76 % 69 % 16 % 37 %-- Female single earner 12 % 52 % 23 % 37 % 35 % 70 % 24 % 55 % Male single earner 8 % 23 % 4 % 14 % 24 % 50 % 8 % 39 % Majority female earners 3 % 57 % 9 % 28 % 9 % 28 % 15 % 45 % Equal contribution 10 % 24 % 17 % 16 % 23 % 63 % 8 % 41 % Majority male earners 5 % 16 % 15 % 19 % 20 % 52 % 9 % 39 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Overall, the results show that besides gender, displacement status and the number of household contributors plays a key role in the identification and level of poverty. In comparison with female-headed non-displaced households, more female-headed displaced households are classified as multidimensionally poor. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "26 value of consumption flow of durable goods. 22 While monetary poverty can measure temporal resource holdings, multidimensional poverty, as a more comprehensive measure, includes chronic and exacerbating sources of poverty. This difference explains the existence of mismatches between individuals identified as monetary versus MPI poor, which are often more prominent in poorer countries (Evans et al 2020). This section examines these differences in the contexts of displacement. Table 9. Percentage of the sample in each poverty category: Rows sum to 100 % Non-poor by both measures Only Monetary Poor Only Multidimensional Poor Monetary and multidimensional poor Ethiopia 38 % 23 % 12 % 27 % N. E Nigeria 13 % 69 % 4 % 15 % Somalia 20 % 32 % 14 % 34 % Sudan 33 % 47 % 4 % 17 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). This table presents the distribution of households in each of the categories in the columns. Thus, each row adds up to 100 %. South Sudan is excluded from this analysis as monetary data is not available for the country. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The lack of overlap might be explained by the relatively recent start of the displacement situation in 2014, when Boko Haram appeared in the north-eastern part of the country. Pape et al (2018) identify two groups of IDPs in this situation: one group representing 74 % of the IDP population that was more engaged in wages and non-farm business before displacement, and another group representing about 26 % of the population, that had significantly more unemployed women. Most of the displaced populations from the first group live in host communities with good access to basic services such as sanitation and water, and safety nets. However, they are disproportionally more likely to be female-headed households and lack access to education, health services, and may face more stringent labor-market barriers. In other words, this group has relatively better housing conditions, but may lack short-term resources that reduce their consumption expenditure. 22 In summary, expenditure in these three categories is computed based on the quantities and prices of a selected list of items in each category. See more details about the computation of the consumption aggregate in Appendix A of the Somali Poverty Profile (Pape et al, 2017). A similar procedure was followed in the other countries of analysis. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "% 83 % 28 % 9 % 14 % Equal contribution 93 % 81 % 83 % 16 % 17 % 17 % Majority male earners 78 % 56 % 57 % 19 % 15 % 16 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 8. Conclusion This paper contributes to the literature by analyzing multidimensional poverty among refugees and internally displaced populations. We observe that forcibly displaced communities are poorer than host communities in each of the five countries ’ sub-populations covered in the surveys, with the difference in incidence between displaced and non-displaced population ranging between 15 and 19 percentage points in South Sudan and Somalia to over 30 percentage points in Ethiopia and Sudan. Displaced communities also experience greater deprivations in nearly every indicator, although there is significant variation in which indicators are the most salient, with having a bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria showing the largest differences between the two populations. The results also indicate gender differences in the experience of multidimensional poverty, with female-headed households more likely to be poor than male-headed households in most of the countries. In addition, displaced households headed by women have a higher incidence of poverty and MPI than non-displaced female-headed households. Particularly, female-headed households in camps have higher multidimensional poverty and intensity compared to their counterparts living outside camps. Dissaggregating further, we find heterogeneity among de facto and de jure female heads. This variation lends itself to further research questions about Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 1 ‐ Number of Forcibly Displaced Persons (1951 ‐ 2015) Source: Constructed from UNHCR population data (http: / / popstats. unhcr. org / en / time_series). Note: 2015 data are mid ‐ year and lower than end of year data. 5 http: / / www. unhcr. org / en ‐ us / figures ‐ at ‐ a ‐ glance. html. 0 10000000 20000000 30000000 40000000 50000000 60000000 70000000 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Figure 4 ‐ Research on Migration, Refugees and IDPs (% of total hits) Source: Authors ’ estimations based on Econpapers, SSRN and Google Scholars searches. This phenomenon can be explained by essentially two factors. The first relates to the humanitarian ‐ development nexus. For the longest time, refugees and IDPs remained the quasi monopoly of humanitarian organizations whose mandate is essentially the humanitarian protection of refugees and IDPs. These organizations are not typically staffed by economists and analysists but by field workers and lawyers. There was, therefore, little demand for hard economics on forced displacement for a very long time. This is changing as development organizations typically staffed by economists have started to work on forced displacement situations. The second factor relates to lack of good data. As we will see in the data section, data collection of mobile populations is complex and the main organizations in charge of data collection of refugee and IDPs data are humanitarian organizations that do not necessarily have the complex skills required for issues like sampling, questionnaire design and data analysis and have a duty to protect data by mandate. This, in turn, has resulted in very few micro data that would be both of good quality and accessible to researchers. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Hence, the authors introduce the notion of decision weight ߨ to weigh the importance that people give to different probabilities so that the expected utility function becomes ܧሺ ܷ ሻ ൌ ෍ ߨ ൣ ݌ ൫ܧ ௝ ൯൧ ܷ ሺݔ ௝ ሻ ௡ ௝ ୀ ଵ One can also study decision making in a game theory setting. Expected utility can be looked at as a one ‐ person game but the value added of game theory in the context of forced displacement relates to multiple ‐ person games. Suppose that actions taken by individuals under conflict situations affect the actions of others and ultimately one ’ s own action (the classic prisoner ’ s dilemma for example). This is what game theory is good in modeling and it could provide valuable contributions to the study of collective behavior under forced displacement situations (see for example Zeager and Bascom, 1996). New branches of economics such as neuroeconomics and behavioral economics, which combine elements of psychology and neuroscience with elements of economics, offer alternative new avenues to the construction of utility models in a forced displacement context. For example, neuroeconomics developed a hierarchical module oriented approach (Sanfey et al., 2006) whereby individuals take decisions in a hierarchical manner where multiple systems of specialized processing modules transform specific inputs into outputs in organized decision stages. This process can be observed in people ’ s brains with scans and can be modeled empirically using specifically designed questionnaires. This literature shows how the short ‐ term decision process is different from the long ‐ term process in terms of how we value potential Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 people may also gain from conflict in terms of income and wealth and this may explain why some people do not move. The defining attributes of the alternative choices are very different from any other model and the task of economics is to understand what these defining attributes should be. In terms of independent variables, “ push ” factors become more important than “ pull ” factors in forced displacement models. The intensity of a conflict may be more important than the income opportunities in potential destination areas. In addition to the classic socioeconomic variables, risk aversion, stress, anxiety, other traits of personality and behavioral factors in general have to be well understood and measured. Hence, one could think of four essential blocks of independent variables including individual or household socioeconomic characteristics, “ push ” factors, “ pull ” factors and behavioral factors. Also, access to and dissemination of information related to the conflict in the place of origin but also in the potential places of destination may be crucial for people to make choices. This is where social psychology, behavioral economics and neuroeconomics may offer insights into such choices. Forced displacement data are also unusual in their form. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Deciding on whether to flee or not to flee a conflict (the migration choice) can be an individual or household choice and risk coping strategies may include temporary migration, shuttling between places, migration of only selected members of the households or migration of the whole household. This implies that individuals may stay put throughout the period observed, join or leave the household during the period, or have several episodes of out and immigration. Households may decide to leave and come back several times. In econometric terms, this means that longitudinal data may be left and right censored and have spells within. They are therefore the most complex set of panel data possible and require particular treatment of data and modeling. Survival or duration models can usually accommodate many of these complexities but it is very rare to find similar data sets used in published articles. Collecting such type of data is also not obvious, particularly if conflict is intense and survey areas cannot be reached. This is an issue where empirical economics could provide a real contribution by defining the optimal data format and adapting panel models to this format. Macro models Macroeconomics has attempted to model forced migration using models borrowed from the trade and economic migration literature such as the gravitational model (Echevarria and Gardeazabal, 2016) or used other macro models to test the impact of refugees on trade (White and Tadess, 2010). A more recent body of work is adapting trade models to take into account stochastic shocks in a dynamic framework (Cameron et al., 2007; Artuc et al., 2008). These are rational expectations models that are able to model the unpredictability of shocks, and recent work has tried to adapt these models to the context of violent Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Some data challenges common to refugees and IDPs Irrespective of the specific questions related to refugee and IDP data, there are also general questions that refer to the forcibly displaced in general and that are distinct from data collection of regular populations or even migrant populations. We explore here selected issues including sampling, unit of analysis, welfare measurement, multidimensional aspects, and the measurement of risks and vulnerabilities. Sampling. As mentioned, the UNHCR is really the only statistical agency for refugees and the UNHCR registry the only population census. As for any other populations, sampling requires the preparation of a master sample that derives from the population census. With various degrees of knowledge and accuracy, this is also what happens with refugees. However, the master sample is more difficult to construct than for regular populations because refugees live in camps and outside camps and are diluted in a host population with different types of arrangements. Some households rent, others stay at relatives ’ places, other live in makeshift shacks and others stay in camps. The information available in the UNHCR registry (the census) can also be quite inaccurate, as already discussed, and the degree of accuracy changes for different groups of refugees. Stratification by urban and rural areas, a typical approach in sampling, may mean little for a population that is mostly in urban areas whether in camps or outside camps. Refugees and IDPs are also mobile and more difficult to track over time than other populations. Several statistical institutes worldwide have developed methodologies to track and measure mobile populations such as herders, nomads or homeless people. However, tracking refugees from other countries has been in the Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Lebanon. It was the obvious step forward in overcoming these problems and the answer to the increasing demand for evidence around the Syrian crisis. The study benefitted from a unique and unprecedented set of data. The UNHCR registry data in Jordan and Lebanon were among the better quality registry data available worldwide and the UNHCR also conducted home visits in Jordan that, at the time of the study, covered over a third of all refugees. There were also sample surveys in both Jordan and Lebanon that were small in size but representative of the population present in the registry. The home visits and the surveys included questions on income and expenditure that could be used for the welfare assessment. Using these data, the study addressed ten questions defined as follows: 1) Who are the refugees?; 2) How different are refugees from “ regular ” populations?; 3) How poor are refugees?; 4) What are the main predictors of refugees ’ welfare and poverty?; 5) How vulnerable are refugees from a monetary and non ‐ monetary perspective?; 6) Do poverty and vulnerability statuses overlap?; 7) How effective are refugee assistance programs?; 8) What is the potential for alternative policies?; 9) How does welfare compare across countries and data sets?; 10) How transferable are Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These findings were also echoed in the context of other developing countries such as India (Allard et al., 2022) and Zimbabwe (Mabugu, Maisonnave, Henseler, Chitiga-Mabugu, & Makochekanwa, 2023). In the MENA economies ’ context, the evidence is considerably sparcer. Early work on the impact of the COVID-19 pandemic on labor markets in the MENA region re- lied on high-frequency phone surveys and highlight important job losses among wage workers and an uneven impact across industries (Krafft, Assaad, & Marouani, 2021, 2022). Providing evidence from Labor Force Surveys in the Islamic Republic of Iran, Dang and Salehi-Isfahani (2023) find that the pandemic exacerbated the pre-existing low participation of females in the labor force. Wahby and Assaad (2023), on the other hand, focus on the impact of the pandemic on Syrian refugees in Jordan and find a divergence in job finding and separation rates of Syrian refugees relative to their Jorda- nian hosts after the onset of the pandemic. Focusing on cross-border commuters in the West Bank and Gaza, Adnan and Etkes (2022) find that undocumented commuters benefited relative to their documented peers after the pandemic, as Israeli policies inadvertently created incentives for employers to favor the former. This sharply con- trasts the results by Borjas and Cassidy (2020) on the impact of the pandemic on immigrants in the United States. The rest of this paper is organized as follows. Section 2 provides background information on labor markets in the West Bank and Gaza, as well as background in- formation on the COVID-19 pandemic and government responses. Section 3 describes the data. Section 4 discusses our methodology. Section 5 presents the main regression results and investigates heterogeneous effects. Section 6 provides robustness checks. Finally, we provide concluding remarks in Section 7. 4 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, permit-holding status is neither sufficient for, nor necessary to, working in Israel. As of 2019Q4, just under a fifth of West Bank residents had the right to work in Israel and the occupied territories, but almost a quarter among them were not commuting across the border for work. The vast majority of such individuals are holders of Israeli or Jerusalem IDs. Conversely, among those who do commute to Israel and occupied territories, 17 % do not hold valid permits or IDs. Likewise, permit-holding status does not logically affect the formality status of the commuter. The frequent border crossings between the West 2This is derived from authors ’ own calculations using the 2016 Jordan Labor Market Panel Survey. 3It is worth noting that the LFS is representative of the residents of the West Bank and Gaza, whose work may not lie in the country. 6 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Bank and Israel and occupied territories strongly suggest that commuters would be differently affected during the pandemic when border closures were enacted. Another unique feature is the presence of a large refugee population in the West Bank and Gaza. However, it is important to note that refugees in this context are de- fined quite differently from other contexts. Here, not only the individuals immediately displaced are considered refugees, but also their patrilineal descendants, even if born many decades later. In particular, the LFS dataset follows the United Nations Relief and Works Agency (UNRWA) definition of refugees, which is “ persons whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948, and who lost both home and means of livelihood as a result of the 1948 conflict, ” as well as “ the descendants of Palestine refugee males, including adopted children ” (UNRWA, 2023). Consequently, most refugees are indistinguishable in socio-economic outcomes and labor market behavior from non-refugees. However, residence in refugee camps does make a significant difference. As of 2019Q4, 5 % of the West Bank ’ s residents live in refugee camps, as do 14 % in Gaza. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "After a short respite in November, Israel imposed a third lockdown from December to February the next year. Gradual easing continued throughout March. During this lockdown, vaccination was rolled out, including to commuters with valid permits. Given this context, we expect the main shock to occur in 2020Q2 in the West Bank, with negative effects of smaller magnitudes to persist over the next quarters. In Gaza, we expect another major shock in 2020Q3 that carries over to Q4. We expect the commuters ’ outcomes to instead track events in Israel and occupied territories more closely, with a major shock in 2020Q2, recovery in Q3, and another dip in Q4. 3 Data 3. 1 Data sources and definitions We employ data from the Labor Force Surveys (LFS) of the West Bank and Gaza, collected by the Palestinian Central Bureau of Statistics and prepared by the Economic Research Forum. The surveys are conducted on a quarterly basis, covering periods from 2000 onwards. The LFS is meant to represent all households whose ordinary residence is in the West Bank and Gaza, though their place of work need not be. The LFS is representative at the region level (respectively of the West Bank and Gaza), as well as at the level of locality types (urban, rural, and refugee camps) (Palestine- Labor Force Survey, LFS, 2021). Importantly for the purpose of our analysis, the data have a panel dimension, en- abling the study of labor market transitions. The sample rotation scheme is described 9 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "prior to the pandemic, a period characterized by less distinct time trends in the labor market. Since the focus of the paper is on the impact of COVID on labor market stock and dynamics, we also restrict the sample of interest to include only prime-aged working adults (aged 20 to 59). The dataset contains standard variables expected of a labor force survey, including those denoting employment, unemployment, and inactivity. It also contains information on the intensive margin of the labor supply, including hours worked and full-time and part-time status. Information on employment sector, industry, contract status, health insurance coverage, and mode of work (distinguishing between employees and self-employed, for example) is also available, allowing us to construct indicators of formality and to differentiate different modes of employment. Information on occupation is also available, but only at the level of 2-digit ISCO-08 classification. This information is enough to distinguish between white- and blue-collar occupations but it is not enough to observe additional relevant pandemic-related job characteristics such as the degree of contact with the public. 3. 2 Descriptive statistics Figure (1) tracks the evolution of labor market stocks in the West Bank and Gaza respectively over time. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A significant increase in non-participation among men can be clearly seen in the West Bank in 2020Q2, mostly at the expense of the private infor- mal sector. The labor market then quickly bounces back. By 2020Q3, labor market stocks in the West Bank appear indistinguishable from pre-pandemic periods. Gaza, on the other hand, experienced three consecutive quarters of depressed employment from 2020Q2 to 2020Q4. Non-participation spiked twice, first in 2020Q2 and then in 2020Q4, corresponding respectively to the initial lockdown orders and the subsequent outbreak in Gaza. Recovery also appears to be slow and uneven. Figure (3) shows the labor market flows. We exploit the specific panel structure of the LFS dataset, described in Section 3. 1 by focusing on one cohort of the same respon- dents who were surveyed in 2019Q1, 2019Q2, 2020Q1, 2020Q2, and finally 2020Q4. This cohort of individuals allows us to observe labor market transitions into the pan- demic; to compare with a period over the same quarters in 2019; and, finally, to observe their recovery outcomes in 2020Q4. Overall, Figure (3) shows two labor markets with high levels of churning. On average, 29 % of individuals in the sample would change their labor market states after just one quarter. These churns are especially prominent between informal employment and unemployment, and in Gaza between unemployment and non-participation. The figure also illustrates the significant differences between the West Bank and Gaza in labor market dynamics, differences already observed in the labor market stocks presented in Figure (1). In addition to the significant flows between unemployment 11 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the West Bank and Gaza. Out of all the estimated coefficients for each period for both outcomes (job loss and job gain), we only find a negative effect on job gain in 2019Q4, which is very small in magnitude (1 percentage point). Taken altogether, the results presented in this section bolster our confidence that we are correctly identifying the effects of the pandemic shock on labor market outcomes. Figure 12: Placebo effect on labor market flows Notes: The figure shows the output of a placebo test with a set-up analogous to Figures 6 and 9. We perform the same regression as specified in Equation (2). Our sample includes data from 2018Q2 to 2020Q1 and assumes that the pandemic started in 2019Q2. Therefore, the post-pandemic period refers to the quarters between 2019Q2 to 2020Q1. The analysis is restricted to men aged 20-59. 7 Conclusion This paper examines the effect of the pandemic on labor markets in the West Bank and Gaza using quarterly labor market data provided by national labor force surveys. With a focus on men ’ s labor market outcomes, this paper sheds light on how labor markets in the West Bank and Gaza adjusted to the COVID-19 shock examining adjustments at the extensive (employment) and intensive (hours of work) margins. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1 POST-CONFLICT TRANSITIONS WORKING PAPER NO. 16 Population Size, Concentration, and Civil War. A Geographically Disaggregated Analysis * Håvard Hegre Centre for the Study of Civil War, PRIO (CSCW) Clionadh Raleigh CSCW, PRIO & University of Colorado at Boulder Abstract Why do larger countries have more armed conflict? This paper surveys three sets of hypotheses forwarded in the conflict literature regarding the relationship between the size and location of population groups: Hypotheses based on pure population mass, on distances, on population concentrations, and some residual state-level characteristics. The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events. The analysis covers 14 countries in Central Africa. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper develops a statistical method to analyze this type of data. The analysis confirms several of the hypotheses. World Bank Policy Research Working Paper 4243, June 2007 The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post- Conflict Transitions Project can be found at http: / / econ. worldbank. org / programs / conflict). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in these papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http: / / econ. worldbank. org. * Contact author: Håvard Hegre; CSCW, PRIO, Hausmanns gate 7, N-0187 Oslo, Norway. Email: hhegre @ prio. no. Thanks to Joachim Carlsen for writing a program to create the dataset used in the analysis, to Siri Aas Rustad for research assistance, and to Kristian Gleditsch, Anke Hoeffler, Pat Regan, Mike Ward, Nils Weidmann and Jen Ziemke for valuable comments. The research has been funded by the Research Council of Norway, grant no. 163115 / V10. WPS4243 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 population density across a state, the ecological inference issue is alleviated as we directly test the propensity of any population group to experience a conflict. Through disaggregation, we may succeed in supporting explanations based on variables such as the distance from the capital and the overall size of the country's population if we know at which locations conflicts occur. If conflicts are located mainly at some distance from countries'capitals, we might infer that large countries have more conflicts because of the difficulties of projecting governmental power. If they are located in population concentrations irrespective of location relative to the capita, other explanations should be sought. The paper makes use a new dataset called ACLED (Armed Conflict Location and Events Dataset) to allow for this type of disaggregated analysis. The dataset currently codes the location of all reported conflict events in 14 countries in Central Africa in the 1960 – 2004 period. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper suggests some adaptions to a statistical method to allow for analyzing data at this level of analysis. Related to the size of populations is their distributions. The Democratic Republic of Congo, for instance, is not only characterized by being enormously large, but also shows tremendous variation in population densities. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Proposition 6 Concentration and Dispersion: The risk of civil war events at a location increases more strongly in local population concentrations in locations distant from the capital of countries. 2. 5 Residual State-Level Mechanisms We have pointed out a set of location-specific factors, each of which imply a positive relationship between country size and national-level risk of armed conflict. But it is not certain that such location-specific factors are the only relevant ones. The size of a country itself may affect risk over and beyond what is implied by sheer population size, distance, and population distributions. If the economies of scale with respect to defense are sufficiently large, the risk of conflict events at a location at a given distance from the capital may be lower the larger is the country (Collier & Hoeffler, 2002). Moreover, large countries may rather be more conflictual than small ones for several reasons. Fearon & Laitin (2003: 81), for instance, note that insurgency will be favored when potential rebels Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Proposition 7 State-Level Effects of Population Size: The risk of civil war events at a location varies with the size of the population of the country to which the location belongs, controlling for the local effects. 3 Research Design 3. 1 Unit of Analysis To distinguish between the different theoretical statements regarding how population sizes, population concentrations and locations relate to risk of conflict, we need to investigate exactly where conflicts occur. We have created a dataset using a Geographic Information Systems (GIS) program which converted large territories into smaller portions of 8. 6 km x 8. 6 km, totaling 74 square kilometers. Each of these grid squares are our units of observation (we will refer to them as squares). This approach is similar to that of Buhaug & Rød (2006), with two important differences. First, their squares are much larger (100x100km). Second, they code the dependent variable considerably more crudely than is done in the ACLED dataset described below. Buhaug & Rød (2006) use the `scope'and `location'variables in the Uppsala / PRIO dataset. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Although much more suited to geographically disaggregated analysis than other datasets, this location dataset has some limititations, It does not record changes over time in the center location and extent of conflicts, and it reports the total extent of the conflict zone without distinguishing between areas that saw repeated and extensive fighting and those that only experienced scattered activities or individual events far from the center of the conflict. 3. 2 Disaggregated Dependent Variable: ACLED The ACLED dataset (Raleigh & Hegre, 2005) deals with these problems. The dataset takes the PRIO / Uppsala Armed Conflicts Dataset as its point of departure. The dataset is limited to events within conflicts that fall within the Uppsala conflict definition; conflicts involving two parties, one of which is a government, and fighting resulting in at least 25 battle deaths. 3 ACLED is designed to parse out both the temporal and spatial actions of rebels and governments within civil wars. 3See the PRIO / Uppsala Armed Conflict Data codebook for more information (Strand, Wilhelmsen & Gleditsch, 2004). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "14 The fundamental unit of observation in ACLED is the event. Figure 2 illustrates the ACLED data for Central Africa for the 1980s and the 1990s. Each location of a conflict event is represented by a symbol. In several of these locations, multiple events occured over the periods. Events always involve two actors – a rebel group and a government – and are coded to occur at a specific point location and on a specific day. Most of the events are battles, but the dataset also records other activities. The dataset includes information on and distinguishes between six types of events: battles resulting in no change of territory, battles resulting in a transfer of territory to the rebel actor, battles Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 resulting in government forces recapturing rebel held territory, establishment of a rebel base or headquarters, rebel activity that is not battle related (e. g. presence or the killing of civilians), and territorial transfers. The dataset consists of 4, 145 battle events for the 1960 – 2004 period. In the present analysis, we use 2, 530 of these. The remaining events were dropped as they either were in countries not included in the analysis, or because information was missing for one of the key variables. Each conflict event is associated with geographic coordinates and a date of occurrence. This information allows for spatial and temporal modeling of conflict events. The dataset used in this article covers 14 countries in Central Africa. 6 of them had a conflict in the 1960 – 2004 period according to the Uppsala / PRIO Armed Conflict Dataset (Gleditsch et al., 2002): Angola, Burundi, Republic of Congo (Brazzaville), Democratic Republic of Congo (Zaire), Rwanda, and Uganda. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The remaining 8 did not have a conflict: Cameroon, Central African Republic, Equatorial Guinea, Kenya, Malawi, Tanzania, and Zambia. 3. 3 Handling temporal and spatial dependence Both the squares and the conflicts events are obviously not fully independent – all events within one conflict are related to each other as an action in one location leads to a later retaliation by the opposing party or to further advances in proximate locations. Events in one conflict may also affect the likelihood of other conflicts, such as the spillover of the conflict in Rwanda into Eastern DRC. The statistical model employed to analyze these data must handle the dependence between observations. We will do this by explicitly modeling the probability of an event in a location as a function of preceding events in the same and in adjacent squares. We can do this since we know both the precise date and the precise geographic location of each event. We use an adaption of the calendar-time Cox regression model presented in Raknerud & Hegre (1997) for this purpose. In Cox regression, the dependent variable is the transition between `states of nature'-- the transition from peace to conflict in a square. A central concept is the hazard function, () t λ, which is closely related to the concept of transition probability: () t t Δ λ is approximately the probability of a transition in the Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The observations of positive events contain more information than the non-event observations We therefore sample asymmetrically: We sample all of the transition events and 1. 0 % of the non-transition events. 3. 4 Disaggregated Independent Variables Local level data on land, population, and elevation is available in the geospatial format of raster files with a resolution of 1km. Using Geographic Information systems (GIS), attributes from raster and point data are associated with the grid square in which they lie. In this way, spatial data is georeferenced to a location that is defined by the grid cell. This process results in a data structure in which each row has within it combined information on a square defined by the grid, the national level information in which is it located, and () () () ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ ∑ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ = = ∉ = t X t X t d d j j p j R i d j j p j w w t β β 1 1 exp exp at out breaks war a | square a in war Pr Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "18 the local data on physical geography and population from the raster data. These data can then be imported into statistical programs for analysis. We aggregate all data up to a grid of 8. 6x8. 6km squares. Each grid square is assigned attributes of the country it is in along with information from data disaggregated to the level of the individual squares. Figure 3 illustrates this grid as a fictive country somewhat smaller than the average size in our dataset (50x50 squares, or 430x430 km) with a fairly representative but stylized population distribution. The country has three major cities, one of which is the capital, and two smaller ones. A rebel group has its headquarters at the Eastern border. The ACLED data for the Central African conflicts were aggregated up to the 8. 6x8. 6km squares and merged with information on other explanatory variables aggregated to the same level. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 Distance from Rebel Group Headquarters We coded the location of the headquarters of the rebel groups participating in the conflicts under study, and calculated the distance from each square to the most proximate rebel group headquarters (we do not know a priori which rebel group or government that will act in a particular square). As the `distance from capital'variable, it was coded as the distance in terms of squares and log-transformed. Border Square We coded squares as border squares if a national border runs through it. Such squares belong to more than one country and are not straightforward to code. We coded national- level information for border squares according to the following rule: A border square was considered to belong to the country that was most frequent among the eight neighboring squares. In tie cases, we assigned nationality randomly between the tied countries. Interaction country-square population This variable was created to test the population settlement pattern hypothesis. It is an interaction between population count at a location (square) as a portion of the country's total population. Road type Road type is a variable by ESRI that is available in the Digitial Chart of the World Data. It is a high resolution dataset at 1: 1, 000, 000 scale and consists of arcs which indicate road mass. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "A number of different road indicators are available and we choose road line type to use in the analysis. Road type is defined by the following: The reference category (0) points out squares with dual lane / divided highways, other primary roads, or road connectors within urban areas (types 1 or 8 in the ESRI dataset). The second category include secondary roads (type 2), and the third combines squares with informal or tertiary roads (tracks, trails or footpaths) or no road registered at all (types 3 and 0, respectively, in the ESRI dataset). Figure 4 overlays the types of roads in the original dataset before our recategorization. The shaded area represents the portion of Africa for which we code Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 6832 This paper presents findings from the impact evaluation of the Economic Empowerment of Adolescent Girls and Young Women (EPAG) project in Liberia. The EPAG project was launched by the Liberian Ministry of Gender and Development in 2009 with the goal of increasing the employment and income of 2, 500 young Liberian women by providing livelihood and life skills training and facilitating their transition to productive work. The analysis in this paper is based on data collected during two rounds of quantitative surveys in 2010 and 2011, the second of which was conducted six months after the classroom-based phase of the training program ended. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Strong impacts are found on the employment and earnings outcomes of program participants, relative to a control group of non-participants. The EPAG program increased employment by 47 percent and earnings by 80 This paper is joint product of the Poverty Reduction and Economic Management Unit, Africa Region and the Social Protection and Labor Unit, Human Development Network.. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at schakravarty @ worldbank. org. percent. In addition, the impact evaluation documents positive effects on a variety of empowerment measures, including access to money, self-confidence, and anxiety about circumstances and the future. The evaluation finds no net impact on fertility or sexual behavior. At the household level, there is evidence of improved food security and shifting attitudes toward gender norms. These results reinforce the highly positive feedback received from focus group discussions with program participants. Finally, preliminary cost-benefit analysis indicates that the budgetary cost of the EPAG business development training for young women is equivalent to the value of three years of the increase in income among program beneficiaries. These preliminary results provide strong evidence for further investment and research into young women ’ s livelihood programs in Liberia. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1. Introduction Sub-Saharan Africa is the youngest region in the world, and the world ’ s fastest-growing. Between 2010 and 2025, the number of people between 15 and 24 will grow to 250 million – a net increase of nearly 50 percent. Over the next decade, roughly one million young people will enter the labor market each month in Sub-Saharan Africa. However, these young people often enter the labor market too early and unprepared. Although access to education is growing, illiteracy remains high and schooling low: among the 32 Sub-Saharan African countries in the Barro-Lee (2010) data set, nearly 40 percent of women aged 15 and above have received no education at all; and the most recent (2007-2011) statistics in the World Bank ’ s Edstats data reveal that female literacy is less than 60 percent, on average. 1 This lack of preparedness contributes to a growing problem of youth unemployment. Quantifying the level of unemployment is bedeviled by lack of data and measurement issues. Household and labor force surveys throughout Africa usually record unemployment rates of less than 10 percent, 2 but those figures belie the extent of underemployment and vulnerability. Those same surveys indicate that the vast majority of working adults have insecure work in the informal sector, on the family farm, or in less- productive or unremunerated labor. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In 2003, almost 60 % of young girls and 40 % of young boys had no formal schooling (ILO / UNICEF 2005). Data from the Demographic and Health survey shows that more than 40 percent of adult women have no education, compared to fewer than 20 percent of men, while 23 percent of women and 44 percent of men have some secondary schooling (DHS 2007). Happily, access to education is rising rapidly, especially for girls: according to the Liberian labor force survey from 2010, the ratio of girls to boys enrolled in primary school has risen from 72 in 2000 to 90 in 2009. Enrollment levels and sex ratios are lower among older children and youth, as they become increasingly engaged in 1 Both are unweighted averages; Barro-Lee comprises 32 countries with data from 2010; Edstats comprises 43 countries with data from 2007-2011. 2 Defined as without work, available for work, and actively looking for work (LISGIS 2010). 2 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "household and productive work. Just over one-third of young (15-24-year-old) Liberians are in the labor force. Young women are more likely than young men to be out of the labor force because they are engaged in household duties (30. 4 percent v. 18. 9 percent), and young men are more likely to be out of the labor force because they are still in school (75. 8 percent v. 63. 0 percent) (LISGIS 2010). Given their initial disadvantage relative to their male peers, and the sources of this disadvantage in social norms, market failures, and poorly functioning institutions, adolescent girls may require targeted policy and program efforts to achieve better outcomes. However, as in many other post-conflict situations, emergency skills training and public works programs in Liberia have targeted male youth ex- combatants, likely reinforcing rather than reducing adolescent girls ’ disadvantage. The few skills training programs for adolescent girls, run largely by NGOs, have focused on traditional female skills (such as sewing, soap production, tie-dyeing) for which the market is already well-supplied. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Programs designed to enhance employment among young people can focus on the supply side of the labor market, including skills training programs for both wage employment and entrepreneurship training; or programs to augment demand, such as wage subsidies, public works, and community service programs; or programs to help the labor market clear, such as job search assistance and placement services. In addition, it may be that the constraints facing young people are not in the labor market itself, but in other markets, such as for credit. The vast majority of jobs programs have focused on the supply side: training programs make up about 79 percent of over 600 cases included in the World Bank ’ s Youth Employment Inventory database. 3 Although rigorous and general evidence of success is limited, it seems that successful skills training programs share a few key features: they are responsive to local market conditions, they provide more than just technical skills in a specific area (including, for example, “ life skills ”), and they include ancillary services that alleviate other constraints preventing successful labor market integration (e. g., access to credit). Among the most celebrated are the Jovenes programs that provide demand-driven technical training, plus social skills that help in the labor market, plus internships. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our evaluation explicitly investigated these spillovers by including household-level indicators such as food security and attitudes of the household head toward gender norms. Our results show strong impacts on economic outcomes, including large and statistically significant increases in employment and earnings of EPAG participants. We show mixed results on empowerment- related outcomes, and very little evidence of spillovers on non-participants. Self-assessed measures of self-confidence show huge gains, as does ownership and control over monetary resources such as savings. The remainder of the paper is organized as follows: Section 2 describes the EPAG project including some of its innovative design features and implementation details. Section 3 reviews the methodology of the evaluation and Section 4 presents results on the three groups of outcomes discussed above: economic, empowerment, and spillovers. Section 5 includes a short discussion of cost-effectiveness. Section 6 presents a series of robustness checks and Section 7 concludes with a discussion of next steps and policy implications. 2. The EPAG Project The EPAG project is part of a larger Adolescent Girls Initiative (AGI) administered by the World Bank with support from the Nike Foundation and the Governments of Australia, the United Kingdom, Norway, Denmark, and Sweden. Launched in Washington DC in October 2008, the AGI was spearheaded by President Ellen Johnson Sirleaf, who signed on to undertake the initiative ’ s first pilot project in Liberia. The Liberian pilot was launched in March 2010 and has served as a role model to seven subsequent pilot projects in Rwanda, South Sudan, Nepal, Afghanistan, Haiti, Jordan, and Lao PDR. Under the global AGI, young women and adolescent girls are given a package of skills training and complementary services in order to facilitate their successful transition to employment. In the case of EPAG, the intervention consisted of a six month phase of classroom-based training, followed by a six 4 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The curriculum included entrepreneurship principles, market analysis, business management, customer service, money management, and record-keeping. The EPAG program was implemented by four NGOs who were selected by the Liberian Ministry of Gender and Development through a competitive bidding process: the Community Empowerment Program (CEP), Liberia Entrepreneurial and Economic Development (LEED), the International Rescue Committee (IRC), and the American Refugee Committee (ARC). Two of these organizations (ARC and IRC) further subcontracted to four Liberian NGOs. 4 The service providers were responsible for developing training curricula, identifying training venues, 5 making arrangements for childcare services, assisting with the mobilization of the nine target communities, and participating in the recruitment of training participants. The EPAG program differed from many training programs in a number of ways. First, performance bonuses were awarded to training providers that successfully place their graduates in jobs or micro- enterprises. The bonus was the last payment that the service providers received under their contracts. These were paid about 12 months after the start of training, or around the same time as the midline survey. Second, a variety of contests and competitions were also held among EPAG trainees (such as attendance prizes, quizzing contests, business plan competitions, etc.). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Third, the EPAG program was designed around the girls'needs: service providers held both morning and afternoon sessions, to accommodate the participants'busy schedules; trainings were held in the communities where the girls reside; and every site offered free childcare. Fourth, frequent and unannounced monitoring visits by MoGD staff ensured that the service providers created and maintained a high-quality learning 4 There are: National Adult Education Association of Liberia (NAEAL), Community Empowerment Sustainable Program (CESP), EduCare, and Children ’ s Assistance Program (CAP). 5 A total of 19 training venues were used during the first round of training. They were chosen with the following considerations in mind: 1. Girls ’ safety, so that the buildings are not so isolated or otherwise dangerous, raising security concerns for girls. 2. Conducive atmosphere for learning, spacious and sanitary with access to water and latrine facilities. Reasonably outside community noise concentration. 3. Proximity to community center and to security posts such as police depots. 4. Accessible to girls from various parts of the community. 5 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "environment. Any issues discovered during these monitoring visits were brought to the attention of service providers and resolved swiftly in conjunction with the project coordination team at MoGD. Eligibility: The EPAG program was targeted to young women who: i) were age 16 to 27, ii) possessed basic literacy and numeracy skills, iii) were not enrolled in school within several months prior of the program initiation, and iv) resided in one of nine target communities in and around Monrovia. 6 These eligibility criteria stemmed from the project's objectives to reach young women at an early enough age to significantly improve the trajectory of their working years, to focus on girls who already had the basic literacy and numeracy skills needed to succeed in the labor market, and to avoid incentivizing applicants to drop out of school. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "stipend money, and they were formed into small groups or\"EPAG teams\", each with a coach or mentor, to foster support networks and boost attendance. 3. Methodology 3. 1. Research design The impact evaluation of the EPAG project uses a randomized controlled trial, in which eligible applicants to the program were randomly assigned to participate in one of two cohorts (or “ rounds ”) of training. The treatment group is defined as those who were offered a space in the first round of training and the control group comprises those assigned to the second round. Selection into the training rounds was performed on a computer (using Excel) and was stratified by the track choice of the applicant (job skills versus business development skills), community, and service provider. Data were collected using three quantitative household surveys (baseline, midline, and endline) and two sets of qualitative focus group discussions (one after each round of training). A timeline of the impact evaluation is depicted in Figure 1. During both the baseline and midline surveys, the head of the household in which the EPAG participant was residing was also interviewed, in order to examine potential spillover effects of the program on non-treated household members. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The household head interviews also provided an opportunity to gather useful household-level information (including assets, housing characteristics, and household head characteristics) to be used as control variables in our analysis. The baseline survey was conducted before participants were informed of the outcome of the randomization, and the midline survey was conducted one year after the baseline survey, one month after completion of the first round of training and before the start of the second round. The results in this paper are based on a panel data set that includes data from the baseline and midline surveys as well as administrative and monitoring data from the program implementation team. Note that although the midline data collection occurred just one month after the completion of the intervention, the intervention itself includes 6 months of post-training follow-up; hence the midline survey captures outcomes of the first cohort 7 months after they exited the classroom-based training. This timing was necessary to prevent too long of a delay between cohorts; the second cohort of trainees started immediately after the midline survey was completed. Future analysis using endline data will examine the trends in outcomes of both groups after Round 1 completion, but will not involve comparison against a pure control group of non-participants. 3. 2. Sample and attrition Following the communications and outreach campaigns in each of the nine target communities, during which 2, 106 young women were originally recruited to be a part of the EPAG program, 8 a randomized 8 The original recruitment effort fell short of the target of enrolling 2500 participants. Hence, before the second round of training, another recruitment effort was launched and an additional 617 girls were enrolled. These girls are not included in the impact evaluation since they were not subject to the initial random assignment. 7 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "assignment process was conducted to assign the trainees to the first or second round of training. 9 Of those entered into the random selection, 1273 young women were assigned to the first round of training, with the remaining 808 to serve as a control group (the control group would participate in the second round of training starting in July 2011). Of the 1273 assigned to treatment, 118 women were not found or chose not to participate after they were selected. 10 In order to fill at least some of these slots, 39 young women from the control group were randomly issued as replacements, resulting in a modified control group of 769 individuals. In the end, 1191 young women entered the first round of training. 11 The assignment process and all post-randomization modifications are summarized in Figure 2. Table 1 reports the baseline and midline survey response rates leading to the sample used for the analysis in this paper. The target sample for both the baseline and midline survey consisted of the original 2106 EPAG recruits, of which 1989 were successfully interviewed during the baseline survey. 12 At midline, 1736 were interviewed, including 56 who were not interviewed at baseline. For our analysis, we drop individuals who were excluded from the randomization or who were manually re-assigned from control to treatment as replacements after the randomization. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These 25 were included in the surveys but have been dropped from the analysis because they were not assigned randomly. 10 The reasons given for not entering the training included: 1) they were back in school, 2) they had moved to a distant location, 3) they were seriously ill, 4) they had found full-time work, 5) they were not interested or able to make such a big time commitment, or 6) they could not be located despite numerous efforts. 11 This includes 1155 of the original 1273 assigned to treatment, plus 36 out of the 39\"replacements\"- young women from the control group who were offered a chance to be reassigned to round 1. 12 Note that a previously released baseline report for this evaluation was based on 2008 observations. However, after cleaning, 4 were found to be duplicate observations and 15 were not found in the program data and hence were dropped from the midline analysis. This leaves 1989 baseline observations that are included in the midline analysis. 13 These are cases in which the adolescent girl was interviewed but the household head interview was not conducted because the head was not available, could not be found, or declined to take part. 14 This suggests that the loss of the 118 young women who were selected but declined to take part, and the 60 who started but did not complete the program, does not bias the results. 8 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Yit is the outcome of interest for individual i and time t. Treati is an indicator which is equal to 1 for treated individuals and 0 otherwise. Postt is an indicator equal to 1 for midline observations and 0 for baseline. Xit is a vector of controls at baseline (t = 0), including individual characteristics (education, age, current pregnancy, marital, parental, and orphan status) and household characteristics (sex of household head and household size). β1 is the coefficient of interest that defines the “ impact ” of the program on individuals in the treatment group. The model also includes dummy variables for the communities where the program was implemented (and where the trainees resided) as well as the program track (business skills or job skills) to which the respondent was assigned. Finally, in order to control for household wealth, we compute an index based on household asset ownership at baseline using multiple component analysis, similar to the method described in Filmer and Pritchett (2001). After constructing the index, which includes thirteen household assets and six indicators of housing conditions, we control for the quintile of household ’ s overall asset position in all regressions. We augment this basic specification with an individual fixed effects model and find that the results are almost identical. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For binary dependent variables, we estimate both linear probability models and probit models analogous to the one given in equation (1), again finding that the results are nearly identical across all outcomes. We cluster the standard errors by classroom in all models, and to account for the interaction effect for variables such as (PosttxTreati) we additionally follow Ai and Norton (2003) to correct the standard error for interaction terms in probit models. 3. 4. Baseline characteristics Table 2 presents baseline balance tests for survey respondents from the treatment and control groups. 15 In addition to confirming the success of the randomization, as judged by the very few significant differences between the two groups, the table provides a vivid profile of the average EPAG participant. The study population has an average age of 23 years, with 55 % falling between 20 and 24 years. The majority have never been married, while 29 % are cohabiting with a partner and only 5 % are married. The majority of the study population has started or completed high school, which is consistent with the program ’ s target group of young women with basic literacy and numeracy, and with the program ’ s goal not to encourage girls to drop out of school. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Thirty-eight percent of the sample was already engaged in at least one income-generating activity (IGA) at baseline. This is consistent with the national figures from the 2007 DHS survey, which found labor force participation rates of 34 % for women aged 15-19 and 49 % for women ages 20-24. It is also consistent with the Liberian 2010 Labor Force Survey, which found labor force participation rates of 25 % for women aged 15-19 and 47 % for women aged 20-24. For the purposes of this study, to be consistent with program objectives and the Liberian context, our definition of income-generating activity encompasses the full range of activities through which people earn money, including paid employment, either formal or informal, and self-employment in small business or through petty trade. The most common types of IGAs reported at baseline were petty trade, including 15 The balance tests are run on the same sample as will be used in the impact analysis in Section 4, that is, the subset of individuals for whom we have a panel. Balance tests run on the full sample of baseline survey respondents, regardless of whether they also participated in the midline survey, confirm the same findings. A report summarizing the balance tests on the full sample, including comparisons to nationally representative data, is available upon request from the authors. 9 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "street vending (48 % of those with at least 1 IGA), food processing for sale, including baking, cooking, and drying (16 %), and home production of crops, livestock, and fish (11 %). It is important to note that the EPAG program was not targeted toward the most vulnerable segments of Liberian society, but rather toward young women with enough education to be able to benefit from a training program of this nature. Based on comparisons from the 2007 CWIQ (Core Welfare Indicators Questionnaire) survey, the average EPAG participant is more educated, more literate, more likely to be engaged in an income-generating activity, and owns more assets than the average Liberian women of the same age group: mobile phone ownership was high (63 %), as was the proportion reporting that they had some money of their own (79 %). Even compared to other similar residents of Monrovia, the EPAG participants are better educated and have higher income. A strong sense of female empowerment at baseline emerges from the sections of the survey instrument having to do with self-confidence and agency. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "sexual encounter. Finally, about 10 % of study respondents have experienced a forced sexual encounter in their lifetimes, which is consistent with the national figure of 13 % for this age group (DHS 2007). 17 Table 2B presents baseline balance tests for the household characteristics of respondents in the study sample. More than 40 % of households are headed by females, and the average household has slightly fewer than five members. The mothers of EPAG respondents tended to have very low education levels (almost 60 % had never been to formal school), while the fathers had more variance in their education (about a quarter had no schooling, but over 60 % had at least some secondary education). In both treatment and control households, a high proportion of school-aged children are in fact enrolled in school. Less than half of young people aged 13-30 in study households have any employment. Housing conditions are also similar across experimental groups. Tables 2A and 2B include a representative, but not exhaustive, list of indicators that were tested for balance by the authors. We conclude that the presence of very few significant differences between the individual or household characteristics between the two experimental groups indicates a high degree of internal validity for the study. 3. 5. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, the program itself was designed to fit within the young women ’ s lives, specifically with regard to their employment, education, and childcare duties. Training schedules were flexible, to allow participants to continue with their pre-existing educational and income-generating activities (and many participants did report continuing with these activities) and free childcare was provided. Hence, at least for these three dimensions of life, there would not have been much incentive to change one ’ s behavior prior to starting the program. While these explanations do not erase concerns about anticipatory behavior, they at least mitigate them. The generalizability of these results is also limited by the differences between the EPAG target group and the population of young women in Liberia. First, a high proportion of adolescent girls and young women in Liberia are illiterate or have very low literacy, while the participants recruited for the EPAG 17 Gender-based violence questions were administered in line with international ethical protocols, with additional informed consent procedures and referral mechanisms as needed. 18 See Ashenfelter (1978). 11 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "project had to meet a basic minimum literacy level in order to qualify for admission to the program; whereas only half of the 15-29-year-old female respondents to the nationally-representative Core Welfare Indicators Questionnaire (CWIQ) survey report that they can read and write (LISGIS 2007). Fewer than two percent of the girls in the EPAG study responded that they had no education, which is much lower than in the CWIQ survey. Second, the majority of adolescent girls and young women in Liberia reside in rural areas, whereas the survey participants were residing in urban and peri-urban areas, where access to basic social services may be much more improved. Consequently, the results are not representative of adolescent girls and young women in Liberia overall. The results are neither indicative of the average Liberian girl and young woman; nor are they indicative of the average Liberian girl or young woman in the project communities. They are only indicative of the average girl and young woman who are part of the EPAG project. Finally, many of the variables that we examine in this study are measures of self-assessed levels of satisfaction or belief. These are entirely subjective variables, and are subject to significant measurement error. There is considerable evidence that the wording of these questions can affect the answers given, as can the order in which the questions are asked. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "cope with the emergencies that arise all too often. 20 To facilitate successful coping and provide a safe place to save, the EPAG program assisted each participant to set up a savings account at a local bank if she did not already have one. Table 5 presents midline survey results that indicate that the treatment group were nearly 50 percentage points more likely to have savings than the control group, and were saving on average LD 2500 (nearly US $ 35) more than the control group. EPAG graduates were also twice as likely as the control group to have outstanding loans (six percent v. three percent), and have loans from formal lenders (five percent v. two percent), 21 although the overall rate of obtaining credit remains extremely low. 4. 3. Empowerment The Adolescent Girls ’ Initiative is based on the hypothesis that livelihood and life skills training for young women will improve their lives in more than just narrowly-defined economic dimensions. In addition, evidence is increasing that these soft skills are also essential for success in employment (Borghans et al. 2008). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Surveys of employers consistently find that more than hard, technical skills, employers value these harder-to-quantify skills of honesty and integrity, problem-solving ability, work ethic, communication skills, the ability to work productively with others, responsibility and dependability (Blom and Hobbs 2007). The AGI program has focused on the development and measurement of these softer attributes that matter for employment as well as those that matter more to the individual, such as self-confidence and empowerment. Despite the challenges of measuring such subjective outcomes, the survey instruments included panels of questions designed to elucidate a nuanced picture of the personality and psychosocial characteristics that are most relevant for labor market success. Table 6A presents results on empowerment and decision-making. The first series of questions have to do with control over resources, spending decisions and earnings. Respondents were asked how much control they had over how to spend their own earnings; also, whether they had money of their own for basic uses that they alone could decide how to use, without having to ask for permission. The EPAG baseline survey found that respondents reported a high degree of control over resources even before the program started. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In contrast, the self-assessed entrepreneurial score is based on questions asking how well the respondent believes she could perform a series of six tasks related to starting or running a business, and can be considered a task-oriented measure of self-efficacy. The aggregate measure of entrepreneurial ability increased by roughly nine percentage points among EPAG beneficiaries relative to those in the control group, equivalent to a quarter of a standard deviation. Enhancing participants ’ self-confidence to perform these tasks was one of the main immediate objectives of the BDS training program. Table 6B summarizes the results on a series of questions on attitudes and self-confidence that were added during the midline survey only (hence no panel analysis is possible). EPAG graduates report a more positive attitude: they feel more in control and more comfortable, and they have greater confidence in their own business abilities as well as in their personal and social lives. They are also more confident than the control group in their personal relationships with spouses and partners, consistent with the findings in Table 6A. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Both treatment and control group respondents report equally high confidence in their ability to return to school “ should [she] decide to do so. ” These findings from the quantitative impact evaluation complement the results from a set of qualitative focus group discussions that were held with participants at the end of the 6 months of classroom training. Twenty-five percent of the trainees from Round 1 participated in a total of 34 focus group discussions that covered a variety of topics including their satisfaction with the program and their empowerment in both social and economic realms. The trainees overwhelmingly voiced a high degree of satisfaction with the training, and trainers commented on how the motivation or “ seriousness ” of the participants grew over the 6 month period. The trainees credited the transport allowance and free childcare in particular as features that facilitated their full participation; as one trainee commented, 22 Questions adapted from the Adolescent Self-Regulation Inventory, developed and validated for youth in the United States by Moilanen, 2006. The questions were revised and translated into an 11-item for the Liberian context. In the future, we plan to conduct basic testing on this scale on internal consistency and reliability. 17 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "their earnings on household expenses than men. Most of these studies have focused on adult women, specifically married women with children. It is not known whether the same holds true for young women, who may have other spending priorities, have less experience in managing households, and have younger children. Given the large increases in employment and earnings documented above, the EPAG program serves as a good setting to examine these types of spillovers. The evaluation included detailed interviews with the heads of the household in which EPAG participants were residing. The purpose of the household questionnaires was precisely to examine the hypothesis that investing in young girls would benefit her household. A secondary hypothesis was that EPAG participation may change gender-related attitudes in the participants ’ households. Household data was collected for 1601 out of the 1622 individuals who were interviewed at both baseline and midline; this same sample of 1601 individuals serves as the basis for both the individual and household level analysis in this paper. The estimated impact of the program on a broad range of household outcomes is summarized in Tables 8 and 9. Panel A of Table 8 examines the household size. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The household and family dynamics in Liberia, as in other African settings, can be complex, with families often sending their children to live with relatives who are more able to provide for their schooling and basic needs. Young parents, in particular, often leave home to migrate for work, leaving their children back home with relatives until they are able to establish themselves and send for their children. If the economic success of the EPAG participant allowed her to bring non-resident family members into her household (including but not limited to her own children), then overall household size may have been expected to increase as a result of the program. This does not appear to have happened, at least in the short term. The results in Table 8 show that overall household size was not affected by the program. It is possible that the increase in earnings due to EPAG was too small, or too short-lived, to have induced the kinds of migrations described above. Other measures of household well-being, including food security and asset ownership, reflect shorter- term investments that might be influenced by the economic success of EPAG participants. Panel B of Table 8 shows the impact of EPAG on a broad range of food security measures. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Household food security was measured using two methods: dietary frequency of high-value protein-rich foods, and two subjective questions on food shortages adapted from USAID ’ s FANTA questions (Coates 2007). On two of the four of the dietary frequency questions and on both of the food shortage questions, the treatment groups ’ dietary situation improved as a result of the EPAG program. Weekly consumption of fish and meat rose significantly by four percentage points in treatment households (from a high baseline value of 84 % for meat / chicken and 90 % for fish), and weekly consumption of dairy and eggs did not change significantly. Household heads report worrying less about insufficiency of household food supplies, and the reported incidence of household members going to bed hungry also decreased in treatment households relative to control. Combined, the impacts across these indicators portray a situation of improved food security and dietary composition, consistent with the hypothesis that the increased earnings of the EPAG participants were spent in part on food. 20 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "any change in the amount of time spent on domestic work, 25 we have no evidence that these shifting norms have affected the division of labor in practice. Further research would be needed to disentangle the issues of changing gender norms in theory versus practice, and attitudes toward norms in general versus those at play in one ’ s own household. 5. Cost Effectiveness Although the first task of any evaluation is to demonstrate the effectiveness of an intervention – that is, whether or not the program actually has a measurable and attributable impact – this is not enough to recommend the program to policymakers. This requires also that the program can show that it is worth spending scarce public resources to do it. Ideally, a program worth doing will be both effective and cost- effective. One can measure cost-effectiveness in terms of the number of physical outputs produced or outcomes achieved, e. g. the number of people employed per dollar spent, or one can measure achievements in terms of the value of the benefits acquired relative to the amount of money spent. In the case of the EPAG, the unit cost of training in Round 1 was roughly $ 1200 for the Business Skills track and $ 1650 for the Job Skills track. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The EPAG Job Skills track consisted of more hours of training, and required purchasing practical equipment for each trade area. Some Job Skills trainers, especially those with more specialized skills and experience, were also more costly. The estimated unit costs cover trainer salaries (EPAG has mostly college-educated trainers), training materials (not including curriculum development), training venue rental, administrative and overhead costs of the training provider, childcare costs, event costs (job fairs, etc.), stipends to mentors, trainee transport allowances and completion bonuses. The costs also cover the withheld incentive payment to the training provider, based on how many trainees find employment. Although high relative to most developing-country budgets, these costs are well within the range of the Jovenes youth training programs implemented in Latin America (cf. Ibarrarán and Rosas 2009). The Jovenes programs were estimated to cost between $ 700 and $ 2000 per participant, depending on the country (Betcherman 2007). Not only were the costs of the EPAG program within international norms, the program itself is also cost-effective. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6. Robustness Checks 6. 1. Sample attrition Many challenges were encountered during survey implementation, due to the context – the densely populated and impoverished communities in and around Monrovia are difficult settings in which to find and track respondents — as well as the transience of a young study population. Despite vigorous efforts to track and interview each individual in the sample, a certain amount of survey attrition was expected. As the survey response rates in Table 1 show, 1622 (or 80 %) of the individuals in the study sample were successfully interviewed in both the baseline and midline surveys. Another 305 respondents were interviewed at baseline but not at midline and hence are not in the panel used for the analysis in this paper. 27 This survey attrition, while not much higher than other program evaluations in Africa, may cause concern that the results of this evaluation are biased, especially if the loss to follow up is correlated with individual characteristics that might affect the outcomes. To address this concern, Table 10 presents regressions on the likelihood of panel inclusion, that is, the likelihood of being interviewed at both baseline and midline. The first column indicates that treated individuals are significantly more likely than control to have been interviewed twice. This result persists even after controlling for individual characteristics and community dummies. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Columns 2, 3, and 4 show that age, school attendance, and employment status at baseline are all correlated with survey attrition. To further investigate whether these characteristics lead to differential attrition between the treatment and control groups, we interact treatment with particular characteristics at baseline. Columns 5 and 6 show that, conditional on being in the control group, the most likely predictor of attrition is having a child. By itself, being a mother does not predict attrition, but when interacted with treatment, we see that control group mothers are more likely to have dropped out of the panel than their treated counterparts. Employment at baseline, while positively correlated with attrition, does not differentially affect treated and untreated individuals. Because the differential attrition between treatment and control groups may bias our results, we use Inverse Probability Weighting (IPW) as outlined in Wooldridge (2002) to adjust the estimates of our key outcomes, using the inverse probability of inclusion in the panel as a probability weight. As a first step, we use the probit model in Column (6) of Table 10 to regress the likelihood of being observed twice on baseline individual characteristics, including those likely to affect attrition, such as employment and parental status. In the second step, we use the inverse of the predicted values from that probit model as probability weights to redo the difference-in-difference regressions for our key outcomes of interest. This method gives more weight to the individuals with the highest chance of attrition, giving them more influence on the estimate of the impact than those with a low probability of attrition. The results are reported in Table 11. The results show a high degree of similarity between the original (unadjusted) and the adjusted estimates. Across all outcomes, the point estimates and standard errors vary only slightly. 27 Of these 305 cases, nine are dropped from the attrition analysis because the household head was not interviewed at baseline, hence the household level control variables are not available. 23 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The EPAG program, which delivered six months of classroom-based skills training followed by six months of job placement support for either self or wage employment, led to a 47 % increase in employment and 80 % growth in earnings, relative to a randomly selected control group of non-participants. The program ’ s Business Skills track had markedly higher impacts on employment and earnings than the Job Skills track, which focused on wage employment. These impacts vary somewhat but remain consistently positive and significant across almost all communities, educational backgrounds, and wealth levels. The highest impacts were obtained for those in the middle of the wealth distribution, and for girls with higher educational levels, which is consistent with the program ’ s initial screening for young women with basic literacy who would be able to make use of a classroom-based skills course. These strong impacts on employment and earnings translated into positive impacts in other realms of the participants ’ lives. Our results show striking improvements in various empowerment measures, including access to and control over monetary resources, including savings, where the program led to a sizeable difference of 35 USD in savings between treated and control individuals. The study also documents significant improvements in a wide range of subjective outcomes including measures of worry, life satisfaction, self-regulation, self-confidence, and self-perceptions of social abilities. In the area of fertility and sexual behaviors, the results paint a somewhat more nuanced picture. The EPAG program had no discernible effect on the desired number of children or on the actual number of children, conditional on having any children. There was a weak reduction in the likelihood of having any children, and a stronger increase in the likelihood of being pregnant, even after excluding those who were pregnant at baseline. On net, these impacts appear to cancel each other out, consistent with a hypothesis that treated individuals waited until the end of the EPAG program to become pregnant. The third main area of outcomes looks at household-level measures. Consistent with the wide body of literature on the benefits to the household of women ’ s increased resources, the results show a 24 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, an endline survey was conducted after the second round of the EPAG program as per the timeline depicted in Figure 1. Examination of this endline survey data will permit a descriptive analysis of the outcomes of the first group of trainees 12 months after they completed the EPAG program, as well as examination of the outcomes of the second batch of trainees. The second round included not only the control group from this impact evaluation but also newly recruited participants who were offered brief basic literacy and numeracy training program prior to program entry. Work is already underway to design and implement the third round of EPAG, with a substantial redesign of the Job Skills track, an emphasis on reaching younger girls with lower literacy, and expansion to communities outside of Monrovia. If the high success rates found in this study are replicated for these future cohorts, the EPAG program should serve as a model for policy makers in Africa and the world seeking to improve lives and livelihoods of all youth, male and female. 25 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Religious School Enrollment in Pakistan A Look at the Data Tahir Andrabi1 Pomona College Jishnu Das The World Bank Asim Ijaz Khwaja Harvard University Tristan Zajonc Harvard University Abstract Bold assertions have been made in policy reports and popular articles on the high and increasing enrollment in Pakistani religious schools, commonly known as madrassas. Given the importance placed on the subject by policy makers in Pakistan and those internationally, it is troubling that none of the reports and articles reviewed based their analysis on publicly available data or established statistical methodologies. This paper uses published data sources and a census of schooling choice to show that existing estimates are inflated by an order of magnitude. Madrassas account for less than 1 percent of all enrollment in the country and there is no evidence of a dramatic increase in recent years. The educational landscape in Pakistan has changed substantially in the last decade, but this is due to an explosion of private schools, an important fact that has been left out of the debate on Pakistani education. Moreover, when we look at school choice, we find that no one explanation fits the data. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While most existing theories of madrassa enrollment are based on household attributes (for instance, a preference for religious schooling or the household ’ s access to other schooling options) the data show that among households with at least one child enrolled in a madrassa, 75 percent send their second (and / or third) child to a public or private school or both. Widely promoted theories simply do not explain this substantial variation within households. 1 Corresponding Author: Tahir Andrabi (tandrabi @ pomona. edu). This study would not have been possible without the enthusiasm and continuous support we received from Tara Vishwanath. Charles Griffin first encouraged us to look at the data. We thank Veena Das, Shehla Andrabi, Sehr Jalal, Ritva Reinikka and Carolina Sánchez for their encouragement and to Hedy Sladovich for her excellent editorial suggestions. The paper has also benefited from comments by Ismail Radwan, Naveeda Khan, Shahzad Sharjeel and Shanta Devarajan. The research department of the World Bank provided funding for this study through the Knowledge for Change trust fund. The findings, interpretations and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent. Working papers describe research in progress by the authors and are published to elicit comments and to further debate. Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized WPS3521 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 The only publicly available sources of data to document patterns of enrollment and available educational options for Pakistani families are household-based surveys. These are the official 1998 Census of Population (Government of Pakistan) 3, the 1991, 1998, and 2001 rounds of the Pakistan Integrated Household Survey4, and a 2003 census of schooling choice conducted by our research team. The fact that three sources use different definitions of madrassa enrollment, and were collected at different times by individuals with very different institutional affiliations provides independent verification of enrollment estimates and allows us to determine the sensitivity of our results. The household data tell us whether a child is enrolled full-time in a madrassa, but not whether a child goes for an hour on any given day to study the Quran. Therefore this data does not confound full-time with part-time attendees — a child who attends a public school during the day and a madrassa in the evening is recorded as enrolled in a public school. This is an important distinction since parents might use a modicum of madrassa or mosque based education to teach their children about religion. Consequently, if we contrast these household-based numbers with numbers from establishment-based reports, discrepancies can arise. From virtually any policy perspective, including evening quran classes in enrollment figures seems misguided. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Regrettably, until now almost all enrollment numbers cited have been based on establishment surveys which do just that. These data sources show that around 200, 000 children were enrolled full-time in madrassas before 2001. Since 2001, our school census suggests that these numbers may have increased somewhat, although the experience varies across districts. To put this number in context, total primary enrollment (grades 1-5) in public and private schools stood at 17. 4 million in 2003 (Government of Pakistan, Ministry of Finance, 2003). The choice of madrassa schooling viewed as either the percentage of eligible children or the percentage of enrolled children, is statistically insignificant for the average Pakistani household. Enrollment in madrassas accounts for approximately 0. 3 percent of all children between the ages of 5 and 19. Given that the overall enrollment rate for this age group is roughly 42 percent, this represents less than 0. 7 percent of all enrolled children, an order of magnitude less than the 33 percent cited by the International Crisis Group report (2002). 3 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 to provide statistics for private versus public enrollment. 4The PIHS is the equivalent of the widely used Living Standard Measurement Surveys (LSMS) implemented in various countries. See http: / / www. worldbank. org / lsms for extensive notes on the 1991 PIHS. See also www. statpak. gov. pk for information on the census and the Federal Bureau of Statistics data. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 3. 1 Data Sources We use three different types of data to verify our estimates and determine how sensitive they are to changes in definition and the year of the survey. Two sources are nationally representative, but date from 2001 or before, the third is data from a census of households carried out by the authors in 2003 as part of a project on educational choice. The first source is the “ long ” form of the population census in 1998, which is a large sample-based survey with information on enrollment. This survey is representative at the level of the district and region (rural or urban) and provides comprehensive coverage of the entire country. 7 We use this data to examine enrollment patterns across districts. The second type of data, based on household surveys, are different rounds of the Pakistan Integrated Household Survey (PIHS) carried out in 1991, 1998 and 2001. While the data is not as extensive as the census, it contains detailed household information on schooling and income, and has been used extensively by researchers both in Pakistan and the United States. Finally, we use the census of schooling choice among households that our research team conducted in August 2003 (referred to as the project on “ Learning and Educational Achievement in Punjab Schools ”, or LEAPS). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "11 There may be grounds for skepticism about these estimates for madrassa enrollment. Since the data were collected prior to 2001, geopolitical changes after September 11 could have led to greater madrassa enrollment. In addition, the household-based survey faces the usual problems of accurately estimating a low-probability event — although enrollment is less than 1 percent in these surveys, the sampling error is large (see Bauman, 2001, for a description of similar problems in estimating home-schooling in the United States). Finally, while the census of populations does not face the problem of small samples, it is not that recent (1998) and some may have reservations regarding the quality of government data. 10 The LEAPS census of schooling choice conducted in 2003 provides a rough check on these numbers (see appendix for details). This census was conducted in three districts of Punjab and villages were chosen randomly based on the criterion that each village must have at least one private school. Typically, this means that the villages lies somewhere between fully urban and fully rural populations and are not representative of the districts that they are in. Estimates from the LEAPS census show that as a percentage of enrolled children, the numbers in two of the three districts are slightly higher than those of the population census. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the third (Rahim Yar Khan) there is a large difference, with the census reporting that 1 percent of all school-going children attended madrassas, and the LEAPS showing that the fraction is closer to 3. 7 percent (Table II). There are three potential explanations for this difference. First, the LEAPS data is not representative of the district and could be off the mark for districts with wide variation in madrassa enrollment across rural and urban samples. Second, the experience of the last five years could have varied dramatically across districts — in some, the enrollment fractions did not change and in others it increased substantially. Third, the data could point to systematic problems with the census estimates from certain districts, or the statistical problems that arise when we try to estimate low-probability events. 3. 3 Explaining the Differences A number of reasons could account for differences between the estimates presented here and those in the popular press. 1. Differences in the sampling unit. Our estimates are all based on household surveys — an interviewer goes to a household and asks about the enrollment status of every child. Some census estimates of home rather than religious schooling in the United States — the former ranges from 1 to 2 percent (Bauman 2001) while the latter is closer to 8 percent (National Center for Education Statistics, 2001). 10 In our own analysis, we find the quality of the data generated by the Federal Bureau of Statistics in Pakistan to be consistently high. We have used the FBS Census of Private Schools (PEIP, 2000) to guide our fieldwork and feasibility study for LEAPS and found it tallied with the situation on the ground quite well, even in remote villages. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Another option is to use the equivalent of the gross enrollment ratio (GER), defined as the total enrollment divided by the number of “ eligible ” children — in this case, children between the ages of 5-19. This statistic provides an estimate of the “ penetration ” of madrassas, but it does not take into account the overall enrollment decision of the family. Thus, a district with two children enrolled in madrassas, and 20 children enrolled in private or public schools out of a total of 100 children will have exactly the same gross enrollment ratio (GER) as a district with two children enrolled in madrassas and 98 children enrolled in regular schools. To the extent that we want to distinguish between these two districts, a third statistic, the ratio of children enrolled in madrassas to total enrollment (the madrassa fraction of enrollment or MFOE), can also be used. The picture changes dramatically depending on whether we use the raw numbers or the ratio of children enrolled in madrassas to total enrollment. However, since enrollment in madrassas is highly correlated with total enrollment, there is little difference in the pattern of madrassa enrollment whether we use the GER or the fraction of enrolled children in madrassas. Figure 1a shows the number of children enrolled in madrassas for every district in the country. As expected, numbers are closely linked to population size — the three most populated districts account for one-quarter of the enrollment, with the bulk of enrollment in large urban Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "List of acronyms CAR Central African Republic DHS Demographic and Health Surveys DRC Democratic Republic of Congo DTM Displacement Tracking Matrix FCS Fragile and Conflict-affected Situations GIDD Global Internal Displacement Database GIS Geographic Information Systems IASC Inter-Agency Standing Committee ICRC International Committee of the Red Cross IDMC Internal Displacement Monitoring Centre IDPs Internally Displaced Persons ILO IOM International Labour Organization International Organization for Migration IRRS International Recommendations for Refugee Statistics JIPs Joint IDP Profiling Service LSMS Living Standards Measurement Study MICS Multiple Indicator Cluster Surveys NGOs Non-Governmental Organizations NRC Norwegian Refugee Council OCHA Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat OAU Organization of African Unity ODA Official Development Assistance OECD Organisation for Economic Co-operation and Development SDG Sustainable Development Goal SKOPE Somalia Knowledge for Operations and Political Economy SuTPs Syrians under Temporary Protection UAV Unmanned Aerial Vehicle UNDP United Nations Development Programme UNHCR United Nations High Commissioner for Refugees UNITAR United Nations Institute for Training and Research UNOSAT UNITAR ’ s Operational Satellite Applications Programme UNRWA United Nations Relief and Works Agency for Palestine Refugees in the Near East UNSD United Nations Statistical Commission WFP World Food Programme Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "displaced due to armed conflict, situations of generalized violence and violations of human rights. 28 Data on IDPs monitored by IDMC are disaggregated and currently published separately for conflict-induced displacement and disaster-induced displacement. 29 At the country level the IOM ’ s Displacement Tracking Matrix (DTM) 30 provides data on IDPs in both conflict and natural disaster settings (activated in all major natural disaster contexts in recent years). Global data on conflict-induced internal displacement reflect variations in how IDPs are defined across situations. There is no consensus on how far a person must flee in order to be considered internally displaced. The definition of internal displacement for nomadic populations, which account for a significant share of IDPs in the Horn of Africa and increasingly in the Sahel, is open to controversy. 31 Moreover, while some countries register IDP children born in displacement (e. g. Azerbaijan, Cyprus and Georgia), other countries do not (IDMC 2015). The crafting of a definition for IDPs and its application in a particular context may be heavily influenced by local and national politics in conflict and post-conflict countries, as well as the direct link between estimates of displaced populations and humanitarian assistance, which can lead to both over- and under-reporting. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In certain contexts, there can be significant overlaps in these two groups; however data systems may be maintained separately for conflict-induced displacement and natural disasters (e. g. in Afghanistan) leading to possible gaps or double counting if these categories are combined. 30 The IOM Displacement Tracking Matrix (DTM) is a system to track and monitor displacement and population mobility. It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route. It has been active in over 40 countries since its inception in 2004. See http: / / www. globaldtm. info /. 31 This is typically defined as nomads not having access to their traditional routes, but routes can vary. 32 IDMC has recently adjusted their methodology to facilitate greater comparability across situations and improvements are reflected in IDMC ’ s end-2015 data. 33 This is not necessarily a problem if the purpose of the registration system is to delineate entitlements to assistance rather than to determine status. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": " The Sri Lanka: Emergency Northern Recovery Project aimed to support government efforts to resettle IDPs in the Northern Province by creating an enabling environment through: (i) emergency assistance to IDPs; (ii) a work-fare program; and (iii) rehabilitation and reconstruction of essential public and economic infrastructure. The project closed in December 2013 and was rated satisfactory.  The Mitigate the Impact of Syrian Displacement on Jordan Project assisted the government to maintain access to essential healthcare services and basic household needs for the Jordanian population affected by the influx of Syrian refugees. The project closed in July 2014 and implementation was rated satisfactory.  The ongoing Azerbaijan IDP Living Standards and Livelihoods Project aims to improve living conditions and increase economic self-reliance of targeted IDPs.  The ongoing Lebanon Municipal Services Emergency Project addresses urgent community priorities in selected municipal services, targeting areas most affected by the influx of Syrian refugees in order to mitigate the impact on host communities, including: (i) provision of high priority municipal services and initiatives that promote social interaction and collaboration; and (ii) larger works to rehabilitate / develop critical infrastructure in the areas of solid waste management, roads improvement, water and sanitation and community infrastructure.  The ongoing Jordan Emergency Services and Social Resilience Project aims to assist municipalities and host communities to address the immediate service delivery Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Historical trends and patterns of forced displacement: Insights from available global data This section provides an overview of the available global data on conflict-induced forced displacement, drawing largely on UNHCR ’ s published data on asylum-seekers, refugees and IDPs. 41 Data are presented visually in a series of figures to highlight the scope and character of the current global forced displacement crisis and identify historical trends and anomalies. These data largely focus on the scale and trends in conflict-induced displacement (i. e. the numbers of forced displaced) with some coverage of other elements such as demographics, location and accommodation. Globally, there has been an unprecedented increase in the numbers of displaced people over the last decade, largely explained by the expansion in the number of reported IDPs. Historical data show a substantial increase in the numbers of forced displaced (see Figure 3), however the expanding geographical scope and quality of displacement monitoring systems are likely to account for much of the increase in forced displacement figures. The numbers of refugees under UNHCR ’ s mandate have recorded a number of variations over time, peaking in the early 1990s (at a level 10 percent over 2015 numbers) with the conflict and displacement associated with the end of the Cold War. The number of Palestinian refugees steadily has increased steadily over time, largely as a result of natural growth. IDP numbers (for which the underlying data are the least robust) have recorded the largest progression as a consequence of: (a) the expanded scope of monitoring efforts (IDPs were not counted before 1989 and methodologies were 41 UNHCR ’ s data only include IDPs protected or assisted by the agency. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 4: Refugees and Asylum-Seekers by Migratory Path 1951 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the case of refugees, host countries rarely facilitate naturalization, only a minority of refugees ever gets resettled in third countries and voluntary repatriation is frequently not a realistic option for several reasons. International law provides for three possible durable solutions for refugees, including integration within the area of displacement, repatriation to their home country or resettlement in a third country; refugee status can also cease when there are no longer compelling reasons for an individual to refuse to avail themselves of the protection of their country of origin. In 2015 only 119, 265 refugees under UNHCR ’ s mandate were either resettled, naturalized53 or ceased to be refugees; and there were only 201, 415 voluntary returns, mostly Afghanistan, Sudan, Somalia and CAR (see Figure 17). These statistics highlight the significant gap between the unprecedented numbers of refugees and the capacity of the international community to provide durable solutions. For the 85 percent of refugees hosted in developing countries, there are only minute prospects for resettlement. Figure 17: Durable Solutions Relative to Refugee Stock 2015 Source: UNHCR Global Trends 2015 Global statistics that show the low rate of refugee returns masks the variation in returns over historical periods and across displacement crises — with significant voluntary returns for some countries. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 18: Significant Refugee Returns by Country of Origin 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Countries selected based on their cumulative returns of refugees over the period 1991-2015. Return does not necessarily lead to the full reintegration of a person into their home country or area of origin. In the absence of global data on the success of reintegration following return, data on returns appear to be taken as indication of sustainable return. In reality, many returnees face impediments to reintegration and continue to have specific economic and social vulnerabilities linked to their displacement. They may not be able to reclaim land, access sufficient financial resources (e. g. accumulated during their displacement) or reestablish social networks in areas of origin, which are critical factors for successful reintegration (World Bank 2015). Sustainable refugee return is therefore not a one-off event but a process that provides returnees with adequate safety, housing, livelihoods and services that address their specific vulnerabilities and reduce the likelihood of secondary displacement (World Bank 2015). Figure 19: Voluntary Returns of Refugees 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of refugees and people in refugee- like situations protected or assisted by UNHCR. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 20: Returns of IDPs Protected or Assisted by UNHCR 1993 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of IDPs and people in IDP-like situations assisted and protected by UNHCR. Consequently, the average length of protracted refugee situations has increased over the past two decades according to UNHCR estimates (see Table 2). UNHCR estimates that the average length of ongoing Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes high-income (OECD and non-OECD) countries. Excludes Palestinian refugees under UNRWA ’ s mandate. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Humanitarian organizations such as UNHCR and OCHA as well as international organizations such as IOM are also involved in the collection of data on IDPs, often involving international and local 59 OCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. See http: / / www. unocha. org /. 60 This section draws heavily on the “ Report of Statistics Norway and the Office of the United Nations High Commissioner for Refugees on statistics on refugees and IDPs ” presented at the UNSD in March 2015. 61 The number of countries where UNHCR exclusively collects data on refugees declined from 76 in 2010 to 72 in 2014, while the proportion of countries where refugee data were exclusively provided by governments gradually increased over the same period from 33 to 38 percent. In 2014, the proportion of countries where data were provided through collection conducted jointly by governments and UNHCR was 15 percent, while in the remaining proportion (13 percent), refugee data were provided exclusively by NGOs and other organizations. In 2014, more than 173 countries and territories provided data on refugees. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "NGOs. Initiatives such as JIPS, a collaborative project of UN and NGO actors, have also been established to support governments and operational organizations to design and implement data collection processes. There are a variety of data sources for generating statistics on forced displacement, each of which has strengths and weaknesses. Despite the significant challenges, large amounts of data are collected and disseminated every year. The main data sources and methods for the generation of statistics on forcibly displaced populations include: (a) registration of refugees and asylum-seekers; (b) registration of IDPs; (c) profiling of IDPs; (d) population movement tracking systems; (e) national population censuses; (f) sample surveys; (g) border crossings; (h) administrative records and registers; (i) general population registers; and (j) a variety of estimation methods for producing statistics when adequate and reliable data on individuals are unavailable (UNSD 2014). Several of these data sources might be used together to triangulate estimates of stocks and flows for a particular displacement situation. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For example, IDPs who subsequently cross international borders may be counted as both IDPs and refugees (e. g. in the case of the Syrian displacement crisis). Table 4: Stocks and Flows Stock Increases Decreases Asylum- seekers  New applications for asylum, separately identifying individuals who were previously IDPs  Positive decisions (convention status, complementary protection status)  Rejected  Otherwise closed Refugees  Spontaneous arrivals (group recognition, temporary protection, individual recognition), separately identifying individuals who were previously IDPs  Resettlement arrivals  Births  Administrative corrections  Repatriation  Resettlement  Cessation  Naturalization  Deaths  Administrative corrections IDPs  New internal displacement  Births  Administrative corrections  Cross border flight, becoming an asylum-seeker or refugee  Return  Settlement elsewhere in the country  Local integration  Administrative corrections Source: UNHCR Global Trends, IDMC Forced Displacement Data Model Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "If registration is linked to the provision of services or other entitlements, there may be strong incentives to register births and new arrivals and weak incentives to deregister, leading to the inflation of the register over time or even instances of fraud and abuse (e. g. multiple registration, “ borrowing children ” etc.) (UNHCR 2003). Consequently, data from a refugee register may overestimate the number of refugees, requiring periodic corrective action through the verification of records. For example, in 2014 a verification of registration records for Somali refugees in the Dadaab camps in Kenya led to the deactivation of tens of thousands of records for individuals that are believed to have returned spontaneously to Somalia (UNHCR 2015). Additional problems with refugee registers include security concerns or inclement weather preventing refugees from accessing registration sites (UNHCR 2003) and the application of data protection principles. Registration of IDPs Individual registration is not as common a method of estimating numbers of IDPs as it is for refugees. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In principle, individual registration of IDPs is not used to determine the ‘ status ’ of an IDP, since IDPs have the same rights and entitlements as other citizens and do not need to apply or be granted a special legal status. 66 Rather, registration of IDPs can provide a basis for: (a) establishing the number, location, and key demographic characteristics of displaced populations; (b) providing protection and assistance; (c) keeping track of family relationships; (d) preventing fraudulent access to scarce humanitarian assistance; (e) facilitating the issuance of temporary identity cards to replace lost personal documentation (Brookings 2008); and (f) providing social security benefits. 67 Full IDP registration by international organizations is not 62 By the end of 2014, individual refugee registration was the source of about 77 percent of the data on refugees; estimation accounted for 13 percent of data, combined estimation and registration for 5 percent and other sources for 5 percent (UNHCR 2016). 63 UNHCR may undertake registration activities when national governments do not have the capacity to do so. 64 Additional data can also be recorded such as education and occupation. 65 Insufficient budgetary resources, staff, training or materials. 66 Countries with national legislation that provides a legal status for IDPs are an exception to this international standard. 67 The scope of data collected depends on the objectives of the registration exercise, for example in Kenya, registration of individuals displaced by the 2007 and 2008 post-election violence excluded ‘ integrated ’ IDPs, i. e. those who had Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "When national or international actors provide assistance, there may be an incentive for people to register in camps even if they are staying elsewhere, or to register in multiple locations (Brookings 2011). 69 Furthermore, registration data provide only a snapshot of the stock of IDPs at a particular point in time and may be out of date if registers are not maintained regularly. Registration methodologies can vary across displacement situations. For example, families may be registered rather than individuals and the population estimated based on an assumption of average family size, which can differ among organizations (UNSD 2014). IDPs may be required to present documentation, meet specific criteria or re-register periodically to maintain their benefits, which affects aggregate numbers (IDMC 2015). For example, in Ukraine, pre-requisites for IDP registration (including valid documentation, arrival from a recognized conflict zone and permanent residence registration in recognized conflict zone) means that people displaced within a non-government controlled area, people displaced from a non- recognized conflict zone in a government controlled area, unaccompanied children or people without current / valid identification are not counted as IDPs (IDMC 2015). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "this way, profiling of IDP situations aims to underpin advocacy, protection and assistance activities as well as support the achievement of durable solutions by informing joint strategies between government, humanitarian and development actors. Profiling provides an overview of displacement-affected populations through the collection and analysis of minimum core data (number of IDPs, disaggregated by location, age and sex) and where possible additional quantitative and qualitative data (causes of displacement, patterns of displacement, protection concerns, humanitarian needs, vulnerabilities, and aspirations and prospects for durable solutions). Profiling may utilize data collection techniques at individual, household and community levels, often combining population estimation methods, a review of secondary data, focus group discussions, household surveys and key informant interviews targeted specifically at forcibly displaced populations (UNSD 2014). 70 Profiling methods focus on displacement situations, rather than only on displaced populations, and therefore includes comparisons to conditions in the host population. IDMC estimates that humanitarian profiling data forms the basis for 18 of their 60 country estimates and around 63 percent of their annual estimates (IDMC 2015), with the largest volume of data on conflict-induced internal displacement provided by OCHA followed by IOM. There are several practical challenges associated with IDP profiling exercises in displacement situations. Insecurity or terrain may impede access to displaced populations in conflict-affected or hard to reach areas. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For example, profiling exercises in Pakistan do not cover all IDPs or areas affected by displacement due to insecurity, and in Afghanistan profiling of IDPs by UNHCR underestimates the scale of the initial displacement as IDPs are only interviewed once displacement sites are accessible, if they are profiled at all (IDMC 2015). Additional challenges include unwillingness of IDPs to participate due to fear of persecution, and mobile populations (IDMC 2008). There may also be political pressures to inflate or reduce numbers. Population movement tracking systems In situations where the movement of displaced populations is fluid or continuous, a movement tracking system can be a useful tool for providing rough estimates of population flows, including recurrent displacements. Movement tracking systems are useful for monitoring fluid population movements (including spontaneous and organized, internal and cross-border, and returns and resettlement) in remote or inaccessible routes and locations (including displacement sites, places of origin, and places of return and resettlement). UNHCR, IOM and other organizations have developed methods for tracking and monitoring movements of IDPs in over 30 countries, particularly in cases of disaster-induced displacement, but also in some cases of conflict-induced displacement (UNSD 2014). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These systems employ a combination of data collection techniques including key respondent interviews, focus group discussions, registration, observations and physical counts, samplings and other statistical methodologies. For examples, UNHCR ’ s population tracking systems identifies and trains local NGOs to monitor key locations such as IDP settlements, bus stations and roads to report on movements. The accuracy of data from movement tracking systems is subject to several caveats. These include: limited access to locations and routes due to insecurity; vast geographical areas to monitor; mixed population flows that include refugees, IDPs, pastoral and seasonal movements and economic migrants; massive population flows that overwhelm monitoring capacity; disinclination of individuals to provide information when there is no assistance being offered; pressures from communities to inflate figures to maximize future assistance; and political pressures to suppress accurate reporting on IDP movements. Additionally, due to the fluid nature of displacement in many contexts and the likelihood of recurring displacements, it is not possible to use movement data to provide estimates of population stocks. Population censuses National population and housing censuses often provide the most comprehensive source of population data and offer the potential for estimating numbers of forcibly displaced people. To estimate the size of displaced populations a census would need to include questions on country (and / or place) or birth, year of (internal) 70 Other data collection methods may be used such as movement tracking systems, registration, big data etc. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "migration, and reason for movement including asylum / refugee protection (or conflict-induced internal migration) (UNHCR 2016), or a specific question to identify IDPs or refugees. However, not all censuses cover refugees and asylum-seekers (if foreigners are considered outside the scope of the census or because they are considered a special category), 71 nor is it common practice for national censuses to include questions related to forced displacement. 72 Nevertheless, there are several examples of national censuses that have included relevant questions on forced displacement. 73, 74 In the case of protracted internal displacement situations, IDPs are likely to be included in national censuses; however, census instruments may be subject to manipulation for political purposes. There are several drawbacks of population censuses including their cost, the significant training required for enumerators to ensure consistent answers to questions on forced displacement, impediments to field operations and data processing (such as weather conditions and technical problems), the relative infrequency with which they are carried out, and the long processing time before data and statistics become available, which have consequences for the timeliness of data. Moreover, often censuses are not conducted in contested territory or conflict zones where many displaced persons reside, and this limits the completeness of the data. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Nevertheless, censuses are the only nation-wide source of population data for potentially providing estimates of displaced populations, especially those in non-camp settings, and often provide a basis for sampling frames for survey instruments (UNHCR 2016). Capturing displacement situations in official statistics also increases their visibility. Sample surveys Sample surveys can potentially provide a rich source of data on forcibly displaced populations. Compared with censuses and registers, sample surveys allow more detailed questions to be asked about the characteristics and situations of households. If survey instruments identify displaced populations based on individuals ’ and households ’ self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status (UNSD 2014). There are opportunities to mainstream forced displacement into international survey instruments, but this has only been done in a handful of cases. Several standardized international sample surveys have been designed for special purposes including the Living Standards Measurement Study (LSMS), 75 Labor Force 71 The UN census recommendations for the 2010 World Population and Housing Census Programme stipulate that refugees and IDPs living in camps should be counted and their numbers disaggregated in population statistics, however there is no requirement to separately distinguish displaced people living outside of camps (UNHCR 2016). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Border crossings The registration of people crossing internationals borders is conducted in many countries, and in some cases these data are used to estimate migration flows. Identifying refugees among people crossing borders is a significant challenge, particularly if individuals decide not to apply for asylum or refugee status (UNSD 2014). Additional problems associated with the collection of data on border crossings include: (a) difficulties distinguishing migrants from other people crossing a border, such as tourists, commuters, traders and truck drivers; (b) lack of capacity of many border posts and officials to handle large migration flows; (c) less scrutiny and diligence of emigration flows compared with immigration flow; and (d) lack of tight controls at most borders and the high incidence of undocumented or irregular crossings (UNSD 2014). Administrative records and registers Many countries have administrative records or registers of immigrants that could generate statistics on asylum-seekers and refugees. In particular, data on residence permits issued to refugees or asylum- seekers could be used to generate statistics on both flows and stocks of refugees. 79 For example, Eurostat collects and disseminates data on residence permits granted to those with refugee status and subsidiary protection (UNSD 2014). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "seekers. 81 There are several challenges associated with using central population registers to estimate refugee and asylum-seeker populations, including: consistency of the definition of refugees with the definition in the 1951 Convention and 1967 Protocol; difficulty and cost of establishing and maintaining a population register (UNSD 2014); and confidentiality safeguards. Compilation of statistics on forcibly displaced populations Several international organizations are involved in the compilation, analysis and dissemination of statistics on forced displacement including UNHCR, 82 Eurostat, IDMC, OCHA, International Committee of the Red Cross (ICRC), 83 WFP84 and IOM. Each of these actors has their own thematic focus and specific objectives, and applies their own methodologies. Asylum-seekers and refugees UNHCR is the principal organization responsible for the compilation, analysis and dissemination of data on asylum-seekers and refugees. UNHCR maintains a publicly available statistical online database85 with data for the period 1951-2014 on refugees (including people in refugee-like situations), asylum-seekers (pending cases), returned refugees, IDPs protected or assisted by UNHCR, returned IDPs previously protected or assisted by UNHCR, stateless persons and others of concern to UNHCR, disaggregated by country of origin and asylum. 86 Data are also provided on demographics, location, asylum-seekers (refugee status determination and monthly data) and resettlement. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "UNHCR regularly publishes statistical reports, including “ Global Trends ”, “ Mid-Year Trends ”, “ Asylum Trends ” and “ Statistical Yearbook ”. Additionally, UNHCR hosts interagency information sharing portals for significant emergencies. 87 These portals provide data on populations of concern at regional and country levels, including time series data, demographics, location and accommodation information. 88 Eurostat compiles and publishes data on asylum (applications and decisions) and managed migration in European Union member countries. Countries and national and international NGOs also publish these statistics, based on sources of various completeness, quality and timeliness (UNSD 2014). There are sometimes substantial inconsistencies between the numbers published by different organizations for the same country, including high-income countries with good statistical systems, usually due to differences in definitions, times and statistical methods, including the mixing of data on flows and stocks (UNSD 2014). There are several challenges associated with the compilation of data on asylum-seekers and refugees. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "IDMC reports that of the 52 countries it monitored in 2015, it was only able to obtain data on new displacements in 20 countries, 92 on returns in 20 countries, on integration in one country, on resettlement in two countries, on children born in displacement in two countries and on deaths in one country; no data was obtained for any county on cross-border flight in 2015 (IDMC 2016). Moreover, existing systems for collecting data on refugees and asylum-seekers make it difficult to know how many were formerly IDPs, and it is possible for some people to be simultaneously counted in both categories, e. g. in the case of the Syrian displacement crisis (IDMC 2016). If no data on returns are available, IDMC risks overstating the number of IDPs (IDMC 2015). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Disaggregated data on IDPs who are not protected or assisted by UNHCR are collected by other agencies, including IOM, but data are not comprehensive and therefore not published in IDMC ’ s global reports. 93 The difficulties of collecting disaggregated data on locations of IDPs are compounded by the fluidity of IDP movements — IDPs might suffer multiple displacements or they might resort to changing locations as a coping strategy (e. g. moving between their homes and place of displacement or testing different locations before deciding where to stay) (Brookings 2011). In recent years, efforts have been made to improve data collection for IDPs living outside of camps by employing a range of techniques including: (a) profiling; (b) household surveys; (c) collecting information on IDPs who come to camps to visit family members or collect relief items; and (d) community outreach programs (Brookings 2013). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, there is no consensus or agreed best practices on the use of these methods in different contexts or stages of displacement (Brookings 2013). 92 The absence of data on new displacement may simply mean that no displacement has taken place (IDMC 2016). 93 IDMC reports that data, disaggregated by age and sex, were available for 15 of the 60 countries it monitored in 2014, however these data were not comprehensive and are not published. Additionally, in some countries there are data provided by IOM on IDP populations by location from which the urban or rural character of the population may be inferred (e. g. if the camp is located in the capital), but data are not comprehensive and not published. While the majority of humanitarian profile data does not typically cover IDPs living outside of camp or camp-like settings (the large majority of IDPs), IOM ’ s DTM in countries such as Nigeria, Iraq, Yemen and Libya do include information about those residing in host communities. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 5: Coverage of Published Data on Location, Accommodation and Demographics 2015 Population end- 2015 (millions) Urban or Rural Location Accommodatio n Age Sex Refugees and people in refugee-like situations 16. 1 85 % 83 % 58 % 68 % Other people of concern to UNHCR 47. 8 (including 37. 5 IDPs) 70 % 47 % 25 % 39 % UNHCR ’ s total population of concern 63. 994 73 % 56 % 33 % 46 % IDPs monitored by IDMC but not included in UNHCR's data 3. 3 0 % 0 % 0 % 0 % Source: UNHCR Global Trends 2015 Note: Other people of concern to UNHCR include asylum-seekers, IDPs and people in IDP-like situations protected or assisted by UNHCR, stateless persons, and ‘ other ’. Overall robustness of current data The robustness of data is difficult to estimate. A review of data collection and compilation methodologies shows broad variations in terms of the accuracy and reliability of the global estimates of forced displacement that are widely used. Headline figures on forced displacement are significant in shaping public opinion and are critical for sound decision making, both to inform the allocation of resources and to design effective humanitarian and development responses. However, the available estimates are potentially misleading and should not be referred to without appropriate caveats and qualifiers. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": " In Somalia, the Mogadishu Household Survey covered both residential areas and IDP camps, collecting data on expenditures, demographics and living conditions [survey completed; analysis forthcoming]. As part of the Somalia Knowledge for Operations and Political Economy (SKOPE) initiative, the Puntland Household Survey will also cover both residential and IDP populations [ongoing].  An IDP study in South Sudan [ongoing] aims to assess the economic needs of IDPs and host communities in urban areas, covering livelihoods, water and sanitation, infrastructure as well as intentions and conditions to return. Basic information about education, employment and general health variables will also be collected.  The Iraq Crisis Response Study [ongoing] will assess the impact of the Islamic State and oil price-related crises on IDPs and households left behind in IS controlled areas. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The study: (i) compares the socio- economic profile of refugees with that of the Syrian population before the crisis and with the hosting populations of Jordan and Lebanon; (ii) provides a welfare and vulnerability assessment of Syrian refugees including a poverty profile, the socio-economic characteristics of higher poverty and where pockets of deep poverty are located; (iii) analyzes key drivers of welfare and poverty; and (iv) models monetary and non- monetary vulnerability.  In Lebanon, Jordan and Iraq, the Bank is leading an initiative to evaluate the socio-economic impact of the regional crises on the welfare of Syrian refugees and host communities in neighboring countries [ongoing]. Data on living conditions, access to services and economic opportunities, coping strategies and economic status are to be collected via a specialized household survey and a sub-component of the survey will be carried out on a semi-annual basis to continue to monitor and adapt support as needed.  A recent Bank paper, “ Turkey ’ s Response to the Syrian Refugee Crisis and the Road Ahead ” [completed in 2015] assessed the impact of Syrian refugees on host areas in various sectors. It found that the presence of Syrian refugees is placing a strain on municipal services, housing rental markets, social relations, and education services for Turkish households. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Impacts on the labor market are unclear; initial research suggests that there has been a supply shock to informal labor markets. This has had a large-scale impact on the employment of natives in the informal sector. At the same time, research suggests there has been a boost to formal employment for the Turks, but this has been uneven: the low educated and women experience net displacement from the labor market and, together with those in the informal sector, declining earning opportunities.  The “ Socio-economic Assessment of the Impact of Syrians under Temporary Protection (SuTPs) on Turkish Hosting Communities ” [ongoing], to be undertaken in partnership with the Government of Turkey, will include a nationally representative household survey with SuTP and local Turkish households including camp and non-camp environments. The questionnaire will cover welfare (assets, income, expenditure), municipal services, labor and employment, education, social networks and quality of life. F. Options to improve forced displacement statistics Significant efforts are needed to enhance the reliability, comparability, quality and scope of the global data on forced displacement. In particular, more robust estimates are needed of the scale (stocks, flows and locations) and typology (demographics, location and accommodation) of forced displacement crises. This requires substantial improvements in the rigor of data collection and compilation methodologies including: (a) Harmonization of definitions and methodologies used in the collection and analysis of statistical data on forced displacement — covering stocks and flows of refugees, asylum- seekers and IDPs — to ensure comparability across regions and countries; Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(a) High frequency sample surveys using smartphone and cellular technologies. For example, a high-frequency survey initiative in Somalia employs a dynamic questionnaire loaded onto smartphones, which enables data to be collected from household interviews in 60 minutes. This approach was developed to overcome the challenges of insecurity, limited data gathering capacity and budgetary constraints. (b) The use of mobile phones to conduct surveys or follow up interviews following face-to-face household surveys. For example, a Bank paper on the impact of the 2012 crisis in Mali on IDPs, refugees and returnees used information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones. This combination provided a mechanism to monitor the impact of conflict on hard-to-reach populations who at times live in areas inaccessible to enumerators. And in Sierra Leone and Liberia, the Bank supported the use of mobile phones to collect key socio-economic data on the effects of the Ebola virus. (c) Crowdsourcing data on displacement. Platforms such as the Kenyan Ushahidi has crowd- sourced data on displacement in Kenya and eastern DRC by encouraging IDPs and host communities to report incidents using their mobile phones or the internet, including information about living conditions. The platform references these reports geo-spatially. (d) Geo-mapping of data on displaced populations and affected host communities. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In settings with clearly distinguishable individual structures, these methods can also be reasonably accurate for the purposes of rapid estimation of displaced population (Checchi, et al. 2013). 101 The use of unmanned drones is also becoming more popular as the cost of this technology falls. This technique has been used by UNHCR to update its estimates of IDPs in Somalia ’ s Afgooye corridor (IDMC 2015) and by IOM to monitor disaster-induced displacement in Haiti, including the use of Unmanned Aerial Vehicles (UAVs) in collaboration with UNOSAT. (g) Open data initiatives. There are several initiatives to provide free and open data that enable Internet users to independently mine and analyze data and generate customized summaries, charts and visualizations. For example, the Bank has provided free, open access to its development data since the launch of its Open Data Initiative in 2010, however there is little open data on asylum-seekers, refugees and IDPs. JIPS has developed a web-based platform that allows users to explore, analyze and visualize profiling data online, and IDMC has 100 See http: / / www. flowminder. org /. 101 These methods are not effective in settings with connected structures, a complex pattern of roofs or multi-level buildings, as are prevalent in urban areas. Additionally, cloud cover and dense foliage can also obscure structures. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Institutional arrangements would need to delineate data collector and data compiler roles, as well as reflect the following principles to ensure sustainability: (a) Pursue additional activities within the overall framework of existing initiatives to ensure coherence with the activities of other actors. (b) Primary responsibility for data collection rests with national statistical agencies (with adequate arrangements in the case of IDPs to mitigate political risks). (c) Definitions and methodologies should be harmonized across countries, through a process managed under the auspices of the UN Statistical Commission. (d) Agencies such as UNHCR and IDMC can play a leading role in ensuring quality, providing technical assistance as may be needed, and aggregating data for global analyses. 102 See http: / / www. internal-displacement. org / database. 103 See http: / / unstats. un. org / unsd / statcom / doc15 / 2015-9-RefugeeStats-E. pdf. 104 See conference documentation at http: / / www. efta. int / seminars / refugee. 105 See http: / / unstats. un. org / unsd / statcom / 47th-session / documents / 2016-14-Refugee-statistics-E. pdf. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 7825 This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at assaad @ umn. edu. The impact of the growth of the local supply of public schools in the post-Colonial period on intergenerational mobility in education is a first-order question in the Arab World. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This question is examined in Jordan using a unique dataset that links individual data on own schooling and par­ents ’ schooling for adults, from a household survey, with the supply of schools in the subdistrict of birth at the time the individual was of age to enroll, from a school census. The identification strategy exploits the variation in the supply of basic and secondary public schools across cohorts and subdistricts of birth in Jordan, controlling for year and subdistrict-of-birth fixed effects and interactions of gov­ernorate and year-of-birth fixed effects. The findings show that the local availability of basic public schools does, in fact, increase intergenerational mobility in education. For instance, a one standard deviation increase in the supply of basic public schools per 1, 000 people reduces the father- son and mother-son associations of schooling by 18 – 20 percent and the father-daughter and mother-daughter asso­ciations by 33 – 44 percent. However, an increase in the local supply of secondary public schools does not seem to have an effect on the intergenerational mobility in education. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 Over the past three or four decades, the Arab world has experienced a massive expansion in educational attainment. According to the Barro and Lee educational attainment dataset, seven out of the top 20 countries in terms of increase in number of years of schooling from 1980 to 2010 were Arab countries (Barro and Lee 2013). 1 Jordan, the subject of this paper, had the seventh highest increase in educational attainment in the world, with an increase of about five years in the average years of schooling over the period. This increase is widely believed to be attributable to a massive public investment in the supply of schooling in the postindependence period in the context of a state-led development model, which virtually guaranteed employment in the public sector for graduates (Assaad 2014; Saleh 2016). The rapid increase in educational attainment has continued unabated despite the fall in returns to education that accompanied the demise in the state-led model and its employment guarantee schemes (Pritchett 2001). A slew of recent literature on the drivers of the Arab Spring protests, some of which occurred in Jordan, has identified the low economic returns to this massive increase in education as the single most important cause of the uprisings (Goldstone 2011; Campante and Chor 2012a, 2012b, 2014; Sanborn and Thyne 2014). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "4 The article employs a unique data source from Jordan, the 2010 Jordan Labor Market Panel Survey (JLMPS 2010), which includes information on parents ’ schooling for every adult in the sample, along with the 2010 School Census produced by the Jordanian Ministry of Education (Hashemite Kingdom of Jordan, 2010). The school census provides the subdistrict, type, and date of establishment of every school in Jordan, allowing us to measure the local supply of each type of schools in each subdistrict in every year (under the presumption that there were no significant school closures or changes in type over time, which is likely the case). The exposure of an individual in the JLMPS 2010 sample to the supply of public schooling is then determined by the number of sex-appropriate basic (or secondary) public schools (per 1, 000 individuals) that were available to them in their subdistrict of birth at the time they were of age to enroll in that school level (six years of age for basic and 15 years for secondary). The richness of the data set makes it the first in the Middle East to allow such a study. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Most importantly for the purpose of the analysis is the fact that the survey provides individual-level data on own schooling and parents ’ schooling for all adults in the sample, which is quite rare in household surveys from developing countries. Also, the survey provides the actual years of schooling completed and not only the highest educational degree attained, which allow observing the schooling variable with precision. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "13 Second, each individual in the JLMPS restricted sample is matched to the 2010 Jordanian school census. The matching process determines for each individual the number of sex- appropriate public basic and secondary schools per 1, 000 individuals available in the individual ’ s subdistrict of birth when the individual was of age to accede to this educational level (six years of age for the basic level and 15 years of age for the secondary level). 7 A school is considered sex-appropriate for a female if it is a girls ’ or a mixed school and for a male if it is a boys ’ or mixed school. The empirical analysis is also performed by entering boys ’, girls ’, and mixed schools separately. 8 Measuring the local supply of public schools at the subdistrict of birth of the individual (i. e., the child) mitigates potential endogeneity originating from parents who had a higher taste for schooling moving to subdistricts where public schooling was more abundant when their child was of school age, although it is not possible to rule out that parents might have moved across subdistricts prior to the birth of their child. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The JLMPS sample is restricted, after matching it to the 2010 school census, to individuals born in Jordan who are aged 25 to 70 in 2010 and who have nonmissing information on age, subdistrict of birth, years of schooling, father ’ s schooling, mother ’ s schooling, and local supply of schools in subdistrict of birth. 9 These exclusions resulted in a sample of 4, 139 males and 4, 131 females, which are referred to as the male and female full samples, respectively. 7. Because of the absence of annual estimates of subdistrict populations, the population used to normalize the supply of schooling at the subdistrict level is the 2004 population of the subdistrict. There are 86 subdistricts in Jordan. If subdistrict populations are growing at different rates, this could introduce some measurement error of the true supply of schooling available to different cohorts. 8. Secondary schools include both general and vocational secondary schools. Public schools include schools under the jurisdiction of: (i) Ministry of Education, (ii) Ministry of Higher Education, (iii) Ministry of Defense, (iv) Ministry of Social Development, (v) Ministry of Religious Endowments (Awqaf), and (vi) UNRWA. 9. The original sample size of all individuals who are aged 25 to 70 years in 2010 and are born in Jordan is 8, 312 observations. The sample restrictions on the missing values result in the exclusion of 34 observations (missing age), 1 observation (missing father ’ s schooling), and 7 observations (missing mother ’ s schooling). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "23 and females. 10 The results are shown in table S. 1 in the supplemental appendix. The baseline effects of basic schools on educational attainment and their effects on mobility are stronger and often more significant than those estimated in table 4 for both males and females. A second concern is that Jordan received a large influx of Palestinian refugees in the aftermath of the 1967 Arab-Israeli War. While refugees benefited from the UNRWA basic schools, their educational attainment and intergenerational educational mobility were perhaps subject to a different set of constraints than those facing other Jordanians. Thus, as a robustness check, individuals who are likely to be Palestinian refugees were excluded from the sample. Since the JLMPS 2010 does not allow directly identifying Palestinian refugees who are now mostly Jordanian citizens, two indirect methods were employed to identify individuals who are likely to be Palestinian refugees. Method 1 excludes individuals born in subdistricts where the percentage of individuals who were ever enrolled (or are currently enrolled) in an UNRWA school exceeds ten percent out of all individuals below 36 years of age in the sample. Method 2 excludes individuals born in subdistricts where the percentage of UNRWA schools exceeds ten percent of the total number of schools. The results for the restricted sample according to both methods are shown in tables S. 2 and S. 3 respectively. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Overall, the results remain unchanged from those in table 4. A third concern is that the samples of males and females that are employed in the analysis may include adult siblings who belong to the same household. This could introduce intra- household serial correlation among these observations. Thus, as a robustness check, the sample is restricted to males and females who are household heads or their spouses, hence excluding adult 10. This results in excluding 381 “ movers ” among males and 371 “ movers ” among females, or roughly 9 percent of the original samples. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 8012 This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at jhoogeveen @ worldbank. org or at mariacristina. rossi @ econ. unito. it or ds1289 @ georgetown. edu. This paper uses a unique data set to analyze the migration dynamics of refugees, returnees, and internally displaced people during the Northern Mali conflict. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "5 3. Data The data used in this paper have been collected through the Listening to Displaced People Survey (LDPS). 4 The baseline face-to-face interviews were executed between June and August 2014. The following 12 monthly interviews – from August 2014 until August 2015- were conducted using mobile phones. 5 The original sample comprised 501 respondents (51 % Male, 49 % Female) and was divided between internally displaced people (IDPs) located in the capital city Bamako, 6 refugees living in refugee camps in Mauritania and Niger, as well as returnees living in the regional capitals Gao, Timbuktu and Kidal in Northern Mali. This survey did not collect information on individuals who were never displaced. The attrition rate was very low, always around 1-2 % per wave. We need to stress that the locations were not randomly selected. Bamako was selected because it hosted a large number of IDPs. Furthermore, the main cities in the north of Mali were chosen to obtain a large sample of returnees given the funds available. Finally, a refugee camp was located in Niger since bureaucratic issues did not allow the inclusion of a camp in Burkina Faso. Nevertheless, households were selected randomly within each location. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "21 5. 5 Intention to return (Y / N) – Fixed-Effect This last empirical section represents an attempt to use the panel dimension of our data set to estimate causal effects. In particular, for 12 consecutive waves, refugees and IDPs were asked whether they were considering going back to Northern Mali in the subsequent month. We tried to test how employment, security and expectations affect these decisions. We did so by estimating a fixed-effect linear probability model (LPM). The estimated coefficients are shown in Table 6. Columns 1 and 2 have been estimated using the whole sample, while only respondents who were the household heads or the spouses were included in the regressions presented in Columns 3 and 4. The main conclusion is that being employed reduces the intention to go back to the regions in Northern Mali by around 8 percentage points. This result persists across all specifications, even when we control for immigration status, i. e., whether the individual is a refugee or an IDP. Indeed, refugees are more likely to be willing to go back. Estimating the same model for refugees and IDPs separately does not change our conclusions. In line with the previous findings, whether an individual felt safe during the day (or at night) did not affect the likelihood of planning to go back. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, another regressor indicates that security may still be pivotal: those who owned a weapon were up to 30 percentage points more likely to plan to go back. In addition to this, it is quite surprising that, if the respondent thought that the Northern Mali crisis was improving, he or she was less likely to plan a return to that area. From a technical point of view, we should point out that we have used an LPM even if the dependent variable was a binary outcome. This choice has been made since in this linear model it is straightforward to add fixed-effects. Furthermore, the coefficients can be interpreted as average partial effects. A simple logit or probit model would not have allowed the inclusion of individual fixed-effects because of the incidental parameter problem. An alternative approach would have been to estimate a conditional logit model. However, since the distribution of the fixed effects is unknown, it would not have been possible to estimate the average partial effects in this model, but only the effect of the regressors on the log-odds ratio. 13 We conclude by stressing that the monthly phone interviews were relatively short, so we did not have a rich panel data set. This may have led to omitted variable biases. Indeed, there may still be time varying factors which could have affected both the probability of being employed and the respondents ’ intentions to go back. Nevertheless, we believe that our model managed to control for 13 See (Wooldridge, 2010) page 639. Conclusions from the conditional logit model are qualitatively similar. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 7402 This paper is a product of the Social Protection and Labor Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at xdelcarpio @ worldbank. org and mathiswagner @ gmail. com. Currently 2. 5 million Syrians fleeing war have found refuge in Turkey, making it the largest refugee-hosting country worldwide. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turk­ish Labour Force Survey to assess their labor market impact. Syrian refugees are overwhelmingly employed informally, since they were not issued work permits, making their arrival a well-defined supply shock to informal labor. Consistent with economic theory our instrumental variable estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupa­tional upgrading, there are increases in formal employment for the Turkish- though only for men without completed high school education. Women and the high-skilled are not in a good position to take advantage of lower cost informal labor. The low educated and women experience net dis­placement from the labor market and, together with those in the informal sector, declining earning opportunities. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 1. INTRODUCTION Refugees pose a massive moral, political and economic challenge for potential host countries. 1 The scale of the challenge is larger than ever, with 60 million people forcibly displaced by conflicts across the world (UNHCR, 2014). War in Syria has produced more refugees than any other conflict of the past two decades: around 4. 6 million have fled the country, with an additional 7. 6 million internally displaced. 2 About 2. 5 million Syrians have found refuge in Turkey, making it the largest refugee-hosting country worldwide. This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turkish Labour Force Survey to assess the impact on Turkish employment and wages. The large majority (85 percent) of Syrians have left the refugee camps and entered the Turkish labor market. 3 They are overwhelmingly employed informally, since they were not issued work permits. This makes their arrival a well-defined supply shock to informal labor, and a particularly good context in which to test the predictions of basic economic theory. We instrument for refugee flows using travel distance between 13 origin governorates in Syria and 26 Turkish subregions (338 origin-destination pairs). This allows us to also control for distance from the Syrian border, and thus any confounding factors that are correlated with proximity to Syria. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 permits and subsequently for the right to work. In practice, this is a long and cumbersome process and by late 2015 at most several thousand had been issued. 14 The economic impact of Syrian refugees in Turkey extends beyond changes in the potential labor supply of informal workers in important ways. There has been extensive humanitarian aid provided to the refugees, overwhelmingly by the Turkish government. Reportedly, by early 2015 the Turkish state had spent $ 6 billion (with total outside contributions $ 300 million). 15 Much of these funds have been spent on food, various services, non-food items such as medicines, clothing, shelter, and housing-related goods. In particular, there are 20 accommodation centers (camps) in 10 cities in Turkey. 2. 2 Data Sources We use the Turkish Household Labor Force Survey (LFS) micro-level data sets compiled and published by the Turkish Statistical Institute. The data contains a rich set of labor market variables along with individual-level characteristics and the region of residence. We primarily rely on two years of LFS data: 2011 (just before the arrival of the refugees) and 2014 (the last year available). 16 By design the LFS does not contain any information on Syrian refugees. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "12 though somewhat lower than that of the Turkish at 30- 50 percent. Child labor is also quite prevalent, though there have been extensive efforts made to ensure that refugee children attend school. 19 Publicly available information on refugees comes from an AFAD survey of 2, 700 households in June and July 2013. Figure 2, using data from AFAD (2013), provides an overview of the Syrian governorates from which the refugees to Turkey originated. The refugees primarily come from northwest Syria. The largest source regions are Aleppo (36 percent), Idleb (21 percent) al-Raqqah (11 percent), Lattakia (9 percent), and Hamah (8 percent). Consistent with travel distance being a good predictor of refugee flows to Turkey, 80 percent of respondents report that they chose to flee to Turkey, instead of another country, due to the ease of transportation. The refugees in Turkey, unlike the later 2015 refugee flows to Western Europe, are nearly 50 percent female. Slightly over 50 percent are minors (under the age of 18). These facts reflect that to large extent Syrian families fled to Turkey together. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Then the absolute refugee-induced wage change is given by Δݓഥ ൌ ݓഥଵ െ ݓഥ ଴ ൌ ൫ ݁ ஓ ෝ ೢ ∗ ଶ െ 1൯ ∗ ݓഥ ଴. 3. 3 Instrument To allow for a causal interpretation of the impact of refugee flows, see equations (1) and (4), we instrument for the ratio of refugees to working-age population (ܴ ௥ ௧ሻ. 24 Our instrumenting strategy is based on the idea that travel distance, from the Syrian governorate from which the refugee is fleeing to each potential destination Turkish subregion, is a key determinant of refugee location decisions. We use Google Maps to calculate the travel distance Tsr from each Syrian governorate capital (s), to the most populous city in each Turkish NUTS 2 subregion (r). The instrument for the number of refugees at a given point of time in each Turkish subregion is given by: ܫ ܸ ௥ ௧ ൌ ෍ 1 ܶ ௦ ௥ ߨ௦ ܴ ௧ ௦, (5) where Rt is the total number of registered Syrians in Turkey in a year and ߨ௦ the fraction of the Syrian population that lived in each governorate in 2010 (pre-war). 25 Since all our 23 The education categories are at most primary school, secondary school, and higher education. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The age categories are 15 – 19, 20 – 24, 25 – 29, 30 – 34, 35 – 39, 40 – 44, 45 – 49, 50 – 54, 55 – 59, and 60 – 64 years. There are 183 groups since we exclude groups containing less than 40 observations. 24 An additional advantage of the IV approach is that it helps deal with measurement problems. Despite the improved measures of refugee numbers in Turkey by subregion starting in 2014, there is likely considerable measurement error, resulting in attenuation bias in the OLS estimates. For the IV estimates to be consistent, it is only necessary that- conditional on the fixed effects and control variables- the flows of Syrian refugees are uncorrelated with the instrument. 25 Using data from AFAD (2013) we can also weight the aggregate refugee numbers using the Syrian source governorates of refugees in 2012-13 (see Figure 2). Results are qualitatively robust to this alternative instrument and first-stage F-statistics about the same. We prefer the use of the pre-war distribution of population in Syria, Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Second, the Turkish LFS asks respondents whether they had previously lived in a different province (one of Turkey ’ s 81 NUTS 3 regions), and if so in what year they moved to their current province. We estimate the impact of refugees on the probability a native moved to a subregion in the past year. Table 9 reports OLS and IV estimates of the impact of refugee on net population growth in subregion (Columns 1 and 2) and gross population inflows (Columns 3 and 4). Net population growth is estimated at the level of NUTS 2 subregions. Population inflows to a subregion are estimated at the individual level (and standard errors clustered by subregion- year). All regressions include subregion and year fixed effects and a year-specific control for log distance from the Syrian border. The first column presents the estimates for the whole sample, subsequent columns for different sub-samples by gender, age and education. For the full sample the net population growth in a subregion is positively correlated with refugee flows, while the IV point estimate is negative (though neither estimate is statistically significant). The probability of a Turkish person migrating to a subregion is negatively correlated with refugee flows (the OLS estimate is highly statistically significant). The IV estimate is of a similar magnitude, but no longer statistically significant. This same pattern broadly holds for both women and men. The only other statistically significant IV estimates are a decrease in the population aged 15 – 24, an age group that is likely more mobile, and of those with medium educational attainment. There is also a decrease in the inflow of low Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "26 education Turkish to a subregion. In sum, there is some evidence that the inflow of Syrian refugees results in a decrease in the number of Turkish living in a subregion. The evidence, however, is weak and the impact unlikely to be very large. 5. PLACEBO TESTS AND ROBUSTNESS CHECKS 5. 1 Placebo Tests The key threat to the validity of our instrument is that there are subregion specific economic trends that are correlated with the instrument, and not fully controlled for by the inclusion of the log distance of a Turkish subregion from the Syrian border. A priori this seems unlikely since the instrument is also based on travel distances, but we can test for the existence of such trends in a pre-period. Specifically, we run regressions that are analogous to those reported in Tables 5, 6 and 7 using data from the LFS 2009 and 2011. As a placebo test we pretend that the Syrian refugees had arrived between 2009 and 2011, rather than between 2011 and 2014, to see if the instrument is correlated with Turkish outcomes in this pre-period. Table 10a presents the results of our placebo tests. For the overall sample there is no statistically significant trend that is correlated with subsequent (instrumented) refugee flows in formal or informal employment, or in log wages. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As expected the positive correlation between refugee flows and 2011 school attendance rates is significant at the one percent significance level (controlling for the gender, age and education composition of a subregion the point estimate is- 0. 17). Even once we instrument for refugee flows the correlation remains significant at the one percent significance level (a point estimate of- 0. 30). However, once we control for the log distance for the Syria border there is no longer a statistically significant relationship, in either the OLS or IV, between school attendance rates in 2011 and subsequent refugee flows. This suggests that, on account of the inclusion of our distance from the border control, we can rule out the 2012 education reform confounding our estimates. 5. 3 Robustness to Varying Sample of Turkish NUTS 2 Subregions Throughout this paper we use all 26 NUTS 2 subregions of Turkey for identification. However, the results are robust to varying the particular sample of subregions. We report results for two alternative samples. First, we drop the Gaziantep subregion from the estimation. Gaziantep has the highest refugee to population ratio among all regions and reportedly towns with a refugee share of over 30 percent. The inclusion of Gaziantep may skew results if there are any non-linearities in the impact of refugees. Second, we follow Ceritoglu et al. (2015) in only considering nine subregions of Turkey. These are the five Syrian border regions of southeastern Anatolia (Hatay, Gaziantep, Sanliurfa, Mardin, and Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Women experience particularly pronounced displacement in the informal sector and no formal job gains. In contrast, for men displacement in the informal sector is fully offset by employment growth in formal sector, with no net job losses. Clearly, our main findings do not depend on the particular sample of subregions we analyze, and importantly are robust to restricting the analysis to a more homogenous group of regions. 6. CONCLUSIONS This paper combines newly available data on the 2014 distribution of 1. 6 million Syrian refugees across subregions of Turkey and the Turkish LFS, to assess the impact on Turkish labor market conditions. The Syrian refugees in Turkey are overwhelmingly employed informally, since they were not issued work permits, and so their arrival was a well-defined supply shock to informal labor. Consistent with economic theory our IV estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupational upgrading, there are increases in formal employment for the Turkish. This increase though only occurs among men without completed high school education. The employment patterns of women and the high-skilled mean they are not in a good position to take advantage of lower cost 36 Results are also robust to dropping all subregions with close to no refugees. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Anderson, J. (2011): “ The Gravity Model, ” Annual Review in Economics, 3 (1), 133 – 160. Angrist and Pischke (2009). Mostly Harmless Econometrics, Princeton University Press. AFAD (Disaster and Emergency Management Presidency of Turkey) (2013). Syrian Refugees in Turkey, 2013: Field Survey Results. Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015a). “ The Impact of Refugee Crisis on Host Labor Markets: The Case of the Syrian Refugee Crisis in Turkey. ” IZA Discussion Paper 8841. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015b). “ The Impact of Refugee Crises on Firm Dynamics and Internal Migration: Evidence from the Syrian Refugee Crisis in Turkey, ” mimeo. Aydemir, Abdurrahman and Murat Kırdar (2013). “ Quasi-Experimental Impact Estimates of Immigrant Labor Supply Shocks: The Role of Treatment and Comparison Group Matching and Relative Skill Composition, ” IZA Discussion Paper 7161. Baez, J. (2011). “ Civil Wars Beyond their Borders: The Human Capital and Health Consequences of Hosting Refugees. ” Journal of Development Economics 96 (2) November: 391 – 408. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "33 In school: an indicator for whether the respondent currently attends regular education (schooling). This does not preclude also being employed. Retired: an indicator for a respondent who declares that they are not engaged in job search because they are retired. Full / part-time employment: an indicator for whether a person works full or part-time for all people in private sector employment (see above definition of employment). Full-time employment is defined as usual working hours of 30 or more hours per week, part-time employment as usual working hours of less than 30 hours per week. We do not use the indicator provided in the LFS data since there seems to be some confusion in which category 30 hours per week falls (with these evenly divided between full and part-time). Education: we classify people into three education categories. Low education is defined as those with no completed formal education. Medium education is defined as those with at least completed primary education but no high school completion. Higher education is defined as people who have at least completed high school. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Wages: the earnings measure we use is the response to the question “ how much did you earn from your main job activity during the last month? ” In the LFS 2011 there is further information on how much of that income was irregular, for example a bonus payment, but the LFS 2014 no longer provides that breakdown. There is also a measure of the “ number of hours per week worked in the main job ” (both usual and total hours), which can be used to construct hourly wages. Since the hours worked measure does not correspond exactly to the earnings measure and introduces additional measurement error, our preferred wage measure is the monthly wage. We exclude wage observations were respondents report having usual working hours of less than 14 or more than 84 hours per week. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Policy Research Working Paper 10099 Hosting New Neighbors Perspectives of Host Communities on Social Cohesion in Eastern DRC Phuong Pham Thomas O ’ Mealia Carol Wei Kennedy Kihangi Bindu Anupah Makoond Patrick Vinck Social Sustainability and Inclusion Global Practice June 2022 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Hosting new neighbors: Perspectives of host communities on social cohesion in eastern DRC * Phuong Pham, † Thomas O ’ Mealia, ‡ Carol Wei, § Kennedy Kihangi Bindu, ¶ Anupah Makoond, | | & Patrick Vinck * * * Pham and O ’ Mealia are co-first authors. Acquisition of the data used in this manuscript was supported by the United Nations Development Programme (UNDP). The funder played no role in the analysis, inter- pretation or writing of the results and decision to submit the manuscript. This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “ Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts. ” The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. This work is part of the program “ Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership ”. The program is funded by UK aid from the United Kingdom ’ s Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This work does not necessarily reflect the views of FCDO, the WBG or UNHCR. † Assistant Professor, Harvard TH Chan School of Public Health and Harvard Medical School, USA ‡ Postdoctoral Fellow, Harvard TH Chan School of Public Health, USA § Research Consultant, Department of Emergency Medicine, Brigham and Women ’ s Hospital, USA ¶ Professor, Universit ´ e Libre des Pays des Grands Lacs, DR Congo | | Research Manager, Harvard Humanitarian Initiative, USA * * Assistant Professor, Harvard TH Chan School of Public Health and Harvard Medical School, USA JEL Codes: D74, F22, C83, N47, O15, R23 Keywords: Displacement, Hosting, Social Cohesion, Surveys, Democratic Republic of Congo Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1 Introduction One of the most challenging steps toward building a peaceful and just society after violence is the mending of broken relationships and establishing new ones between people, communities, and institutions. The international community has recognized this challenge, adopting social cohesion as a core objective and tool of peacebuilding (UNDP 2020, UNICEF 2021). Forced displacement creates major disruption to social dynamics among both displaced persons and host communities. The influx of displaced people has the potential to put strain on the host community by creating inequalities in access to services, resources, and income. At the same time, social cohesion can facilitate collective action for example by allowing pop- ulations to preemptively evacuate and escape (Arnon, McAlexander & Rubin 2021). High levels of social cohesion can improve outcomes after traumatic events either providing individual or so- cial assets, such as resilience (¨ Ozc ¸ ¨ ur ¨ umez, Hoxha & ˙ Ic ¸ duygu 2020) or mental stability (Greene, Paranjothy & Palmer 2015). But understanding what dimensions of social cohesion are especially salient – and how those dimensions are related to changing dynamics such as forced displacement – requires understanding how communities perceive what constitutes social cohesion in their lived experiences. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Despite a growing body of work that analyzes the relationship between forced displacement and social cohesion, there remains a lack of clear definition of social cohesion in the context of forced displacement (De Berry & Roberts 2018). Most research focuses on refugee situations and the resulting relationships with host communities in the global north. But the salience of different elements of social cohesion may be contextually driven and differ across several dimensions, such as the type of forced displacement experienced locally (IDP versus refugee, for example) 1 and local 1We employ the following definitions for different types of displacement to ensure analytical consistency: refugees are “ someone who has been forced to flee his or her country because of persecution, war or violence. ” Internally displaced persons (IDPs) are “ someone who has been forced to flee their home but never cross an international border. ” Returnees are “ someone who was of concern to UNHCR when outside their country of origin and who remains so for a limited period (usually two years) after returning home to their country of origin. It also applies to internally displaced persons who return home to their prior place 3 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "context (urban and rural). It is commonly accepted that displacement negatively affects social cohesion, and that forced displacement occurs in contexts with low levels of social cohesion to begin with. Protracted displacement can lead to political tensions, as associations are formed along ethnic or political lines and new grievances emerge. In contexts of internal displacement, ethnic and social tensions (which may have been the drivers or consequences of the displacement) are exacerbated by the presence of IDPs. For refugees, deeper social and cultural divides may exist, hindering social cohesion (De Berry & Roberts 2018). Indeed, in some situations, protracted displacement and expectations of retaliation on return create greater politicization of the displaced along ethnic lines (Harild, Vinck, Vedsted & de Berry 2013). More generally, economic competition, poor governance, lack of rule of law, and limited access to scarce resources are features of the living conditions for most IDPs and refugees and oftentimes the host population, further hindering social cohesion (Munoz & Shanks 2019). This paper analyzes the relationship between displacement and social cohesion in the eastern provinces of the Democratic Republic of Congo (DRC). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In 2020 alone, the country recorded 2 million new conflict displacements according to UNHCR, the majority of whom are in the eastern provinces of Ituri, North Kivu, and South Kivu (UNHCR Global Focus N. d.). Large segments of the civilian population are displaced regularly and temporarily live with hosts in neighboring communities until the local security situation improves. Additionally, political violence and in- stability in neighboring states (Burundi, South Sudan, Rwanda, and Uganda especially) produce refugee flows into eastern DRC. 2 Eastern DRC is therefore host to large numbers of both IDPs and refugees. The dynamics of hosting displaced persons in eastern DRC thus represent a fundamen- of residence. ” These categories are in contrast to migrants, “ someone who leaves their country purely for economic reasons unrelated to the refugee definition, or in order to seek material improvements in their livelihood. ” 2The conflicts in eastern DRC also produce refugee flows out of the country and into neighboring states. These flows are beyond the empirical scope of our paper but are connected to regional displacement dy- namics. 4 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "tally different set of challenges to social cohesion than the more commonly analyzed camp-based displacement or refugee flows into European countries. Due to contextual differences, the salient dimensions of social cohesion may not match aca- demic definitions derived mainly within western contexts. To address this challenge, this project employed participatory research methods to identify the elements of social cohesion considered relevant in eastern DRC. By adopting this design, the project iteratively built a set of research questions and methodological tools to ensure locally appropriate decisions to measure contextu- ally appropriate concepts. The insights from the focus groups dictated our measurement strategy of social cohesion when analyzing a series of surveys conducted in eastern DRC between 2017 and 2021. The findings contribute to a growing research agenda on how hosting forcibly displaced per- sons impacts perceptions of social cohesion. The results are consistent with findings from Zhou, Grossman & Ge (2021), who find that proximity to refugee settlements can improve goods provi- sion to host communities and that the presence of displaced persons does not necessarily negatively affect host community attitudes towards the displaced persons they host. Similarly, Aksoy & Ginn (2021) also find that the arrival of migrants do not necessarily have negative effects on host com- munity attitudes. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Consistent with the results in this paper, Betts, Stierna, Naohiko & Sterck (2021) find that local context – including urban versus rural settings – matters in how host community interactions with displaced populations impact social cohesion. 2 Context: Eastern DRCongo For the past five decades, eastern DRC has experienced varying levels of conflict. The violence has been fueled by complex and interlinked domestic and foreign competition over access to resources and political power, deepening long-standing inequities and conflicts along ethnic lines. 3 Mobutu ’ s 3For details on the roots of the conflicts in eastern DRC, see Reyntjens (2001), Vlassenroot & Raeymaekers (2004), and Vlassenroot & Raeymaekers (2009). 5 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "thirty years of autocratic rule that followed the Congolese independence from Belgium colonial rule paved the way to a violent transition, culminating in two internationalized wars from 1996 to 1997, in the aftermath of the Rwandan genocide, and from 1998 to 2003. Since the end of the Second Congo War in 2003, eastern Congo has remained unstable and violent, with many domestic and foreign-backed armed groups – including elements of the state military, FARDC – using violence against civilians and each other (Autesserre 2010). The violence and instability have resulted in poor living conditions and regular forced displacement for Congolese civilians. This project focuses on three provinces of eastern DRC that are especially impacted by forced displacement and political violence: North Kivu, South Kivu, and Ituri. 4 These three provinces account for 4. 5 million out of an estimated 5. 268 million total (85 %) IDPs in DRC 2020 (UNHCR Operational Data Portal: Democratic Republic of Congo 2021). Other provinces not included in this study but hosting IDPs include southern and central provinces such as Kasai, Kasai-Central, Kasai-Oriental, Lomani, Sankuru, and Tanganyika. The analysis in this paper focuses exclusively on dynamics in eastern Congo where the authors collected data. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The large number of interconnected conflicts in these provinces involving non-state armed groups and state actors create a continuous ebb and flow of displacement in eastern DRC (Jacobs & Kyamusugulwa 2018). In June 2020, UNHCR estimated that over 4. 5 million persons were internally displaced in Ituri (1. 6M), North Kivu (1. 9M) and South Kivu (1M) provinces alone (UNHCR 2020). DRC hosts an additional 536, 000 refugees (UNHCR 2020) from neighboring countries with recent experiences of violence, especially Burundi, Uganda, CAR, and South Sudan. Figure 2 plots the trend in the new IDPs in the DRC between 2009 and 2020. 5 Most IDPs in DRC favor staying with host families as opposed to camp displacement (Haver 2008, Rohwerder 2013). In 2017, UNOCHA estimated that around 500, 000 IDPs were in camp- like settings, whereas 3. 3 million sought refuge in host communities (Jacobs & Kyamusugulwa 4The qualitative analysis covers only the Kivu provinces, but the quantitative analysis covers all three. 5Data provided by the Internal Displacement Monitoring Center DRC Page, accessed May 14, 2021. 6 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 15 ° E 20 ° E 25 ° E 30 ° E 10 ° S 5 ° S 0 ° 5 ° N South Kivu North Kivu Ituri (a) DRC, with North Kivu, South Kivu, and Ituri Shaded 24 ° E 26 ° E 28 ° E 30 ° E 32 ° E 34 ° E 4 ° S 2 ° S 0 ° 2 ° N 4 ° N G G G G G G G (b) Kivu Provinces, Territoires, and Focus Group Locations Figure 1: Area of Interest: Kivu Provinces Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "500000 1000000 1500000 2000000 2012 2016 2020 Year Newly Displaced Persons Due to Conflict New Displacements, DRC Figure 2: Temporal Trends in IDP Flows, Democratic Republic of Congo 2018). Many are displaced multiple times in short bursts as the security situation in their com- munities fluctuates (Zeender & Rothing 2010). When fleeing violence, IDPs in DRC oftentimes attempt to stay close to their home communities so they can monitor their properties with the intention of returning once the security situation improves (White 2014). In areas near violence, host communities are frequently and repetitively asked to host IDPs: by one count, host families often host IDPs around three to four times, for around three months on average (Simpson 2010). Alternatively, many IDPs flee to urban areas, increasing slum areas in cities like Goma and Bukavu (Zeender & Rothing 2010). Urban IDPs are less reliant directly on host families and many have found work in urban areas (Jacobs, Lubala Kubiha & Katembera 2020). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In summary, the dynamics of displacement in eastern DRC are fluid, with vulnerable popula- 8 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "tions frequently coming and going as conflict dynamics evolve. IDPs primarily rely on informal networks when seeking refuge, leveraging their ethnic, religious, and other social networks to find safety. Communities host displaced persons informally and long-term camp-based displacement is relatively rare. In contrast to most research that focuses on the impacts of tightly concentrated pop- ulations, displacement in eastern DRC lacks the sort of static geographic concentration of displaced populations. Such dynamics may have fundamentally different implications for social cohesion and require different policy. 3 Defining Social Cohesion in Contexts of Forced Displacement Existing definitions of social cohesion motivate the participatory research strategies used define social cohesion in this project. Social cohesion is a conceptual construct for which there is no universally agreed upon measure. In a comprehensive study reviewing whether community driven development positively impacts social cohesion, (King, Samii & Snilstveit 2010) note that both the attitudinal and behavioral measures of social cohesion manifest in highly context specific ways, making it difficult to identify universal and cross-cutting measures of social cohesion. In general terms, social cohesion is defined as a set of societal characteristics or attributes that foster “ mutual moral support, which instead of throwing the individual on his own resources, leads him to share in the collective energy and supports his own when exhausted ” (Berkman, Kawachi & Glymour 2014). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Scholars and practitioners studying social cohesion in the last two decades generally agree that social cohesion consists of two intertwined features of society: 1) the absence of latent social conflict, including income inequality, racial / ethnic tensions, and disparities in political participa- tion and 2) the presence of strong social bonds such as high levels of trust, norms of reciprocity, presence of associations and the presence of institutions of conflict management (Jenson 2010). Increasingly, and especially in the literature that explores the role of social cohesion in economic 9 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "growth and development, a third component, sometimes included in the second above-mentioned dimension is made explicit: the presence of effective institutions and governance. These under- standings are mainly based on research conducted in Europe or North America, which excludes contextual factors in other societies, “ such as focusing on the impact of minority groups on social majorities and the effect of integration (or lack of integration) on social cohesion or theoretical blind spots, such as risks to good governance ” (De Berry & Roberts 2018). The relevant set of relationships and institutions that matter to social cohesion vary according to context. Although some studies use social cohesion and social capital interchangeably, King, Samii & Snilstveit (2010) argue that social cohesion emphasizes the group and the patterns of cooperation rather than the assets that give rise to them. Yet the question remains as to whether there is a set of common thematic concepts that can be operationally measured. De Berry & Roberts (2018) note “ initiatives to improve the definition and measurement of social cohesion have involved the development of subjective and objective indicators across the horizontal (inter-group) and vertical axis (person-state). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For example, the horizontal could be evident in the levels of trust in other social groups and the vertical evident in the level of trust in the institutions of the state. ” Social cohesion thus represents a broader social fabric, not just a particular circumstance, issue, or event. To understand how dynamics such as displacement are related to social cohesion, it is crucial to first what elements of the social fabric are considered most salient by those who live in these communities. 3. 1 Towards a Locally-Led Definition of Social Cohesion: Focus Group Ev- idence To ensure that the conceptualization of social cohesion used in this paper is appropriately contex- tualized to local understanding, the project consulted with local communities using a sequential mixed method approach to produce a locally driven definition of social cohesion. First, a qual- 10 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "They then developed a series of questions that they could ask this person to understand their per- ception of social cohesion. This participatory process based on “ personas ” developed a better understanding of the concepts and outlined key questions that participants felt were relevant to social cohesion in eastern DRC. The findings from the seven focus groups were combined to draw a single concept map (Figure 3). Together, the participants outlined three broad domains that can be subsequently divided into dimensions, sub-dimensions, and finally indicators. Some overlap between dimensions is unavoid- able because of the conceptual proximity of many of the topics discussed in the focus groups. As such, the three domains of social cohesion, as well as their respective dimensions and sub dimen- sions, are interrelated. These represent a subjective and synthesized conceptualization of social cohesion in eastern DRC according to Congolese participants. Three main domains emerged as particularly salient for focus group participants: solidarity, relationships and governance. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Figure 3: Concept Map Produced from Focus Groups on Understandings of Social Cohesion in Eastern DRC 12 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In addition to the formal forms of collaboration and to the importance of equity between in- dividuals and groups, persons consulted also evoked the importance of basic, quotidian solidarity, which we label as support. Examples included the willingness to help a neighbor who is sick or to assist someone in need of credit. Support did not give rise to many sub dimensions during the consultations, but scales can be developed to measure the attitudes and behavior of individuals in relation to quotidian solidarity. 3. 1. 3 Governance Finally, some participants (in Goma especially) noted the accountability of leaders to the popula- tion as a factor contributing to social cohesion. Elsewhere, participants discussed governance but in an indirect, and often negative, manner. The governance factor can be broken down into three dimensions. In almost all consultations, participants expressed that access to basic needs and services is key 15 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "group will benefit more in communities where elites and the excluded groups seek to compete to capture aid. As such, the evidence on the consequences of forced displacement for perceptions of local social cohesion is mixed. Existing findings are based on research conducted mainly in western Europe, particularly those that analyze large refugee flows from the Middle East and show that hosting refugees can negatively impact social cohesion. But other research demonstrates that, especially among those who are most directly exposed to displaced populations, hosting does not necessarily result in a backlash effect and may even be associated with limited economic benefits. 3. 2. 1 Fluid Displacement Flows and Perceptions of Social Cohesion In scenarios where displacement is more fluid, frequent, and informal amidst ongoing conflicts, host communities may have incentives and experiences that may differ in fundamental ways than in refugee contexts or more permanent displacement. These differences may result in unexpected relationships between hosting displaced populations and perceptions of social cohesion. Aggregate relationships between levels of displacement and social cohesion are likely influ- enced by the fact that areas that experience higher levels of forced displacement are precisely the areas that experience the most violence. As a result, aggregate displacement levels and perceptions of social cohesion are likely negatively related, but this is not necessarily a function of hosting displaced persons. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Instead, displacement is a manifestation of a broader erosion of social cohe- sion at the vertical level. Hosting IDPs may still impact host community perceptions in several ways, but more dis-aggregated analysis is required to unpack the relationship between hosting and perceptions of social cohesion. At the individual level, the relationship between hosts and IDPs may be more positive than other host-displaced community relationships. First, hosting IDPs populations can improve per- ceptions of relationships both with in-groups and out-groups by increasing contact and reliance. Hosting displaced populations can force people to rely on their own communities to respond to the 19 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "are combined. The surveys follow a repeated cross-sectional design and are not panels (i. e. the same administrative units, not the same people, are re-sampled), so responses are aggregated to the groupement level, the lowest level at which the project consistently collect representative data. Table 2 provides a summary of the dates, sizes, and percent of respondents who report being displaced within each survey wave. This aggregated temporal analysis can show, associations between fluctuations in displacement and perceptions of social cohesion over space and time at the groupement level. Question coverage varies across survey waves, but a battery of core questions enables consistent observation of how many individual respondents self-report being displaced at the time of the survey and being involuntarily moved within the past year. Poll Date N % Currently Displaced % Displaced Last Yr % Hosting Displacees # 11 July 2017 5834 4. 35 7. 42 – # 12 September-October 2017 4013 1. 62 2. 62 – # 13 December 2017 4883 3. 50 7. 97 – # 14 March-April 2018 1933 4. 97 8. 85 31. 35 # 15 June-July 2018 5951 3. 70 8. 35 30. 33 # 16 October 2018 1112 6. 47 4. 68 – # 17 December 2018 5918 5. 86 11. 20 – # 19 July-August 2019 5961 5 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": ". 12 10. 45 – # 20 December 2019 5752 4. 71 8. 14 – # 21 November 2020 2627 4. 19 5. 14 – # 22 February-March 2021 5847 6. 86 9. 30 – Overall July 2017-March 2021 49831 4. 64 8. 19 30. 58 Table 2: Details on Surveys and Displacement Trends Second, the paper conducts an individual-level analysis of two cross-sectional surveys of 1, 933 and 5, 951 individuals conducted in March-April 2018 and June- July 2018, respectively, to probe the relationship between hosting displacees and social cohesion in more detail. This survey wave 22 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "included a specific battery of questions that provided respondents the opportunity to report their perceptions of whether IDPs or refugees were present in their communities and, if so, what impact hosting displaced persons had on social cohesion their communities. Additionally, respondents reported whether their communities hosted IDPs (displaced persons from within DRC) or refugees (displaced persons from Burundi, Rwanda, Uganda, South Sudan, or other countries). Given the lack of reliable census data and frequent population movements in eastern DRCongo, sampling and weighting procedures are necessarily conservative. All of the surveys randomly select groupements (or quartiers in cities) in each territoire. Within selected groupements, select villages are drawn (or avenues in cities), which are clusters per territoire. Enumerators carry out 8 interviews per cluster using a random walk procedure. Responses are weighted to adjust for differences in probability of selection at the territoire level and all samples are gender-balanced. Additional details on the survey design are provided in the Appendix, Section D. 4. 1 Measuring Displacement Context Because this paper is interested in a context of frequent, unregistered, and informal displacement, it relies on self-reported measures for all variables included in the analysis. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "displacees in their communities, the survey asked an additional question in which respondents re- ported whether their communities hosted IDPs (displaced persons from within DRC) or refugees (displaced persons from Burundi, Rwanda, Uganda, South Sudan, or other countries). These re- sponses are used to create a categorical variable that measures whether respondents report hosting IDPs, hosting refugees, or not hosting displaced persons in their communities. Table 3 summarizes the measurement strategies for hosting status. It is possible that respondents may misreport whether they are displaced or whether displaced people are present for a number of reasons. First, they might not know that displaced persons are present, a risk that is especially acute for IDPs. Because the paper is primarily interested in how knowledge of hosting impacts perceptions of social cohesion, this measurement challenge is not as acute a problem as it may at first seem. Respondents must know that IDPs or refugees are present in their community for hosting to impact their perceptions of social cohesion. If they are not aware of the presence of IDPs or refugees, their presence is unlikely to systematically impact their perceptions. Second, there might be incentives to either hide or, alternatively, to over-claim the presence of IDPs or refugees. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Enumerators reminded respondents that the survey was part of an academic study and not connected to service provision decisions, which we hope alleviate some of these incentives. That said, it is important to note that this project analyzes self- reported perceptions of the presence of IDPs or refugees, not the confirmed presence of displaced populations. Hosting Status Aggregate Individual % Respondents Displaced Currently Self-Reported Hosting % Respondents Recently Displaced Self Reported Hosting IDPs Self Reported Hosting Refugees Table 3: Operationalizing Hosting Status 24 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "All results in the main text are presented as odds ratio plots with 95 % confidence intervals (CIs). On each of the plots, the X-axis is the Odds Ratio (log scale), the dashed vertical line is the “ line of null effect, ” and the colored point is the estimate from each regression, with 95 % confidence intervals. Estimates to the right of the dotted line signify positive and statistically significant relationships, estimates that intersect with the dotted line indicate results that do not reach statistical significance (defined as p =. 05), and estimates to the left of the dotted line indicate negative and statistically significant relationships. Each regression includes relevant controls, but the graphs only plot the displacement or hosting status variables to ease interpretation. Full regression tables are available in the Appendix, Section F. 26 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Needs Odds ratio (log scale) Variable Gender G Female Male Hosting displaced refugees (c) Host Refugees Figure 6: Summary Plots of Logistic Regressions by Gender Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "complete census of land parcels in Barafu and Kati, known as the Tanzanian Land Rights\n\n\nSurvey (TLRS). Households were identified using records and maps from the Kinondoni\n\n\nMunicipality, which had created a listing of all households in the area to assist with the\n\n**Notes:** data are from Tanzanian Land Rights survey. Sample restricted to dual-headed households in\ntreatment blocks.\n\n\nlevels of female land ownership: investigating the gender breakdown of land ownership in\n\nusing baseline data from the experimental intervention, which is discussed in more\n\n\ndetail in the following section. Households in two unplanned settlements in Dar\n\n\nes Salaam were asked a series of questions about the _de_ _facto_ ownership of land,\n\n5To avoid priming, households were not asked directly about female ownership. Instead, they were\nasked to list all members of the household that were default owners, must be consulted before a sale, or\nwould be included on a CRO.\n6Section 191(2) of the 1999 Land Act and section 58 of the (1971) Law of Marriage Act.\n7Authors' calculations using data from the Kinondoni municipal data.\n8Section 159(6) of the 1999 Land Act.\n\n\n8\n\n\n\n<!-- PAGE 12 -->\nTable 1: Female land ownership in Dar es Salaam", "output": {"entities": {"named_data": ["Tanzanian Land Rights survey", "Tanzanian Land Rights\n\n\nSurvey (TLRS)"], "descriptive_data": ["baseline data from the experimental intervention", "Kinondoni municipal data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6For more information on ENOE, see http://www.beta.inegi.org.mx/proyectos/enchogares/regulares/enoe/. 7For more information on ENILEMS, see http://www.beta.inegi.org.mx/proyectos/enchogares/modulos/enilems/.\n\n<!-- PAGE 13 -->\nin ENILEMS 2010 using the official algorithm for generating the CURP. [8]\n\nA simple merge of ENILEMS and ENLACE Grade 12 using the pseudo-CURP and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ASPIRE does not cover all countries in the world. To expand the set of countries in this study, we proceed as follows. First, we estimate an econometric model that explains the share of income from transfers, for poor and nonpoor people in each country by the share of GDP spent on social protection at the national level (from the International Labour Organization), an indicator of government effectiveness (from the Worldwide Governance Indicators), and whether the country used to be in the Soviet Union. (Other explanatory variables we tested include World Bank income category of the country, region, and GINI coefficient, but they turn out to have no explanatory power.) This provides an estimate of income from transfers in 26 additional countries (from 98 using only ASPIRE data).\n\nDiversification also comes from financial inclusion. We use the fraction of the population with savings at a financial institution, from the Global Financial Inclusion Database (FINDEX, 2015). The Findex database provides these values for the bottom 40% and for the top 60%, which we use for poor and nonpoor people respectively. We assume that the fraction of income that is diversified increases by 10% for people who have bank accounts.\n\nRather, we proxy the ability of a country to borrow with sovereign credit rating, using the average (long-term) rating of from three agencies: S&P, Moody's and Fitch.\n\nOur study takes advantage of newly available panel data from the Indian Human Development Survey (IHDS), which includes a question asking households to estimate the daily average duration of outages.", "output": {"entities": {"named_data": ["Worldwide Governance Indicators", "Global Financial Inclusion Database", "Indian Human Development Survey (IHDS)", "ASPIRE data", "Findex database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Gibson and Kim (2012) use a HCES with direct measures of consumption from food stocks and find an error of up to 300 KCal per person per day from ignoring destocking of one major calorie-source (rice) that is subject to bulk buying and storage.\n\nthese reports when made by people who never transact in metric units. A typical HCES consumption\n\nOnce the food reported in an HCES is converted into calories, the household's calorie intake is compared\n\nWhile the potential sources of mismeasurement in HCES are numerous, this study systematically\n\nusing a survey experiment conducted in Tanzania. There were a total of eight alternate designs, which", "output": {"entities": {"named_data": ["HCES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Use of state level data on rental restrictions, together with a nationally representative survey from India, suggests that, contrary to original intentions, rental restrictions negatively affect productivity and equity.\n\n1 Using census figures, Appu (1997) estimates that, to avoid having to give rights to tenants, landlords evicted about 30 mn tenants or about one third of the total agriculturally active population.\n\nThe data used in the analysis come from two rounds of NCAER's ARIS/REDS survey conducted in 1982 and 1999, respectively. This survey, the first rounds of which were implemented in 1968-71 to evaluate the impact of an agricultural development program, covers all of India's major states.\n\nof observation 4980 1279 1613 670 1417 7476 1705 2479 1307 1985\n_Source:_ Own computation from 1982 and 1999 ARIS/REDS surveys.\nAll values are in 1982 Rs with 1999 values having been deflated by state level deflators.", "output": {"entities": {"named_data": ["NCAER's ARIS/REDS survey"], "descriptive_data": [], "vague_data": ["state level data on rental restrictions", "nationally representative survey", "census figures"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the ENILEMS-ENLACE panel. The first panel is formed by a cohort of students' test\n\npanel merges a special module of the Mexican labor survey, _Encuesta Nacional de Empleo_\n\n_y_ _Ocupaciones_, ENOE, applied to individuals aged 18, 19 and 20 years during the third\n\nquarter of 2010, with students that sat for the ENLACE test in Grade 12 in 2008, 2009,", "output": {"entities": {"named_data": ["ENILEMS-ENLACE panel", "Encuesta Nacional de Empleo", "ENOE"], "descriptive_data": ["Mexican labor survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The FAO now uses survey data to calculate the CV and coefficient of\nskewness. This revision also took into account the average physical stature of each age-sex group derived from\nDHS data. The FAO updated the CV estimate for 37 countries, and, for the other countries (where they did not\nobtain HCEs), they used the same CV as in the past.\n\nThe FAO method has been widely critiqued by the research community (Svedberg, 1999, de Haen et al.\n\nThe FAO calculates the CV of food availability directly for only for a limited number of countries and", "output": {"entities": {"named_data": ["DHS data"], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The paper assembles a new, regionwide panel data set that measures local economic activity using nightlights, potential hurricane damages using a detailed hurricane windstorm model, and mangrove protection by mapping the width of mangrove forests on the path to the coast.\n\nSpecifically, we use remote sensing data on nightlights to measure local eco nomic activity. Nightlights have been shown to be a good proxy of economic activity (see Donaldson and Storeygard (2016) for a review), and their high spatial resolution is ideal because the economic impact of tropical cyclones has been shown to be highly localized\n\nWe measure potential hurricane damages using predicted wind speed from the wind field model of Pita et al. (2015). The model is calibrated for Central America and has been validated with historical data.\n\nWe measure local economic activity using imagery from four weather satellites that are part of the United States Air Force Defense Meteorological Satellite Program. These satellites record daily cloud formation by measuring the amount of moonlight reflected by clouds at night.\n\nSpecifically, we use the annual composites produced by the National Oceanic and Atmospheric Administration (NOAA). These composites predominantly measure man-made lights because they only use information from cloudfree days and because NOAA's methodology filters transient sources of light.", "output": {"entities": {"named_data": [], "descriptive_data": ["annual composites", "remote sensing data on nightlights", "composites produced by the National Oceanic and Atmospheric Administration (NOAA)"], "vague_data": ["panel data set"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Increasing the schooling attainment of girls is a challenge in much of the developing world. In\nthis paper we evaluate the impact of a program that gives scholarships to girls making the transition\nbetween the last year of primary school and the first year of secondary school in Cambodia. We show\nthat the scholarship program had a large, positive effect on the school enrollment and attendance of girls.\nOur preferred set of estimates suggests program effects on enrollment and attendance at program schools\nof 30 to 43 percentage points; scholarship recipients were also more likely to be enrolled at any school\n(not just program schools) by a margin of 22 to 33 percentage points.\n\nThe impact of the JFPR program\nappears to have been largest among girls with the lowest socioeconomic status at baseline. The results we\npresent are robust to a variety of controls for observable differences between scholarship recipients and\nnon-recipients, to unobserved heterogeneity across girls, and to selective attrition out of the sample.\n\nto the 2000 Demographic and Health Survey (DHS), 85 percent of 15 to 19 year olds had completed", "output": {"entities": {"named_data": ["2000 Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "shows that 86.7 percent of recipients, but only 64.6 percent of non-recipients were enrolled in a JFPR\n\n<!-- PAGE 7 -->\nprogram school; 80.2 percent of recipients, but only 57.9 percent of non-recipients were attending a JFPR\n\nscholarship recipients but only 76.5 percent of non-recipients were enrolled in any school, JFPR or\n\notherwise. [2] Table 1 is consistent with a large program effect on school enrollment and attendance.\n\npercentage points more likely to be enrolled at a JFPR school than girls ranked 46 or higher (p-value:", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The data for this paper come from the European Social Survey (ESS) for the survey years 2002,\n\nobservations per country/year. The ESS covers 36 countries, 24 of which are included in our\n\nCard et al (2012) investigates the drivers of attitudes towards immigrants in Europe using,\n\n\namong others, the following variables of the ESS:", "output": {"entities": {"named_data": ["European Social Survey (ESS)", "ESS", "European Social Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "on violence using data from the Armed Conflict Location & Event Data Project (ACLED) on\n\nSecurity Council meeting and subsequent news reporting- Nigeria, Somalia, South Sudan, and Yemen (e.g., World Bank 2018b). 7In the 2016 IPC classification, the mVAM was actually the key component of determining food access across the country.\n\nAuthors' calculations using the 2014 Household Budget Survey. 16Registration for the World Bank's cash transfers program being implemented by UNICEF, which covers approximately one-quarter of the total population and is aimed at relatively poorer households, demonstrates the vast majority of households can be reached via phone (e.g., World Bank 2018c); evidence from different WFP surveys of food aid benefic\n\n<!-- PAGE 11 -->\nsurvey is very similar in its relative ranking of governorates based on food access as compared\n\nto the face-to-face Emergency Food and Nutrition Security Assessment that was undertaken\nduring the survey period. [20] Additionally, the regions that the WFP survey identifies as receiving\n\nproduced by another famine early warning system (FEWS NET 2017). These unofficial updates", "output": {"entities": {"named_data": ["Armed Conflict Location & Event Data Project (ACLED)", "2014 Household Budget Survey", "Emergency Food and Nutrition Security Assessment"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This paper contributes to the existing literature, by focusing on the impact of drought on poverty in\nSomalia. Four consecutive seasons of poor rains between April 2016 and December 2017 resulted in a\nsevere drought across Somalia (FEWSNET, 2018). The drought exacerbated preexisting food insecurity, as\nhalf of the population faced acute food insecurity in mid-2017 (FEWSNET, 2016; FSNAU, 2017). The\ndrought threatened the livelihoods of many Somalis.\n\nThis analysis uses a regression framework similar to Hill and Porter (2016) to estimate the effect of the\ndrought on poverty and consumption. To isolate the drought effect, the analysis exploits two\ncharacteristics of the SHFS data set. First, fieldwork timing was such that data were collected before the\ndrought shock (wave 1) and during the drought (wave 2), allowing for a before-and-after comparison.\n\nJournal of\nDevelopment Economics 106, 132-143.\nDercon, S., Krishnan, P., 2000. Vulnerability, seasonality and poverty in Ethiopia. The Journal of\nDevelopment Studies 36, 25-53.\nFEWSNET, F., 2018.\n\nFood Security and Nutrition Analysis Unit - Somalia and Famine Early Warning Systems Network. FSNAU, 2017. Special Brief: Focus on Post Gu 2017 Early Warning.", "output": {"entities": {"named_data": ["SHFS data set"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2.0 percentage points more likely to be enrolled at any school, JFPR or otherwise (p-value: 0.29). Note\n\nscholarship receipt, therefore provide further evidence of a JFPR program effect.\n\nthe 2000 Demographic and Health Survey; here too there is a clear schooling gradient. [3] Figure 1 shows,\n\n2 Comparisons for the full sample of girls, not just those with completed applications, also suggest that scholarship recipients were more likely to be enrolled and attending school: In the full sample, the difference in the probability of enrollment at a JFPR school is 22.1 percentage points; the difference in the probability of attending is 21.9 percentage; and the difference in the probability of enrollment at any school, not just a JFPR school, is 12.1 percentage points.", "output": {"entities": {"named_data": ["2000 Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The second source of data we draw on is a short survey on COVID-19 vaccination collected inperson as part of the Ethiopia Socioeconomic Survey (ESS 5), a nationally representative household survey that was implemented between April and June 2022 by the Ethiopia Statistical Service with support from the World Bank's LSMS program. This survey contained a similar module as the phone surveys and collected information on the vaccination status of all household members.\n\nThe source of administrative data for our study is the Our World in Data (OWID) COVID-19 vaccination dataset (Mathieu et al. 2021) that compiles administrative data on COVID-19 vaccine coverage. Amongst others, the dataset contains information on the number of total doses administered, the share of the country population that has received at least one dose, and the share of the population that is fully vaccinated. [3] It covers the period from December 2020 when the first COVID-19 vaccines achieved approval and is regularly updated as new data becomes available on a per-country basis. The data is compiled from country reports (such as government websites, dashboards, or the social media accounts of national authorities) and in some cases third-party aggregators (where national authorities do not publish data in a machine-readable format) and is regularly audited for inconsistencies and technical errors.\n\nWe additionally access a second source of administrative data stemming from the WHO's COVID19 vaccination dashboard (WHO 2020b). The dashboard does not provide longitudinal information for public access but reports the latest available COVID-19 vaccine coverage figures at the time of data access (April 2, 2023, in our case).\n\nLastly, we use data from the World Bank's Statistical Performance Indicators (SPI) available through the World Bank's Open Data library (World Bank n.d.). The SPI is a composite index between 0 - 100 scoring countries' statistical systems across the five pillars of data use, data services, data products, data sources, and data infrastructure (Dang et al. 2023). To capture the performance of administrative data systems in particular, we also use the SPI's indicator of administrative data capacity (Dimension 4.2) that records the availability of Civil Registration and Vital Statistics (CRVS).", "output": {"entities": {"named_data": ["Ethiopia Socioeconomic Survey (ESS 5)", "Our World in Data (OWID) COVID-19 vaccination dataset", "WHO's COVID19 vaccination dashboard", "Statistical Performance Indicators (SPI)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "observations. The ENILEMS-ENLACE panel also includes the information from ENOE\n\nfor all 3,714 matched observations plus information from ENLACE's context question\n\nin means between the observations of ENILEMS that were merged with ENLACE scores", "output": {"entities": {"named_data": ["ENOE", "ENLACE panel", "ENILEMS-ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For rainfall data, I use a historical daily grid of rainfall, which is interpolated based on readings\n\nthat suddenly drop to zero purchasers is suspicious, especially since the BASIX data does not contain\n\nerror I run simulations where I assume that the BASIX data has been matched completely correctly,\n\nI start by examining whether there is actual autocorrelation in the rainfall data. To test for", "output": {"entities": {"named_data": ["BASIX data"], "descriptive_data": ["historical daily grid of rainfall"], "vague_data": ["rainfall data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "As with the earlier two matching estimations, we matched the children within each DHS year.\n\nThis is around 7, 6, and 5 percentage points for the 2007, 2011, and 2014 DHS, respectively.\n\n\"The Effect of Water and Sanitation on Child Health: Evidence from the Demographic and Health Surveys 1986-2007.\n\n16 According to the Joint Monitoring Programme (JMP), an improved sanitation facility is defined as \"one that hygienically separates human excreta from human contact.\n\n\" The JMP considers the following categories as improved sanitation: flush to piped sewer system, flush to septic tank, flush to pit (latrine), flush to unknown place, ventilated improved pit latrine, pit latrine with slab, and composting toilet.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": ["DHS year"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using data from the Armenia Land Tenure and Area study - a study designed specifically for this analysis - we compare the implications of the use of a proxy respondent versus the recommended self-respondent approach and the use of aggregated land data versus parcel-level land data (recommended).\n\nThe ALTA study was implemented with the technical guidance of the lead author by the International Centre for Agribusiness Research and Education (ICARE) in partnership with the Statistical Committee of the Republic of Armenia (ArmStat) and with the financial support of the 50x2030 Initiative.\n\nThe paper is organized as follows: section II describes the land tenure system in Armenia, providing context for the results that follow; Section III describes the ALTA study design and data; Section IV discusses SDG indicator computation and methods; Section V presents the results; and Section VI concludes.\n\nThe sample of the ALTA study consists of 1,200 households, scientifically selected from 100 urban and rural enumeration areas (EAs) covering both agricultural and non-agricultural households.\n\nIn addition to the module on land tenure, the ALTA questionnaire included a brief set of modules capturing\nindividual socio-demographic characteristics, educational attainment, employment status, and agricultural activity. [6]\nAn additional focus of the ALTA study was the validation of measurement approaches to land area estimation. To\nthis end, both Arms 1 and 3 included a module on land area measurement that incorporated GPS and satellite\nimagery-based parcel-level area measurement. A series of cognitive interviews were conducted to ensure that the\nland tenure questions were translated as intended from English to Armenian, as well as to understand how the\ninterpretation of questions varied across individuals, with a view to understanding male versus female", "output": {"entities": {"named_data": ["Armenia Land Tenure and Area study"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This assessment is made possible through the use of state of the art transportation modeling techniques and by combining three data sets: 1) An innovative General Transit Feed Specification (GTFS) data set collected for the purpose of this analysis under normal (dry) and flooded (wet) conditions, 2) a travel survey data set with travelers' socioeconomic attributes and Origin-Destination (OD) information, as well as 3) a set of high-resolution global flood maps that capture both the extent and depth of pluvial and fluvial floods.\n\n**3.2** **General Transit Feed Specification (GTFS) Public Transit Feed**\n\nWong, J. C. (2013). _Use of the general transit feed specification (GTFS) in transit performance measurement_ [Thesis,\nGeorgia Institute of Technology]. https://smartech.gatech.edu/handle/1853/50341\n\nTo understand the impact of flooding on public transit, we compared the GTFS transit feed mapped under dry and wet conditions and evaluated changes in headways, blockages of roads and rerouting, and travel speeds.", "output": {"entities": {"named_data": ["General Transit Feed Specification (GTFS)", "General Transit Feed Specification (GTFS) Public Transit Feed", "GTFS transit feed"], "descriptive_data": ["travel survey data set"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "composite of the effect of skills at Grade 6, as captured by ENLACE, on future outcomes\n\nFurthermore, we find evidence that strongly supports the case that ENLACE captures\n\nmain specification and run a regression of ENLACE test scores in a particular subject\n\n<!-- PAGE 8 -->\nscribes the ENLACE national examination and the steps followed to construct the longi\n\ntudinal data. Section 3 of the paper describes the empirical approach followed to study\n\nWe construct two longitudinal datasets for this paper: (1) the ENLACE panel and (2)", "output": {"entities": {"named_data": ["ENLACE national examination", "ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "15 We followed Anginer, Demirgüç-Kunt, and Zhu's (2013) methodology and computed the index by aggregating the answers to 14 selected questions on supervisory powers that were collected in the 2003, 2007 and 2011 surveys conducted by Barth, Caprio, and Levine (2008). 16 The methodology for assessing central banks' FSRs was introduced by Čihák (2006) in the first worldwide survey of FSRs. 17 Alternatively, one could use as the control the availability or even better the actual implementation of macroprudential tools prior to the 2008 crisis.\n\nIt derives from the database developed by Melecky and   Podpiera (2013) and the Bank Regulation and Supervision Surveys carried out by the World Bank in\n\nempirical link between a simple publication of FSRs and financial stability. The data on FSR publication\n\nare available for only 78 countries, covering 22 of the 25 crisis countries. The data on the quality of FSRs", "output": {"entities": {"named_data": ["Bank Regulation and Supervision Surveys"], "descriptive_data": ["2003, 2007 and 2011 surveys", "database developed by Melecky and   Podpiera", "data on FSR publication"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our main specification for the poverty line estimations defines an expenditure threshold of R$500 to\nclassify lumpy (all above) and not lumpy (all below) items. Items that are above the threshold are thus not\nincluded in the consumption aggregate as it is assumed that they are infrequently purchased, and in many\ncases, an accurate incorporation into the poverty line calculation would be by estimating a value of the\nflow of services they provide to the owner. The data required to run those estimations is not available in\nPOF 2017/18.\n\nBelow we list the items that were excluded in our main specification by POF module.\n\nby floods, and which strategies they use to cope and adapt.\nThese insights are based on firm survey data collected in\n\n2018 using a tailored questionnaire, covering a sample of\n\nmore than 800 firms. To assess the impact of disasters on\nbusinesses, the study considers direct damages and indirect\neffects through infrastructure systems, supply chains, and\nworkers.\n\nThis study presents evidence from a dedicated Tanzanian firm survey conducted with 837 businesses in Dar es Salaam and the provinces of Tanga and Dodoma.\n\nThe survey's target population was all 58,959 firms in Dar es Salaam, Tanga, and Dodoma that were registered with the National Bureau of Statistics' (NBS) in 2015.", "output": {"entities": {"named_data": ["POF 2017/18"], "descriptive_data": ["firm survey data", "dedicated Tanzanian firm survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "_replications. Contemporaneous cross-unit correlations are generated by computing the cross-correlations of residuals from_ _AR(I) regression of the first difference of Gross State Product for 20 U.S. states from 1963 to 1986 and of Gross Domestic_ _Product for 24 O.E. C.D. economies from 1960 to 1991._\n\nareas, of which 72 percent depends on agriculture (Census of India, 2011). Agriculture accounts for 17 percent of GDP and employs 51 percent of the total labor force (World Bank, 2010). [2]\n\n_Source_ : Author's analysis based on the 1982 and 2006 rounds of the Additional Rural Incomes Survey and Rural Economic & Demographic Survey (ARIS-REDS)   from the National Council of Applied Economic   Research.", "output": {"entities": {"named_data": ["Census of India", "ARIS-REDS", "Additional Rural Incomes Survey and Rural Economic & Demographic Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "_Source:_ ODHD (Sustainable Development Observatory) database 2013.", "output": {"entities": {"named_data": ["ODHD (Sustainable Development Observatory) database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "###### **2.1 The ENLACE test**\n\n_colares_, ENLACE. The test gathered information at the end of each academic year on\n\nin private and public schools. Starting in 2008, ENLACE was also applied to students\n\nfinishing upper secondary school (Grade 12). ENLACE was originally designed as a low\n\nyear when ENLACE was applied.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Firstly, the Hydrosheds variant of SRTM is used, both at 3 and 30 arc second resolutions. A number of additional corrections are applied to the terrain data including a systematic vegetation correction procedure in vegetated areas and an urban correction procedure in urbanized areas. A detailed description of the modeling framework is provided by (Sampson et al. 2015).\n\nFor this study, we developed flood hazard maps representing riverine, flash-flood and coastal flood risk for Vietnam. These flood hazard maps estimate the inundation depth at a grid cell level of 3 arc-seconds, (~ 90m) and provide coastal surge hazard layers, along with pluvial and fluvial layers. The maps provide information on the extent and depth of flood hazard for a specific location. For the coastal component, we explicitly model four return periods - 25, 50, 100, and 200 year events, under current and future climate conditions.\n\nData on natural capital is sourced from a World Bank dataset on national wealth. The dataset disaggregates the three components of national wealth-produced capital, intangible capital and natural capital-and it decomposes the natural wealth stock into renewable and nonrenewable resources.\n\nThe data on income inequality is sourced from the World Bank's \"All-the-Ginis Database,\"\nwhich was last updated in 2014. [8] The database collects Gini indexes from multiple sources\ninto long time series. The data has been standardized for this analysis via the so-called\n\"choice-by-precedence approach,\" which reflects each dataset's reliability, degree of\nvariable standardization, and consistency of geographical coverage. GDP and population\nfigures have been collected from the United Nation's UNCTAD-STAT database. Table 2\npresents the descriptive statistics for per capita GDP and the Gini index by country\nincome group. The Gini index peaks among the upper-middle-income group, falls among\nthe lower-middle-income and low-income groups and is lowest among the high-income\ngroup. These data are consistent with the relationship between inequality and GDP\ndescribed by Kuznets (1955).", "output": {"entities": {"named_data": ["All-the-Ginis Database", "UNCTAD-STAT"], "descriptive_data": ["World Bank dataset on national wealth"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Respondents should be asked to estimate the number of purchases made over a fixed reference period, anchoring questions on consumption rather than on acquisitions (as in the 2016 Barbados Survey of Living Conditions and the 2017/18 Jordan Household Income and Expenditure Survey).\n\n**GDP.** We use GDP data from the Word Development Indicators of the World Bank. It is\n\n\nmeasured using constant 2010 prices in US dollars. [8] For our analysis, GDP series need to be\n\n\nfiltered in order to extract the business cycle component from the trend. We use three different\n\n8We used the data serie called \"NY.GDP.MKTP.KD\"\n9With this setting, we mostly keep fluctuations that have a frequency between 8 and 32 quarters.\n10We keep fluctuations between 32 and 200 quarters, following Comin and Gertler (2006a)\n11The concordance table from SITC Rev2 to BEC can be found on the UN Trade Statistics webpage: `[https:](https://unstats.un.org/unsd/trade/classifications/correspondence-tables.asp)`\n`[//unstats.un.org/unsd/trade/classifications/correspondence-tables.asp](https://unstats.un.org/unsd/trade/classifications/correspondence-tables.asp)` .\n\n**Trade** **Proximity.** We collect data on bilateral trade flows from the Observatory of Economic Complexity (MIT). This database covers 215 countries over the period 1962-2014.\n\nIn this paper, we use the recent GVC indicators from Borin and Mancini (2019). They offer a new toolkit for value-added accounting of trade flows at the aggregate, bilateral, and sectoral levels", "output": {"entities": {"named_data": ["2017/18 Jordan Household Income and Expenditure Survey", "Word Development Indicators", "NY.GDP.MKTP.KD", "Observatory of Economic Complexity"], "descriptive_data": [], "vague_data": ["GVC indicators"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "9 Beans, for instance, which are one of the most important food purchases and items in Brazil, have a raw calorie intake of 306 kcal per 100 g in TBCA (Feijão, carioca), while in the prepared form, TBCA assigns 71 kcal per 100 g, assuming a composition of 50 percent beans and 50 percent water to the meal.\n\nTable 2 shows the top 10 food groups in POF in terms of average calorie intake per 100g. It shows that\n'Meat' is the most expensive food group, followed by 'Breads, Cakes and Pies'. On the other hand, 'Kitchen\nOil' is displayed as the most caloric food group, as expected. It is followed by 'Flour Derivatives' (which\nincludes pasta). The cheapest food group is 'Beans and Legumes', while the least caloric is 'Fruits'.\nTherefore, the data - including our calorie mapping - show some expected patterns of caloric and price\ndistribution across food groups.\n\n_Source:_ Own calculations using POF 2017/18 data and TBCA, based on food items categorizations by IBGE.\n\nFor this, we construct a consumption aggregate using the data from POF. The consumption aggregate is based on household expenditures on goods and services.", "output": {"entities": {"named_data": ["TBCA"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Firms listed in the NBS registry without a contact telephone number were also excluded from the sampling\nframe. Hence, all survey results should be interpreted as representative of more formal business activities.\n\nTo ensure that robust estimation results can be obtained for different subpopulations, the survey used a\ndedicated sampling strategy. Based on information from the NBS, all registered firms were divided into\nfive distinct strata, depending on their reliance on transport systems. Ordered from low to high transport\nreliance, these strata contain firms from the following sectors:\n\nDirect asset losses are the most tangible impact of disasters on firms. In high-income countries, a range\nof institutions, such as insurance companies or governmental organizations, collect data on direct losses\n(for the United States, see, for example, Smith and Katz 2013). In developing countries, where insurance\nmarkets and data collection are limited, less is known about the direct losses that firms incur. While\ndatabases such as EM-DAT provide some data on aggregate direct disaster losses, few quantitative firmlevel studies have been conducted. One exception is De Mel et al. (2012), who analyze the impact of the\n\nFirms incur a wide range of losses due to disasters, both in terms of direct damages and indirectly\ntransmitted costs. Based on the data collected for this study, Appendix B offers a full discussion of the\nscale and type of firms' disaster losses.\n\n\nThe survey data reveals that flood risks are high throughout most of Tanzania and confirm that firms face\nsubstantial recurring losses.", "output": {"entities": {"named_data": ["NBS registry", "EM-DAT"], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "**6. Conclusions**\n\n\nThis paper has used historical rainfall data to estimate a putative history of payouts on\n\n\nIndian rainfall insurance policies. We have found that indemnities are concentrated in the\n\nAccording to Census data (CNBS, 2001), shares of these crops in five of China's provinces\n\nThe climate data (monthly temperature and precipitation) were gathered from the National   Meteorological Information Center in China.  The data are based on actual measurements in 753   national meteorological stations that are located throughout China. The temperature and   precipitation data were collected from 1951 to 2001.\n\nSocio-economic data come from China's National Bureau of Statistics (CNBS). The data   were collected by a highly trained, professional enumeration staff in 2001 as part of the annual,   nation-wide Household Income and Expenditure Survey (HIES). The data cover 45,700 farm   households in 4365 villages, 533 counties and 31 provinces.\n\naccount for soils, we downloaded a soil map from FAO's website. There are three major soil\n\n\ntypes-clay, sand and loam soils. The final set of variables for our analysis was created by", "output": {"entities": {"named_data": ["Household Income and Expenditure Survey"], "descriptive_data": [], "vague_data": ["historical rainfall data", "Census data", "climate data", "soil map"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "assemble annual observations as follows for each grid square: the first 40 from the CRU\n\nscores). We do this for both future periods, as well as for the historical CRU data (here\n\nof rainfall and temperature for the past (from the benchmark CRU series that we have\n\ntable presents average R [2] scores for the bivariate relationships between the CRU", "output": {"entities": {"named_data": ["CRU\n\nscores"], "descriptive_data": [], "vague_data": ["CRU data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "evidence is consistent with studies that used cross-section surveys, panel data and cross-country\n\nSome of the work with panel data has also gone further in an effort to establish a causality link\n\nOswald (2007) use information on lottery winnings in the British Household Panel Survey", "output": {"entities": {"named_data": ["British Household Panel Survey"], "descriptive_data": [], "vague_data": ["cross-section surveys", "panel data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "data are collected from the World Bank's World Development Indicators (WDI). In this context, TFP growth is:\n\nOil Metal **Trade Openness** Percent of GDP 1990 - 94 64 95 65 56 2010 - 14 77 94 93 70 **Herfindahl Index** Product Concentration 1990 - 94 0.41 0.84 0.69 0.29 2010 - 14 0.30 0.67 0.46 0.21 Market Concentration 1990 - 94 0.20 0.30 0.08 0.19 2010 - 14 0.20 0.15 0.28 0.18 **Natural Resources** Percent of GDP 1990 - 94 17.0 46.9 26.8 9.8 2010 - 14 13.2 43.0 16.3 8.9 Percent of total exports 1990 - 94 0.82 0.99 0.95 0.78 2010 - 14 0.73 0.89 0.71 0.72 _Sources: WDI, and author's calculations using WITS, COMTRADE_", "output": {"entities": {"named_data": ["World Bank's World Development Indicators (WDI)", "WDI", "WITS", "COMTRADE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To estimate the fraction of income of the poor and non-poor that comes from social protection and transfers, we calibrate �� using the \"average per capita transfer from all social protection and labor (for beneficiaries)\" and the coverage in each quintile from the ASPIRE database [10] :\n\n<!-- PAGE 18 --> that quintile. Poor and non-poor correspond to the bottom 20% and the top 80% respectively. ASPIRE at present does not cover developed countries, so we use data from the US Consumer Expenditure Survey (CES, 2015), Canadian National Household Survey (CNHS, 2015), Australian Household Wealth and Wealth Distribution Survey (AWWDS, 2015), and European Union Survey of Income and Living Conditions (EU- SILC, 2015).", "output": {"entities": {"named_data": ["ASPIRE database", "US Consumer Expenditure Survey", "Canadian National Household Survey", "Australian Household Wealth and Wealth Distribution Survey", "European Union Survey of Income and Living Conditions"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The primary data source of undernutrition in this study is the Ethiopia Mini Demographic and Health Survey\n\n\n(EMDHS) 2014 [2]. The EMDHS 2014 is a stratified nationally representative survey, including 5,579 children\n\n1. Consensus C Copenhagen Consensus 2012 : Expert panel findings.\n2. Agency CS (2014 ) The Ethiopia Mini Demographic and Health Survey (EMDHS).\n3. Ethiopia GotFDRo (2013) National Nutrition Programme June 2013-June 2015.\n4. Andrew Sunil R, Christopher G, Jessica T (2012) Combating Malnutrition in Ethiopia : An Evidence-Based Approach for", "output": {"entities": {"named_data": ["EMDHS", "EMDHS 2014", "Ethiopia Mini Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the section above, we showed that our results are robust to the expansion of the calorie-food mapping between POF and TBCA. We also find that applying a parametric or nonparametric approach to the estimation of the lines does not affect the results greatly.\n\nBased on this, and using the detailed data on Brazil's demographic profile from the POF 2017/18 data, we calculate a population-weighted average required calorie intake.\n\n16 An alternative method could also be to directly use the information from the POF 2017/18 and the consumption\npatterns of the consumption per capita distribution to estimate the actual kcal consumed and apply those to\nestimate the food poverty line. However, as has been documented in many other studies (for instance World Bank,\n2019; Myanmar Ministry of Planning and Finance and WBG, 2017), the relatively poor households do not typically\nconsume enough to cover their caloric needs. In POF 2017/18, a simple extrapolation of the average calories\nconsumed in the bottom 40 percent is about 1,190. Thus, the definition of a food poverty line is normative and uses\na benchmark instead of actual caloric consumption.\n\n**Poverty** **Poverty**\n\nParametric R$276 R$487\nNonparametric R$276 R$484\n_Source:_ Own calculations using POF 2017/18.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Malaita is one of largest of the Solomon Islands, with an estimated population of 170,883 (according to\n2012/13 HIES). The three primary roads of Malaita, namely the North Road (112.2 km), South Road (75.6\nkm) and East Road (41.7 km), together constitute nearly 60% of the road network on the island and carry\nthe majority of vehicular traffic (Figure 1). These roads connect 19 of the 33 wards and provide access to\n70% of the population.\n\nIt combines nationally representative pre- pandemic household survey data with follow-up phone survey data from Mali and exploits sub- national variation in the intensity of pandemic-related disruptions between urban and rural areas.\n\nWe combine nationally representative data collected between October 2018 and July 2019 with follow-up phone survey data collected between May and June 2020.", "output": {"entities": {"named_data": ["2012/13 HIES"], "descriptive_data": ["pre- pandemic household survey data", "follow-up phone survey data", "nationally representative data collected between October 2018 and July 2019"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "<!-- PAGE 7 -->\ncities in the sample, providing a consistent mid-day activity benchmark for comparing CO2\nconcentration anomalies. [3] OCO-2 has an observation repeat time of 16 days. We have downloaded\ngeoreferenced measures of XCO2 (the column-averaged dry air mole fraction of CO2).\n\nWe compute the daily median XCO2 for each 10-degree latitude band and linearly interpolate the result to each OCO-2 observation with 1-degree resolution.\n\nMistry (2019) has provided global estimates of monthly heating and cooling degree days at 25 km\nresolution. [4] We compute population at 25 km resolution by aggregating data from CIESIN (2021) at\n5 km resolution. Monthly estimates are interpolated from data provided for 2010, 2015, and 2020. We\nuse two sources to construct our georeferenced measure of income per capita. From the G-Econ\ndatabase (Nordhaus et al.\n\nWe merge the results with annual UN estimates of GDP per capita in constant $US 2015 (UN 2021), and use the cell ratios to estimate annual GDP per capita for each cell.", "output": {"entities": {"named_data": [], "descriptive_data": ["data from CIESIN", "UN estimates of GDP per capita"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the alternative specification, an additional step has been taken to assign calories to food items registered in the POF household expenditure questionnaire on frequent purchases (POF 3). The cost per calorie in this alternative scenario is minimally higher, leading to a food poverty line of R$259 in 2018 urban Southeast prices.\n\n_Source:_ Own calculations using POF 2017/18.\n_Note:_ Main specification estimates the cost per calorie based on assigning food items in POF to the TBCA and imputation for food\nitems at the 5-digit level (i.e., steps 1 and 2 described above). The alternative specification extends the imputation to larger food\ngroups based on categorization by IBGE (2020).\n\n**Lower** **Upper**\n**Total poverty line** R$455 R$1,061\n𝒔 [𝑭𝑭] 0.235 0.243\n_Source:_ Own calculations using POF 2017/18 data.\n_Note:_ These total poverty lines are based on the food poverty line of R$258, coming from our main specification, which is based\non the minimum calorie requirement of 2,100 calories per person per day and average cost per calorie of the bottom 40 percent\nof the welfare distribution, with calories having been assigned in a two-step procedure and deflated by household-specific foodprice Paasche indices, as described earlier in this section.", "output": {"entities": {"named_data": ["POF household expenditure questionnaire"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "this approach to calculating hunger the HCES-direct method. Of note is that neither the FBS-CV nor the\n\nHCES-direct method actually measures what individuals ate (as in a food intake survey) or ask about\n\nThe FBS-CV and HCES-direct methods both rely on household surveys. In the case of the FBS-CV method,\n\nthe second moment of the calorie distribution comes from the surveys, while the HCES-direct method\n\nrelies on the surveys for all moments. Yet, the design of HCES varies over several key dimensions, such", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Although the JFPR program is known as a \"scholarship\" program, it does not directly subsidize\n\nreason\" fewer than 10 days in a year. [1] The JFPR program therefore functions much like a \"conditional\n\nAs we show below, the JFPR scholarship program had a large, positive effect on the school\n\npercentage points. The impact of the JFPR program appears to have been largest among girls with the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "each country. However, while we are aware of this limitation, the ESS has been extensively\n\n(2012) mentions that these questions in the ESS eliminate ambiguities by referring to _people_   _who come to live in a country_, rather than to _immigrants_ . In countries where citizenship is   based on blood ancestry such as Germany, a translation of immigrants would include people   who were born in the country but are not citizens.\n\nsection survey data. The second specification incorporates age, year of birth and survey year   dummy variables as explanatory variables (blue line). According to the model without any   cohort controls, older people are less likely to exhibit positive attitudes toward immigrants than   their younger peers in almost all countries considered.\n\n_Is the period of time long enough to capture life cycle effects?_\n\n\nIt could be argued that the period of time covered by the ESS - from 2002 to 2012 - may not be\n\n\nlong enough to capture life cycle patterns. Preferences may change over a longer period of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "rents to GDP from WDI), we can implicitly compute 𝛾.\n\nSource:_\n_Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._\n\n\nFigure 2 plots the sources of growth for the Sub-Saharan Africa region as well as the different groups according\n\nSource: Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._\n\nFrankel, J. A., & Romer, D. H. (1999). \"Does trade cause growth?\" American Economic Review 89(3): 379-399.\n\nreported in the Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015). [15]", "output": {"entities": {"named_data": ["Penn World Tables 9.0", "Penn World Tables"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "20 For more details on the 1997 IFPRI Egypt Integrated Household Survey, see Guarav Datt, Dean Joliffe\n\n***Total food subsidies in real terms calculated by deflating nominal costs by CAPMAS Consumer Price\nIndex for urban areas (1 986-87 = 100).\nn.a. = Not available.\nSources: Food subsidy data:\n1970-71 to 1980-81 from Harold Alderman, Joachim von Braun and Sakr A. Sakr, Egypt's Food Subsidy and\nRationing System: A Description, Research Report 34 (Washington, DC: International Food Policy Research\nInstitute, 1982), Table 2.\n1981-82 to 1996-97 from unpublished data, Ministry of Planning, and Ministry of Trade and Supply.\n\n\nTotal government expenditure data:\n1970-71 to 1979 from Alderman, von Braun and Sakr, op. cit.\n1980-81 to 1991-92 from unpublished data, Central Agency for Public Mobilization and Statistics.\n1992-93 to 1996-97 from unpublished data, Ministry of Trade and Supply.\n\nSource: IFPRI Egypt Integrated Household Survey, 1997.\n\napplying it to data from a household survey in northern Mali. In this application, we find", "output": {"entities": {"named_data": ["1997 IFPRI Egypt Integrated Household Survey", "Egypt Integrated Household Survey, 1997"], "descriptive_data": ["Total government expenditure data", "household survey in northern Mali"], "vague_data": ["Food subsidy data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "_Source_ : EM-DAT (2008) _Notes_ : Figures greater than 1,000 such as the total number affected by storm in 1995 (  1,145 affected per 1,000 people) are possible due to instances where there are multiple occurrences of a particular disaster in one country in a given year.\n\nThis paper presents a set of estimates of the impacts of natural disasters on different forms of capital (physical, human, natural and energy), and thereby on real GDP per capita. The capital database is compiled by the World Bank for three periods - 1995, 2000 and 2005 and for 210 countries. This was combined with data on four types of natural disasters - droughts earthquakes, floods, and hurricanes/storms - for 196 countries, taken from the Emergency Events Database (EM-DAT). The disasters database lists a total of 55 events of drought, 82 events of earthquake, 447 events of flood and 303 events of storm that started during the three years of 1995, 2000 and 2005.\n\nEmergency Events Database (EM-DAT). 2008. _Data on Natural Disasters by type_ . Centre for Research on the Epidemiology of Disasters (CRED), School of Public Health of the Université Catholique de Louvain, Brussels, Belgium.", "output": {"entities": {"named_data": ["EM-DAT", "Emergency Events Database (EM-DAT)"], "descriptive_data": [], "vague_data": ["Data on Natural Disasters", "capital database"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1) _Food consumption._ We include the value of food consumed from all possible sources, as measured in POF. This includes food acquired for consumption at home, which is collected in the household questionnaire of frequent acquisitions (POF 3). Food consumed at home can be acquired via monetary means, obtained through own production, or received in kind or via other means that do not involve monetary payment.\n\nThe value of consumption of FAFH is collected in the individual-level expenditure questionnaire on nonhousehold goods and services (POF 4). Again, this component of food consumption might have been acquired via monetary as well as nonmonetary means.\n\nFinally, POF also collects information on food consumption outside the home - in particular, meals consumed in settings like at school or on vacation.\n\nPOF collects information on a wide range of nonfood items, but several are typically not included in the construction of a consumption aggregate used for welfare analysis.\n\nFor this, a detailed inventory of durable goods is needed, including information on their value (either original purchase date and value, or current replacement value). POF, however, collects information only on quantity, mode of acquisition, year, and state (new or used) in its durable goods inventory, but not on values.", "output": {"entities": {"named_data": ["POF"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Nevertheless, we believe that this analysis provides suggestive   13As mentioned earlier, this is per capita income for the three months prior to the ENIGH survey.\n\nCADENA now offers weather index insurance   for a variety of perils (e.g., drought, flood, hail), as well as area-based yield index insurance, which   2Subsidies from the federal government depend on the marginality index of the insured municipality, as computed by CONAPO.\n\nNote. The data used to created this table comes from the 2010 census. % Illiterate is defined as the number of\nilliterate individuals over the age of 15 divide by the total population over 15.\n\n**Karlan,** **Dean,** **Robert** **Osei,** **Isaac** **Osei-Akoto,** **and** **Christopher** **Udry**, \"Agricultural\n\n\nDecisions after Relaxing Credit and Risk Constraints,\" _Quarterly_ _Journal_ _of_ _Economics_, 2014,\n\nIn addition, this study computes environmental risks at the commune level [2] to relate it to household information based on the Vietnam Household Living Standard Surveys (VHLSS) for 2010, 2012, and 2014. Benefiting from the panel", "output": {"entities": {"named_data": ["ENIGH survey", "2010 census", "Vietnam Household Living Standard Surveys (VHLSS)"], "descriptive_data": [], "vague_data": ["marginality index"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "<!-- PAGE 9 -->\nThe purpose of ENLACE was to strengthen school accountability by informing parents\n\n\nand society at large about the levels and evolution of learning outcomes. In this regard,\n\nthe results from ENLACE received wide attention from the Mexican public. Every year,\n\nENLACE results made it to the front page of most newspapers in Mexico; civil society\n\norganizations were empowered thanks to ENLACE results. In an effort to use all the\n\ninformation produced by ENLACE to guide school improvement plans, SEP produced", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The DLHS-3 data do not provide details on the number of households that  share the latrines reported, whether the shared latrine belongs to the household or another\n\nin access to sanitation in the span of 10 years (as of the 2011 census), an estimated 69  percent of households in India still do not have a sanitation facility in their household\n\nhouseholds in the primary sampling unit (PSU), define as village in DLHS-3, that has access to that kind\n\nsample of households of each village, making the ratio reliable. The distribution presented on the figure\n\n\nwas estimated using a kernel density estimation with a bandwidth of 5.\n\nfrom de Ministry of Agriculture regarding WII's coverage. These data include municipality level\n\ncoverage information in terms of weather stations used, insured crops (maize, beans, sorghum", "output": {"entities": {"named_data": ["DLHS-3 data", "DLHS-3"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We use the FAO Food Composition Tables for the Near East to convert daily food quantities into kilocalories; we then divide by the effective household size to get per capita daily caloric intake. [6]\n\nThe amount of assistance delivered during conflict is increas ing to support the growing share of the extreme poor living in these settings. However, little is known about how well assistance is targeted, which can have implications for conflict itself. Using a novel data source in Yemen that tracks food assistance, which accounts for the largest share of assistance in the country, the authors find that the share of households receiving food assistance significantly increased following the U.N. announcement of a food emergency and that the increases were larger in regions identified by the U.N. as being closer to famine. Furthermore, the increases in\n\nThis article investigates the change in humanitarian assistance following a particularly in teresting event- the announcement of a food emergency in Yemen in 2017. The United Na tions announced the 2017 Integrated Food Security and Phase Classification (IPC) in March\n\nutilizes a novel mobile phone survey conducted by the World Food Programme (WFP) that pro vides high-frequency data on food assistance and food access that are regionally disaggregated.\n\n_∗_ The authors thank the World Food Programme for sharing the data from the November 2017 Yemen mVAM survey and commend the team on the impressive data they have collected over the course of the conflict in Yemen. We thank the editor and two anonymous reviewers for excellent comments and suggestions.", "output": {"entities": {"named_data": ["FAO Food Composition Tables for the Near East", "Integrated Food Security and Phase Classification (IPC)", "November 2017 Yemen mVAM survey"], "descriptive_data": ["novel data source in Yemen that tracks food assistance", "mobile phone survey conducted by the World Food Programme"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "_enlace_ _[math]_ _it_ = _β_ 0 + _β_ 1 _enlace_ _[math]_ _it-_ 1 [+] _[ β]_ [2] _[enlace]_ _it_ _[language]_ _-_ 1 + _τf_ + _ϵit_ (4)\n\nFinally, we go back to the ENILEMS-ENLACE panel and look at average ENLACE\n\nfield of studies (see Figure 5). If ENLACE captures specific skills and students tend to\n\nENLACE indeed captures the specific skills it is designed to measure. Furthermore, to\n\nUsing the Mexican census-based standardized test ENLACE, we construct a longitudinal", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Data on municipal level requests and approvals for disaster declarations were constructed from\n\nthe archives of Mexico's official diary. Data on municipal level Fonden expenditures, and\n\nlevel. Specifically, we calculate by municipality, from the 2005 population conteo and the 2010 population census, the number of dwellings with the following characteristics: dwelling has non\n\n12The source of state level GDP data is INEGI. GDP is measured in constant 2008 pesos.\n\nUNDP estimates of municipal GDP per capita in 2000 by municipal population. Third, we", "output": {"entities": {"named_data": [], "descriptive_data": ["municipal level Fonden expenditures", "2005 population conteo and the 2010 population census"], "vague_data": ["state level GDP data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "daily changes in the amount of time spent at different places within a given geographic area. These data\n\n\ncapture daily percent changes from February 15 through July 15 relative to a baseline representing the\n\nFigure 2 displays the Google Community Mobility data for both the entire country of Mali and\n\n\nfor Bamako specifically. Google aggregates locations into six types of places: Grocery and Pharmacy,\n\nFinally, we use information from the COVID-19 phone panel survey, our third source of information used to investigate the intensity of pandemic-related disruptions within Mali.\n\nas suggested by the Google Mobility Data, price effects could influence behavior on the intensive margin.\n\n\nMore research is needed to fully understand these dynamics.\n\n\nFigure 4 shows self-reported estimates of the impact of the coronavirus pandemic on economic out", "output": {"entities": {"named_data": ["Google Community Mobility data", "COVID-19 phone panel survey", "Google Mobility Data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To further control for geological and agro-ecological characteristics at the community level, elevation figures were included, taken from the GLOBE 1 kilometer elevation database (GLOBE Task Team et al. 1999). Elevation differences were also incorporated as a measure of terrain roughness. These were computed using the SRTM3 data (Jarvis et al. 2006) by subtracting the minimum elevation in a 1 kilometer grid from the maximum elevation in the 1 kilometer grid. Finally, urban/rural and regional indicator variables help capture unobserved (time invariant) location specific characteristics related to the urban and rural livelihood systems as well as those related to regional variation in the agro-ecological, economic and political environment. Year dummies were included to control for the survey year.\n\nGiven the focus here on riverine and coastal floods and to better pinpoint the locations that actually experienced riverine or coastal floods, the DFO flood maps were overlaid with the location of the rivers taken from the Hydroshed project (Lehner et al., 2008). Only the major rivers were mapped as these will have sufficiently large catchment areas exposing the downstream areas they traverse to (non-localized) flooding. Riverine and coastal flood bands were then derived by considering DFO flooded areas within 2, 5 and 8 meters elevation difference from the closest point on the major river or the coast using a Digital Elevation Model from GLOBE (GloBe Task team, 1999).\n\nTo reflect access to public services, indicator variables are included that take the value of one if clean water [32], sanitary latrines, [33] or electricity are present in the community, and zero otherwise. To proxy a household's integration in the overall economy, the community data were further augmented with the distance of the centroid of the community (in meters) to the nearest part of the nearest primary or secondary road, as designated in the VMAP0 dataset (NIMA 1997). These measures were computed that using Arc View 3.2. In addition, the estimated travel time to the nearest town or city of at least 25,000 people, the nearest city of at least 100,000 people, and the nearest city of at least 500,000 people were included. The travel times were computed in Arc View from a friction grid that assigned differing travel times to various classes of roads, urban areas, water bodies, off-road, and crossing international boundaries. [34]\n\nThe expenditure data are expressed in January 2002 prices using the CPI provided by the Government Statistical Office. They are also corrected each year for regional and rural/urban differences across the 8 regions.", "output": {"entities": {"named_data": ["GLOBE 1 kilometer elevation database", "SRTM3", "Hydroshed project", "VMAP0"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "<!-- PAGE 16 -->\n**Appendix 1: Comparing applicants with full application forms to those with only partially**\n**completed forms**\n\n**All applicants** **Full application form**\n**Outcomes**\nEnrolled in JFPR school 0.81 0.82\nAttending on the day of school visit 0.74 0.75\nEnrolled in any school 0.87 0.87\n\nDas, Jishnu, Quy-Toan Do, and Berk Özler. 2005. \"Reassessing Conditional Cash Transfer Programs.\"\n_World Bank Research Observer_ 20(1): 57-80.\n\nDeolalikar, Anil. 1993. \"Gender Differences in the Returns to Schooling and School Enrollment Rates in\nIndonesia.\" _Journal of Human Resources_ . 28(4): 899-932.\n\n\"Educational Attainment and Enrollment Profiles: A Resource Book based on an Analysis of Demographic and Health Survey Data.\" Development Research Group.\n\n<!-- PAGE 19 -->\n**Figure 1: Attendance and enrollment status by decile, JFPR application data**\n\nJFPR applicants: Enrollment at a JFPR school, by JFPR applicants: Attendance on day of school visit, scholarship status and decile by scholarship status and decile", "output": {"entities": {"named_data": ["Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In doing so, this paper also relates to recent research using firm-level data to document effects of foreign acquisitions on firm performance, including Arnold and Javorcik (2009), Bloom et al.\n\nIn order to estimate equations (1), (2) and (3), we use data from the Orbis database\nof Bureau Van Dijk. The Orbis dataset provides retrospective information on\ncompany ownership and reports the country of origin of each foreign shareholder,\nalthough the share value is often unknown.\n\nThe data covers about 58% of Polish firms as reported by Eurostat and coverage varies significantly across regions (Farole et al., 2017). [5] Figure A.4 shows the evolution of the percentage of companies with at least one foreign shareholder from either Germany, French, the United States or the United Kingdom.", "output": {"entities": {"named_data": ["Orbis database"], "descriptive_data": [], "vague_data": ["firm-level data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "total area of 122,437 sq. km. Rainfall data from more than 200 rainfall stations and\n\npotential evapotranspiration data of around 30 evaporation stations have been used in the\n\nis estimated by overlaying the inundation risk map with the population map for 2001 using\n\nAhmed, A.U., Alam, M. 1998.: Development of Climate Change Scenarios with General\nCirculation Models in Vulnerability and Adaption to Climate Change for Bangladesh, S.\nHuq, Z. Karim, M. Asaduzzaman and F. Mahtab (Eds.), Kluwer Academic Publishers,\nDordrecht, pp.13-20.\n\n\nBangladesh Bureau of Statistics (BBS), 2007. Population Census-2001: National Series,\nVolume-1 Analytical Report. Dhaka, Bangladesh.\n\n2050. The best available spatially-disaggregated maps and data for these assets have been", "output": {"entities": {"named_data": ["Population Census-2001"], "descriptive_data": ["evapotranspiration data of around 30 evaporation stations"], "vague_data": ["Rainfall data", "spatially-disaggregated maps and data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using a survey of 4,500 manufacturing firms for the year 2010-11, this paper estimates the impact of electricity shortages on firm productivity in Pakistan.\n\nWe match firm-level data from the Census of Manufacturing Industry conducted by the Pakistan Bureau of Statistics with district-level power shortage data reported by distribution utilities.\n\nThe Census of Manufacturing Industries provides a thorough annual overview of firmlevel activities, including detailed information on a variety of input costs, including labor, capital and electricity, as well as revenue data. The 2010-2011 census mainly covers firms in Punjab province.\n\nThe census covers 4,499 firms in 23 sectors at the 2-digit level of Pakistan Standard\nIndustrial Classification (PSIC). [ 4] The distribution of firms by the 2-digit classification are\nshown in Table 1. Of the 23 divisions, number 13 (Manufacturing of Textiles) covers\nroughly 28 percent of our sample, division 10 (Manufacturing of Food Products)\naccounts for 15 percent, and division 32 (Other Manufacturing) is the third-largest with\n11 percent of the sample. The 23 sectors can be further broken down into 236\nsubcategories at the 5-digit level of PSIC.", "output": {"entities": {"named_data": ["Census of Manufacturing Industry", "Census of Manufacturing Industries"], "descriptive_data": ["survey of 4,500 manufacturing firms"], "vague_data": ["power shortage data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Notes: (1) The table displays the estimation of the probability of being enrolled in college, employed, or employed in a formal firm given ENLACE grade 12\nscores and a set of socioconomic variables. (2) Sample: ENILEMS-ENLACE panel. (3) All specifications include school fixed effects\n\n45\n\n\n\n<!-- PAGE 48 -->\nTable A.6: Probit         - ENLACE test scores and post-secondary school outcomes\n\nEnlace Score 0.301*** -0.0474 -0.0474 0.114*** -0.0152 -0.0175 (0.0499) (0.0870) (0.0811) (0.0186) (0.0280) (0.0298) Upper secondary GPA 0.223*** 0.128* 0.0667 0.0846*** 0.0409* 0.0246 (0.0427) (0.0671) (0.0661) (0.0162) (0.0216) (0.0245) Girl -0.194** -0.661*** -0.0615 -0.0736** -0.212*** -0.0227 (0.0792) (0.132) (0.127) (0.0291) (0.0339) (0.0473) Private upper secondary 0.275*** -0.277 -0.271 0.104*** -0.0886 -0.100 (0.102) (0.186) (0.173) (0.0392) (0.0585) (0.0646) Urban resident 0.244*** -0.0709 0", "output": {"entities": {"named_data": ["ENLACE panel", "ENILEMS-ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The COVID-19 Panel Phone Survey of Households sample is nationally representative, representative of Bamako, and representative of both urban and rural areas.\n\nThe EHCVM sample itself covered 8,390 households across Mali and is nationally representative, representative of Bamako, and representative of both urban and rural areas. The survey relies on a multi-module instrument covering topics including a household's socio-economic characteristics, time use, production activities, and welfare indicators such as consumption expenditure and food security.\n\nuse sampling weights derived from the 2018 EHCVM sampling frame and adjusted for response rates\n\n\nin the COVID-19 Panel Phone Survey of Households. These sampling weights are applied both in our\n\nIn this study, we use the FAO's Food Insecurity Experience Scale (FIES) as primary outcome of interest. The FIES aims to measure food insecurity based on the direct experiences of people relating to food security (Ballard _et_ _al._, 2013; Smith _et_ _al._, 2017).", "output": {"entities": {"named_data": ["COVID-19 Panel Phone Survey of Households", "EHCVM", "Food Insecurity Experience Scale (FIES)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For example, the baseline year for children sampled in the 2007 DHS will be the year 2000.\n\nWe do not  have any particular expectations regarding the relationship between DHS years and the likelihood of  enrollment (Year [2011], Year [2014]).\n\n[14] Clusters from  DHS 2000 were geomatched with DHS 2007 to estimate the exposure to unimproved sanitation when children in the 2007 sample were 1 or 2 years old.\n\nOur data set is a pseudo-panel with children ages 6-9 sampled from three cohort pairs of  independent repeated cross-sections of DHS 2000-07, 2004-11, and 2007-14, following Deaton (1985),  Magadi (2016), and Ncube and Shimeles (2012), respectively.\n\nWe matched the children within each DHS year, using the Stata user-written program _cem_ introduced in Blackwell et al.", "output": {"entities": {"named_data": ["2007 DHS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "market outcomes (Kotsadam and Tolonen 2016). Mining is also associated with more economic\n\n\nactivity measured by nightlights (Benshaul-Tolonen, 2019; Mamo et al, 2019).\n\n\nKotsadam and Tolonen (2016) use DHS data from Africa, and find that mine openings cause\n\nand GLSS with production data for 17 large-scale gold mines in Ghana. We find that a new\n\n\nlarge-scale gold mine changes economic outcomes, such as access to employment and cash\n\nincrease in total production that rose from 541,147 oz in 1990 to 3,119,823 oz in 2009 according\n\n\nofficial Ghana statistics (Bloch and Owusu, 2012). This production increase led to an increased\n\nSouth Africa\nTeberebie 1990 2005 Anglogold Ashanti South Africa\nWassa 1999 active Golden Star Resources USA\n_Source:_ InterraRMG 2013.\n_Note:_ Active is production status as of December 2012, the last available data point.", "output": {"entities": {"named_data": ["DHS data"], "descriptive_data": [], "vague_data": ["production data", "official Ghana statistics"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 2 reports average daily hours in the three locations for a representative sample of 4,612 individuals drawn from 600 households in rural, peri-urban and urban areas of seven Bangladeshi regions\n\nA previous paper by the authors (Dasgupta, et al., 2004) has documented extensive indoor air-quality monitoring in the peri-urban area of Narshingdi (Dhaka region).\n\nDrawing on information from continuous, 24-hour monitoring of PM10 in 27 households in Narshingdi, Figure 2 displays a typical daily pollution cycle as\n\ncloser to cooking-area concentrations, and our 24-hour monitoring data indicate that daily pollution", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["monitoring of PM10", "24-hour monitoring data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "effects is Calahorrano (2011). Using panel data for Germany between 1999 and 2008, she finds   that immigration concerns decrease over the life-cycle.\n\nsimilar to Calahorrano (2011). However, given the lack of comparable panel data surveys for a   large group of countries, we use pooled cross-sections from the European Social Survey (ESS)", "output": {"entities": {"named_data": ["European Social Survey (ESS)"], "descriptive_data": [], "vague_data": ["panel data", "panel data surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "race or ethnic group as the majority (ESS). _Education_ is the average number of years of full-time education completed (ESS).   _Age_ is the median age of all respondents (ESS). All summary statistics use design and population size weights.\n\nThe estimates are small area estimations\nbased on the 2014 Demographic and Health Survey and\n\n\n\nthe latest population census. It is shown that small area\nestimations are powerful predictors of undernutrition, even\ncontrolling for household characteristics, such as wealth and\neducation, and hence a valuable targeting metric.\n\n**Keywords:** Child malnutrition, Small Area Estimation, Demographic and Health Survey, Targeting, Ethiopia", "output": {"entities": {"named_data": ["2014 Demographic and Health Survey", "Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Survey (UNHS) and the 2009/10 Uganda National Panel Survey (UNPS), both implemented\n\nThe 2005/06 UNHS covered all the districts in Uganda surveying 7,421 households\n\nfrom 783 Enumeration Areas. The 2009/10 UNPS collected information on 2,975 households", "output": {"entities": {"named_data": ["Survey (UNHS)", "2009/10 Uganda National Panel Survey", "UNPS", "2009/10 Uganda National Panel Survey (UNPS)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The algorithm is fed with 24 different satellite bands, measuring surface reflectance in the visible and infrared spectra, topographical data from the Shuttle Radar Topography Mission (SRTM), and road network data from the OpenStreetMap project.\n\nInformation on aquaculture production in Vietnam's two main river deltas was obtained from two data\nsources. In the Red River Delta, a vector dataset of the locations of individual aquaculture ponds was used.\nIt was created and provided by researchers of the German Aerospace Center (DLR). As part of their\nmethodology, an algorithm extracted individual ponds from time series of radar satellite data. Specifically,\n83 Sentinel-1 scenes from between 2014 and 2016 were processed. Radar data, such as the observations\nfrom the Sentinel-1 series of satellites, enables the detection of water-covered surfaces irrespective of\ndaylight and clouds.\n\nOverall, an\naccuracy of 0.84 is reported for pond detection in the Red River Delta (Ottinger et al. 2018). In the Mekong\nDelta, a vector dataset containing the locations of individual aquaculture ponds was provided by SWIRR.", "output": {"entities": {"named_data": ["Shuttle Radar Topography Mission", "OpenStreetMap project"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Both the EHCVM sample and the COVID-19 phone survey include each of the eight FIES questions, allowing us to get a measure of food insecurity for both periods.\n\n_Notes:_ These figures come from Google's COVID-19 Community Mobility Data. With the same kind of aggregated and anonymized data used in Google Maps, these data show changes for each day in time spent at specific types of places relative to the baseline period.\n\nBaqueano, F., Christensen, C., Ajewole, K. and Backman, J. (2020). International food security\n\n\nassessment, 2020-30. _GFA-31,_ _U.S._ _Department_ _of_ _Agriculture,_ _Economic_ _Research_ _Service_ .\n\n\nBarrero, J., Bloom, N. and Davis, S. (2020). Covid-19 is also a reallocation shock. _NBER_ _Working_\n\nThe Kinshasa Commuter Travel Survey (CTS), conducted by the Japan International Cooperation Agency (JICA, 2018), reports information on local commuting patterns as well as socio-economic attributes at both household (income/expenditure, number of vehicles, members, etc.) and individual (age/sex, work/school type and place, industrial category, income, vehicle availability, etc.) levels.\n\nBuilding on the resulting wet network, we then ran travel simulations for a total of 8,866 commuters [13] in Kinshasa using the origin and destination location information provided in the JICA commuter travel survey under both dry and wet conditions for five flood return periods.", "output": {"entities": {"named_data": ["EHCVM", "Google's COVID-19 Community Mobility Data", "Kinshasa Commuter Travel Survey (CTS)", "JICA commuter travel survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Self-reported data from this survey suggest that firms in Dar es Salaam, Dodoma, and Tanga incurred at least $7.6 million in direct losses and damages due to flooding on business premises in 2018.\n\nThe data collected through this survey suggest that the primary reasons for general supply and delivery delays in Tanzanian firms are upstream supply chain issues.\n\nRentschler et al. (2019a) build a microdata set of about 143,000 firms to estimate the monetary costs of infrastructure disruptions in 137 low- and middle-income countries.\n\nThe lack of reliable and resilient infrastructure systems causes economic efficiency losses. A global study\nby Rentschler et al (2019) highlights the substantial drag that unreliable infrastructure imposes on firms\nin developing countries. In Tanzania, firms are incurring estimated utilization losses of nearly $670 million\na year (1.8 percent of national GDP) from power and water outages and transport disruptions. The firm\nsurvey collected for this study allows us to estimate the share of these utilization losses that are caused\nby natural shocks.\n\nWe use consumption and price data from the National Risk and Vulnerability Assessment (NRVA) 2007/08, conducted by the Government of Afghanistan Central Statistics Organization and the Ministry of Rural Rehabilitation and Development. The survey was administered between August 2007 and September 2008 and covered over 20,500 households (over 150,000 individuals) in 2,572 communities in all 34 provinces of Afghanistan.", "output": {"entities": {"named_data": ["National Risk and Vulnerability Assessment"], "descriptive_data": ["Self-reported data from this survey", "microdata set of about 143,000 firms"], "vague_data": ["data collected through this survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "sample variation between the population of twins and the total population of ENLACE\n\nEven\nthough these estimates are smaller than those reported in previous specifications, they are still significant\nand economically large.\n\n\n17\n\n\n\n<!-- PAGE 20 -->\nates measured in the ENLACE context questionnaire, which allows us to also control for\n\nENLACE context questionnaire, which are similar to those already reported. Columns\n\nteristics. As expected, ENLACE test scores at Grade 6 are strong predictors of on-time\n\n###### **4.2 ENLACE Test Scores and Labor Market Outcomes**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Strzepek K & McCluskey A, 2006. District level hydroclimatic time series and scenario analysis to assess the impacts of climate change on regional water resources and agriculture in Africa. CEEPA Discussion Paper No 13, Centre for Environmental Economics and Policy in Africa, University of Pretoria.\n\n#### 2 The Survey Data   Progressing in the e¤orts to better understand the development economics of public  debt management strategies across di¤erent country groups and individual coun tries, the Banking and Debt Management Department of the World Bank conducted  a survey on public debt management strategies.\n\n#### 5 Conclusion   This paper analyzed survey data on public debt management strategies across income  groups, regions and levels of indebtedness using graphical tools.\n\nAs mentioned in the introduction, we see this paper as a ...rst attempt to charac terize the variations in the survey data on public debt management strategies across  countries where establishing a regularly repeated survey would be incredibly bene... cial.\n\ntotal government debt to GDP, should result in an e¤ort to consolidate government\n\n...nances and adopt a debt management strategy. One can also expect that if this\n\nindicator reaches high levels the government may give up on debt management and", "output": {"entities": {"named_data": [], "descriptive_data": ["survey on public debt management strategies", "survey data on public debt management strategies"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "While emission factors for each technology were taken from ESMAP (2007) and the Royal Academy of Engineering (2004), electricity prices were derived from IEA (2012).\n\n(2004), electricity prices were derived from IEA (2012). The levelized cost estimates are based\n\non an overview of a large number of recent studies presented in IEA (2010). [10] Obviously, using", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Next, we evaluate firm level data on ownership to get a better understanding of what foreigners\n\n2Aitken and Harrison (1999) argue a similar point. Using plant level data on productivity in Venezuela, they find\n\nhypotheses testing. Data on mergers and acquisitions in Korea are from Securities Data Corporation\n\nfrom Securities Data Corporation (2000) or have a foreign ownership share exceeding 25 percent.\n\nHandbook of Listed Companies, Daewoo Securities (1998 and 1999). In cases where the World", "output": {"entities": {"named_data": ["Securities Data Corporation"], "descriptive_data": ["plant level data on productivity in Venezuela"], "vague_data": ["firm level data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "prediction model between the z-score and observable household and location characteristics in EMDHS. This\n\nrelated to the sampling design of EMDHS, as it is designed with few EAs within each region and relatively large\n\nareas, the sample from EMDHS 2014 is used.", "output": {"entities": {"named_data": ["EMDHS", "EMDHS 2014"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Poor households in Bangladesh depend heavily on wood, dung and other biomass fuels\nfor cooking. This paper provides a detailed analysis of the implications for indoor air\npollution, drawing on new monitoring data for respirable airborne particulates (PM10) in a\nlarge number of Bangladeshi households. Concentrations of 300 ug/m [3] or greater are\ncommon in our sample, implying widespread exposure to a serious health hazard. For\ncomparison, Galassi, Ostro, et al. (2000) find substantial health benefits for PM10\nreduction in eight Italian cities whose annual concentrations are far lower: 45-55 ug/m [3] .\n\nhouseholds, using new air monitoring data from Bangladesh. Recent technical advances\n\nIn this paper we have investigated the determinants of indoor air pollution in\n\n\nBangladesh, using monitoring data for a stratified sample of 236 households in the region\n\nof Dhaka. Extrapolating from our results, we have estimated indoor air pollution levels\n\n\nfor a random sample of 600 rural, peri-urban and urban households in six regions:\n\n3 The 24-hour cycle of ambient PM10 concentrations is very similar to the pattern of average hourly\nresiduals from a panel regression that controls for differences in average PM10 concentrations for\nhouseholds that use biomass fuels.\n4\nFor comparison, we cite PM10 concentration measured by the Bangladesh Air Quality Management\nProgram monitor situation at the Parliament building in Dhaka. From March, 2002 to February, 2003, the\nmean daily concentration was 137 ug/m [3] . Our thanks to our colleague Paul Martin for this contribution.", "output": {"entities": {"named_data": [], "descriptive_data": ["new monitoring data for respirable airborne particulates (PM10)", "new air monitoring data from Bangladesh"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Annual grid-level GDP data between 1990 and 2014 at a 0.5-degree resolution come from Kummu, Taka and\n\nGuillaume (2018a). The data are primarily based on sub-national GDP per capita data constructed by Gennaioli,\n\nIs the relationship between rainfall and GDP explained by agriculture? We use data from the ESA CCI project\n\n\nto determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split\n\ndifferent weights. Population is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).\n\nThe work compares food prices subnationally and finds that increasing prices are the most significant factor driving recent food insecurity but documents strong spatial heterogeneity in both market price dynamics, as well as relations to food insecurity, and provides examples of markets where prices are more sensitive to localized shocks. 4For instance, the International Finance Statistics (IFS) data base of the IMF reports monthly price data at Consumer Price Index (CPI) component level, but few developing countries report food price data without substantial delays.", "output": {"entities": {"named_data": ["ESA CCI project", "HYDE 3.2", "International Finance Statistics (IFS) data base of the IMF"], "descriptive_data": ["grid-level GDP data", "sub-national GDP per capita data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "strong association between ENLACE test scores at Grade 6 and secondary school out\n\nA one standard deviation (SD) increase in ENLACE test scores at Grade 6 is asso\n\nilar story goes for future test scores. A one SD increase in ENLACE test scores at Grade\n\non taking the ENLACE exam. Again, these results are statistically significant at the\n\nENLACE Panel and run a specification including a vector of twin (family) fixed effects.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "very likely to enroll in a JFPR school if they were to enroll anywhere; similar incentives did not exist for\n\nsignificant program effects of the scholarship program on enrollment at any school, not just at JFPR\n\nThe estimated program effect on enrollment in a JFPR school in the specification with a quadratic and a log term in SES is 0.255, with a standard error of 0.121, that in the specification with a quartic in SES is 0.291, with a standard error of 0.249.\n\nFor example, the estimated program effect on enrollment in a JFPR school in the cubic specification with a threshold at 42 is 0.340 (with a standard error of 0.155); when the threshold is 48, the estimated program effect is 0.361 (with a standard error of 0.255).\n\ntotal enrollment among non-recipients is 0.879, and the estimated JFPR program effect on enrollment is", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Specifically, the monthly mVAM began in August 2015 and has been conducted in nearly every month since. [7] The 10-minute survey is conducted by mobile phone and reaches approxi mately 2400 households every month through random digit dialing. The survey is stratified by\n\nEvery month, the survey collects information necessary to construct the Food Consumption Score (FCS) [10], information on food coping strategies necessary to construct the Reduced Coping Strategy Index (rCSI) [11], information on whether the household received food assistance in\n\npublicly available. However, the WFP publicly shares each month the governorate-level averages and confidence intervals of all key variables collected [13], and these monthly governorate-level  estimates are used in the empirical analysis to analyze changes in food assistance and food access following the 2017 IPC announcement. [14] We further merge the mVAM data with data", "output": {"entities": {"named_data": ["Food Consumption Score (FCS)", "Reduced Coping Strategy Index (rCSI)"], "descriptive_data": ["monthly governorate-level  estimates"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "where _β1_ = 0.19 and _β2_ = 0.81 denote mt of soybean oil and soybean meal, respectively, produced from one mt of soybeans in a representative large country (FAPRI 2012), [6] and _c0s_ denotes the (fixed) processing cost per mt of soybean oil (the crushing margin).\n\ncalibrate our constant price elasticity demand and supply curves, we use elasticities found in the literature as well as the elasticities used for similar developing countries in the FAPRI model.\n\nWe use Hamilton's (2009) estimate of fuel demand elasticity of -0.26. [15] The demand for soybean\n\ndetermination of this compensatory payment is crucial for modeling the effects of alternative\n\nbiodiesel policies.\n\nSinkala (2011) reports a processing cost of soybean-based biodiesel in Zambia of\n\nSinkala, T. (2011). \"Economics of Biofuels: Country Case Study for Zambia\". Report for the\nWorld Bank, DEC-Research Group, Environmental and Energy Unit.\n\nZDA. (2011). \"Agriculture, Livestock and Fisheries - _sector profile 2011_ \". Published by Zambia\nDevelopment Agency.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "school report cards and a related online platform. Still, few schools viewed ENLACE\n\nBy design, ENLACE had a national mean score of 500 and a standard deviation of 100\n\nand excellent. ENLACE's methodology followed item response theory (IRT), allowing\n\nIn 2008, SEP decided to use ENLACE scores to measure teacher performance in\n\nsubject to yearly evaluations. In 2008, ENLACE scores among their students were given", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the ALTA study, perception of tenure security is asked on a scale from 1 to 5, ranging from \"not at all likely [to involuntarily lose land]\" to 'extremely likely [to involuntarily lose land]\", in line with the joint module by FAO, World Bank, and UN-Habitat (2019).\n\nIn the ALTA study, the non-response rates at the individual level for Arms 1 and 2 are 13% and 12%, respectively, with the difference not statistically different from zero.\n\nThis paper uses data from a methodological experiment in Armenia to assess the implications of survey design-namely, respondent strategy and the level of disaggregation of land data-on the measurement of individual land rights and SDG indicator monitoring.\n\nEvidence from the Uganda Methodological Survey Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA), which fed into the development of the UN EDGE guidelines, illustrates the asymmetric impacts of respondent approach by gender.\n\n3 For reports on the LSMS+ surveys in Tanzania, Malawi, Ethiopia, and Cambodia, see Hasanbasri et al. (2021a) and\nHasanbasri et al. (2021b).", "output": {"entities": {"named_data": ["Uganda Methodological Survey"], "descriptive_data": ["LSMS+ surveys in Tanzania, Malawi, Ethiopia, and Cambodia"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, specifying a control variable for which national accounts or survey data is needed (such as the share of agriculture in GDP or the share of the population engaged in agriculture) would run counter to the purpose of this study, which is to see whether proxies such as nightlights can be used to estimate growth in areas (sub-national or supra-national) for which there are no national accounts or survey data. One option is to use rainfall or vegetation data, which share many of the desirable features of nightlights data, to proxy the performance of the agricultural sector.\n\nIn the first step, we estimate national GDP based on specification (1). The estimated GDP is divided into two parts based on the share of agriculture in the economy (based on actual national accounts data): Agricultural GDP and industrial and services GDP. In the second step, we distribute industrial and services GDP among the subnational units in proportion to their share of national nightlights.\n\n6 This is in contrast to Ghosh and others (2010) who use the Landscan data to distribute agricultural GDP among\nsub-units.\n\n\n16\n\n\n\n<!-- PAGE 19 -->\n##### **5.1 Kenya**", "output": {"entities": {"named_data": ["Landscan data"], "descriptive_data": [], "vague_data": ["survey data", "rainfall or vegetation data", "nightlights data", "national accounts data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "www.gasflaretracker.ng](http://www.gasflaretracker.ng/) has the geographic coordinates of each flaring point in Nigeria as well as monthly estimates of the flare volume from each location.\n\nsatellite-detected estimates with the official estimates reported by the Department of Petroleum\n\n\nResources [DPR] (2018). The satellite-detected estimates are broken down by location, while the\n\nThe paper uses the Romanian firm-level data from the Ministry of Finance covering enterprises of all sizes from 2011 to 2020, combined with the new World Bank Businesses of the State dataset, which tracks ownership of state business entities with at least 10 percent stake in Romania.", "output": {"entities": {"named_data": ["gasflaretracker.ng", "World Bank Businesses of the State dataset"], "descriptive_data": ["Romanian firm-level data from the Ministry of Finance"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "percentage of government debt ...nanced by means of concessional resources, e.g. from\n\nmultilateral and bilateral donors. We assume that the higher the concessional share\n\nof government debt the lower the incentive for the government to adopt a strategy\n\nThe  standard deviation of exchange rate is computed using the exchange rate series from   18     <!-- PAGE 24 -->  the Penn World Tables (Heston et al, 2006).\n\nstandard deviation, creates a genuine dilemma for country authorities of whether to\n\n\n21We have carried out the estimation using LOGIT and LP models as well to check on any\nmispeci...cation problems. We did not detect any. The estimation results are available from the\nauthor.\n\n[7] Heston, A., Summers, R., Aten, B. (2006). Penn World Table Version 6.2. Center\n\nSince price stability is the basic objective and goal of monetary policy we ...nd the\n\nstandard deviation of in‡ation indicative of the quality of internal macroeconomic", "output": {"entities": {"named_data": ["Penn World Tables"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The only question included in the WVS related to attitudes towards immigrants - and available   for a sufficient number of survey years - is: _On this list are various groups of people.\n\nUsing household surveys for 24 countries between 2002 and 2012, we find that in most\n\n<!-- PAGE 24 -->\nDeaton, Angus. The analysis of household surveys: a microeconometric approach to\n\nSource: Attitudes toward migrants were estimated from ESS (see data section for more details). The gap reflect the difference between the positive attitudes of\n\ngroup as the majority (ESS). _Proimmigdiff_ is the average pop. share (%) who would like many or some immigrants of a _different_", "output": {"entities": {"named_data": ["WVS", "ESS"], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "set that satisfies this requirement is the World Value Survey (WVS) spanning the years from   1990 to 2011, that is, a period covering about 20 years.\n\nin the WVS regarding immigrants are quite different from those of the ESS. Moreover, they are   not available for all years, so we end up with only two countries from the Europe area: Spain", "output": {"entities": {"named_data": ["World Value Survey (WVS)", "WVS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "When the stable lights data set is examined, most countries display positive growth in _AoL_ and negative\ngrowth in _R_ . There is a weak negative relationship in levels (Figure 7) and a somewhat stronger negative\nrelationship in growth rates (Figure 8). This outcome is not surprising. For all but a handful of rapidly\ngrowing countries, the periphery of an expanding _AoL_ is not as bright as the center. Thus, _R_ must fall\nbecause _R_ = _SoL_ ÷ _AoL_ .\n\nWe use four independent variables. Data for GDP, non-agricultural GDP, and manufacturing value-added\nare all measured in constant 2005 US dollars. These data come from the World Development Indicators\nsupplied by the World Bank. Data for electricity consumption in kilowatt-hours come from the\nInternational Energy Agency via the World Development Indicators. Data for population come from the\nUN Population Division, again via the World Development Indicators. Data for the value of the stock of\nphysical capital, denominated in constant 2005 US dollars, come from the Penn World Tables Version 8.0.", "output": {"entities": {"named_data": ["World Development Indicators", "Penn World Tables Version 8.0"], "descriptive_data": ["stable lights data set", "International Energy Agency via the World Development Indicators"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The final steps consist of setting a threshold for when a stream flow is strong enough  to flood the basin and then weighting this with the nightlight data and aggregating up to a  district level.  5  <!-- PAGE 8 --> The earthquake index is constructed from computer generated contour maps by the US  Geological Survey (USGS) of earthquake intensity data, commonly used as potential dam age proxy (De Groeve _et_ _al._, 2008; GeoHazards International and United Nations Centre  for Regional Development, 2001; Federal Emergency Management Agency, 2006).\n\nThe  data are then combined with the nightlight data and aggregated up to district level.  Finally, the 2004 Christmas tsunami has been modeled following a method where Heger  (2016) uses inundation maps to construct a district level damage index assuming uniform  6  <!-- PAGE 9 --> damage across all flooded areas.\n\naggregation of the disaster indices in the main paper. To weight the indices, nightlight data  are used as a proxy for economic activity.  **A.1** **Flood** **Index**  The flood index is made from stream flow modeled in GeoSFM, a software that uses remotely  sensed data as inputs, which are weather and soil and terrain based.\n\nthe contour maps as a base for damage infliction, we combine them with the nightlight and  building type data from the USGS building inventory for earthquake assessment to create  fragility curves by building type; see Jaiswal & Wald (2008) and GeoHazards International  and United Nations Centre for Regional Development (2001).", "output": {"entities": {"named_data": ["USGS building inventory"], "descriptive_data": [], "vague_data": ["nightlight data", "earthquake intensity data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This means that night lights data can be used to estimate economic activity at levels that are not usually captured in national accounts, such as subnational administrative units (provinces, districts, counties, cities, etc.) or regions not coinciding with national borders (coastal vs inland regions, connected vs unconnected regions, etc.). For instance, Henderson, Storeygard and Weil (henceforth HSW) estimate, counter to intuition, that coastal areas in Sub-Saharan Africa are growing slower than the hinterland.\n\nData on night lights are provided by the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). The DMSP satellites circle the earth 14 times a day and record the intensity of Earth-based lights.\n\nWe used stable light imagery of SSA derived from scores of orbits of the DMSP OLS in from 1992-2013 since this product inter-calibrated where fires and other ephemeral lights have been removed, although there are noteworthy blunders associated with over-glow effects where lighting spreads to neighboring pixels (and hence economic activity is wrongfully attributed to certain places). The stable lights imagery has annual quantized pixels with values (Digital Numbers) with integers ranging from 0 to 63.\n\nData on GDP and other indicators used in the analysis (surface of country territory, electricity consumption, etc.) are provided by the World Development Indicators (2014). Following HSW, we use constant GDP in local currency units.", "output": {"entities": {"named_data": ["Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS)", "DMSP OLS", "World Development Indicators"], "descriptive_data": [], "vague_data": ["night lights data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "There are different ways to arrive at a value for housing services consumed by nonrenters, which have different advantages and disadvantages (see Deaton and Zaidi, 2002). In POF, households that own their dwelling (even if still paying the mortgage) or that\n\n_Temporal and spatial price adjustment._ To make the consumption aggregate and average costs per calorie\ncomparable across households, we adjust expenditure values temporally and spatially. POF was collected\nthroughout a full year and its reference periods go back up to 12 months. Figure 2 shows the temporal\nvariation of prices for different Brazilian states over the reference period of the survey. On average, prices\nincreased by 26.8 percent over the reference period - a monthly average of 1 percent. IBGE (2020)\nprovides temporal deflators in the publicly available data, which we use to deflate prices and the\nconsumption aggregate to express all monetary values in January 2018 values.\n\n13 There are several differences between our consumption aggregate and other studies using POF data. In one of the\nmost recent ones (Oliveira et al. 2016), the authors aim to measure and analyze welfare, poverty (using the\nadministrative \"poverty'' line equivalent to half a minimum wage), inequality, and vulnerability.\n\nMidwest-Rural 0.997 0.671 0.933\n_Source:_ Own calculations using POF 2017/18 data.\n_Note:_ Prices in the urban Southeast are the reference prices. While we use household-specific price indices in our estimations,\nfor ease of presentation we calculated regional averages of household-specific indices and divided them by the\nSoutheast-Urban average.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "\"Life in Transition Survey, Transition Report 2020-2021: The State Strike Back\", [https://www.ebrd.com/publications/transition-report-](https://www.ebrd.com/publications/transition-report-202021) [202021.](https://www.ebrd.com/publications/transition-report-202021)\n\n\"Strengthening the business environment for productivity convergence,\" in OECD Economic Surveys: Romania 2022, OECD Publishing, Paris, [https://doi.org/10.1787/63318cf5-en.](https://doi.org/10.1787/63318cf5-en)\n\n_Encuesta_ _Dirigida_ _a_ _la_ _Población_ _Venezolana_ _que_ _Reside_ _en_ _El_ _País_ _(ENPOVE)_ is a special ized survey of Venezuelans living in Peru conducted by the National Institute of Statistics (INEI) in December 2018. The sample covers five main urban areas in the country where Venezuelan immigrants were most likely to be present. The survey collects data on the immi grant's origin, migration date, and details on their current employment. Importantly, a full module asks about the immigrant's experiences with locals, which includes questions about discrimination and hostile attitudes towards them. The respondent's current location is iden tified down to the _centro_ _poblado_ level, which roughly corresponds to an urban neighborhood or a rural town.\n\n_Encuesta_ _Nacional_ _de_ _Hogares_ _(ENAHO)_ is the Peruvian version of the Living Standards Measurement Survey, e.g. a nationally representative household survey collected monthly on a continuous basis. For our analysis, we use data from January 2007 to December 2020. The survey covers a wide variety of topics, including basic demographics, educational back ground, labor market conditions, crime victimization, and a module on respondent's percep tions about the main problems in the country and trust on different local and national level institutions. Observations are also spatially identified at the municipality level, but here we focus on variation in the Venezuelan share of the population at the province level, of which there are 196, as these are best representative of local labor markets.\n\n_Latin_ _American_ _Public_ _Opinion_ _Project_ _(LAPOP)_ is a opinion survey conducted bi-annually in all countries in Latin America and designed to be representative of urban populations. This was fielded in Peru in 2010, 2012, 2014, 2017 and 2019 and consists of about 2,000 observations from mostly urban areas. The survey questions are centered around politics,", "output": {"entities": {"named_data": ["Life in Transition Survey", "ENPOVE", "ENAHO", "LAPOP"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The surveys were conducted\neach year between May and November of the survey year, with expenditures referring to the past\n12 months.\n\n\nReal total per capita expenditure less per capita expenses on health care is taken as the\ndependent variable.\n\nIn particular, using the historical daily rainfall records over 1980-2006 in 166 geo-referenced weather stations from across Vietnam (see Figure 1) [9], monthly rainfall grids of 0.1 degree resolution are constructed [10] using both inverse distance weighting (IDW) and inverse elevation difference weighting (IEW).\n\nTo also capture riverine floods, which may occur downstream following heavy rainfall in the mountains or as a result of storm surges in coastal areas, riverine flood indicators will be constructed based on the satellite based flood data from the Dartmouth Flood Observatory (DFO) (2008). The 1980-2006 geo- referenced UNEP/GRID-Europe storm track dataset is used to separately explore the effects from high winds and gusts from cyclones. As the damage from heavy rainfall associated with cyclones will already be captured by the localized and riverine flood indicators, these measures will capture the wind damage associated with cyclones. [5]\n\nTropical storms approach Vietnam from the east. They typically arrive during the Southeast and Northeast monsoon (Christiaensen et al., 2009), with 90 percent of them occurring between June and November, and almost half of them coming ashore in the northern part of the country. To generate annual cyclone maps, a GIS dataset of areas affected by hurricane force winds was developed from the UNEP/GRID-Europe (2007a, 2007b) tropical cyclones databases. In particular, the storm track and wind speed data were used to create symmetric polygons about each path, based on the algorithm by Klotzbach and Gray (no date). [21]\n\n4 The frequently used CRU TS2.1 monthly rainfall database (Mitchell and Jones, 2005) only has a half degree resolution, which is around 55 kilometres at the equator. 5 See Thomas (2009) for a detailed description of the different data sources and a more elaborate description of the construction of the different natural hazard maps. 6 The nearest neighbour method assigns the value of the weather station nearest to the grid cell centre. 7 Radial basis functions are functions of distance used for exact interpolation of point data.", "output": {"entities": {"named_data": ["satellite based flood data from the Dartmouth Flood Observatory (DFO)", "UNEP/GRID-Europe storm track dataset", "CRU TS2.1"], "descriptive_data": [], "vague_data": ["daily rainfall records"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The monthly precipitation data comes from the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003). This dataset, created by the National Oceanic and Atmospheric Association's Climate Prediction Center, is based on ground station measurements of precipitation over the period 1948-2001.\n\nSoil data was obtained from FAO (2003). The FAO data provides information about the\nmajor and minor soils in each location. Data concerning the hydrology was predicted from a\nhydrological model for Africa (Strzepek & McCluskey 2006). The model calculated the\nwater flow through each district in the surveyed countries. Data on elevation at the centroid\nof each district was obtained through GIS manipulation using data from the United States\nGeological Survey (USGS, 2004). The USGS data are derived from a global digital elevation\nmodel with a horizontal grid spacing of 30 arc seconds (approximately one kilometer).\n\nWeng F & Grody N, 1998. Physical retrieval of land surface temperature using the Special Sensor Microwave Imager. _Journal of Geophysical Research_ 103: 8839-8848. World Bank, 2003. Africa rainfall and temperature evaluation system (ARTES). World Bank, Washington DC.", "output": {"entities": {"named_data": ["Africa Rainfall and Temperature Evaluation System (ARTES)", "ARTES"], "descriptive_data": [], "vague_data": ["Soil data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Macro International, Demographic and Health Surveys, Beltsville, MD.\n\n<!-- PAGE 33 -->\n8. **Comparison with Monitoring Results for India**\n\n\nA recent monitoring study for Indian households (World Bank, 2002; Balakrishnan,\n\n\net al., 2002; Parikh, et al., 2001) has provided useful comparative information about\n\nsurvey suggests that progress has been quite limited. Of 686 biofuel-using households in\n\n\nour 7-region survey (including Dhaka), only 9 (1.3%) report using an improved stove: 4\n\nIn this paper, we investigate individuals' exposure to indoor air pollution (IAP). Using new survey data from Bangladesh, we analyze exposure at two levels: differences within households attributable\n\nIn this paper, we use our survey data to estimate the incidence of IAP exposure for family", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": ["monitoring study for Indian households", "7-region survey", "new survey data from Bangladesh"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "survey, which did not include a benchmark measure as did the Dantlait survey. Figure 3\n\nDantlait survey results can be extrapolated to a sample of households in other parts of Niger,\n\nIt is important to note, however, that the Dantlait survey was limited to cattle. Small\n\nprecision of these estimates, and the Dantlait survey only collected data on cattle. Throughout", "output": {"entities": {"named_data": ["Dantlait survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "factor between nightlights and economic activity as measured by Gross Domestic Prod uct (GDP). Using World Bank data on GDP in Parity Purchasing Power (PPP) in 2011\n\nThe data source for the terrain roughness is the Global Land Cover Dataset 2000 and for the topography characteristics is the Shuttle Radar Topography Mission database.\n\nThe local wind speeds experienced due to a hurricane are derived from a spatial hurricane windstorm model developed by the World Bank Group Latin America and the Caribbean Disaster Risk Management team (Pita et al., 2015). The model estimates maximum wind speeds at the height of 10 meters with a spatial resolution of 1 km [2] for observed hurricanes and tropical storms in Central America during our sample period.\n\nThe focus in the paper is on one specific family of household surveys, the Living Standard\n\nMeasurement Study (LSMS). This is one prominent type of household survey widely", "output": {"entities": {"named_data": ["Global Land Cover Dataset 2000", "Shuttle Radar Topography Mission database"], "descriptive_data": ["World Bank data on GDP"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Of all countries in the World Bank's WDI, only 60%\n(40% of the analyzed countries) had an inflation figure for the most recent 2019-2020 period. The IFS\ndata reported similarly on only half of the countries. This was last checked on August 31, 2021; WDI\ndata identifier FP.CPI.TOTL.ZG, and IFS data identifier PCPI ~~P~~ C ~~P~~ P ~~P~~ T.\n\nThe paper highlights the new price monitoring capabilities using surveys from the World Food Programme (WFP) gathered in 25 fragile and conflict-affected countries.\n\nSubnational food prices have been surveyed in many countries for years by\nhumanitarians to inform their country operations. Well-known data bases are\nthose from the WFP, FEWS NET and the Food and Agricultural Organization\n(FAO). [6] The paper focuses on raw monthly data from the WFP, but parts of the\ndiscussion, and particularly the methods developed here, could apply to similar\ndata sets. [7]\n\nThe paper gathered all end-of-August data available from the WFP Vulnerability Analysis and Mapping (VAM) unit as of September 21, 2021.", "output": {"entities": {"named_data": ["World Bank's WDI", "WFP Vulnerability Analysis and Mapping (VAM) unit"], "descriptive_data": ["surveys from the World Food Programme"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "6 However, this may reflect differences in rainfall or farming intensity.\n7 We use two poverty maps based on the General Population and Housing Censuses of 1998 and 2009. The poverty\nmap for 1998 was developed by GREAT (Applied and Theoretical Economics Research Group) and combines the\n1998 census and the household survey ELIM (Integrated Light Household Survey) of 2006.", "output": {"entities": {"named_data": ["General Population and Housing Censuses", "1998 census", "household survey ELIM (Integrated Light Household Survey)"], "descriptive_data": ["General Population and Housing Censuses of 1998 and 2009"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Data on the structural and cyclical characteristics of national economies comes from the World Bank's World Development Indicator Database [9] and UNCTAD-STAT; [10] the figures on education are sourced from Barro & Lee (2013); [11] those on the real effective exchange rate (REER) are taken from Darvas (2012); [12] and metal- and oil-price figures are sourced from the IMF's Primary Commodity Prices database. [13] Referring to the original datasets will provide further details on the methodology and sources used.\n\nWe need first to describe inequality and poverty levels in the affected region. We define the poor as the individuals in the bottom quintile in terms of consumption or income. Therefore, the parameter �� is equal to 20%. To estimate �� and ��, we use the World Development Indicators database, which provides the income share of the bottom 20%. Figure 3 shows the result, highlighting the large variability - and lack of correlation - between inequality and national income level.\n\nIn this national-level analysis, and consistent with our focus on the socioeconomic drivers of resilience, we use a very simple methodology. We use the Global Building Inventory database from PAGER, by USGS, which provides a distribution of building types (buildings only, not contents) within countries across the world (USGS 2015). This typology has been developed to assess vulnerability to earthquakes but here we use it for all hazards.", "output": {"entities": {"named_data": ["UNCTAD-STAT", "Primary Commodity Prices database", "World Development Indicators database"], "descriptive_data": ["Global Building Inventory database from PAGER"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "using purchasing power parity rates. It comes from the World Development Indicators\n\nThe governance indicators come from the International Country Risk Guide (ICRG)\n\nData on the Gini coefficient were drawn from a more complete World Bank source\n\n100 km of ice-free coast) come from Gallup et al. (1999). Data on total forest area (km [2] ),\n\nprecipitation come from WDI (2010). Latitude (in absolute value), mean elevation (meters", "output": {"entities": {"named_data": ["World Development Indicators", "International Country Risk Guide"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The FAO estimates of the hunger rate combine aggregate food balance sheets for every country with\n\nrepresentative household consumption expenditure surveys (HCES) available for 129 developing\n\ncountries. [1] These HCES are already being used to monitor global poverty trends (Chen and Ravallion", "output": {"entities": {"named_data": ["HCES"], "descriptive_data": [], "vague_data": ["food balance sheets"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The second source is Giri et al. (2011), who use 1997 to 2000 Landsat data, together with supervised and unsupervised digital image classification, to construct a 30 square meter resolution map of the global distribution of mangrove.\n\n4Nordhaus (2006) considers areas with elevation less than 8 meters as vulnerable to storm surge. We\nuse a less stringent definition because Shuttle Radar Topography Mission (SRTM) elevation estimates\nbelow 10 meters are not considered reliable (McGranahan et al., 2007). We thank Eric Strobl for\nproviding us with GIS boundaries for global coastal lowlands constructed from STRM data.\n\n\n7\n\n\n\n<!-- PAGE 10 -->\nshortest path to the coast. We find that there are 3,853 cells (49% of cells in storm surge\n\nWe measure local economic activity using remote sensing data on nightlights; potential hurricane de struction using a damage index derived from a wind field model calibrated for Central America; and mangrove protection by calculating the cumulative width of mangroves along the closest path to the coast.", "output": {"entities": {"named_data": ["Shuttle Radar Topography Mission"], "descriptive_data": [], "vague_data": ["remote sensing data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "data source listed most prominently in the announcement was the Emergency Food Security  and Nutrition Assessment (EFSNA), which was conducted between November and December\n\nthe IPC classification. For example, the EFSNA avoided the two most conflict-affected gover norates, was potentially plagued by a host of logistical difficulties given the security situation\n\nby the WFP used in this analysis and the Gallup World Poll both find that the size of the\n\ndisplaced population is up to three times as large as is being identified by the Task Force for  Population Movement (TFPM), which is the official source used in the IPC 2017 classification\n\noccur. This article utilizes the mobile Vulnerability and Assessment Mapping Survey (mVAM)", "output": {"entities": {"named_data": ["Emergency Food Security  and Nutrition Assessment (EFSNA)", "Emergency Food Security  and Nutrition Assessment", "Gallup World Poll", "Task Force for  Population Movement (TFPM)", "mobile Vulnerability and Assessment Mapping Survey (mVAM)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "therefore be dedicated to juxtaposing the FBS-CV and HCES-direct methods, and considering their\n\nwhich sources of error we should expect in HCES and why we should expect a subset of these errors to\n\n#### **3. POTENTIAL SOURCES OF ERROR IN HCES**\n\nWhile the potential usefulness of HCES for measuring hunger is apparent, there are several recording,\n\nprocessing, and analytic steps to take before the entries from a HCES can be converted into meaningful", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Several studies have examined the benefits of rural electrification in India. For example, van de Walle et al. (2015) estimate the long-run effects of electrification on household consumption in rural India based on data for 1981-98; Burlig and Preonas (2016) investigate the effect of India's national rural electrification program on labor force participation, living standards, and other village-wide outcomes using census data for 2001 and 2011; and Khandker et al. (2014) estimate the benefits of electrification projects in rural India based on cross-sectional data for 2005.\n\nFor example, using night lights data, Min and Gaba (2016) show that many villages in India that were officially classified as electrified under the RGGVY program remained in the dark for years after the completion of electrification projects.\n\nThis paper estimates the welfare impact of rural electrifi cation in India using nationally representative household panel survey data for 2005 and 2012.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["census data", "cross-sectional data", "data for 1981-98", "night lights data", "nationally representative household panel survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "[2 Available at http://faostat3.fao.org/home/index.html (accessed 11 June 2013)](http://faostat3.fao.org/home/index.html) 3 Smith (1998) reports that, at the time, for 18 out of the 99 countries, the CVs are estimated based on analysis of nationally representative HCES. The rest of the countries' CVs are predicted either from measures of income distribution or as the mean CV estimated for other countries in the same region.\n\n4 In 2012 the FAO revised its distribution to be the skew-normal distribution (Azzalini, 1985), which generalises the normal distribution to allow for skewing.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["HCES"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Power shortage data are reported annually by distribution companies (DISCO) to NEPRA and are published in the DISCO Performance Evaluation Report. The latest evaluation report includes shortage data at the DISCO level from year 2010-2011 to year 2014-2015.\n\nSource: DISCO Performance Evaluation Report 2014-2015 by NEPRA.\nNotes: SAIFI is the average number of interruptions that a customer experiences in a year.\nSpecifically, SPAIFI is calculated as the total annual number of consumer supply\ninterruptions divided by the total number of consumers that the distribution company\nserves in any a given year. For the purposes of illustration, the index has been\ntransformed using the inverse hyperbolic sine.\n\nThe second measure, the System Average Interruption Duration Index (SAIDI), captures the outage duration (in minutes) that an average customer experiences in a year.\n\n- (2016). Enterprise Surveys. Available http://www.enterprisesurveys.org,\nAccessed June 10, 2017.\n\n\n13\n\n\n\n<!-- PAGE 16 -->\n### **Figures**\n\n**Figure 1 Power outages and their impact**\n\nSource: World Bank Enterprise Survey for Pakistan (2013)", "output": {"entities": {"named_data": ["System Average Interruption Duration Index (SAIDI)", "Enterprise Surveys", "World Bank Enterprise Survey for Pakistan"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "food consumption expenditure in Tanzania based on the Household Budget Survey 2000/01. To make\n\nrepresentative 2006-07 Household Budget Survey (the comparison results are not presented here but\n\nThis model is estimated using a sample of 206,414 children from house holds in rural villages of India from the District Level Household Survey 2007-08 (DLHS-3).\n\nThe most relevant for the current study is a recent paper that  used propensity score matching to estimate the effect of access to improved sanitation on  diarrhea in children under five using the District Level Household Survey (DLHS-3) (Ku\n\nAnother  study (Bose, 2009) using similar methods from Nepal looks at access to sanitation using  a 2006 Demographic and Health Survey (DHS) finding reductions of 5 percent from mean", "output": {"entities": {"named_data": ["Household Budget Survey 2000/01", "2006-07 Household Budget Survey", "District Level Household Survey 2007-08 (DLHS-3)", "District Level Household Survey (DLHS-3)", "2006 Demographic and Health Survey (DHS)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Source: Electricity Demand Forecast by Planning Power of National Transmission and\nDispatch Company (NTDC) (2014).\n\n\n15\n\n\n\n<!-- PAGE 18 -->\n**Figure 3 System Average Interruption Frequency Index**\n\n30 Manufacture of other transport equipment 89\n31 Manufacture of furniture 39\n32 Other manufacturing 480\nSource: Authors' calculations using 2010-11 Census of Manufacturing Industries.\n\nForce Defense Meteorological Satellite Program-Operational Linescan System (DMSP\n\nThe source for hurricane tracks is the HURDAT Best Track Data, which provides\n\nend quarterly series of national GDP is available for the Dominican Republic.  We thus", "output": {"entities": {"named_data": ["Census of Manufacturing Industries", "HURDAT Best Track Data"], "descriptive_data": ["quarterly series of national GDP"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "However, the carbon savings and reduced deforestation will only be realized if the stoves\n\n\ncontinue to be consistently used (Simon et al. 2012). The results from the choice experiment\n\nIn this paper we use a choice experiment survey to elicit preferences for improved stoves\n\nthat used a stove satisfaction survey and controlled cooking tests to evaluate actual Mirt Stoves.\n\n8 In a stove satisfaction survey 100% of the users rated the MIRT stoves as good or very good, 90% said that they would buy the stove at full market price.", "output": {"entities": {"named_data": [], "descriptive_data": ["choice experiment survey", "stove satisfaction survey"], "vague_data": ["choice experiment survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Comparing our results with the World Bank poverty lines used for global poverty monitoring, by\nexpressing them in 2011 purchasing power parities (PPP), we find that the total poverty line of R$455 is\nequivalent to approximately US$6.10 a day, and hence in the range of upper-middle income countries. [17]\nMoreover, it is very much in line with the societal poverty line one would obtain for Brazil using POF\n2017/18 data. [18] The food poverty line of monthly R$258 is equivalent to a daily US$3.46 in 2011 PPP,\nabove the international poverty line of US$1.90 per person per day.\n\nIn Brazil, poverty monitoring is not based on POF. Instead, another survey - the Pesquisa Nacional por Amostra de Domicílios Contínua (PNADC), which collects information on income instead of consumption - is used.\n\nDeveloping approaches to use the poverty line estimated here for regular poverty monitoring with the income aggregate based on PNADC is beyond the scope of this paper; however, related work has shown that very\n\n18 The societal poverty line defines someone as suffering from societal poverty if they live on less than $1 plus half of what the median person in their country consumes (see Jolliffe and Prydz, 2017). With median consumption per capita per person per day from POF 2017/18 expressed in 2011 PPP US$ being 10.18, the societal poverty line would be US$6.09 in 2011 PPP per person per day for Brazil.", "output": {"entities": {"named_data": ["POF", "POF\n2017/18 data", "Pesquisa Nacional por Amostra de Domicílios Contínua (PNADC)", "PNADC"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Markandya and Pedroso-Galinato (2007) dealt with cross-sectional data of 208 countries for 2000. The underlying production function was assumed to take a nested CES form. The results indicated a relatively high elasticity of substitutability between different types of capital; for example, loss of natural capital could be made up relatively easily by increases in human and physical capital. In addition, the paper also showed that the efficiency of all capital is significantly influenced by changes in economic indicators (trade openness and private sector investment).\n\nData will be gathered for \"household final consumption expenditure (current US$)\" as an alternative.|World Bank- World Development Indicators (WDI) database| |Wages (10-45 years old)|Wages affect schooling outcomes but the direction of influence is uncertain.\n\nData for the following will be gathered: `o` Public spending on education, total (% of GDP) `o` Public spending on education, total (% of government expenditure)\n\n|Determinants|Description|References|Notes|Data Source| |---|---|---|---|---| |Income from agriculture|This variable captures the importance of agriculture to the economy.|Turner, et al. (1993); Lopez and Galinato (2005a,b); Irwin (2006)|Agriculture, value added (% of GDP)|WDI| |Demographic factors|Major determinants of land use include demographic factors such as population size and density.|Major determinants of land use include demographic factors such as population size and density.|Data needed: `o` Total population `o` Population density (people per sq.\n\nWe already have data for the following indicators of governance: `o` Government effectiveness - perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies.", "output": {"entities": {"named_data": ["World Development Indicators (WDI) database", "WDI"], "descriptive_data": ["cross-sectional data of 208 countries"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To complete this analysis with information on how these losses are distributed in the population and especially on the poorest, a survey was conducted with informal dwellers affected by the 2005 floods. It was found that the aggregated losses they suffered from are about $250 million (for total losses due to the 2005 flood of about $2 billion), but the relative impact on their savings and consumption was extremely large, with average capital losses of the same order of magnitude than their average total savings (in other terms, their savings were totally wiped out by the event).\n\n<!-- PAGE 12 --> _Figure 2: Roofer wages in an area where losses have been significant after the 2004 hurricane season in_ _Florida. Data from the Bureau of Labor Statistics, Occupational Employment Surveys in May 03, Nov 03,_ _May 04, Nov 04, May 05, May 06, May 07._\n\nThe box figure illustrates the effect of insurance penetration [13] on the household's budget, for a July 2005 like flood estimated using the ARIO model. Three scenarios are included: (i) γ=0, equivalent to the absence of insurance system, but with an access to credit; (ii) the current value of flood insurance penetration estimated by RMS (γ=0.08 for households, γ=0.15 for firms); and (iii) γ=1, representing the situation where all the reconstruction is paid for by insurance.\n\n<!-- PAGE 5 --> methodologies and approaches, and they often reach quite different results. In the US, for instance, a systematic analysis by (Downton and Pielke, 2005) showed that loss estimates differ by a factor of 2 or more for half of the floods that cause less than $50 million in damages.\n\nOne of the appealing features of night lights data is their availability on every level. Night lights\ndata are measured on the so-called 30 arc sec level, corresponding to roughly 1 square kilometer\nat the equator.", "output": {"entities": {"named_data": ["Bureau of Labor Statistics, Occupational Employment Surveys"], "descriptive_data": [], "vague_data": ["night lights data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "<!-- PAGE 7 -->\nAs we did not have meteorological data for each and every municipality in Chile, this\ninformation was estimated. Since average annual temperature in any particular location\ndepends principally on distance from the equator and elevation above sea-level, we\nestimated a simple model (see Table 2) using information on average annual temperature,\nlatitude, and altitude for all the Chilean stations for which we could obtain \"normal\"\ntemperature data (from www.worldclimate.org). In order to get sufficient variation in the\nelevation variable, we included 4 stations located close to Chile (Catamarca, Mendoza,\nArequipa, and Oruro).\n\nIn this section we will analyze climate data for Chile from May 1948 to March 2008 to test\nwhether there are any significant trends, and whether these trends differ between regions.\n\n\nWe will use the Monthly Climatic Data for the World database collected by the National\nClimatic Data Center (NCDC) in the US. This project started in May 1948 with 100\nselected stations spread across the World, including five in Chile. Since then, many more\nstations have been included in the data base, but only seven stations in Chile have\ncontributed systematically throughout the period, with only inconsequential gaps. These are\nlisted in Table 5, ordered from north to south.\n\nAccording to the model simulations reported in Christensen et al (2007), temperatures are going to increase faster in the northern part of Chile than in the southern part\n\ntemperature data are extracted from the New et al. (2000) gridded 0.5 degree dataset. Calculation of\n\nindices, such as the Palmer Drought Severity Index which uses precipitation and temperature to measure", "output": {"entities": {"named_data": ["Monthly Climatic Data for the World", "Palmer Drought Severity Index"], "descriptive_data": [], "vague_data": ["gridded 0.5 degree dataset"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The program we evaluate is the Japan Fund for Poverty Reduction (JFPR) scholarship program.\n\na girl is selected for a JFPR scholarship, she is automatically eligible to continue receiving a scholarship\n\nfor the three years of the lower secondary cycle. The JFPR program therefore attempts to increase the\n\nsecondary schools in Cambodia, so the JFPR scholarship program covered approximately 15 percent of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(henceforth CRU), provided by the Climatic Research Unit of the University of East\n\nand satellite-based observations. The data enable us to characterize historical climate\n\n<!-- PAGE 6 -->\n**2.1 Assignment of reliability weights to the eight GCMs, based on their**\n**historical \"goodness of fit\" to the CRU data**", "output": {"entities": {"named_data": ["CRU data"], "descriptive_data": [], "vague_data": ["satellite-based observations"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "**Several key messages emerge.** **First, SOEs in Romania were larger-employed more people and had**\n**larger assets per worker-and paid better wages, on average, than their POE peers from 2011 to**\n**2019.** On average, they had lower revenue per worker than POEs over the same period. These results are\nrobust for the various SOE ownership degrees (i.e., minority and majority owned SOEs) and align with\nother studies. In addition, the average SOE experienced higher job growth, investment, and labor\nproductivity growth but slower wage growth than the average POE over the same period. Nevertheless,\nthese growth effects are not uniform across the various ownership degrees.\n\nemployment Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Sector size in economy Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations **[†]** 704 704 704 704 704 704 704 697 704 704 Within R-squared **[†]** 0.095 0.009 0.039 0.267 0.147 0.193 0.046 0.072 0.045 0.040 Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019.\n\n<!-- PAGE 43 -->\nSource: World Bank staff analysis using Romania MoF firm-level data from 2018 to 2020. The sample includes all firms. Growth is calculated as the difference between the values\nin year t and t-1 divided by the average of the values in year t and t-1.\n\n5 According to the World Bank Businesses of the State database, SOEs with at least 10% state ownership accounted for 3.6% of the formal employment as of 2019 in Romania.\n\n(2022b) to supplement the World Bank BOS database.", "output": {"entities": {"named_data": ["World Bank BOS database", "World Bank Businesses of the State database"], "descriptive_data": ["Romania MoF firm-level data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For this reason, the World Bank's Development Economics Vice Presidency (DEC) has established an open web facility, the XCO2 database, [19] that pre-filters the OCO-2 data and publishes monthly mean concentration anomalies for all terrestrial cells of a 25 km global grid (G25). The website will also publish annual change estimates for urban areas by statistical significance class and provide information that links G25 grid cell IDs to IDs for urban areas and national administrative units (levels 0, 1 and 2). We believe that this web facility will contribute to the global effort to reduce CO2 emissions as rapidly as possible.\n\nByers, L., J. Friedrich et al. 2021. A Global Database of Power Plants. World Resources\nInstitute.\n\nJPL/NASA. 2021. OCO-2 Data Set. Jet Propulsion Laboratory, California Institute of\nTechnology. https://co2.jpl.nasa.gov/?mission=oco-2", "output": {"entities": {"named_data": ["XCO2 database", "Global Database of Power Plants"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the flagship publication of the Food and Agriculture Organization (FAO) of the United Nations -- _The_\n\n_State of Food Insecurity in the World_ . Over the same period, the slow fall in the proportion of hungry\n\npeople, from 19% to 13% (FAO 2012), suggests that the goal of halving the hunger rate will not be met", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Are ENLACE test scores at Grade 12 a good predictor of college enrollment and labor\n\nFigure 4 (top panel) plots local means of both outcomes by ENLACE score ventiles in\n\nthe top part of the distribution, because there are few people at this level of ENLACE\n\ncolumn 1). A 1-SD increase in the ENLACE score is associated with a 10 percentage point\n\nground, ENLACE scores could capture both the numeracy and literacy skills that the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "UNHS. Data were collected over a 12-month period. The sample used in this paper is\n\ncharacteristics also differ between the two groups. Table 1 summarizes relevant socio\neconomic and child anthropometric variables by ownership and per capita consumption\n\n\n8\n\n\n\n<!-- PAGE 11 -->\nexpenditure terciles for the 2005/06 UNHS and the 2009/10 UNPS. [3] Descriptive statistics on\n\nDHS. (2011). Uganda Demographic and Health Surveys, 2011. Data accessed via & Hautvast, J. (1989)\nIncreased Risk of Vitamin B-12 and Iron Deficiency in Infants on Macrobiotic Diets. _The_\n_American Journal of Clinical Nutrition_, 50, 818-824.\n\nPAGE 31 --> Kabubo-Mariara, J., Ndenge, G. & Mwabu, D. (2009). Determinants of Children's Nutritional Status in Kenya: Evidence from Demographic and Health Surveys. _Journal of African_ _Economies_, 18(3), 363-387.", "output": {"entities": {"named_data": ["Uganda Demographic and Health Surveys", "Demographic and Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "come ratios relative to extractives,<br>2008-13|\n|---|---|\n||Kayes<br>Sikasso<br>Other<br>regions<br>Total<br>Agriculture<br>6%<br>37%<br>60%<br>40%<br>**Extractives**<br>**100%**<br>**100%**<br>**100%**<br>**100%**<br>Industry<br>111%<br>50%<br>110%<br>85%<br>Construction<br>14%<br>75%<br>413%<br>214%<br>Services (tradable)<br>129%<br>68%<br>107%<br>84%<br>Services (non tradable)<br>76%<br>73%<br>183%<br>126%|\n|_Source:_ CPS/SME (Mining and Energy Sector Planning and<br>Statistics Unit), 2013.|_Source:_ EPAM (Permanent Household Survey), 2010.|", "output": {"entities": {"named_data": ["EPAM (Permanent Household Survey)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The study mainly relies on household data from the VHLSS 2010, 2012, and 2014. These surveys are conducted by the General Statistics Office (GSO) with technical support from the World Bank in Vietnam. They are nationally and regionally representative and contain detailed information on individuals, households and communes. In total 9,400 households nationwide are included in each round. Half of these households were also interviewed in the previous round so that the data set includes a short-term panel.\n\n**1)** **Air pollution** is measured by the area-weighted mean of concentration (measured as micrograms per cubic meter) of particulate matter with a diameter of 2.5 micrometers or less (PM2.5) taking the 10 years- average value for 2000-2010. The data is based on satellite imaginary using the total column aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer satellite instruments, which is combined with chemical transport model simulations, and ground measurements from 79 countries to produce a global spatial data set with 0.1° × 0.1° resolution (Brauer et al., 2015). PM2.5 includes dust, dirt, soot, smoke, and liquid droplets, which can lodge deeply into the lungs due to their small size. PM2.5 air pollution has been identified as a leading risk factor for global diseases (Forouzanfar et al., 2015).\n\n**2)** **Tree cover loss** is calculated as the share of the area under tree cover in 2000 that suffered from a tree cover loss between 2000 and 2010. Tree cover is defined as canopy closure for all vegetation taller than 5m in height and is calculated from imagery from the Landsat 4, 5, 7, and 8 satellite data used to produce a global\n\n**3)** **Land degradation** is measured by the share of land area that experienced a significant biomass decline. This loss is calculated based on the inter-annual mean trend of Normalized Difference Vegetation Index based on data from Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA) satellite between 1982 and 2006, which is corrected for climate effects to only measure human-induced degradation (Vu et al., 2014b). Soil fertility, agricultural productivity and ultimately food security of smallholder farmers can be largely compromised on degraded lands (Von Braun et al., 2013).", "output": {"entities": {"named_data": ["VHLSS 2010, 2012, and 2014", "Moderate Resolution Imaging Spectroradiometer (MODIS)", "Landsat 4, 5, 7, and 8 satellite data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "-- PAGE 12 --> For the decomposition analysis of emissions intensity, we obtain annual country-level total passenger and freight transport traveled by road and rail from the World Road Statistics and the International Transport Forum databases.\n\nThe level of urbanization, and the value added from agriculture, manufacturing, and service sectors are all obtained from the World Bank World Development Indicators database.\n\nData on country-level BRT systems are obtained from global BRT database.\n\nDiesel and gasoline prices are compiled from fuel price documentation from the Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ).\n\nFarm data were collected by conducting a face-to-face survey among a", "output": {"entities": {"named_data": ["World Road Statistics", "World Bank World Development Indicators database", "global BRT database"], "descriptive_data": ["fuel price documentation from the Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 For a disaster to be listed in the EM-DAT database, at least one of the following criteria should be met: (i) 10 or more people are reported killed; (ii) 100 people are reported affected; (iii) a state of emergency is declared; (iv) a call for international assistance is issued. 4 The study was carried out by a consortium including the OECD, Risk Management Solutions, CIRED, Météo-France, NATCOM PMC, and the Indian Institute for Technology Bombay at Mumbai, and published in Ranger et al. (2011).\n\nThen, the population and assets exposed to flood risks is assessed, using data on population and assets collected by Risk Management Solutions from an insurance database developed for the assessment of earthquake risks.\n\nIn the absence of vulnerability curves for the buildings that can be found in Mumbai, the analysis uses \"average damage ratio\". It is assumed that when a property is flooded, a constant share of its value is lost, regardless of the water level and the detailed characteristics of buildings. Using three different techniques (based on published loss estimates for the 2005 floods, insurance data for the 2005 floods, and simple", "output": {"entities": {"named_data": ["EM-DAT database"], "descriptive_data": ["data on population and assets", "insurance data for the 2005 floods"], "vague_data": ["insurance database"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our results suggest that any comparative assessment of hunger prevalence using HCES should clearly\n\nfrom HCES, such as poverty counts and inequality measures (Beegle et al. 2012). The reason is that the\n\nexpected when measuring hunger directly from HCES and how some of these errors likely differ by\n\nhunger numbers across the various arms of the survey experiment and verifies whether the magnitude\n\nAgriculture Organization (FAO) of the United Nations in a series of reports tracking world hunger, with", "output": {"entities": {"named_data": ["HCES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "and hourly wages. For this objective, we merge upper secondary ENLACE test scores\n\nlimitations, large-scale standardized tests like ENLACE capture skills that are important\n\nuna comparación entre ENLACE, Excale y Pisa,\" 2014. Nexos.\n\n_Promise_, Washington, DC: World Bank, 2018.\n\n\n28\n\n\n\n<!-- PAGE 31 -->\n#### **Tables**\n\nTable 1: ENLACE Panel       - Means and Standard Deviations", "output": {"entities": {"named_data": ["ENLACE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Still, despite these critiques, the evidence in this paper cautions against a naive switch to the HCES\ndirect method. In our survey experiment, we calculate hunger to range between 19 and 68 percent -this\n\nis a difference of more than 23 million people in Tanzania (a country with a population of 45 million\n\npresenting a strong challenge to both the HCES-direct and the FBS-CV methods. The FBS-CV is\n\nNote: estimates of Equation (1). Each column represents the results of a (separate) regression (OLS or LPM) of a selected HCES-derived measure (mentioned in the titles of the panels) on 7 module assignment dummies, a single selected household characteristic (mentioned in the column headings) and 7 interaction terms of that household characteristic with the module assignment dummies.\n\nSmith L. 1998. Can FAO's Measure of Chronic Undernourishment be Strengthened? _Food Policy_ 23(5):", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "note about the survey experiment. The recall modules administered in the survey experiment ask the\n\nboth the HCES-direct method that is advocated by numerous researchers and the FBS-CV method that is\n\nused by the FAO when making global hunger counts. Since the HCES modules we use are typical of those", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Although the OCO-2 satellite platform provides the best available database, its coverage for our 25-km grid cells is limited by its 16-day repeat cycle, relatively narrow observation track, and the frequent occurrence of cloud cover over some areas.\n\nFor the translation of regression residuals to emissions deviations, we use the EDGAR gridded database of CO2 emissions estimated from sectoral activity data and standard emissions parameters (Crippa et al.\n\nWe translate these residuals to emissions using the EDGAR global database of gridded CO2 emissions estimated from local activity measures and standard emissions parameters (Crippa et al.\n\n2020). The current EDGAR database terminates in 2018, so we perform the conversion using data for 2015 - 2018, the period of overlap with our OCO-2 database.", "output": {"entities": {"named_data": ["EDGAR gridded database of CO2 emissions", "EDGAR global database", "EDGAR database", "OCO-2 database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Martin et al. (2018) propose a measure of business relationships' stickiness based on the duration of firm-to-firm trade and use firm level data in France to measure a \"stickiness\" index for more than 4,000 HS6 products.\n\nWe use data consolidated banking statistics from the Bank for International Settlement to construct an index of financial proximity. [12] We use the total bilateral cross-border claims (including bank and non-bank sectors for all maturities)\n\n**Foreign** **Direct** **Investments.** Data from the UNCTAD's Bilateral FDI Statistics provides up-to-date and systematic FDI data for 206 economies around the world, covering inflows, outflows, inward stock and outward stock by region and economy.\n\nan index of \" _proximity_ _in_ _sectoral_ _composition_ \" based on the World Development Indicators. We use the share in value added of main sectors: service and agricultural sectors and we decompose manufacturing sectors into 7 main sub-sectors.", "output": {"entities": {"named_data": ["UNCTAD's Bilateral FDI Statistics", "World Development Indicators"], "descriptive_data": ["consolidated banking statistics"], "vague_data": ["firm level data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Nassar, R., J. Mastrogiacomo, W. Bateman-Hemphill et al. 2021. Advances in quantifying\npower plant CO2 emissions with OCO-2. Remote Sensing of Environment. 264: 112579.\n\nNordhaus, W., A. Azam et al. 2006. The G-Econ Database on Gridded Output: Methods and\nData. Yale University.\n\nYe, X., T. Lauvaux, E. Kort, T. Oda, S. Feng, J. Lin, E. Yang and D. Wu. 2020. Constraining\nfossil fuel CO2 emissions from urban area using OCO-2 observations of total column CO2. JGR\nAtmospheres, 125(8).\n\n<!-- PAGE 41 -->\n**Appendix: Mobilizing OCO-2 Data for the Stakeholder Community**\n\nIn this paper, we have shown that a relatively simple tracking model can provide useful information\nabout changes in local CO2 concentration anomalies for areas of interest. However, we recognize that\nmost stakeholders do not have the requisite hardware and software for mobilizing the OCO-2 data\ndirectly. Accordingly, the World Bank's Development Economics Vice Presidency (DEC) has\nestablished an open web facility with the following features. We believe that it will contribute to the\nglobal effort to reduce CO2 emissions.", "output": {"entities": {"named_data": ["G-Econ Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We test the two parts of the redistribution hypothesis using the most recent data from Luxembourg Income Study for 20 OECD countries covering the period 1967-2005 (total number of country/years is 110).\n\n_Economy_ (Milanovic, 2000) and which for the first time used household level data derived from household income surveys to test the hypothesis, and in the process\n\nindividual national identification\", which he argues, based on World Values Survey data, to be stronger among the poor voters, and which reduces their propensity to vote for\n\nseemingly depending on author's preferences, availability of the data (OECD provides the 90-10, 90-50 and 50-10 gross wage ratios), or perhaps contingent on the formulation\n\nis why, in their analysis of eight advanced economies, they use scores of \"intended generosity\" (developed by Scruggs, 2004) of the pension system, unemployment benefits, child benefits and social assistance.", "output": {"entities": {"named_data": ["Luxembourg Income Study", "World Values Survey"], "descriptive_data": [], "vague_data": ["household income surveys", "gross wage ratios"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The SIRRV question on road building clearly notes to exclude \"rehabilitation.\" 10 Later, after discussing our estimation methods, we ascertain that this holds for almost all communes when we predict the counterfactual kilometers of rehabilitated roads in the absence of the project.\n\nAdditional corroborating evidence comes from available SIRRV data on other\n\n(iii) ** and * indicate significance levels of 5 and 10%. (iv) Average commune household consumption is predicted using a consumption model calibrated to the 1998 VLSS.\n\nThe dataset for this analysis comes from an extensive economic survey involving over 9000\nfarmers in ten African countries: Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, Kenya,\nNiger, Senegal, South Africa and Zambia. Data was gathered from Zimbabwe but the livestock\nobservations had to be dropped because of the turbulent conditions in this country during the\nsurvey period. The data was collected for the GEF project studying the impact of climate change", "output": {"entities": {"named_data": ["SIRRV data", "VLSS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "**Electricity** **Capital** **GDP** **Consumption** **Stock** **Population** Median 3.84 3.65 3.45 1.44 Average 3.48 4.10 3.88 1.43 Standard Deviation 4.37 6.87 2.77 0.97 Skewness -1.58 1.56 0.81 0.11 Kurtosis 9.83 20.48 1.10 -0.33 _Sources: World Development Indicators, Penn World Tables Version 8, and author's calculations._\n\n<!-- PAGE 19 -->\n#### **VII. Hypothesis Testing**\n\nWe explore two basic questions. First, could night-time lights data serve as a good proxy for any of the\neconomic variables considered in this paper?\n\nA new satellite is generating superior night-time lights data. The data are generated by the Suomi National Polar-orbiting Partnership (SNPP) satellite series operated by the NASA - NOAA Joint Polar Satellite System that was launched in late 2011.\n\nobserved in cross-section surveys - even when controlling for other observable characteristics -   cannot directly be interpreted as reflecting a change in attitudes over the life-cycle.", "output": {"entities": {"named_data": ["World Development Indicators"], "descriptive_data": [], "vague_data": ["cross-section surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We use two main data sources for our estimation of a poverty line for Brazil: the 2017/18 Household Budget Survey (Pesquisa de Orçamentos Familares; POF) and the Brazilian Table of Food Composition (Tabela Brasileira de Composição de Alimentos; TBCA). POF is a nationally representative semiregular survey on income and expenditures in Brazil, conducted every six to nine years.", "output": {"entities": {"named_data": ["Pesquisa de Orçamentos Familares", "POF", "Brazilian Table of Food Composition", "Tabela Brasileira de Composição de Alimentos", "TBCA"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Afrobarometer data reveal that technical capacity and not corruption or lack of accountability may be the reason local governments are not successful in transforming revenues into better public goods and services for better welfare outcomes.\n\nEmployment estimates in the artisanal gold-mining sector vary considerably-from 6,000\naccording to a Sustainable Development Observatory (ODHD) survey (2011) to 1 million in the\nthree active mining regions, according to the Chamber of mines (2013), and 200,000 according to\na survey by Central Bank of West African States (BCEAO 2013). The latest population census\nestimates the number of people involved in artisanal gold mining at 25,000. The national labor\nsurvey (the Permanent Household Survey, EPAM) of 2010 estimates that 28,000 people are\ninvolved primarily in extractives. In addition, 14,000 people work in extractives as a secondary\nactivity, with farming being the primary activity of 76 percent of these people. The difficulty in\nestimating the number of people employed in artisanal gold mining arises from the fact that the\nactivity is sometimes practiced in a \"rush\" manner, which may not coincide with a census or survey", "output": {"entities": {"named_data": ["Afrobarometer data", "Permanent Household Survey, EPAM"], "descriptive_data": ["survey by Central Bank of West African States"], "vague_data": ["population census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The coefficient for storm intensity was estimated using aggregate damages per storm and storm characteristics at landfall from US storms since 1960 (NOAA 2009).\n\nand income at each impacted coastal area. These data were inferred for each year from\n\n\ndecennial Census data by county (US Census of Population 1960, 1970, 1980, 1990,\n\nA separate damage analysis was then conducted of tropical cyclones around the globe (EMDAT 2009). The international data set was used to estimate the coefficients for vulnerability (income and population density).\n\nNote: There were 111 observations in the damage regression and 40 observations in the fatality regression. The t statistics are in parenthesis. The functional form of the regression is log log. Source: NOAA(2009).", "output": {"entities": {"named_data": ["US Census of Population", "EMDAT 2009"], "descriptive_data": [], "vague_data": ["Census data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "These climate scenarios are then used to predict how tropical cyclones may change into the future using a newly developed tropical cyclone simulator (Emanuel et al. 2008).\n\nThe historic relationship between aggregate damages and the magnitude of each storm is estimated using data from storms that have hit the United States since 1960.\n\nThe damages are matched with characteristics of the storm including minimum barometric pressure, maximum wind speed, and location at landfall (NOAA 2009).\n\nspeed, and location at landfall (NOAA 2009). Estimates of county income and\n\n\npopulation density are inferred from Census data for the five counties closest to the point\n\n\nof contact. A regression estimates the relationship between storm intensity and damages.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["data from storms", "Census data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "**Total Poverty** **Food Poverty** **Lower Total**\n**Estimation** **Poverty**\n\nParametric R$287 R$507\nNonparametric R$287 R$503\n_Source:_ Own calculations using POF 2017/18.\n\n**Total Poverty** **Food** **Lower Total**\n**Estimation** **Poverty** **Poverty**\n\nParametric R$251 R$443\nNon-parametric R$251 R$441\n_Source:_ Own calculations using POF 2017/18.\n\nDeaton and Zaidi (2002) advert to the fact that implicit rent, as elicited in POF and used here, is a hypothetical concept that could lead to estimations that are not usable.\n\nIn this paper we have presented our estimate of a poverty line for Brazil, using the CBN approach and based on the most recent data (POF 2017/18). Our preferred specification results in a food poverty line, accounting only for nutritional requirements, of R$258 (in 2018 Southeast urban prices) per person per month.\n\nIn comparison with earlier work, mainly based on POF 2003, our poverty lines are generally similar in real\nvalues, although methodologies differ, and consumption patterns have likely changed over time.\nConverted to January 2018 prices and considering São Paulo (mostly metropolitan) lines in the case of\nregional lines, previous estimates range from R$485 to R$532 (Rocha, 2007; Silveira et al., 2007). Ferreira\net al. (2003) used POF 1996 and estimated a lower poverty line of R$477 in January 2018 metropolitan\nSão Paulo prices. Only World Bank (2007) estimated a considerably lower poverty line of R$272 in January\n2018 metropolitan São Paulo prices.", "output": {"entities": {"named_data": ["POF 2017/18", "POF"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "was that it did not collect expenditure data on different kinds of subsidized and\n\nEgypt Integrated Household Survey (EIHS) -- was designed to collect detailed\n\nexpenditure data on all of the main subsidized and non-subsidized foods in Egypt. The\n\nselecting households was supplied by CAPMAS from its 1990/91 HIES survey. [20]\n\nmade. First, per capita total expenditure is calculated from the IFPRI survey data for each", "output": {"entities": {"named_data": ["Egypt Integrated Household Survey", "1990/91 HIES survey", "IFPRI survey data"], "descriptive_data": [], "vague_data": ["expenditure data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The firm-level financial data for 1997 are primarily from the Worldscope database. The World\n\nin Australia and Canada, respectively. Using industry data from Mexico, Blomstrom and Persson\n\nCommission on an annual basis. We use [group-affiliation data from the 1994-1997 lists of business]\n\nby the level of growth of a sector or a country. Indeed, using firm level data, Haddad and Harrison", "output": {"entities": {"named_data": ["Worldscope database"], "descriptive_data": ["industry data from Mexico"], "vague_data": ["group-affiliation data", "firm level data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "At present, the FAO hunger estimates rely on CVs which are not always country specific, do not vary\n\nranging from 19% when the personal diary is used to 72% when the estimates come from an HCES that\n\nhousehold surveys (HCES-direct method), as opposed to calculating them from a combination of food\n\nbalance sheets and household surveys (FBS-CV method) as currently done by the FAO. The FBS-CV", "output": {"entities": {"named_data": ["HCES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "weather policy, this is deemed a rainfall shock. As the quality of the rainfall data is related to the\n\n11The APHRODITE weather data provides information about how many local weatherstations contributed to a\ncertain rainfall reading. Since some of the rainfall observations are likely to be more accurate than others, I weight\nthem according to accuracy. If there are no rainfall stations contributing to the APHRODITE data within a .75 _[o]_ x.75 _[o]_\n\nWhen the regressions are run with the village characteristics from the 2005 Indian census, the coefficients of interest do not change significantly. Also, most village-level characteristics had insignificant coefficients, with the exception that a more literate population\n\nWhile the BASIX data set does not offer the opportunity to test the direct effects of a cash payment\n\nRobust standard errors in parentheses Obervations weighted by quality of rainfall data", "output": {"entities": {"named_data": ["APHRODITE weather data", "2005 Indian census", "BASIX data set"], "descriptive_data": [], "vague_data": ["rainfall data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "and more immediate impact on food access, we are able to find additional empirical support\n\nfor the last explanation. Given data and information constraints, it is not possible to estimate\n\nSundberg, R., and E. Melander. 2013. \"Introducing the UCDP Georeferenced Event Dataset.\"\n\nNotes: This table compares mobile phone ownership in the November 2017 WFP mobile phone survey and the 2014 Household Budget Survey (HBS), where the 2014 HBS summary statistics are restricted to the share of the population that resides in a household that owns at least one mobile phone.\n\nWe gratefully thank Claudio Montenegro, David Newhouse and Minh Nguyen for their help with the I2D2 database. We gratefully acknowledge the generous support of the World Bank (Office of the Senior Vice-President and Chief Economist and Social Urban Rural and Resilience Global Practice), the Cities Program of the International Growth Center (Grant number 89408), the GWU Institute for International Economic Policy and the GWU Center for International Business Education and Research.\n\nData Used to Estimate the Returns**   **Sources.** The data source for the analysis is the _International_ _Income_ _Distribution_   _Database_ (I2D2) of the World Bank. The database consists of a large number of   individual-level surveys and census samples.", "output": {"entities": {"named_data": ["UCDP Georeferenced Event Dataset", "2014 Household Budget Survey", "I2D2 database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "<!-- PAGE 9 -->\nthe CDM: parties in NA1 countries could carry out GHG mitigation which could be paid for by parties in\nA1 countries, thereby equivalently reducing the GHG emissions obligation in the latter. Since GHG\nmitigation costs have been far lower in non-Annex 1 countries than in A1 countries, such trades have\nbeen highly beneficial to both groups. The aggregate amount of CER issuance under the CDM by today is\nmore than 1,600 million tons CO2-e; see UNFCCC (website).\n\n\nA similar rationale and functioning can be found for JI.\n\nThe JI market has, however, turned out to be far less active than the CDM market, with smaller aggregate volumes for emissions reduction units (ERUs) so far. Still, as of today more than 850 million tons of CO2e has been reached in terms of issuance.\n\nOnly a quick glance at data from the CDM market reveals that offset prices (per ton of CO2-e) achieved by project hosts have varied a lot across both projects and host countries, and have often (or even typically) been substantially below the respective (policy bloc-internal) quota prices.\n\nRosendahl and Strand (2011) have simulated likely leakage levels, and come up with \"best estimates\" for leakage of around 30% of credited emissions reductions, as a rough average for the CDM projects carried out by 2010 (when disregarding HFC-23 related projects).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["data from the CDM market"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For this study, data on the four types of capital are obtained for three periods - 1995, 2000 and 2005 and for 210 countries, from an updated database underpinning the wealth estimates of nations (World Bank, 2006; G. Ruta and K. Hamilton, personal communication, 2009). The data are measured in per capita values at 2005 constant prices. An econometric analysis is conducted for a panel data of capital, along with the data on the magnitude of natural disasters for the same 2 <!-- PAGE 5 --> periods. Because of limitations in the data on human capital related to education (HS), the intangible capital residual (HR) is used as a proxy measure of human capital in this study.", "output": {"entities": {"named_data": [], "descriptive_data": ["updated database underpinning the wealth estimates of nations", "data on the magnitude of natural disasters"], "vague_data": ["data on human capital related to education"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This paper combines district-level government spending data from Indonesia and natural disaster damage indices to analyze the extent to which districts are forced to reallocate their expenditures across categories after the incidence of floods, earthquakes, and volcanic eruptions.\n\nlocal economic activity. The natural disaster damage indices in Skoufias _et_ _al._ (2017) are  constructed by modeling the local strength of each disaster using its physical characteristics  and taking account of local exposure to these aspects using nightlight intensity derived from  satellite imagery.\n\n<!-- PAGE 7 --> ##### **2 Natural Disaster Damage Indices** The methodology and data sources used to make damage indices for natural disasters are  extensively covered in Skoufias _et_ _al._ (2017), and there are also additional details in Ap pendix A.\n\nBased on Remotely Sensed Data: An Application to Indonesia. _Policy_ _Research_ _Working_\n\n\n_Paper_, **8188** . https://openknowledge.worldbank.org/handle/10986/28365.", "output": {"entities": {"named_data": ["natural disaster damage indices"], "descriptive_data": ["district-level government spending data"], "vague_data": ["natural disaster damage indices"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Notes: The graph plots local means of post-secondary school outcomes by ENLACE test\nscore ventile in grade 12. The solid line shows a linear fit estimated using the grouped data.\nPanel (a) reports the probability of college enrollment; (b) reports the probability of being\nemployed conditional on not being enrolled in college; and (c) and (d) report, respectively,\nthe logarithm of the hourly wage and the probability of working in a formal firm conditional\nin both cases on being employed. Outcomes are measured in the ENILEMS survey at ages\n18 to 20 in the third quarter of 2010. ENLACE test scores come from the years 2008, 2009\nand 2010. Data: ENILEMS-ENLACE panel.\n\nTable A.1: ENLACE Twins and Non-twins: Means and Standard Deviations\n\n(1) (2) T-test\n0 1 Difference\nVariable N Mean/SE N Mean/SE (1)-(2)\nEnlace Score Spanish Grade 6 1942306 512.842 20982 515.377 -2.536***\n(0.075) (0.719)\n\n\nEnlace Score Math Grade 6 1942246 514.237 20982 516.474 -2.238***\n(0.079) (0.762)\n\nEnlace Score Math Grade 6 1942246 514.237 20982 516.474 -2.238***\n(0.079) (0.762)\n\n\nEnlace taker Grade 9 1943583 0.702 20995 0.742 -0.040***\n(0.000) (0.003)", "output": {"entities": {"named_data": ["ENILEMS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "disappears entirely among girls who receive the JFPR scholarships. For example, the first column\n\nSES and school enrollment. The regression results in Table 4 therefore confirm the patterns observed in\n\nIn this paper we estimate the impact of a scholarship program for girls on school enrollment and\n\nfarther away from a secondary school. As a result, the JFPR program appears to have dramatically\n\neffectiveness comparisons, or estimate whether the scholarship amount set by the JFPR program was \"too", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Notes: The graph plots local means of secondary school outcomes by ENLACE test score\npercentile in grade 6 in 2007. The solid line shows a linear fit estimated using the grouped\ndata. Panel (a) reports the probability of on-time graduation from grades 9 and 12, proxied\nby siting in the Enlace exam in those grades. Panel (b) reports Enlace test scores in grades\n9 and 12 (normalised with mean 0 and SD 1) conditional on taking the Enlace exam in 2010\nand 2013. Data: ENLACE panel.\n\n\n38\n\n\n\n<!-- PAGE 41 -->\nFigure 4: ENLACE test scores and post-secondary school outcomes\n\n(a) College Enrollment<br>1<br>(%)<br>College .8<br>in<br>Enrolled<br>.6<br>.4<br>0 10 20 30 40 50 60 70 80 90 100<br>Enlace Test Score Percentile in Grade 12\n\nd<br>not .5<br>if<br>(%),<br>Employed .4<br>.3<br>0 10 20 30 40 50 60 70 80 90 100<br>Enlace Test Score Percentile in Grade 12|\n|---|---|\n|2.7<br>2.8<br>2.9<br>3<br>3.1<br>3.2<br>Wage hourly earnings (ln)<br>0<br>10<br>20<br>30<br>40<br>50<br>60<br>70<br>80<br>90<br>100<br>Enlace Test Score Percentile in Grade 12<br>(c) Wage Earnings|.2<br>.3<br>.4<br>.5<br>.6<br>Employed in formal firm (%), if employed<br>0<br>10<br>20<br>30<br>40<br>50<br>60<br>70<br>80<br>90<br>100<br>Enlace Test Score Percentile in Grade 12<br>(d) Employed in Formal Firm|\n\n\n\nNotes: The graph plots local means of post-secondary school outcomes by ENLACE test\nscore ventile in grade 12.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We also simulate the livestock effects across three AOGCM climate scenarios. The AOGCM scenarios predict a 2-3% increase in the probability of owning livestock by 2020, a 4-7% increase by 2060, and a 0-13% increase by 2100.\n\nWashington W et al., 2000. Parallel Climate Model (PCM): Control and transient scenarios.\n_Climate Dynamics_ 16: 755-774.\n\n<!-- PAGE 28 -->\n**Table 7: AOGCM climate scenarios**\n\nThe empirical analysis is based on a household survey conducted of 11 countries across Africa: Burkina Faso, Cameroon, Egypt, Ethiopia, Kenya, Ghana, Niger, Senegal, South Africa, Zambia and Zimbabwe (for more information about the entire study, see Dinar et al. 2006).\n\nIn this study, we relied on monthly temperature data collected from US Department of Defense satellites (Basist et al. 2001). This set of polar orbiting satellites obtain measurements at a given location on earth at 6am and 6pm every day. The satellites are equipped with sensors that measure surface temperature by detecting microwaves that pass through clouds (Weng & Grody 1998).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey", "monthly temperature data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "**(1)**\n**Enrollment at a JFPR**\n**school**\n\nUsing local-level survey data collected for this purpose, we test whether the evidence supports the standard economic argument that there will be little or no impact on rural roads rehabilitated, given fungibility.\n\nindependent administrative data reports that an average of 4.6 kilometers per commune\n\nRoads in Vietnam\" (SIRRV) is a panel data set of pre-project baseline and post-project\n\n6 Least cost techniques refer to the minimum-cost engineering solution that ensures a minimum level of\nmotorized passability.\n7 For a more detailed description of the SIRRV see van de Walle (2007).", "output": {"entities": {"named_data": ["SIRRV"], "descriptive_data": [], "vague_data": ["local-level survey data", "independent administrative data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Coastline changes in the Mekong Delta, including Ho Chi Minh City and Bà Rịa-Vũng Tàu, were evaluated\nbased on data provided by the Viet Nam Disaster Management Authority in vector format [2] . By analyzing\nthe coastline in 1988 and 2015 in imagery from Landsat satellites, sediment changes in this period could\nbe quantified.\n\n\nSimilarly, coastal erosion data in the provinces further north were developed by companies Deltares and\nRoyal Haskoning DHV (Deltares at al. 2017). Coastline changes in the period between 1990 and 2015 were\ndetected automatically based on imagery from Landsat and Sentinel satellites. In this vector format\ndataset, average erosion or accretion is available by coastline segments with a length of 500 meters.\n\nTo analyze this hazard, a dataset in raster format describing the worst saline intrusion event in 2016 was provided by the (Southern Institute of Water Resources Research (SWIRR). With a resolution of about 50 by 50 meters, it shows the salinity level at each point in the delta during the 2016 event\n\nThe exposure of various types of agricultural production and urban areas is estimated based on land use\ndata with a very high spatial resolution. This dataset was developed by the Japanese Aerospace Exploration Agency and is openly and freely available online (JAXA EORC, 2018).\n\nThe land use map provides a fine-grained overview of land use for all of Vietnam for 2015, for northern regions and for central and south Vietnam in 2017.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["land use map"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Based on specification (1), we estimate total 2013 GDP for Kenya at $26.8 billion (2005 USD), close to its actual level of $26.9 billion (WDI, 2014). On county-level, we find that Nairobi had the highest overall GDP in 2013 ($3.4 billion), followed by Kiambu ($3.0 billion), Nakuru ($2.3 billion), Nyeri ($1 billion), and Kilifi ($1 billion). Counties with the lowest GDP are the sparsely populated counties of Isiolo ($56 million), Lamu ($58 million), Samburu ($67 million), Elgeyo Marakwet ($108 million), and Tharaka Nithi ($109 million). Nairobi has the highest contribution to national GDP (13 percent), followed by Kiambu (11 percent),\n\nBased on specification (1), we estimate total 2013 GDP for Rwanda at $4.58 billion (2005 USD), only slightly higher than the national-accounts estimate of $4.57 billion (WDI, 2014). On the district-level, we find that Gasabo district had the highest GDP in 2013 ($925 million), followed by the two other districts of Kigali (Kicukiro at $538 million and Nyarugenge at $371 million). Districts in the secondary urban centers come next: Rubabu ($219 million), Rusizi ($186 million), Huye ($166 million), and\n\nAs evident from the title of this paper, we also tried to estimate poverty levels and changes based on nightlights data. The estimated associations were however not robust, and more time and effort would need to be invested in examining this relationship in a more detailed fashion.\n\nBased on national labor survey data (EPAM 2010), it appears that mean income from mining activity for each active worker is higher than the average income for all other activities, especially the agricultural and industrial sectors in the Sikasso region.", "output": {"entities": {"named_data": ["WDI, 2014", "national labor survey data (EPAM 2010)"], "descriptive_data": [], "vague_data": ["nightlights data", "national labor survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In short, these contour maps are ShakeMaps from USGS, which are automatically generated maps providing several key parameters following an earthquake, such as peak ground acceleration (PGA), peak ground velocity (PGV) and modified Mercalli intensity (MMI), are used as a base for localized impact.\n\nFor the actual construction of the damage index, two types of data will be used; the intensity data - expressed as PGA - and building inventory data. The building type data stems from the USGS building inventory for earthquake assessment, which provides estimates of the proportions (based on total number of buildings) of building types observed by country; see\n\n\\n<!-- PAGE 17 -->\\nJaiswal and Wald (2008). The data provide the share of 99 different building types within a country separately for urban and rural areas. For Indonesia the building type information was compiled from a World Housing Encyclopedia (WHE) survey.\n\nDamage curves by building type are derived from the curves constructed by the Global Earthquake Safety Initiative (GESI) project; see (International and Regional Development 2001).\n\nThe source for the localized wind speeds is the IBTrACS database that provides the strength and tracks every 6 hours of all typhoons that affected Southeast Asia during the period.", "output": {"entities": {"named_data": ["ShakeMaps from USGS", "World Housing Encyclopedia (WHE) survey", "IBTrACS database"], "descriptive_data": ["USGS building inventory for earthquake assessment"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Climate data on temperature and precipitation were taken from Bitan and Rubin (2000). Average annual temperature calculations are based on data collected in 38\n\nTable 3 presents the results of the two models. In the first model, linking farm profits to\n\n\nfarm exogenous variables, the irrigation water quota was omitted. The second model in\n\nand 2 percent for flowers and other garden plants (Israeli Central Bureau of Statistics, 2005). Almost all the crops excluding field crops are irrigated. Field crops are grown on\n\n_Source: World Bank calculations based on Ministry of Energy and Mineral Resources 2007 data._\n\n(2007), reports that from a survey of 51 countries, nearly half had an ad hoc pricing mechanism where the government adjusted the price level irregularly, 14 percent an automatic adjusting mechanism that holds the margin between world and local prices constant while allowing domestic prices to adjust, and 37 percent enjoyed a liberalized pricing system.", "output": {"entities": {"named_data": [], "descriptive_data": ["Ministry of Energy and Mineral Resources 2007 data", "survey of 51 countries"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "**3.3** **Open Street Map (OSM) Road Network Data Set**\n\nIn addition to the five public transportation networks, we also added the drive as well as the pedestrian network leveraging the Open Street Map data set to account for commuters who walk or drive to work.\n\n**3.4** **Fathom Flood Maps**\n\n<!-- PAGE 10 --> Pluvial and fluvial flood maps from the Fathom global flood model were first clipped to the bounding box [9] of the Kinshasa city and then mosaiced together using the maximum operator so that the maximum flood depth from both fluvial and pluvial flood estimates were preserved.\n\nFlood models are an integral tool for understanding and managing flood risks on transportation networks. In\nthe past decades, increased computing power and precision of remote sensing data sets have led to the\ndevelopment of multiple global flood models (Bernhofen et al., 2018; Wood et al., 2011). Among them, the\nlastest flood map products from Fathom (A. Smith et al., 2015) are selected to provide flood depth and extent\nestimates in Kinshasa for the following reasons: 1) the map products from Fathom have relatively high\nspatial resolution (90 meters) which is sufficient for this study given the geographic extent of the Kinshasa\ncity; 2) flood extent and depth estimates for both pluvial and fluvial floods under 10 return periods [8] are\nincluded in this data product; 3) it uses 2D hydrologic flood model which is more advanced in mapping flood\nplain and modeling dynamic water flows.", "output": {"entities": {"named_data": ["Open Street Map (OSM) Road Network Data Set", "Open Street Map data set", "Fathom Flood Maps", "Fathom global flood model"], "descriptive_data": ["flood map products from Fathom"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "FAO (2012) being the latest. More recently, the FAO indicator is used to track progress toward the first\n\ncountry. [2] Combining this with population data allows the FAO to estimate the total kilo calories available\n\nnumber of HCES. [3] For most countries the CV was kept constant across years and only the mean was\n\nrevised. [4] Finally, the FAO estimates the required energy of a population by determining age-sex specific", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "10 The SETAM platform has only one electronic trading platform that is run by the system administrator, creating a conflict of interest through the possibility of accessing auction data and using these to interfere in the auction process.\n\nAuction data for our analysis come from two sources. Information on 31,483 auctions of public land for\n\nSpecifically, we use data from longitudinal and cross-country comparable national phone surveys implemented between March 2021 and January 2023. These multi-topic phone surveys were conceived in order to track the effects of the COVID-19 pandemic in the absence of in-person data collection (Himelein et al. 2020). Our experimentation with survey design choices draws on five of these surveys in Sub-Saharan Africa that were supported by the Living Standards Measurement Study (LSMS) team at the World Bank and implemented by the respective National Statistical Offices.\n\nThese surveys are re-contact surveys, drawing their samples from the latest nationally representative, in-person LSMS-ISA household survey conducted in each country before the pandemic. As part of the LSMS-ISA surveys, phone contact numbers of all household members (where available) as well as from a reference contact such as a neighbor were collected (Gourlay et al. 2021). The list of households with a phone contact, or a random subset of it, constituted the sample to be contacted for the phone surveys and covered between 73% (Malawi) and 99% (Nigeria) of households included in the in-person LSMS-ISA survey.", "output": {"entities": {"named_data": ["LSMS-ISA surveys"], "descriptive_data": ["longitudinal and cross-country comparable national phone surveys"], "vague_data": ["auction data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Landscan Population Data. Oak Ridge National\nLaboratory.\nHallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D., Vogt-\nSchilb, A., 2016.\n\nAt the national-level analysis, we overlay the flood hazard maps developed for this study with spatial socioeconomic data. For Vietnam, the World Bank has produced estimates of the number of people within each district who live below the poverty line: this \"poverty map\" is displayed in Map 4a, and the full methodology can be found in (Lanjouw, Marra, and Nguyen 2013). In addition, we use gridded population density data with a 1km resolution from Landscan (Geographic Information Science and Technology 2015). This \"population map\" is displayed in Map 4b.\n\nThe spatial socioeconomic data set used for Ho Chi Minh City is a data set of potential slum areas and of urban expansion from 2000 to 2010, from the Platform for Urban Management and Analysis (PUMA), a city-level data set developed by the World Bank (World Bank 2015).This data was collected via satellite in the year 2012, through a combination of visual interpretation of various sources and vintages of imagery.\n\nThe inundation maps were used in an earlier flood risk study of HCMC (Lasage et al. 2014), and were\ncomposed with the MIKE 11 hydraulic modeling software (DHI 2003).", "output": {"entities": {"named_data": ["Landscan Population Data", "Platform for Urban Management and Analysis (PUMA)"], "descriptive_data": [], "vague_data": ["inundation maps"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "- The XCO2 database (https://datacatalog.worldbank.org/search/dataset/0062760), an OCO2 panel database for a 25 km grid (G25), in Stata format, beginning in September 2014 and\nupdated regularly. The database includes G25 grid cell ID numbers, cell centroid coordinates,\nmonthly means of measured CO2 concentrations, and monthly means of Hakkarainen prefiltered CO2 concentration anomalies. The JPL/NASA database publishes OCO-2 data with\na lag of approximately two months.\n\n\n  - For functional urban areas (FUAs) with sufficient data, annually-updated change parameter\nestimates for models (6) and (7), with statistical significance categories [p>.05, ≤ .05, ≤ .01,\n≤ .001].\n\nUsing a rich dataset consisting of geocoded household data combined with detailed information on gold mining activities, the authors conduct two types of difference-in-differences estimations that provide complementary evidence.\n\nWe use two complementary geocoded household data sets to analyze outcomes in Ghana: the Demographic and Health Survey (DHS) and the Ghana Living Standard Survey (GLSS), which provide information on a wide range of welfare outcomes.", "output": {"entities": {"named_data": ["XCO2 database", "OCO-2", "Demographic and Health Survey (DHS)", "Ghana Living Standard Survey (GLSS)"], "descriptive_data": ["geocoded household data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "They can be viewed and downloaded at [https://doi.org/10.48529/2ZH0-JF55](https://doi.org/10.48529/2ZH0-JF55) or, currently, [https://microdata.worldbank.org/index.php/catalog/4218.](https://microdata.worldbank.org/index.php/catalog/4218) The citation is Andr´ee (2021). 6There are also country-specific data gathered by FSNAU (Somalia) and CLiMIS (South Sudan). More recently, the International Food Policy Research Institute has piloted gathering high frequency prices in several countries. 7For instance, estimates have also been developed for Papua New Guinea in collaboration with IFPRI in a separate pilot project.\n\nFor example, maize, sorghum, millet, wheat, vegetable oil, to name a few common food items, are also tracked in the World Bank Commodities Price Data (The Pink Sheet) that is used to construct the World Bank Food Price Index used to track international food price developments.\n\nUsing detailed data on expenditures from a 2017/18 household budget survey and caloric information from the Brazilian Table of Food Composition, calorie intake is assigned to more than 1,400 items to estimate the cost per calorie for a representative group of the population.", "output": {"entities": {"named_data": ["World Bank Food Price Index", "Brazilian Table of Food Composition"], "descriptive_data": ["country-specific data gathered by FSNAU", "household budget survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In terms of statistically insignificant effects, Block et al. (2004) examine the effect of Indonesia's drought and financial crisis of 1997-98 using time-age-cohort decomposition analyses employing high-frequency nutrition data.\n\nWith regards to social shocks, in Rwanda, Akresh et al. (2011) find that an additional month of exposure to civil war lowers child HAZ by 0.11 SD. Using qualitative data, the authors argue that this effect is due to increased theft of livestock or crops and exposure to water- and vector-borne diseases, with internal displacement driving this exposure to disease.\n\n7 Measurement error in consumption data could be behind this.\n\nWe use weather data taken from the _Terrestrial Air Temperature and Precipitation Version 4.01_ compiled by\n\nWe use recent global sub-national aggregated output data (Kummu, Taka and Guillaume 2018a) derived from", "output": {"entities": {"named_data": ["Terrestrial Air Temperature and Precipitation Version 4.01"], "descriptive_data": ["global sub-national aggregated output data"], "vague_data": ["high-frequency nutrition data", "consumption data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The novelty is that a new, more spatially and temporally detailed data set for nightlights, the Visible Infrared Imaging Radiometer Suite (VIIRS) product from the National Oceanic and Atmospheric Administration (NOAA), following the work of Elvidge et al. (2017), is employed. In addition, settlement layers have been used to remove nightlight cells with zero population to reduce the impact that \"empty\" cells could have on the analysis.\n\nThis paper utilizes a newer nightlight data set, the VIIRS Day/Night Band (DNB) provided by\nThe Earth Observations Group (EOG) at NOAA/NCEI. The data are produced following the\nmethodology of Elvidge, et al.\n\nThe VIIRS data have a number of advantages over the widely used, but now discontinued,\nDefense Meteorological Satellite Program (DMSP). Firstly, they have a higher resolution,\n450m by 450m compared to 1km by 1km for the DMSP.\n\nThe WorldPop data sets have been used to identify settlement areas in Myanmar and Vietnam\n(WorldPop 2013) (Worldpop 2016). These data sets have a spatial grid cell resolution of\napproximately 100 meters at the equator and estimate the number of persons per square.\n\n\\n<!-- PAGE 13 -->\\nFor Indonesia, the Philippines and Thailand high resolution settlement layers from Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University was used.", "output": {"entities": {"named_data": ["Visible Infrared Imaging Radiometer Suite (VIIRS) product from the National Oceanic and Atmospheric Administration (NOAA)", "Defense Meteorological Satellite Program (DMSP)", "WorldPop"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 All data on energy access, use and expenditures come from the 2008 ILCS.\n\n5Ideally, the ILCS data would be matched with household-level data from the utility companies on gas and other alternative energy consumption and payment to allow a more accurate analysis of the residential demand and the distributional consequences of price changes.\n\n_Source:_ Author's calculations based on 2008 ILCS and 2009 GDP data\n\nmore than half of it on gas. According to the ILCS, which provides self-reported data on energy\n\n0.135 (= 3,600/26,582) metric tonnes of oil per one tonne of soybean, while the share of soybean\n\nmeal is given by the residual, _β2_ ' = 0.865 (=1 - 0.135). [13] Then the production of soybean meal in", "output": {"entities": {"named_data": ["2008 ILCS", "ILCS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the World Bank's Poverty and Inequality Platform (PIP). PIP is managed jointly by the Data and\n\nResearch Groups in the World Bank's Development Economics Division. It reports both poverty\n\nmeasures and inequality measures, including the Gini Index for 168 countries from 1968 to 2022. The\n\nGini index is based on primary household survey data obtained from government statistical agencies\n\nalternative dataset that provides inequality measures. Specifically, we use the Standardized World\n\nIncome Inequality Database (SWIID). [5] The SWIID maximizes the comparability of available income\n\n<!-- PAGE 10 -->\nIncome Study database.\n\nData on natural disasters is from the EM-DAT database maintained by the Centre for Research", "output": {"entities": {"named_data": ["Poverty and Inequality Platform", "Gini Index", "Income Study database", "EM-DAT database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Production of soybeans QSB 41,000 metric tonnes Faostat [a] Imports of soybeans MSB 27 metric tonnes Faostat [b] Export of soybeans XSB 14,445 metric tonnes Faostat [b] Diesel consumption D 596.84 mil. liters Energy Sector Report [c]\n\nDiesel price PD 0.92 $/liter Energy Sector Report [c]\n\n\nReport of the Status of\nDiesel tax t 0.30 $/liter\nPetroleum Industry [d]\n\nMPG biodiesel/MPG diesel λ 0.91 de Gorter et al. (2011)\nLocal price of soybeans at Beira linked to Argentian price PSB 370.24 $/metric tonne HighQuest Partners (2011)\n\nBiodiesel price in Germany PB 1.64 $/liter UFOP [e]", "output": {"entities": {"named_data": ["Faostat"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "From 1997-98, 274 households, randomly selected from 10 purposively sampled villages, [2] were surveyed in each of four periods, including the 1997 post-harvest and the subsequent 1998 hunger season.\n\navailability. Women in these households were asked to recollect the quantities of\n\n\ndifferent foods consumed in the previous seven days. These data were converted into\n\n\n\n<!-- PAGE 12 -->\n8\n\ndaily per capita caloric availability using household size and locally adapted tables that\n\n\nconvert physical units of food into calories. Were data available, however, the measure\n\nThe incidence and depth of current caloric shortfall can be directly calculated\n\n\nfrom the survey data collected in November 1997. We take 2,345 kcal/person/day as\n\n\ncaloric threshold which corresponds to the needs of a 60 kg male, aged 30-59,\n\nGovernment of Mali and the USAID sponsored [Famine Early Warning System for Mali]\n\n\nuse it as a leading indicator of food insecurity. Since [the availability of food is necessary]", "output": {"entities": {"named_data": ["Famine Early Warning System for Mali"], "descriptive_data": ["survey data collected in November 1997"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This rate decreases as we move away from mining areas (figure 12a and 12b). Not only are the rates higher in the first two types of communes mentioned, but the rates in those communes also increased faster from 1998 to 2009, the two years being the years of the last two General Population and Housing Censuses in Mali.\n\n|Figure 12a Net primary enrollment (%)|Figure 12b Net primary enrollment (%) mining<br>communes|\n|---|---|\n|<br>|<br>|\n|_Source:_RGPH (General Population and Housing Census) 1998 and 2009.|_Source:_RGPH (General Population and Housing Census) 1998 and 2009.|\n\n5 We could not use the 1987 census data because communes were not created at that time.", "output": {"entities": {"named_data": ["General Population and Housing Censuses", "General Population and Housing Census", "RGPH (General Population and Housing Census)"], "descriptive_data": ["1987 census data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(1) GDP growth, (2) agricultural GDP value added, (3) industrial GDP value added, and (4) poverty\n\ngrowth, (2) agricultural GDP value added (%), (3) industrial GDP value added (%), and (4) poverty\n\n<!-- PAGE 28 -->\n###### **_References_**\n\nAlley, William: The Palmer Drought Severity Index: Limitations and Assumptions, Journal of Climate and Applied\nMeteorology, 1984, 23,1100 - 1109.\n\n16 For this research, the model was updated by the Institute of Water Modeling (IWM) with river alignments of GBM basins using available physical maps of India, Nepal, Tibet; and sub catchments of the GBM basins were redelineated using the Digital Elevation Model (DEM) based on SRTM-version 3. The model has been first calibrated using round the year hydrological feature of 2005 and validated for two subsequent years 2006 and 2007. Then the model was further updated using most recent data of last hydrological year 2009. 17 Bathymetries of the rivers have been updated incorporating the available latest cross-sections and bathymetries of floodplain routing channels are taken from national land terrain model developed in FAP (Flood Action Plan) 19.\n\nchannels. It is based on the existing national DEM for Bangladesh and model simulations", "output": {"entities": {"named_data": ["Palmer Drought Severity Index"], "descriptive_data": [], "vague_data": ["agricultural GDP value added"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "<!-- PAGE 17 -->\n**Figure 2: Relative Fit to CRU Data: 8 Global Climate Models**\n\n<!-- PAGE 22 -->\n**Appendix 1: CRU and GCM Sources**\n\nHistorical Data: 1961-2000\n\nCRU: UK Climatic Research Unit, University of East Anglia, UK\n[http://www.cru.uea.ac.uk/](http://www.cru.uea.ac.uk/)\n\nGCM Data: 1961-2100\n\neach country and year (using household-level data from surveys available through the Luxembourg Income Study, LIS), individuals were ranked by their (household per capita) market income, from the poorest to the richest, and grouped in deciles.", "output": {"entities": {"named_data": ["CRU", "Luxembourg Income Study"], "descriptive_data": [], "vague_data": ["GCM Data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The decision to link it with CM made ENLACE a de facto high-stakes test encouraging\n\nin grade inflation (Contreras and Backoff, 2014). Anomalies in ENLACE and the creation\n\n<!-- PAGE 10 -->\n###### **2.2 The ENLACE Panel**\n\nENLACE created a unique personal identifier ( _Clave_ _Única_ _de_ _Registro_ _Poblacional_ or\n\nthe ENLACE dataset included a school identifier, socioeconomic information for each", "output": {"entities": {"named_data": ["ENLACE dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "<!-- PAGE 10 -->\nILRI, and the World Bank (as part of the LDIA and LSMS-ISA projects [2] ) to start the survey\n\nvalidation work that is documented in this paper.\n\n\n2.2 _Milk production recall methods_\n\nUsing data from two microenterprise surveys in Sri Lanka, De Mel et al. (2009) find that\n\nthe same survey experiment in Tanzania to obtain evidence on the nature of measurement\n\nScott and Amenuvegbe (1990) conduct an experimental study on 135 households in Ghana.\n\nDantlait survey. The fieldwork was managed by two experienced enumerators, and a", "output": {"entities": {"named_data": ["LSMS-ISA projects", "Dantlait survey"], "descriptive_data": ["microenterprise surveys in Sri Lanka"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "8 Flood return periods of 5, 10, 20, 50, 75, 100, 200, 250, 500 and 1000 are included in the Fathom data product.\n\nWe leverage a unique large-scale experiment on consumption measurement in Iraq designed for the Iraq Household and Socio-Economic Survey (IHSES) in 2012.\n\n[1] In recall interviews, households are typically asked to report on food _consumption_ during the specified period and 1The World Bank's Living Standards Measurement Study (LSMS) survey finder includes a list of household consumption and expenditure surveys: of almost 90 surveys from more than 25 countries, more than 75 use recall.\n\n(2017) compare recall questions on food spending from the Canadian Food Expenditure Survey to data from expenditure diaries.\n\nThe availability of repeated diary-recall measurements of food spending or acquisition is not unique to our setting, the Consumer Expenditure Surveys in the United States and the Canadian Food Expenditure Survey being notable examples.", "output": {"entities": {"named_data": ["Fathom data product", "Iraq Household and Socio-Economic Survey", "Living Standards Measurement Study", "Canadian Food Expenditure Survey", "Consumer Expenditure Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "different social norms and customs, as well as ethnic prejudice. Card et al (2012) use the ESS to\n\ncountries. In order to identify age patterns using repeated cross-section data, Deaton and\n\ndegree of pro-immigration attitudes, we estimate the models using stacked micro-data from   the cross-section surveys instead of averaging over time-invariant characteristics.\n\nthe cross-section surveys instead of averaging over time-invariant characteristics. As a\n\nTo identify the effect of age on attitudes toward migration, we append household surveys from", "output": {"entities": {"named_data": ["ESS"], "descriptive_data": [], "vague_data": ["repeated cross-section data", "stacked micro-data", "cross-section surveys", "household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "3 The exact list of socioeconomic variables in the DHS is not identical to that on the JFPR application form. We\nderive the index of socioeconomic status in the DHS from variables describing: the ownership of a bicycle, cart,\nboat, motorbike, car, truck, radio, television; the conditions of the dwelling such as hard roofing and finished\nflooring; the availability of electric lighting; the main source of drinking water; the type of toilet facilities; and the\nmain type of cooking fuel used.\n\nbeneficiaries was done by the Local Management Committee (LMC) of a JFPR _secondary_ school. In\n\nThe JFPR scholarship program established a cut-off in the maximum number of scholarships\n\nFinally, the paper presents evidence of heterogeneity in the JFPR program effects. For this", "output": {"entities": {"named_data": ["DHS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "under field-based controlled cooking tests in a related study (Gebreeziabher et al., 2015). Later\n\nD., Bluffstone, R., Martinsson, P., Mekonnen, A., &\n\nToman, M. A. (2018). Fuel savings, cooking time and user satisfaction with improved\n\nthe adoption of improved stoves using both revealed and stated preference data from India. Our\n\nshocks, such as drought or conflict, in causing and perpetuating poverty is critical to designing policies aimed at building resilience and contributing toward the goal of ending poverty. This paper uses micro-data from two waves of the Somali High Frequency Survey to assess the impact of the severe drought that Somalia experienced in 2016/17 on poverty, hunger, and consumption. The analy sis uses a regression framework to quantify the effects of the drought, relying on spatial variation in drought exposure\n\nThis analysis uses cross-sectional household-level data from two waves of the SHFS. Wave 1 interviewed 4,117 urban, rural, and IDP households in February and March of 2016, representative of 9 of 18 Somali pre-war regions, excluding inaccessible areas in the south. Wave 2 expanded coverage to all but one,", "output": {"entities": {"named_data": ["Somali High Frequency Survey"], "descriptive_data": [], "vague_data": ["revealed and stated preference data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In the absence of objective measures of these factors at the commune level, we leverage the Afrobarometer [15] surveys to investigate at the individual level the effect of the proximity to gold-mining activities on perceptions of governance and quality of public services.\n\nWe use the 2005, 2012, and 2014 rounds of the nationally representative Afrobarometer survey in\nMali. We focus on variables of political responsiveness, corruption, and quality of public services\nthat are similar across the three rounds. Geocoded enumeration areas are combined with location\nof industrial mine sites to construct our treatment variable, which is the cumulative gold production\nwithin 20 kilometers of the mine at the time of the survey. This assumes that both distance to mines\nand intensity of production matter for the effect. We then estimate the following logistic regression\nof the form:\n\n<!-- PAGE 24 -->\n**Figure 17 Locations of Afrobarometer enumeration areas**", "output": {"entities": {"named_data": ["Afrobarometer survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Finally, Gunther and Fink (2010) analyze data from 172 DHS  surveys and find that households having flush toilets have 13 percent lower odds of diarrhea\n\nWe use a subsample of 209,762 children between 0 to 48 months of age that live in ru ral areas from the Third Round of the District Level Household Survey (DLHS-3). The\n\nThe sample  design of the DLHS-3 survey makes this measure possible since in rural areas the DLHS-3  uses census villages as PSU (International Institute for Population Sciences (2010)).\n\nDLHS is a nationwide survey with district level representation of India's households, which\n\ncollects information on family planning, maternal and child health, reproductive health of\n\nThese ratios are reasonably comparable with the estimations of JMP for rural  areas in India. Table 2 presents the summary statistics of these variables for the 209,762", "output": {"entities": {"named_data": ["Third Round of the District Level Household Survey (DLHS-3)", "DLHS-3 survey", "District Level Household Survey (DLHS-3)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Notes: The table displays the mean and standard deviations of several characteristics of all students\nmatched in the ENILEMS-ENLACE (column 1), students that reported to be in college (column 2),\nout of college (column 3) or employed (column 4). Data: ENILEMS-ENLACE panel.\n\n\n30\n\n\n\n<!-- PAGE 33 -->\nTable 3: OLS - ENLACE test scores and secondary school outcomes: simple correlations\n\nNotes: (1) The table displays the estimation of the effect of early test scores on grade 9 and 12 test scores and enrollment by subject and\ngrade. (2) Specifications for each subject and grade include as independent variables grade 6 test scores of the same subject. (3) Sample:\nTwins (students in the same school in grade 6, with identical last names and birth date. (4) All specifications include twins fixed effects. (5)\nRobust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05,  - p _<_ 0.1.\n\n\n\n<!-- PAGE 46 -->\nTable A.4: OLS       - ENLACE mathematics and Spanish test scores and post-secondary school outcomes\n\nEnlace Score 0.102*** -0.00750 0.0681** -0.000444 (0.0154) (0.0297) (0.0304) (0.0298) Upper Secondary GPA 0.0688*** 0.0412* -0.00579 0.00507 (0.0146) (0.0233) (0.0268) (0.0254) Girl -0.0500* -0.237*** -0.0321 0.00994 (0.0263) (0.0442) (0.0501) (0.0478) Private Upper Secondary 0.113*** -0.0962 0.0606 -0.0842 (0.0320) (0.0649) (0.0736) (0.0653) Urban resident 0.269*** 0.0373 0.0944* 0.137** (0.0375) (0.0472) (0.0535) (0.0566)", "output": {"entities": {"named_data": ["ENILEMS-ENLACE", "ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Enlace taker Grade 9 1943583 0.702 20995 0.742 -0.040***\n(0.000) (0.003)\n\n\nEnlace taker Grade 12 1943583 0.317 20995 0.368 -0.052***\n(0.000) (0.003)\n\n_Notes_ : The value displayed for t-tests are the differences in the means across the groups.\n***, **, and - indicate significance at the 1, 5, and 10 percent critical level.\n\n\n42\n\n\n\n<!-- PAGE 45 -->\nTable A.3: OLS  - ENLACE mathematics and Spanish test scores and secondary school outcomes\n\nEnlace Spanish 0.0885*** -0.0285 0.0619** -0.00724 (0.0149) (0.0280) (0.0266) (0.0264) Enlace mathematics 0.0749*** 0.0132 0.0474* 0.00573 (0.0129) (0.0249) (0.0277) (0.0267) Upper Secondary GPA 0.0763*** 0.0442* -0.000549 0.00642 0.0733*** 0.0370 -0.00313 0.00312 (0.0146) (0.0228) (0.0260) (0.0242) (0.0145) (0.0233) (0.0271) (0.0259) Girl -0.0751*** -0.233*** -0.0439 0.0116 -0.0362 -0.232*** -0.0222 0.0108 (0.0262) (0.0441) (0.0497) (0.0474) (0.0266) (0.0450) (0.0516) (0.0492) Private Upper Secondary 0.111*** -0.0974 0.0553 -0.0842 0.115*** -0.0957 0.0626 -0.0833 (0.0319) (0.0644) (0.0\n\nEnlace Score Grade 6 0.234*** 0.335*** 0.0853*** 0.110*** (0.0298) (0.0277) (0.0101) (0.0102) Girl 0.178*** 0.0851* 0.0648*** 0.0280* (0.0499) (0.0464) (0.0191) (0.0150) Private School Grade 6 0.0638 0.120 0.0232 0.0396 (0.176) (0.146) (0.0641) (0.0479) Mother has lower secondary school 0.282*** 0.228*** 0.103*** 0.0750*** (0.0529) (0.0538) (0.0204) (0.0167) Father has lower secondary school 0.201*** 0.107* 0.0732*** 0.0352* (0.0559) (0.0606) (0.0209) (0.0193) Mother is white collar 0.0522 0.0631 0.01", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "PAGE 9 --> To calculate a cost per calorie for each food item, we assign caloric intake values to the quantities purchased, as reported in POF. The TBCA contains the caloric information for a myriad of items, but there are no common food item identifiers with the POF 2017/18 that would allow directly matching the information across the two data sources.\n\nThus, we use the food item description in POF to look up corresponding items and calorie intake per kilogram (kg) in TBCA. Given the large amount of individual food items in POF (4,549) and the fact that their contribution to total average consumption differs, we opted for a simplified approach to assign calorie intake values from TBCA to food consumption in POF. This approach can be divided into two main steps.\n\nWe rank all food items in POF 2017/18 and find 350 food items that together account for about three-quarters (73.3 percent) of average total expenditures on food consumed at home and about half (49.2 percent) of all expenditures on food.", "output": {"entities": {"named_data": ["POF", "TBCA"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ratings. Where GDP per capita growth data have been recorded, it appears that FCS countries have stronger growth trends than non-FCS countries and in both groups of countries growth is correlated with stronger outcome ratings.\n\ncategorization is derived from the FCS lists for FY06 to FY13. [1] Partial FCS countries are those classified as FCS in at least two years during this period.\n\n7For example that would be detected through the implementation status reports (ISRs).\n\nwe rely on the data from Melecky and Podpiera (2013) and the 2003, 2007, and 2012 Bank Regulation\n\n\nand Supervision Surveys of the World Bank. For banking crisis classification, we rely on Laeven and\n\n\nValencia's (2013) database and cross-check our results against the crisis classification by Reinhart and\n\ncountries.\n\n\n8\n\n\n\n<!-- PAGE 11 -->\n_**Regression Model**_\n\n\nWe use the systemic banking crisis database of Laeven and Valencia (2013) to identify banking", "output": {"entities": {"named_data": [], "descriptive_data": ["systemic banking crisis database"], "vague_data": ["GDP per capita growth data", "FCS lists"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Aside from the various reporting errors, other criticisms of using HCES for nutritional assessments relate\n\nto easily avoidable design mistakes. Many HCES omit details on meals consumed outside the home by\n\nAnother example is that HCES sometimes ask about food acquisition rather than consumption. As food\n\ncomes to the harmonization of HCES for measuring food consumption. In fact, Deaton and Zaidi (2002),\n\nthe most common reference for designing HCES and calculating consumption aggregates, explicitly", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "<!-- PAGE 41 --> SOE dummies Yes Yes Yes Yes Yes Yes Age dummies Yes Yes Yes Yes Yes Yes Size dummies No No Yes Yes Yes Yes Subsidies received No Yes No Yes No Yes Industry effects Yes Yes Yes Yes Yes Yes Year effects Yes Yes Yes Yes Yes Yes Observations **[†]** 2,253,381 2,253,381 2,165,653 2,165,653 2,249,388 2,249,388 Pseudo/Within R **[†]** 0.018 0.018 0.049 0.049 0.029 0.029 Source: World Bank staff analysis using Romania MoF firm-level data from 2016-20.\n\nSOE dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Age dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Size dummies No No No Yes Yes Yes Yes Yes Yes Value of subsidies received Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Year effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations **[†]** 2,098,570 47,698 11,260 2,016,257 45,597 10,878 2,094,821 47,624 11,249 Within R-squared **[†]** 0.018 0.016 0.013 0.048 0.034 0.042 0.028 0.023 0.030 Source: World Bank staff analysis using Romania MoF firm-level data from 2016 to 2020.\n\n<!-- PAGE 29 -->\nSource: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. Exit is a dummy variable equal to 1 for the last\nyear the firm exited the sample. Firm-level measure of allocative efficiency is computed as a cross product between two terms\ndefined at the 4-digit NACE sector, year, and county level: (a) the deviation of a firm's market share from the average market share\nat the sector-year-county level, and (b) the deviation of a firm's (labor) productivity from the average firm-level productivity at the\nsector-year-county level.\n\nemployment Yes Yes Yes Yes Sector size in economy Yes Yes Yes Yes Industry effects Yes Yes Yes Yes Year effects Yes Yes Yes Yes Observations 704 704 704 704 Within R-squared 0.021 0.019 0.031 0.032 Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019.", "output": {"entities": {"named_data": [], "descriptive_data": ["Romania MoF firm-level data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Existing coastal flood maps covering Vietnam, most prominently the Global Tide and Surge Reanalysis (GTSR) data set (Muis et al. 2016), are expected to underestimate coastal flood risk in Vietnam.\n\nIt is based on a global digital elevation map with a resolution of 90 meters at the equator that has been corrected for several errors typically present in spaceborn elevation models (Yamazaki et al. 2017).\n\nThese curves have been generated by statistically postprocessing the results from storm surge modeling with a wide range of possible typhoons (Ministry of Agriculture and Rural Development, 2012).\n\nTyphoon wind speeds used in this analysis were produced by a global model of cyclone winds calibrated\non over 2,500 past cyclones, terrain composure, and ocean depth. It contains the modelled maximum\nwind speed at every location in Vietnam for typhoons occurring, on average, every 50, 100, and 1000\nyears (figure 2.2). The data is in raster format has a grid resolution of roughly 30 by 30 kilometers (UNDRR,\n2015).\n\n\n**Figure 2.2: Wind speeds over Vietnam in typhoons of varying severity**", "output": {"entities": {"named_data": ["Global Tide and Surge Reanalysis"], "descriptive_data": ["global digital elevation map"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "for the exam in 2007, we were able to identify 71 percent three years later in ENLACE\n\nwe use administrative data from the annual school census ( _Formato_ _911_ ) to estimate the\n\nENLACE panel has a survival rate that is 6 and 7 percentage points lower vis-a-vis the\n\n<!-- PAGE 11 -->\nmatching. Take-up rate in the ENLACE test is high, 87.3 percent of the population\n\nsecondary in 2013. The difference in graduation or survival rates between the ENLACE", "output": {"entities": {"named_data": [], "descriptive_data": ["annual school census"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "most recent year in our CRU dataset). We begin by computing average annual rainfall\n\nand temperature for the nine datasets (CRU; 8 GCMs). Then we use a least-squares fit\n\ngenerates nine benchmark annual datasets - CRU and eight GCMs - for each of the 372", "output": {"entities": {"named_data": ["CRU dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "[8] Daewoo Securities (1988 and 1999) _Handbook of Listed Companies,_ Daewoo Securities: Korea.\n\nscope data were missing or incomplete, we collected additional company information from Wisenet\n\nKorea. [7] This internet server provides detailed financial information, including income statements\n\nSecond, using data on foreign ownership from 1998 and 1999 and firm characteristics from the\n\n'° Of course, a drawback of this approach is that the estimates of elements of E will not be very precise, as the crosssectional dimension in these regressions is large relative to the time dimension. For this reason, we use only 20 states\nbecause annual data for gross state product is only available for 23 years, from 1963-1986. To give some idea of the\nmagnitude of the spatial correlations, note that the average cross-sectional correlation for the U.S. state data is .193, with\na maximal value of .629, while for the O.E.C.D., the corresponding figures are .312 and .761.", "output": {"entities": {"named_data": [], "descriptive_data": ["annual data for gross state product"], "vague_data": ["data on foreign ownership"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "**Figure** **2.** Source: WDR 2020 team, based on World Bank's World Development Indicators (database).\nNote: The blue line represents the average of all pair-wise GDP correlation taken over all country-pairs\nthat include China. The orange line represents the average taken over all countries that include the\nUnited States. The grey line represents country-pairs that contain neither the United States nor China.\nThe date corresponds to the midpoint of a 10-year rolling window.\n\nChange in Production Connectivity\n\n\n**Figure 4.** WDR 2020 team, based on World Bank's World Development Indicators (database) and World\nIntegrated Trade Solution (database). Note: Each dot represents a pair of regions-for example, East\nAsia and Pacific and Sub-Saharan Africa, and Latin America and Caribbean and South Asia are two\ndifferent observations). The horizontal axis measures the change over time in production connectivity\ndefined as the total trade in intermediates as a share of GDP of both regions. The vertical axis measures\nthe proportional change in GDP correlation over time.\n\nSource: WDR 2020 team, based on World Bank's World Development Indicators (database) and a classification of sticky vs. non sticky trade from Martin et al. (2018).", "output": {"entities": {"named_data": ["World Development Indicators"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "which collected heretofore unavailable expenditure data on each type of subsidized food\n\n4. New 1997 Household Budget Survey\n\nEgyptian food subsidy system.' [8] This study used expenditure data from the 1990/91\n\nHousehold and Income Expenditure Survey (HIES), which was a large, nationally\n\nand Statistics (CAPMAS).1 [9] One of the main problems with this 1990/91 HIES survey", "output": {"entities": {"named_data": ["1997 Household Budget Survey", "Household and Income Expenditure Survey (HIES)", "HIES"], "descriptive_data": [], "vague_data": ["expenditure data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Our nightlights data set is composed of 22 satellite-year composites for the period 2000 2013. Each composite covers Central America and contains information on 604,473 one kilometer square grid-cells.\n\nTo measure the distribution of surface winds from hurricanes in Central America during our sample period, we use the wind field model developed by Pita et al. (2015). This model uses an asymmetric Holland equation that has been specifically calibrated for Central America.\n\nData on the distribution of mangroves come from two sources. The first is a collection of harmonized maps, 1960 to 1996, that was assembled for the Mangrove World Atlas (Spalding et al., 1997). We use this map to identify areas that have historically supported mangrove habitats.", "output": {"entities": {"named_data": ["Mangrove World Atlas"], "descriptive_data": ["nightlights data set"], "vague_data": ["harmonized maps"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "cereal production (in kilograms) per residential [household member reported by the] household head in the immediate post harvest [period as the measure of agricultural] production.\n\nindex. [8] Here, we construct a food variety score (FVS) to combine the diversity of a\n\n\nperson's diet into a single index (Hatloy, _et al.,_ 1998). The FVS is based on the number\n\n\nof different food items eaten over a registration period. We evaluate two versions: 1) a\n\nassociated with food shortages into a numerical index. Our third alternative indicator is\n\n\nan index of these 'coping strategies'. We asked the most knowledgeable woman within\n\n1996 and the World Bank's forthcoming World Development Report on Poverty 2000/1, the demand for such comprehensive measures is more urgent than ever.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "using spatial information. Geographical coordinates of enumeration areas in GLSS are from\n\n\nGhana Statistical Services (GSS). [2] Point coordinates (global positioning system [GPS]) for the\n\nThe Raw Materials Data are from InterraRMG (2013). The data set contains information on\n\n\npast or current industrial mines. All mines have information on annual production volumes,", "output": {"entities": {"named_data": ["GLSS", "Raw Materials Data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "<!-- PAGE 9 -->\n|Figure 6a Share of population working primarily<br>in agriculture (%) in mining communes|Figure 6b Share of population working primarily<br>in extractives (%) in mining communes|\n|---|---|\n|||\n|_Source:_ RGPH (General Population and Housing Census) 1998 and 2009.|_Source:_ RGPH (General Population and Housing Census) 1998 and 2009.|", "output": {"entities": {"named_data": ["RGPH (General Population and Housing Census)", "General Population and Housing Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Additionally, local and\nupstream shares of forest cover are measured using satellite data from the European Space Agency.\n\n###### 3. Empirical Strategy\n\nOur econometric specification uses a panel fixed-effects model to link data on droughts to data on\neconomic growth at the level of 0.5-degree grid cells (approximately 56 kilometers x 56 kilometers at the\nequator) between 1991 and 2014, the period for which economic data is available at a granular scale.\n\nIn robustness checks we also make use of the Standardized Precipitation Evapotranspiration Index (SPEI) (Vicente-Serrano, et al., 2010) that integrates evapotranspiration into the standard precipitation index.\n\nWe use data from the ESA CCI project to determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split the sample based on different shares of cropland ranging from less than 20 percent to more than 75 percent.\n\n2 UNCCD Report \"Drought in Numbers 2022.\"\n3 Defined here as shocks that are at least 2 standard deviations (SDs) below the long-term mean. Note that a 2 SD dry\nshock is a very rare event and includes the driest 2.5 years in a century.\n\nThe magnitudes of the effects are also consistent with the recent 2022 Global Assessment Report on Disaster Risk Reduction which looks at all types of disasters from rapid onset events like typhoons, floods, earthquakes to other events like droughts, saltwater intrusion, air pollution.", "output": {"entities": {"named_data": ["Standardized Precipitation Evapotranspiration Index (SPEI)", "ESA CCI project"], "descriptive_data": ["satellite data from the European Space Agency"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "this purpose. Enumerators were given a list of applicants to each JFPR school, without information on\n\nthe JFPR school she had applied to. Note that a girl would only figure as enrolled by this measure if she\n\nenrollment; attendance, conditional on enrollment; and adequate grade progress in a JFPR school. The\n\nregardless whether this is a JFPR school or not. To construct this third variable, we proceeded as follows.\n\nWhen an applicant to a given JFPR school did not appear on the 8 [th] grade enrollment rosters, enumerators", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "proxied by observing student \"i\" sitting for ENLACE in Grades 9 and 12, respectively.\n\nWe also use the ENLACE panel to examine the relationship between Grade 6 test scores\n\nThe ENILEMS-ENLACE panel is used to examine the relationship between Grade 12\n\nLearning achievement is measured using an aggregated ENLACE test score, the simple\n\nderstand how much of ENLACE's predictive power is related to the skills it captures as", "output": {"entities": {"named_data": ["ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Oseni and Pollitt (2013) use cross-sectional data on electricity outages collected by the 2007 World Bank Enterprise Survey of 6,854 firms in 12 African countries.\n\n** **Data** We used the social accounting matrix (SAM) of year 2007.\n\nWe use a choice experiment study to fill this knowledge gap and identify preferences for\n\ngoods, when market data are not available for assessing these valuations. A choice experiment\n\n\nsurvey presents the respondent with choice scenarios where each choice scenario has\n\nThe enumerators spoke local languages and were trained in conducting choice   experiment surveys (and most had previous experience with collecting choice experiment data", "output": {"entities": {"named_data": ["2007 World Bank Enterprise Survey", "social accounting matrix (SAM) of year 2007"], "descriptive_data": [], "vague_data": ["choice experiment\n\n\nsurvey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "exports. For this information, we used the World Integrated Trade Solution (WITS) database on the SITC, Revision\n\ndiversification. We use COMTRADE data on exports by product at a 4-digit disaggregated level from the SITC Revision\n\n1.0 to compute Herfindahl indices of export product concentration. Bilateral data from COMTRADE are also used to\n\nThe CO2 emissions and energy consumption data are from International Energy Agency (IEA) Fuel Combustion Statistics database and World Energy Statistics and Balances database, respectively.", "output": {"entities": {"named_data": ["World Integrated Trade Solution (WITS) database", "COMTRADE data", "International Energy Agency (IEA) Fuel Combustion Statistics database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Notes: (1) The table displays the results of the estimation of Equation 1. (2) The dependent variables are labor market\noutcomes: a dummy indicating enrollment in college (column 1), a dummy indicating if employed (column 2), ln of\nhourly wage (column 3) and a dummy for being employed in a formal firm (column 4). (3) All specifications include\nage and State dummies. (5) Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05,  - p _<_ 0.1.\n\n\n34\n\n\n\n<!-- PAGE 37 -->\nTable 7: OLS  - ENLACE test scores and later test scores by subject\n\n<!-- PAGE 38 -->\n#### **Figures**\n\nFigure 1: ENLACE take-up\n\n\nNotes: The graph presents historical ENLACE take-up by schooling levels: primary, lower\nand upper-secondary. Source: SEP.\n\nNotes: The graph presents the observations found in the ENLACE panel in 2007, 2010 and\n2013 and the expected observations given the school trajectories in secondary school. Data:\nauthors' estimations based on ENLACE panel and Formato 911.\n\n\n37\n\n\n\n<!-- PAGE 40 -->\nFigure 3: ENLACE test scores and secondary school outcomes", "output": {"entities": {"named_data": ["ENLACE panel", "Formato 911"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "_Thailand_ . As for Thailand, it is the third largest country, fourth most populous and boasts the\nsecond largest GDP of our countries. In terms of disasters, Thailand is mostly at risk to floods\nand typhoons, but earthquakes happen occasionally, with the largest recorded being a 6.1\nmagnitude earthquake that occurred in May 2014. The lower risk compared to our other\ncountries is also mentioned by INFORM in their country profile of Thailand (INFORM 2019).\n\n\n_Vietnam_ . The final country is Vietnam, which is the smallest, but has the third highest\npopulation just below 100 million people. In terms of GDP, Vietnam rank fourth among our\nfive countries. When it comes to disaster risk, they are on par with Thailand in that INFORM\nranks them low and exposed primarily to floods and typhoons (INFORM 2019).\n\nNational Disaster Management Agency, B. N. P. B. 2016. \"DiBi database (Data and Information\non Disaster in Indonesia).\" http://dibi.bnpb.go.id/.\n\n\nRahman, Md. Mizanur, Dhyan Singh Arya, Narendra Kumar Goel, and Ashis Kumar Mitra. 2011.\n\"Rainfall statistics evaluation of ECMWF model and TRMM data over Bangladesh for\nflood related studies.\" _Meteorological_ _Applications_ (Wiley) 19: 501-512.\ndoi:10.1002/met.293.\n\nThe underlying data are based on 2015 and combines census data with satellite images from DigitalGlobe. The population is allocated according to subdivision censuses once settlements have been identified from the satellite images.\n\n(2018) used the underlying NPP-VIIRS DNB Daily Data to analyze selected natural disasters. They found that the images were useful for detecting damages and power outages, but that cloud coverage was a major limitation in the assessment.\n\nThe first dataset combines information on flood extent and flood depth from pluvial and fluvial flooding into one flood map. It was developed by the company Fathom, formerly known as SSBN. The map covers all of Vietnam at a resolution of about 90 by 90 meters.", "output": {"entities": {"named_data": ["NPP-VIIRS DNB Daily Data"], "descriptive_data": ["satellite images from DigitalGlobe", "flood map"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Findings from the 2014 Agricultural Census suggest there are over 317,000 family farms [4] of 1.48 hectares on average (Statistical Committee of the Republic of Armenia, 2016), which produce approximately 95% of the country's gross agricultural output (Statistical Committee of the Republic of Armenia, 2021).\n\n4 This estimate of over 317,000 farms includes those farms that had their own land (including leased land) at the time of the 2014 Agricultural Census, as well as farms that leased land but did not have their own land.\n\nStatistical Committee of the Republic of Armenia. (2016). Main Findings of Agricultural Census 2014 of the\nRepublic of Armenia. [http://armstat.am/en/?nid=82&id=1860](http://armstat.am/en/?nid=82&id=1860)\n\n<!-- PAGE 8 --> of enumeration areas was based on the Population Census 2011, and a fresh household listing was conducted in each of the selected EAs to attain a current household sampling frame from which to randomly select 12 households in each EA.\n\nTodorov, A., & Kirchner, C. (2000). Bias in proxies' reports of disability: data from the National Health\nInterview Survey on disability. _American Journal of Public Health_, 90(8), 1248.\n\n\nUNECE. (2005). Inventory of Land Administration Systems in Europe and North America. Produced and\npublished by HM Land Registry. London, United Kingdom on behalf of the UNECE Working Party\non Land Administration.", "output": {"entities": {"named_data": ["2014 Agricultural Census", "Population Census 2011"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In order to take account of the building types in Indonesia we use  information from the USGS building inventory for earthquake assessment, which provides  estimates of the fractions of building types observed by country; see Jaiswal & Wald (2008).  The data provides the share of 99 different building types within a country separately for  urban and rural areas, where - due to lack of other information - a homogenous distribution  of buildings is assumed.  Then fragility curves by building type are derived from the curves constructed by the  Global Earthquake Safety Initiative project; see GeoHazards International and United Na tions Centre for Regional Development (2001).\n\nIn order to use these vulnerability curves for Indonesia we first allocated each of the 99  building types given in the USGS building inventory to one of the 9 more aggregate cate gories of the GESI building classification.\n\nof ongoing eruption as a threshold of when to include an eruption in the data set or not.  Second, images containing sulphur dioxide data from the OMI/AURA satellite are used to  model the intensity of the eruptions.\n\nTo construct an inundation map of the affected areas, a  map based on MODIS satellite pictures from Anderson _et_ _al._ (2004) is used with spatial al gorithms to detect the difference in color between inundated and non-inundated areas.", "output": {"entities": {"named_data": ["USGS building inventory"], "descriptive_data": ["MODIS satellite pictures"], "vague_data": ["sulphur dioxide data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "2 Data from the EM-DAT database, created and maintained by the Center for Research on the Epidemiology of Disasters (CRED) at the Catholic University of Louvain. 3 There are multiple formal definitions.\n\nFor a disaster to be listed in the EM-DAT database, at least one of the following criteria should be met: (i) 10 or more people are reported killed; (ii) 100 people are reported affected; (iii) a state of emergency is declared; (iv) a call for international assistance is issued.\n\nHazard maps for countries can provide this information<br>(with uncertainty) based on historical data; hazard maps<br>can be adjusted based on climate models to investigate<br>future conditions (even larger uncertainty)\n\nnd evacuation<br>schemes.<br>Information and education<br>campaigns on risk maps|\n|Vulnerability|The vulnerability of the<br>exposed capital𝑉 (or<br>equivalently, total asset<br>losses )|Fraction of the population covered by an early warning<br>system and with ability to prepare and evacuate|Fraction of the population covered by an early warning<br>system and with ability to prepare and evacuate|\n|Macro-economic<br>resilience ( )|The interest rate and<br>marginal<br>capital<br>productivity ( );|Macroeconomic data provide this information|Policies<br>to<br>improve<br>the<br>macroeconomic context|\n\n_Figure B1: Roofer wages in an area where losses have been significant after the 2004 hurricane season in_\n_Florida. Data from the Bureau of Labor Statistics, Occupational Employment Surveys in May 03, Nov 03,_\n_May 04, Nov 04, May 05, May 06, May 07._\n\nFigure B2 is a classical quantity-price plot, showing the long-term demand and supply curves for a goods\nor service aggregated at the macroeconomic level. The green line is the demand curve: it shows how the", "output": {"entities": {"named_data": ["EM-DAT database", "Occupational Employment Surveys"], "descriptive_data": [], "vague_data": ["historical data", "Macroeconomic data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "_Source: World Bank calculations based on Ministry of Finance data. DAU data is for 2007, Natural Resource_\n_Revenues represent 2006 data._\n\nFour types of natural disasters are considered in the study: droughts, earthquakes, floods, and hurricanes/storms. Data on the number of natural disasters are obtained for 196 countries from the Emergency Events Database (EM-DAT) [1] . _Drought_ is characterized by a shortage in a region's water supply as a result of constantly below average precipitation. _Earthquake_ is characterized by the shaking and displacement of ground due to seismic waves. This variable refers to the occurrences of earthquakes only without secondary effects. _Flood_ is defined by a significant rise of water level in a stream, lake, reservoir or coastal region. _Storm_ is represented by wind with a speed between 48 and 55 knots.", "output": {"entities": {"named_data": ["DAU data", "Emergency Events Database (EM-DAT)"], "descriptive_data": ["Ministry of Finance data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Survey data, for example from large household survey programs such as the Demographic and Health Surveys (DHS), Multiple Indicators Cluster Survey (MICS), or Living Standards Measurement Study (LSMS) is independent of public record keeping and can also capture vaccination obtained through private or non-governmental providers\n\nThe data that support the findings of this study are available from the from the Central Statistical Office of Poland and the Orbis database of Bureau Van Dijk.", "output": {"entities": {"named_data": ["Demographic and Health Surveys (DHS)", "Multiple Indicators Cluster Survey (MICS)", "Living Standards Measurement Study (LSMS)", "Orbis database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "_Source: Authors' calculation based on Dantlait survey data._\n\n_Source: Dantlait survey_\n\nin 2009 and 2010 for the Livestock Climate and Society (ECliS) project (final report and", "output": {"entities": {"named_data": ["Dantlait survey"], "descriptive_data": ["Dantlait survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "the General Social Survey - showed no trend. This paradox acquired quickly popularity across the\n\nlongitudinal data.\n\nof studies, those that rely on one cross-section survey focusing therefore on individuals or\n\nProvided that cross-section and longitudinal data are available, one can of course try to reconcile", "output": {"entities": {"named_data": ["General Social Survey"], "descriptive_data": [], "vague_data": ["longitudinal data", "cross-section survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The remote sensing inputs comprise  weather data, such as rain and temperature, as well as soil and terrain data. These sources  are then used by GeoSFM to model basins across Indonesia and the stream flow in each of  these.\n\nfrom ENLACE, a census-based standardized test that primary and secondary school\n\nof the Mexican labor force survey (ENOE) applied to individuals aged 18 to 20 years old", "output": {"entities": {"named_data": ["Mexican labor force survey (ENOE)", "ENOE"], "descriptive_data": ["ENLACE, a census-based standardized test"], "vague_data": ["weather data", "soil and terrain data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "CIESIN (Center for International Earth Science Information Network), Columbia University; IFPRI (International Food Policy Research Institute); The World Bank; and CIAT (Centro Internacional de Agricultura Tropical). 2004a. \"Global Rural-Urban Mapping Project (GRUMP), Alpha Version: Settlement Points\". Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available at http://sedac.ciesin.columbia.edu/gpw. Downloaded March 17, 2005.\n\nFor rivers and lakes, the CIA World Data Bank II (CIA 1972) was used. The population centers are from the GRUMP settlement points dataset (CIESIN et al. 2004a) and the World Gazetteer database (Helders 2005). Urban boundaries are from the GRUMP urban extents database (CIESIN et al. 2004b). The international boundaries are from the World Bank mapping office. 35 To maintain confidentiality, the geographic coordinates of each household in the VHLSS are not made publicly available, only the shapefiles for the different communes surveyed.\n\n30 Communes (the primary sampling units) were selected in the first stage with a probability proportionate to population size based on the 1999 Population census.\n\nThe average current global damage from tropical cyclones is currently $26\n\n\nbillion/year (EMDAT 2009). Several authors have relied on the general result by", "output": {"entities": {"named_data": ["Global Rural-Urban Mapping Project (GRUMP)", "CIA World Data Bank II", "World Gazetteer database", "1999 Population census", "EMDAT 2009"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "PAGE 25 --> similar income aggregates can be obtained between POF and PNADC (Paffhausen et al., 2021), which could be exploited in further work in this direction.\n\nMyanmar Ministry of Planning and Finance & World Bank Group. (2017). Technical Poverty Estimation\nReport: Myanmar Poverty and Living Conditions Survey. World Bank, Yangon.\n\nOliveira, L. S., De Souza, D. F., Dos Santos, L. A., Antunes, M., Brendolin, N. C., & Quintaes, V. C. (2016). \"Construction of a Consumption Aggregate Based on Information from POF 2008-2009 and Its Use in the Measurement of Welfare, Poverty, Inequality and Vulnerability of Families.\" _Review of Income and_ _Wealth_, 62, 179-S210.\n\nRodrigues, C.T., & Helfand, S., & Lima, J.E. (2018). Novas linhas de pobreza para o Brasil: Uma análise a partir das Pesquisas De Orçamentos Familiares (POF) 2002-2003 e 2008-2009.", "output": {"entities": {"named_data": ["PNADC", "Myanmar Poverty and Living Conditions Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Drawing on two years of panel survey data, this paper studies the dynamics of vaccine acceptance, its correlates, and reasons for hesitancy over time. The data come from multiple rounds of national High-Frequency Phone Surveys in five countries in East and West Africa (Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda), covering the period between 2020 and 2022.\n\nWe use data from High-Frequency Phone Surveys (HFPS) in five countries in Sub-Saharan Africa: Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda. The surveys were conducted by study countries' national statistical organizations (NSOs), supported by the World Bank's Living Standards Measurement Study (LSMS). Since May 2020, the LSMS-supported HFPS have collected cross-country comparable longitudinal data on a wide range of topics, focused on COVID-19 impacts on households and individuals.\n\nThe HFPS have national coverage and draw their samples from nationally representative samples of households interviewed in pre-pandemic face-to-face surveys of the LSMS-Integrated Survey on Agriculture (LSMS-ISA) series. In the settings at hand, mobile phone coverage is not universal, and the sample selection of phone surveys may not yield samples fully representative of the general population.\n\nWe use weather data taken from the _Terrestrial Air Temperature and Precipitation Version 4.01_ compiled\nby the University of Delaware (Willmott and Matsuura 2001) that has widely been used in the economics\nliterature (Dell Jones and Olken 2012, Burke Hsiang and Miguel 2015, among others). This data set provides\nmonthly total precipitation and temperature at a 0.5-degree spatial resolution. Data are available for each\nmonth between 1901 and 2014. Annual precipitation at the cell level between 1990 and 2014 ranged\nbetween 3mL in Sudan to 10,187mL in India.", "output": {"entities": {"named_data": ["High-Frequency Phone Surveys", "LSMS-Integrated Survey on Agriculture", "Terrestrial Air Temperature and Precipitation Version 4.01"], "descriptive_data": [], "vague_data": ["panel survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Annual grid-level GDP data between 1990 and 2014 at a 0.5-degree resolution come from Kummu, Taka\nand Guillaume (2018). The data are primarily based on sub-national GDP per capita data constructed by\nGennaioli, _et al._ (2013) and covers 82 countries, representing 85% of the global population and 92% of\nglobal total GDP (PPP) in 2015. Population data is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).\n\nwe use World Bank income group classifications to divide the world into developing countries (that includes low-income, lower-middle and upper-middle income countries), and high-income countries.\n\nWe also use the Global Aridity Index and Potential Evapotranspiration Climate Database (Trabucco and Zomer 2019) to differentiate grid cells based on their aridity.", "output": {"entities": {"named_data": ["HYDE 3.2", "Global Aridity Index and Potential Evapotranspiration Climate Database"], "descriptive_data": ["Annual grid-level GDP data between 1990 and 2014", "sub-national GDP per capita data", "World Bank income group classifications"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Using observations from NASA's OCO-2 platform, we develop the template from the data filtering techniques and econometric analysis employed by Dasgupta, Lall and Wheeler (2022). For a large sample of urban areas, we compare alternative trend estimation models and conclude that the template can be constructed from a simple model that estimates trends directly from OCO-2 data that are prefiltered to isolate local concentration anomalies.\n\nIn an additional exercise, we use our regression model results to compute expected emissions from\nurban areas. The regression residuals identify the directions and relative magnitudes of departures\nfrom expected values for individual areas. We convert the residuals to their emissions equivalents\nusing high-resolution gridded information from the EDGAR global database (Crippa et al. 2020). For\n1,306 urban areas with populations greater than 500,000, we find a rough balance between cities whose\nemissions are higher and lower than their expected values. Converting deviations to percentages of\nexpected values, we find that percent deviations are typically greater in absolute value for cities with\nlower-than-expected emissions.\n\n1 Recent research has used Google Traffic to infer vehicular emissions from high-resolution traffic congestion data for\nsome cities (Heger et al. 2018; Dasgupta, Lall, and Wheeler 2021). However, no currently available technology enables\ndirect estimation of global vehicular emissions at 25 km resolution.\n2 Household air conditioning is powered by fossil-fired home generators in many hot low-income areas where utility-scale\npower is either nonexistent or unreliable. For a detailed assessment, see Lam et al. (2019).\n\nThe design of OCO-2 supports comparative exercises like our analysis. It follows a sun-synchronous\nnear-polar orbit, crossing the equator in ascending mode around 1330 hours local time. This means\nthat the OCO-2 observations for our study are collected between 1200 and 1500 local time for all", "output": {"entities": {"named_data": ["EDGAR global database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To examine the impact of different electricity price assumptions on hurdle prices, we use data from only the two most comprehensive studies (EC (2008) and IEA (2005)) presented in IEA (2012), which implies that the levelized costs for different technologies are more comparable.\n\nUsing aggregate energy consumption data and a nationally representative household survey immediately before the crisis, this paper provides an overview of household energy consumption patterns, highlights Armenia's energy vulnerability, and estimates the direct poverty and distributional impacts of the increase in the cost of imported gas.\n\nUsing data from a nationally representative household survey of 2008 and national energy", "output": {"entities": {"named_data": [], "descriptive_data": ["nationally representative household survey"], "vague_data": ["aggregate energy consumption data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "For each of these food items we obtain calorie intake values from TBCA that are based on the descriptions of the food item in POF and TBCA. TBCA has calorie intake values for different preparation forms of the food item.\n\nA second step is applied in order to increase the list of food items that have calorie intake assigned to\nthem-and thus contribute to the cost per calorie estimation. In this step, we create food groups by\naggregating POF's original 7-digit item code to a 5-digit item code. Based on this, we impute calorie intake\nto items that were not mapped to calorie intake in step 1.\n\nFor instance, different types of rice ( _arroz_ _hibrido_, _arroz bica corrida_, _arroz quirera_ ) were not found in the TBCA, but by creating a 5-digit aggregation, we can assign them to the same group as _arroz polido,_ for which we do have a caloric intake estimate.\n\nFor all of these types we assign the average value of calorie intake for rice at this 5-digit item code that had calories assigned from TBCA in step 1 (in this case, _arroz polido_ ). After this step, we have raw calorie intake values for 1,413 items, which account for 86.6 percent of average total expenditures on food consumed at home and 58.1 percent of all food expenditures.", "output": {"entities": {"named_data": ["POF"], "descriptive_data": ["calorie intake values from TBCA"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "It consists of seven questionnaires that collect information on demographics, work and income, quality-of-life perceptions, expenditures, and food consumption (at the household and individual level). For the estimation of the poverty line, we use data from the three expenditure questionnaires (POF 2-4), in addition to key demographic data collected in the general household questionnaire (POF 1). The expenditure questionnaires collect information on monetary consumption expenses as well as the value of nonmonetary consumption.\n\nThis information can be obtained from the TBCA. It has nutritional values per 100 grams, including calorie intake, for an extensive list of meals and food items typically consumed in Brazil.\n\n[7 The TBCA can be accessed at http://www.tbca.net.br/base-dados/composicao_alimentos.php.](http://www.tbca.net.br/base-dados/composicao_alimentos.php)\n\n_Estimating the cost per calorie._ To estimate the cost per calorie, we use information on food purchases (expenditures and quantity consumed) by food item from POF. POF collects information on food purchases for consumption at home as well as food consumed away from home (FAFH). Food purchases for consumption at home are collected in the household expenditure diary, which collects expenditures on frequent purchases over a reference period of one week (POF 3). It collects expenditures and quantities purchased by food item, containing 4,549 different food items.\n\n8 We assessed the possibility of addressing this shortcoming by using data from another questionnaire (POF 7) that\nregisters quantities of items consumed, location, time, and its calorie intake. However, it was not possible to match\nthe data from POF 7 (quantities) and POF 4 (expenditures) in a reliable way. First, item specifications differ across\nthe two questionnaires. Food items in POF 4 are mostly vague. The item with highest expenditure is \"takeaway meal\n(lunch/dinner).\" In contrast, POF 7 has items as detailed as white rice, beans, eggs, potatoes, and so on consumed\nduring lunch or dinnertime.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "composite measure for human capital. We extract real GDP and human capital data from Penn World\n\nTable 10 (Feenstra et al., 2015), using the \"rgdpna\" series, which measures real GDP in constant 2017\n\nschooling and returns to education (Inklaar and Timmer, 2013). We extract the population from World\n\nDevelopment Indicators (WDI).\n\n#### **3.3 Sample Construction**\n\nThe baseline populations served by the East Road (within the buffer zones) were established by utilizing\nWorldPop open source data. To estimate the number of people affected by rain event, however, a series\nof models were run to simulate the locus of impacts for each rain event (3-, 10-, and 30-year events) and\ntheir subsequent effects on access. Repeated events might affect the same people, and so, the cumulative\nimpacts over the lifetime of the road can be thought of as 'person-disruptions' - i.e., the sum of individual\ndisruptions. If, for example, an individual living on the East Road was cut off from access to hospitals three\ntimes over the thirty-year period, this experience would account for three person-disruptions.\n\nUsing Global Positioning System (GPS) tracking records for the Malaita East Road from Atori to Dala, 5,856\nroad segments with gradient details were delineated and mapped using geographic information system\n(GIS) mapping software. These segments were mapped for the current road and also used to model surface\nconditions defined by the proposed upgrading projects.\n\nCost data for similar road projects also informed assumptions about per-unit (km) repair costs\nfor each type of road surface subject to various damage levels.", "output": {"entities": {"named_data": ["WorldPop open source data"], "descriptive_data": ["Global Positioning System (GPS) tracking records"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In order to understand the role of vulnerability, we turn to international data. The international regressions measure the relationship between damage and fatalities and population and income (using international data EMDAT 2009).\n\n<!-- PAGE 17 -->\nto follow projections made by demographers (United Nations 2004). GDP is assumed to\n\nDartmouth Flood Observatory's (DFO) Global Archive of Large Flood Events, which is\n\n\nhoused at the University of Colorado (floodobservatory.colorado.edu). The DFO is funded\n\nEmergency Events Database (Cavallo and Noy 2010), which is affiliated with the World\n\nmaps from the Gridded Population of the World v3 (CIESIN-CIAT 2005) to obtain", "output": {"entities": {"named_data": ["EMDAT 2009", "Dartmouth Flood Observatory's (DFO) Global Archive of Large Flood Events", "Emergency Events Database", "Gridded Population of the World v3"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "income has not led to more happiness using Eurobarometer and US GSS data. Variables used\n\nor panel data) but a few use variables transformations of income and relative income that would\n\nproblem one would need panel data, good instruments for personality traits or variables that", "output": {"entities": {"named_data": ["Eurobarometer", "US GSS data"], "descriptive_data": [], "vague_data": ["panel data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "They indicate that a 10% increase in Germany's export share is associated with a 8% increase in German ownership (0.04 percentage points over the average ownership of 0.5%). We do not find evidence of border regions receiving more FDI from Germany (Figure A.8 of the Appendix). As a falsification experiment, we employ the same specification on similar data for Romanian firms, also from the Orbis database. Since Romania acceded the EU in 2007, we would not expect to observe a significant effect in this case.\n\nIt is worth noting that the sharp increase in foreign acquisitions was not likely driven by the lift of restrictions on FDI. OECD data on FDI restrictiveness for Poland show that screening and legal restrictions on FDI in manufacturing sectors had already been greatly removed by the time of accession.\n\nTable 2 presents summary statistics from the pooled 2013 and 2018 Demographic Health Surveys for our child health outcomes, parental and household characteristics in the oil producing states\n\nOur child health data come from the 2013 and 2018 Nigerian Demographic Health Survey (DHS). These nationally representative cross-sectional surveys have demographic and health details for women aged (15-49) and for children aged (0-5).\n\nof flare volumes. These come from the Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the", "output": {"entities": {"named_data": ["Orbis database", "Nigerian Demographic Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Data for these measures of German influence are obtained from Eurostat, OECD and UNIDO. Finally, we use data on exports from COMTRADE to construct a measure of Germany's revealed comparative advantage (RCA) relative to Poland and to the world.\n\nResults in this section draw on the Orbis data set, and for this reason refer only to\nemployment as the outcome variable. Unfortunately, the variable turnover presents\nan excessive amount of missing values, which makes it unreliable.", "output": {"entities": {"named_data": ["Eurostat", "OECD", "COMTRADE", "Orbis data set"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of data is the National Water Commission; the data consist of daily rainfall measures in\n\nThird, we use the geographic location data of electoral sections obtained from the Department of\n\nelections in 2000 and 2006 by electoral section are public from the Federal Electoral Institute\n\n(IFE) website. In addition to these, we use complementary information on socio-demographic\n\ncharacteristics of municipalities from the 2000 Population Census and the 2005 Short Census or", "output": {"entities": {"named_data": ["2000 Population Census", "2005 Short Census"], "descriptive_data": ["geographic location data of electoral sections"], "vague_data": ["daily rainfall measures"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We develop and use a model to estimate expected asset losses and expected welfare losses, and quantify socioeconomic resilience in 117 countries. The model builds on Hallegatte et al. (2016b), which only considered river floods and 90 countries. Here we add new countries thanks to new socioeconomic data, and we introduce additional hazards - coastal floods and storm surge, windstorm, earthquakes, and tsunamis - using the risk assessment provided in the Global Assessment Report (UN-ISDR 2015). We also simplify the model - focusing on the factors that were found to have most influence on our previous assessment and disregarding other factors - and improve our modeling of insurance and social protection.\n\nIn our national indicator, we include the role of early warning system using data reported in the context of the Hyogo Framework for Action [7] (UN-ISDR, 2015a). The priority for action #2 (\"Identify, assess and monitor disaster risks and enhance early warning\") includes an indicator (P2-C3) related to \"Early warning\n\n<!-- PAGE 16 --> overlays flood maps from the GLOFRIS global model and poverty maps from the World Bank. For countries where this study does not provide data, we use an older, similar study by (Winsemius et al. 2015). This other World Bank study overlays the same GLOFRIS flood maps as above with geo-localized household surveys (using the Demographic and Health Surveys [8] ) to assess the exposure of poor people to river floods relative to the exposure of non-poor people.\n\nWe calculate the average capital productivity as output-side GDP divided by total reproducible capital within a country, both variables from Penn World Tables.", "output": {"entities": {"named_data": ["GLOFRIS", "Demographic and Health Surveys", "Penn World Tables"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The data sources are (1) Romania MoF firm-level data for the period 2011-20, and (2) the World Bank Businesses of the State (BOS) database for Romania, and (3) the taxonomy of sectors developed by Dall'Olio et al.\n\n<!-- PAGE 8 -->\nthe period 2011 to 2020. It is based on financial statements and contains balance sheet information such as\nfirm tax identification number, year of incorporation, operating revenue, average number of employees,\nnumber of employees at the end of the year, labor cost, fixed assets, total assets, amount of subsidies\nreceived from the government, [6] 4-digit NACE industry code, and the county location of the firm.\n\nThe Romania MoF firm-level data did not include a variable that identifies the ownership status of the firm (whether the firm is an SOE), which is the key explanatory variable of interest.\n\n<!-- PAGE 14 -->\n2014 428,618 1,031 0.24 86.8 13.2 25.2 71.2\n2015 440,445 1,092 0.25 87.5 12.5 23.7 72.7\n2016 457,273 1,136 0.25 88.1 11.9 22.9 73.7\n2017 475,757 1,209 0.25 88.9 11.1 21.4 75.4\n2018 491,289 1,277 0.26 89.4 10.6 20.5 76.4\n2019 511,863 1,299 0.25 89.5 10.5 20.1 76.9\nTotal 4,027,783 0.24 87.6 12.4 23.7 72.8\nSource: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. For purposes of providing the\nsummary statistics, the sample is restricted to firms with revenue and employment data. SOEs are defined as firms\nwhere national or subnational governments have ownership stake of at least 10%, while POEs are firms where national\nor subnational governments own 0-9.9%. The percentages for centrally owned or locally owned may not add up to\n100 because there are \"other\" SOEs that are neither owned by the central or local government.", "output": {"entities": {"named_data": ["World Bank Businesses of the State (BOS) database"], "descriptive_data": ["Romania MoF firm-level data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The raw data has been sourced from Visible Infrared Imaging Radiometer Suite (VIIRS) sensors mounted on US satellites, and has been cleaned to remove irregularities, such as lighting used for the year-round growing of dragon fruit.\n\nIn order to analyze the exposure of industrial activity to natural hazards, a dataset of 372 industrial zones in Vietnam was obtained (World Bank, 2020).\n\nThis analysis circumvents the lack of available data sources by using data from the OpenStreetMap (OSM)\nproject. OSM is an open and free initiative that aims to create and continuously update a map of the world\nthat is created entirely from user-contributed data. Next to detailed street networks, the locations of\nvarious points of interests are published by community volunteers. From this source, a dataset of 5,658\nhotels, hostels, guest houses, and motels was downloaded in early 2019 (OpenStreetMap, 2019). Each\ndatapoint includes an exact point location. Of the hotels, 3,309 are located in coastal provinces and 1,531\nare located within 5 kilometers of the coastline.\n\nInsights into the exposure of health care facilities are based on a dataset provided by a collaboration between the Government of Vietnam, the World Bank, and the World Health Organization. The dataset contains 1,583 geocoded public hospitals and district health centers.\n\nAs no official datasets of school locations could be obtained for this analysis, just as with hotels, volunteer-contributed geolocations of schools were sourced from the OSM project (OpenStreetMap, 2019).", "output": {"entities": {"named_data": ["OpenStreetMap"], "descriptive_data": [], "vague_data": ["OSM project"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "twins) variation on 6th grade ENLACE, identifying, therefore, the relationship between\n\nas captured by ENLACE, on future outcomes and the (within-family) individual-level\n\ncharacteristics. In the ENLACE panel, this last specification uses the sample of students\n\nwho answered the ENLACE context questionnaire to control for differences in household\n\nENILEMS-ENLACE panel regressions, _Xi_ _[′]_ [includes] [upper] [secondary] [school] [grade] [point]", "output": {"entities": {"named_data": ["ENLACE", "ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Little documentation exists, apart from a few conspicuous examples related to HFC-23-related emissions, in China under the CDM; see Wara and Victor (2008); and in the Russian Federation under JI.\n\nactivity by using imagery from the United States Air Force Defense Meteorological Satellite Program (DMSP). Specifically, we use imagery gathered by three satellites: F15, F16 and F18.\n\nOperational Linescan System (OLS) sensors to measure the intensity of earth based lights. The\n\nforest fires, see Elvidge et al. (1997) for details on the filtering process. The resulting stable cloud-free night light composites measure, by and large, man-made lights. These measures of\n\nNOAA publicly provides composites in yearly frequency covering the 1992 to 2012 period. These", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We also use a project level database constructed from provincial and central\n\nHowever, the household survey was designed in view of combining it with the nationally representative 1998 Vietnam Living Standards Survey (VLSS) to predict baseline consumption expenditures for SIRRV households (van de Walle, 2006).\n\nWe then compare this to the independent administrative data on the aggregate allocation.", "output": {"entities": {"named_data": ["1998 Vietnam Living Standards Survey", "VLSS"], "descriptive_data": [], "vague_data": ["project level database", "independent administrative data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "approximated by wealth measured by the EMDHS asset index; and e)mothers' education. Note that the alternative\n\nThe asterisks indicate the significance level: *** p<0.01, ** p<0.05, * p<0.1. Standard errors in parentheses. Regressions take sample\ndesign and household weights into account by using Stata's svy command. Data: EMDHS 2014.\n\n2007 Census. The 2007 census has two formats - a long and a short format. The long format is richer in terms of\n\nThe Ethiopian census has both a short and a long form, and to increase model fit, the models use the long\n\nobserved in Ethiopia before, based on other data sets [30]. SAE results in addition to these variables are highly", "output": {"entities": {"named_data": ["EMDHS", "EMDHS 2014", "2007 Census", "Ethiopian census"], "descriptive_data": [], "vague_data": ["other data sets"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 9 presents the effects of mining on asset wealth and on asset wealth inequality. Wealth\n\n\ndata are available in the form of a wealth index, but only for the two last DHS surveys.\n\nactive*mine -0.086*** -0.055** 3.705 -0.058 -0.032 0.125**\n(0.025) (0.025) (2.898) (0.086) (0.032) (0.051)\n\n\nMean dep var 0.715 0.705 45.71 0.491 0.259 0.028\n\n_Note:_ The table uses GLSS data for Ghana for the survey years 1998, 2005, 2012. The sample is restricted to\nwomen and men aged 15-49.\n\nprevious analyses have found evidence for parallel pre-trends in infant mortality and night lights (Benshaul-Tolonen, 2019) for gold mining countries in West and East Africa (including Ghana).\n\nWe use rich geocoded data with information on households and mining production over time to evaluate the gold boom at the local and district levels in difference-in-differences analyses.\n\npresents the details from a new, 1997 household budget survey in Egypt. This sunrey,", "output": {"entities": {"named_data": ["GLSS"], "descriptive_data": ["1997 household budget survey"], "vague_data": ["wealth index", "geocoded data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Recall that, by definition, attritors are not enrolled in a JFPR school. Arguably, the probability of\n\n6 Smith and Welch parametrize this as the enrollment ratio between attritors and non-attritors. We report this ratio\nin columns 2 and 5 of Table 3.\n\n\n11\n\n\n\n<!-- PAGE 13 -->\nprobability of other girls who were turned down for scholarships and did not enroll in a JFPR school, but\n\nscholarships and did not enroll in a JFPR school, but whose enrollment status could be established; this\n\nenrollment of attrited recipient and non-recipient girls, the estimated JFPR program effect is 0.191. Note,\n\ngirls who received JFPR scholarships and other girls-they correspond to the raw difference in", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Climate data came from two sources: US Defense Department satellites and weather station\nobservations. We relied on the satellite data for temperature observations and the ground station\ndata for interpolated precipitation observations (Mendelsohn et al. 2006). Soil data were obtained\nfrom the FAO digital soil map of the world CD ROM. The data was extrapolated to the district\nlevel using GIS (Geographical Information System). The dataset reports 116 dominant soil types.\n\nThese climate scenarios reflect the A1 scenarios in the IPCC's Special Report on Emissions Scenarios (SRES) (IPCC 2001) from the following models: Canadian Climate Center (CCC) (Boer et al.\n\nThese climate scenarios reflect the A1 scenarios in the IPCC's Special Report on Emissions Scenarios (SRES) (IPCC 2001) from the following models: Canadian Climate Center (CCC), Center for Climate System Research (CCSR), and Parallel Climate Model (PCM). For each climate scenario, we add the climate model's predicted change\n\n\n��\n\n\n\n<!-- PAGE 12 -->\nin temperature to the baseline temperature in each district. We also multiply the climate models\npredicted percentage change in precipitation by the baseline precipitation in each district or\nprovince. This gives us a new climate for every district in Africa.", "output": {"entities": {"named_data": ["FAO digital soil map of the world"], "descriptive_data": [], "vague_data": ["satellite data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Total Consumption (percent, Production) 66 66 66 73 81 78 77 1.2 _Source:_ Publications of and contacts with Public Services Regulatory Commission, Republic of Armenia\n\n_Source:_ International Energy Agency, _Energy Statistics, 2007_\n\nThe 2008 ILCS data is used in the simulation of the impact of gasp price hike on April 1, 2010.\n\n3 For detailed description of the 2008 ILCS, please refer to NSS (2009).\n\nThe ILCS collects data from nearly 8,000 Armenian households surveyed year round. It is based", "output": {"entities": {"named_data": ["2008 ILCS", "ILCS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "measures of the adequacy of calorific intake for a survey sample or before the CV needed for the FAO\n\n(2012b) present a useful list of various HCES in low and middle income countries highlighting substantial differences in their design across a select number of\n\nHCES in all their relevant dimensions (requiring a 22-page form to cover all variations). Drawing on\n\nConsequently, HCES with different methods of data capture (diary versus recall questionnaires), levels of\n\ncomparable. The survey experiment we use in this paper was designed in part to assess the extent to", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["HCES"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The Normalized Deviation Vegetation Index (NDVI) is used to determine the exposure of households to\nthe drought. The NDVI is derived from satellite images measuring the health of vegetation.\n\nIn controlling for potential confounding factors, we rely on geo-coded conflict fatality data provided by the Armed Conflict Location Event Dataset (ACLED) and on data on the percentage of target beneficiaries reached with aid by pre-war region coming from the Food Security Cluster Somalia (Table A.1).", "output": {"entities": {"named_data": ["Armed Conflict Location Event Dataset (ACLED)"], "descriptive_data": ["geo-coded conflict fatality data provided by the Armed Conflict Location Event Dataset"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "_Gallup_ _World_ _Poll_ _(GWP)_ is a nationally representative opinion survey and has been con ducted annually since 2006 in a wide range of countries around the world. The sample collected in Peru is a repeated cross-section of about approximately 1,000 observations each year. For our analysis, we use data from 2013 to 2020. The survey questions are centered around politics, governance and opinions on current events. We make use of several opinion indices provided by Gallup that measure individual opinions on various domains. Observa tions are spatially identified at the region level for Peru, which is our level of analysis in this case (there are 25 regions in Peru).\n\n_PTP_ We measure the location of Venezuelan immigrants on a monthly basis from January 2015 to December 2020 using administrative data on the district Venezuelan immigrants register at with the Peruvian authorities to obtain access to social services. There are strong incentives to register as this is also a prerequisite for applying to obtain the PTP. This data only records monthly gross arrivals so we do not know the outflows of Venezuelans to other locations within Peru or out of the country entirely. However, in ENPOVE, 84% of Venezuelan immigrants in Peru report having lived in the same district during their entire time since arriving in the country. The data shows the arrival of 511,223 Venezuelans as of December 2020, which, while somewhat lower than estimates of the actual number of Venezuelans living in Peru, is quite substantial.\n\nWe also use data from the _National_ _Census_ _2007_ _and_ _2017_ . We use the 2017 Census data to measure the share of workers in the formal and informal sector in each centro poblado as well as the total local population in each centro poblado, province and region. We use the 2007 data to construct both of our instruments discussed in more detail below as well as to create additional controls for the local economic environment.\n\nTo construct the _Trade_ _shock_ instrument for the first part of our analysis, we also use trade data from the reports of TradeMap. From this website, we are able to identify export and import values for Peru on a monthly basis since 2006 at the HS 6-digit product revision. In addition, correspondence tables of HS 6-digit product revision to ISIC 3.1 revision (United Nations) are used to harmonize products with their corresponding industry sector in order\n\nSpecifically, we look at the reported (log) number of crime in each district from administrative data split into non-violent and violent crimes (data starting in 2011, means 3.54 for log violent crime and 3.31 for log non-violent crime), from ENAHO whether crime is a major national problem (12.7%), from LAPOP whether they have been a crime victim in the last two months (32.0%) and standardized variables from LAPOP on opinions about neighborhood safety and from Gallup on personal security.", "output": {"entities": {"named_data": ["GWP"], "descriptive_data": ["trade data from the reports of TradeMap", "administrative data split into non-violent and violent crimes"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "<!-- PAGE 5 -->\ntracks ownership of state business entities with at least 10 percent stake in over 90 countries, including\nRomania. The first part of the paper examines whether the degree of ownership and control levels of SOEs\nmatter for differences in performances-in terms of employment, average wages, assets per worker,\ninvestment, and (labor) productivity-between SOEs and POEs over the 2011-2019 period.\n\n**that justifies an SOE presence** . The taxonomy was developed by Dall'Olio et al. (2022b) in conjunction\nwith the BOS database. It classifies 4-digit NACE industries into three broad sectors (competitive, partially\ncontestable, and natural monopoly) based on the economic rationale-the intrinsic features and associated\nmarket failures-that justifies SOE presence in an industry (see Table 2). Other NACE codes are excluded\nfrom the sector classification of SOEs because firms in those industries provide public goods (e.g., public\nadministration and defense and activities of extraterritorial organizations). In contrast, others are\ncharacterized by externalities (e.g., education and human health activities).\n\n3.2 World Bank Global BOS Database\n**The World Bank BOS database maps the footprint of the state within the corporate sector and across**\n**economic activities based on a uniform definition** . The BOS dataset tracks all corporations where\nnational or subnational governments have an ownership stake of at least 10%, either directly or indirectly\n(Dall'Olio et al. (2022a)). In this dataset, corporations are business entities that are (a) capable of generating\na profit or other financial gain for their owners, (b) recognized by law as legal entities separate from their\nowners and with limited liability, and (c) set up for purposes of engaging in market production.", "output": {"entities": {"named_data": ["BOS database", "World Bank BOS database", "BOS dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Since 2006, Rwanda is divided into 30 districts, which are the main service-delivery units. All\nhousehold surveys are representative at the district level, but there is no information on districtlevel economic growth. Estimating district-level growth based on nightlights is however\ncomplicated by the low intensity of lights: Four districts did not emit any observable lights at all\nduring 2000-2011, and ten districts only have nonzero observations towards the end of the 20002011 period. As a result, we can only estimate nighlights-based GDP growth for 16 districts.\n\nNotes: Dependent variable is the natural log of GDP in constant LCU. All specifications include country and\nyear fixed effects. Robust standard errors clustered by countries in brackets. ***: significant at 1%-level; **:\nsignificant at 5%-level; *: significant at 10%-level. Data source: NGDC (2014) and WDI (2014).\n\nIn Column (3) we replace lights observed from space by data on electrical power consumption (in kilowatt hours, obtained from the World Development Indicators). As data on electricity consumption are only available for 22 countries in SSA, the number of observations drops from 966 to 425 (not all 22 countries have data for all 21 years). We find a strong and statistically significant association between electricity consumption and GDP in the reduced sample (elasticity of _0.37_ ). The coefficient is however considerably smaller than the one estimated between night lights and GDP ( _0.58_ ). This may potentially be explained by the relatively high use", "output": {"entities": {"named_data": ["WDI (2014)", "World Development Indicators"], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Having seen that mandatory e-auctions significantly increased land prices, we estimate (1) using data from   the entire 2015-22 period as an additional robustness check and using data from 2018/19 when e-auctions\n\nthe nearest main road, grain elevator, and city. A land use map, constructed based on remotely sensed data\n\nAdding data on (offline and online) auctions conducted in the 2015 to 2020 period serves not only as a", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["data from", "remotely sensed data", "data on (offline and online) auctions"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The model is estimated using data from the Turkish HBS in 2008 and 2009. The HBS is conducted annually by the Turkish Statistical Institute to collect information on household socioeconomic status, living standards, income, and consumption expenditures.\n\nThe survey data are then combined with the actual monthly rate schedule. The Turkish electricity\ntariff data have two advantages that are fairly unique to the study. First, in the retail sector,\nTurkey has applied a price equalization mechanism to maintain a nationwide uniform tariff until\n2011. Under such a system, all residential consumers face a uniform flat-rate price schedule.\n\nation to fish & fishery products     -    GB 14939Fish Hygienic standard for canned fish CAC 70:1995 NEQ 1994    GB 2715Cereal Hygienic standard for grains - 2005    GB 19303Meat 2003   GB/T 22388Milk 2008   GB/T 23376Tea 2009    Hygienic practice of cooked meat and meatproducts factory    Determination of melamine in raw milk and                                          -                                         dairy products   Determination of pesticides residues in tea                                          -                                         GC/MS method    CAC/RCP13 MOD 1976    Vegetabl  es    GB 2714Hygienic standard for preserved vegetables         -         2003    GB 13104- CAC Sugar Hygienic standard for sugars NEQ 2005 212:1999   _Source:_ Authors‟ calculations based on SAC National Standards Query   20     <!-- PAGE 24 -->  **Figure 2.\n\nTo ensure reliability and completeness, the standards have been cross  checked with the \"Chinese Bulletin of Standards\" (SAC 2011a) and the German-Chinese  Standards Portal (DIN and SAC 2011).\n\n(HS 1992) and tariffs are compiled from the TRAINS data base. Consumption variables are\n\ncomputed from the Food and Agriculture Organization of the United Nations‟ statistical\n\ndatabase FAOSTAT. [6] We deflated our data US Bureau of Labor Statistic's HS Import Price", "output": {"entities": {"named_data": ["Turkish HBS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "returns to human capital. Then, we find that relative _pcGDP_ is 53 _._ 5 percent, accounting\n\n\n48See Web Appx. Fig. 8(a) for the mortality distributions.\n49See Web Appx. Fig. 8(b) for the distribution of schooling we use, based on the I2D2 data (1990-2016).\n50We compare GDP per capita in levels. An alternative approach would be to compare GDP per capita\nin log levels. In that case, the gap is of 51%. In any case, the main point of our inquiry in this section is to\ncompare the relative contribution of education and experience, so the absolute metric is not important.\n\nAccording to I2D2 data, workers in developed economies work   43 hours per week on average compared to 50 in developing economies. Assuming three   weeks of time off, we get that workers in developed economies on average train for 3 _._ 4%   of their working time, compared to 2 _._ 0% in developing economies.\n\nTo see whether the differences in the experience premium might be accounted for   by differences in hours spent training, we draw on recent data from _OECD.Stat_ (OECD,   2021). The data contain information on 33 developed economies and 4 developing   economies.\n\n[2 Global SDG Indicators Database, accessed April 26, 2022: https://unstats.un.org/sdgs/indicators/database/](https://unstats.un.org/sdgs/indicators/database/)\n\nUsing data from the Armenia Land Tenure and Area (ALTA) study and in the context of land tenure rights, this paper addresses questions on: (i) the data quality implications associated with respondent strategy; (ii) the data quality implications associated with level of data collection; (iii) the interaction of biases stemming from the use of proxy respondents and aggregated level data collection, as relevant; and ultimately, (iv) the implications of these design decisions on the computation and monitoring of SDGs 1.4.2 and 5.a.1.", "output": {"entities": {"named_data": ["I2D2", "OECD.Stat", "Global SDG Indicators Database", "Armenia Land Tenure and Area (ALTA) study"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "fact that they are time consuming and therefore expensive to collect. At least three other less resource\nintensive alternatives to the FAO approach have been suggested to derive hunger numbers:\n\nanthropometric data, self-assessments, and direct use of HCES.\n\nquicker and cheaper to collect than full HCES efforts. However, how well they correlate with other\n\nThe third approach, and the one that we concentrate on here, is to use HCES to derive hunger statistics\n\nHCES are positioned between the single subjective hunger question and the intensive 24-hour recall.", "output": {"entities": {"named_data": ["HCES", "direct use of HCES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "random variation, seasonal variation, and measurement error. Furthermore the FAO forces any CV to lie\n\nmotivation for this second approach, which is purely illustrative, is that the FAO derives this mean from\n\nThe third and final component needed to replicate the FAO calculations is to use the sample average of\n\ndaily energy requirement (2068 kcal per person per day). Using these estimates, the FAO approach\n\n11 This is the last FAO report on which we have a detailed description of the exact mechanisms used. The\nmethodology used has been modified slightly since then, as explained Annex 2 of FAO (2012) and footnote 4.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The final row of the table presents estimates of the impact of the JFPR program on enrollment in\n\nany school, regardless whether this is a JFPR school or not. These estimates of program impact drop\n\ngenerally smaller than those for enrollment at a JFPR school. The smaller program effects when the\n\ndependent variable is enrollment at any school, not just a JFPR school, is not entirely unexpected: JFPR\n\nenrolled in the school they applied to. Girls who were selected for JFPR scholarships would therefore be", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 2 presents hunger estimates derived from HCES alone. The calorie measure displays a great\n\nOne of the arguments for favoring the HCES-direct method over the FBS-CV method is that it allows for\n\npatterns of hunger are sensitive to the type of HCES that is used. For example, for each standard\n\nour experiment to calculate a CV of calorie availability, following FAO (1996, Appendix 3). [11] Specifically,\n\nwe collapse the data to 10 deciles of daily per capita kilocalories available and then calculate the CV\n\nFAO motivates these manipulations, which serve to lower the CV, by the desire to purge the CV of", "output": {"entities": {"named_data": ["HCES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "(2022b) to supplement the World Bank BOS database.\n\nThe database was built using data from ORBIS and complemented with data from government sources, such as business registries, central depositories, central oversight bodies, and the Ministry of Finance.\n\nEC (European Commission). 2015. \"Macroeconomic imbalances Country Report - Romania 2015,\"\nOccasional Papers 223. Directorate-General for Economic and Financial Affairs,\n\nUsing 1995, 2004, and 2008 data from the Chinese Industrial Census, Brandt, Kambourov, and Storesletten (2020) indicate that a key factor underlying the dispersion and dynamics of aggregate total factor productivity and wages across Chinese prefectures were entry barriers, which in turn were linked to significant state presence in economic activities.", "output": {"entities": {"named_data": ["World Bank BOS database", "Chinese Industrial Census"], "descriptive_data": ["data from ORBIS"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "**4)** **Slope** is measured by the area weighted average of slope categories. This variable is calculated based on data from the Harmonized World Soil Database version 1.2 with eight slope classes: 1 for least steep (elevation of 0-0.5 percent) and 8 for most steep slope (elevation greater than 45 percent).\n\n**5)** **Rainfall variability** is defined as the 1981-2010 standard deviation of monthly rainfall levels. The variable is constructed from the global CRU TS3.21 dataset from the University of East Anglia, containing a long-term time series of monthly rainfall levels at 0.5x0.5 grid resolution, which was produced using statistical interpolation based on data from 4,000 weather stations (Harris et al., 2014).\n\nThe VHLSS provide detailed information to estimate consumption expenditure, which can be used to estimate poverty rates. This study uses district-level poverty maps based on estimates from the VHLSS 2010 combined\n\n<!-- PAGE 6 --> with the 15-percent sample of the 2009 Population and Housing Census as calculated by Lanjouw et al. (2013). In addition, household consumption is calculated from the VHLSS 2010, 2012, and 2014 based on detailed expenditure data in line with the methodology for determining the GSO-World Bank poverty line. All consumption values are expressed in 2011 Purchasing Power Parity (PPP) values using data on the Consumer Price Index from the World Development Indicators.", "output": {"entities": {"named_data": ["Harmonized World Soil Database", "CRU TS3.21 dataset", "2009 Population and Housing Census"], "descriptive_data": ["district-level poverty maps", "Consumer Price Index from the World Development Indicators"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Usual) which would lead to even higher emissions.\n\n\nWe rely on four climate models: CNRM (Gueremy et al. 2005), ECHAM\n\n\n(Cubasch et al 1997), GFDL (Manabe et al. 1991), and MIROC (Hasumi and Emori\n\nshown in Figure 1. Note that a range of temperature changes are predicted for this\n\n\nemission scenario.\n\n\nThis study\n\n\nUsing a tropical cyclone generator in each ocean basin, the climate data is used to\n\n\nproject 17,000 tropical cyclone tracks (Emanuel et al. 2008). There are 3,000 tracks in", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "7 Annex 9 describes the distribution of refined fuel products by overall consumption decile, based on the 2004 National Household Survey. 8 About 15 percent of the population was defined as poor in 2008.\n\nSubsidies Capital invest't Social programs _Source: Ministry of Finance, 2008 APBN-P, assuming oil at US$95;_ _social program expenditure data from 2008 APBN._\n\nMore detailed data would enable better estimates, for example monthly data on fuel consumption in the provinces near neighboring economies and for otherwise similar provinces where fuel is more likely to only be consumed locally.\n\n_Source: World Bank calculations based on data from the Ministry of Finance._\n\nTo explore how important is such an effect, we use the average em  ployee cost in electricity generation sector (NACE 3511) reported in AMADEUS database   maintained by Bureau Van Dijk as an approximation for labor cost of wind generation.", "output": {"entities": {"named_data": ["2004 National Household Survey"], "descriptive_data": ["social program expenditure data from 2008 APBN"], "vague_data": ["monthly data on fuel consumption", "data from the Ministry of Finance"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "lending operations database in each fiscal year.\n\n\nIn sections 2 and 3 all operations that are IDA financed are considered. This includes development policy\n\nof outcome ratings provided by the World Bank's Independent Evaluation Group (IEG). The portfolio review\n\n\nexamines how the IDA portfolio has evolved since 2001, in terms of the number of projects, size of the portfolio,\n\nRegression analysis comparing IEG outcome ratings of projects in FCS and non-FCS countries shows that\n\ncome rating from IEG (see Table 2). There are also no differential time series trends for this measure of outcome\n\nstated project development objectives, as well as macro level factors, such as GDP growth and country policy and institutional assessment (CPIA) ratings.", "output": {"entities": {"named_data": [], "descriptive_data": ["IEG outcome ratings"], "vague_data": ["lending operations database"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "[7] We use the expanded December 2017 vintage of the I2D2   database. Only select members of the research team or individuals in charge of   harmonizing the data can access the database.\n\n**Sample** **Size.** The version of the I2D2 database that we use includes about 1,500   survey/census samples. However, wages are only reported for about two thirds of them.\n\nThe baseline sample that we obtain includes 24,437,020 individuals from 1,073   surveys and 11 censuses in 145 countries from 1990-2016 (median number of samples   7The I2D2 database has been used to study labor markets or returns to education (Montenegro and Patrinos, 2014; de Hoyos et al., 2015; Gindling and Newhouse, 2014; Gindling et al., 2016).    <!-- PAGE 10 --> 7   per country = 6; mean = 7.5; min = 1; max = 44).\n\nAlso, the I2D2 team does not provide details on how the   harmonized variables were created for each survey, and thus the level of consistency   across surveys. However, given the wide use of the data across flagship World Bank   reports, we feel compelled to trust that the I2D2 team did a good enough job that the   results generated in our study do not capture statistical artefacts.\n\nIn addition, we measure _potential_ work   experience as I2D2 does not include direct measures of work experience. Ability (e.g.,   test scores) is also not measured.", "output": {"entities": {"named_data": ["I2D2", "I2D2 database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The poverty map for 2009 is the result of collaboration between the World Bank and INSTAT (National Statistics Institute) and uses data from the 2009 census and the 2010 ELIM. One major methodological difference between the two maps is that the map of 1998 is based on a consumption model that considers urban and rural areas inside each administrative region with a total number of 17 models, whereas the map of 2009 uses only 8 regional models.\n\n|Figure 16a Own revenues (CFA francs/capita),<br>2005-08|Figure 16b Capital investment and Current<br>expenditures (CFA francs/capita), 2000-08|\n|---|---|\n|<br> <br>|<br>|\n|**Figure 16c Capital investment by sector (CFA**<br>**francs/capita, 2000-08)**|**Figure 16d Components of current expenditures**<br>**(CFA francs/capita, 2006-08)**|\n|||\n|_Source:_ Authors' calculations from ODHD (Sustainable Development Observatory) data 2003, 2006, 2008.|_Source:_ Authors' calculations from ODHD (Sustainable Development Observatory) data 2003, 2006, 2008.|\n\nA clean econometric identification of the impact of mining activities on local government budget\noutcomes would require data on budgets for both mining and non-mining _communes_ during preand post-mining. However, we have annual budget data available for only 2006 to 2008, during\nwhich most of the mines had already opened. Industrial gold production started with the first site\nin Sadiola in 1996, just two years before the second population census of Mali. Fortunately, we\nare able to use the 1998 population census to fully control for some initial differences across\ncommunes, such as poverty and education levels prior to the mining boom. We also test whether\nlarger mines have bigger impacts on budget outcomes by using the cumulative level of production\nmeasured in metric tons as an alternative measure of mining activity.", "output": {"entities": {"named_data": ["2009 census", "2010 ELIM", "ODHD (Sustainable Development Observatory) data"], "descriptive_data": [], "vague_data": ["annual budget data", "population census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In addition to the publicly available imagery, NOAA has produced, especially for this paper, monthly frequency composites for the 2004 to 2013 period. These satellite-month datasets\n\nfrom the labor force survey (LFS) ENOE. Specifically, we produce a quarter-year panel of\n\nThe national water commission (CONAGUA) provided us with three datasets: (i) Data on historical rainfall at the day-weather station level, this dataset spans the 1920 to 2015 period,\n\nand contains the universe of weather stations. (ii) The weather station-month level triggers for Fonden eligibility. (iii) The mapping between municipalities and representative weather stations.", "output": {"entities": {"named_data": ["labor force survey (LFS) ENOE"], "descriptive_data": ["Data on historical rainfall"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "monthly temperature and rainfall data for the period 1961-2000 (CRU), provided for\n\nWe compute R [2] 's between CRU and each of the eight GCMs for temperature and rainfall\n\nWe require separate benchmarks for the CRU and each of the GCMs. We establish", "output": {"entities": {"named_data": ["CRU"], "descriptive_data": [], "vague_data": ["monthly temperature and rainfall data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 2 presents summary statistics for the ENILEMS-ENLACE panel dataset. Columns\n\n12 ENLACE scores than individuals out of college (by around 0.38 SD), but they are also\n\nWe are interested in the predictive power of ENLACE test scores over future schooling\n\nindividual's ENLACE test score in Grade 6 as a predictor of future education outcomes or\n\nWe use the ENLACE panel to study the relationship between Grade 6 test scores", "output": {"entities": {"named_data": ["ENILEMS-ENLACE panel dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Night-time lights data are a beneficial by-product of a meteorological satellite program. The data are\ncollected by the United States Air Force Defense Meteorological Satellite Program (DMSP). DMSP\nsatellites have been circling the earth since the 1970s in a polar orbit that allows observations of every\n\n1. Number of illuminated pixels with _DN_ >=6 within the borders of a country.\n2. Average _DN_ within all illuminated pixels.\n_Sources:_ NGDC v4, World Development Indicators for land area, and author's calculations.\n\nLong-run patterns in _AoL_ and _R_ are consistent with some country circumstances. For example, as shown\nin Table 1, a country with a rapidly expanding _AoL_ is more likely to be a country with a high urban\npopulation growth rate. China, Indonesia, Malaysia, Vietnam, and Yemen are examples. A country with\nshrinking _AoL_ could be in the early, painful stages of transition from a planned economy to a market\neconomy. Azerbaijan, Tajikistan and Ukraine are examples. Countries with growing average radiance, _R_,\n\nSouknilanh et al (2015) find their night-light based _GDP_ estimates are improved when supplemented by ground cover data from a second satellite (MODIS).\n\n16 Normally there is a quasi-fixed ratio of intermediates to gross output which slowly falls as productivity improves. Countries that import most of their intermediates will be subject to external shocks (trading partner demand, terms of trade) that disrupt this relationship. 17 From a sample of 166 countries in 2010, from the World Development Indicators.\n\n[26] The result is the v.4 DMSP stable lights data set with between 20 and 100 observations per year per pixel depending upon circumstances (Baugh et al.", "output": {"entities": {"named_data": ["Defense Meteorological Satellite Program", "World Development Indicators"], "descriptive_data": [], "vague_data": ["stable lights data set"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "scholarship program, and data on school enrollment and attendance from an unannounced school visit.\n\n<!-- PAGE 5 -->\nrandom sample of 6 [th] grade girls in the primary feeder schools to the JFPR secondary schools, or even of\n\nLocal Management Committee (LMC) of the relevant JFPR secondary school. The LMCs were then\n\nThe second source of data for this evaluation is based on an unannounced school visit to each one", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "and Fonden threshold information for 1,745. As shown in table 1, using 2000 census data, we\n\nvarious administrative records that allow us to observe at the municipal level: the number of\n\n**Keywords:** Disaster Risk Financing and Insurance; Financial Institutions; Climate Change and Disaster Risk; Shocks and Vulnerability to Poverty   _∗_ We would like to thank Artemio Couti˜no of SAGARPA who provided administrative CADENA data, as well as information about program rules.\n\nTo determine the effect of   insurance payments on yields and area sowed, we use agricultural production data from SAGARPA   detailing the annual hectares sowed, hectares harvested, and total production in metric tons at the   municipality-crop level.\n\nNote. Standard errors are clustered at the state level. Asterisks indicate statistical significance: _∗_ _p_ _<_ 0 _._ 10, _∗∗_\n_p_ _<_ 0 _._ 05, _[∗∗∗]_ _p_ _<_ 0 _._ 01. Observations are at the municipality-year-level. Hectares sowed are defined as the total\nhectares growing any rainfed agricultural crop as reported in SAGARPA production data.", "output": {"entities": {"named_data": ["administrative CADENA data", "SAGARPA production data"], "descriptive_data": ["2000 census data", "agricultural production data from SAGARPA"], "vague_data": ["administrative records"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "To mitigate issues arising from the time interval between the 2007 Population Census and EMDHS 2014, only\n\nEMDHS at regional level. The table shows that measured undernutrition rates in EMDHS and the estimated rates\n\nregression of z-scores is estimated in the EMDHS with addition of the SAE estimates. The regression includes the", "output": {"entities": {"named_data": ["EMDHS 2014", "2007 Population Census", "EMDHS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "future year by the counterpart observation in the CRU benchmark dataset (from the most\n\nrepresentative temperature/rainfall combination, derived from CRU data for 1980-2000).\n\ndeparture: the benchmark series from the CRU data. This translation step is necessary", "output": {"entities": {"named_data": ["CRU benchmark dataset"], "descriptive_data": [], "vague_data": ["CRU data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "lived more than 4 kilometers from the JFPR secondary school at the time they completed the application.\n\neffects with three dependent variables: Enrollment at the JFPR school that a girl applied to, school\n\nattendance at this school on the day of the unannounced school visit, and enrollment at any school.\n\nTable 2 suggests that the JFPR scholarship program had a large, positive effect on school\n\nnon-recipients, scholarships had an impact on enrollment at a JFPR school or attendance on the day of the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Finally, we show using the IPUMS dataset that children born in the United States to men born in China are more likely to be boys, but this finding does not hold for children born to women from China.\"\n\nA very clear pattern emerges from the third of a million births in the one percent sample of the\n1990 Chinese census. Note that this pertains to births during 1989-90, which is close in time to\nthat for which Oster made her calculations on the proportion of the female deficit in China\nattributable to HBV (Oster 2005, Table 11).\n\nUsing data on expected average annual loss (AAL) and estimates of AAL reductions resulting\n\nSurvey results indicated that only 55 percent of respondents reported understanding the insurance\n\naversion may influence adoption of index insurance (Bryan, 2010), in the Gujarati data the", "output": {"entities": {"named_data": ["IPUMS dataset", "1990 Chinese census"], "descriptive_data": ["data on expected average annual loss (AAL)", "Gujarati data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "<!-- PAGE 2 -->  ## WPS4426 Policy Research Working Paper 4426  # Statistical Analysis of Rainfall Insurance Payouts in Southern India  #### _Xavier Giné_ _Robert Townsend_ _James Vickery_ The World Bank Development Research Group Finance and Private SectorTeam December 2007     <!-- PAGE 3 -->  Policy Research Working Paper 4426  ### **Abstract**   Using 40 years of historical rainfall data, this paper estimates a distribution for payouts on rainfall insurance policies offered to farmers in the State of Andhra Pradesh, India, in 2006.\n\nThis paper uses historical rainfall data to estimate the distribution of payouts on a   rainfall index insurance product developed by the general insurer ICICI Lombard and   offered to rural Indian households since 2003.\n\nCorrespondingly we can use historical rainfall data to calculate a putative history of   insurance payouts for insurance contracts written against the 2006 monsoon.\n\nApplying the insurance contract terms to historical rainfall data, we calculate the   hypothetical payout on the contract for each station, phase and year.\n\nDependence on Insurance Payouts**   To calculate the degree of cross-sectional dependence in payouts, we calculate the   standard deviation of phase payouts for each weather station, restricting analysis to the 11   contracts for which we have the most historical rainfall data.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["historical rainfall data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "a country belongs to. The regional classi...cation corresponds to that used throughout the graphical analysis in section 2. [16] When constructing the regional dummy\n\nof public sector (government) in economic performance of a country. One may expect\n\nthat a larger share of government on real GDP would result in a greater e¤ort to\n\nstabilize government ...nances in the sake of greater macroeconomic stability. Sim\nilarly, if government actions are important for an economy the public will require\n\nHousehold survey data point towards a large and possibly widening gap between rural and urban levels of consumption and recent increases in poverty. After decreasing from 59.7% in 1992 to 37.4% in 1999/2000, rural poverty increased to 41.1% in 2002/2003.\n\nThe data for our study come from the 2005/2006 Uganda National Household Survey (UNHS), fieldwork for which was conducted by the Uganda Bureau of Statistics from May 2005 to April 2006. The survey collected information at the community, household, and parcel level for about 7,500 households in 753 EAs including 30 IDP camps.\n\nThe 2002 population census was used as a sample frame. Following stratification into urban (30%) and rural (70%) sub samples, enumeration areas (EAs) were chosen with the probability of selection being proportional to size.", "output": {"entities": {"named_data": ["2005/2006 Uganda National Household Survey (UNHS)", "2002 population census"], "descriptive_data": [], "vague_data": ["Household survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The ODHD survey (2011) found that about 7 in 10 gold washers practice\nagriculture in parallel, and about 1 in 10 of them practice a trade. The higher estimates of 200,000\nto 1 million consider the possibility of artisanal gold mining being practiced as a secondary or\ntemporary occupation.\n\nOn the positive side, mining could create a mini-boom in the local economy-that is, higher\nemployment and higher wages leading to an increase in local aggregate demand for food crops.\nHowever, due to lack of geocoded agricultural modules in censuses and household budget surveys,\n\n_**Governance**_ **Afrobarometer Question** **Coding**\n\nimagery has inspired many researchers to investigate the\nuse of earth observation data for monitoring economic\nactivity around the world. One of the most popular earth\nobservation data sets is the so-called nighttime lights from\nthe Defense Meteorological Satellite Program. Researchers have found positive correlations between nighttime\nlights and several economic variables.\n\nThe study finds that the Defense Meteorological Satellite Program data are quite noisy and therefore the resulting growth elasticities of Defense Meteorological Satellite Program nighttime lights with respect to most of these socioeconomic variables are low, unstable over time, and generate little explanatory power.", "output": {"entities": {"named_data": ["ODHD survey", "Defense Meteorological Satellite Program"], "descriptive_data": [], "vague_data": ["household budget surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We complete this data with exact geographic location data from MineAtlas (2013), where satellite imagery shows the actual mine boundaries, which allows us to identify and update the center point of each mine.\n\n1 In the 2010 Ghana population census average district size is 112,000\n\nWe combine the respondents from all four DHS standard surveys in Ghana for which there are geographic identifiers. The total data set includes 19,705 women (of which 12,392 live within 100 km of a mine) aged 15-49 from 137 districts. They were surveyed in 1993, 1998, 2003, and 2008,\n\nWe complement the analysis with household data from the GLSS collected in the years-1998-\n\n\n99, 2004-05, and 2012-13. These data are a good complement to the DHS data, because they", "output": {"entities": {"named_data": ["MineAtlas (2013)", "2010 Ghana population census", "DHS standard surveys"], "descriptive_data": ["household data from the GLSS"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Table 3 shows summary statistics for a select group of baseline characteristics, as well as a\n\n<!-- PAGE 5 -->\nland owners in all countries in sub-Saharan Africa for which survey data are available\n\ngenerally lasts 35-40 days. During this time, rainfall data is collected daily at a designated weather\n\nThe data set consists of the entire set of BASIX's purchasers of rainfall index insurance from 2005-2007,\n\nthat season. The BASIX data covers 42 weather stations, and includes a total of 19,882 customers from", "output": {"entities": {"named_data": ["BASIX data"], "descriptive_data": ["entire set of BASIX's purchasers of rainfall index insurance"], "vague_data": ["survey data", "rainfall data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "from the Ethiopia Demographic and Health Surveys. International Journal of African Development 1.\n8. Christiaensen L, Alderman H (2004) Child malnutrition in Ethiopia: Can maternal knowledge augment the role of\n\nfor height). The z-scores were calculated using EMDHS and the 2006 WHO growth standards [24].\n\nTo obtain estimates of undernutrition rates at the woreda level, EMDHS is combined, through SAE, with the\n\nThe EMDHS and a 10 percent sample of the 2007 census are available on request from the Central Statistical", "output": {"entities": {"named_data": ["Ethiopia Demographic and Health Surveys", "EMDHS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Fortunately, there is a very large national longitudinal dataset from Taiwan (China), which permits a robust estimation of the impact of mother's HBV status on sex ratios at birth.\n\nHowever, a large medical dataset from Taiwan (China) shows that hepatitis B infection raises women's probability of having a son by only 0.25 percent.\n\nHowever, the findings from the detailed medical dataset from Taiwan (China), indicate that she has massively overestimated the impact of maternal hepatitis B infection on the sex ratio at birth.\n\nShe therefore compiles data from the Demographic and Health Surveys (DHS) carried out in 18 Sub-Saharan African countries, cautioning that these survey data suffer from defects such as recall bias.\n\nHer third approach uses United States census data to look for patterns in the sex ratios of children born to Chinese immigrants, who are assumed to have levels of HBV prevalence similar to their place of origin.", "output": {"entities": {"named_data": ["Demographic and Health Surveys (DHS)", "United States census data"], "descriptive_data": ["national longitudinal dataset from Taiwan", "medical dataset from Taiwan"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The daily precipitation data used are from the 3- hourly data set from the Tropical Rainfall Measurement Mission Project (TRMM), which is aggregated up to daily data.\n\n\\n<!-- PAGE 28 -->\\ndata set product 3B42RT 3 hour product gives the best results in a basket of 8 near real-time rainfall products. The 3B42RT daily derived product is what is used in this paper.\n\n\\n<!-- PAGE 8 -->\\nthrough 2015. During the period from 1985 to 2016, the Dartmouth Flood Observatory (DFO) registered 3,808 floods of magnitude 4 or more and 1,175 floods of magnitude 6 and up. [3]\n\nAccording to the Indonesian National Disaster Management Authority (BNPB), there were more than 19,000 natural hazards in the period 2001 - 2015 (National Disaster Management Agency 2016), making Indonesia a useful country for any natural hazard analysis.\n\nAccording to the Global Facility for Disaster Reduction and Recovery (GFDRR), the Philippines is at high risk from several types of natural hazards (GFDRR 2019). Prime among them are cyclones, where an average of 20 make landfall every year. In 2013, typhoon Yolanda led to 6,000 casualties and damaged more than 1.1 million houses. The Philippines are also exposed to earthquake and flood risks.", "output": {"entities": {"named_data": ["Tropical Rainfall Measurement Mission Project (TRMM)", "3B42RT daily derived product"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "According to 2017 data from GSO, these 864 schools represent about 12 percent of all schools in coastal provinces (General Statistics Office of Vietnam, n.d.).\n\nData on power plants was sourced from the free and open Global Power Plant Database created and maintained by the World Resources Institute (WRI) (Global Energy Observatory et al. 2019).\n\nA digital spatial representation of Vietnam's electricity transmission network in vector format was prepared by a team at the World Bank and is freely and openly available online (World Bank, 2017).\n\nTo approximate the economic impact of hazards on the tourism sector, macroeconomic estimates were\nsourced from World Bank open data [4] and publications. National tourism GDP was obtained by applying\nthe contribution of tourism to GDP in 2017 (WTTC, 2018) to the national GDP in 2017 (World Bank, 2019).\nThe number of direct jobs in the tourism industry in 2017 are obtained from numbers published by the\nWorld Travel and Tourism Council (WTTC, 2018).\n\n\nIn the fisheries sector, aquaculture output in tons per province in 2017 was sourced from the General\nStatistics Office of Vietnam (GSO, 2019).", "output": {"entities": {"named_data": ["Global Power Plant Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The effort was inspired by the U.S. Drought Monitor (USDM) and involved USDM experts and experienced officials from Mexico, which had recently implemented a similar drought monitor process.\n\nThe World Bank performed a disaster risk financing diagnostic to gain a more nuanced understanding of the economic impacts of the drought throughout the Eswatini economy.\n\nWorld Development 157, 105932\nAnseeuw, W., Bache, W., Bru, T., Giger, M., Lay, J., Messerli, P., Nolte, K., 2012. Transnational land deals for agriculture in the\nglobal South: Analytical report based on the land matrix database.\n\nby SGC** 2015 1,935 1,530 28,345 0 0.54 2016 2,817 1,916 42,754 0 0.87 2017 3,388 2,139 43,358 0 0.84 2018 5,108 3,519 65,859 0.06 0.85 2019 8,710 5,327 83,269 0.51 0.69 2020 2,431 847 13,408 0 0.50 2021 7,805 5,232 58,442 0.09 0.38 Pre-Prozorro 7,094 4,774 54,425 0 0.41 Prozorro 711 458 4,017 1 0 2022 2,041 1,043 9,964 1 0 Total 34,235 21,553 345,399 0.22 0.61 _Source:_ Own computation from SGC and Prozorro data as described in the text.\n\n0.322 0.255 0.469 0.202 0.174 0.319 0.203 Contract length (years) 8.61 8.33 9.51 8.84 9.04 7.98 7.08 _**Land use**_ Crops 0.48 0.48 0.47 0.65 0.66 0.61 0.69 Pasture 0.35 0.34 0.36 0.29 0.27 0.37 0.38 Forest 0.15 0.15 0.13 0.09 0.09 0.08 0.06 _**Distance in km to**_ Main road 8.65 8.48 9.22 9.31 9.30 9.32 9.38 Nearest city 15.64 15.66 15.58 16.58 16.59 16.57 17.16 Grain elevator 13.39 13.55 12.84 13.56 13.50 13.84 14.43 Kyiv 297.22 295.17 304.04 312.33 309.85 323.26 334.55 _Source:_ Own computation from SGC and Prozorro data as described in the text.", "output": {"entities": {"named_data": ["U.S. Drought Monitor", "land matrix database"], "descriptive_data": ["SGC and Prozorro data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Chinese Exports in Agriculture and Food Products**   Value of Exports in Billion USD        _Source_ : COMTRADE Database, Authors calculation    19     <!-- PAGE 23 -->  **Table 1.\n\n6 [http://faostat.fao.org/](http://faostat.fao.org/)   6     <!-- PAGE 10 -->  standards and heavy regulation (Chen and Findlay 2008).Chinese exports in agricultural and  food products are increasing.\n\nAlthough there is no data on the implementation of  voluntary standards, the China Statistical Yearbook of Certification and Accreditation (cited in  Jin et al.", "output": {"entities": {"named_data": ["COMTRADE Database", "faostat", "China Statistical Yearbook of Certification and Accreditation"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ENLACE included a context questionnaire which was applied to a random sample\n\nWe use student identification variables to identify 20,187 twins in the ENLACE panel.\n\nfor 1.02 percent of the ENLACE panel in 2007, a level close to the prevalence of multiple\n\nTable 1 presents summary statistics for the ENLACE panel dataset. Columns 1-3\n\nreport statistics for the ENLACE survey sample and the twins sample, respectively, in", "output": {"entities": {"named_data": ["ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This is exactly what we will do in the present paper. Using data from 333 municipalities\n( _comuna_ s) in Chile, we will estimate short-run relationships between climate variables\n\n\n1\nFor example, an extensive drought in Central America might damage its coffee-production and the reduced\nsupply of coffee would increase the world price of coffee, which could benefit coffee-producers in other parts\nof the world.\n\nTwo different types of climate change will be assessed. First, the documented recent\nclimate change in each of the 333 municipalities, as estimated from average monthly\ntemperature series from 1948 to 2008 for all the Chilean meteorological stations that have\ncontributed systematically to the Monthly Climatic Data for the World (MCDW)\npublication of the US National Climatic Data Center. Second, we will use the predictions\nof the Fourth Assessment Report of the Intergovernmental Panel on Climate Change\n(IPCC4) climate models to simulate the likely effects of projected future climate change in\nChile.\n\nThe municipal level cross-section data-base, which is used to estimate the relationship between climate and development in Chile, is constructed using data from different sources. Table 1 lists the variables, their definitions, and the sources of the information.", "output": {"entities": {"named_data": ["Monthly Climatic Data for the World", "US National Climatic Data Center"], "descriptive_data": ["municipal level cross-section data-base"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "WDI data on electricity consumption only encompass output from power plants and hence may underestimate the intensity and volume of night lights in countries with a strong reliance on private generators.\n\nWhat about the economy of Somalia? National accounts data for Somalia have been non-existing since 1991, when the country sunk into chaos following the toppling of the Badre regime.", "output": {"entities": {"named_data": ["WDI data"], "descriptive_data": [], "vague_data": ["National accounts data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "This multiresolution decomposition is performed using Maximum Overlap Discrete Wavelet Transformation (MODWT) on first difference of the annual series from Penn World Tables 7.0.\n\nNext, we consider actual data. We use annual series of income, consumption, and investment data from Penn World Tables 7.0, all in 2005 international dollars per capita terms. The income series are provided by the Purchasing Power Parity (PPP) converted GDP per capita (chain series) data, whereas consumption and investment series are computed by using the share of actual consumption and investment in the PPP converted GDP series at 2005 prices.\n\nFigure 9: Wavelet cross-correlation between the monthly Industrial production index\nchanges of USA (a) TUR, (b) BRA, (c) IND, and (d) PAK.\n\n\nof industrial production series between Turkey and United States at 2 month frequency\n\nThe selection of the countries roughly reflects a cross-section of the geographic spread, as well as per capita income levels as defined by the World Bank classification using 2011 Gross National Incomes.\n\ninconsistency between longitudinal and cross-section data, a paradox that would eventually lead", "output": {"entities": {"named_data": ["Penn World Tables 7.0", "Industrial production index"], "descriptive_data": [], "vague_data": ["cross-section data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "villages in total), based on the 2008 national census.\n\nLC questionnaire and a 12-month AMAD recall had been included in the national ECVMA  survey implemented in 2011 by the 'Institut National de la Statistique' (INS) of Niger, with\n\nvia the AMD and LC methods are observed in the data collected via the national ECVMA  survey, which did not include a benchmark measure as did the Dantlait survey.\n\n**Figure 3.** _Comparison of mean, median and standard deviation measures of milk off-take_ _estimates from AMD and LC methods in Dantlait and ECVMA surveys (liters)_\n\n_Source: Dantlait and ECVMA surveys_", "output": {"entities": {"named_data": ["2008 national census", "national ECVMA  survey", "ECVMA survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "We then use international data (EMDAT 2009) to estimate the relationship between storm damages and national income and population density (vulnerability).\n\nThe analysis relies on the A1B SRES emissions scenario generated by the Intergovernmental Panel on Climate Change (IPCC 2000). The scenario assumes that mitigation is tightened gradually over time so that greenhouse gas concentrations finally peak and stabilize at 720 ppm.", "output": {"entities": {"named_data": ["EMDAT 2009"], "descriptive_data": [], "vague_data": ["international data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "<!-- PAGE 3 -->\n**Estimating a Poverty Line for Brazil Based on the 2017/18 Household Budget Survey** **[+]**\n\n**Keywords:** poverty lines; food poverty line; basic needs; household budget survey; Brazil", "output": {"entities": {"named_data": ["2017/18 Household Budget Survey", "Household Budget Survey"], "descriptive_data": [], "vague_data": ["Household Budget Survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "In  recent years enrollment rates have increased sharply and are higher for girls than boys in Bangladesh's urban areas, according to UNICEF.\n\nFemale children are more likely to be enrolled than male children in primary  schools, a result in line with recent UNICEF findings.\n\nThe data for the Netherlands is taken from the Dutch National Institute for Public Health and Environment (RIVM). [2] The data for Germany is from the Robert Koch Institute. [3] The data for Italy can be viewed via a live dashboard, [4] and the raw data is well organized and available on a github page. [5] The Spanish data was taken from this link. [6]\n\nThe COVID-19 data is taken from the RIVM. [8] The first data snapshot includes all confirmed\ncases as of March 22 (a total of 4,004 with known residence out of 4,157 confirmed cases).", "output": {"entities": {"named_data": ["Dutch National Institute for Public Health and Environment (RIVM)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "<!-- PAGE 10 --> ##### **3 District Expenditure Data** The financial data used are the District budget data for the years from 2001 to 2012 (Fiscal  Year of January-December every year).\n\nThey were derived from the Regional Financial In formation System (Sistem Informasi Keuangan Daerah, SIKD) of the Ministry of Finance.  The district expenditures are available for 12 different sectors/functions (such as agriculture,  health, education, etc.) and for four economic classifications (personnel, goods and services,  capital, and other).\n\n- floods, earthquakes, volcanic eruptions and the 2004 tsunami - that are combined with  nightlight data - used as a proxy for economic activity - to construct an index that estimates  the impact on districts and provinces.  More specifically, the nightlight data used provide a normalized annual light value ranging  from 0 (no light) to 63 (maximum light) and are from the Defense Meteorological Satellite  Program (DMSP) satellites.\n\nRaschky (2014) and Michalopoulos & Papaioannou (2014). In our case, the nightlight data  have been employed as a weight for the economic impact of disasters.  Floods are modeled through a combination of remote sensing images and GIS-modeling  using the Geospatial Stream Flow Model (GeoSFM).", "output": {"entities": {"named_data": ["Sistem Informasi Keuangan Daerah"], "descriptive_data": ["District budget data"], "vague_data": ["nightlight data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "graduates aged 18, 19, and 20. The objective of ENILEMS was to provide information on\n\nThe ENILEMS-ENLACE panel merges information from the respondents of the ENILEMS\n\n2010 survey with their results in the ENLACE Grade 12 taken in May of 2008, 2009, or\n\n2010. Although ENILEMS 2010 did not capture the CURP, it included all the necessary", "output": {"entities": {"named_data": ["ENILEMS\n\n2010 survey"], "descriptive_data": ["ENILEMS-ENLACE panel"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The 355 municipalities are grouped into 25 GGD areas, each covering a population of approximately 600,000 inhabitants. The GGD borders are visible in figure 5 which visualizes the hospital admissions. The data is combined with demographic statistics (2019) obtained from the Dutch Central Bureau of Statistics.\n\nA number of surveyed health\nstatistics (2016) have been obtained as well from the RIVM (maps can be viewed in the\nsource link). [11] . The data is based on a survey of 457,000 people and includes the share of\npopulation in each district with a documented long-term illness (illnesses over 6 months), the\nprevalence of overweight and obesity, alcohol abuse, smoking and noise due to traffic.\n\nFor the main analysis, annual average particulate matter concentrations from the RIVM are used to capture long-term exposure (2017, published September 2019).\n\nhe temporal lag in the pollution data also ensures that there is no endogeneity due to feedback between case incidence and changes in pollution levels that follow lock-down policies. To test whether the main findings of the analysis generalize to other pollution data sets, a second analysis presented in the appendix uses the coarser grids from the global PM2 _._ 5 data set of van Donkelaar et al. (2016).\n\nCases are reported to the RIVM by the Municipal Health Service (GGD).The GGD is organized as collaboration between municipalities to provide base level public health service", "output": {"entities": {"named_data": [], "descriptive_data": ["annual average particulate matter concentrations from the RIVM"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "more recent data. This suggests that ambiguity about index insurance can fall over time, with\n\nloan restructuring after early flooding. The contract would be underwritten against recorded\n\nwater levels at a main river gauge station, using this data as a proxy for flood damage. This\n\n10 This form of cat bond trigger is more analogous to a traditional insurance policy with its loss settlement process.\nOther triggers are on _modeled losses_ or _industry losses_ . For _modeled losses_, instead of dealing with Proactive's\nactual losses, an exposure portfolio is constructed for use with catastrophe modeling software. When there is a\ndisaster, the event parameters are run against the exposure database in the cat model. If the modeled losses are above\na specified threshold, the bond is triggered. For _industry losses_, the cat bond is triggered when an entire industry loss\nfrom a certain peril for the insurance industry doing business in this country reaches a specified threshold.\n\ninformation on renewable policy design was obtained mostly from IEA's renewable policy   database, and cross-checked with government websites, legislative texts and other related\n\ncollect generator-level wind installation data from a global wind farm dataset [20] to match", "output": {"entities": {"named_data": ["IEA's renewable policy   database"], "descriptive_data": [], "vague_data": ["recent data", "exposure database", "global wind farm dataset"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "The study is based on two-period panel data collected by the IHDS, which was jointly carried out by researchers from the University of Maryland and the National Council of Applied Economic Research (NCAER) in New Delhi. This nationally representative survey covers a wide-ranging set of topics, including energy use, income, expenditure, education, health, and employment. The survey covers all of India's key states and union territories except Andaman and Nicobar Islands and Lakshadweep. The first round of the survey was carried out in 2004-05 (mostly in 2005) and collected information on 41,554 households in 33 states and union territories, 383 districts, 1,503 villages, and 971 urban blocks. The second one, conducted in 2011-12 (mostly in 2012), re-interviewed 83 percent of the original households and split households (if located within the same village or town), and interviewed 2,134 new households, for a total of 42,152 households.\n\nAccording to IHDS 2012 data, the average duration of power outages of households who have less than 24 hours of power supply is about 12.5 hours a day. Increasing the supply of electricity to 24 hours a day would lead to an estimated income gain for the rural population of US$6.5 billion annually.\n\n2 In the following, we use the terms \"access\" and \"grid-connection\" interchangeably.\n3 World Bank Enterprise Surveys (http://www.enterprisesurveys.org).", "output": {"entities": {"named_data": ["IHDS", "World Bank Enterprise Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "obtained from the Center for Research in Security Prices (CRSP). Stocks with prices less than\n\nof all stocks included in CRSP.\n\nFirm characteristics are obtained from COMPUSTAT. Size is computed as the log of total assets.\n\nof stock returns on the Fama and French (2015) 5 factors.", "output": {"entities": {"named_data": ["Center for Research in Security Prices (CRSP)", "COMPUSTAT"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Polity data - political regime or patterns of authority. Countries with larger positive (negative) polity values have a more democratic (autocratic) system.\n\nThis paper describes an approach to forecasting future climate at the local level using historical weather station and satellite data and future projections of climate data from global climate models (GCMs) that is easily understandable by policymakers and planners.\n\npolicymakers. It draws on historical climate data from weather stations and satellites;\n\nWe begin with monthly temperature and rainfall data for the period 1961-2000", "output": {"entities": {"named_data": [], "descriptive_data": ["historical climate data from weather stations and satellites", "monthly temperature and rainfall data"], "vague_data": ["station and satellite data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "_returns to education are heterogeneous across countries. The second set of Mincerian returns are those estimated by Montenegro and Patrinos (2014)._\n_Source: The data has been collected from PWT 9.0 (Feenstra, Inklaar and Timmer 2015)._\n\nnatural capital: (i) it represents only the contribution of the physical capital stock from PWT 9.0 in the conventional\n\n<!-- PAGE 12 -->\ndecomposition, and (ii) it shows the contribution of the physical capital stock (from PWT 9.0) and natural capital (Lange\n\n\net al. 2018) in the natural resource decomposition. The average annual rate of growth in output per worker for the\n\nrespectively- are then computed using the estimated relative income shares (which vary across groups) and the PWT   9.0 labor share (which varies across countries and over time).\n\nSince  is calculated from the share of labor force in PWT 9.0 and 𝛼�1 �𝛾� is proxied by the ratio of natural resource", "output": {"entities": {"named_data": ["PWT 9.0"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "ENLACE test scores are likely not independent of other student characteristics which\n\nthat ENLACE does capture and propose an empirical strategy to identify the impacts of\n\n###### **4.1 Grade 6 ENLACE and Secondary School Outcomes**\n\nENLACE score percentile in Grade 6. We find a clear and positive relationship between\n\ntest scores in Grades 9 and 12 on ENLACE test scores at Grade 6, and a dummy for", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "###### **2.3 ENILEMS-ENLACE**\n\nthrough the _Encuesta Nacional de Ocupación y Empleo_ (ENOE), a nationally- and state\n\nrepresentative rotating household survey. [6] In most quarters ENOE's core survey is com\n\nENOE's special module was the _Encuesta_ _Nacional_ _de_ _Inserción_ _Laboral_ _de_ _los_ _Egresa-_\n\n_dos de Educación Media Superior_ (ENILEMS), a survey targeting upper secondary school", "output": {"entities": {"named_data": ["ENOE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1 2 3 4 5 6 7 8 9 10\nDecile\n\n\n\nJFPR applicants: Enrollment at any school, by\nscholarship status and decile\n\n<!-- PAGE 20 -->\n**Figure 2: Proportion of girls who complete grade 7 among those who completed grade 5,**\n**by economic status decile, DHS data**\n\nOutcomes** Enrolled in JFPR school 0.87 0.65 0.22 0.00 Attending on the day of school visit 0.80 0.58 0.22 0.00 Enrolled in any school 0.90 0.77 0.13 0.00\n\n**form)** **variables)**\n\n**Enrolled at JFPR school** 0.222*** 0.292*** 0.303*** 0.413*** 0.065* 0.302*\n(0.018) (0.021) (0.022) (0.056) (0.036) (0.166)\n\n**Enrolled at JFPR school** 0.222*** 0.292*** 0.303*** 0.413*** 0.065* 0.302*\n(0.018) (0.021) (0.022) (0.056) (0.036) (0.166)\n**Attending JFPR school on day of visit** 0.223*** 0.299*** 0.313*** 0.426*** 0.094** 0.436**\n(0.018) (0.022) (0.023) (0.056) (0.040) (0.188)", "output": {"entities": {"named_data": ["DHS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "decreased satisfaction. For example, using the German Socio-Economic Panel Survey (SOEP),\n\nTwo studies that use the German SOEP data (D'Ambrosio and Frick, 2007 and 2012) find that\n\nusing a set of European panel surveys concludes that absolute income is more important than\n\nCurrent global counts rely on combining each country's total food balance with information on distribution patterns from household consumption expenditure surveys. Recent research has advocated for calculating hunger numbers directly from these same surveys.\n\ndetails about how household surveys are designed and\nhow these data are then used. Using a survey experiment\nin Tanzania, this study finds great fragility in hunger\ncounts stemming from alternative survey designs. As a\nconsequence, comparable and valid hunger numbers will\nbe lacking until more effort is made to either harmonize\nsurvey designs or better understand the consequences of\nsurvey design variation.", "output": {"entities": {"named_data": ["German Socio-Economic Panel Survey (SOEP)", "German SOEP data"], "descriptive_data": ["household consumption expenditure surveys"], "vague_data": ["European panel surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "1 We use the generic term HCES to refer to a range of household survey efforts to capture total household\nconsumption expenditures. This can include surveys described as household budget surveys, living standards\nsurveys, or others.\n\nWe explore a unique survey experiment which randomly assigned seven different HCES methods to\n\nof Sub-Saharan environments where, according to the FAO, the proportion of hungry people is highest\n\nsurvey experiment, described below, ensure that any differences in derived hunger numbers are solely", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["HCES", "survey experiment"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "discussed above, using data on _recorded_ COVID-19 infection and death counts we observe that both of\n\n\nthese indicators are dramatically skewed toward Bamako. Although these recorded infections and deaths\n\n\nlikely underestimate the true incidence of infections and deaths, particularly outside Bamako (UNICEF,\n\n_Notes:_ These figures come from the Humanitarian Data Exchange (HDX) COVID-19 sub-national case data, supported by the United Nations Office for the Coordination of Humanitarian Affairs.\n\nThe Oxford COVID-19 government's response tracker (Hale _et_ _al._, 2020) suggests that the Mali government, in order to control the spread of the virus, imposed restrictions as stringent as in North American and Western European countries (see Figure A1 in the Supplemental Appendix).\n\nWe now turn to Google's Community Mobility Reports, our second source of information on the intensity of pandemic-related disruptions in Mali. For the purpose of contributing to an understanding of the consequences of the coronavirus pandemic, Google released anonymized and aggregated data from users who have turned on the location history setting of their Google account.", "output": {"entities": {"named_data": ["Humanitarian Data Exchange", "Humanitarian Data Exchange (HDX)", "Oxford COVID-19 government's response tracker", "Google's Community Mobility Reports"], "descriptive_data": ["COVID-19 infection and death counts"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "of the true JFPR program effects under different assumptions about the enrollment behavior of attrited\n\nhouseholds; for example, the first row in this panel suggests that the JFPR program increased enrollment\n\nat JFPR schools by 24.3 percentage points for girls with above-median SES, and by 43.9 percentage\n\ngirls who live at least four kilometers from the JFPR secondary school they applied to; the first row in this\n\nthe JFPR secondary school should be less likely to know girls who live far away, which minimizes the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Survey data\nare available for the period 1995-2013 and contain information on a set of firm\n\n\n5On the other hand, when comparing the distribution of firms across sectors in Orbis and in\nthe survey of firms provided by the Central Statistical Office of Poland (Figure A.3), the two\ndistributions are broadly aligned suggesting that coverage is likely to be similar across sectors.\n6In our empirical analysis of firm performance, we exclude the year 1999 due to anomalies in\nthe data.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of firms", "survey of firms provided by the Central Statistical Office of Poland"], "vague_data": ["Survey data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Section A.1, [9] we verify that the I2D2 database generates   global patterns that are broadly consistent with patterns observed when using other   global databases such as the _World Development Indicators_ (WDI) database of the World   Bank. More precisely, we find that our samples are globally representative in terms of   per capita incomes, age structure, education, and self-employment.\n\n10For example, we find that log mean earnings in I2D2 are strongly correlated across country-years with log per capita GDP in WDI. We show that wage variance is in line with what is expected.\n\nIn our case, I2D2 calculates their wage as the amount of salary taken from the business; and (iii) public sector workers because they receive nonwage compensation and their wages may not reflect the full payment for their labor. 23Results hold if we use 12 or 14 years or information about high school graduation (Web Appx.\n\nIf   34Since most low-income countries are in sub-Saharan Africa and to ensure that our results are not driven by a lower quality of surveys from the region, we verify that the patterns that we obtain for subSaharan Africa in I2D2 are consistent with the patterns observed when using other global databases such as the _World Development Indicators_ database of the World Bank (see Web Appx. Section A.1).\n\nWe set the schooling transition probabilities _πs_ ( _·_ ) to match the schooling  distribution in developed and developing economies (I2D2). [49] We assume agents may   only accumulate up to 25 years of schooling as some countries only record schooling   up to 25.", "output": {"entities": {"named_data": ["I2D2 database", "I2D2"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Rural households are more likely to openly defecate compared to  urban households (DHS, 2015).\n\nRural households are about twice as likely to have access to unimproved sanitation compared to their urban counterparts (DHS, 2014).\n\n[4]  Sixty-five percent of the households in the country have access to a private sanitation facility in  2014 (DHS, 2016).\n\nIn 2014, only 45 percent of the households had a private improved sanitation facility -  an increase from 39 percent in 2011 (DHS, 2016).\n\npdf 2   <!-- PAGE 5 --> an unshared toilet facility compared to urban households (DHS, 2016).", "output": {"entities": {"named_data": ["DHS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "CURP, respectively, was able to match 2,820 observations (40 percent of the ENILEMS\n\nuals in the ENILEMS sample were matched to their ENLACE Grade 12 test scores. After\n\neliminating missing observations in the ENLACE score, the panel reaches a total of 3,714", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Score Grade 6 512.2 527.3 550.0 527.8 515.6 (100.8) (98.96) (95.74) (105.8) (100.7) Enlace taker Grade 9 0.712 1 1 0.769 0.764 (0.453) (0) (0) (0.421) (0.425) Enlace taker Grade 12 0.323 0.453 1 0.370 0.379 (0.468) (0.498) (0) (0.483) (0.485) Girl 0.469 0.498 0.524 0.473 0.535 (0.499) (0.500) (0.499) (0.499) (0.499) Private School Grade 6 0.0822 0.0970 0.119 0.0578 0.0954 (0.275) (0.296) (0.323) (0.233) (0.294) Mother has lower secondary 0.539 (0.499) Father has lower secondary 0.586 (0.493) Mother is white collar 0.114 (0.318) Father is white collar 0.207 (0.405)\n\nNotes: The table shows the mean and standard deviations of all students matched in the ENLACE panel in 2007\n(column 1), 2010 (column 2) and 2013 (column 3). Column 4 displays additional variables from student and parents\nsurveys that were applied to a sample of ENLACE takers. Column 5 reports statistics for the sample of identified\ntwins in grade 6. Data: ENLACE panel.\n\nEnlace Score 0.213 0.375 -0.0630 0.114 (0.854) (0.856) (0.778) (0.808) College Student 0.630 1 0 0.437 (0.483) (0) (0) (0.496) Employed 0.379 0.263 0.577 1 (0.485) (0.440) (0.494) (0) Upper Secondary GPA -0.0219 0.135 -0.289 -0.0835 (1.007) (0.995) (0.970) (0.987) Girl 0.564 0.546 0.596 0.502 (0.496) (0.498) (0.491) (0.500) Private Upper Secondary 0.175 0.203 0.129 0.134 (0.380) (0.402) (0.335) (0.340) Urban resident 0.848 0.912 0.740 0.814 (0.359) (0.283) (0.439) (0.389) Age 19.18 19.16 19.21 19.23 (", "output": {"entities": {"named_data": ["ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Data and Variables Definition The data used to conduct this study are the ARIS-REDS data from the National Council of Applied Economic Research (NCAER). Since 1971, the NCAER has been conducting surveys on a _sample_ of households in 232 villages in the 17 major states of India.\n\ndevelopment improves. According to FAO data, in the last five decades per capita milk\n\ndevelopment partners) as part of the Living Standard Measurement Study - Integrated Survey  on Agriculture (LSMS-ISA) program [2] . The paper aims to contribute to building an evidence\n\n2 More information on the program is available at www.worldbank.org/lsms-isa.", "output": {"entities": {"named_data": ["ARIS-REDS", "Living Standard Measurement Study - Integrated Survey  on Agriculture (LSMS-ISA) program", "LSMS-ISA", "Living Standard Measurement Study - Integrated Survey  on Agriculture (LSMS-ISA)"], "descriptive_data": [], "vague_data": ["FAO data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "Therefore, in addition we adopted another adapted calculation method which is based on travel paths simulations using OD pairs from the JICA travel survey.\n\nThe conventional approach calculates shortest paths between every possible pair of nodes within the network whereas the adapted approach simulates the travel trajectories based on the multimodal transportation model and accounts for public transport waiting times, road speed limits, and designated origin and destination pairs acquired from the JICA commuter travel survey.\n\n**Acknowledgments:** We would like to thank GoMetro for leading the public transport mapping, and in\nparticular Bob Kabeya and Clayton Lane, as well as the enumerators that conducted these surveys. We thank\nShohei Nakamura, Takaaki Masaki and Mervy Ever Viboudoulou Vilpoux for their help in accessing\nKinshasa commuter and household surveys. We are grateful to Laurent Corroyer, Stephane Hallegatte and\nKirsten Homman for their inputs and feedback throughout this project. We are also grateful to Catalina\nOchoa for her review of an earlier draft of this paper and her thoughtful comments. This study was supported\nby the Global Facility for Disaster Risk Reduction and Recovery (GFDRR).\n\nThese data sets were combined with travel survey data containing travelers' socioeconomic attributes and trip parameters, as well as a high-resolution flood maps.", "output": {"entities": {"named_data": ["JICA travel survey", "JICA commuter travel survey"], "descriptive_data": ["Kinshasa commuter and household surveys", "travel survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "NASA EOSDIS Land Processes DAAC. World Bank, 2018. Somalia drought impact and needs assessment: synthesis report (English). World Bank Group, Washington, D.C.\n\nI consider 10 years of daily data for US Treasury zero rates (provided by Bloomberg):\n\nIn addition to the return matrix, some model versions presented here augment the returns data with cross-sectional data. These models are referred to in this document as 'mixed' models. The basic data used in these cases are presented in table 3.\n\n**Keywords:** CO2 emissions, OCO-2, Urban pollution, Emissions tracking\n\nHigh-resolution observations of atmospheric GHG concentrations are now available from several platforms, including NASA's OCO-2 and OCO-3 instruments, the European Space Agency's METOP-A and TROPOMI (Sentinel-5P) platforms, China's TANSAT and the Japan Space Exploration Agency's GOSAT and GOSAT-2.", "output": {"entities": {"named_data": [], "descriptive_data": ["US Treasury zero rates"], "vague_data": ["cross-sectional data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "prwp"}
-{"input": "# **YOUTH REPORT** **Protection barriers and risks**\n\n**NORTH-WEST SYRIA | Nov 2024**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The NWS Protection Cluster would like to thank all partner organizations involved in the development of this\nreport, particularly the organizations engaged in the Protection Monitoring and Analysis Working Group (PMA WG)\nand the Inclusion Technical Working Group (ITWG) for their support with the data collection and analysis of the\nfindings.\n\nWe would also like to thank all Protection Cluster donors for their constant engagement and collaboration.\n\n_**Photo credits:**_\n\n_Upper left: Violet Organization/ Idleb/Salqin city/Youth camping_\n\n_Upper right: Violet org/ Idleb/Salqin City_\n\n_Center: Door Beyond War org/ Idleb/Idleb City_\n\n_Lower Left: Door Beyond War org/Idleb/Darkosh_\n\n**For additional information please contact** :\n\nLorena Nieto Savser Talostan\nNWS Protection Cluster Coordinator NWS Protection Cluster Co-Coordinator\n[nieto@unhcr.org](mailto:nieto@unhcr.org) [savser.talostan@rescue.org](mailto:savser.talostan@rescue.org)\n\n[Türkiye Cross-border: Protection | ReliefWeb Response](https://response.reliefweb.int/turkiye-cross-border/protection)\n\n**1**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**KEY HIGHLIGHTS** **[1]**\n\n Most of the youth in NWS **face protection risks,** with 53% reporting homelessness and 55% experiencing\nexploitation, while 44% face gender-based violence.\n\n 75% of youth **lack access to protection services**, highlighting a significant gap in availability and\naccessibility.\n 77% of respondents indicate **no organizations offer tailored activities or services for youth** in NWS,\n\n**Specific youth groups**, such as those with disabilities 70%, adolescent females 40%, IDPs 48%, and\nhomelessness 36%, are identified as experiencing significant protection risks.\n 82% of **youth believe newly graduated youth have fewer working opportunities**, 66% of Youth actively\nparticipate in community-based structures, with 74% volunteering and 53% involved in outreach teams,\npromoting skill development, relationship building, and community contribution.\n\n 76% of youth in northwest Syria **perceive unequal opportunities for social participation,** with\ndiscrimination (85%), exclusion (34%), and stigmatization (15%) as key contributing factors.\n\n Only 12% of **youth have access to capacity-building or empowerment activities**,\n\n Expanded initiatives for **youth empowerment, leadership, and conflict resolution** are needed, with a\nfocus on essential skills development and resilience building.\n\n 20% of **youth have been threatened or felt afraid**, while 24% sometimes experience such feelings,\nimpacting their well-being.\n\n---\n[1] The Protection Monitoring and Analysis working Group (PMA WG) of the Northwest Syria Protection Cluster, with support from the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": " 25% of youth face **difficulties obtaining official documents due to their age**, hindering access to essential\nservices and resources.\n\n **13% of youth report the presence of Unexploded** **Ordnance** (UXO) in their area, with 25% receiving\nsupport for clearance and 79% receiving awareness sessions, highlighting the need for increased support\nand safety measures.\n\n 83% of youth confirm **their family’s own property in NWS,** 100% of them have ownership documentation,\n53% of them are occupied and 10% has been destroyed.\n\n Youth engaged in the FDGs agreed that young people in NWS they face discrimination due to poverty,\nunemployment, war, and social factors. Women face sexual exploitation, harassment, early/forced\nmarriage, and lack of support. Men are neglected and face risks of recruitment, exploitation, and\npersecution. Addressing these issues requires combating corruption, raising awareness, and ensuring\nequal opportunities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Detailed overview of findings**\n\n## **PROTECTION RISKS**\n\n**Family compositions and access to services**\n **Exploitation** **emerges as the most pressing protection risk, with 55%** of respondents identifying it as a\nsignificant concern. This high percentage suggests that youth in NWS are particularly vulnerable to various\nforms of exploitation, which may include labor exploitation, sexual exploitation, or other forms of abuse\nwhere individuals are coerced or taken advantage of in exchange for survival or basic needs.\n **Closely following exploitation, homelessness was reported by respondents (53%)** as a significant risk.\nThis statistic highlights the acute housing crisis faced by youth in NWS, likely exacerbated by ongoing\nconflict and instability.\n **Gender-based violence (GBV** ) is a critical issue for 44% of youth particularly related to physical, sexual,\nand psychological violence due to entrenched gender inequalities and the destabilizing effects of conflict.\nSexual violence has been identified as a grave impact that affects both men and women, particularly when\nlinked to detention.\n **Drug abuse** is a concern for 31% of respondents; stress and trauma because of the ongoing conflict and\nmultilayered impacts of the war (lack of economic and educational opportunities, livelihoods) have driven\nyouth to substance abuse, also used as a method of recruitment to arm fractions.\n **Forced displacement** is a pressing risk faced by respondents (28%), with direct impact on their family and\ncommunity networks, their mental health and their HLP rights.\n **Recruitment** into armed groups, is also a pressing protection risks (11%) as the only alternative to secure\nbasic needs for them and their families in absence of educative and livelihoods opportunities.\n **Disappearance is another significant risk, noted by 11%** of respondents. **7% of respondents identified**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**arbitrary detention** as a pressing risk with direct impact on their freedom of expression and movement,\nas well as other rights violations.\n **Torture is identified by 2% of respondents.**\n\nParticipants in the FDGs highlighted:\n Need to **stress the role of women in society** and the need to enhance their involvement in mitigating these\nrisks; while **male emphasized the need to enhance digital security and regulating transportation to**\n\n**prevent abductions.**\n Both male and female participants agreed on the **need to address the risks of torture, arbitrary detention,**\n\n**and enforced disappearance,** with a focus on awareness campaigns, community education, and\nsupporting local organizations.\n The male group emphasized the importance of **strengthening local authorities' roles**, providing legal and\npsychological support, and ensuring a safe environment for young people. They also advocated for the\ncreation of specialized offices to receive reports of kidnappings or detentions, and the need for centralized\nsecurity under one authority.\n **Psychological pressure and** traditional social roles were seen as restricting their freedom, with young\nwomen facing additional challenges such as forced marriage to relatives, and gender-based discrimination\nin job opportunities.\n\n**3**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": " Male participants highlighted the **risks of violence, including indirect targeting through military**\n\n**operations, arbitrary detention, and torture** . They also emphasized the lack of control over the spread of\ndrugs, political exploitation of youth, and the dangers of illegal migration.\n Young women also **endured physical and psychological violence**, limited access to education, and\ndeprivation of rights such as inheritance.\n Female participants focused on the **negative consequences of early marriage, sexual exploitation, and**\n\n**the societal stigma attached to divorced women or those who delay marriage** .\n Both genders mentioned concern regarding **increasing spread of electronic blackmail and defamation**,\nwhich exacerbated young people's vulnerabilities.\n\n## **ACCESS TO PROTECTION SERVICES**\n\n Youth in NWS face a significant gap in the availability\nand accessibility of essential protection services to\nmitigate the exposure to risks.\n For those who have access, psychosocial support\n(68%), legal (41%), and case management (32%) are\nthe most frequent service they can access.\n Despite the relevance of these services, through them,\nYouth can’t address, in an effective manner, the\nexposure to, exploitation (55%), homelessness (53%)\nand gender-based violence (44%).\n\nWhen asked about the reasons why they were not able to\naccess these services, respondents said:", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Does youth have access to protection services**\n\n**to mitigate risks?**\n\n **Discrimination** is the standing\nreason from their perspective,\nprobably associated with the way\ntargeting criteria has been\ndeveloped and implemented in\nNWS, prioritizing the same\npopulation groups across the year\nbased, mainly on vulnerability, age\nand gender, but forgetting to assess,\neffectively the diversity and\ndisproportionate exposure to\nparticular risks from specific\npopulation groups.\n\n**According to the FDGs:**\n\nLack of capacity and expertise\n\nExclusion\n\nData/Information gaps\n\nFear\n\nStigmatization\n\n**4**\n\n**Why do you think youth is not being prioritized?**\n\nDiscrimination\n\nLack of awareness\n\n**66%**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": " Youth noted that the **continuation of armed conflict increases vulnerability to exclusion** . Both groups\nagreed that addressing these issues requires tackling corruption, improving awareness, and ensuring equal\nopportunities for all.\n **Absence of data** is also a relevant finding. Protection risks analysis on youth have not been produced in a\nsystematic manner in NWS, thus the identification of the particularity of the risks the population group\nfaces has not been documented, or even assessed. Concrete risks such as exploitation, forced labor, forced\nrecruitment have been addressed, to some extent, for children (until 18 years), but not for young people\n(18-23).\n Findings also highlight the **need to have enhanced technical expertise** to assess, analyze and design\nadequate responses for youth, that take into consideration the specific exposure to specific risks by young\nmen and young women, and doesn’t neglect these considerations in an equal manner.\n When asking youth if there are any mitigation strategies in place for these risks, results indicate that 47%\nof youth in northwest Syria report the presence of mitigation strategies for protection risks, while 53% do\nnot. Among those who have access to mitigation strategies, the most common approaches include", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**awareness sessions (90%), community networks (53%) protection analysis (44%). The** main gap remains\non specific strategies to mitigate impacts on **the safety, evacuation routes and well-being** .\n According to the survey findings, a significant majority of respondents (77%) indicated **that there are**\n\n**currently no organizations**, institutions, agencies, or entities offering tailored and specialized activities or\nservices specifically designed for youth in northwest Syria.\n\n 79% of the respondents believe some groups within youth are facing **disproportionate impacts** :\n\nYoung parents, the only segment of this\npopulation groups with some level of access to\nservices and programs, was not identified by\nrespondents as one of the groups at heightened\nrisk. The attribution of 15% to **young people**\n\n**belonging to minorities** is relevant finding,\nconsidering that, it is a high percentage for a\nsmall group. **IDPs** as the second group exposed\nto higher impact represents a need to better\nunderstand the specificities of the risks faced by\nthem; also, relevant to see that adolescent men\nseem to face increased risks than adolescent\nwomen. Absence of data on the particularities\nof these risks faced by both genders limits the\naccuracy of the programmatic response.\n\nThose that belong to minorities", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey findings"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Belonging to host communities\n\n**5**\n\n**If yes, which groups?**\n\nThose with disabilities\n\nIDPs\n\nAdolescent men\n\nAdolescent women\n\nHomeless\n\nThose with mental health issues\n\nSurvivors of violence\n\n**70%**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "## **LEGAL AND PHYSICAL PROTECTION**\n\n 36% of the respondents perceive the **security situation as challenging**, 34% as not stable and 29% as stable\nand safe. However, when asked whether they had felt threatened of afraid, 55% said no, 24% sometimes\nand 21% yes.\n When asked about **access to official documents**,\nyouth seem to have ambivalent feelings. Half of **Have you ever encountered difficulties**\n\n**obtaining official documents due to being**\n\nthem answered they didn’t face challenges, and the\n\n**young?**\n\nother half said yes or sometimes. Withing the\nchallenges identified through FDGs youth\n\ndiscrimination, and confusion related to **23%**\noverlapping procedures.\n On this regard, the survey showed, that 13% of the\nrespondents **did not have any civil documentation**\ndue to:\n\n**Have you ever encountered difficulties**\n\n**obtaining official documents due to being**\n\n**young?**\n\n**23%**\n\ni) **financial constraints** : the cost associated with\nacquiring these documents whether it be fees,\ntransportation costs, or other related expenses.\n\n**52%**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "ii) **Lack of knowledge about the process** : gap in knowledge and awareness, possibly due to insufficient\noutreach or complex bureaucratic procedures that are not well-communicated to the public. Additionally,\nthe presence of multiple authorities complicates the process of acquiring recognized identification\ndocuments, which is essential for accessing various services and opportunities.\n\niii) **Absence of supporting documents** : this may reflect broader issues such as displacement, loss of\ndocuments due to conflict or disaster, or systemic barriers that prevent individuals from ever being\ndocumented in the first place\n\niv) **Fear** : including distrust in government authorities, fear of discrimination or persecution, or concerns\nabout potential repercussions in conflict-affected areas. In the FDGs youth also mentioned fears related\nto recruitment, arbitrary detention, disappearance, and torture due to lack of documentation or during\nthe process to obtaining it.\n\n**If no, why you don't have civil documentation?**\n\n**68%**\n\nDont have the money to get it Dont know the process to get\nit\n\n**6**\n\nThey/I do not have any\nsupporting civil documents\n\nI am afraid of getting it", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "FDGs also showed:\n\n Both male and female participants noted a **lack of coordination among government institutions**, with\nmales emphasizing the need for centralization and the bureaucratic nature of the process, while females\nhighlighted challenges posed by the diversity of authorities in different regions, making it difficult to obtain\nrecognized identification documents.\n\n **Bureaucratic Challenges:** Male participants pointed out complex, costly procedures reliant on paper\ndocumentation, while females raised concerns about the loss of identification papers due to conflict and\nthe forgery of documents for benefits.\n **Costly procedures:** The male group further detailed the issues surrounding complex and costly\nprocedures, including the non-use of electronic forms and a complete reliance on paper documentation.\nCorruption within government institutions, coupled with nepotism and the widespread practice of bribery,\nexacerbates these challenges. They also highlighted the physical distance between government centers,\nthe difficulty of accessing them, and the high fees involved in obtaining official documents.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": " **Calls for Reform** : Both groups advocated for streamlined processes, improved accessibility to government\ncenters, and awareness campaigns to help young people secure necessary official documents efficiently.\n **Loss of Documentation:** Female participants focused on the loss of important identification papers due\nto conflict or displacement, and the inability to register marriages in courts during previous periods, which\ncreated long-term issues. They also mentioned the forgery of documents for material or relief benefits,\nadministrative inefficiencies, and the overcrowding of government centers, which slows down the\nprocess. Additionally, they pointed out that local documents were not recognized internationally, leading\nto exploitation as individuals are forced to request documents from regime-controlled areas for exorbitant\nfees.\n\n The survey results suggest that **elevated tensions and frequent clashes are a significant concern** for many\nyoung people in northwest Syria. Specifically, 22% of the respondents reported that their community or\nliving area is currently experiencing elevated tensions and frequent clashes, while 38% sometimes\nexperienced such situations.\n\n**Is your community experiencing elevated**\n\n This suggests that a substantial proportion of the youth **tensions and frequent clashes?**\nin the region are living in a state of insecurity and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "40% of the respondents reported that their community **38%**\nor living area is not currently experiencing elevated\ntensions and frequent clashes. This perception is\nconsistent with the available surveys and assessments\nrun by the NWS PC, on the escalation of hostilities\nstarted in October 2023 and still ongoing.\n The survey also explored the **presence of Unexploded Ordnance (UXO)** in the living areas of youth in\nnorthwest Syria, along with their experiences of receiving support for clearance and awareness, 13%\nreporting presence in their area. Among those who reported the presence of UXO, 25% received help or\nsupport for clearance, and 79% of those who received support reported that it included an awareness\nsession about explosive ordnance exchange.\n\n**Is your community experiencing elevated**\n\n**tensions and frequent clashes?**\n\n**38%**\n\n**7**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "## **LIVING CONDITIONS**\n\n Majority of youth in northwest Syria **live with their**\n\n**family** (85%), some (10%) live with their family and\nother families, and 3% live with younger brothers\nand sisters, 1% live alone, 1% live with friends, and\nless than 1% live with neighbors.\n\nCamps\n\n**Where do you live?**\n\nHouse\n\n**52%**\n\n- **The overwhelming majority of youth living**\n\n**with** **their** **families** **underscores** the\nimportance of family structures in providing\nstability and support. This arrangement\nlikely offers a sense of security, emotional\nsupport, and shared resources, which are\ncrucial, especially in uncertain or crisis\nsituations.\n\nCollective Center\n\nUnfinished Shelter\n\nApartment\n\nTent (Outside camp)\n\nCaravan\n\n- **Youth living with their family and other**\n\n**families,** might reflect socio-economic\npressures that lead to shared living arrangements to pull resources. Such arrangements can offer\nsocial and economic support, but may also introduce complexities such as overcrowding, privacy\nconcerns, exposure to protection risks, increased tension due to differing household dynamics.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- **Youth living with only their younger siblings,** suggests they may be in a caregiving role, possibly\ndue to the absence of parents or other guardians. This situation can place additional stress,\nimpacting their ability to focus on education, work, or social and Mental health activities. It may\nalso highlight the vulnerability of these households, where young people are taking on adult roles\nat an early age.\n\n- **Youth living alone 1%,** might reflect a variety of circumstances, such as orphanhood,\nestrangement from family, or personal choice housing arrangements for these young people might\nbe scarce and lead to homelessness with enhanced exposure to protection risks. Groups living in\nthis situation might be facing increased challenges to access services. **Respondents living with**\n\n**friends** might also be exposed to disproportionate impacts, particularly recruitment, exploitation,\ntrafficking, and smuggling.\n When asking if they were responsible **for providing financial support at least to one family member**,45%,\nindicate that they do. This finding highlights the significant economic pressures faced by youth who may\nbe shouldering the burden of supporting their families in the context of limited employment opportunities,\ndisplacement, and poverty.\n\n**House, land, and property related rights**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "83% of the respondents **confirmed their family-owned property in Syria**, and 100% of them confirmed they had\nownership documents at hand. When asked about the status of those properties, more than half confirmed them to\nbe occupied.\n\n**8**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Throughout the war, HLP rights violations has been one\nof the main impacts, particularly related to\n\n**secondary/unauthorized occupation, destruction,**\n\n**and confiscation** . Absence of ownership documents\n(either due to loss or verbal agreements) increased the\nexposure to house, land, and property related\nviolations. In the case of youth, these risks are\nenhanced, due to **lack of procedures to secure tenure**\n\n**for youth when parents have died** .\n\nOccupied I don't\nknow\n\n**What is the status if your familys property?**\n\n**54%**\n\nAbandoned Destroyed Rented Confiscated\n\nFamily separation, lack of access to legal services,\ninformality on tenure, ethnic background,\ndiscrimination, are some of the existing drivers for **additional HLP violations for youth** . The percentage of destroyed\nproperties documented by the survey raised additional concerns related to future restitution of HLP rights for young\npeople and the need to safeguarding these documents for transitional justice purposes.\n\nYouth participating in the FDGs also mentioned:", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": " **Property Ownership Dynamics** : Both male and female participants recognized three main groups in property\nissues related issues, local owners, internally displaced persons (IDPs), and tenants, with many properties\noccupied by military factions or families unable to return. The male group highlighted that many properties in\nregime-controlled areas have been taken over by military factions or families, often due to disputes or conflict.\nSimilarly, female participants noted that most properties are occupied by military factions or families unable\nto return, with only a small percentage of original owners still in possession of their homes.\n **Risks to Property Security** : Participants identified significant threats to property, including displacement,\nconflict-related damage, and natural disasters, with males highlighting challenges like non-recognition of\ndocuments and military control over properties. The male group added challenges like non-recognition of\nofficial documents, forced displacement, and demographic changes that prevent property recovery. They also\nmentioned military control over properties, particularly those near conflict zones and foreign military bases,\nas well as the conversion of public assets into private projects. Female participants emphasized poverty,\nfactional control, and the absence of legal authority as barriers to reclaiming property.\n **Barriers to submit HLP claims:** Both groups noted that poverty, factional control, and a lack of legal authority\nhinder property restitution, emphasizing the complex challenges faced by owners due to ongoing conflict and\nweak legal frameworks. Both groups identified significant risks to property, including displacement, migration,\nartillery and air bombardment, and vulnerability to natural disasters like earthquakes. These discussions\nhighlight the severe challenges faced by property owners, exacerbated by conflict, poverty, military control,\nand weak legal frameworks in the region.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "## **ACCESS TO EMPLOYMENT AND LIVELIHOODS**\n\n The results show a diverse range of activities that\nthe youth are participating in, with some\nparticipating in multiple activities. A significant\nproportion of young people, (42%), are **engaged**\n\n**in work/employment** . Identification of risks of\nexploitation, particularly in connection with the\nspecific groups mentioned above to asses’ risks\nof rights violations remains as a pressing need. In\nsome areas in NWS, like Al Bab, youth is engaged\nin informal petroleum refineries with a\nheightened exposure to risks to their health and\nsafety.\n\n**What activities are youth in NWS engaged in?**\n\nWork\n\n**42%**\n\n An alarming 27% of the respondents said that\n\nEducation\n\nUnpaid work\n\nSports activities\n\nCommunity-based work\n\nReligious activities\n\nNone\n\nCultural activities\n\nthey are doing **unpaid work** . The risks of\nexploitation and abuse linked to hazardous jobs\nand possible slavery need to be assessed further, particularly if any of these activities is under the control\nof armed groups in northwest Syria.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": " Despite the challenges, the survey also reveals a positive trend of **youth engagement in their communities**\n(20%), however, 17% are not accessing any form of work-related activity, situation that can also lead to\nrisks of recruitment or engagement in illegal activities (including smuggling), as well as drug abuse.\n\nThe results also show that a significant majority of young people, (82%), **do not believe that newly graduated**\n\n**youth have the same working opportunities** as those who have already graduated or have previous work\nexperience. Lack of job opportunities represent the biggest barriers for recently graduated youth, most likely, in\nthe case of NWS, humanitarian organizations constitute the biggest employment alternative, however, lack of\nexperience can also represent a barrier for youth to access.\n\nFDGs showed that:\n\n **Young women are especially vulnerable to sexual exploitation, harassment, and early or forced**\n\n**marriages**, leading to social exclusion and lack of support, particularly for divorced women. Femaleheaded households and displaced individuals also face discrimination.\n\n The male group highlighted that **corruption, regionalism, and favoritism in employment** and resource\ndistribution exacerbate discrimination, with young people often being excluded from opportunities. They\nalso noted that the continuation of armed conflict increases vulnerability to exclusion.\n\n**10**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "## **SOCIAL PARTICIPATION AND COMMUNITY- BASED ENGAGEMENT**\n\n Engagement of youth in community-based structures\n(34%), is mainly through **volunteer groups** (74%), **Do the Youth actively participate in Community-**\n\n**Based Structures?**\n\n**outreach teams** (53%), **safety brigades and clubs**\n(30%).\n\nthese structures, perceived or actual barriers to\n\nbelief that these structures do not address their\nneeds or concerns. **n mplaints and feedback**\n\n**mechanisms**\n **Regarding social participation, youth believe they do not have equal opportunities** (76%), 85% attributed\nthis perception to discrimination, 34% to and 15% to stigmatization. These findings suggest that a\nsignificant portion of youth feel marginalized and unable to fully participate in social activities within their\ncommunities.\n Only 12% of respondents reported **having access to capacity building and empowerment activities** . The\nmain areas of training accessed by this 12% are capital for citizenship, peaceful coexistence, social and\ncommunity networks and peaceful resolution of conflict, as well as peacebuilding and restitution of rights.\nExpansion of these capacities across NWS remains as a priority to guarantee effective engagement and\nsupport to the future generations in the reconstruction of their social and capital networks.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": " The survey results indicate that a significant\nmajority of youth in Northwest Syria, 92% **Which services are requiered by youth to**\n\n**improve their situation?**\n\nindicated that **youth require various services to**\n\n**address their needs and aspirations** . The most\nidentified services required by youth include\nimproving **livelihoods and mental wellbeing** .\n\nhumanitarian aid. **Literacy** (23%) is also a\nsignificant finding of this study that should be\neasily address by humanitarian actors.\n\nDuring the FDGs youth also mentioned:\n\n**Do the Youth actively participate in Community-**\n\n**Based Structures?**\n\n**Which services are requiered by youth to**\n\n**improve their situation?**\n\nFunding for small projects\n\n**84%**\n\nVocational Training\n\nGroup PSS\n\nLiteracy\n\nIndividual PSS\n\nMHPSS\n\nSexual and Reproductive…\n\nCyber Security\n\n **Shared Commitment to Youth Empowerment** : Both male and female participants emphasized the critical\nrole of education, training, and job opportunities in fostering community engagement and societal growth.\n\n**11**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": " **Divergent Priorities** : The male group focused on leadership development, youth representation in\ndecision-making, and creating legal protections, while the female group prioritized psychological support\nand safe spaces for self-expression.\n **Support for Initiatives** : Both groups called for educational courses, volunteer programs, and funding for\nyouth-led initiatives, highlighting the need for continuous support and reduced barriers to enhance job\nopportunities.\n **Networking** and supporting youth unions, associations, and groups to reduce risks by being a strong\norganized entity.\n Both groups called for **educational courses, development projects, volunteer programs, and funding for**\n\n**youth-led initiatives to foster societal growth** .\n\n`o` The males group emphasized **leadership development**, youth representation in decision-making\nbodies, and creating legal and financial protections through infrastructure. They also highlighted\nremote work, digital security, and special environments for females ensuring privacy and safe\naccess.\n\n`o` The female group focused on **psychological and social support**, promoting safe spaces for youth\nto express themselves and discover talents, and suggested reducing experience barriers to widen\njob opportunities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "`o` They also pushed for career rotation and continuous support for **education** as a foundation for\nsociety. While both groups shared a commitment to youth empowerment, the female group\nleaned more toward social and psychological well-being, whereas the male group prioritized\nleadership, structure, and legal safeguards.\n\n## **HEALTH AND DISABILITY**\n\n**Health and disability**\n\nA small percentage (7%) identify as having a disability [2] according to the\nWashington Group classification. Within youth with disabilities, **males**\n\n**constitute the majority** (72%) compared to females (28%).\nDisproportionate impact of disability on young men should be the driver\nof additional data collection to develop specific programs to address\ntheir needs and exposure to risks.\n\n**Person with disabilities by**\n\n**gender**\n\n**Male**\n\n**72%**\n\n The majority of young people in northwest Syria, (84%), do not\nface any health problems conditions. However, a significant 16%\nreported facing health issues, with varying levels of access to\nhealth services and medication.\n Regarding access to health services, the results indicate that only 47% of those with health problems,\nconditions or disabilities have access to health services, while 53% do not.\n 56% of youth with health problems, conditions or disabilities **do not have access to adequate medication** .\n\n---\n[2] https://www.washingtongroup-disability.com/question-sets/", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Disability types among youth** :\n\n- **Walking:** significant portion of the population faces\nmobility challenges, which can severely impact their daily **Disability Type**\nactivities and quality of life.\n\nWalking\n\n- **Remembering:** This type of disability can lead to\n\nfurther complicating the lives of those affected.\n\n- **Washing:** broader challenges related to personal hygiene\n\nnavigate their environment, access written information,\nand engage in daily activities, highlighting the need for targeted support and resources.\n\n- **Communicating** : This can hinder social interactions and access to information, services and further\nisolating individuals with disabilities. It can also affect their capacity to self-protect and request support\nand help.\n\n- **Hearing:** significant barrier to communication and social engagement.\n\n**Disability Type**\n\nWalking\n\n**40%**\n\nRemembering\n\nWashing\n\nSeeing\n\nCommunicating\n\nHearing\n\n**Key Recommendations** **[23]**\n\n**Voices of NWS young men and women:**\n\n- Improve education, support remote work opportunities, and reduce the cost of higher education.\n\n- Collaborate to address the challenges faced by young people.\n\n- Support networking and youth unions, associations, and groups to reduce risks.\n\n- Improve transportation services for both young males and females to reduce security risks.\n\n- Provide awareness programs about the dangerous impact of drug abuse and its effects on youth.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Encourage and fund sports, cultural, and social activities as positive alternatives for youth.\n\n- Target youth in all humanitarian funded projects.\n\n- Ensuring safe work environments for women that guarantee their rights.\n\n- Improving access to education services and providing a safe environment for young males and females.\n\n- Reduce discrimination.\n\n- Design concrete measure to avoid additional rights violations.\n\n- Acknowledge youth role on peacebuilding for the prevention of new cycles of violence.\n\n**Recommendations for Donors:**\n\n1. **Request prioritization of youth sectorial and intersectoral program designs,** pooled fund allocations; as\nwell as in yearly strategic documents developed for calls for applications in the humanitarian development\nsectors.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "2. **Prioritize Funding for Comprehensive Services for Youth:** Allocate funds specifically for programs\naddressing the needs of at heightened risk youth groups, including those with disabilities, homeless, with\nan ethnic background and facing protection impacts. S\n\n3. **Support community-based initiatives** that provide safe spaces, counseling, and support services for youth\nat risk particularly to address exposure to trafficking and smuggling, recruitment, exploitation, etc.\n\n4. **Support Youth Empowerment and Participation:** Invest in capacity-building programs that provide youth\nwith essential skills such as leadership, conflict resolution, and entrepreneurship, while also supporting\ninitiatives that promote their participation in community-based structures.\n\n5. **Advocate for equal opportunities for social participation** by addressing issues of discrimination,\nexclusion, and stigmatization.\n\n6. **Fund Programs Addressing Civil Documentation:** Fund programs that facilitate access to official\ndocuments for youth, particularly those facing financial barriers or lack of knowledge.\n\n7. **Partnerships and Collaboration:** Engorge collaboration among humanitarian organizations to ensure a\ncomprehensive response to the needs of youth. Encourage partnerships with local communities to\nidentify and address their most pressing needs.\n\n8. **Support advocacy efforts to promote policies and funding** that support the protection, empowerment,\nand well-being of youth.\n\n**Recommendations for Cluster Coordinators, UN Agencies, and HLG Members:**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**1.** **Develop data collection and surveys to identify the specific needs of youth,** considering also cross cutting\nissues including ethnic and religious background, gender, age and living conditions (ex. homelessness)\n\n**2.** **Design youth-oriented strategies to address existing needs, gaps, and risk,** with specific consultation and\nparticipation mechanisms to guarantee modalities and interventions are discusses and agreed with them.\n\n**3.** **Increase the availability and accessibility of tailored services for young people**, focusing on addressing\ngender-based violence, exploitation, recruitment, trafficking and smuggling and homelessness from an\nintersectional perspective.\n\n**4.** **Prioritize youth as a at heightened risks population group** during the development allocation strategies for\npooled funds and UN Funding mechanism (CERF).\n\n**B. Recommendations for National and International NGOs:**\n\n1. **Expand Protection Services and Targeted Interventions:** Increase the availability and accessibility of\nprotection services particularly related to adequate housing, livelihoods, access to health and medication\nand support to those facing drug addiction. focusing on the unique needs of\n\n2. **Prioritize youth in programs design:** particularly youth lead HHs, those with disabilities, homeless and\nseparated from their families.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "3. **Enhance Capacity Building to strengthen your engagement in community led processes and networks:**\nbuilding on the experience they already have on peaceful coexistence, pacific resolution of conflict, rule\nof law and overall engagement on governance and risk mitigation.\n\n**14**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "4. **Foster Partnerships and Collaboration:** Strengthen collaboration and coordination among humanitarian\norganizations to ensure a comprehensive response to the needs of youth, engage with local communities\nand organizations to identify and address the most pressing needs.\n\n5. **Invest in Mental Health and Well-being:** Provide accessible mental health services to address the\npsychosocial needs of youth, including those affected by conflict, violence, and trauma by Promote\nresilience-building activities to help youth cope with adversity and develop healthy coping mechanisms\nand offering specialized support for youth at risk of self-harm or suicide.\n\n**6.** **Address Access to Essential Services and Resources:** Facilitate access to official documents for youth,\nespecially those facing financial barriers or lacking knowledge, by implementing subsidized programs to\nlower the costs associated with obtaining civil documentation. Additionally, conduct public awareness\ncampaigns to highlight the importance of civil documentation and inform young people about the\nresources available to them.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**7.** **Strengthen Safety and Security:** Prioritize efforts to clear unexploded ordnance (UXO) to reduce risks for\nyouth and communities, while also providing comprehensive awareness sessions and training on UXO\nsafety measures. Additionally, offer support and assistance to communities impacted by UXO\ncontamination to help them recover and rebuild. In addition to coordinate with the local authorities and\nto improve transportation services for both young males and females to reduce security risks associated\nwith using unsafe alternatives.\n\n**15**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Annex 1**\n\n**Demographics information.**\n\n This report is based on the information provided by 2176\nyoung Syrians, between 18 – 23 ages, living in 34\ndifferent sub-districts within the governorates of Aleppo\nand Idleb in Northwest Syria, covering 225 communities,\nand 106 IDPs camp sites. 47% identified as IDPs and 53%\nas part of the host community.\n\n**Are you from the Host community or**\n\n**IDPs?**\n\n**Internally**\n\n**displaced**\n\n**person …**\n\n**Host**\n\n**Community**\n\n**53%**\n\n In terms of marital status, the results showed a close split\n\nbetween married (48%) and single (49%).\n With a representative number of respondents (at least 200) as widows and same amount for divorced.\nAdding a layer of complexity and needs probably related to management of loss, livelihoods, and social\nstigma.\n\n**Marital status of interviewee**\n\n**48%** **49%**\n\nMarried Single Widow Divorced Seperated\n\n**16**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Annex 2**\n\n**FGDs Questionnaire**\n\nFGDs Questions :\n\n1. Torture, Arbitrary detention, and Forced Disappearance, are some of the protection risks faced by youth\nin NWS, from your point of view What solutions or actions would help mitigate/overcome these\nprotection risks?\n2. The survey results conducted earlier in NWS indicated that 66% of youth are facing discrimination, if you\nagree with that statement, can you please clarify and elaborate the factors contributing to\ndiscrimination, exclusion, and stigmatization among youth in the region?\n3. What challenges do young people encounter when trying to acquire official documents?\n4. Which are the top 5 risks you believe young males face? and which are the tops 5 protection risks young\nfemales face?\n5. How can organizations engage young people in NWS in the rebuilding of their communities?\n6. 54% of the surveyed youth in NWS who themselves or their family have properties and 83% of them\nhave property documents, could you explain more by whom it is occupied?\n7. What are the main protection risks related to your/your family's properties?\n\nDemographics:\n\nFemale Participants: Total of 50 across all FGDs, 25 in Aleppo/ A'zaz and 25 in Idleb/Idleb.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey results"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Male Participants: Total of 50 across all FGDs, 25 in Aleppo/ A'zaz and 25 in Idleb/ Idleb.\n\n**17**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "# **From Stateless to Citizens: the journey** **of the Shona community in Kenya**\n\nPreliminary results from a socioeconomic study on the impact of citizenship on socioeconomic and living conditions.\n\n\" _Getting a national ID has really helped me. I can now move freely without fear of getting arrested. I have access to better job_\n\n_opportunities too because of the access the ID has been able to give me. My kids can now pursue their education comfortably because_\n\n_I know I can support them with access to more work opportunities._ \" - Azariah Samuel, 27-year-old father of two during a follow-up\n\nsocioeconomic study on the Shona community in Kenya. © UNHCR/Charity Nzomo.\n\n**[www.unhcr.org](http://www.unhcr.org/)** 1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "A stateless person is someone who is not considered as a national by any\n\nState under the operation of its law.\n\n- 1954 Convention relating to the Status of Stateless Persons (Article 1).\n\n**Globally, an estimated 4.4 million people lack a nationality, leaving them stateless, according to statistics published**\n\n**by UN Refugee Agency (UNHCR)** . [[1]] In the East, Horn, and Great Lakes of Africa (EHAGL) region, this includes\n\n97,591 stateless persons or persons of undetermined nationality, of whom 9,800 live in Kenya. Given the lack of\n\nuniversal reporting and other gaps in statelessness data, these figures are likely to be an underestimate. Stateless\n\npeople often live on the margins of society and remain invisible in national statistics and other government\n\npopulation databases due to the complexities of accurate registration and data collection.\n\n**Lack of identifying documents confirming nationality frequently precludes stateless persons from working in the**\n\n**formal economy, owning property, accessing basic services such as health and education, moving freely or**\n\n**accessing financial services, leaving them at an elevated risk of poverty.** [[2]] Children may face restrictions on\n\nattending school, while exclusion from public health services imposes costs on families and creates risks for", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "surrounding communities. [ [3], [4]] Acquiring citizenship, brings the promise of improved employment and earnings, [[5]]\n\nespecially among women and the poorest, [[6]] while also offering broader social and economic benefits, as increases\n\nin talent, skills and taxation accompany legalization.\n\n**Since 2014, more than 500,000 stateless people have acquired a nationality globally.** Statelessness for many\n\nothers has been prevented as a result of efforts to improve birth registration and issuance of nationality documents\n\nto populations at risk of statelessness and legislative changes, including those that eliminate discrimination on the\n\nbasis of gender, allowing women to pass on nationality to their children on an equal basis as men, However,\n\nacceleration of efforts is needed to ensure that everyone can enjoy the fundamental right to a nationality, which\n\noften enables the enjoyment of other rights and full participation in the society.\n\n**Kenya has emerged as a global leader in the eradication of statelessness, setting an example for other nations**\n\n**through its continued efforts to reduce the number of stateless persons within its borders.** The country has made\n\nsignificant strides, beginning with the recognition of 1,496 members of the Makonde community as nationals of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Kenya in 2016, followed by the provision of nationality to 1,659 Shona in 2020-21, and most recently, the\n\nrecognition as nationals of approximately 7,000 Pemba in 2023. These milestones underscore Kenya’s commitment\n\nto addressing statelessness, a commitment reaffirmed at the Global Refugee Forum (GRF) in December 2023\n\nthrough four strategic pledges aimed at the progressive eradication of statelessness. Despite these achievements,\n\nan estimated 9,800 individuals in Kenya remain stateless today, highlighting the need for sustained efforts to fully\n\nresolve this issue. Gaps in law continue to be a contributing cause of statelessness, due to Section 15(1) of the\n\nCitizenship and Immigration Act, which limits the scope of the definition of a stateless person to those who were\n\nin Kenya when it gained independence on 12 December 1963, excluding many individuals who arrived later, as well\n\nas their descendants. In addition, neither the Constitution nor the Citizenship and Immigration Act contain a legal\n\nsafeguard to ensure that a child born on the territory who would otherwise be statelessness is granted Kenyan\n\nnationality.\n\n**[www.unhcr.org](http://www.unhcr.org/)** 2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**The Shona originally came from Zimbabwe and migrated to Kenya as missionaries majorly between the 1950s and**\n\n**1970s, settling in the Greater Nairobi area.** According to community elders, the Shona were issued certificates of\n\nregistration under the Alien Restriction Act upon arrival. However, a change in the Registration of Persons Act of\n\n1978 prevented them from accessing identity cards. As a result, most of those who were among the first wave of\n\narrivals do not hold valid legal identity documents. At the same time, Zimbabwe and Zambia do not consider them\n\nas nationals since many of those who were born there did not have their births registered and lost all traces of their\n\nancestry since they never returned, hence rendering them stateless. [[8]] Subsequently, they settled and assimilated\n\nwith the local Kikuyu community which welcomed them. Since their arrival, they have practiced wood carving for\n\nmen and basket weaving for women as their primary economic activity outside of preaching activities. Ten\n\nindividuals from the Shona community were granted Kenyan citizenship on 12 December 2020 and a further 1,649\n\nwere recognized as Kenyan citizens and issued with registration certificates on 29 July 2021.\n\n- **Box 1: Data Sources**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The primary data for this report come from a comprehensive 2024 socioeconomic survey conducted by UNHCR\n\nwhich recontacted households which were initially surveyed in 2019, prior to the Shona acquiring nationality. The\n\nfollow-up survey aimed to capture the transformative impact of citizenship on the Shona community in terms of\n\nemployment, education, income, and access to essential services, while also incorporating new modules on social\n\ncohesion, community engagement, and civic participation to better understand the broader integration of the Shona\n\ninto Kenyan society. The 2024 survey provides rich insights into the Shona’s post-citizenship journey and the\n\nchallenges and opportunities they face as newly recognized citizens of Kenya.\n\nThe findings are complemented by the 2019 household survey of the Shona community, conducted jointly by\n\nUNHCR and the World Bank [[8]] establishing a baseline of the Shona community’s socioeconomic conditions while\n\nthey were still stateless. This study revealed significant disparities in access to services and opportunities compared\n\nto Kenyan nationals, highlighting the detrimental effects of statelessness on employment, financial inclusion, and\n\neducational attainment. These findings informed policy recommendations that ultimately contributed to the\n\nrecognition of the Shona as Kenyan citizens in 2020-21, providing a crucial foundation for evaluating their post\ncitizenship outcomes.", "output": {"entities": {"named_data": [], "descriptive_data": ["2024 socioeconomic survey", "2019 household survey of the Shona community"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Additionally, insights were drawn from the Kenya COVID-19 Rapid Response Phone Surveys (RRPS), [[9]] conducted\n\nbetween 2020 and 2022, which included the Shona as a distinct stratum. The RRPS monitored the impact of the\n\npandemic on vulnerable groups, capturing critical data on employment disruptions, income losses, and food\n\ninsecurity during the crisis.\n\n**The Shona community in Kenya has faced significant socioeconomic challenges, exacerbated by their stateless**\n\n**status and the impacts of the COVID-19 pandemic.** Before acquiring citizenship, Shona households faced a 24\n\npercent higher likelihood of living in poverty compared to the urban Kenyan population and were more likely to\n\nreside in larger, overcrowded households. Although primary school enrollment rates were similar to those of their\n\nKenyan counterparts, many Shona children struggled to transition to secondary education. Limited access to formal\n\nemployment, driven by a lack of legal documentation, has forced many Shona individuals into self-employment or\n\ninformal work. Even with high employment rates within the community, this does not translate into reduced\n\npoverty, as most of the employment is low-income and lacks stability. [[8] ]\n\n**[www.unhcr.org](http://www.unhcr.org/)** 3", "output": {"entities": {"named_data": ["Kenya COVID-19 Rapid Response Phone Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "widespread among Shona households, and access to external support was minimal. Although employment levels\n\nbegan to recover a year later, they remained below pre-pandemic levels, underscoring the prolonged adverse\n\nimpact of the pandemic on this already vulnerable community. [[9]]\n\n_Figure 1: Labor force status after the COVID-19 outbreak (18-64 years)_\n\n_Source: Kenya COVID-19 Rapid Response Phone Surveys (RRPS)_ _[[9]]_\n\n**Two rounds of household surveys taking place over five years before and after the transition of the Shona in**\n\n**Kenya from statelessness to citizenship help us to understand this community – and present one of the first**\n\n**socioeconomic pictures of the impact of citizenship on the welfare of stateless persons.** Between 2019 and 2024,\n\nthe median age of Shona household members remained unchanged at 18 years, underscoring a predominantly\n\nyouth demographic profile. Gender distribution is even, showing only a marginal change, with the female population\n\nincreasing slightly from 49 percent to 50 percent. Average household size has risen modestly from 4.9 to 5.4\n\nmembers, which may reflect greater household consolidation or socio-economic stability following their\n\nnaturalization. The most notable shift is the significant reduction in the proportion of female-headed households,", "output": {"entities": {"named_data": ["Kenya COVID-19 Rapid Response Phone Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "dropping from 33 percent to 21 percent.  The literacy rate has remained constant at 93 percent, reflecting sustained\n\naccess to education. Overall, the Shona community's demographic profile suggests stability and gradual socio\neconomic integration following their acquisition of citizenship.\n\n_Table 1: Demographic characteristics of Shona community in Kenya._\n\n_Source: 2019 Shona socioeconomic survey_ [[8]] _and the authors’ calculation of 2024 survey data._\n\n**[www.unhcr.org](http://www.unhcr.org/)** 4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Access to services**\n\n**Since 2020, the Shona community has made significant strides in the enjoyment of rights and access to essential**\n\n**services and social, economic, and financial opportunities.** The acquisition of citizenship has enabled the\n\ncommunity to access critical documentation, with 86 percent of individuals now holding a national ID, citizenship,\n\nor birth certificate. This remarkable progress has facilitated their inclusion in various social and economic\n\nopportunities.\n\n**Access to financial services has also improved significantly.** The proportion of community members with bank\n\naccounts increased from 9 percent in 2019 to 35 percent in 2024, while mobile wallet usage saw a surge from 52\n\npercent to an impressive 97 percent. This suggests that the community has leveraged citizenship to integrate more\n\nfully into the formal financial system, enhancing their economic participation.\n\n**Health insurance coverage has nearly tripled over the past five years.** The percentage of Shona individuals with\n\nhealth insurance rose from 4 percent in 2019 to 11 percent in 2024, compared to 24 percent nationally _._ [[10]] Although\n\nthe current coverage rate remains low, this growth indicates an increasing awareness and ability to access\n\nhealthcare services, possibly due to better employment prospects and social benefits associated with citizenship.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Net primary school enrolment shows a slight decline but is attributed to changes in the education system rather**\n\n**than reduced access.** The enrolment rate decreased from 81 percent in 2019 to 75 percent in 2024. However, this\n\nis likely due to the adjustment in the age bracket for primary school in Kenya under national education reforms\n\nlaunched in in 2022, which has led to a temporary underestimation of the enrollment rate. Despite this, the Shona\n\ncommunity's access to primary education remains strong and stable.\n\n_Figure 2: Access to essential services, pre- and post-citizenship_\n\n_Source: 2019 Shona socioeconomic survey_ [[8]] _and the authors’ calculation of 2024 survey data._\n\n**[www.unhcr.org](http://www.unhcr.org/)** 5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**pandemic, regional drought, inflation crisis and continuing global economic turbulence.** According to data from\n\nthe Kenya COVID-19 Rapid Response Phone Survey (RRPS), the Shona community experienced significant job\n\nlosses during the pandemic, particularly between July and September 2020, when unemployment increased\n\ndramatically. [[9]] This period of economic disruption caused by the pandemic led to fluctuating employment rates, as\n\nseen in the figure below.\n\n_Figure 3: Labor force participation as a percentage of working age population, between 2019 and 2024_\n\n_Source: 2019 Shona socioeconomic survey,_ [[8]] _Kenya COVID-19 Rapid Response Phone Surveys (RRPS),_ _[[9]]_ _and the authors’ calculation_\n\n_of 2024 survey data._\n\n**While employment has started to improve, it has not yet returned to pre-pandemic levels, especially for women.**\n\nThe employment rate for women decreased from 72 percent in 2019 to 61 percent in 2024, which can largely be\n\nattributed to the job losses incurred during the pandemic and slower recovery in female-dominated sectors that\n\nwere further setback by the food price crisis and global economic headwinds. Meanwhile, male employment\n\nincreased slightly from 74 percent to 76 percent during the same period, reflecting a quicker rebound in male\ndominated occupations.", "output": {"entities": {"named_data": ["Kenya COVID-19 Rapid Response Phone Survey", "2019 Shona socioeconomic survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**The overall decline in total employment from 74 percent in 2019 to 70 percent in 2024 is indicative of the sum of**\n\n**these shocks.** Although men have re-entered the labor force at a faster rate, the community as a whole is still in\n\nthe process of economic recovery. The proportion of unemployed individuals has remained relatively low,\n\nsuggesting that many are still re-entering the labor market or seeking labor market opportunities. Only 13 percent\n\nof the Shona working wage-earners have some form of employment contract, demonstrating a high prevalence of\n\ninformal work arrangements.\n\n**The increase in the number of individuals out of the labor force, especially among women, underscores the**\n\n**difficulties of returning to the labor market and securing timely employment.** The pandemic's effects have pushed\n\n**[www.unhcr.org](http://www.unhcr.org/)** 6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_Figure 4: Labor force participation by gender as a percentage of working age population across 2019 and 2024_\n\n_Source: 2019 Shona socioeconomic survey_ [[8]] _and the authors’ calculation of 2024 survey data._\n\n**Household income**\n\n**Income from business is the primary source of livelihood for the Shona community, followed by wages and other**\n\n**minor sources.** 73 percent of household income in the Shona community comes from business activities like\n\ncarpentry, weaving baskets and hawking on the streets, indicating a strong reliance on entrepreneurship and self\nemployment. Wages from employment and daily labor activities constitute 25 percent of the income, while other\n\nsources like asset earnings or assistance contribute a mere total of 2 percent, showing limited diversification in\n\nincome streams. This heavy dependence on business shows that the Shona community still relies on their traditional\n\nincome sources and suggests that the community might face heightened vulnerability to market fluctuations and\n\neconomic shocks affecting their businesses.\n\n**A majority of households report that their income has remained stable since gaining citizenship, but a notable**\n\n**proportion has experienced an increase.** Half of the households indicated their income stayed the same, while 38", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "percent reported an increase, and 12 percent experienced a decrease since December 2020. This distribution\n\nsuggests that while citizenship has had a stabilizing effect for many households, it has not yet resulted in broad\nbased economic gains. However, the increase in income for nearly 4 out of every 10 households is a positive sign\n\nof economic progress and potential for further growth as the community continues to integrate more fully into the\n\nKenyan economy.\n\n**[www.unhcr.org](http://www.unhcr.org/)** 7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_Source: Authors’ calculation of 2024 survey data._\n\n**Livelihood opportunities for the Shona community have improved significantly since gaining citizenship,**\n\n**particularly in terms of financial access and job prospects – as well as protection outcomes, such as reduction in**\n\n**harassment by law enforcement.** According to the survey data, over half of the Shona community reported that\n\ncitizenship has enhanced their access to banking and mobile wallet services, making it easier for them to save,\n\ninvest, and manage their finances. Increased job opportunities were cited by 47 percent of respondents, reflecting\n\nhow the ability to present formal identification has opened new avenues for employment and economic\n\nparticipation. Importantly, 46 percent of the Shona noted a reduction in harassment by law enforcement, which\n\nhad previously hindered their movement and limited their economic activities. This improvement has enabled more\n\nindividuals to seek employment and conduct business without fear of arbitrary detentions or fines.\n\n_Figure 6: Impacts on receiving citizenship on livelihood opportunities_\n\n_Source: Authors’ calculation of 2024 survey data._\n\n**[www.unhcr.org](http://www.unhcr.org/)** 8", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "to credit, which is critical for expanding small businesses or starting new ventures. These changes underscore the\n\ntransformative role of citizenship in enabling the Shona community to overcome previous barriers to economic\n\nparticipation and build sustainable livelihoods.\n\n**Moreover, citizenship has also led to increased access to training and educational opportunities, while reducing**\n\n**discrimination in the workplace.** Over a third of the Shona noted that they have had more opportunities for training\n\nor education since becoming citizens, helping them to build skills and enhance their employability. Another 30\n\npercent reported experiencing less discrimination at work, which has contributed to more positive employment\n\noutcomes and a more inclusive work environment. Overall, these improvements indicate that legal recognition has\n\nbeen pivotal in unlocking the economic potential of the Shona community, setting the stage for greater\n\nsocioeconomic mobility and long-term development.\n\n- **Box 2: Findings from focus group discussions**\n\nMany of the survey results are consistent with findings from qualitative focus group discussions, including\n\nchallenges associated with securing economic opportunities. The Shona community faces challenges predominantly\n\nstemming from low education levels and the absence of formal qualifications to which the lack of nationality and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "documentation has been a major contributor. The acquisition of citizenship has provided some new avenues,\n\nespecially for the youth, such as working in the hospitality industry and the ability to obtain driving licenses, which\n\nhas opened job opportunities in the transportation sector. However, most of the community still relies on traditional\n\ntrades such as carpentry and basket weaving. The competitive nature of these trades, coupled with a lack of\n\ndiversification in skills and economic opportunities, is prompting members to explore new economic avenues. This\n\nsituation highlights the need for economic development initiatives that are tailored to the community's unique\n\nneeds and skills, offering sustainable and diversified economic opportunities.\n\nThe youth within the Shona community demonstrate a keen interest in enhancing their skills and seeking\n\nopportunities beyond conventional trades. The improvement in access to education is a positive development, yet\n\nthe challenge of transitioning into the formal labor market remains significant. Citizenship has facilitated easier\n\nmobility and enabled traditional vending activities, but it has not led to substantial changes in long-term career\n\nprospects. This underscores the need for targeted youth development programs focusing on vocational training,\n\ncareer counseling, and job placement services to help bridge the gap between education and employment.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The primary needs identified within the Shona community include access to affordable housing, better integration\n\ninto the labor market, financial support for business development, and political representation. External support in\n\nthese areas, such as land allocation advocacy, education and skills training programs, and inclusion in national safety\n\nprograms, is essential for providing a foundation for sustained community growth and prosperity. Partnerships with\n\ngovernment agencies, NGOs, and international organizations could be instrumental in addressing these needs,\n\nensuring that the Shona community has the necessary resources and support to thrive in a post-citizenship era.\n\n## **Nationality as a tool for development**\n\n**Inclusive national identification systems are recognized as key enablers for development.** Allowing people to\n\nestablish and verify their identity is often a prerequisite for access to services and economic opportunities and\n\nexercising a range of rights, such as property ownership and public participation. For governments and businesses,\n\n**[www.unhcr.org](http://www.unhcr.org/)** 9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "can promote reconciliation and a sense of national unity and identity. [[11]]\n\n**Acquiring Kenyan citizenship has significantly improved the socioeconomic conditions of the Shona community,**\n\n**enabling them to access essential services, economic opportunities, and social benefits.** Before gaining citizenship\n\nin 2020, the Shona faced numerous barriers due to their stateless status, which excluded them from formal\n\nemployment, financial services, and government programs. Following the acquisition of nationality, the Shona now\n\nenjoy access to legal identity documents, increased livelihood opportunities, better health insurance access, and\n\nfinancial inclusion. These positive changes align with findings in the academic literature, which highlight the\n\nimportance of citizenship in reducing poverty and enhancing human capital by allowing individuals to leverage\n\ngovernment services, engage in formal employment, and contribute more productively to society.\n\n**Citizenship has also enhanced the Shona community’s labor market potential, though employment rates are still**\n\n**recovering from the adverse impacts of the COVID-19 pandemic.** Legal identity has granted many Shona\n\nindividuals the ability to seek formal employment and broaden their economic activities beyond informal self\nemployment. This has enabled the community to slowly rebuild their livelihoods post-pandemic. Nevertheless,\n\nemployment levels remain below pre-pandemic rates, especially for women, reflecting challenges in labor market", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "reintegration. The employment rate for women dropped from 72 percent in 2019 to 61 percent in 2024, suggesting\n\na slower recovery in sectors traditionally dominated by women. Overall, the Shona community’s gradual\n\nemployment recovery indicates the value of citizenship in building economic resilience and highlights the need for\n\ncontinued support to ensure inclusive recovery for all members of the community.\n\n**The Shona experience provides valuable lessons for addressing statelessness and promoting inclusive**\n\n**development for other stateless communities globally.** Gaining citizenship has been transformative for the Shona,\n\ngranting them access to a range of socioeconomic benefits and reducing their vulnerability to poverty and exclusion.\n\nThis case underscores the importance of grant of nationality to stateless populations and its potential to unlock\n\neconomic and social opportunities that drive sustainable development. As more countries around the world seek\n\nto address statelessness through legislative changes and the expansion of citizenship rights, the Kenyan experience\n\nwith the Shona can serve as a model. By providing legal identity and addressing structural barriers, policymakers\n\ncan empower stateless communities to participate more fully in their economies and societies, leading to improved\n\nlivelihoods, social cohesion, and economic growth. These findings have broader implications for the many stateless", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "individuals in the East, Horn, and Great Lakes of Africa region, as well as the millions of stateless people globally,\n\ndemonstrating that resolving statelessness is not only a matter of human rights but also of inclusive economic\n\ndevelopment.\n\n## **Recommendations**\n\n**1. Develop holistic, long-term policy frameworks that address the socioeconomic and human rights dimensions of**\n\n**statelessness.**\n\nNational governments and international stakeholders should adopt inclusive development policies that go beyond\n\nlegal recognition to ensure the full social and economic integration of stateless and formerly stateless populations.\n\nThis entails ensuring equitable access to education, healthcare, employment, and social protection services for\n\nnewly recognized citizens. Governments should be encouraged to introduce targeted social safety nets and\n\nemployment support programs to reduce poverty and promote resilience among stateless and formerly stateless\n\ngroups. UNHCR and the World Bank, in partnership with national governments and NGOs, can provide technical\n\n**[www.unhcr.org](http://www.unhcr.org/)** 10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**2. Support economic integration and empowerment of the Shona community through targeted financial inclusion**\n\n**programs and skills development initiatives.**\n\nThe Government of Kenya, in collaboration with development partners, is encouraged to implement tailored\n\nfinancial inclusion strategies to help the newly recognized Shona citizens access credit, savings, and insurance\n\nproducts that are crucial for business growth and household resilience. Establishing entrepreneurship training\n\nprograms, mentorship networks, and providing access to capital for small and medium-sized enterprises would\n\nempower the Shona community to leverage their skills in wood carving, basket weaving, and other economic\n\nactivities. Additionally, promoting employment opportunities in the formal sector for Shona individuals through job\n\nplacement services and partnerships with the private sector will ensure that the community’s transition to full\n\ncitizenship is accompanied by tangible improvements in income and welfare.\n\n**3. Reaffirm and expand Kenya’s commitment to eradicating statelessness by adopting a comprehensive national**\n\n**strategy.**\n\nBuilding on the success of granting citizenship to the Makonde, Shona, and Pemba communities, a National Action\n\nPlan to identify and resolve remaining cases of statelessness, including the estimated 9,800 individuals who remain\n\nstateless, offers many benefits. This strategy should include amending the relevant Sections of the Kenya", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Citizenship and Immigration Act to remove the time-limitation for registration of stateless persons and align the\n\ndefinition of a stateless person with the definition in the 1954 Convention; acceding to the two UN statelessness\n\nconventions and ratification of the Protocol to the African Charter on Human and Peoples' Rights on the Specific\n\nAspects of the Right to a Nationality and the Eradication of Statelessness in Africa; enhancing birth registration\n\nsystems to prevent future statelessness; simplifying procedures for nationality application and documentation; and\n\nimplementing community outreach programs to raise awareness about the rights and entitlements of citizenship.\n\nThe Kenyan government is also encouraged to continue its leadership on this issue by advocating for regional and\n\ninternational cooperation to address statelessness, including sharing best practices and lessons learned from its\n\nnational experience.\n\n**4. Promote international collaboration and investment to support stateless populations in gaining nationality and**\n\n**legal identity and accessing essential services.**\n\nGovernments, international organizations, and development agencies should work together to ensure stateless\n\nindividuals receive the support needed to obtain nationality and legal documentation. This includes nationality law\n\nand policy changes enabling stateless people to acquire nationality, investing in robust and inclusive civil registration", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "systems, conducting community-based identification campaigns, and facilitating cross-border collaboration to\n\naddress statelessness among migrant populations. Providing technical and financial assistance to countries with\n\nhigh numbers of stateless individuals would further enable the implementation of comprehensive registration and\n\ncitizenship programs. The new Global Alliance to End Statelessness provides a multi-stakeholder platform to\n\nstrengthen these joint efforts.\n\n**5. Expand efforts to generate evidence on the impacts of statelessness and post-citizenship integration globally,**\n\n**with a focus on Kenya.**\n\nStakeholders should invest in continued, longitudinal studies to track the Shona community’s socioeconomic\n\nprogress over time and take advantage of the pre- and post-citizenship data that is already available, enabling a\n\nbetter understanding of post-citizenship outcomes. Additionally, research should be expanded to include the\n\nMakonde, Pemba, Rundi and other communities alongside the national population, providing comparative insights\n\n**[www.unhcr.org](http://www.unhcr.org/)** 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["pre- and post-citizenship data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "## **Works cited**\n\n[1] UNHCR, ‘Global Report 2023 - Executive Summary’, UNHCR, Geneva, Jun. 2024. Accessed: Jul. 13, 2024. [Online]. Available:\nhttps://reporting.unhcr.org/global-report-2023-executive-summary\n\n[2] UNHCR, ‘This is our home: stateless minorities and their search for citizenship’, 2017, [Online]. Available:\nhttps://www.unhcr.org/ibelong/wp-content/uploads/UNHCR_EN2_2017IBELONG_Report_ePub.pdf\n\n[3] D. S. Weissbrodt and C. Collins, ‘The Human Rights of Stateless Persons’, _hrq_, vol. 28, no. 1, pp. 245–276, Feb. 2006, doi:\n10.1353/hrq.2006.0013.\n\n[4] E. Abuya, ‘Out of the Shadows: towards ensuring the rights of stateless persons and persons at risk of statelessness in Kenya’, Kenya\nNational Commission on Human Rights (KNCHR) and UNHCR, Nairobi, Jul. 2010. [Online]. Available:\nhttps://www.unhcr.org/media/out-shadows-towards-ensuring-rights-stateless-persons-and-persons-risk-statelessness-kenya\n\n[5] B. Blitz and M. Lynch, ‘Statelessness and the Benefits of Citizenship: A Comparative Study.’, _Statelessness and Citizenship: A Comparative_\n_Study on the Benefits of Nationality_, Jan. 2011.\n\n[6] S. W. Goodman, ‘Citizenship Studies: Policy Causes and Consequences’, _Annu. Rev. Polit. Sci._, vol. 26, no. 1, pp. 135–152, Jun. 2023, doi:\n10.1146/annurev-polisci-051921-102729.\n\n[7] UNHCR, ‘High-Level Segment on Statelessness: Results and Highlights’. Accessed: Oct. 10, 2024. [Online]. Available:\nhttps://www.unhcr.org/ibelong/high-level-segment-on-statelessness-results-and-highlights/", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "[8] F. N. P. Nimoh, T. P. Beltramo, J. R. Fix, F. K. Appler, U. J. Pape, and L. A. Rios Rivera, ‘Understanding the Socioeconomic Conditions of\nthe Stateless Shona Community in Kenya: Results from the 2019 Socioeconomic Survey’, Dec. 2020. Accessed: Apr. 02, 2024. [Online].\nAvailable: https://documents.worldbank.org/en/publication/documentsreports/documentdetail/356511608745182603/Understanding-the-Socioeconomic-Conditions-of-the-Stateless-Shona-Community-inKenya-Results-from-the-2019-Socioeconomic-Survey\n\n[9] U. J. Pape _et al._, ‘How COVID-19 Continues to Affect Lives of Refugees in Kenya : Rapid Response Phone Survey - Rounds 1 to 5’, World\nBank Group, Washington, D.C., Policy Note 166098, Oct. 2021. Accessed: Oct. 10, 2024. [Online]. Available:\nhttps://documents1.worldbank.org/curated/en/202201637042522937/pdf/How-COVID-19-Continues-to-Affect-Lives-of-Refugeesin-Kenya-Rapid-Response-Phone-Survey-Rounds-1-to-5.pdf\n\n[10] Kenya National Bureau of Statistics and ICF, ‘Kenya Demographic and Health Survey 2022. Key Indicators Report’, KNBS and ICF,\n\nNairobi, Kenya, and Rockville, Maryland, USA, 2023. Accessed: Oct. 10, 2024. [Online]. Available: https://www.knbs.or.ke/wpcontent/uploads/2023/08/Kenya-Demographic-and-Health-Survey-2022-Key-Indicators-Report.pdf", "output": {"entities": {"named_data": ["2019 Socioeconomic Survey", "Kenya Demographic and Health Survey 2022"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION \nBRIEF \nSUDAN \nJULY 2023 \nWomen and children line up to fetch water from the tap stands in Um Sangour camp, White Nile State. Services in the camp have been \nstretched as a result of the influx of new arrivals from Khartoum and other conflict areas. Photo: UNHCR/Isadora Zoni", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The security situation in Sudan continues to deteriorate, with increased armed\nconfrontation, including in urban centres such as Khartoum, Geneina, Zalingei, Nyala, El\nFasher and rising criminality reported in various locations following the general breakdown\nof law and order [1] . The overall situation continues to severely impact the lives of civilians,\nwho remain exposed to repeated violations in the conduct of hostilities by all parties to the\nconflict. The disregard for basic principles of international humanitarian law (IHL) has led\nto estimated casualties of more than 3,000 civilians, including several refugees and IDPs\nin attacks in Khartoum and North Darfur [2] . Safe passage to secure areas within and outside\nthe country remains highly problematic, with first hand credible reports of hundreds of\ncivilians, including refugees, being denied safe exit from urban and semi-urban areas of\nKhartoum by armed groups and prevented from crossing the borders to seek international\nprotection.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The security situation in Sudan and the consequent collapse of State institutions has\nresulted in the lack of basic services, food, medical supplies, fuel, communications and\ncash. Negotiated humanitarian ceasefires or self-declared truces, have been repeatedly\nviolated by the parties as the fighting rages on. When a lull in fighting enables civilians to\naccess markets, little primary commodities are available due to widespread looting of\ncivilian and public places. This also affected humanitarian premises and warehouses and\nimpeded the delivery of much needed assistance to the displaced population. Illegal\noccupation of and attacks on public institutions, notably health and education facilities,\ngovernmental offices, banks, and other structures essential for the survival of the\npopulation continue to be reported, depriving all civilian population of access to the most\nessential and life-saving services. Escalation of conflict in Khartoum and in the Darfur\nregion, including inter-communal clashes, as well as the re-ignition of other conflict\ndynamics between the Sudanese Armed Forces and other non-State armed groups in the\nKordofan region have triggered additional massive internal displacement of civilians. As of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_**Refugees and asylum seekers fleeing fighting in Khartoum and other conflict areas continue to arrive in White Nile State in search of safety and security.**_\n_**Photo: UNHCR/Ibrahim Mohamed**_\n\nIn Darfur, the confrontation between the Sudanese Army and the RSF has unleashed a\nmarkedly ethnic or intercommunal dimension igniting tribal rivalries. This situation affected\nparticularly El Geneina with reports of widespread ethnically motivated killings and\ndeliberate attacks on civilians and civilian objects, as well as other severe human rights\nviolations. Also in West Darfur, mass killings have been reported in Misterei during attacks\nthat led to the complete destruction of the town. Given the lack of information from West\nDarfur due to the absence of reliable means of communication and the lack of access for\nhumanitarian partners, the reported incidents of human rights violations from West Darfur,\ngathered through the accounts of refugees in neighbouring Chad, likely represent a fraction\nof the overall devastating effects of the conflict. In addition, the past months have seen a\nsignificant escalation of violence in North Darfur, which, amongst others, has resulted in\nthe complete destruction of the Kassab IDP camp in Kutum, with a significant number of\nfatalities, injuries, several reported cases of sexual violence and the displacement of\n22,000 people. Clashes in Tawila town, resulted in the displacement of large numbers of\nIDPs to El Fasher. In South Darfur, areas in and around Nyala saw fierce clashes, the\ndestruction of government and humanitarian facilities and severe challenges in reaching\nthe population with humanitarian assistance. 15 people were reportedly killed by\nunexploded ordnance (UXO) in Otash IDP camp. In Zalingei, Central Darfur, aerial attacks\nas well as tactics adopted by parties to the conflict have left the city under military siege.\nThis, coupled with the suspension of telecommunications has severely hampered\nhumanitarian access. This situation is the same in other cities and locations (Geneina,\nGarsila, Habila, Foro Baranga and Masterei), where freedom of movement for the civilian\npopulation has been severely curtailed, with populations unable to meet their basic needs.\nAs a result, almost 180,000 Sudanese, mainly from Darfur, have sought asylum in Chad,\nenduring perilous journeys and reportedly at times denied safe passage by parties to the\nconflict.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The severe escalation of the conflict has led to significant destruction of UNHCR property.\nDuring the month of June, both UNHCR offices in Khartoum were destroyed, the UNHCR\nwarehouse in Al Obeid was looted, and other UNHCR warehouses remain inaccessible or\nhave recently been looted such as in South Darfur. In locations accessible to humanitarian\nworkers, UNHCR and partners continue to scale up humanitarian delivery despite capacity\nlimitations. UNHCR has established a small operational presence in Wadi Halfa while\nscaling up its footprint in Wad Madani, Kosti, Gedaref, Kassala and Port Sudan. Further in\nNorth Darfur, in collaboration with the sectors, UNHCR has been able to continue delivering\nNFIs to IDP sites in El Fasher as well as conduct protection monitoring of new arrivals from\nKutum and Tawilla.\n\nUNHCR 3", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The conflict in Sudan continues to steadily trigger waves of population outflows to\nneighbouring countries. At the beginning of July, based on registration and government\nstatistics, almost 490,000 newly arrived refugees and asylum seekers were recorded in\nneighbouring Chad, Egypt, Ethiopia and South Sudan, and almost 142,000 South\nSudanese were recorded as returnees in their country of origin.\n\n_[Source: UNHCR Sudan Situation, Operational Data Portal https://data.unhcr.org/en/situations/sudansituation](https://data.unhcr.org/en/situations/sudansituation)_\n\nWithin Sudan, the internal movement of refugees fleeing insecurity and active conflict\ncontinued unabated, particularly from Khartoum, which traditionally hosted the highest\nnumbers of refugees, mainly from Eritrea and Ethiopia [4] . UNHCR estimates that more than\n187,000 refugees may have left their areas of residence to seek safety in other regions of\nSudan unaffected by the conflict. UNHCR continues to work with concerned authorities,\nincluding the Commissioner for Refugees (COR) to identify their locations to provide the\nneeded support. The tracking and registration so far conducted highlighted how White Nile\nState has been one of the main areas of initial destination, followed by other States of East\nSudan, notably Gedaref and Kassala, where refugees continue to seek shelter in the\nexisting sites, as well as the Red Sea State.", "output": {"entities": {"named_data": ["UNHCR Sudan Situation, Operational Data Portal"], "descriptive_data": [], "vague_data": ["registration and government\nstatistics"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_Source: UNHCR Internal movement of refugees in Sudan as of 16 July 2023_\n\n_4 See Sudan Protection Brief, June 2023 and UNHCR Sudan- Overview of refugees and asylum seekers distribution and_\n\n_movement in Sudan Dashboard as of 16 July 2023_\n\nUNHCR 4", "output": {"entities": {"named_data": ["UNHCR Internal movement of refugees in Sudan"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Khartoum, require refugee registration. In the Kordofan region, COR is registering refugees\nnewly displaced from Khartoum into North and South Kordofan states, with challenges in\nWest Kordofan due to the security situation and lack of fuel. In East Darfur, the arrivals of\nmore than 1,000 South Sudanese, predominantly from Khartoum, has been reported with\nsmaller numbers of new arrivals to Al Lait settlements in North Darfur.\n\nThe rainy season has started to affect registration operations in several locations. During\nthe second half of June, access to hosting sites in the west part of White Nile State has\nbeen hindered by weather conditions, while in Gedaref notably in Um Rakuba, strong\nstorms have damaged the registration centre and some equipment, affecting the planned\nregistration activities. It is anticipated that disruptions to registration activities will persist\nand access will remain constrained for the period of the rainy season which is expected to\nlast a few more weeks.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "On the internal displacement front, the number of IDPs continues to surge, including\nsecondary or tertiary movements. Current interagency estimates for internal displacement\nare higher than the recorded IDP movements of the last four years combined. It is estimated\nthat over 2.4 million people have been displaced within Sudan since the onset of the current\nconflict, mainly from the most severely hit States of Khartoum and Darfur. The fleeing\npopulation is currently recorded predominately in River Nile (16.57%), Northern (14.71%),\nWhite Nile (10.82%), and Sennar (8.66%) states. [5] The conflict in the Darfur Region has led\nto massive displacement within the region, and particularly in West Darfur, where the\nmajority of the population remain at heightened risk of violence. Given the significant\nescalation of localised violence in certain locations, the Kordofan Region has also become\nboth a hosting area particularly for IDPs who fled Khartoum, and an area of origin of newly\ndisplaced population.\n\n### Protection Risks\n\n##### Safety and security", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Civilians caught up in conflict.** Despite high level advocacy and several attempts to call\nparties to the conflict to respect the basic rules of international humanitarian law, the\nneglect towards imperative principles on the protection of the civilian population remains a\nconstant feature of the situation in Sudan. Coupled with a security vacuum also conducive\nto increased criminality, the situation in Sudan continue to claim lives and to take a toll on\nthe security and safety of the population. According to the Federal Ministry of Health by\nmid-June, more than 3,000 fatalities and at least 6,000 injuries were recorded because of\nthe conflict [6] . Actual figures are undoubtedly higher, with unverified reports of deaths in El\nGeneina alone exceeding that figure.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The past weeks have witnessed serious incidents affecting civilians across the country,\nlargely stemming from the confrontation between the SAF and the RSF, but also linked to\nthe inter-communal conflict affecting particularly West Darfur. Over 100 IDPs lost their lives\nin the clashes in and around IDP sites in Kutum, North Darfur, where tribal dynamics\naugmented the level of violence, including incidents of sexual and gender-based violence.\nReportedly, attempts to evacuate injured civilians to nearby health facilities were thwarted\nby hostile acts and carjacking of vehicles and ambulances [7] .\n\n_[5 Displacement Tracking Matrix, Sudan Situation Report 11, July 2023 https://dtm.iom.int/reports/sudan-situation-report-12](https://dtm.iom.int/reports/sudan-situation-report-12)_\n_6 UNHCR-led Protection Sector Sudan, At a Glance: protection impacts of the Conflict, Update no. 8, 2 July 2023. Available on_\n\n_request._\n_7_ _UNHCR-led Protection Sector Sudan, Darfur Protection of Civilians Flash Update North Darfur: Attacks on Kutum town and_\n_Kassab IDP camp, 8 June 2023. Available on request._\n\nUNHCR 5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Since the onset of the conflict, the urban area of Khartoum continued to see the highest\nlevels of violence and grave breaches of international humanitarian. On 25 June, 28\nrefugees hosted by Sudan were killed, and additional refugees injured, in the outskirt of\nKhartoum, when the area in which they lived was suddenly engulfed by the fighting [9] .\n\n##### Child Protection and Gender-Based Violence", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Gender-based Violence, including conflict-related sexual violence.** Reported incidents\nof conflict-related sexual violence continues to rise alongside the escalation of the conflict,\nin an atmosphere of total impunity. On 10 June, the Sudanese Unit for Combating Violence\nAgainst Women and Children (CVAW) reported that since the outbreak of the conflict,\nseveral documented cases of sexual assault in Khartoum and in Darfur were recorded, with\nmost survivors being between the ages of 12 and 17 years [10] . They warned how foreign\nwomen and girls, notably Ethiopian and Eritrean refugees or migrant women and girls, were\nconsidered at heightened risk in Khartoum-North and Omdurman. In the Kordofan region,\nat least 12 incidents of GBV against refugees and IDPs were reported by community\nleaders in one day, all allegedly perpetrated by the warring parties against women and girls\nbetween the ages of 15 and 60, including one pregnant refugee woman. Multiple reports\nof conflict-related sexual violence perpetrated by parties to the conflict but also as a result\nof the escalating inter-communal violence coupled with the collapse of law and order are\nemerging in the Darfur region, including from refugee women and girls who have crossed\ninto Chad and Egypt.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_8 UNHCR-led Protection Sector Sudan, Darfur Protection of Civilians Flash Update - North Darfur: Attacks on Tawila town, 19_\n\n_June 2023. Available on request._\n_[9 UNHCR Press Release - 4 July 2023 - After 28 refugee deaths in Khartoum, UNHCR urges Sudan’s warring parties to allow](https://www.unhcr.org/news/press-releases/after-28-refugee-deaths-khartoum-unhcr-urges-sudan-s-warring-parties-allow-safe)_\n\n_[safe passage for civilians](https://www.unhcr.org/news/press-releases/after-28-refugee-deaths-khartoum-unhcr-urges-sudan-s-warring-parties-allow-safe)_\n_10 Sudan: top UN officials sound alarm at spike in violence against women and girls, 5 July 2023_\n\n_https://www.who.int/news/item/05-07-2023-sudan-top-un-officials-sound-alarm-at-spike-in-violence-against-women-and-girls_\n\nUNHCR 6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "neighbouring countries disclose episodes of violence and cite the risk of conflict-related\nsexual violence as one of the main reasons for flight. Accounts of combatants looting\ncivilians’ homes and deliberately targeting women and girls, as well as cases of harassment\nat checkpoints, and of sexual violence and exploitation during their journeys to\nneighbouring countries are emerging in Chad, Ethiopia, South Sudan and Egypt.\n\nWithin Sudan, the rising number of survivors have limited possibilities to approach service\nproviders and report incidents, given the lack of public health services, closure of many\nfacilities and the unavailability of specialised staff and health personnel, from both the\ngovernment and the humanitarian community sides. Consequently, there is considerable\ndelay in providing medical services to survivors, including clinical management of rape and\nadministration of PEP kits, with detrimental effects on possible HIV transmission and on\nthe rate of unwanted pregnancies.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "While several UN agencies and other humanitarian actors strive to maintain or re-establish\nservices, including through courageous local organisations that remain active in conflictaffected areas, critical gaps remain in human resources for much needed psychosocial\nsupport interventions, availability of safe and confidential spaces for women and girls as\nwell as medical supplies. Amidst this overall stretched capacity, service providers remain\noperational in Blue Nile, White Nile, Kassala, Gedaref, Port Sudan and Wad Madani.\n\n**Impact of violence on children.** While child protection issues predated the current crisis,\nthe situation of children caught up in the ongoing conflict is now considered alarming.\nChildren have been caught up in the fighting, killed and injured in aerial attacks in Darfur\nand Khartoum. They are put at risk due to the widespread presence of unexploded\nordnances and other remnants of war in urban areas and near IDP sites. Children who\nsurvived attacks, such as the one against the IDP site in Kutum in North Darfur, and\nmanaged to reach safety, suffer from the psychological consequence of having witnessed\ndeadly violence, and their needs often remain unattended due to lack of functional services\nand social protection structures.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Children and youth have reportedly been abducted and forcibly recruited into armed forces\nand groups. Unverified but credible reports of abduction of children from hosting sites have\nemerged from West Darfur, as well as from the Kordofan region, from where UNHCR\nreceived information that at least 25 refugee youth of unverified age were kidnapped by a\nwarring party in early June 2023.\n\nInvoluntary family separation due to the killing of family members, or because of the flight,\nremains a widespread child protection risk. In pre-conflict times, protection partners\nestimated that 3 to 5 % of IDP children in Sudan were unaccompanied and this figure is\nnow believed to have exponentially increased. Opportunities for alternative care\narrangements are precarious, given the disintegration of social and community-based\nsafety nets. Children in institutional care, generally not a solution in the best interests of\nthe child, are even more at risk due to the conditions of these social institutions, mostly left\nunattended, with power outages, and with lack of resources and personnel.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The humanitarian needs of children in all locations continue to grow, due to the collapse of\nsocial services and the halting of humanitarian assistance. In the Darfur region, the death\nof children in camps due to food insecurity and malnutrition is frequently reported by local\norganizations. A surge in the number of measles cases was also reported in the East and\nNorth Darfur regions, with ten deaths reported so far and with families resorting to\ntraditional medicine in the absence of access to functioning hospitals.\n\nUNHCR 7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "attendance and access have been completely disrupted, also due to the reported\ndestruction and occupation of several education facilities. Course of studies have been\ninterrupted, with children deprived of the possibility to attend final exams in many areas of\nSudan.\n\n##### Access to safety", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Situation at the borders.** Increased access restrictions to some neighbouring countries\nrisk to further aggravate the humanitarian situation and needs within the country. At the\nborder with Egypt continuous flows of displaced Sudanese continue to arrive with the hope\nof being authorised to cross into Egypt after obtaining the necessary visa documentation.\nGiven the length of the process, the situation of the stranded population in Wadi Halfa is\nincreasingly concerning due to the absence of accommodation, facilities, medical services\nand other adequate humanitarian services. Considering the visa restrictions imposed by\nneighbouring countries, it is anticipated that more individuals will make their way to Wadi\nHalfa and Port Sudan to access available consular services with the risk of remaining\nstranded until they are granted entry visas and arrange onward transportation via land or\nair. Very little information is available on the route to Libya due to the escalation of conflict\nin North Darfur and the remoteness of the border areas.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In West Darfur, where conflict has been particularly intense, there have also been\nconfirmed reports provided by refugees in Chad that civilians were prevented from leaving\nEl Geneina for their safety and security to safe locations, including across the border into\nChad. Several reports of looting, assaults, harassment, and other violations against\ncivilians seeking to flee the violence in El Geneina, West Darfur, into Chad have also been\nreceived. These incidents underscore the immense challenges faced by civilians seeking\nsafety and security away from conflict zones.\n\n_**The situation in the gathering sites in Wadi Halfa is becoming increasingly dire due to the lack of humanitarian activities. The continuous arrival of**_\n_**individuals hoping to cross into Egypt is exerting immense pressure on local resources. Photo: UNHCR/Rached Cherif**_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### Response\n\n**Scale-up of registration and verification activities.** UNHCR and COR continued the\nverification of refugees that have self-relocated from Khartoum and other states to East\nSudan and White Nile State. So far, over 38% of new arrivals have been recorded, while\nregistration activities have resumed in Kassala state after a period of suspension. UNHCR\nteams on the ground continue their efforts in supporting authorities on population fixing,\nverification, and registration, including to ensure proper identification and unhindered\naccess to services.\n\n**Support to new arrivals from Khartoum.** UNHCR has increased its operational presence\nand protection activities in Wad Madani as a first point of contact for refugees fleeing\nKhartoum. The operational presence in Wad Madani will continue to grow and serve as a\nhub to support those fleeing ongoing conflict in Khartoum. In White Nile State, UNHCR is\ncoordinating with WFP the food assistance for all refugees arriving from Khartoum,\nestimated to be around 144,000.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Support in border areas.** Through frequent missions, UNHCR is increasing its field\npresence in Wadi Halfa, actively engaging communities stranded in this border area while\nwaiting to process their documentation for entry into Egypt with the consular authorities.\nProtection monitoring and awareness activities are being carried out on relevant issues\nsuch as risks of GBV and trafficking in persons. The team distributed NFI kits to 410\nhouseholds at ten spontaneous gathering sites and further distributions are ongoing,\ntargeting the most vulnerable households and individuals. Interventions for persons with\nspecific needs in those sites also include the provision of mattresses, repair and installation\nof fans, as well as lighting.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Protection monitoring.** Where security and mobility permit UNHCR continues to monitor\nthe situation of persons of concern through direct visits. UNHCR also strives to access\nhard-to-reach areas remotely, connectivity allowing. UNHCR has also monitored the\nmovement of refugees and IDPs within Darfur and Kordofan region through phone calls or\ninteractions with peers who fled to the same areas. In East Sudan, protection monitoring\nwas conducted on the ground in Babikri, Shagarab and Wad Sharifay camps. It includes\nthe provision of information on available services and means to access them to new arrivals\nas well as older refugees. Remote protection monitoring is ongoing in the Darfur region but\nhampered by connectivity challenges. Physical protection monitoring is done where\npossible, such as in El Fasher, North Darfur and sporadically in South Darfur. In East\nDarfur, together with refugee communities and COR, the protection situation for refugees\nis also being monitored.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "seeker children at risk. In Gedaref, identification of children at risk continues through a\nvariety of means including house to house protection monitoring, community-based\nprotection networks, community volunteers, protection desks, referrals from partners\nincluding Government and at the point of registration. Case management is done in line\nwith the Best Interests Procedure through BID panels and case management taskforces,\nwhose members have undergone training either by UNHCR or partners. Children in need\nof mental health and psychosocial support (MHPSS) are supported through Child Friendly\nSpaces and Child Rights Clubs, in collaboration with the Education Working Group which\nprovides recreational activities. Procedures are in place for onward referral for more\nspecialist medical care where required. On IDPs, UNHCR engages with partners through\nrelevant coordination bodies, at national and state levels, mainly through State level\nCouncils for Child Welfare (with SCCW, Child Protection partners, UNICEF and other\nstakeholders) and with the Education cluster and Child Protection AOR led by UNICEF at\nnational level. These structures continue highlighting the dire situation of children and\nadvocate for additional support, including through targeted funding in the field of child\nprotection and education, as well as in health and MHPSS. In Eastern Sudan, the Telling\nthe Real Story project continues to work with adolescent boys and girls, sensitizing them\non the risks of irregular onward movement and their vulnerability to trafficking.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Gender-based violence.** In Eastern Sudan, services to support GBV survivors are\nongoing. In Gedaref, 100% of survivors who approached service providers have received\ncounselling and services in accordance with case management procedures. In the\nKordofan region, El Obeid, UNHCR is working with community leaders to find alternative\nlocations for women who have experienced conflict-related sexual violence, along with their\nfamilies. Due to lack of access, UNHCR is not able to relocate refugees to safer areas out\nof Khartoum, the Darfur or the Kordofan region but the Office continues to establish\nlinkages between survivors and Community-Based Protection Networks to help them reach\nthe support needed.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Two-way Communication with Communities and feedback mechanisms.** Hotlines\nremain functional but dependent on the network connectivity. 1,072 calls were attended\nduring the reporting period through active hotlines dedicated to Khartoum, the Darfur\nRegion, the Kordofan Region, East Sudan and the Southern Corridor. Orientation training\nfor hotline operators was held to further strengthen capacities to respond and make\neffective referrals. Nevertheless, providing information on access to services in areas of\n[conflict remains a major challenge. Apart from the hotlines, UNHCR’s Help Page for Sudan](https://help.unhcr.org/sudan/)\ncontinues to be updated on a regular basis, and the [Telegram channel provides helpful](https://t.me/UNHCRSudaninformationchannel)\ninformation in English and Arabic. UNHCR is working to improve the complaints feedback\nmechanism across all offices and is engaged with the inter-agency AAP working group in\nthe planned conduct of community consultations to further improve the response across\nthe country. Measures will be put in place to enable remote consultations exercise (by\nphone) in areas where there is no physical presence.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Durable solutions.** UNHCR Sudan continues to capitalize on outreach efforts to identify\nrefugees who may have been already identified and considered for resettlement and\ncomplementary pathways options, some who may have been already in an advanced stage\nof the procedures, including scheduled for imminent departure. Prior to the conflict, 3,250\nindividuals had been submitted to various resettlement countries for consideration, and 849\nindividuals were under various stages of resettlement processing. Cooperation with\nUNHCR neighbouring offices to track those refugees who were in the resettlement process\nand have crossed the border continue, to ensure that pathways towards solutions are not\ninterrupted. In parallel, the office in Sudan has used the information collected to advocate\nfor continued acceptance of cases from Sudan and is closely coordinating with relevant\npartners the way forward, including on exit procedures the situation permitting.\n\n**Protection from Sexual Exploitation and Abuse (PSEA).** UNHCR Sudan continues to\nwork with partners and government counterparts in areas where operations are ongoing,\nto ensure that the IASC’s Six Core Principles Related to SEA and the Secretary-General’s\n\nUNHCR 10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "staff. UNHCR continues to disseminate information and to sensitize persons of concern,\npartners and government staff on PSEA, referral pathways and reporting mechanisms.\nUNHCR is also an active member of the PSEA network.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Coordination and advocacy.** In response to the significant conflict-driven internal\ndisplacement that has occurred, UNHCR has begun scaling up protection coordination in\nareas where new IDP responses are now underway. UNHCR and partners are also taking\npragmatic approaches to address the mixed nature of displacement flows, i.e. refugees\nand IDPs. Protection Sector coordination platforms have been established in White Nile,\nKassala, Gedaref, Madani, and Wadi Halfa. Additionally, in areas where protection sector\ncoordination platforms were already present, outreach to protection partners planning to\ninitiate responses in new areas affected by conflict and displacement is underway. In\naddition, since the outbreak of the conflict on 15 April, UNHCR-led Protection Sector has\nissued nine flash updates titled ‘At a Glance: Protection Impacts of the Conflict’. These\nweekly updates, primarily based on desk review of secondary data, highlight the severity\nof the protection impacts experienced by the civilian population as a result of the conflict.\nThe specific protection concerns around the intensity of the violence and its intercommunal\ndimension in Darfur were underscored in dedicated Darfur Protection of Civilians Advocacy\nNotes. These have included two regional-level advocacy notes and one focused\nspecifically on West Darfur. Flash updates were also issued in relation to the attacks on\nKutum and Tawila in North Darfur. Finally, key advocacy messages related to urgent\nprotection of civilians’ priorities were drafted in preparation for the high-level pledging event\nto support the humanitarian response in Sudan on 15 June, in collaboration with the Global\nProtection Cluster.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### Challenges & Opportunities\n\n**Security and other constraints for humanitarian operations.** The security situation\ncontinues to deteriorate putting the civilian population more at risk. Aerial attacks and\nbombardments have increased, affecting infrastructures, services, health facilities\nnecessary for the implementation of humanitarian activities, notably offices, warehouses,\naccommodations of humanitarian staff. As a result, despite the determination to deliver\nassistance where needs are most acute, more humanitarian services continue to be\nsuspended, especially in Darfur and Kordofan regions, aggravating further risks of\ndeprivation and death. Communication blackouts continue to hamper operations in the\nDarfur region, with several locations completely cut-off for more than a month.\n\n**Returns under adverse circumstances.** In their quest for safety, and with increased\nborder restrictions, many refugees see no other options but to flee the ongoing conflict in\nSudan and are eventually opting to return to their country of origin under adverse\nconditions. While many of those individuals continue to fear return, they see no other\noptions but to flee the ongoing conflict in Sudan.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Civil documentation. Registration of vital events and delivery of civil documentation**\ncontinue to be suspended in many parts of the country, affecting new-born registration and\naccess to public services where available. Many Sudanese, refugees and asylum seekers\nhave either left documentation behind or lost it during flight.\n\n**Safe passage and freedom of movement.** As a result of the current humanitarian\nsituation, refugees have engaged in self-relocation in various parts of the country to escape\nrisks of conflict and seek safety. Regulations in place requires refugees to request a permit\nprior to leaving their place of registration. UNHCR has undertaken strong advocacy with\nauthorities in the East to lift this requirement, considering the current context. UNHCR has\nsuccessfully advocated for the release of 49 refugees from detention in Gedaref after they\n\nUNHCR 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Severe economic constraints.** As the conflict enters its third month, banking systems\nhave yet to resume. Thousands of people who have savings in Sudanese banks continue\nto be unable to access them and rely on in-kind support or on selling personal assets.\nHumanitarian assistance is also challenged, especially with the inability to disburse cash\nassistance due to the unavailability of banking. Liquidity challenges hamper the efficient\ndelivery of most forms of humanitarian assistance, even where partners are fully\noperational.\n\n**Rainy season and its effect on humanitarian operations.** The rainy season started in\nmany parts of Sudan, with risks of floods in many areas where refugees and IDPs are\nsettled, and humanitarian operations are ongoing. During the rainy season, roads become\nlargely impassable, preventing access and assistance delivery to refugee camps, notably\nin Eastern Sudan and White Nile State. Flash flooding can also lead to the destruction of\nshelters and other infrastructure. Further, in Darfur, areas where refugees and IDPs live,\nhave become inaccessible due to poor road conditions and seasonal flooding.\n\n_**Ongoing flood mitigation work in various camps in White Nile State in anticipation of the rainy season. Photo: ADRA**_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION BRIEF > SUDAN (JULY 2023)\nUNHCR  \n13 \nKey Messages \n■ Sudan is facing a complex, large-scale protection crisis characterised by \nunprecedented levels of violence and human rights violations inflicted on \ncivilians.  The situation requires an urgent and comprehensive response that \nhas as primary focus the protection of civilians including IDPs, refugees, and \nasylum seekers.  \n■ The parties to the conflict should immediately comply with their obligations \nto protect civilians as prescribed by international humanitarian and human \nrights law, including by bringing an end to the conflict and by restoring peace \nand security across the country.  \n■ Parties to the conflict are urged to ensure the commitment by all persons \nacting under their instructions, direction or control to abide by their core \nobligations under international human rights and humanitarian law. This \nincludes, inter-alia, upholding at all times the principle of proportionality and \ndistinction between military and civilian objects; refraining from attacks \nexpected to cause excessive civilian harm in relation to the military; allowing \ncivilians to voluntarily and safely leave areas of active conflict and \nguaranteeing the protection of those that may not be able to do so; facilitating \ncivilians’ access to humanitarian assistance and enabling unhindered access \nto humanitarian organisations.  \n■ As stressed recently and jointly by all humanitarian actors operating in the \ncountry, there is an immediate need to put an end to all forms of gender-\nbased violence, including sexual violence being used in armed conflict as a \nweapon of war. Parties to the conflict are urged to immediately end rape and \nother forms of sexual violence and respect the integrity of women and girls. \nEqually, parties to the conflict must refrain from recruiting children and from \nexploiting the vulnerable conditions of those without parental care and \nsupport.   \n■ There is an urgent need to scale up GBV prevention, mitigation and response \nservices within Sudan, for survivors of various forms of conflict-related GBV, \nincluding through the provision of specialized and survivor-centred support. \nThese efforts should also capitalize on the presence and strength of local \norganizations – including those led by women – who continue to \ncourageously provide frontline responses in the conflict affected areas.  \n■ The protection of all children affected by the conflict remains a priority and \nneeds to be reinforced. Increased outreach to identify and support children \nat risk is required, also for sensitisation purposes on the presence of \nexplosive hazards. Strengthened family tracing and alternative care \narrangements are urgently needed, as well as investment in specialized \nservices for children at heightened protection risks, particularly survivors of \nviolence, children in need of alternative case and family tracing/reunification", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION BRIEF > SUDAN (JULY 2023)\nUNHCR  \n14 \nReinforcement of interventions and services to address the critical mental \nhealth and psychosocial distress of children and caregivers, including \nthrough community-based support mechanisms, is also required.  \n■ UNHCR continues to appreciate the efforts made by the Government of \nSudan to facilitate the upscaling of humanitarian response in areas not \naffected by the conflict, including refugee-hosting areas in the Southern and \nEastern parts of the country. UNHCR encourages national and local \nauthorities to continue granting freedom of movement to refugees and other \ncivilians on the move, to help them reach and stay in safe areas within or \noutside Sudan, and to monitor and curb exploitative activities by \nunscrupulous smuggling and trafficking rings.  \n■ UNHCR is appealing to all neighbouring countries and those further afield to \ncontinue allowing those fleeing conflict and persecution to find safety across \nborders by keeping their borders open. The reduction of bureaucratic or \nfinancial requirements that hinder access to asylum for persons with \ninternational protection needs is needed.  \n■ UNHCR expresses gratitude for the pledges made by several donors in \nsupport to the humanitarian efforts in Sudan. Donor support to the \nrequirements of the Protection Sector and its partners, including local \npartners and community-based organisations who remained active \nthroughout the conflict is particularly needed.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION BRIEF > SUDAN (JULY 2023)\nUNHCR  \n15 \n▪\n\nPROTECTION BRIEF \nSUDAN \nJuly 2023\n\nUNHCR Sudan\n\ndata.unhcr.org/en/country/sdn   \nwww.unhcr.org", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR Sudan\n\ndata"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "# **Population** **Data Analysis**\n\n### **Regional Bureau** **for Southern Africa**\n\n**September 2022**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Population Data Analysis – September 2022\n\nOverview\n\nAs of the end of September 2022, Southern Africa hosts around **8.6 million persons of concern (PoCs) to UNHCR** .\nThis includes 1.1 million refugees and asylum-seekers and 6.9 million internally displaced persons (IDPs), as well as\nothers of concern, refugee returnees and IDP returnees. **The Democratic Republic of Congo (DRC) represents 77**\n\n**per cent of the regional data.**\n\nRefugees, Asylum-Seekers and Others of concern\n\nThe region hosts **785,000 refugees, 278,000**\n\n**asylum-seekers** **and** **36,000** **others** **of**\n\n**concern** . Among those 1.1 million PoCs, 74 per\ncent of them are from the countries outside of\nthe Southern Africa region. [1] The top five\ncountries of origin are Central African Republic\n(243,000), Rwanda (242,000), DRC (228,000),\nBurundi (84,000) and Ethiopia (61,000).\n\nInternally Displaced Persons\n(IDPs)\n\nIn Southern Africa, there are **6.9 million**\n\n**internally displaced persons (IDPs)** . Most of\nthem are conflict-induced, 6.4 million, but there\nare also natural disaster-induced IDPs, 0.5\nmillion. The data on IDPs are reported in DRC,\nCongo, Mozambique and Zimbabwe (see\nFigure 1).\n\nDurable Solutions\n\nFigure 1. Number of IDPs in RBSA by Cause as of 30 September 2022\n\n---\n[1] The Southern Africa region refers to the 16 countries covered by the Regional Bureau for Southern Africa of UNHCR including Angola,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In 2022, 15,402 persons have been repatriated voluntarily thus far, with 2,805 persons repatriated in September.\nThe largest group of returns was from Zambia to the DRC, with 2,159 persons repatriated in September. Further,\nthere has been notable movements from Angola to the DRC in September, where, 47 families of 143 persons were\nrepatriated to destinations in the DRC such as Kassai Province, Kinshassa, Kwilu Province and Goma.\n\nFrom January to September 2022, 4,497 individuals of 1,063 cases were submitted for resettlement consideration.\nAmong these cases, half were male and the other half were female with 57 per cent being children under age 18.\n\nIn the same period, 2,067 individuals departed for resettlement. A half of the departed cases were male and another\nhalf were female, similarly to submitted cases. Most persons have been considered for resettlement to the United\nStates (3,417). The highest number of those departed also headed to the United States (1,285). In terms of country of\nasylums, the highest number of submitted applications were from Malawi (1,438) and Zambia (1,367). Congolese\n(DRC) is the nationality with the highest number of submissions (3,618) and departures (1,729).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Data Sources: proGres v4 (PRIMES) hosts the data of refugees and asylum-seekers in 11 countries. In South Africa, the data are managed by the\ngovernment. In Angola, DRC, Zambia and Zimbabwe, some portions of the data are external. For IDP data, the source of DRC’s IDP figure is the OCHA;\nthe sources in Mozambique and Zimbabwe are the Displacement Tracking Matrix (DTM) of the IOM; and the source in the Republic of Congo is the\ngovernment, the Ministry of Social Affairs and Humanitarian Action (MASAH).", "output": {"entities": {"named_data": ["proGres v4 (PRIMES)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "REGIONAL BUREAU FOR SOUTHERN AFRICA\n\n###### **POPULATION OF CONCERN IN SOUTHERN AFRICA REGION**\n\n30 September 2022\n\n**PoCs IN SOUTHERN** **AFRICA REGION***\n\n**521,512** **REF**\n\n**41,283** **REF**\n\n**13,762** **ASY**\n\n**1,291** **ASY**\n\n**5,526,022** **IDP**\n\n**REF**\n\n**ASY**\n\n**OOC**\n\n**RET**\n\n**KEY FIGURES**\n\n## **8,572,919**\n\nTotal Population of concern\n\n###### **1,099,585**\n\nRefugees, asylum-seekers, other\nof concern & returnees**\n\n**785,119**\n\n**278,090**\n\n**36,165**\n\n**211**\n\n###### **7,473,334**\n\nConflict induced and Natural Disaster IDPs\n\nNatural Disaster IDPs\n\n**528,466**\n\n**7%**\n\n**6,419,356**\n\n**86%**\n\nIDPs RET\n\n**525,512**\n\n**7.0%**\n\ndo not imply official endorsement or acceptance by the United Nations\n\n**Author: UNHCR DIMA - RSA** Contact : rsarbdima@unhcr.org **Source:** UNHCR Primes, Government, IOM, OCHA, UNHCR\n\n**Author: UNHCR DIMA - RSA** Contact : rsarbdima@unhcr.org **Source: REF, ASY, OOC, RET** (UNHCR PRIMES, Government); **IDP DRC** (OCHA); IDP Zimbabwe & Mozambique (IOM); **IDP ROC** (Government, Ministry of Social Affairs and Humanitarian Action (MASAH).\n\n*PoCs = Persons of Concern   ** REF = Refugee; ASY = Asylum-seeker; OOC = Other person of concern; RET = Returnee. DRC = Democratic Republic of the Congo  ROC = Republic of the Congo Date of creation : 30 September\n2022\n\nFor more information visit: UNHCR Data Portal", "output": {"entities": {"named_data": ["UNHCR Primes"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "CROSS BORDER MOVEMENTS KEY FIGURES\n\n200 61 118 21\n\nREGIONAL BUREAU FOR SOUTHERN AFRICA\n\n**PERSONS OF CONCERN INVOLVED IN CROSS BORDER MOVEMENTS IN SOUTHERN AFRICA**\n\nAs of 30 September 2022\n\nMAP OF THE CROSS BORDER MOVEMENTS WHERE THE FLOW INVOLVED 3 POCs AND MORE *\n\n**Inward movements**\n\n**into the region**\n\n**Total cross**\n\n**border**\n\n**movements**\n\n**Intra region**\n\n**movemens**\n\n**Outward**\n\n**movements from the**\n\n**region**\n\nInward\n\n31%\n\nOutward\nmovements\n\n34%\n\nBefore 2019 2019 2020 2021 2022\n\nexclusion.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "REGIONAL BUREAU FOR SOUTHERN AFRICA\n\n**2022 VOLUNTARY REPATRIATION IN SOUTHERN AFRICA REGION**\n\nAs of 30 September 2022\n\nMAP OF VOLUNTARY REPATRIATION WHERE THE FLOW INVOLVE 5 POCs OR MORE\n\nKEY FIGURES\n\n15,042\n\nTotal Individuals\nrepatrieted since\n\nJanuary 2022\n\nIndividuals repatrieted\n\n**within Southern Africa**\n\n**Region** since January\n\n2022\n\n6,786 8,256\n\nIndividuals repatrieted\n\n**from Southern Africa**\n\n**Region** to other countries\noutside of the region since\n\nJanuary 2022\n\nVOLREP* WHERE THE FLOW INVOLVE 5 POCs OR MORE\n\nTRENDS\n\nMONTHLY REPATRIATION SINCE JANUARY\n\n**3,874**\n\nANNUAL REPATRIATION SINCE 2019\n\n*VolRep = Voluntary Repatriation  PoCs = Persons of Concern Source : UNHCR PRIMES   Author : DIMA/RBSA    Data sources: UNHCR PRIMES.  For more information or to contribute, please contact UNHCR RBSA DIMA (rsarbdima@unhcr.org)", "output": {"entities": {"named_data": ["UNHCR PRIMES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "REGIONAL BUREAU FOR SOUTHERN AFRICA\n\n**DEMOCRATIC REPUBLIC OF THE CONGO REFUGEES SITUATION**\n\nAs of 3 0 September 2022\n\n**Author: UNHCR DIMA - RSA** Contact : rsarbdima@unhcr.org **Source:** UNHCR Primes, Government, UNHCR", "output": {"entities": {"named_data": ["UNHCR Primes"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "RESETTLEMENT KEY FIGURES\n\n**Submitted Cases**\n\n1,063 4,497\n\n**Cases** **Case Members**\n\n**Active Cases**\n\n986 4,219\n\n**Case** **Case Members**\n\n**Submitted Cases Member by**\n\n**Country of Submission**\n\nREGIONAL BUREAU FOR SOUTHERN AFRICA\n\n**PERSONS OF CONCERN INVOLVED IN RESETTLEMENT IN SOUTHERN AFRICA**\n\nAs of 30 September 2022\n\nMOVEMENTS OF GROUPS OF 10 OR MORE RESETTLEMENT CASE MEMBERS\n\n**Country of Origin** **Country of Submission** **Country of Resettlement**\n\nMAP OF THE DEPARTURES BY COUNTRY OF SUBMISSION\n\n**Departure Cases**\n\n337 2,067\n\n**Case** **Case Members**\n\n**Quota**\n\n6,483 69%\n\n**Allotcated Quota** **% of Submission vs Quota**\n\n**Balance (Quota/Submission) :** 1,986\n\n**Departure Cases by Age and Gender**\n\n**ZAM**\n\n**MLW**\n\n**RSA**\n\n**ZIM**\n\n**MOZ**\n\n**ANG**\n\n**BOT**\n\n**NAM**\n\n**COB**\n\n**COD**\n\n**USA**\n\n**SWE**\n\n**NZL**\n\n**FIN**\n\n**NOR**\n\n**CAN**\n\n**AUL**\n\n**FRA**\n\n**493**\n\n**303**\n\n**205**\n\n**28**\n\n**27**\n\n**13**\n\n**11**\n\n4%\n\n9%\n\n9%\n\n26%\n\n0%\n\n0-4\n\n5-11\n\n12-17\n\n18-59\n\n60+\n\n3%\n\n12%\n\n9%\n\n1%\n\n26%\n\n**Submitted Cases Members**\n\n**by Top 10 Country of Asylum**\n\n**MLW**\n\n**ZAM**\n\n**3,417**\n\n**1,438**\n\n**1,367**\n\n**Departure Cases Members**\n\n**by Top 10 Country of Asylum**\n\n**1,247**\n\n**464**\n\n**169**\n\n**122**\n\n**16**\n\n**15**\n\n**14**\n\n**12**\n\n**2**\n\n**2**\n\n**RSA**\n\n**ZIM**\n\n**NAM**\n\n**BOT**\n\n**MOZ**\n\n**MAD**\n\n**519**\n\n**487**\n\n**355**\n\n**157**\n\n**96**\n\n**36**\n\n**Submitted Cases Members**\n\n**by Top 10 Country of Origin**\n\n**Departure Cases Members**\n\n**by Top 10 Country of Origin**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Data sources: UNHCR PRIMES, UNHCR Resettlement Statistics Report.   For more information or to contribute, please contact UNHCR RBSA DIMA (rsarbdima@unhc\n\n**1,729**\n\n**154**\n\n**57**\n\n**53**\n\n**34**\n\n**13**\n\n**12**\n\n**4**\n\n**3**\n\n**2**\n\n**COD**\n\n**BDI**\n\n**SOM**\n\n**RWA**\n\n**TUR**\n\n**ZAM**\n\n**ETH**\n\n**UGA**\n\n**ANG**\n\n**AFG**\n\n**COD**\n\n**BDI**\n\n**SOM**\n\n**RWA**\n\n**ETH**\n\n**PAK**\n\n**TUR**\n\n**CAR**\n\n**BOT**\n\n**ERT**\n\n**3,619**\n\n**263**\n\n**235**\n\n**220**\n\n**40**\n\n**36**\n\n**19**\n\n**14**\n\n**8**\n\n**8**", "output": {"entities": {"named_data": ["UNHCR PRIMES", "UNHCR Resettlement Statistics Report"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "REGIONAL BUREAU OF SOUTHERN AFRICA\n\nPROTECTION MONITORING DASHBOARD ON INCIDENTS IN DRC AND MOZAMBIQUE\n\nAs of 30 September 2022\n\nMAP SHOWING THE NUMBER OF REPORTED PROTECTION INCIDENTS PER\n\nProvince in Mozambique\n\n(MOZ) covered by\nprotection monitoring", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "|KEY FIGURES<br>DRC MOZ<br>¯760Kilometers Number of incidents * Number of victims Number of provinces covered<br>by Protection Monitoring<br>4,573 2,930 10,358 101,869 7 1<br>7,503 112,227 8<br>Top 4 incident perpetrators in<br>Victims by Age and Gender Top 5 protection incidents<br>Mozambique (%)<br>47% 53%<br>Armed Groups 89% 53,142 59,085 Homicide 30%<br>Assault and battery 29%<br>Unknown 7.8% 16,326 60+ 13,331<br>15,573 18-59 17,460 Extortion of property 16%<br>Other(s) 2.7% 9,927 12-17 8,134 Destruction of<br>13%<br>properties<br>6,497 5-11 7,704<br>Host Community 0.5% Looting 12%<br>4,819 0-4 12,456<br>Top 4 incident perpetrators in **<br>Victims by Population group top 5 Provinces affected with protection<br>Democratic Republic of Congo (%)<br>incidents<br>Host Community 52% Cabo Delgado 1,837<br>Ituri 1,540<br>Other(s) 44% 99.88%<br>Kasai Central 1,438<br>Unknown 2%<br>Tanganyika 624<br>0.11% 0.01%<br>Armed Groups 2% IDP Refugee Asylum seeker Kasai 529<br>* Number of incidents covered only for those reported in the month of September 2022. The timing of incident occurrence could be September 2022 or before.<br>* *Protection monitoring in Mozambique is done only for IDPs while in DRC it is done for both IDPs and Refugees.|Distr<br>B|\n|---|---|\n|Victims by Population group<br>7<br>8 <br>1 <br>**KEY FIGURES**<br>Top 4 incident perpetrators in <br>Democratic Republic of Congo (%)<br>Top 4 incident perpetrators in <br>Mozambique (%)<br>Victims by Age and Gender<br>0.5%<br>2.7%<br>7.8%<br>89%<br>Host Community<br>Other(s)<br>Unknown<br>Armed Groups<br>2%<br>2%<br>44%<br>52%<br>Armed Groups<br>Unknown<br>Other(s)<br>Host Community<br>Top 5 protection incidents <br>529 <br>624 <br>1,438 <br>1,540 <br>1,837 <br>Kasai<br>Tanganyika<br>Kasai Central<br>Ituri<br>Cabo Delgado<br>top 5 Provinces affected with protection <br>incidents<br>12%<br>13%<br>16%<br>29%<br>30%<br>Looting<br>Destruction of<br>properties<br>Extortion of property<br>Assault and battery<br>Homicide<br>Number of victims<br>101,869 <br>112,227 <br>2,930 <br>7,503 <br>4,573 <br>Number of provinces covered <br>by Protection Monitoring<br>DRC<br>MOZ<br>760<br>**Kil**ometers<br>¯<br>4,819 <br>6,497 <br>9,927 <br>15,573 <br>16,326 <br>12,456 <br>7,704 <br>8,134 <br>17,460 <br>13,331 <br>53,14**2**<br>47**%**<br>~~**59**~~,~~**085**~~ <br>53%<br>0-4<br>5-11<br>12-17<br>18-59<br>60+<br>10,358 <br>**<br>* *Protection monitoring in Mozambique is done only for IDPs while in DRC it is done for both IDPs and Refugees.<br>*<br>* Number of incidents covered only for those reported in the month of September 2022. The timing of incident occurrence could be September 2022 or before.<br>99.88%<br>0.11%<br>0.01%<br>IDP<br>Refugee<br>Asylum seeker<br>Number of incidents|Provinc<br>Repu<br>50 km|", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Data sources: UNHCR PRIMES.  For more information or to contribute, please contact UNHCR RBSA DIMA (rsarbdima@unhcr.org)\n\nThe boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations", "output": {"entities": {"named_data": ["UNHCR PRIMES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "\"\nÛ\nE\n\"\nÛ\nE\n\"\nÛ\nE\n\"\nÛ\nE\n\"\nÛ\nE\n\"\nÛ\nE\n\"\nÛ\nE\n\"\nÛ\nE\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC\n\n#\nC", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "#\nC\nIlemi\nTriangle\n(SSD)\nBOTSWANA\nCENTRAL\nAFRICAN\nREPUBLIC\nMALAWI\nSOUTH AFRICA\nZAMBIA\nZIMBABWE\nUNITED\nREPUBLIC OF\nTANZANIA\nUGANDA\nBENIN\nLESOTHO\nSOMALIA\nBURUNDI\nGABON\nEQUATORIAL GUINEA\nETHIOPIA\nDEMOCRATIC\nREPUBLIC OF\nTHE CONGO\nREPUBLIC OF\nTHE CONGO\nSEYCHELLES\nRWANDA\nMOZAMBIQUE\nMADAGASCAR\nANGOLA\nNAMIBIA\nSAO TOME AND\nPRINCIPE\nCOMOROS\nNIGERIA\nCAMEROON\nMAURITIUS\nKENYA\nESWATINI\nSOUTH SUDAN\nA\nA\nA\nA\nA\nA\nA\nA\nA\nA\nA\nA\nA\nA\nA\nTshikapa\nDundo\nLibenge\nBetou\nGoma\nNampula\nWindhoek\nLilongwe\nLuanda\nMaputo\nLusaka\nBukavu\nHarare\nCape Town\nTongogara\nSolwezi\nBrazzaville\nUvira\nBeni\nAru\nKawambwa\nBunia\nGbadolite\nKaoma\nKinshasa\nKalemie\nGamboma\nYakoma\nPretoria (RO)\nPretoria (LO)\nPemba\nKananga\nMeheba\nMulongwe\nSite du 15 Avril\nBili\nMole\nOsire\nDzaleka\nMongemonge RC\nSangé RC\nMalindza\nModale\nKaka2\nKavimvira TC\nBoyabu\nTongogara\nLóvua\nMantapala\nMayukwayukwa\nMaseru\nMarratane\nBiringi\nLusenda\nMeri\nBouemba\nInke\nNdendere Bukavu\nNkubi\nDukwi\nBele\nWenze\nSidi\nNzakara\nINDIAN OCEAN\nNORTH\nATLANTIC\nOCEAN\nSOUTH\nATLANTIC\nOCEAN\nArabian Sea\nNORTHERN\nKGALAGADI\nATSIMO\nATSINANANA\nSUD-UBANGI\nMASVINGO\nWESTERN\nJWANENG\nEQUATEUR\nKASAI\nCENTRAL\nNGAZIDJA\nCENTRAL\nLUANDA\nITASY\nLIKOUALA\nOSHANA\nANDROY\nKHOMAS\nSELIBE PHIKWE\nNORTHERN\nREGION\nCUVETTE\nCUANZA\nNORTE\nNORTH-WEST\nKASAI\nORIENTAL\nNORTH-EAST\nRODRIGUEZ\nFLACQ\nCOPPERBELT\nNORD-UBANGI\nLUNDA SUL\nLOBATSE\nKUNENE\nMOHELI\nSOFALA\nNIASSA\nTETE\nMONT BUXTON\nGAZA\nMAPUTO\nMASHONALAND EAST\nMUCHINGA\nLUALABA\nMONGALA\nHAUT-KATANGA\nSOUTHERN\nSOUTH-EAST\nNORTH WEST\nNORTH-WESTERN\nMPUMALANGA\nSOUTHERN\nREGION\nNORTHERN CAPE\nEASTERN CAPE\nGAUTENG\nFREE STATE\nATSIMO\nANDREFANA\nKAVANGO\nEAST\nOMUSATI\nHAUT-LOMAMI\nCABINDA\nLEKOUMOU\nPLATEAUX\nMAI-NDOMBE\nSAINT BRANDON\nKONGO CENTRAL\nKASAI\nMOXICO\nNORD-KIVU\nTSHOPO\nAGALEGA\nISLANDS\nKWAZULU-NATAL\nCHOBE\nCUANDO\nCUBANGO\nVAKINANKARATRA\nERONGO\nLOMAMI\nBONGOLAVA\nANALAMANGA\nBETSIBOKA\nZAMBEZI\nCUNENE\nNIARI\nOTJOZONDJUPA\nOSHIKOTO\nNAMIBE\nGHANZI\nANOSY\nBOENY\nOHANGWENA\nBENGO\nHUÍLA\nCENTRAL\nREGION\nBOUENZA\nOMAHEKE\nBAS-UELE\nLUNDA NORTE\nHUAMBO\nKWILU\nTSHUAPA\nCUANZA SUL\nCUVETTE\nOUEST\nMALANJE\nBRAZZAVILLE\nVATOVAVY\nFITOVINANY\nDIANA\nSOFIA\nANALANJIROFO\nSANGHA\nMASHONALAND\nWEST\nMANIEMA\nHAUTE\nMATSIATRA\nWESTERN CAPE\nPORT LOUIS\nSANKURU\nLIMPOPO\nHARARE\nFRANCISTOWN\nBIÉ\nHARDAP\nMIDLANDS\nKOUILOU\nLUAPULA\nMENABE\nKAVANGO\nWEST\nMELAKY\nPOOL\nTAKAMAKA\nSUD-KIVU\nKWANGO\nHAUT-UELE\nZAIRE\nGRAND ANSE PRASLIN\nSOWA\nMASHONALAND\nCENTRAL\nIIKARAS\nMATABELELAND\nSOUTH\nITURI\nTANGANYIKA\nBENGUELA\nMANICALAND\nKWENENG\nLUSAKA\nSAVA\nMATABELELAND\nNORTH\nKGATLENG\nZAMBEZIA\nNAMPULA\nMANICA\nCABO DELGADO\nINHAMBANE\nMAPUTO CITY\nPOINT NOIRE\nKINSHASA\nIHOROMBE\nBULAWAYO\nGABORONE\nEASTERN\nALAOTRA MANGORO\nATSINANANA\nAMORON\nI MANIA\nUÍGE\nLUBOMBO\nMASERU\nSOUTHERN\nCENTRAL\nThe boundaries and names shown and the designa ons used on this map do notimply o\ncial endorsement or acceptance by the United Na ons.\nPrin ng date: 02-11-2022\nSources: UNCS, UNHCR\nAuthor: UNHCR - RBSA DIMA Unit\nFeedback: rsarbdima@unhcr.org\nFilename: rbsa_reference_A2L_2022-11-02\n200km\nA\nA\nA\nA\nUNHCR Field Unit", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "#\nC\n\"\nÛ\nE\nRefugee, centre\nUndetermined boundary\nREGIONAL BUREAU FOR SOUTHERN AFRICA\nREFERENCE MAP / UNHCR PRESENCE\n as of 30 September 2022", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Persons of concern in Southern Africa, Data as of 30 September 2022**\n\nNotes: *'Other' in the location refers to any known location other than camp or settlement sites, covering both urban and rural areas; **self-settled refers to the individuals without available information such as their names and locations, and their locations are categorised to be 'unknown'; those by location in Congo, Democratic Republic of\nthe Congo and Zimbabwe could be different from the numbers operation report due to inconsistency in proGres v4.", "output": {"entities": {"named_data": ["proGres v4"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Regional protection monitoring harmonization outcomes\n\nIn line with the outcomes of the Regional IDP Stocktaking held on 25 February 2022, the Regional Bureau for\nSouthern Africa undertook an ambitious project to harmonize the protection monitoring activities conducted in the\nregion. The envisioned regional harmonization aims to enable an environment that increases persons we serve\nempowerment, inclusion, and protection while strengthening accountability and efficiency in humanitarian and\ndevelopment programs. The regional protection monitoring harmonization focuses to:\n\n§ **Set common definitions of the incident, minimum standards, and objectives of the protection**\n\n**monitoring.**\n§ **Standardize questionnaires to collect data but be flexible enough for field operations to add and amend**\n\n**questions depending on the context and needs.**\n§ **Develop reporting/product templates with a standard design and branding for UNHCR.**\n§ **Develop a narrative to explain externally why and how UNHCR does protection monitoring and identify**\n\n**common indicators for regional reporting.**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The regional protection monitoring harmonization established a regional incident typology framework to efficiently\nanalyze the regional protection monitoring data. To this extent, 7,503 protection incidents with 106,673 victims were\nreported in September 2022 in both countries, which affected 10 provinces in the Democratic Republic of Congo\n(DRC) and 1 province in Mozambique. It is worth mentioning that of the protection incidents reported in September\n2022, some occurred in the same month, and some occurred before September 2022.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The top 5 protection incidents reported in the region are homicide (30 per cent), assault and battery (29 percent),\nextortion of property (16 per cent), destruction of properties (13 per cent), and looting (12 per cent). Also, those\nprotection incidents affected 99.88 percent of Internally displaced persons (IDPs), 0.11 percent of refugees, and 0.01\nper cent of asylum-seekers. The protection incidents affected more males (53 per cent) than females (47 per cent),\nand the protection incidents affected the age group (0 to 17), children, who represent 44 percent of all victims\nreported in September 2022. In addition, the perpetrators of those protection incidents are armed groups (46 per\ncent), host communities (26 per cent), others (23 per cent), and unknown (5 per cent). It is worth mentioning that 1.3\npercent of the regional population, compared to September 2022 regional data, was affected by the protection\nincidents reported in September 2022.", "output": {"entities": {"named_data": [], "descriptive_data": ["September 2022 regional data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In conclusion, the protection incidents evidenced in the analysis partially overview the situation in both countries\nwhere the data were collected. The number of protection incidents and victims is higher than what is represented\nin this report for September 2022. In DRC, the new protection monitoring platform still needs to be fully deployed\ncountrywide, and efforts are underway to have data that will reflect the real situation. In Mozambique, the data\nreported in this report concerns only one province, Cabo Delgado, where the protection monitoring is implemented.\nEfforts are also underway to have functioning protection monitoring in the rest of the affected provinces.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "# **NEW ISSUES IN REFUGEE RESEARCH**\n\n**Research Paper No. 153**\n\n# **Climate change and forced migration**\n\n**Etienne Piguet**\n\nProfessor of Human Geography\nInstitute of Geography\nUniversity of Neuchâtel\nSwitzerland\n\nE-mail: Etienne.Piguet@unine.ch\n\nJanuary 2008\n\n**Policy Development and Evaluation Service**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Policy Development and Evaluation Service**\n\n**United Nations High Commissioner for Refugees**\n\n**P.O. Box 2500, 1211 Geneva 2**\n\n**Switzerland**\n\n**E-mail: hqpd00@unhcr.org**\n\n**Web Site: www.unhcr.org**\n\nThese papers provide a means for UNHCR staff, consultants, interns and associates, as well\nas external researchers, to publish the preliminary results of their research on refugee-related\nissues. The papers do not represent the official views of UNHCR. They are also available\nonline under ‘publications’ at <www.unhcr.org>.\n\nISSN 1020-7473", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Introduction**\n\nThe term \"environmental refugees\" was first coined in 1985 as a report title for the\nUnited Nations Environment Programme (El-Hinnawi 1985). It has since been\nwidely diffused in both political and academic circles (Castles 2002). This growing\nconcern of the international community about the consequences of migration resulting\nfrom environmental deterioration was reinforced in 1990 by the publication of the\nfirst UN intergovernmental report on climate change which stated that \"The gravest\neffects of climate change may be those on human migration as millions will be\ndisplaced\" (Intergovernmental Panel on Climate Change 1990, 20).\n\nIn 1993, the prediction that there would be 150 million environmental refugees by the\nend of the 21st century, as forecast by Norman Myers, further fuelled the fear of mass\nmigrations (Myers 2003). In the review \"Population and Environment\" this respected\nenvironmentalist wrote, four years later, \" the issue of environmental refugees (...)\npromises to rank as one of the foremost human crises of our times\" (Myers 1997, p.\n175).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Filmmaker Roland Emmerich dramatized this fear in 2004, in a scene from the film\n\"The Day After Tomorrow\" where American citizens flee en masse from lightning\nand a terrible climatic disturbance from the north only to find themselves – and here is\nthe irony – running up against the fences of the American-Mexican frontier. Today,\nfor the Israeli geographer Nurit Kliot, who has led a synthetic overview on the\nsubject, \"the fear of mass migration of environmental refugees has become a major\nissue in the international community\" (Kliot 2004, 69).\n\nWith the increasing certainty of global warming, the more precise term of \"climate\nrefugee\" has been swiftly diffused in public discourse. This is exemplified by the\n\"Citizen's guide to climate refugees\" found on the website of the Australian NGO,\nFriends of the Earth (Friends of the Earth Australia 2007). Other instances include the\nrecent series of reports entitled \"Avec les réfugiés climatiques\" by the French\nphotographic collective Argos (http://www.collectifargos.com/ - visited 21 July\n2007).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The links between climate and human migration are not new (Beniston 2004). Thus,\nthe droughts of the 1930s in the plains of the American Dust Bowl forced hundreds of\nthousands of migrants towards California, and those that struck the Sahel between\n1969 and 1974 displaced millions of farmers and nomads towards the cities.\nNotwithstanding the present media focus, the amount of systematic research on\nenvironment and migration remains quite limited.\n\nThere is much vagueness surrounding the concepts employed, the underlying\nmechanisms involved, the number of persons affected and the geographical zones\nconcerned. The use by numerous authors of the term \" refugee \" has also led to certain\nconfusion because it evokes the juridical status recognized by UN Convention of 1951\nreferring to any person having a \"well-founded fear of being persecuted for reasons of\nrace, religion, nationality, membership of a particular social group or political\nopinion\". Although it is clear that environmental reasons are absent from this list, if\nenvironmental deteriorations due to human influence on the climate generate forced\n\n1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "migration flows, then the question of the rights of victims to a form of protection will\nbecome unavoidable.\n\nIn this article we will first try to understand why the environmental aspect of the study\nof migration and refugees has, up until now, been neglected. We will then propose a\ndefinition of population movements induced by environmental factors, before\nconcentrating on climate aspects by providing a synthesis of results put forward by\nresearchers. Finally, we will examine forecasts for future developments.\n\n**A neglected topic**\n\nFor many years, population geography has, of course, acknowledged the role played\nby environmental factors in explaining the history of population and the emergence of\ncities. Thus, for mankind, the passage across the Bering Straits from America 13,000\nyears ago was possible due to the low sea levels of the ice Age, while the Medieval\nClimate Optimum which lasted between 8th and 13th centuries AD seems to have\nstimulated the population of Polynesia by making navigation relatively easy thanks to\nregular winds and clear skies (Perch-Nielsen 2004, 39).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Paradoxically, it also appears that the desertification of the Sahara and the Arabian\npeninsula has played an important part in the densification of the population on the\nbanks of the Nile, and has consequently contributed to the birth of ancient Egyptian\ncivilization (Hammer 2004, 238). Fifty years ago, the strong link between\npluviometry and population density has been shown, for example, in the American\nGreat Plains (Robinson, Lindbergh, and Brinkman 1961).\n\nWith industrialization, however, the importance of the role given by population\ngeographers to the environment declined progressively. Already at the end of the 19th\ncentury, the famous \"migration laws\" of E. G. Ravenstein held that economic factors\nwere of prime importance. Their pre-eminence was almost exclusive in the\ntheorization of migration flows during the second half of the 20th century (Massey\nand al. 1993).\n\nWhile certain environmental characteristics of areas studied were taken into\nconsideration, generally only the positive factors received any serious attention.\nGreenwood (1969) highlights in this respect the favourable effect of high average\ntemperatures on internal migration on the US mainland, while Graves measures the\neffect of climate mildness in general on migration (1980).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Up until recently, the environment, especially when considered as a negative factor\ninducing forced displacements, has been absent from the study of migrations on\naccount of the dominance of what we can call an \" Economic Paradigm \". One can\nadd that migrations linked to the environment are frequently internal and affect\nSouthern countries. It is noteworthy that these two aspects of migration have been\nneglected by researchers to the advantage of studies in international and North-South\nmigrations.\n\nA similar result stems from the \" Political Paradigm \" that characterizes the specific\nstudy of refugees ( _Refugee studies_ ). The latter give only limited attention to the link\nbetween environment and migration and often reduce the object of enquiry to political\n\n2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "refugees, as defined under the 1951 UN Convention. It is consequently not surprising\nthat surveys of refugee studies only give very limited attention to environmental\naspects, except for the degradation that refugees might cause themselves (Black and\nRobinson 1993; Richmond 1988; Zolberg, Suhrke, and Arguayo 1986).\n\n**A problematic concept**\n\nOne last element that may have curbed the study of links between environment and\nmigration is that several researchers have rejected the very concept of environmental\nrefugees (Black 2001). Rightly highlighting the shaky empirical character and sloppy\nnature of most work on the subject, they have brought to the fore problems arising\nfrom a unidirectional link between environmental changes and migrations in the face\nof well-established results from research on population flows. For Castles, \"the term\nenvironmental refugee is simplistic, one-sided and misleading. It implies a\nmonocausality which very rarely exists in practice (…) [Environmental and natural\nfactors] are part of a complex pattern of multiple causality, in which [they] are closely\nlinked to economic, social and political ones.\" (Castles 2002, 5).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["surveys of refugee studies"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Numerous works confirm this: when environmental deteriorations cause\ndisplacements, they are often the by-product of economic, demographic or political\nfactors (Hugo 1996). Moreover, vicious circle phenomena are very frequent and it is\nnot easy to isolate primary causes. Hence, population displacements will induce\nenvironmental problems that will have an effect on conflicts which themselves risk to\nexacerbate environmental deterioration (Hagmann 2005).\n\nThere is agreement today that natural factors are not the sole cause of migration and\nthat the economic, social and political situation of the zone under threat can,\ndepending on the case, increase or decrease the flow of migrants. Apart from the\nscientific error of oversimplifying the processes taking place, the danger here is also\none of “evacuating political responsibility by overplaying the hand of nature\"\n(Cambrézy 2001, 48).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Another serious criticism has been addressed to the advocates of the environmental\nrefugee concept and, in particular, to Norman Myers and his estimation of a potential\n150 million refugees. They are accused of brandishing the spectre of a flood of\nmigrants towards rich countries, thus reinforcing the position of governments that\nhave policies of closed borders and are hostile to refugees. For MacGregor: \"In so far\nas the term environmental refugee conflates the idea of disaster victim and refugee, its\nuse brings with it the danger that the key features of refugee protection could be\nundermined and the lowest common denominator adopted.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Because environmental can imply a sphere outside politics, use of the term\nenvironmental refugee may encourage receiving states to treat the term in the same\nway as economic migrants to reduce their responsibility to protect and assist\" (1993,\n162). The High Commissioner for Refugees, being very aware of this risk of\nconfusion between political and non political refugees, has always treated with the\nupmost prudence the idea of including environmental motivations in the international\ndefinition of refugees, even if he also deems this category of the population as a\npossible part of his protective mandate toward displaced persons within states (IDPs)\n(see number 127, vol. 2, 2002 of the review \"Refugees Magazine\").\n\n3", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Even if they have dampened the enthusiasm of certain researchers, reservations\nregarding the concept of environmental refugees seem to be fully justified. They have\nobliged the scientific community to be mindful of the consequences of their\nterminological choices and point to the need for clear definitions of the different\naspects of the phenomenon.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Indeed, a considerable number of terminological variants have been used by\nresearchers to refer to persons fleeing climate hazards, and more generally,\nenvironmental disturbances. While the term environmental refugees (sometimes\necological refugees) was more frequent in the English language (El-Hinnawi 1985;\nJacobson 1988; Myers 1993; Myers 1997; Westing 1992) during the 1990s as well as\nin German ( _Umweltflüchtling_ (Bächler 1994; Richter 1998)) and French (réfugiés de\nl’environnement (Gonin and Lassailly-Jacob 2002)), more neutral terminology has\nemerged vis-à-vis the 1951 Convention, such as environmental or ecological\nmigrants, ecomigrants or ecomigrations (Wood 2001). As time has passed, the\nnumber of push factors included under this terminology has become greater. While El\nHinnawi in 1985 focussed on deterioration of soils and rural exodus, Jacobson\nbroadened the definition to include persons displaced by development projects (The\nThree Gorges Dam...) or industrial accidents (Bhopal, Chernobyl...) (Jacobson 1988).\nToday, the acronyms EIPM (Environmentally Induced Population Movements) and\nEDP (Environmentally displaced person) are well suited to describe a general\ncategory of migration movements where the environmental factor is decisive, but not\nnecessarily unique. As Lonergan notes (1998), five groups of factors can be singled\nout as environmental push elements that might lead to migration:", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "1. Natural disasters;\n\n2. Development projects that involve changes in the\nenvironment;\n\n3. Progressive evolution of the environment;\n\n4. Industrial accidents; and\n\n5. Environmental consequences due to conflicts.\n\nIn the following part of the paper we are going to concentrate on factors 1 and 3,\nwhich concern environmental changes that are relatively independent of short-term\nhuman activity and that might be linked to climate change. Migrations linked to\ndevelopment projects, industrial accidents or conflicts are, on the other hand, singular\nevents directly linked to human activities and as such not easy to anticipate or\nsummarize.\n\n**Past experiences and the consequences of global warming**\n\n\"Greater resource scarcity, desertification, risks of droughts and floods, and rising sea\nlevels could drive many millions of people to migrate\". This alarming prediction\nappears in the review of the economic consequences of global warming delivered to\nthe British government by Sir Nicholas Stern at the end of November 2006 (Stern\n2006, 111). One year earlier, the authorities of Papua New Guinea appeared on the\n\n4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "front page of the British newspaper The Guardian in a leading article entitled \"The\nFirst Refugees of Global Warming,\" announcing their decision to progressively\nevacuate all one thousand inhabitants of the small atoll of Carteret (Kilinailau) which\nis being slowly submerged by rising seas.\n\nWhile it is extremely difficult to elaborate scientific predictions by combining climate\nand migration models (Perch-Nielsen 2004), the expected consequences of climate\nchange can be enumerated and compared to past experiences so as to establish a list of\nthe populations most at risk and the possible resulting emigration flows. Three\nconsequences of climate warming, as forecast in the latest report of the IPCC for the\nend of the 21st century, appear to be the most threatening potential causes of\nmigrations (Intergovernmental Panel on Climate Change 2007b):\n\n- The increase in the strength of tropical hurricanes and the\nfrequency of heavy rains and flooding, due to the rise in\nevaporation with increased temperatures.\n\n- The growth in the number of droughts, with evaporation\ncontributing to a decrease in soil humidity, often associated\nwith food shortages.\n\n- The increase in sea levels resulting from both water\nexpansion and melting ice.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "While the first two consequences are the direct result of sudden natural disasters, the\nthird is a long-term process, which, as we will see, has very different possible\nimplications in terms of migrations. We leave aside other effects of global warming\non health or the viability of certain economic activities that may have additional\nconsequences for migrations but which remain subject to speculation.\n\n_Hurricanes, torrential rains and floods_\n\nThe impact of hurricanes and floods on population displacement is among the easiest\nto identify, as they manifest themselves in a brutal and direct manner. Particularly\nwell publicised, the flooding due to Hurricane Katrina, in August 2005, necessitated\nthe evacuation of hundreds of thousands inhabitants of New Orleans while tens of\nthousands of others, primarily Afro-Americans, remained trapped in the city due to a\nlack of transport amenities (Cresswell 2006).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "While we know approximately the number of persons affected by flooding worldwide\n(106 million, on average, between 2000 and 2005 according to the _International_\n_Disaster Database_ ), and by hurricanes (38 million), the total number of people\nthreatened by an eventual increase of this kind of disaster is, however, very difficult to\nestimate (EM-DAT). No climate model is able to predict with accuracy whether or not\nthe affected zones will be densely populated and whether the damage will have tragic\nconsequences.\n\nApart from this difficulty of forecasting, the studies carried out after such events tend\nto relativize their effects in terms of migration in general, and long-term migration in\nparticular. Living mainly in poor countries, the victims have little mobility (Lonergan\n\n5", "output": {"entities": {"named_data": ["EM-DAT"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "1998) and the majority of the displaced return as soon as possible to reconstruct their\nhomes in the disaster zone (Kliot 2004; Naik, Stigter, and Laczko 2007). The results\nfrom numerous research projects conducted worldwide on the subject tend to confirm\nthis point with remarkable regularity. Thus, a synthesis of results on migration choices\nof victims of natural disasters displaced in eighteen sites confirms, with rare\nexceptions, the strong propensity to return (Burton, Kates, and White 1993).\n\nIn a much more indirect and incomplete fashion, studies on persons seeking asylum in\nEurope indicate no correlation between asylum applications and natural disasters\nrecorded in the zones of departure. On the contrary, a significant link is confirmed\nregarding the political situation in these same zones (Neumayer 2005).\n\nAccording to some authors, the case of Bangladesh nevertheless remains an important\ncounter example where, in contrast with the image portrayed in most literature on the\nsubject, natural disasters would be the major cause of forced migration (Haque 1997).\nOn a global level however, the general conclusion is that the potential of hurricanes\nand torrential rains to provoke long-term and long-distance migrations remains\nlimited.\n\n_Drought and desertification_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In the recent past, the number of persons affected by drought has been comparable to\nthat of victims of hurricanes and floods (146 million, on average, between 2000 and\n2005 according to the EM-DAT). The latest report of the IPCC predicts increased\nwater shortages in Africa (74 to 250 million people affected in 2020) and Asia:\n\"Freshwater availability in Central, South, East and Southeast Asia particularly in\nlarge river basins is projected to decrease due to climate change which, along with\npopulation growth and increasing demand arising from higher standards of living,\ncould adversely affect more than a billion people by the 2050s.\" (Intergovernmental\nPanel on Climate Change 2007a, 10). Case studies, however, paint a contrasting\npicture. The effect of a lack of drinking and irrigation water on migration is actually\nless sudden than that of the meteorological events mentioned in the previous chapter,\nand only generates progressive departures.", "output": {"entities": {"named_data": ["EM-DAT"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "On one hand, there are many well-known cases of mass population departures, in\nparticular in Africa (Sahel, Ethiopia) but also in South America (Argentine, Brazil), in\nthe Middle East (Syria, Iran), in Central Asia and in Southern Asia. Hammer presents\nan impressive table of forced migration due to droughts and floods during the period\n1973 – 1999 in the Sahel with a maximum figure of one million displaced persons\nduring the drought in Niger in 1985 (Hammer 2004, 232).\n\nHe affirms, \"It seems very likely that hundreds of thousands of people from rural\nSahel regions are displaced every year as a consequence of environmental change and\ndesertification\" (234). Likewise, for Leighton, \"The periodic drought and\ndesertification plaguing northeast Brazil contributed to factors causing 3.4 million\npeople to emigrate between 1960 and 1980\" (2006, 47).\n\nOn the other hand, many researchers strongly relativize the possible direct link\nexisting between drought and emigration by highlighting the fact that the latter, in\ngeneral, is the last resort when all other survival strategies have been exhausted.\n\n6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Consequently, during the 1994 drought in Bangladesh, only 0.4 per cent of\nhouseholds had to resort to emigration (Smith 2001). Other researchers hold similar\nviews to the Nobel Prize winner for Economics, Amartya Sen, in remarking that\nfamines are, in general, only marginally the direct result of environmental factors, but\nmuch rather political ones (Sen 1981) and add that this also holds for migrations.\n\nA multivariate analysis on interprovincial migrations in Burkina-Faso thus shows that\nenvironmental variables, in general, only explain 5 per cent of migrations and drought\nitself only 0.8 per cent (Henry, Boyle, and Lambin 2003). In certain contexts, the\neffect can even be inversed. This was the case in Mali during the drought of the mid\n1980’s: a reduction in international emigration was observed due to the lack of\navailable means to finance the journey (Findley 1994).\n\nThe general conclusion to be drawn here is that forecasts of increased migrations\nlinked to drought related phenomena remain hazardous. Consequently, it would be\ndifficult to put a figure on the magnitude of populations at risk and the eventual\nmigrations arising from global warming induced droughts.\n\n_Rising sea levels_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "While the first two climatic hazards mentioned do not foreshadow massive population\ndisplacements due to climate change, the potential for migration when linked to an\nincrease in sea level is considerable. Contrarily to hurricanes, rains and droughts, this\nphenomenon is virtually irreversible and manifests itself over a long period of time.\nThis could make migration the only possible option for the population affected.\n\nThe localization of the consequences of rising sea levels is a relatively easy task\nbecause the configuration of coastlines, their altitude and population are well known\nand thus easy to integrate into geographical information systems (GIS) that permit\nsimulations and forecasts. Hence, it is possible to calculate, on a global scale, the\nnumber of persons living in low elevation coastal zones and threatened by either\nrising water levels, higher tides or further-reaching waves. McGranahan, Balk and\nAnderson define \"Low elevation coastal zones\" as those situated at an altitude of less\nthan 10 metres (2007).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Even though these zones only account for 2.2 per cent of dry land, they currently are a\nhome for 10.5 per cent of the world population, some 602 million people, of whom\n438 live in Asia and 246 in the poorest countries of the world (other authors furnish\nslightly lower figures totalling 397 million persons, but these, nevertheless, remain\nimpressive (Anthoff, Nicholls, Tol, and Vafeidis 2006)).\n\nIt would certainly be an exaggeration, however, to consider that these hundreds of\nmillions of people are all potential migrants in a near future. The latest report of the\nIPCC describes, of course, the possible melting of Greenland ice cover and the\nconsequent 7-metre rise in sea level, but this would occur over several thousand years.\nOf more concern to us here is the scenario of thermic expansion of the oceans.\nAccording to a future CO2 emission estimate based on continuing economic growth\nbut with a moderation of fossil fuel use (scenario A1B of the IPCC) one could\nwitness an increase of 0.3 to 0.8 metres of the oceans by 2300 (Intergovernmental\nPanel on Climate Change 2007b).\n\n7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "On this basis it seems reasonable to consider populations living at an altitude of less\nthan 1 metre as being directly vulnerable by the next century. A study commissioned\nwithin the framework of the Stern report estimates that this group would comprise a\nconsiderable 146 million people (Anthoff, Nicholls, Tol, and Vafeidis 2006). Mainly\nsituated in the major rivers, deltas and estuaries, the flood zones are particularly\npopulated in South Asia (Indus, Ganges-Brahmaputra etc.) and East Asia (Mekong,\nYangtze, Pearl River, etc.). These two regions account for 75 per cent of the\npopulation at risk. Certain Pacific states such as Tuvalu or Kiribati are, in the shortterm, among the most threatened, as they are situated only centimetres above water.\nAlthough far less populated, they nevertheless have several thousand inhabitants.\n\nThe increase in sea levels appears to be the aspect of global warming that represents\nthe greatest direct threat for numerous populations. Contrary to hurricanes and\ndroughts, the localization of potential victims is ascertainable. If no measure of\nmoderation is taken and if no effort is made to protect the groups at risk, then they\nwill have no alternative but to emigrate.\n\n**Conclusion**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Our summary clearly shows that environmental degradation can generate migration\nflows. Global warming could, in particular, lead to major forced displacements. This\nwill result principally from rising sea levels, but will only progressively manifest itself\nover the coming centuries, with the exception of the flooding of certain islands. The\nincrease in droughts and meteorological disasters predicted by climatic models will\nalso have impacts in terms of migrations, but these will remain regional and shortterm, and are at present difficult to estimate.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Existing research shows that due to the number of factors involved, no climatic or\nenvironmental hazards inevitably result in migrations. Many authors note that even if\ndisasters become more frequent in the future, political efforts and measures of\nprotection will be able to lessen the need to emigrate provided that the necessary\nfinancial means are made available. Even rising sea levels could be partially\ncounteracted by the erection of dykes or the filling in of threatened zones. The Stern\nreport is clear in this respect and states that \"the exact number who will actually be\ndisplaced or forced to migrate will depend on the level of investment, planning and\nresources\" (112), before estimating the cost of mitigation to be several billion dollars.\n\nThe overview we have carried out also shows that the very concept of climate or\nenvironmental refugees, because of its connotations of urgency and unavoidability, is\nto be handled with care. It actually evokes fantasies of uncontrollable waves of\nmigration that run the risk of stoking xenophobic reactions or serving as justification\nfor generalized policies of restriction for people seeking asylum.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The question of what the international system of protection should put in place to face\nthese challenges remains unanswered, and is all the more important because of the\nclear responsibility of rich countries for global warming. Simply including\nenvironmental motives in the 1951 definition of refugees seems politically unfeasible\ndue to the very likely opposition of receiving countries. It would probably not achieve\nits objective of protection as the majority of displacements take place in the interior of\nthe countries affected. It would also risk threatening the coherence of an international\n\n8", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "framework of refugee protection that already has difficulty in obliging states to\nrespect their commitments. As stated in 2005 by the then Under Secretary General of\nthe UN, Hans van Ginkel \"This is a highly complex issue, with global organizations\nalready overwhelmed by the demands of the conventionally-recognized refugees as\noriginally defined in 1951. We should prepare now, however, to define, accept and\naccommodate this new breed of refugee within international framework\" (United\nNations University 2005) _._\n\nIt seems that two possibilities can be envisaged with regard to this: on one hand, an\nincreased international cooperation with a view to collective burden sharing of\nassistance and prevention in countries confronted with disasters, and on the other, the\nopening of emigration channels with the recognition of environmental push factors in\nsubsidiary international instruments of protection such as temporary protection\nschemes. This second option seems more viable for urgent cases but brings with it\nnumerous problems, in particular the question of responsibility for the displacement\nof the person from the disaster zone to the receiving zone.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The discussion of these possible solutions is largely beyond the scope of this article\nbut it is evident that without firm preventative action, global warming could have\nserious consequences in terms of forced migrations. This must be more widely\nrecognized and stimulate scientific and political awareness.\n\n9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "# **NEW ISSUES IN REFUGEE RESEARCH**\n\n**Research Paper No. 244**\n\n# **‘Because I am a stranger’** **Urban refugees in Yaoundé, Cameroon**\n\n**Emily Mattheisen**\n\nCentre for Migration and Refugee Studies\n\nThe American University in Cairo\n\nE-mail: EmilyMattheisen@gmail.com\n\nSeptember 2012", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Policy Development and Evaluation Service**\n\n**United Nations High Commissioner for Refugees**\n\n**P.O. Box 2500, 1211 Geneva 2**\n\n**Switzerland**\n\n**E-mail: hqpd00@unhcr.org**\n\n**Web Site: www.unhcr.org**\n\nThese papers provide a means for UNHCR staff, consultants, interns and associates, as well as\nexternal researchers, to publish the preliminary results of their research on refugee-related issues.\nThe papers do not represent the official views of UNHCR. They are also available online under\n‘publications’ at <www.unhcr.org>.\n\nISSN 1020-7473", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Introduction**\n\nAs of January 2011, UNHCR reported that there were more than 106,000 refugees and asylum\nseekers living in Cameroon, over 14,000 of them living in urban and peri-urban areas. The\nmajority come from neighbouring and nearby states such as Burundi, the Central African\nRepublic, Chad, Democratic Republic of Congo, Guinea, Nigeria, Rwanda and Sudan.\n\nCameroon is signatory to most conventions and treaties that articulate human rights, including\nthe 1951 Convention relating to the Status of Refugee and 1967 Protocol (hereafter referred to as\nthe “1951 Convention”) and the 1969 OAU Convention Governing the Specific Aspects of\nRefugee Problems in Africa (hereafter referred to as the “OAU Convention”). Cameroon adopted\nnational legislation regarding the status of refugees in 2005. Of particular importance are the\nprovisions found in Chapter III of the law, which outline the rights and obligations of refugees\nhosted in Cameroon.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Article 9 grants refugees several important rights, including the right to practice religion freely,\nthe right to property, freedom of association, the right to sue, the right to work, the right to\neducation, the right to housing, the right to social assistance, freedom of movement, the right to\nobtain identity and travel documents, the right to transfer of assets, and the right to naturalisation.\nAdditionally, Article 10 states that refugees are required to comply with the same laws and\nregulations on the same basis as nationals. Essentially, it is expected that refugees comply with\nthe same standards and laws that apply to nationals and in turn be treated as nationals.\n\nArticle 16 of the law indicates that the government will create a national committee for\ndetermining refugee status and appeals, however this has not been done and the UNHCR still\nshoulders the responsibility in determining refugee status. In reference to this law, UNHCR\nCameroon country representative Aida Haile Mariam, statea that “it’s a very good law, with very\ngood principles, but to apply this law there should be a Presidential Decree and we are waiting\nfor this Presidential decree for the application of the law.”", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In addition to providing a commission for RSD and appeals, Article 9 of the 2005 refuge law\nindicates that refugees have the right to government issued identity documents. This\ndocumentation has not yet been issued, and has been quite problematic for many refugees living\nin Cameroon. Several of those interviewed for this research claimed that authorities often\nharassed them because they did not recognise UNHCR refugee documentation.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR refugee documentation"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "To try to alleviate this issue, UNHCR has sent a specimen of documents to the authorities and\ninstitutions (i.e. banks, money transfer companies, etc.) so that they are able to become familiar\nwith the various documents that refugees will use for identification, however this problem still\npersists. In regard to this issue of identification documents, a UNHCR official stated that “it\nbrings about a certain vulnerability for the refugees with police men who will harass them,\nsaying ‘I don’t know this, I don’t recognise this’, so if the government had issued the ID card we\nbelieve that it will enhance the protection for refugees, or at least make life easier [for the\nrefugees]”.However, as UNHCR, other organisations, and the refugee community await the\ndecree on this law, the refugees still must live and work and carry on their everyday lives.\n\n1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Urban refugees**\n\nThe regional dynamics of many of the conflicts in Sub-Saharan Africa generate a perspective of\nrefugees as a potential security threat, as governments fear that some refugees will involve the\ncountry in the conflict, for example by using the host country as a base for rebel groups to attack\ntheir home country. [1] The host country’s desire to protect and separate itself from the effects of\nconflict, and the conflict itself, influence the way in which treatment, assistance, and polices are\nformed towards refugees. This is often achieved by confining refugees into camps and\nsettlements where they are prevented from moving freely, which is essential to their ability to\naccess many of their economic and social rights, such as employment. [2]\n\n---\n[2] Harrell-Bond, _supra_ note 5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Refugees in Cameroon are mostly self-settled, as there is only one refugee camp in the country,\nthe Langui camp, located in the extreme north of the country near the border of Chad, and thus is\nhome to a large number of Chadian refugees; however the UNHCR is working to repatriate some\n1,000 Chadian refugees upon the signing and finalising of an agreement with the government of\nCameroon.3In Cameroon the large majority of refugees in country are coming from the\n\nneighbouring country CAR, with 80,900 living in the East and Adamaoua regions of Cameroon. [4]\n\nThe refugees from CAR are mostly from the Mboro ethnic group, which are nomadic cattle\nherders found in CAR, Cameroon, parts of DRC and Chad; refugees from CAR are not accepted\non an individual basis, but rather _prima facie._ Many of the Mboro refugees stay in the rural parts\nof the East and Adamaoua regions, among the Cameroonian population, because many\nCameroonian Mboro live there, and according to representatives of UNHCR, the Mboro peoples\nfrom CAR know the land, so when they came as refugees they knew where they were able to\nstay.\n\n---\n[4] UNHCR, _supra_ note 1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Although most refugees stay in more rural areas, many refugees, including those from CAR,\nchoose to move into the larger cities of Cameroon, mainly Yaoundé and Douala. The refugees\nmove towards the cities for many reasons, but those interviewed indicated two primary reasons\nfor coming to Yaoundé: the first was to find work, and the second was because they already\nknew some people in the city, who presumably came to find work. Refugees living in urban\nareas have different needs and obstacles than those who live in camps or rural settlements, and in\norder to understand the needs and protection issues for urban refugees it is important to discuss\nsome of the literature on the subject of integration and urban refugees.\n\nMany refugees enter urban settings hoping to have the opportunity to retain self-sufficiency and\nearn an income in order to support their family, but the reality of living as a refugee in a city can\nbe difficult without proper support mechanisms. The reality is that many refugees in the Global\nSouth face grave rights violations and extreme levels of poverty. [5]\n\n---\n[5] Harrell-Bond, _supra_ note 5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The spatial dispersion of urban refugees makes it difficult for aid organisations to easily identify\nrefugees and access them, and for organisations such as UNHCR, identifying and registering\nrefugees is an important component to assessing how much aid is needed. On the other side of\n\n1Akokpari, _supra_ note 13", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "this issue, often times it is difficult for urban refugees to reach out to UNHCR, as locating the\noffice may be difficult for a newcomer to that particular city, which is made increasingly difficult\nif there is a language barrier between the refugee and the host population. [6] Unfortunately, host\ngovernments within the Global South often restrict services available to urban refugees, as they\nfear it will create “pull factors” that make their city more appealing for more refugees. [7]\n\n**Livelihood strategies**\n\nIn Cameroon however, the vast majority of the refugees come from similar cultural backgrounds\nand countries where French is the primary language, so for most, communication and language is\nnot much of a barrier. However, despite these similarities, finding employment and getting by in\nan urban area is not easy for refugees. Although according to domestic law refugees are able to\naccess employment in Cameroon, the reality is that it is not easy for them to find opportunities to\nearn an income.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "For refugees living in urban areas, usually among the local poor, they will have to compete for\njobs and resources among the local population, further exacerbating the vulnerability of the\nresident poor and increasing social tensions. [8] Refugees are able to compete with, and potentially\ndisplace local workers; this could happen when the skills of the refugee(s) are greater or when\nthey are willing to accept lower wages and work conditions. [9] The other view is that allowing\nrefugees to integrate into the local community can produce multiplier effects, “by expanding the\ncapacity and productivity of the local economy”, contributing their skills, labour and resources. [10]\n\nWith a high unemployment rate among the local population of Cameroon, the ability for refugees\nto compete for limited job opportunities is more difficult. According to UNHCR representatives,\n“Cameroon has a lot of educated people, so competing in the job market is not easy for refugees,\nas highly qualified Cameroonians are already employed”. All refugees interviewed in this\nresearch were very clear to make the point that it is not easy for them to find work in Cameroon,\nand that every day is a struggle living in Yaoundé.\n\n---\n[8] A. Tibaijuka, A., ‘Adapting to urban displacement’, _Forced Migration Review_, February 2010, p. 4.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Prior to arriving in Cameroon, many of the refugees had a stable income, or at least were able to\nmeet their needs; many of those interviewed had fields and livestock in their home countries, and\nsome worked office jobs. Several refugees in Cameroon are unable to find a job that matches\ntheir skill set, and because of non-recognition of education or previous experience/gained skills,\nmany refugees often suffer from underemployment, which is defined as “holding a job which\ndoes not require the level of skills or qualifications possessed by the jobholder”. [11]\n\nThis is not an issue unique to Cameroon, but also in other refugee-hosting countries in both the\nGlobal South and North. One woman from Rwanda who has been living in Cameroon for over\n15 years, since July 1995, said that “here [in Cameroon] they do not recognise our diplomas, the\n\n6 D. Buscher, ‘Case Identification: Challenges Posted by Urban Refugees’ _,_ NGO Note for the Agenda Item, _Annual_\n_Tripartite Consultations on Resettlements_, Geneva 18–19 June 2003.\n7 _Id._", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "refugees come after the Cameroonians…in Rwanda I was a computer secretary, but here I cannot\nfind any work.” For this woman she is not able to use the skill set she possess, to make ends meet\nshe used to sell mobile phone cards and credit, or other random items on the street, but in her\nwords “it is difficult to find money, to work, to get food . . . there are a lot of problems.”", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In order to get by, many refugees work odd jobs in the informal sector, or sell things on the\nstreet, and some refugees interviewed even admitted to resorting to begging on the streets to\nmeet their financial needs. A 16 year old boy interviewed said that “my father is working in a\nconstruction site but is a victim of discrimination and the money is not being paid to him…I am\nalways searching for jobs to help my father and junior brother.” Unfortunately, according to\nDamien Eloundou of the organisation RESPECT, employers under payment and lack of pay, is\nsomething that many refugees experience. On the flipside of this issue, a representative at CRAT\nindicated that being withheld pay is something that happens to many Cameroonians as well.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Some refugees have been able to find temporary work with friends or odd jobs, but none\ninterviewed have found a permanent or stable solution. Although there is a lack of jobs in\nCameroon, there are several other reasons that are preventing refugees from finding gainful\nemployment. Mr. Moundzego of _Réfugiés sans Frontiérs_ stated that “Within the society the\nperception of refugees is changed with time, however there is still the idea that a refugee is\nsomeone who is a criminal and who came to the steal the work of the Cameroonians…they are\nsomeone who has no money.”\n\nThe majority of refugees that were interviewed during this project support this view. An article\nwas also found in the _Cameroon Tribune_ newspaper, which had an interview with a refugee\nliving in Yaoundé, Cameroon, he explained that he moved to Yaoundé in order to find a job, but\nhad not been successful, which he also indicates is a problem for many refugees living in that\ncity: \"Whenever refugees go to look for jobs anywhere people fear to recruit them because they\nbelieve that refugees are thieves. They don't have confidence in refugees…\" [12]\n\n---\n[12] E. Mosima, ‘Refugees at ease in Yaounde’, _Cameroon Tribune_, 24 June 2009, retrieved 24 April 2011 from", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Several of the refugees interviewed for this research indicated that no one wanted to give them\nwork because of their status as a refugee in Cameroon. When discussing the treatment they\nreceived from the host population and the ability to find a job, all refugees interviewed explained\nthat it was difficult for them “because I am a stranger”, “from the outside” or because “I am\ndifferent”.\n\nAlthough there are many similarities between the refugee population and the host community,\nthere is also a clear distinction that exists; all refugee interviewed indicated that they experienced\nsome sort of harassment and/or discrimination because they were refugees, and most connected\nthis directly to their ability to find wage-earning work in Yaoundé. However, several refugees\nstated that the discrimination they face is not experienced with all Cameroonians, in some cases\nthey indicated that the locals supported them and treated them well; simply put by one refugee\nfrom CAR, “some Cameroonians are nice, some are not.”\n\nAs discussed previously, identity documentation is a big problem for refugees living in\nCameroon, as many authorities and institutions do not recognise UNHCR identity cards and", "output": {"entities": {"named_data": ["UNHCR identity cards"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "papers. This issue extends to finding employment in the formal sector of Cameroon. For refugees\nwho wish to participate in the trade and commercial activities, not having national identity\ndocuments hinders this desire, so some, in addition to their refugee documentation card, possess\nthe _carte de séjour_ (a two year residence permit)or _carte de resident_ (a ten year residence\npermit).\n\nAccording to UNHCR’s Deputy Representative, for those who are involved in trade or\ncommercial activities, they purchase this permit “in order to pay for their taxes and to be\nregistered and really to be seen as a credible commercial actor they will need to present this\ndocument- even though by law they should not need it, in practices it’s what the people will ask\nfor.” However, only those who have the means for the permit are able to obtain one; it is\nprimarily Rwandans who have this permit, as most of them have been in Cameroon for over ten\nyears and are more involved in commercial activity compared to refugees from other\nnationalities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Directly connected to the ability to earn money is the ability to find adequate housing. Urban\nrefugees within the cities of Sub-Saharan Africa usually become part of the urban poor, as such\ntheir marginalised position in the city means they often live in the slums of the city. Although\nrent may be cheaper in the poor, slum areas of the cities, if one is not earning an income rent\nbecomes near impossible to pay. According to the UNHCR Deputy Representative, “the issue of\nrent is a huge problem in an urban setting, and we do not have the resources to pay for rents”. In\nsome cases, UNHCR is able to assist with paying rent, however, “it’s a temporary measure for an\non-going problem”.\n\nIn addition to the difficulties in paying rent, many refugees in Yaoundé live in small, unfinished\nslum housing. Most of the refugees interviewed lived with many people in a small one-room\nhouse, often sharing one bed with several people. Two refugees interviewed were squatting in\nunfinished construction sites, which were dangerous, with partial floors leading to large drop offs\namong other things [13] ;one young woman from CAR with three small children said, “we will stay\n\n---\n[13] Based on observations from field visits in July 2011", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "here until someone removes us”.\n\nAccording to Damien Eloundou of RESPECT, it is very common for refugees and some very\npoor Cameroonians to live in this type of housing because they only have to pay a very small\namount, however when the owner decides to resume construction they must leave. Many of the\nrefugees have changed houses several times during their stay in Yaoundé.\n\nA refugee man from CAR, who had seven children with him, has moved seven times since he\nfirst arrived in Cameroon in 2006. This man was recently removed from his house, and without\nmoney to find a new residence, the landlord allowed him to build a small “house” on the adjacent\nempty lot. Using spare wood he could find, this man built a makeshift, one room house for him\nand his family, however he was not able to construct a roof for the house, and as it was the rainy\nseason, this posed many difficulties. [14]\n\nA female refugee from DRC said that in Cameroon there is “no consideration, no rights, no\nknowledge of these rights…it is very difficult to live in Cameroon, especially with children.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Difficult and frustrating.” This frustration, expressed by many refugees, is rooted in the difficult\nlives they face living in Cameroon. Refugees move to the cities often times with the intention of\nbeing able to work and provide for their families’ basic needs, as stated previously. The\norganisation CRAT, which primarily focuses on mental health issues, recognises that\nemployment/livelihood strategies have a direct connection to the mental health of refugees.\nAccording to a psychologist working at CRAT, “the first is the issue [for refugees] in Cameroon\nis of employment, it is very difficult for refugees here … there are many, and they are jobless.”\nThe inability to find work and meet basic needs may trigger mental health issues, or exacerbates\nexisting conditions.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "As has been demonstrated, jobs are not easy to come by for most refugees, and in order to earn\nmoney some choose to invest in small businesses. Some organisations assist refugees in their\nlivelihoods through allowing them to be able to gain independence and earn their own money. In\n2009-2010 CRAT, in conjunction with the US Embassy in Cameroon, began a project entitled\n“Improving the Coping Status of Urban Refugees”. This project was initiated after a survey and\nresearch project was done with many of the torture victims that CRAT assists, the results of\nwhich showed a strong link between Post-Traumatic Stress Disorder, anxiety and depression,\nwith a lack of livelihood support. According to a psychologist at CRAT, when people are unable\nto meet their basic needs, it is very difficult to treat their mental health issues.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey and\nresearch project"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "This project took 50 refugees living in Yaoundé, mostly women, and assisted them with skills\ntraining for managing a small business as well as a start-up kit, and the refugees were also given\nassistance in saving money through CRAT. As of summer 2011, approximately 75% of the\nbeneficiaries were still doing well and earning an income from the business they started.\nAlthough this project was only able to target a small number of refugees, the success that many\nof the recipients experienced is encouraging, and supports projects directed more towards\nempowerment and assisting refugees in earning their own money over direct financial aid.\n\nAnother, similar type of livelihood assistance is also provided by UNHCR in Cameroon. For\nthose refugees that show initiative and have the desire to start their own small businesses,\nUNHCR offers skills training to develop their business skills, as well as further training for some\nin business management. This is done in conjunction with an NGO who has a focused capacity\non business training, and upon completion they are given a start-up kit for their business.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "According to UNHCR officials, many of the small business include activities like selling peanuts\nand other small food items, as well as small shops that sell various goods. The organisation\nRESPECT offers training in sewing skills for many refugee women, so they can work as\ndressmakers and tailors. There are many women who want to participate in this program, but\naccording to the organisation, the problem is having money to purchase materials for the women\nto work with and sell.\n\nAll organisations interviewed stressed the importance of skills training and assisting the refugees\nto have the ability to earn their own money, however, for many refugees they are heavily\ndependent on aid. According to UNHCR country representative, “what [the refugees] would\nprefer is to receive regular assistance…a sort of salary, but we do not have the means to do that\nand I do not think it is a desirable assistance program in this way- they should bring a\ncontribution also to help themselves and we try encourage them”, she went on to indicate that\n\n6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "those who really want to succeed in becoming independent are the ones who are pursuing the\nvarious training programs offered by UNHCR.\n\nThis opinion of assistance programming is consistent with the information in the UNHCR policy\non urban refugees, which states that “UNHCR will support the efforts of urban refugees to\nbecome self-reliant, both by means of employment or self-employment.” [15] Regarding the\npsychosocial opinion on the issue of self-reliance and independence, one researcher notes,\n“ultimately a population recovers from war not as recipients of aid or as patients but as active\ncitizens. Structural poverty, landlessness, and lack of violable jobs too often retard this\nrebuilding of lives”. [16]\n\n---\n[15] UNHCR, _supra_ note 46\n[16] D. Summerfield, ‘War and Mental Health, a brief overview’, _BMJ_, Vol. 321, 2000, p. 234.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Many of the refugees interviewed have spent time in and IDP or refugee camp, sometimes both,\nwhich has made them more dependent on financial assistance, as psychologist working at CRAT\nstated that “many refugees are actually less eager to do something [work] because they expect\neverything from the UNHCR…they have dependency on the help they receive from UNHCR,\nwhich is a big difficulty”. In an urban setting where aid is not distributed like in a camp, it is\nimportant that refugees are equipped with the skills and tools they need successfully meet their\nneeds. Although it has not reached all refugees in the city, the organisations working in Yaoundé\nhave been working towards the goal of independence and empowerment for refugees.\n\n**Other assistance**\n\nRefugees living in Yaoundé do have some aid assistance available to them. Several refugees\ninterviewed complained of medical problems, especially with the children. [17] Access to primary\n\nhealth care is provided through UNHCR’s implementing partner, the Cameroonian Red Cross,\nand all refugees interviewed (25) indicated that they have received assistance from this\norganisation.\n\n---\n[17] No persons specified what illnesses they had but rather referred generally to “sickness”, but in discussion with", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "A problem for some was illness related to malnutrition, such as calcium deficiency and anaemia;\nfor these cases, UNHCR assists the refugees with their special diet needs. For those who have\nmedical problems or are recovering from treatment that requires them to have a caretaker,\nUNHCR will pay for the refugee who volunteers to care for the patient. Unfortunately medical\nproblems tend to persist because of unsanitary living conditions, malnutrition, and unsafe\ndrinking water, among other things.\n\nAs mentioned briefly in a previous section, the organisation CRAT works with urban refugees in\nYaoundé to assist with mental health needs. According to a psychologist working at CRAT,\nmany [refugees] come from rural areas, and in urban areas this is a problem- they feel\ncompletely lost in the city”, many of the refugees coming from urban areas feel equally\noverwhelmed when coming to Yaoundé; for some, “depression has become a normal part of their\nlives.” To assist with their needs, CRAT offers several different therapies, including cognitive", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "behavioural therapy and narrative therapy, among others, as well as the plan for a music and\ndance therapy program.\n\nMany refugees interviewed discussed the importance of religion and spirituality in their lives,\none woman who was in a particularly difficult situation, stated, “God is the only one who helps\nus.” CRAT offers individualised approaches to therapy, however a psychologist at CRAT stated\nthat through his personal experience, “many of [the refugees] have been more effectively helped\nusing their belief in god... they can better express their issues through their religion – through\ntheir spiritual beliefs we can help them find solutions.” As discussed previously, mental health\nissues can be exacerbated by the living conditions and life struggles of refugees living in an\nurban setting, however with mental health assistance, refugees can receive the support that they\nneed.\n\nThrough UNHCR refugee children are able to access primary and secondary education.\nAccording to UNHCR Cameroon representative, education is a critical component to protecting\nrefugee children in Cameroon, as well as in all countries. The domestic refugee law in Cameroon\nallows for refugee children to attend the local schools [18], however attending school costs money\n\nthat many refugee families do not have.\n\n---\n[18] Article 9 and 10(1) of Cameroon’s Domestic Refugee Law, see _supra_ note 29", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "\\In Cameroon, UNHCR assists all refugee children to be able to attend primary school, and is\nalso striving to offer assistance for children in higher grade levels, as “we [UNHCR] understand\nthe risk if we abandon them after completing their primary education”. For some refugees,\nassistance is available for university education, and even a Masters level degree; the scholarships\navailable for this level of education are made possible through a German organisation, and it is a\ncompetitive program.\n\nIn addition to UNHCR assistance, the organisation RESPECT offers some scholarship assistance\nfor children to attend schools. This organisation also has implemented a letter exchange program\nwith a school in Canada; this allows the refugee children to practice their writing and\ncommunication skills, as well as learn about children in Canada and have the opportunity to\nteach them about the lives of refugees. This program has been very successful, and when asked\nabout the program, the children involved seem to be very happy to have the opportunity to have\n“friends” to share with in Canada.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Unfortunately, even with this assistance, not all refugee children are permitted to attend school.\nFor many families, when older children are in school, they are not able to work and help provide\nfor the family. One woman from Chad was living in Yaoundé with her two older daughters (15\nyears and 17 years) and son (6 years), however only one of the daughters was able to attend\nschool because the boy was too sick, and the older girl needed to stay home and help care for\nhim. This problem of education is not unique to Cameroon, and as livelihood challenges persist,\nwill continue to be a protection challenge for UNHCR and other assistance organisations\n\n**Conclusion**\n\nUrban refugees face different problems and in many cases are more vulnerable than refugees\nliving in camps. Living in an urban area means that refugees must earn money to be able to meet", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "their other needs, which is an issue that was consistently articulated during the research and\ninterviews for this project. General academic information provided by various scholars and\nUNHCR indicate that urban areas pose particular difficulties for organisations as well as\nrefugees.\n\nHowever it is not likely that the trend of refugees living in cities will change anytime soon, so\norganisations are compelled to adapt. As discussed previously, UNHCR issued a new policy for\nthe treatment of urban refugees in 2009, which had several improvements and changes from the\nprevious policy (1997), which treated urban refugees as an exception rather than the norm. One\nof the primary goals of this new policy is to increase the protection space for refugees,\nconceptualised as an environment in which internationally recognised rights of refugees are\nrespected and their needs are met. [19]\n\nUNHCR’s new policy also notes that movements to urban settings can “place considerable\npressure on resources and services that are already unable to meet the needs of the urban poor”20,\n\npressure on resources and services that are already unable to meet the needs of the urban poor”20,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "this situation makes it challenging to provide a protection space for refugees; these are issues that\nface aid and assistance organisations in Cameroon. It is difficult to focus attention (both financial\nand research) to the refugee situation in Cameroon when compared to other refugee hosting\ncountries in Sub-Saharan Africa, which have a significantly larger “population of concern” [21] such\nas South Africa, Chad, Uganda, Kenya and Tanzania. [22] This lack of focus and attention makes it\n\nas South Africa, Chad, Uganda, Kenya and Tanzania. [22] This lack of focus and attention makes it\n\nvery difficult to fundraise and attract international NGOs to provide assistance; compared to\nsituations in other parts of Sub-Saharan Africa, as UNHCR country representative noted “we\ncannot compete with those kind of high, complex, emergency programs”.\n\n---\n[22] Total populations of concern for these countries: Cameroon-106,658; South Africa- 229,601; Chad- 529,090;", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The UNHCR offices in Cameroon, as well as the other organisations, have challenges meeting\nall refugee needs with a limited amount of resources. UNHCR representatives indicated that they\nreceive between 30 and 40 requests for financial assistance every day, and every week the staff\nmust go through the applications and choose who are the most “needy” and grant them\n“exceptional allowances”. In terms of assistance, UNHCR deputy representative stated that, “we\nhave different types of assistances, it's definitely not enough, but because our resources are so\nlimited we have to share in between a large number of people- and at the end of the day\neverybody doesn’t get a lot but it can make a difference for a few people”.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "However, UNHCR in Cameroon is making efforts to use the resources available in an effective\nmanner through working with the refugees and working towards better understanding their\nneeds. There are eleven different nationalities of refugees in Cameroon, and every two years\neach community elects a community leader that also acts as a link between the community and\nUNHCR. UNHCR, in conjunction with the Adventist Development and Relief Agency,\nconstructed a refuge community centre, located next to the UNHCR office, so each community\nhas a space to work and discuss issues and concerns. Every three months, UNHCR staff meets\nwith the refugee leaders to discuss concerns and work to find solutions together. The opinions\nand viewpoints of the refugee community are an invaluable resource and crucial to implementing\n\n19 UNHCR, _supra_ note 48\n20 _Id._\n21 “Population of concern” includes refugees, asylum seekers, returned refugees, stateless persons, IDPs, and\nreturned IDPs.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "projects that yield beneficial results; as the country representative stated, “[many of the refugees]\nhave been here longer than us- some for twenty years... they know how things work and they\nhelp us identify problems in the community and how to go about finding solutions.”\n\nFor UNHCR working together with the refugee community in Yaoundé is a key component to\nproblem solving and finding adequate solutions. The other organisations interviewed for this\nresearch, _Réfugiés sans Frontiérs_, CRAT and RESPECT, seemed to be the primary\norganisations, other than UNHCR and the Cameroon Red Cross, that offered some assistance\nand advocated for urban refugee rights in Yaoundé. These organisations are deeply rooted in the\nrefugee community and in tune with their needs and the issues they face on a daily basis,\nhowever there seemed to be an extreme lack of communication and working relationship\nbetween organisations.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "When asked if there were partnerships between refugee organisations in Cameroon (apart from\nUNHCR), they all stated that there was not. After spending two months researching and working\nwith refugee organisations in Yaoundé, it is in the opinion of this researcher that the\norganisations, and the refugee community, would benefit greatly if they were to collaborate on\nprojects and share resources, and because of the small amount of refugee specific organisations,\nit may be important and useful to reach out to other domestic aid and assistance NGOs in\nYaoundé which do not solely focus on refugee issues.\n\nThere are many refugees to care for both in urban and rural areas of Cameroon, and everyday\nmore asylum seekers are arriving in the country. Although limited by resources, the\norganisations working with refugees in Yaoundé, including UNHCR, have identified the primary\nprotection needs for refugees and are working towards achieving a greater realisation of refugee\nrights in Cameroon.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The amount of work, along with political and social situation of Cameroon can make providing\nassistance for refugees difficult, whether a domestic NGO or an international organisation such\nas UNHCR, but as the UNHCR Cameroon country representative stated, “we cannot be\ndiscouraged because there is so much to be done.” The organisations providing protection for\nrefugees in Yaoundé have much work to do and a long road ahead, however the organisational\nand leadership foundations that are now in place will allow them to continue working towards\nproviding effective protection for refugees.\n\n10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### IN OPERATIONALIZING THE GLOBAL COMPACT ON REFUGEES AND COMPREHENSIVE RESPONSES", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### INTRODUCTION\n\nWithin UNHCR public health covers various areas including primary health\ncare, nutrition and food security, reproductive health and HIV, mental\nhealth and integrated refugee Health Information Systems (iRHIS). Sectoral\nprogramming in a comprehensive response context means applying a wholeof-government (i.e. relevant national and local authorities for health and\nnutrition response) multi-stakeholder approach and planning with relevant\npartners. The overall responsibility of coordinating the health sector\nresponse in refugee-only situations will be with the Ministry of Health, with\nsupport of UNHCR and relevant partners. A wide range of partners play\na role in planning and delivering public health interventions in different\nareas and at different stages of the refugee response. For an effective and\ncomprehensive response it is therefore essential to know how and when to\nengage these various partners. Though the establishment of refugee-specific\nservices may be needed in the early phases of a refugee situation, longer\nterm solutions are required to ensure that refugees have access to services\nthrough the national health system. Host countries may require assistance\nfrom other partners, including international organizations but also local\npartners, to make the necessary adjustments to comprehensively include\nrefugee health needs into national development and local health plans,\nto strengthen/reinforce national and local resilience of national and local\nhealth systems to meet the health needs of refugees and host communities.", "output": {"entities": {"named_data": ["integrated refugee Health Information Systems"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### OBJECTIVES OF HEALTH AND NUTRITION PROGRAMMING AS PART OF COMPREHENSIVE RESPONSES\n\n## **1.**\n\nRefugees have access to quality, comprehensive health and nutrition\nservices from the onset of the emergency to stabilization which\naddress the main causes of morbidity and mortality, including\nthe needs of the most vulnerable and marginalized.\n\n## **2.**\n\nInclusion of refugees into the national / development response in\nthe health sector is accelerated as part of global efforts towards\nuniversal health coverage (UHC) as per the United Nations 2030\nAgenda for Sustainable Development (“leave no one behind’’).\n\n## **3.**\n\nNational health systems are strengthened at the local and national level.\n\n## **4.**\n\nHost communities benefit from improved access to quality health\nservices alongside refugees in an equitable manner.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### UNDERLYING POLICIES/PRINCIPLES/STANDARDS\n\n» » UNHCR’s global public health strategy 2019-2023 (forthcoming) aims to ensure\nthat all refugees are able to exercise their **rights** in accessing life-saving and\nessential health care, mental health, HIV prevention, protection and treatment,\nreproductive health and nutrition services.\n\n» » UNHCR promotes **Universal Access** to Health Care and Equity Principles in\nsupport of Sustainable Development Goal (SDG) 2 and 3 and through a **primary**\n\n**health care (PHC)** approach embedded into the national public health system.\nWhile supporting global efforts towards UHC, access to primary health care\nand to cost effective interventions at secondary health care level will take\nprecedence over long term and costly secondary and tertiary care, and be based\non country level standard operating procedures for referral care [1] .", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "» » Wherever **National Health Service delivery programmes** are available, these\nare preferred to setting up parallel services for refugees. In emergencies and\nin refugee camp situations, UNHCR and partners may have to establish health\ncentres, due to lack of availability or poor absorption capacity of the national\nhealth care system. These health centres should be integrated, and where\nfeasible, accredited by the Ministry of Health and be part of the national health\nsystem. Structures, equipment and design should be in line with the national\nstandards for health facilities to avoid the need for expensive rehabilitation/\nupgrade of facilities during the handover phase.\n\n1 UNHCR’s Principles and Guidance for referral Health Care for Refugees and Other Persons of Concern. UNHCR, 2009.\n\n» » UNHCR works to ensure that refugees have access to health and nutrition\nservices at **equal levels** and at similar costs to that of nationals of the host\ncommunity once ensuring that minimum standards have been met.\n\n» » Effective **coordination** between the Ministry of Health (MoH) and other line\nministries is of paramount importance including in exploring opportunities for\nintegration of services.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "» » Not all refugee situations start with an influx, and all emergency responses need\nto transform into a more consolidated and stable programmatic response in\nthe mid- to long-term. This would include seeking the engagement of relevant\nnational and local government authorities and development actors. The Global\nCompact on Refugees envisages that refugee responses would be designed in a\nmanner that would pave the way for more **sustainable support and responses**,\nwhere possible, integrating responses for refugees into national systems while\nensuring these are adequately supported.\n\n» **Regardless of the location** » (camp, settlement, out of camp, urban [2], rural etc.), it\nis critical to ensure (and support directly if necessary) refugee access to quality\nhealth services and means to meet their basic needs. Advocate to ensure that\nexisting social protection systems (including cash-based transfers as part of\nsocial safety nets) are available for vulnerable refugees so that they can access\nservices equitably.\n\n---\n[2] Ensuring access to health care: Operational guidance on refugee protection and solutions in urban areas. UNHCR, 2011.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### ROLES AND RESPONSIBILITIES\n\n» » States are primarily responsible for ensuring refugees are protected.\n\n» » UNHCR retains the overall accountability for Persons of Concern\n\n» » UNHCR’s role includes the following key elements:\n\n–– **Coordination:** As the agency ultimately responsible for refugee responses,\nUNHCR has a role in coordinating UN and partner responses for refugees\nincluding convening and catalysing the engagement of a broader array of\nstakeholders in line with the GCR.\n\n4 PUBLIC HEALTH AND NUTRITION\n\n–– Ensuring that **protection considerations** are taken into account in the healthrelated interventions of the refugee response, including those of partners\n\n–– **Advocacy and technical support to legislative, policy or strategy changes**\nwhere relevant, to facilitate **inclusion in national systems and plans** : UNHCR\nadvocates with relevant counterparts (Ministries, UN) to include refugees in\nhealth service delivery at national and local levels, and in national planning\ndocuments (National development plans (NDP) and support frameworks\nsuch as UN Sustainable Development Cooperation Framework (UNSDCF).\nWhen needed and feasible, UNHCR to work with relevant partners [relevant\nline ministries, international organizations such as WHO and local partners]\nwho would provide support to host governments to strengthen national\nhealth systems and health service provision.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "–– Ensuring that a **situation analysis and mapping of relevant actors**\n(Government, UN agencies, NGOs, multilaterals and donors) in the health\nsector is done in collaboration with the line ministry to inform the design of a\nresponse in each area of public health and nutrition and for every stage of the\nresponse.\n\n–– **Facilitating data driven responses** Facilitate and support the collection,\ncompilation, analysis, interpretation and dissemination of health program\ndata. Support inclusion of refugees in national data systems and tools\nincluding disaggregation of data by nationality to the extent possible.\n\n–– **Refugee participation and consultation** : wherever possible, continue to\ndevelop and support consultative processes that enable refugees and host\ncommunity members to assist in designing appropriate, accessible and\ninclusive responses.\n\n–– **Providing technical expertise & support** : UNHCR will seek to provide\nor facilitate technical and general support to partners on program\nimplementation and support for inclusion of refugees in national data\nsystems..", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["health program\ndata"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### TIMELINE AND SPECIFIC CONSIDERATIONS [3]\n\n---\n[3] Inclusion is cross-cutting and starts at onset of the response planning and should be gradually and contextually formulated as per guidance in this document.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PREPAREDNESS\nEMERGENCY\nTRANSITION\nLONG-TERM INCLUSION\nKEY PARTNERS\nMinistry of Health (MoH) to include refugees in \nnational programs with support from UN agencies \nand NGOs (national and international).\nUN agencies: support the MoH in preparedness \nand contingency planning for scenarios with \na refugee influx, within the broader work on \nstrengthening MoH capacities in health emergency \nrisk management.\nDevelopment partners and international financial \ninstitutions: strengthen institutions for the \nmanagement of a refugee influx; develop financial \ninstruments to facilitate the flow of financial \nsupport; and to establish surge capacity for service \ndelivery.\nMoH to include refugees in national programs with support \nfrom UN agencies and NGOs (national and international).\nGlobal Fund: for HIV, TB, malaria program support where \napplicable including emergency fund grants.\nGavi: For vaccine support to MoH for refugee response with \npossibility of waiving co-financing obligation of MoH.\nWHO: support MoH on health system capacity needs, \ntechnical support for the public health situation analysis and \nrisk assessment.\nWFP: Providing food and nutrition assistance to refugees in \ncollaboration with UNHCR.\nUNICEF: support refugees access to immunisation \nprogrammes; vitamin A and deworming campaigns, \ncommodities relating to maternal newborn and child health; \nnutrition materials and supplies for the treatment of Severe \nAcute Malnutrition (SAM), Infant and Young Child Feeding \n(IYCF) support, nutrition in the surrounding community, \nsupport to nutrition assessments and surveys and behaviour \nchange communication (BCC).\nUNFPA: support refugees and hosting communities to access \nreproductive health care services and commodities.\nMoH to include refugees in national \nprograms with support from development \npartners (multilateral development banks \nand bilateral donors), UN agencies, NGOs \n(national and international) among other \npartners.\nWHO: Health system strengthening; support \nthe MoH with an assessment of the impact \non the national health system.\nMoH to include refugees in national programs with \nsupport from development partners, UN agencies, \nNGOs (national and international) among other \npartners.\nUN agencies: Support MoH in developing health \nand related policies and strategies and in activities \nfavourable towards inclusion of refugees and to \nenhance the capacity of the national systems to \nequitably and sustainably integrate refugees.\nDevelopment partners and international financial \ninstitutions: Support to MoH & partners in health \nsystem strengthening for sustainable inclusion of \nrefugees in national systems and equitable health \nservices for both refugees and host communities.\n6\nPUBLIC HEALTH AND NUTRITION", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### OVERALL RESPONSIBILITIES OF VARIOUS ACTORS [6]\n\n---\n[6] Inclusion is cross-cutting and starts at onset of the response planning and should be gradually and contextually formulated as per guidance in this document.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PREPAREDNESS\nEMERGENCY\nTRANSITION\nLONG-TERM INCLUSION\nOTHER ACTIVITIES (PUBLIC HEALTH, NUTRITION, FOOD SECURITY)\nReview national health plans and policies \nand advocate that activities for refugees \nare included and equitably provided as \nnationals.\nMeasles, polio vaccination on arrival and vitamin A \nsupplementation.\nExpand vaccination to national immunization schedule (EPI) and supplementary immunization activities (SIA).\nAdditional support for vaccines and human resources may be needed and may be sought as per the GAVI, Fragility, \nEmergencies, and Refugees Policy.\nUNICEF may support cold chain capacity, training of health workers and vaccine related activities\nAs above\nEssential primary health care.\nIntegrated primary health services.\nGradually integrate refugees into national health services, support services if need be with human resources for \nhealth, medications, medical supplies and equipment.\nEngage other UN agencies (UNICEF, UNFPA and WHO) to support efforts to include refugees in national program/\nsystems.\nIf refugee standalone facilities, aim for accreditation and inclusion in national system.\nEngage supervision from Ministry of Health especially for malaria, TB, HIV, nutrition, reproductive health and \nimmunization.\nUse national clinical management protocols.\nInternational (and in exceptional situations, local) procurement of medicines in line with UNHCR policy. Review of \nand support to MoH procurement protocols/systems and integrate where applicable (context-specific and based on \nquality assurance assessment).\n8\nPUBLIC HEALTH AND NUTRITION", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**PREPAREDNESS** **EMERGENCY** **TRANSITION** **LONG-TERM INCLUSION**\n\nAs above Comprehensive nutrition services integrated as much as possible into MoH systems (often supported by UNICEF in collaboration with UNHCR).\n\nNutrition assessments of refugees and national populations.\n\nPrevention of acute malnutrition, anaemia and stunting by gradually integrating refugees into national programmes of fortification, deworming and supplementation as well\nas close nutritional monitoring. If need be in collaboration with UNICEF, WFP and other partners for human resources, nutritional supplies and equipment.\n\nAdvocate for eligible refugees to receive therapeutic feeding products (ready-to-use therapeutic food, F75, F100) and medications (systemic treatment and ReSoMal) through\nnational system.\n\nEngage UN agencies (UNICEF, WFP) to support treatment and prevention of acute and other forms of malnutrition.\n\nEngage supervision from MoH on nutrition service provision.\n\nInclude refugees in national Vitamin A, deworming, school feeding and micronutrient fortification programmes.\n\nPrevention of micronutrient deficiencies and anaemia by gradually integrating refugees into national programmes of Vitamin A supplementation, deworming, school\nfeeding and micronutrient fortification programmes as well as close nutritional monitoring and enhanced collaboration with reproductive health programmes. If need be in\ncollaboration with UNICEF, WFP and other partners for human resources, nutritional supplies and equipment.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Include refugee nutrition programme staf (government and local partners) in national capacity building programmes for improved/integrated health and nutrition services.\n\nAs above Infant and young child feeding in emergencies (IYCF-e).\n\nPrioritize life-saving IYCF activities (defined by context)\nand advocate for needs of infants and pregnant and\nlactating women to be considered in all sectors.\n\nReference: UNHCR/Save the Children IYCF in Refugee\nSituations: A Multi-Sectoral Framework for Action.\n\nMulti-sectoral integrated IYCF programmes. Engage other sectors e.g. WASH, camp management, security,\nsettlement and shelter, health, food security and livelihoods, logistics, child protection, general coordination.\n\nBuilding systems and capacity to promote IYCF support (often in collaboration with UNICEF).\n\nInclude refugees in national IYCF and child health programmes.\n\nIN OPERATIONALIZING THE GLOBAL COMPACT\nON REFUGEES AND COMPREHENSIVE RESPONSES 9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**PREPAREDNESS** **EMERGENCY** **TRANSITION** **LONG-TERM INCLUSION**\n\nAs above Minimum Initial Service Package for RH (MISP) including Scale up to comprehensive reproductive, HIV/TB health services.\nEmergency Obstetric Care.\n\nAdvocate for inclusion of refugees into national HIV, TB and malaria programmes for provision of ART, malaria\nand TB drugs, rapid testing kits, early infant diagnosis, GENExpert and viral load, bed nets (LLIN) etc. Global\nFund support may be needed including emergency funds for large influxes, reprogramming of existing grants or\ninclusion into new grants. (Reference GFATM’s’ Challenging Operating Environment Policy and UNHCR’s Global\nFramework Agreement).\n\nInclude in national cervical cancer screening programmes and obstetric fistula programs where they exist.\n\nInclude refugees health workers or staf working in refugees sites in national trainings\n\nlevel referral system.\n\nAdditional support to referral facilities may be needed in terms of equipment support, payment of referral costs\n\n|As above|Life-saving referral care and logistics support.|\n|---|---|\n|As above|Identifcation of NCD patients & ensure continuity of<br>care.<br>Support training/refreshment of health providers on NCD<br>updated protocols.<br>Prioritize patients considered to be at higher risk<br>of complications – symptomatic; those for whom<br>medication interruption is likely to have signifcant<br>consequences; those who have had recent disease<br>instability; and those with multiple co-morbidities.|\n\n10 PUBLIC HEALTH AND NUTRITION", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Integrate NCD care into primary health care and ensure congruence with the national health system.\n\nAdvocate for inclusion into existing national NCD services and programs.\n\nIn the absence of functioning local facilities, identifying and supporting a reliable health partner is important and,\nequally, referral systems should be established where specific care is not provided directly by the health partner.\n\nSupport the local health system to maintain and enhance their NCD services.\n\nReference: NCDs in Humanitarian Settings- Operational guidelines (contact UNHCR Public Health Section).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**PREPAREDNESS** **EMERGENCY** **TRANSITION** **LONG-TERM INCLUSION**\n\nAs above\n\nPromote the dissemination and use of\ninternational guidance documents such\nas IASC Guidelines for Mental Health\nand Psychosocial Support (MHPSS) and\nmental health entries in Sphere Minimum\nStandards and UNHCR Emergency\nHandbook.\n\nReview national health plans and policies\nand advocate that activities for refugees\nare included and equitably provided as\nnationals.\n\nTraining of health workers and other\nrelevant actors on culturally-sensitive\nservice delivery, including interpreters;\nharmonized with health system processes.\n\nProtect the rights of people with severe mental health\nconditions in the community, hospitals and institutions.\n\nOrient staff and volunteers on how to offer\npsychological first aid.\n\nMake basic clinical mental healthcare available at every\nhealthcare facility.\n\nMake psychological interventions available where\npossible for people impaired by prolonged distress.\n\nWork with protection actors to strengthen community\nself-help and social support.\n\nComprehensive food security interventions to include\nprovision of blanket assistance to meet basic needs\n(food- in-kind or cash, with partners).\n\nJoint Needs/Vulnerability Assessments (refugee/host).\n\nNutrition sensitive agriculture, livelihood programmes.\n\nDevelopment of Self-Reliance Strategy for food/\nnutrition.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Comprehensive food security interventions to include\nprovision of targeted assistance to meet basic needs\n(food- in-kind or cash, with partners) where government\nsocial protection programmes do not yet include\nrefugees and needs are identified.\n\nContinue food security activities as per Self-Reliance for\nfood and nutrition Strategy.\n\nLink and support to social protection systems for most\nvulnerable.\n\nAs in emergency stage PLUS:\n\nOrganize a referral mechanism among mental health specialists, general healthcare providers, community-based\nsupport and other services.\n\nDevelop plans with the MoH, development donors and NGOs to develop a sustainable mental health system.\n\nAssessments with line ministries to determine\nvulnerability (poverty and food security).\n\nIntegration into government social protection system.\n\nTraining of refugees on key health and nutrition and Continuous training of health workers in refugee settings linked with MoH national training curricula.\nhygiene promotion messages.\n\n\u0007Support efforts to ensure qualified refugee health workers are able to work similar to national system health\nworkers.\n\nCapacity/skill building of refugees.\n\nIN OPERATIONALIZING THE GLOBAL COMPACT\nON REFUGEES AND COMPREHENSIVE RESPONSES 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Global Public Health, June 2006; 1(2): 147�156\n\n# HIV behavioural surveillance surveys in conflict and post-conflict situations: A call for improvement\n\nP. B. SPIEGEL & P. V. LE\n\nUnited Nations High Commissioner for Refugees", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["HIV behavioural surveillance surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Abstract\nBehavioural surveillance surveys (BSSs), an evolution from the knowledge �attitudes practice surveys (KAPs), are a tool to track trends in HIV/AIDS knowledge, attitudes\nand risk behaviour among populations. The data collected support organizations in\ntargeting specific HIV/AIDS prevention and care activities, monitoring their effectiveness\nand coverage, and allocating scarce resources. The objectives are to evaluate the quality and\nstandardization of BSS-like surveys undertaken in conflict and post-conflict situations, and\nto provide recommendations to humanitarian agencies and governments on how to\nimprove their quality. Survey methodology was classified as reproducible if the populationbased sampling defined a sampling frame using probabilistic sampling. Survey indicators\nwere compared to internationally-accepted HIV indicators. The results showed that 14\n(45.2%) of the 31 BSS-like surveys evaluated between 1998 and 2005 in 14 countries were\nclassified as reproducible. Surveys undertaken by non-governmental organizations\n(NGOs) were significantly less reproducible than those undertaken by non-NGOs (p �/\n0.05). The majority of surveys used at least one identical or similarly worded\ninternationally-accepted HIV indicator for prevention and misperception but not for\npractice and attitudes. Few reported disaggregated indicators according to age or gender. It\nwas concluded that the majority of BSS-like surveys are of insufficient methodological rigor\nto be reproducible. Few surveys reported internationally-accepted HIV indicators by\ngender and age which makes interpretability and comparison difficult. United Nations\nagencies, NGOs, and governments undertaking BSSs in conflict and post-conflict settings\nshould proceed with a BSS survey once the design and plan for execution has been prepared\nby experienced and qualified experts. These experts should then oversee the survey, assure\ndata quality and incorporate training of others in the process. A practical and field userfriendly BSS manual is needed for conflict affected and displaced population situations,\none which is customized to take into account the special circumstances of such populations.", "output": {"entities": {"named_data": ["Abstract\nBehavioural surveillance surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Keywords: HIV, AIDS, behavioural surveillance survey, humanitarian emergency,\nconflict, post-conflict, refugee, IDP, methodology and quality\n\nCorrespondence: Paul B. Spiegel, MD, MPH, UNHCR, Case Postale 2500, Geneva 1211,\nSwitzerland. Tel: 41 22 739 8289. Fax: 41227397366. E-mail: spiegel@unhcr.org\n\nISSN 1744-1692 print/ISSN 1744-1706 online # 2006 Taylor & Francis\nDOI: 10.1080/17441690600679764", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["behavioural surveillance survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "148 P. B. Spiegel & P. V. Le\n\nIntroduction\n\nThe human immunodeficiency virus (HIV) behavioural surveillance surveys\n(BSSs), an evolution from the knowledge-attitudes-practice surveys (KAPs), are\nan assessment, monitoring and evaluation tool designed to track trends in HIV/\nAIDS knowledge, attitudes and risk behaviour among populations. When used\ntogether with qualitative and quantitative research and proper measurement of\nappropriate programme indicators, the data collected from BSSs can assist\norganizations in targeting specific HIV/AIDS prevention and care activities,\nallocating scarce resources, and monitoring and evaluating the interventions’\neffectiveness and coverage. BSSs are useful because they alert policy makers and\nprogramme managers to emerging or changing risks in existing behaviour, reveal\ngaps in knowledge and attitudes, help to identify vulnerable segments of\npopulations, contribute to improved programme content, provide data on specific\ntarget groups and ensure compatibility and standardization of data collection\n(Family Health International 2000).\nThe core BSS indicators have been evolving over time (Table I). Until the\nUnited Nations General Assembly Special Session on HIV/AIDS (UNGASS)\nindicators were developed in 2002, there were no internationally-accepted HIV\nindicators. The UNGASS indicators were followed by the development of the\nMillennium Development Goal (MDG) indicators in 2003 and, subsequently,\nthe US President’s Emergency Preparedness Fund on AIDS Relief (PEPFAR)\nindicators in 2004. Although all of these indicators are similar to one another,\nthere are minor differences. Thus, it is difficult for persons implementing BSSs to\nchoose which indicators to use and complicated for others to compare studies\nwhich use different indicators. Furthermore, there are numerous other indicators\nthat can be used in BSSs depending upon the target groups and objectives of the\nsurvey.\nConflict, displacement, food insecurity and poverty have the potential to make\naffected populations more vulnerable to HIV transmission. The UNGASS\nDeclaration of Commitment on HIV/AIDS, states that ‘populations destabilised\nby armed conflict . . . including refugees, internally displaced persons, and in\nparticular women and children, are at increased risk of exposure to HIV infection’\n(United Nations General Assembly 2001). However, the common assumption\nthat this vulnerability necessarily translates into increased HIV infections and\nconsequently fuels the epidemic is not supported by data (Spiegel 2004). In the\nrecent past, HIV/AIDS interventions were generally not included by humanitarian organizations as part of their immediate response to conflict; HIV/AIDS was\nconsidered more of a developmental issue and not an immediate life threatening\ndisease such as malaria or cholera. However, thinking has evolved and it is now\ngenerally accepted that HIV/AIDS programmes must begin at the onset of a\nhumanitarian emergency, be multisectoral, and continue at every stage thereafter\n(Inter-Agency Standing Committee 2003). Furthermore, for refugees and\ninternally displaced persons (IDPs), HIV/AIDS programmes should be integrated", "output": {"entities": {"named_data": ["knowledge-attitudes-practice surveys (KAPs)", "Millennium Development Goal (MDG) indicators"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "HIV behavioural surveillance surveys 149\n\nTable I. Internationally accepted key BSS indicators.\n\nKnowledge International standard\n\nPrevention Percentage of young women and men\naged 15 �24 years who, in response to\nprompted questions, say that:\n1) people can protect themselves from\ncontracting HIV by having sex with only one\nfaithful, uninfected partner. [a]\n\nUNGASS, MDG, PEPFAR\n\n2) people can protect themselves from UNGASS, MDG, PEPFAR\ncontracting HIV by using condoms. [a]\n\nMisconceptions Percentage of young women and men\naged 15 �24 years who, in response to\nprompted questions, correctly reject that:\n1) A person can get HIV from mosquito UNGASS, MDG, PEPFAR\nbites. [a]\n\n2) A person can get HIV from sharing a UNGASS, MDG, PEPFAR\nmeal with someone who is infected. [a]\n\nGeneral Percentage of young women and men\naged 15 �24 who, in response to\nprompted questions, know that:\n1) A healthy-looking person can have UNGASS, MDG, PEPFAR\nHIV. [ab]\n\nAttitudes\n\nCare and support The number of respondents who report\nan accepting or supportive attitude of:\n1) Would be willing to care for a family\nmember who became sick with the AIDS\nvirus.\n\nPEPFAR\n\n2) Would buy fresh vegetables from a vendor PEPFAR\nwhom they knew was HIV�/.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["HIV behavioural surveillance surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "3) Female teacher who is HIV�/ but not sick\nshould be allowed to continue teaching in\nschool.\n\nPEPFAR\n\n4) Would not want to keep the HIV�/ status PEPFAR\nof a family member a secret.\n\nPractices\n\nCondom use 1) Percent of men and women (aged 15 �24)\nwho used a condom at last sex with a\nnon-marital, non-cohabiting partner, of\nthose who have had sex with a non-marital,\nnon-cohabiting partner in the last 12\nmonths. [bc]\n\nUNGASS, MDG, PEPFAR\n\na prior to the UNGASS indicators in 2002, the UNAIDS stated indicators did not specify youth\n(15 �24 years).\nb the MDG indicator replaces ‘have’ with ‘transmit’.\nc the MDG indicators do not specify ‘non-marital, non-cohabiting’ but add ‘high risk’. PEFPAR\nuses 15 �49 years.\n\nwith the surrounding host population response and a sub-regional approach\nundertaken in order to take into account the displacement cycle (UNHCR 2005).\nTimely and accurate data are needed to provide targeted and effective\ninterventions in conflict and post-conflict settings. Unfortunately, due to unstable", "output": {"entities": {"named_data": ["UNGASS indicators"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "150 P. B. Spiegel & P. V. Le\n\nsituations and lack of epidemiological expertise in many humanitarian agencies,\nthe data provided are often unreliable (Boss et al. 1994, Spiegel et al. 2004). To\nassess the quality and standardization of BSSs, and its predecessor, the KAP\nsurvey, undertaken in conflict and post-conflict situations, we evaluated the\nmethodological quality and use of internationally-accepted indicators of these\nsurveys conducted among refugee, IDP, host community, returnee, conflict and\npost-conflict populations. Recommendations were then provided to humanitarian\nagencies and governments on how to improve the quality and standardization of\nBSSs among conflict and post-conflict populations.\n\nMethods", "output": {"entities": {"named_data": ["KAP\nsurvey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "We collected all available HIV BSSs and reproductive health KAP surveys with an\nHIV component in refugee, IDP, host population, returnee, conflict and postconflict settings. This was accomplished by undertaking a literature review using\nPubMed, by searching the internet for key words (i.e. HIV, behavioural\nsurveillance survey, BSS, knowledge, attitudes practice, KAP, refugee, internally\ndisplaced person, IDP, returnee, conflict and post-conflict), and by contacting\nUN offices in affected countries and non-governmental organizations (NGOs)\nwhich undertake such surveys (e.g. Reproductive Health Response in Conflict\nConsortium, International Rescue Committee) and organizations which specialize\nin undertaking such surveys (e.g. Family Health International [FHI], US Centers\nfor Disease Control and Prevention [CDC]). When titles or abstracts of surveys\nwere found, but the actual report was not available, authors and organizations\nresponsible for the report were contacted directly. Inclusion criteria were any BSS\nor KAP survey with a quantitative HIV component undertaken among affected\npopulations, listed above, where a written report was available. Exclusion criteria\nwere surveys with an HIV component in the affected populations that were solely\nqualitative, and nationwide surveys that may have included one or more of the\naffected populations but where results were not disaggregated to differentiate\nthem from the overall population, and where reports were unavailable.\nThe survey reports were collected, evaluated, categorized and entered into an\nEpiInfo 3.2.2 (CDC, Atlanta, GA; version 4/26/2004) database under four broad\ncategories: background; methodology; report; and indicators. Sampling procedures were classified as reproducible if the population-based sampling defined a\nsampling frame and used probabilistic sampling, including proportional to\npopulation size (PPS) sampling if cluster sampling was used during the first\nstage, and all persons in the household within the stated age range were surveyed.\nAdditional indicators for the quality of survey methodology were assessed,\nincluding essential steps of survey preparation and report writing.\nInternationally-accepted standardized indicators were recorded as being\nincluded in the survey if the wording was the same or similar to the indicators\nin Table I. If information regarding survey methodology or indicators was not\nprovided in the reports, it was recorded as non-reproducible or accepted\nindicators not used, respectively.", "output": {"entities": {"named_data": [], "descriptive_data": ["reproductive health KAP surveys"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "HIV behavioural surveillance surveys 151\n\nResults\n\nA total of 40 BSS or KAP surveys were identified, of which 31 (77.5%) were\neligible according to the inclusion criteria. The 31 eligible surveys were undertaken between 1998 and 2005 in 14 countries. There were 12 countries (25\nsurveys) in Africa: Angola (1), Eritrea (1), Ethiopia (2), Kenya (2), Rwanda (2),\nSierra Leone (3), Somalia (1), South Africa (1), Sudan (2), Tanzania (3), Uganda\n(5) and Zambia (2); and 2 countries (six surveys) in Asia: Nepal (2) and Thailand\n(4). Eight (25.8%) surveys were undertaken in conflict settings, nine (29.0%) in\npost-conflict settings and 14 (45.2%) in relatively stable countries hosting\nrefugees. Among some of the eligible surveys, more than one affected population\nwas studied; refugees were included in 28 (90.3%) surveys, IDPs in six (19.4%)\nsurveys, returnees in three (9.7%) surveys and surrounding host populations\nin six (19.4%) surveys. The primary organizations responsible for the surveys\nwere NGOs, (23 surveys, 74.2%), CDC four surveys (12.9%), a United Nations\nagency three surveys (9.7%) and one government survey (3.2%). The sample\nsizes of the surveys ranged from 148 to 7,484, with a mean of 1,261 and a median\nof 549 persons. Fourteen (45.2%) of the 31 surveys were classified as\nreproducible (see Table II). Surveys undertaken by NGOs were significantly\nless reproducible than those undertaken by non-NGOs (chi-square test,\np �/0.05).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["HIV behavioural surveillance surveys", "BSS or KAP surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Survey methodology\n\nThe number of households or persons refusing to participate in the survey was\nreported in nine (29.0%) surveys, absent households or persons reported in seven\n(22.6%) surveys and the use of household replacement was reported in nine\n(29.0%) surveys. Eighteen (58.1%) used purely descriptive analysis while 13\n(41.9%) used both descriptive and comparative; for the latter, 11 (84.6%) of the\n13 reports stated which statistical tests were used to make comparisons. Ten\n(32.3%) of the survey reports did not state which statistical software was used. Of\nthe 21 (67.7%) reports that mentioned the type of statistical software, some used\nmore than one type: EpiInfo (11), SPSS (8), SAS (3), CSPro (2), SUDAAN (1)\nand SSP (1). Fourteen (45.2%) of the surveys had both a qualitative and\nquantitative component while 17 (54.8%) were solely quantitative.\nIn the written survey reports, 27 (87.1%) stated objectives, 10 (32.3%) stated\nthey asked for informed consent, 17 (54.8%) pilot tested the questionnaire, six\n(19.4%) of 30 surveys back-translated the questionnaire (one survey was\nundertaken in English so did not need back-translation), 27 (87.1%) provided\ntraining for interviewers (range of 1�16 days with median of 3 days), 11 (36.7%)\nof 30 surveys had gender balance among interviewers (one survey required\nrespondents to write answers on the questionnaire so there were no face to face\ninterviews), nine (29.0%) stated definitions (e.g. high risk sex, non-regular\npartner), 13 (41.9%) stated limitations and biases, 23 (74.2%) stated conclusions", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "152 P. B. Spiegel & P. V. Le\n\nTable II. Sampling methodology and reproducibility for BSS and KAP surveys (N �/31).\n\nSampling method Frequency Percent\n\nRandom sampling 23 74.2%\nConvenience 4 12.9%\nNot mentioned 4 12.9%\nTotal 31 100.0%\n\nIf random, what type?\nSimple random 4 17.4%\nSystematic 7 30.4%\nCluster sampling 8 34.8%\nNot mentioned 4 17.4%\nTotal 23 100.0%\n\nIf cluster sampling, was PPS used?\nYes 3 37.5%\nNot mentioned 5 62.5%\nTotal 8 100.0%\n\nAll eligible persons in household surveyed\nYes 31 100%\nNo 0 0\nTotal 31 100.0%\n\nSampling frame\nYes 23 74.2%\nNo 8 25.8%\nTotal 31 100.0%\n\nReproducibility\nYes 14 45.2%\nNo 17 54.8%\nTotal 31 100.0%\n\nand recommendations based on study data, 16 (51.6%) provided references and\n16 (51.6%) appended the questionnaire to the report.\n\nKey indicators", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The majority of surveys reported the same or similarly worded internationallyaccepted HIV indicators for prevention and misconception (except for the\nmisconception question on the possibility of getting HIV from sharing a meal with\nsomeone who is infected with HIV); however, few reported disaggregated\nindicators by age and gender (Table III). The majority of surveys did not report\nthe same or similarly worded internationally-accepted HIV indicators for practice\nand attitudes; few reported disaggregated indicators by age or gender (Table III).\nTwenty-six (83.9%) of the surveys asked an HIV practice question with 14\n(45.2%) asking the question that was the same as or similar to the internationallyaccepted practice indicator that we chose (Table I); five (35.7%) disaggregated\nthe indicator by gender and age (Table III). Twenty-one (67.7%) of the surveys\nasked HIV attitude questions with less than the majority asking at least one\nquestion that was the same as or similar to the internationally-accepted attitude\nindicators (Table I); few disaggregated by gender and age (Table III).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["internationallyaccepted HIV indicators for prevention and misconception"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "HIV behavioural surveillance surveys 153\n\nTable III. HIV/AIDS knowledge, practices and attitudes questions and indicators (N �/31).\n\nNumber* Percent\n\nKnowledge\nDid the study include prevention questions, even if 26 83.9%\nthey did not include the standard questions below?\n\n1) Sex with only 1 partner\nSame or similar wording 21 67.7%\nDisaggregated by gender and age 6 (N�/20) [�] 30.0%\n\n2) Using condoms\nSame or similar wording 26 83.9%\nDisaggregated by gender and age 6 (N�/25) [�] 24.0%\nDid study include misconception questions, even if 20 64.5%\nthey did not include standard questions below?\n\n1) Mosquito bites\nSame or similar wording 16 51.6%\nDisaggregated by gender and age 3 (N�/15) [�] 20.0%\n\n2) Sharing a meal\nSame or similar wording 14 45.2%\nDisaggregated by gender and age 5 (N�/13) [�] 38.4%\n\n3) Healthy-looking person can have/transmit HIV\nSame or similar wording 18 58.1%\nDisaggregated by gender and age 4 (N�/17) [�] 23.5%\n\nPractices\nDid the study include practice questions, even if they 26 83.9%\ndid not include standard questions below?\n\n1) Condom at last sex with a high risk partner\nSame or similar wording 14 45.2%\nDisaggregated by gender and age 5 (N�/14) 35.7%", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["HIV behavioural surveillance surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Attitudes\nDid the study include attitude-related questions, even 21 67.7%\nif they did not include standard questions below?\n\n1) Care for family with HIV/AIDS\nSame or similar wording 14 45.2%\nDisaggregated by gender and age 3 (N�/14) 21.4%\n\n2) Would buy fresh vegetables\nSame or similar wording 5 16.1%\nDisaggregated by age 1 (N�/5) 20.0%\n\n3) Teacher who is HIV�/\n\nSame or similar wording 6 19.4%\nDisaggregated by age 1 (N�/6) 16.7%\n\n4) Would not want to keep secret\nSame or similar wording 7 21.4%\nDisaggregated by gender and age 1 (N�/7) 14.3%\n\n- N�/31 unless specified.\n\nN�/1 less the total number of surveys with same or similar wording because one survey targeted\none subpopulation of a specific gender and age.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "154 P. B. Spiegel & P. V. Le\n\nDiscussion\n\nOverall, the majority of surveys that met the inclusion criteria were of insufficient\nmethodological rigor to be reproducible. Other important methodological issues,\nsuch as households or persons who were absent or refused to participate, as well\nas whether replacement of such households or persons occurred, were not stated\nin most of the reports. Sample sizes had a wide range and some may have been of\ninsufficient size to be precise enough to interpret results or make meaningful\ncomparisons with future surveys. The majority of questionnaires were not field\ntested or back-translated nor were qualitative methods used to complement the\nquantitative methodology used in many of these surveys. Only a minority of\nsurveys obtained informed consent from participants. Few provided definitions\nof essential terms, such as high risk sex or non-regular partners. Although most\nof the surveys included training of surveyors, few reported gender balance of\ninterviewers. All of the above methodological flaws inject sufficient biases into\nthese surveys to make the most of them unacceptable.\nThe majority of the written reports lacked sufficient detail and structure to be\nanalysed and interpreted by the reader in a meaningful way. Most reports\nprovided objectives for the survey as well as conclusions and recommendations.\nHowever, few stated limitations and biases and only half appended the\nquestionnaire to the report. Important methodological details were missing in\nmany surveys, including information on how the sample size was chosen, the\nspecifics of the sampling methodology and whether replacement was used. Most\nof the analyses were descriptive in nature with few reports using comparative\nstatistics. The majority of reports used some variation of the internationallyaccepted HIV indicators for knowledge (e.g. prevention and misconceptions) but\nonly a minority used some variation of these indicators for practice and attitudes.\nThe disaggregation by age or gender varied considerably among reports which led\nto difficulties in comparing results; furthermore, few studies reported indicators\naccording to both gender and age.\nThere are limitations to this article. Despite an attempt to search as widely as\npossible in the published and grey literature, as well as to contact organizations\nknown to undertake BSSs in conflict and post conflict settings, some surveys will\nhave been missed. In addition, those included are not just BSSs but reproductive\nhealth KAP surveys with an HIV component. The latter may not contain as much\ndetail on HIV as BSSs, however, this would neither affect the basic methodological weaknesses nor the absence of key internationally-accepted standardized\nHIV indicators. Some of the BSSs examined in this report were conducted before\n2002 when the internationally accepted UNGASS indicators were developed; this\ntogether with the changing of indicators over time makes it difficult to interpret\nthe usage of internationally-accepted indicators. Misclassification may have\noccurred because results were based on findings written in the reports reviewed.\nFor those reports that omitted key methodological issues or results, the data were\nrecorded in a negative fashion (e.g. if type of sampling was not mentioned, the\nsurvey was recorded as not employing random sampling).", "output": {"entities": {"named_data": ["internationally accepted UNGASS indicators"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "HIV behavioural surveillance surveys 155\n\nBSSs are expensive and time consuming. In conflict and post-conflict settings,\nthe costs of BSSs vary according to population and geographic size, but generally\ncost at least US$50,000 for a typical refugee camp when conducted by qualified\nspecialists (source: UNHCR based on 11 BSSs in five countries from 2004 to\n2005). Anecdotally, some organizations having insufficient funding undertook\nsurveys on their own or with inexperienced consultants; these surveys often had\npoor results. Therefore, adequate funding is needed before such surveys are\nundertaken.\nBSSs collect crucial HIV/AIDS data to inform local and national interventions\nas well as for programme monitoring and evaluation, and for the allocation of\nscarce resources. Scientifically sound and reproducible studies with structured\nand detailed reports are needed. Our results showed that organizations that are\nspecifically trained and have extensive experience in doing these surveys, such as\nCDC, undertook surveys and produced reports that were superior to those of\nNGOs. As was previously recommended for nutrition surveys in humanitarian\nemergencies, NGOs, governments, and UN agencies interested in undertaking\nBSSs in conflict and post-conflict settings must ensure that the process is\ndeveloped and directed by qualified and experienced experts (Spiegel et al.\n2004). This may require hiring organizations that specialize in undertaking such\nsurveys. The decision to undertake such surveys should be made in a coordinated\nfashion with all relevant organizations, governments and affected populations.\nPersons writing such proposals should be aware of the financial, time and\nlogistical constraints in correctly undertaking BSSs. Donors should only fund\nrealistic and technically sound proposals. Systematic training and ongoing\nadvocacy on these issues among NGOs, UN agencies, governments and donors\nis needed. An inventory of international experts that can assist in planning and\nundertaking field missions should be developed.\nUnlike nutrition surveys in humanitarian settings (Spiegel et al. 2004), there is\ncurrently no standard questionnaire, methodology nor practical manual on how\nto undertake BSSs in conflict and post-conflict settings. Many BSSs are\nundertaken on a nationwide scale with large samples that require significant\ntechnical expertise and resources (e.g. demographic and health surveys with an\nHIV component). The FHI BSS guidelines for repeated behavioural surveys in\npopulations at risk of HIV (Family Health International 2000) are often used as\nthe standard manual for undertaking BSSs. However, this 350 page manual\ndescribes how BSSs can be undertaken in numerous different situations and is not\ndesigned to be a ‘how to manual’ such as those designed for nutrition surveys in\nhumanitarian emergencies (Me´decins Sans Frontie´res 1995, Save the Children\n2004). Furthermore, conflict and post-conflict situations are unique and require\ndifferent information from other populations (Spiegel 2004, UNHCR 2005).\nThese include questions on displacement and interaction with surrounding host\npopulations as well as sensitive questions on sexual exploitation and violence.\nRecognizing this need, the United Nations High Commissioner for Refugees in\ncollaboration with the World Bank, UNAIDS and the Great Lakes Initiative on", "output": {"entities": {"named_data": [], "descriptive_data": ["demographic and health surveys with an\nHIV component"], "vague_data": ["HIV behavioural surveillance surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "156 P. B. Spiegel & P. V. Le\n\nAIDS are engaged in a process that will produce a BSS manual that can serve as a\ngeneric BSS tool for conflict-affected and displaced populations. This manual will\ncontain practical sections on methodology, analysis and results, indicators, report\nwriting as well as sample questionnaires that contain modules on pre-displacement, displacement and post-displacement/interaction with the surrounding host\ncommunity. Emphasis on the latter and the need to undertake such surveys\namong both the displaced populations and surrounding host communities is\nemphasized. This effort will ultimately aid in the provision of integrated HIV/\nAIDS programmes for refugees and surrounding host populations (UNHCR\n2005).\n\nAcknowledgements\n\nRichard Brennan, Laurie Bruns, Ann Burton, Marelize Gorgens, Sara Hersey,\nMichelle Hynes, Amey Kouwonou, Njogu Patterson, Susan Purdin, Marian\nSchilperoord, Richard Seifman and Dieudonne Yiweza.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "# - Guidance note ## _Informing Afghanistan Protection Cluster strategic priorities for 2017-2018_ **INTRODUCTION**\n\nA round of consultations with key stakeholders related to the efficiency of the Afghanistan Protection\nCluster (APC) highlighted the need to review and rethink strategic priorities and coordination\nmechanisms to enhance protection outcomes for populations affected by the conflict. Main findings\nindicate that there is a lack of conflict analysis, lack of prioritization of protection concerns, limited\nfocus on protection of civilian in conflict areas, poor quality of the protection response plan and weak\ninformation management. The paper outlines key elements and findings to be considered or\nincorporated when developing the 2017-2018 APC Strategy, while taking into account responsibilities,\nchallenges, limitations and opportunities.\n\n**The APC guidance note will serve as a basis to organize a strategic APC workshop with key APC**\n\n**members that will to discuss, define and agree the following elements:**\n\n- **Information management** : Agree figures and data set to be regularly updated by APC\nmembers, especially AoRs (monitoring framework provided by HNO/HRP)\n\n- **Protection analysis** : identify key protection risks to be tackled by APC members and gap\nanalysis (to inform HNO/HRP)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- **Protection approach** : identify the most relevant approach to tackle identified protection risks\n(prevention, mitigation and response) through addressing threats, vulnerabilities, and\ncapacities using the protection risk equation.\n\n- **Key geographical areas to focus on** : Defined by the HAG analysis and need assessments\nconsidering the nexus with development actors\n\n- **Theory of change** : define protection narrative using the HCT protection strategy template and\ndescribe key expected protection outcomes\n\n## **SITUATIONAL ANALYSIS AT A GLANCE**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Despite considerable support from the international community over the past 15 years, the security\nenvironment has continued to deteriorate. This has led to widespread poverty, shrinking humanitarian\nspace, increased violence against civilians and vulnerabilities of population affected by displacement,\nlimited prospect for stabilization, peace, durable solutions and development. Global and regional\npolitical developments have also resulted in shrinking protection space for Afghan refugees in Europe,\nPakistan and Iran. The complexity of emergency humanitarian needs versus chronic poverty in host\ncommunities has evoked a shift to a needs versus status based approach to assistance. Limited funding\nhas also highlighted the requirement to develop thresholds to prioritize assistance. Humanitarian\naccess is a key protection concern, especially for IDPs in rural and remote areas where humanitarian\nactors have limited coverage due to internal security policies and on-going conflict.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "This is underscored by the findings from 2016, which indicate that 33 out of 34 provinces experienced\nconflict between non-state armed groups and government forces throughout the year while districts\nin over half the country reported conflict induced displacement. Approximately 20% of IDPs were\ndisplaced to hard-to-reach areas. As a result of ongoing conflict with AGEs and Governmental forces\nin a large part of Afghanistan, the following patterns of displacement have been identified:\n\nPage **1** of **12**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Significant number of new IDPs (141,775 as of June 2017) due to the recurrent military clashes,\ndecrease of 25% compared to 2016;\n\n- Increase number of refugee returnees as of June 2017 compared to the previous year due to\nshrinking protection space in Pakistan and Iran;\n\n- Increased trends in short term displacement due to the lack of humanitarian access, with\npeople often returning to their place of origin after the engagement has finished (ex. Situation\nin Kunduz in 2016: 118,166 people were displaced from Kunduz in September-October, and\n69,916 Kunduz IDPs that were displaced within the Northern and North-Eastern provinces\nreturned to their places of origin within several weeks – a month period);\n\n- Pattern of secondary and multiple displacement in rural and urban centers (according to the\nREACH study on prolonged displacement, some 23% IDPs were displaced twice or more [1] );\n\n- Pattern of return in unsafe areas due to limited livelihood opportunities in urban centers and\nthe need to tend to crops [2] ;\n\n- Pattern of local integration in urban center of protracted IDPs due to lack of security in area\nof origin;", "output": {"entities": {"named_data": [], "descriptive_data": ["REACH study on prolonged displacement"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Recurrent shocks and stressors have been putting an incredible pressure on capacities of the Afghan\npopulation to cope with the substantial movement of IDPs and returnees hence dramatically\nincreasing community tensions, negative coping mechanisms, violations of human rights, and overall\nvulnerabilities [3] . Children are particularly at risk, with incidences of child labor, child marriage and\nchildren out of school being higher within IDP/returnee groups than the quiescent population [4] . The\nfocus of emergency humanitarian assistance on those displaced within the last six months and limited\nassistance for returnees past immediate survival have also contributed to a gap in reintegration and\nearly recovery, with affected populations often returning to worse conditions.\n\nMany contested areas are rural and remote, however assistance is focussed on urban and peri-urban.\nPattern of displacements towards urban centers has increased the risk to civilians due to non-respect\nof IHL and IHRL, indiscriminate targeting and use of civilian assets in the conduct of military operations.\nIt has also strongly diminished the capacities for stabilization and development of the country with\nregard to the gradual loss of control of large parts of the territory by the GoIRA.\n\n---\n[4] Source: UNICEF Sit reps", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Lack of livelihood opportunities in areas of displacement combined with wide spread insecurity, lack\nof freedom of movement, and limited humanitarian access to AGEs controlled areas is generating an\nalarming humanitarian and protection crisis that requires immediate attention and a new strategic\napproach by the APC.\n\n## **PATTERNS OF ABUSE**\n\nAnalysis of protection risks needs to be improved to better prioritize and mitigate risks in 2017-2018.\nThe APC will dedicate further time and resources to producing a comprehensive protection risk\nanalysis. Information available suggest the following patterns of abuse:\n\n1 REACH PIDP study, p. 17\n\n2 IDMC. Afghanistan: New and long-term IDP risk becoming neglected as conflict intensifies.\n\n3 The findings of the Protection community assessment in the East indicate that additional influx of people into communities, that already\nhave limited service providers’ capacities, overstretches the resources, like water, health and education, leading to the overcrowded\nschools, hospitals facing challenges with the number of people and water sources being not sufficient to meet the needs of the local and\ndisplaced population.", "output": {"entities": {"named_data": ["REACH PIDP study"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Protection of civilians related to IHL/IHRL violations**\n\nCivilians bear the brunt of the conflict, as they are caught in the crossfire, victimized by indiscriminate\nattacks or deliberately targeted. Alongside more traditional guerrilla warfare tactics, a visible intent\nby Taliban to shift tactics towards large-scale attacks, particularly on urban areas, poses grave risks for\ncivilian protection and results in substantial levels of forced population movements.\n\nFamilies often leave villages abruptly and with little prior warning, in response to rapidly encroaching\nclashes or military operations. IDPs often flee only with what they can carry, surrendering key assets\nin exchange for relocation to safer areas. Displaced populations in Afghanistan often benefit from the\nsupport of host communities, largely relying on tribal affiliations or the support of established kinship\nnetworks. Spontaneous camps and settlements are therefore the exception rather than the rule.\nHowever, widespread poverty among host communities and the rapid depletion of existing resources\ngenerally necessitates a humanitarian response to address acute needs (food, shelter, and basic relief\nand hygiene items) to address relatively high levels of vulnerability in the initial phases of\ndisplacement, particularly among those with weak support from family and community networks.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In its Protection of Civilians in Armed Conflict Annual Report 2016, UNAMA recorded the highest\nnumber of civilian casualties since it began systematic documentation of civilian casualties in 2009.\nThe increase in casualties among children is particularly alarming. Key objectives of the Strategy are\nto be action-oriented, consider new trends in conflict and displacement and align with priorities\nidentified by the reinstated Humanitarian Access Working Group (HAG). A draft of an advocacy\nstrategy has been revisited in 2017 with the support of the Afghanistan Protection Cluster (APC) and\nProtection of Civilians Working Group (PoCWG). It identified the following focus areas for advocacy:\n\n- Adherence to IHL principles of distinction, proportionality and precaution to cease the use of\nexplosive weaponry and aerial attacks in civilian populated areas, the indirect use of weaponry\nand deliberate targeting by parties to the conflict;\n\n- Protection of humanitarian space including aid workers and delivery, healthcare\ninfrastructures in line with ICRC/MSF #NotATarget campaigns and education infrastructures\nin line with the Oslo Safe Schools Declaration;\n\n- A safe passage for fleeing civilians in times of military operation and conflict induced\ndisplacement (especially in large scale emergencies and when military operations aim at\ncapturing or re-capturing cities);", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Continued pressure on the National Unity Government to publish and implement its National\nPolicy on Civilian Casualty Prevention and Mitigation, including all aspects of IHL, with the\nsupport of UN and civil society;\n\n- Development of services to protect children affected by conflict, especially to prevent child\nrecruitment and exploitation of children (particularly, bacha bazi) by parties in the conflict.\n\n**Protection from explosive remnants of war**\n\nMore than three decades of armed conflict in Afghanistan has left widespread mine and ERW\ncontamination across the country. It is estimated that 3,511 minefields, 309 battlefields and 52\ncontaminated firing ranges remain throughout the country, which affects 1,500 communities. These\nimpacted communities are spread out in 256 districts, in 33 out of 34 provinces, affecting an estimated\n910,000 people (figures from UNMAS).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In terms of mines, the priority areas to be cleared are those contaminated sites that are close to\ncommunities and where high levels of casualties are being reported. For the current planning period\n(according to the Mine Action Programme for Afghanistan strategic plan) the top 20 districts that\nreport the highest number of casualties (since Jan 2015 until now) are spread across 9 provinces,\npredominantly in the South and South East regions. They are: Maywand, Nad Ali, Tirin Kot, Shah Wali\n\nPage **3** of **12**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Kot, Qalat, Lashkar Gah, Dihrawud, Shahjoy, Andar, Nawa-I- Barak Zayi, Nahri Sarraj, Gelan, Ghazni,\nArghistan, Chaghcharan, Qaysar, Puli Khumri, Kandahar, Bala Buluk, Spin Boldak.\n\nAfghanistan still reports the highest number of casualties from mines and explosive remnants of war\nin the world (according to the 2016 Landmine Monitor). In total UNAMA documented 326 incidents\nof explosive remnants of war detonation resulting in 724 civilian casualties (217 deaths and 507\ninjured), an increase of 66 per cent compared to 2015, rendering explosive remnants of war\nresponsible for six per cent of all civilian casualties in 2016. Men accounted for 13 per cent and women\naccount for 3 per cent of these casualties.\n\nEven more worryingly, children comprised 86 per cent of all civilian casualties caused by the\ndetonation of explosive remnants of war in 2016 – making it the second leading cause of child\ncasualties after ground engagements (609 child casualties documented, with 183 deaths and 426\ninjuries). In the first three months of 2017, once again children comprised the vast majority – 81 per\ncent – of the casualties caused by the detonation of explosive remnants of war.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNAMA documented a further increase in civilian casualties from unexploded ordnance during the\nfirst three months of 2017, recording 203 civilian casualties (50 deaths and 153 injured), a one percent\nincrease compared to the same period in 2016.\n\nIn April 2017, UNAMA again drew attention to explosive remnants of war, citing them as one of the\nmain causes for a steep rise in child casualties during the first three months of 2017. Danielle Bell,\nUNAMA’s Human Rights Director stated, “The 17 per cent increase in child casualties reflects the\nfailure of parties to the conflict to take adequate precautions to protect civilians, including through\nmarking and clearing unexploded ordnance after fighting ends.”\n\nThe priority areas with regards to unexploded ordnance (UXO) are those that see the heaviest fighting\nand thus shift as the fighting shifts. The mine action sector responds by deploying cross trained teams\nto the areas contaminated once safe. The teams survey and clear spot tasks posing immediate danger.\nMobile mine/ERW Risk Education Teams are also deployed where needed, in places of displacement\n(preparing for people to return), in contaminated areas of return, and at encashment and transit\ncenters.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The returnee population is relatively more prone to lethal landmine and ERW accidents than the\ncivilian population living in their community of origin. According to data from DMAC and UNMAS,\ntravelers including returnees and IDPs account for 30% of all landmine, ERW and Pressure Plate IED\naccidents. Returnee populations are particularly vulnerable due to their unfamiliarity with the overall\nthreats posed by explosive hazards; lack of information about how to identify them and lack of the\npotentially life-saving behaviour to adopt in response, in addition to their unfamiliarity with their new\nsurroundings, including the history of armed clashes and potential for explosive contamination in\nareas of settlement.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "All surviving casualties of ERW face social, economic and psychological problems impacting not only\nthe individual, but their families, communities and the Afghan society as a whole. As such, there is a\ncritical need for physical and social rehabilitation programmes throughout Afghanistan.\nApproximately 90% of the Afghan population lives more than 100Km far from a rehabilitation centre;\nwhile 20 Provinces out 34 have no prostheses and orthoses services available. With more than 1,360\nimpacted communities located 10km – 50km from adequate health centres, and a further 26 impacted\ncommunities located more than 50km from adequate health centres, the likelihood of fatalities due\nto mine/ERW and PPIED incidents increases. These 1350 communities are spread across 32 provinces,\nwith a map of the hazards and health centres at the end of this guidance note.\n\nPage **4** of **12**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The main challenge to respond the above are related to funding shortfalls. Afghanistan has the\ncapacity to respond to the threat of mine and ERW and to respond to the needs of victims, but with\nfunding to the sector having dropped by 80 per cent since 2013, the sector is now unable to mobilize\nsufficient teams to respond to the demand.\n\nThus going forward, in addition to regular operations, the sector will focus on:\n\n- Maintaining a quick response team on standby who can quickly deploy to areas where fighting\nhas occurred to survey for unexploded ordnance, clear critical tasks and deliver risk education\nto at risk populations;\n\n- With funding decreasing, but casualty numbers rising or remaining the same, the sector needs\nto enhance efforts to do more with less, or in other words to be more efficient. Significant\ncuts have already been made in the cost to clear 1 square meter of contaminated land. Going\nforward, efforts will be made to make the coordination part of the sector more efficient\nthrough finalizing the handover to national counterparts and thereby reducing the expensive\nUN footprint.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Advocacy vis-à-vis allied, Government and AGEs with regards to the use of weaponry that may\nleave ERW in heavily populated areas;\n\n- Advocacy on the need to clear battle sites post kinetic engagement, especially in populated\nareas;\n\n- Strengthen resource mobilization efforts to ensure the sector can function at full capacity. The\ntotal funding for mine action requested through the HRP is $3,080,676 (across MRE,\nEOD/survey and coordination), of which 80% ($2,485,695) has been mobilised.\n\n**Child protection**\n\nDuring displacement and emergencies and conflict, violations of child rights occur in multiple forms.\nThe increased insecurity caused by violence and conflict has exacted an increasing toll on children in\nterms of the number of civilian casualties along with serious child protection concerns.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The rapid humanitarian assessments of newly conflict-induced displaced populations often detect\nchildren amongst those injured by the armed clashes. Aside from material hardships, the psychological\nimpact of the conflict and subsequent flight is deemed to be severe. Recruitment and use of children\nby armed forces and armed groups remains a significant risk in light of the fragmentation of NSAG and\nvarying degrees of interest in compliance with IHL. Active conflict led to 11,418 civilian casualties [5 ] in\n2016 - approximately 11% were women and 31% were children. In 2016, the Country Taskforce on\nMonitoring and Reporting (CTFMR) verified 57 incidents of recruitment and use of children in the\nconflict (89 boys) who were recruited and mainly used for planting IEDs, transporting explosives,\ncarrying out suicide attacks and spying. Forced recruitment is primary reason given by Afghan asylum\nseekers in Sweden and Norway. Poverty, coercion and lack of livelihood opportunities, including\nduring the more prolonged phases of displacement, is also a factor that contributes to the recruitment\nof children, particularly adolescents. Access to education in displacement is generally hindered by\nseveral factors: Poverty and destitution, with a loss of assets and means of livelihood, often forces\ndisplaced families to engage children in support of family resilience and interim livelihood strategies.\nLack of civil documentation, cultural and social norms, threats and intimidation, social status and\npoverty are significant obstacles. In 2016, 423 schools were intermittently closed due to conflict and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "5 UNAMA Protection of civilians 2016 Annual Report. Available here:\nhttps://unama.unmissions.org/sites/default/files/protection_of_civilians_in_armed_conflict_annual_report_2016_feb2017.pdf\n\nPage **5** of **12**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "insecurity, affecting at least 11,200 students, the highest number of closures recorded by the Ministry\nof Education and UNICEF since 2003 [6] .\n\nBatcha Bazi [7] is also widespread in the country and the penal code is currently being changed to make\nthis practice illegal. Parties to the conflict and the police are key perpetrators.\n\n**Unaccompanied and separated minors and families is an important concern**\n\nThe same poverty and instability caused by conflict and natural disasters force many Afghans to leave\nthe country to seek better economic opportunities abroad. Adolescent boys in particular enter\nneighboring Iran or Pakistan unaccompanied on a regular basis in the hope of finding job opportunities\nand to contribute to their household income. Some of them move through these countries as transit\nlocations with the ultimate intention being to reach European countries. However, the reality is that\nmany of these children face extreme conditions, often fall into the hands of human traffickers, and\nget abused, imprisoned or even killed. Many are caught by authorities and sent back to Afghanistan.\n\n---\n[7] Bacha bāzī (Dari:](https://en.wikipedia.org/wiki/Dari_language) بازی بچه [, literally \"boy play\"; from بچه bacha, \"child\", and بازی bāzī, \"game\") is a slang term in Afghanistan for a wide](https://en.wikipedia.org/wiki/Slang)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Afghan children being formally sent back to Afghanistan from Iran return through one of two border\ncrossings, either Islam Qala close to Herat or Zarang in Nimroz province in the South. During the year\n2016, a total of 4,396 UASC were sent back from Iran. It is estimated that 20-30% of these children do\nnot go through a formal return process and do not receive any support. Many of these children have\nreported horrific stories of abuse and exploitation, including from Iranian police and detention\npersonnel. The experience faced by those adolescents are quite harsh especially as they leave\nAfghanistan via irregular routes, they are more likely to experience exploitation, abuse and violence\nnot to mention detention prior to their return to Afghanistan. Based on the need presented, a package\nof services are being offered to children at the Islam Qala border including psychosocial support and\nfamily tracing and reunification by UNICEF and its partner in coordination with IOM, DoRR and\nDoLSAMD. From the period between July 2016 and March 2017, approximately 1,500 children\nreturning through the Islam Qala/Milak border received the above mentioned services with UNICEF\nsupport.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "A decision was made by the Iranian Government to divert the deportation route from the Islam Qala\nborder to the Milak border in March 2017 which resulted in no flow of UASC to Islam Qala while the\nflow via Milak border doubled. In order to ensure those children received appropriate services,\nUNICEF temporarily shifted the partner from Islam Qala border to Malik border until the expected reopening of the Islam Qala border in July 2017. In Milak border, approximately 150 children are assisted\nby the end of May 2017 with the same package of services as they were offered in Islam Qala and\nresettled in with their families. Additional UNICEF NGO partners were brought on board (HRDA) in\nJune 2017, to assure coverage along the entire border.\n\n**Protection of women and girls**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Women and girls in Afghanistan continue to suffer, directly and indirectly, from the impact of the\nconflict and of the displacement. The respect and fulfilment of their rights during displacement remain\nchallenged. UNAMA documented 1,218 women casualties in 2016, a 2% decrease from 2015. So far\nthere is little evidence that sexual violence is used as a targeted strategy in the conflict. Obstacles\nrelated to social and cultural norms and lack of identification and response capacity does not allow to\nmeasure patterns and indications of episodes of GBV perpetrated by parties to the conflict. However,\nit is presumed that gender based violence occurs widely like in any other armed conflict and\n\n6 Consolidated input (UNAMA Human Rights, OCHA, UNHCR, Afghanistan Protection Cluster, IOM) for the Security Council's informal\nExpert Group on the protection of civilians’ discussions on Afghanistan", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "displacement setting. In addition, the emergence of new non-State armed groups affiliated with ISIS,\nparticularly in the Eastern, Northern and Southern parts of the country, has contributed to a further\ndeterioration of the situation for women and girls. While most of the facts remain unverified due to\nthe lack of humanitarian access, frequent reports are received from displaced populations on the\nimposition of stringent social and moral codes for women and girls, including stricter limitations to\nfreedom of movement and to seek basic health and education services among others.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Given the protracted conflict and continued displacements caused by the conflicts, a recrudescence\nof certain traditional harmful practices, such as early and forced marriages8, women and girls do not\nhave meaningful access to education and health services in safety and dignity. The worsening\nprotection environment and increase in internal displacement have generated an increase of\nvulnerability which can be correlated with the increase in the incidences of traditional harmful\npractices as negative coping mechanisms. Additionally, an absence of information/awareness about\nrights, negative effects of the harmful practices on women, girls and children and the lack of quality\nGBV services further contribute in already fragile context. In this context, existing capacities of basic\nGBV services including psycho-social counseling, medical, referral and response remain very limited.\n\nFinally, the practice of bacha bazi however, is widespread among the parties in the conflict and\nrepresents a Gender Based Violence for young boys.\n\n**Protection of House Land and Property**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Forced displacement often leads to the loss of land, homes and other property with serious\nconsequences for individuals and communities, who are often deprived of their main source of\nphysical and economic security. Disputes involving housing, land and property (HLP) are both a\nfundamental cause of conflict as well as a result arising in the aftermath of conflict and can pose\nobstacles to return, reintegration and reconciliation. These disputes pose immediate protection and\nearly recovery challenges in humanitarian operations. If left unaddressed, disputes on HLP can\nundermine peace and re-fuel hostilities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Loss of land and property can have serious consequences for the lives, health and well-being of\nindividuals and communities and expose them to various risks. Without access to land, homes and\nproperty people are often deprived of their main source of physical and socio-economic security,\nincluding shelter, water, and food as well as the ability to earn a sustainable livelihood. Lack of a home\nor a fixed residence can also restrict people’s access to assistance and services, including education\nand health care, and limit their access to credit. As a result, displaced persons may suffer increased\npoverty, marginalization and are at risk of harassment, exploitation and abuse. Women and children\noften suffer disproportionately from the loss of land, homes and property. Discriminatory laws and\npractices frequently prevent women and girls from owning, leasing, renting and/or inheriting\nproperty. In case of divorce or the death of husbands, fathers or other male relatives, women and girls\nmay be forced to leave their homes, coerced into marriage, or subjected to other harmful practices.\n\n## **CATEGORIES OF PEOPLE OF CONCERN OF PROTECTION**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Internally Displaced Persons (IDPs)** : Over 652,600 Afghans (approximately 96,000 families) were\nnewly displaced due to conflict in 2016, adding to a protracted IDP population of over 1 million. Most\nIDPs found refuge with host families in neighboring communities, already facing extreme poverty.\nFood, adequate shelter, WASH, and health care remain high priority needs, while efforts to raise\nawareness of mines and ordnance risks are also ongoing. A majority of IDPs live an insecure existence\nin makeshift shelters and informal squatter settlements with irregular access to services, poor\n\n8 8.8% of women aged 20-24 were married or in a union before the age of 15 and 45% before the age of 18 (AfDHS15)\n\nPage **7** of **12**", "output": {"entities": {"named_data": ["AfDHS15"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "sanitation including a lack of latrines, and fragile livelihood strategies; others reside in shared and\novercrowded rental accommodation, or with relatives.\n\n**Returnees** : In 2016, more than 600,000 documented and undocumented Afghans returned from\nPakistan and Iran including 372,577 registered refugees who returned under UNHCR’s facilitated\nreturn program and were provided with UNHCR cash grants as part of their repatriation assistance\npackage. The majority of returnees have indicated Kabul, Nangarhar, Kandahar, Herat, Balkh, Ghazni,\nBaghlan and Kunduz provinces as their intended destination for return, including areas subject to\nattacks by armed groups. Returnees report a lack of land and adequate shelter, insufficient livelihoods,\ninsecurity, and poor access to services as obstacles to sustainable return and reintegration. These and\nother factors have forced many returnees to undertake secondary movement to locations, particularly\nin urban centers, other than their place of origin.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Refugees and Asylum Seekers** : As of December 2016, 208 individuals were recognized as refugees\nwhile 135 had sought asylum. Financial support to these refugees and asylum-seekers, according to\ntheir vulnerability while meeting basic needs for food and shelter, must continue because of their lack\nof income, livelihood opportunities, and effective legal protection. Meanwhile, is estimated that over\n125,000 Pakistani refugees who have fled North Waziristan, mostly in 2014, are still hosted in\nAfghanistan. The lack of formal birth registration for refugee children born in Afghanistan may\nheighten the risk of statelessness.\n\n**Host/Affected Communities** : The year 2016 saw the highest level of security incidents (23,712) in over\na decade, including some 3,498 civilian deaths and 7,920 injured civilians. A similar trend was reported\nin January 2017, amid increasing territorial gains by AGEs. It is projected that conflict will continue to\nfrequently result in substantial levels of forced population movements placing an additional burden\non overstretched resources and support systems with some members of the host communities\nexperiencing the same assistance needs as IDPs.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**People with specific needs** : Conflict-induced displacement disproportionately affects individuals with\nspecific needs, such as children, constituting around 60% of the displaced population, women, older\npersons, and persons with disabilities. These populations are often exposed to the greatest\ndeprivations and harshest conditions. Access to health for conflict-affected and displaced populations\nis gravely compromised by the extremely poor conditions of public health structures. Moreover, the\nnumerous episodes of grave breaches of IHL with respect to medical facilities, medical personnel, and\nmedical transport by NSAG, has led to numerous closures of facilities and the loss of access to lifesaving medical care by local and displaced population. The chronic lack of female personnel and\nlimited outreach hinders access for women to critical services and treatment. There is a duty to ensure\nthat protection is mainstreamed across all response sectors as people with special needs are often\nexposed to protection concerns, including a lack of privacy and GBV due to overcrowded shelters,\ndiversion of food assistance, placement of water sources and cash distribution points at a far distance\nfrom areas of settlement and where markets and community latrines are inaccessible.\n\n## **HUMANITARIAN AND DEVELOPMENT NEXUS**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Returns and displacement are concentrated in time and space, thus posing a disproportionately large\nchallenge to the absorption capacity of some districts and provinces. While the local impact of a\nmassive influx of refugees, and the capacity to reintegrate, depends on a range of factors, one thing\nis clear: local absorption capacity certainly has a limit. Once the limit is reached, competition over\nresources could trigger or reinforce pre-existing causes of conflict, especially since institutions are\nweak. The increase in secondary displacement among returnees is a strong sign that the country’s\ncapacity to absorb and reintegrate additional inflows of returnees was already overstretched before\nthe surge of the recent months’ returns. There is no reason to believe trends will be reversed: a higher\n\nPage **8** of **12**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "number of returns from abroad will likely result in an increase in internal displacement. In particular,\nthe continued deterioration of the security situation and the economic crisis in Afghanistan are likely\nto further challenge the reintegration of more recent returns. Whilst displacement is not the principal\ndriver of vulnerability in this context, many of the factors related to displacement, including high levels\nof poverty, reduced access to informal safety nets, a lack of documentation and the loss of land and\nassets, have increased the vulnerabilities of some displaced households. REACH has estimated that\nsome 759,293 IDPs, returnees and urban poor have settled in the informal settlements across the\ncountry. These settlements rarely offer any kind of formal social protection, education and psychosocial services.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In this context it is important to consider the difference between humanitarian and development\nprinciples and what this means for the neutrality of aid. Furthermore, humanitarian assistance\nmodalities cannot respond properly to the urban displacement phenomena, the scale of displacement\nand nature of needs generated. It requires different types of intervention modalities along early\nrecovery and development types of programming. The concept of the “new way of working” outlines\nthe importance of mobilising development actors and further consider comparing advantages in these\nkind of situations.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The Government of Afghanistan has laid out its vision in the Mutual Accountability Framework (2014)\nand the Afghanistan National Peace and Development Framework (ANPDF 2017-2021) to transform\nAfghanistan in the coming years. In part, the aim of these frameworks is to ensure the rights of all\ncitizens, including returnees and IDPs, to economic and physical security. The ANPDF further\nemphasizes that finding solutions for the needs of IDPs and returnees is a “vital part of the national\ndevelopment strategy”, thereby recognizing that the response requires a ‘whole of Government\napproach’ through its National Priority Programs (NPPs). Land tenure security, property rights, and\nupgrading the informal settlements are prioritized throughout the framework as measures to reduce\npoverty.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "These developments represent a major opportunity to advance on key policy decisions - such as the\nright to settle in the area of choice, the right to obtain civil documentation in the area of settlement\netc. – and to prioritize and target development response to vulnerable populations. However, the\nimplementation of the Displacement & Returns Executive Committee Policy Framework and Action\nPlan at provincial level, taking into consideration an inclusive approach to respond to the needs of\nIDPs, returnees and host communities, represents a challenge but is also an opportunity to bridge the\ndivide between humanitarian and development interventions, implementation modalities and\nfunding streams. The commitment of donors towards a needs based approach based on a planning\nprocess that consolidates the immediate-, medium- and long-term needs and prioritized interventions\nis encouraging. It may finally facilitate the move from a fragmented approach towards an integrated\nresponse with concrete roles and responsibilities allocated to key actors (Government, donors, UN\nagencies, NGOs).\n\n## **KEY GEOGRAPHICAL AREAS AND SCOPE OF WORK**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Protection concerns are tremendous in Afghanistan, some are directly related to ongoing conflict,\nground engagements and forced displacement as other are rather related to widespread poverty and\nstructural deficit. The APC will first and foremost address protection issues related to new shocks and\nwill mobilize development actors to respond to protection issues related to structural deficits.\n\n**Given limited resources and capacities**, it is paramount for the APC to prioritize key geographical\nareas taking into consideration following elements:\n\nPage **9** of **12**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Focus on key protection risks**, **responsive and rehabilitation actions** : Mainly related to Protection of\ncivilian in high combat intensity areas as well as hard-to-reach populations in AGEs controlled areas\nshould access allowed, new displacement due to the conflict, and equity of access to services by\nvulnerable groups.\n\n**Severity of needs and response capacity** : Consideration will be given to the number of affected\npopulations, especially children, in the contested, IEA and government controlled areas. Presence of\noperational partners and response capacity will be also taken into consideration. As of 1 March 2017\nthe access snapshot indicates approximately 54% of the territory is controlled or contested by NSAG.\nAs of January 2017, the 3W Map shows the humanitarian community to be present as follows (if BPHS\nstaff are included then the number of staff will increase in all areas): Areas under control of\ngovernment (30,349 staff), contested areas (13, 279 staff), NSAG control (428 staff).", "output": {"entities": {"named_data": ["3W Map"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Mobilization of early recovery and development actors** on the basis of comparative advantage and\ncollective outcomes: Increased partnership with development actors to address urban displacement,\nprolonged/protracted displacement, durable solutions and the reintegration of returnees,\ndevelopment and structural deficit that generates negatives coping mechanism and acute\nvulnerabilities.\n\nWhere humanitarian access is feasible or could be negotiated, the APC will focus its interventions and\ncoordination efforts on **high combat intensity/contested areas** that generate heightened protection\nrisks and require an immediate response to protect affected population.\n\nThe scope of work for the APC is defined below on the next page and will be considered as key areas\nof priorities for the APC.\n\n## **CONCLUSION**\n\nThe APC Guidance Note facilitates the identification of key areas of work to redefine strategic priorities\nfor the APC. It provides highlights on some patterns of abuse and specific categories of population\nthat are exposed to threats. It also suggests key coordination and partnership areas for the APC to\nstrengthen.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "However, it also highlights the APC’s limited capacity to collect protection data and build up a more\nrobust and comprehensive protection analysis. The main challenges have been identified as limited\nhumanitarian access, sensitivity related to some protection thematic, a lack of information sharing\namong different agencies, limited capacity and knowledge to operationalize the protection risks\nequation, a lack of conflict sensitivity analysis and limited protection assessments. The available\nprotection analysis is usually limited to the overall chronic issues in the country, while conflict and\ndisplacement related risks and threats remain less explored and assessed.\n\nThose limitations can be overcome should the APC members prioritize the resources to fill those gaps\nand increase their commitment to the coordination system for the protection sector.\n\nA workshop with APC members will be organized in the course of August 2017 which will provide an\nopportunity to produce a joint protection analysis that would support a prioritization exercise.\n\nIntegrated protection response plans are also under development at regional level and will provide a\nregional perspective of the protection context and priorities.\n\nPage **10** of **12**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "|Strategic<br>priorities<br>Modalities|Protection of civilians|Displacement|Access to services|\n|---|---|---|---|\n|<br>**Advocacy**| Monitoring and reporting<br>(civilians casualties, MRM)<br> Engagement with parties<br>to the conflict<br> Support to the NUG to<br>publish and implement its<br>National Policy on Civilian<br>Casualty Prevention and<br>Mitigation (Cf. PoC key<br>advocacy areas)| Support to DiREC<br> Information products<br> Briefing notes to<br>HC/HCT, GPC, Donors<br> Adherence to refugee<br>law especially with<br>regard to refoulement| Access to civil<br>documentation and<br>policies for the affected<br>population to be able to<br>access justice, education,<br>finance and inheritance<br>rights in the interim<br> Linkage with Citizen<br>Charter CDCs|\n|<br>**Advocacy**|Protection monitoring|Protection monitoring|Protection monitoring|\n|<br>**Advocacy**|HCT Protection strategy<br><br><br>|HCT Protection strategy<br><br><br>|HCT Protection strategy<br><br><br>|\n|<br>**Access**| Conflict and stakeholder<br>analysis| Protection integration<br>in Health, FS,<br>Shelter/NFI,WASH,<br>cash based<br>programming| Communication and<br>information strategy|\n|<br>**Access**|HAG - Negotiated access strategies of international actors|HAG - Negotiated access strategies of international actors|HAG - Negotiated access strategies of international actors|\n|<br>**Access**|Partnership and capacity building of local actors|Partnership and capacity building of local actors|Partnership and capacity building of local actors|\n|<br>**Access**|Establishment of Community Network|Establishment of Community Network|Establishment of Community Network|\n|<br>**Access**|Mobile outreach and remote monitoring<br><br><br>|Mobile outreach and remote monitoring<br><br><br>|Mobile outreach and remote monitoring<br><br><br>|\n|<br>**Protection **| Mine action| Protection assessment<br>and analysis<br> Alert system<br> Gender analysis<br> Preparedness| PSN Network<br>(Identification and<br>referral)<br> linkage with AIHRC to<br>address areas of human<br>trafficking, lack of access<br>to essential services and<br>justice through a human<br>rights lens|\n|<br>**Protection **|Community Based<br>Measures<br>|Protection Mainstreaming in Health, FS, Shelter/NFI,<br>WASH, cash based programming<br><br>|Protection Mainstreaming in Health, FS, Shelter/NFI,<br>WASH, cash based programming<br><br>|\n|<br>**Protection **| UNAMA PoC<br> Local advocacy| Assistance and<br>Mediation to issues<br>related to HLP| Child Protection (support<br>to CPANs, CFS, packet of<br>services)<br>  GBV capacity<br>(Identification and<br>referral),legal assistance<br> Access to tazkera|\n|<br>**Protection **|Call center|Call center|Call center|", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "#### **Country Summary as at 30 June 2023**\n\n##### (Update of 30 June 2020 summary)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "1. Host Communities\n\n### **RPRF Policy Dimensions**\n\n(as of 30 June 2023)\n\n### **1. Host Communities**\n\n**1.1** **Support for communities in refugee-hosting areas**\n\nThere has been little change since June 2020 in this domain and no specific national fiscal or budget policy\nis yet in place to provide additional financial transfers to areas most affected by the presence of refugees.\nFinancial contributions for these areas still rely on projects and programs funded by external donors.\n\nThe government’s priorities remain focused on: (i) Defence and security, peace, social cohesion and national\nreconciliation; (ii) Good governance and the rule of Law; and (iii) Return to constitutional order. In practice,\nregarding transfers from central to local level, the Government of Chad continues to allocate twenty\npercent of its budget to security, excluding other social sectors, amid ongoing challenges from conflicts in\nneighboring countries, and economic and climatic crises.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "As of 30 June 2023, only 1.6 per cent of Chad’s population is covered by the social protection framework,\nand the country spends 0.1 per cent of the Gross Domestic Product on social protection (excluding health\nand education expenses). After a lengthy process, the document for the new National Social Protection\nStrategy (SNPS) 2022-2026 has been finalized and is awaiting validation and signature by the transitional\ngovernment. The new SNPS spanning four years aims to gradually establish a comprehensive, effective\nand efficient social protection system that addresses financial needs, livelihood security, risk management,\nvulnerability reduction and access to basic social services for all individuals residing in Chad, including\nrefugees.\n\n**1.2** **Social cohesion**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "On 27 June 2023, Chad’s National Transitional Council (CNT) officially adopted the proposed new\nConstitution. This Constitution aims to restore constitutional order and conclude the transition initiated\nafter the passing of former President Idriss Déby on 19 April 2021. Drawing from the 1996 Constitution,\nthe document incorporates recommendations from the national dialogue. Notably, the reinstatement\nof institutions such as the Senate, the High Court of Justice and the Supreme Court occurs, preserving\nthe decentralized unitary state. Half of the cross-cutting recommendations from the Sovereign National\nInclusive Dialogue (DNIS) concentrate on fostering social cohesion. The primary focus of the Post-DNIS\nTransition Specifications’ initial strategic axis revolves around Defence, Security, Peace, Social Cohesion\nand National Reconciliation. Consequently, the Ministry of National Reconciliation and Social Cohesion has\nbeen established.\n\n[As part of the RESILAC project funded by the European Union (EU) and the French Development Agency](https://www.urd.org/fr/publication/rapports-de-capitalisation-du-projet-resilac-2020-2022/)\n[(AFD), the capitalization report on the implementation of social cohesion activities in the Lake Chad basin](https://www.urd.org/fr/publication/rapports-de-capitalisation-du-projet-resilac-2020-2022/)\n[(2022)](https://www.urd.org/fr/publication/rapports-de-capitalisation-du-projet-resilac-2020-2022/) recommended strengthening social cohesion at various levels (community members, groups,\ninstitutions) and adopting a cross-cutting approach coupled with economic recovery and institutional\nsupport activities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "During the covered period, a National Development Plan spanning 2022-2026 has been prepared, to\nsupplant the National Development Plan document (2019-2021) which has lapsed, but it has not been\nadopted. The updated plan maintains provisions for establishing consensual mechanisms for the peaceful\nresolution of conflicts. This involves reinforcing the legal framework to foster trust between communities and\nsecurity forces, facilitating socio-security dialogue, promoting peaceful coexistence and fostering respect\nfor differences.\n\n2 R E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "1. Host Communities\n\nEven though the [Law 021-PR-2019 on legal aid and judicial assistance was enacted in 2019, its implementation](https://www.refworld.org/docid/609f00834.html)\nhas been stalled due to the absence of an implementing Decree. Chad is planning to commit to adopting\nthis Decree at the 2023 December Global Forum on Refugees.\n\nFurthermore, it also important to note that the new asylum legislation enacted during the prescribed period\nhas also incorporated a few provisions to promote peaceful coexistence including through securing the\nright of refugees and asylum-seekers to access State legal aid services to prevent and/or resolve conflict.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Given that the refugee population is up to three or four times larger than the local population in certain\nareas hosting refugees, mainly in Eastern Chad, there is an increased risk of tensions due to already limited\nresources. Nevertheless, on a broader scale and across the entire territory, refugees continue to peacefully\ncoexist with the local population, facilitated by strong ethnic and cultural bonds, along with shared common\ntraditions. Mixed committees, comprising both refugees and members of the host community, remain active\nin all refugee camps and sites. Their objective is to promote peaceful coexistence and proactively address\nconflicts. Women continue to play a crucial role in conveying messages of peace and social cohesion,\ncontributing significantly to dispute resolution efforts.\n\n**1.3** **Environmental management**\n\nThere has been no significant change in national environmental protection and resource management\npolicies. The existing policies continue to lack clarity concerning their application to refugees and/or host\ncommunities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The floods in 2022 had adverse effects in 18 out of the 23 provinces, including Lac, Mayo Kebbi Est, Mayo\nKebi Ouest, Logone Occidental, Logone Oriental, Tandjilé and Mandoul. These consequences resulted in\nthe loss of agricultural land and livestock, and heightened risks to food security. In October 2022, authorities\nresponded by declaring a state of emergency and offering financial assistance to affected individuals,\nincluding refugees hosted in these rural areas, to meet their shelter and essential needs. Specific sites were\nidentified to temporally accommodate the displaced individuals irrespective of their status.\n\n**1.4** **Preparedness for refugee inflows**\n\nThe National Commission for the Reception, Reintegration of Refugees and Repatriates (Commission\nNationale d’Accueil de Réinsertion des Réfugiés et des Rapatriés, CNARR) remains the national institutional\nbody to respond to new refugee inflows. The four-year strategy of CNARR (2019-2023), which includes\nmobilization provisions in case of emergencies, continues to be applied. The CNARR continues to work\nwith UNHCR and other United Nations organizations to support and guide its response to refugee inflows,\nparticularly in cases of emergency.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In response to the recent arrival from Sudan from mid-April 2023 to the present, humanitarian agencies\nacted swiftly to support the government’s request. They coordinated a comprehensive, multisectoral\nhumanitarian response on the ground under the Refugee Coordination Model previously used during\nthe inflow of refugees from Cameroon and Central African Republic. This involved establishing sectoral\ngroups to ensure a consistent and comprehensive approach. While development actors were not directly\nengaged in formulating and financing emergency plans, they received regular updates on the implemented\nresponses by CNARR and UNHCR.\n\nR E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D** 3", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "2. Regulatory Environment and Governance\n\n### **2. Regulatory Environment and Governance**\n\n**2.1** **Normative framework**\n\n[Chad, already a state party to the 1951 Convention relating to the Status of Refugees and its 1967 Protocol,](https://www.unhcr.org/fr/media/convention-et-protocole-relatifs-au-statut-des-refugies)\nand the [1969 OAU Convention Governing the Specifc Aspects of Refugee Problems in Africa, enacted](https://au.int/sites/default/files/treaties/36400-treaty-0005_-_oau_convention_governing_the_specific_aspects_of_refugee_problems_in_africa_e.pdf)\n[a new asylum Law on 31 December 2020 (Loi No. 027/PR/2020 Portant Asile en République du Tchad).](https://www.refworld.org/pdfid/606334e04.pdf)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Despite a historically enabling environment for refugees and other forcibly displaced in Chad, the lack of\na specific refugee legal framework has been a longstanding issue. The new 2020 asylum Law contributed\nto addressing this vacuum and enhances the existing national institutional framework for the protection\n[of refugees and asylum-seekers assumed by CNARR, as established by the 2011 Decree establishing the](https://www.refworld.org/docid/609efe754.html)\n[National Commission for the Reception, Reintegration of Refugees and Returnees](https://www.refworld.org/docid/609efe754.html) (Décret n°839/PR/PM/\nMAT/2011, CNARR). This latter remains in force given the role played by CNARR to supervise the refugee\nmanagement in the country. The new asylum Law clarifies the principles applicable to refugees and asylumseekers to ensure their protection and provides a legal basis for their civil and socio-economic rights, including\nfreedom of movement, access to justice, the right to work, healthcare, education and land. Additionally, the\nnew asylum Law grants refugees the same rights as Chadian citizens regarding education, healthcare and\nsocial protection. It also provides for the recognition of the refugee identity card as a residence permit.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "On 25 April 2023, in the midst of significant arrivals from Sudan, the Transition President promulgated\n[Decree No. 0648/PT/PM/MATDBG/2023 implementing the asylum Law (Decret d’application de la loi d’asile](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf)\n[n°0648/PT/PM/MATDBG/2023 portant modalités d’application de la loi du 31 décembre 2020). This 2023](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf)\nDecree establishes the measures to operationalize the provisions of the 2020 asylum Law. Henceforth, the\ncombined set of legal instruments, comprising the 2020 asylum Law and the 2023 Decree, is referred to as\nthe asylum legislation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The 2023 Decree outlines the procedures for reception and registering asylum-seekers arriving in Chad. It\ndetails the individualized refugee status determination (RSD) procedure, covering interview modalities, RSD\nassessments, and final adjudication by the Sub Eligibility Commission. Additionally, the Decree introduces\nan accelerated RSD procedure for specific groups, including unaccompanied and separated children,\nvictims of torture and gender-based violence, and vulnerable asylum-seekers. It also addresses prima facie\nrefugee recognition for mass influxes due to violence or unrest, or for groups of asylum-seekers with similar\ncharacteristics. The Decree includes appeal procedures for first instance rejected asylum-seekers which is\ndealt with by a Sub-Commission of Appeal stipulating that the decisions taken by this body are reasoned\nand endorsed by an order taken by the Minister in charge of territorial administration. The same Decree also\nindicates that decisions of the Appeals Sub-Commission are subject to appeal before the administrative\nchamber of the Supreme Court. The Decree also outlines the processes for exclusion, cessation, revocation\nand cancellation of refugee status. Additionally, the Decree outlines the specific legal and judicial assistance\nthat asylum-seekers and refugees can benefit from in the national asylum procedure, but also for the any\nother matters/conflict.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Consistent with the recent legislation consolidating past practices, CNARR, through its Eligibility Sub\nCommission, remains the primary authority for asylum at first instance. Over the past three years, CNARR\nhas employed prima facie refugee recognition during the registration phase for asylum-seekers from Central\nAfrican Republican hosted in the South of Chad, those from Nigeria hosted in the Western Chad, and equally\nfor those fleeing Sudan, including for non-Chadian persons forcibly displaced since the outbreak of 15 April\n2023. Other asylum-seekers have undergone an individualized RSD procedure handled by CNARR. While\nthere have been some enhancements in asylum legislation, notably with the recent promulgation of the\n2023 Decree aiming to improve the efficiency of individualized RSD procedures, implementation challenges\npersist. Issues include permanent rotation of CNARR staff, inadequate training for CNARR eligibility officers\nin interview techniques, legal assessments and the use of country-of-origin information. Non-compliance\nwith first-instance asylum processing delays stipulated in the asylum legislation is also observed. Despite", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "4 R E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "2. Regulatory Environment and Governance\n\nthe introduction of accelerated procedures in the 2023 Decree, some asylum-seekers with vulnerabilities\nwait for a first-instance asylum decision for several months. It is crucial to note that asylum-seekers do not\nhave the same rights as recognized refugees, limiting their socio-economic integration. Additionally, the\nbudget of CNARR remains mostly funded by UNHCR.\n\nThe asylum legislation, available in French and Arabic, is not well-known among some local administrative\nauthorities and host communities. Despite ongoing information sessions and capacity development\nconducted by CNARR with UNHCR support, the impact is constrained by high personnel turnover in central\nand provincial administrations. CNARR, supported by UNHCR, has launched a campaign to disseminate\ninformation about the new refugee Law. Additional efforts are essential for effective dissemination, especially\nin remote areas hosting refugees and asylum-seekers, particularly with the recent release of the 2023\nDecree implementing the 2020 Asylum Law.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Furthermore, on 1 June 2023, the Transitional National Council also enacted [Law No. 012/PT/2023](https://www.refworld.org/docid/648710104.html) relating to\nthe protection and assistance of internally displaced persons in the Republic of Chad. This Law establishes\nthe legal framework to assist internally displaced persons, serving as a guiding, preventive and assistance\ntool for both the state and other relevant actors in addressing internal displacement.\nThis has resulted in instances of arbitrary arrests and detentions of refugees and asylum-seekers.\n\n**2.2** **Security of legal status**\n\nThe new asylum legislation establishes predictable legal arrangements for the stay of refugees and asylum[seekers in Chad. Article 31 of the 2020 Law mandates the issuance of a refugee ID card by the competent](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf)\nauthority. This card serves as both an authorization to stay in Chad and a residency permit, allowing freedom\nof movement for refugees within the conditions specified by Law. The second paragraph of Article 14 of the\n2020 asylum Law stipulates that an asylum-seeker certificate is valid for six months and equivalent to an\nauthorization to stay in Chad, is issued by CNARR and renewed until the Eligibility Sub Commission takes\na decision on his/her refugee status.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "[Furthermore, Article 65 of the 2023 Decree specifies that refugees or asylum-seekers with a (provisional)](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf)\nresidence permit have the right to reside and move within the Chadian territory, like Chadian nationals. This\nlegislation aligns with previous practices, and there has been no change in the duration and legality of stay\nfor refugees and asylum-seekers in Chad. In practice, renewals of asylum-seekers’ certificates and refugee\nidentity cards by the authorities continue without difficulty. Over 80 per cent of consulted asylum-seekers in\n2021 reported no difficulties with the renewal of their asylum-seeker certificates.\n\nArticle 36 of the 2020 Asylum Law guarantees protection against refoulement. The 2023 Decree also\ndetails specific provisions to ensure effective respect to the non-refoulement principles including Articles\n51, 52 and 53. The Law also outlines legal provisions for cessation clauses, cancellation and revocation of\nrefugee status, adhering to international refugee law standards. Over the past three years, there has been\nno documented case of refoulement involving a refugee or asylum-seeker, nor has there been any reported\ncase of the unlawful termination of refugee status. Despite over half of arriving asylum-seekers lacking\nidentity or civil registry documentation, there have been no issues reported regarding their admission to\nthe territory.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**2.3** **Institutional framework for refugee management and coordination**\n\nAccording to Article 38 of the [2020 Asylum Law, the institutional framework for the protection of asylum-](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf)\nseekers and refugees remains defined by [Decret n°839/PR/PM/MAT/2011, which establishes CNARR. In](https://www.refworld.org/pdfid/609efe754.pdf)\nessence, the introduction of the new asylum legislation in Chad has not altered the institutional framework for\nasylum. This legal reform has codified existing practices and brought clarity to the implementation of Decree\nNo. 11-839, which outlines the creation, organization and responsibilities of CNARR.\n\nIn practice, CNARR, which still operates under the Ministry of Public Security, Territorial Administration and\nLocal Governance (MSPARGL), remains responsible for coordinating the implementation of legal provisions\n\nR E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D** 5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "2. Regulatory Environment and Governance\n\nrelated to refugees. CNARR continues to be responsible for coordinating the protection of refugees. Its tasks\ninclude safeguarding the well-being of refugees and asylum-seekers, managing issues related to them (such as\nidentification and registration, document issuance and camp administration), and maintaining communication\nwith relevant ministries. These ministries encompass Foreign Affairs, Security, Defence, Justice, Finance, Social\nAffairs, Human Rights, Economy, Education, Health and Water. CNARR also provides advice to the MSPARGL\non sustainable solutions. Challenges faced by CNARR in coordinating efforts among various stakeholders and\ndonors persist, mainly due to a shortage of stable, qualified human resources and limited financial capacity. In\nthese circumstances and as specified by Article 55 of the [2023 Decree, UNHCR continues to support CNARR](http://2023 Decreehttps://www.ecoi.net/en/file/local/2091861/645b938a4.pdf)\nfor the refugee response coordination using the established refugee coordination model. Coordination\nmeetings with external partners are jointly chaired by the government counterpart (CNARR or prefect) and\nUNHCR. CNARR maintains a presence in all refugee camps and most refugee hosting areas.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Furthermore, the Ministry of Education continues to collect data on refugee and asylum-seekers students for the\neducation management information system. The same applies with the Ministry of Health; data on refugee and\nasylum-seekers are included in the national health information system. The government is working on releasing\nbreakdowns of pupils by legal status (nationals and refugees). Additionally, the Government continues to work\nto include refugees in the national civil registry database. However, the technical and financial prerequisites\nfor this are not yet in place. Furthermore, UNHCR has long been advocating for refugees and asylum-seekers\nto be included in the future national population and household census, and there is now apparent agreement\nfrom the government on this principle.", "output": {"entities": {"named_data": [], "descriptive_data": ["national population and household census"], "vague_data": ["education management information system", "national health information system", "national civil registry database"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The established mechanisms in refugee camps and in N’Djamena to ensure substantial refugee participation\nat local levels remain functional. This includes elected committees representing refugees, along with separate\ncommittees for men, women and children/young individuals. Sectoral committees such as those for livelihoods,\nchild protection, education and healthcare are also still active. Additionally, inclusive and mixed committees\nof leaders or sectoral representatives in refugee hosting villages continue to facilitate interaction between\nmembers of both communities, addressing both general and specific issues. During the COVID-19 pandemic,\nwhen physical access to refugees became difficult due to government restrictions, alternative communication\nmethods (e.g. posters, banners, radio announcements, phone calls and SMS), and mobile protection teams,\nwere utilized by CNARR to stay in touch with refugee communities.\n\n**2.4** **Access to civil registration and documentation**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "[The new asylum legislation - specifically Article 31 of the 2020 Law and Article 73 of the 2023 Decree](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf)   provides for the issuance of refugee identity cards by the competent national authority. It outlines that the\nrefugee identity card is valid for five years, renewable and is issued for each refugee of 18 years old and\nabove. Despite this new asylum legislation, UNHCR continues to give substantial support to the Refugee\nGovernment counterpart, CNARR, including by issuing individual refugee cards to adults, family refugee\nattestations and household ration cards. There have been no changes in the documentation process for\nadult refugees and asylum-seekers. Additionally, UNHCR continue to support CNARR to issue asylumseeker certificates valid for six months and their renewal.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "During the 2019 Global Refugee Forum, the Government of Chad committed, among other things, to\nissuing biometric Refugee Identity Cards and Machine-Readable Refugee Convention Travel Documents\n(MRCTDs) to meet international standards. A study by the National Agency for Secure Documents (Agence\nNationale des Titres Sécurisés, ANATS), the only competent authority for issuing biometric identity and\ntravel documents, outlined a technical proposal released in March 2023 for the project’s implementation,\nbut financial resources are currently lacking to operationalize this pledge. As of 30 June 2023, no biometric\nidentity card neither CTD has been delivered by the competent national authorities (ANATS) to refugees.\n\nThe 2023 Decree specifies that CNARR and UNHCR collaboratively handle the reception and pre-registration\nof new arrivals at the international border, following the completion of police formalities. The pre-registration\nspecifically pertains to situations involving arrivals in groups. In practice, the entire registration of newly\narrived asylum-seekers remains jointly conducted by CNARR and UNHCR. This involves collecting all the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "6 R E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "2. Regulatory Environment and Governance\n\nbiometric and individual data of asylum-seekers in the UNHCR-managed refugee management database,\nensuring that this process remains distinct from the registration of an asylum application carried out strictly\nby CNARR.\n\nArticle 31 of the 2020 Law and Article 73 of the 2023 Decree outline that refugees are entitled by the\ncompetent authorities to be issued with civil status documents including birth certificates, death certificates\nand marriage certificates on par with nationals. Furthermore, the specific legal framework on civil status\n[documents consists of the National Civil Status Code and Law No. 008/PR/2013 of 10 May 2013, governing](http://citizenshiprightsafrica.org/wp-content/uploads/2020/11/Tchad-Loi-no-13-08-Etat-Civil-10-mai-2013.pdf)\ncivil status organizations in the Republic of Chad. Under Decree No. 660/PR/PM/MATSP/2015, establishing\nthe modalities of application of the Law of 10 May 2013, all births in Chad are subject to a mandatory\nregistration requirement. In line with this universal principle of civil registration, all foreigners, including\nrefugees and asylum-seekers to whom vital events have occurred in Chad, are allowed to benefit from civil\nregistry services on par with nationals. Additionally, [Ordinance No. 002/PR/2020](https://citizenshiprightsafrica.org/wp-content/uploads/2020/11/Tchad-Ordonnance-002-PR-2020-Etat-Civil-14-fevrier-2020.pdf) on the organization of civil\nstatus in the Republic of Chad has extended the registration delay for births to three months.", "output": {"entities": {"named_data": ["UNHCR-managed refugee management database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Despite the improved legal framework and the free issuance of birth certificates within the stipulated period,\nchallenges in physically accessing civil registration centres, along with a persistent lack of awareness\nregarding legal obligations surrounding births, contribute to the overall low rates of birth registration in\nChad.\n\nIt is worth noting that there has been an increase in the birth registration rate from 15 per cent in 2020 to\n26 per cent in 2023. The strategic partnership between UNHCR and government agencies responsible for\nissuing birth certificates, specifically the Directorate of Political Affairs and Civil Status (APEC until 2020) and\nANATS since 2021, has facilitated the issuance of birth certificates to over 150,000 refugee children and\nchildren from host communities in refugee hosting areas.\n\n**2.5** **Justice and security**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The government of Chad continues to ensure the security of refugees and asylum-seekers through the\nHumanitarian Workers and Refugee Detachment (DPHR). Generally, refugees are not more exposed to\n[existing violence and crime. According to protection data collected through Project 21](https://response.reliefweb.int/west-and-central-africa/protection/projet-21) (‘regional protection\nmonitoring’) approximately 15 per cent of refugees and asylum-seekers claim to have been victims of\nphysical assault, but it is noteworthy that these cases occurred in the country of origin at the time of fleeing.\n\nWhile rape and child marriage remain prohibited by Law, other forms of gender-based violence (GBV)\nare insufficiently covered by existing Laws and policies to protect the Chadian and refugee populations.\nThe national policy to respond to GBV, in effect since 2011, applies in refugee camps and refugee hosting\nvillages but faces challenges due to limited resources (human and logistic) to fully implement this policy. The\nreferral mechanism for refugee victims for medical, legal and psychosocial support remains in place but its\neffectiveness depends on the human and financial resources available in the relevant hosting areas.", "output": {"entities": {"named_data": [], "descriptive_data": ["protection data collected through Project 21", "regional protection\nmonitoring"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The new asylum legislation provides a robust legal framework for guaranteeing access to justice for\nrefugees and asylum-seekers. This includes outlining specific provisions regarding legal assistance and\njudicial assistance to permit access to justice, legal representation and enforcement of ruling. It is important\n[to consider this framework in conjunction with Law 021-PR-2019 on legal aid and judicial assistance, which](https://www.refworld.org/docid/609f00834.html)\nwas enacted in 2019, though not fully operational in absence of an implementing decree. Article 27 of the\n[2020 Asylum Law](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf) recognizes the refugee right to access Chadian Courts. The same provision also provides\nfor treatment regarding access to judicial assistance on par with nationals and exempts refugees from\nproviding a financial deposit to Courts (caution judicatum solvi) applicable to ordinary foreigners.\n\n[Article 65 of the 2023 Decree](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf) further outlines that refugees and asylum-seekers enjoy access to Court on par\nwith nationals. Additionally, the 2023 Decree outlines the rights of asylum-seekers and refugees to access judicial\nassistance and legal assistance in the conditions outlined by the law. A specific legal provision of the 2023\nDecree also details the different modalities composing legal aid accessible to refugees and asylum-seekers", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "R E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D** 7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "3. Economic Opportunities\n\nwhich aims at preventing conflict, enabling resolution and improving the understanding of law and justice.\n\nIn practice, the availability of judicial services, including Court, remains scarce or too distant from refugee\nhosting areas, including camps. The nascent state legal aid services remain constrained in scale and scope,\nfurther exacerbated by the lack of implementation. In addressing the legal and justice needs of refugees\nand asylum-seekers, despite the support provided by UNHCR and its partners for legal aid services, the\npredominant option remains traditional arrangements made under the auspices of local and traditional\nauthorities.\n\n### **3. Economic Opportunities**\n\n**3.1** **Freedom of movement**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "[In addition to the constitutional guarantee of freedom of movement, Article 21 of the Asylum Law](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf) confirms\nthat refugees and asylum-seekers in possession of their identification documents enjoy the right to circulate\nand to reside in Chad in the same conditions as nationals. Furthermore, Article 73 of the [2023 Decree](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf)\noutlines that the refugee identity card allows the free movement of refugees in the conditions specified\nby the law. Additionally, Article 61 of the 2023 Decree specifically addressing local integration of refugees\nrecognizes, as part of the perquisite to achieve self-reliance, among others, the freedom of movement and\nthe right to settle in places favourable to their self-reliance.\n\nHowever, regarding asylum-seekers, it is important to note that Article 20 of the 2020 Law, incorporating\nthe principle of non-penalization for illegal entry, specifies in its second paragraph that the movement of\nasylum-seekers is restricted only if necessary and as long as their refugee status is not determined or until\nthey have been admitted to another host country.\n\nRefugees and asylum seekers continue to have access to a free travel permit issued by CNARR (saufconduits). This document specifies the intended destination and the duration of absence from the camp.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "While refugees and asylum-seekers have the right to reside in Chad, as enshrined in Article 21 of the law, and\nthere are no legislative restrictions on their choice of residence, multisectoral assistance is only provided\nby UNHCR and its partners in camps. Consequently, this assistance remains a decisive factor in refugees’\ndecision to stay in the camps. Refugees with the financial means to support themselves or engaged in\neconomic activities outside the camps are free to choose their place of residence in Chad.\n\n**3.2** **Right to work and rights at work**\n\nArticle 28 of the [Asylum law](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf) in the Republic of Chad grants refugees the most favourable treatment afforded\nunder similar circumstances to foreign nationals regarding the pursuit of gainful employment, whether\nsalaried, non-salaried and/or self-employed. The same provision pursues that refugees shall be exempted\nfrom certain restrictive measures imposed by the prevailing regulations on the employment of foreigners.\nAdditionally, Article 70 of the 2023 Decree stipulates that a refugee legally residing in Chad enjoys the same\nrights and standards of treatment as foreign nationals. Article 61 of the [2023 Decree specifically addressing](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf)\nlocal integration also guarantees the refugee right to access salaried or non-salaried employment to reach\nself-reliance.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Despite the enactment of new asylum legislation, potential inconsistencies may persist with other laborrelated legislation that remain in force or have not been amended in the country. This lack of alignment\ncould create hesitancy among prospective employers when considering the hiring of refugees.\n\nFor instance, [Decree No. 96-189 PR/MFPT of 15 April 1996, and Deecree No. 1793/PR/PM/MECDT/2015 of 24](https://wwwex.ilo.org/dyn/natlex2/natlex2/files/download/97320/TCD-97320.pdf)\nAugust 2015, which address mandatory reporting of hiring, job offers and personnel in companies, as well\nas business-related procedures, are still applicable.\n\n8 R E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "3. Economic Opportunities\n\nIn accordance with Article 28 of the 2020 Asylum Law, the restrictions on the employment of foreigners,\nincluding the prerequisite of obtaining a work permit, should be considered not applicable to refugees.\nHowever, Decree No. 96-189 PR/MFPT, still requiring approval from the National Office for Employment\nProtection (ONAPE) for the employment of foreigners, remains silent on refugees. In addition, the decree\nstipulates that, before submission to ONAPE, contracts for foreigners must receive endorsement from\nimmigration authorities. It is unclear whether refugees should be exempted from this endorsement from\nImmigration Department.\n\nFurthermore, Article 11 of the 1996 Decree, which prohibits hiring foreigners for non-specialized jobs, may still\napply for refugees. Companies hiring foreigners are still obligated to pay fees ranging from FCFA 100,000\nto FCFA 250,000, and it is unclear whether these fees will also apply to refugees. The 2023 Asylum Decree\nhas not clarified the type of exemption of restrictive measures for foreigner employment that should benefit\nto refugees.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNHCR supports CNARR in the popularization of the asylum law and its implementing decree, including\ntowards ONAPE with the aim to develop a Memorandum of Understanding that would lift the challenges met\nby refugees while accessing the job market including in the private sector.\n\nIn the private sector, practical experience shows that some employers still hire refugees without the approval\nof ONAPE and may treat them less favorably in terms of wages and social benefits. There has been no\nlegislative or policy change regarding child labor.\n\nThere is no reliable data on the percentage of refugees employed in the formal sector.\n\nMany refugees work in the informal sector, but data on this is equally unavailable. Most refugees engage\nin independent work, particularly in agriculture, which remains the backbone of the Chadian economy.\nHowever, agricultural value chains are very weak in the country, affecting both refugees and host populations.\nThe main constraints, even more pronounced for women, relate to difficulties in accessing agricultural land,\ninfrastructure, agricultural inputs such as certified inputs, and financial services.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "While Article 28 of the Asylum Law permits refugees also to engage in self-employment, the lack of legally\nrecognized refugee identity cards is likely to remain a major obstacle to meeting administrative requirements\nto open a business and pay the necessary tax.\n\nInformation on the number of refugees owning businesses is also lacking.\n\n**3.3** **Land, housing, and property rights**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "[Article 24 of the Asylum Law accords refugees a treatment as favourable as possible and, in any event,](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf)\nnot less favourable than that accorded to aliens generally in the same circumstances, concerning the\nacquisition of movable or immovable property, lease contracts and other associated rights. Article 67\nof the [2023 Decree](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf) adds that refugees lawfully staying in the country shall have the same treatment\nas foreigners in general concerning that right. Article 29 of the Asylum Law also accords refugees\nlawfully staying in Chad treatment as favourable as possible concerning housing which falls under\nnational legislation, or which is submitted to public control, and, in any event, not less favourable than\nthat accorded to aliens generally in the same circumstances. Article 71 of the 2023 Decree specifies\nthat refugees lawfully staying in Chad should be accorded the same treatment as foreigners in general\nregarding housing subject to the laws and regulations in force. Key to note is that Article 61 of the 2023\nDecree, specifically addressing local integration as a durable solution, specifies that the Government\nprioritizes refugees’ right to access land and secure allocated land to achieve self-reliance.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Land ownership remains a complex issue in the absence of a codified land law, and the general regime\nof land ownership, encompassing both formal and traditional rights, has not undergone any changes in\nthe past three years. Customary and Islamic laws continue to govern access to and control of land and\nnatural resources in urban and rural areas. While these customary systems vary considerably across the\n\nR E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D** 9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "3. Economic Opportunities\n\ncountry, most Chadians traditionally obtain land through the family or lineage, following the principle of\nfirst occupancy and, for women, through marriage.\n\nIn practice, refugees have access to agricultural land through sharecropping, loans or leases.\n\nIt is important to note that before the enactment of the new asylum legislation and in the absence of clear\nrules, refugees theoretically had the right to purchase land. However, this was challenging due to the\nprevalence of the traditional ownership system, their inability to produce financial documents and other\nadministrative obstacles.\n\nIn practical terms, refugees continue to encounter greater challenges than nationals in accessing large,\nfertile land parcels due to their distance from the camps. When engaging in sharecropping agreements\n(métayage), refugees are required to share a portion of the harvest with the landowner or pay rent for\nthese lands, which can sometimes amount to 50,000 FCFA per hectare per year.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "This contrasts with the government’s promotion of free land access which apply to all people involved\nin agriculture. The current arrangements have led to a seasonal migration of refugees from the camps\nto fields located in cantons up to 50 or 70 km away, where they establish temporary settlements. These\nsharecropping agreements offer the advantage of fostering close relationships between refugees,\nlandowners and entire villages, because refugees set up tents with their families throughout the growing\nseason. Furthermore, refugees often do not receive documents confirming their right to sustained access\nto the land used. Between 2020 and 2023, approximately 15,000 hectares of agricultural land have been\nmade available to refugees in Chad for exploitation purposes, under written or verbal agreements lasting\nfrom one to three years.\n\nIn the current Ministry of Social Affairs, there is still no housing assistance program for vulnerable Chadian\nnationals. Furthermore, the temporary support system for relocating populations affected by floods in the\ncapital, primarily backed by the humanitarian actors, does not extend its coverage to refugees.\n\n**3.4** **Financial and administrative services**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The newly enacted asylum legislation does not include a specific legal provision governing access to financial\nservices nor access to administrative documents and certifications, including recognition of educational\nattainments received outside of the national system. However, within the context of local integration and\nself-reliance, Article 61 of the [2023 Decree](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf) outlines that the Government of Chad prioritizes actions for\nrefugees, among others, to access credits, microcredits and subsidies, as well as to obtain the recognition\nof documents issued by competent authorities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In practice, access to these financial services is still hindered by the type of identification documents issued by\nCNARR and UNHCR to refugees. The refugee identity cards, not issued by the national competent authority,\nANATS, is often considered an unofficial document in most cases. Efforts to expand the legally recognized\ndocumentation through the provision of biometric identification and a national identification number by\nANATS for refugees would be the solution to these recurring challenges. Other practical challenges persist\nfor refugees in accessing credit, stemming from their inability to meet the financial guarantees and the\nperception that they pose a high risk profile as borrowers. Some refugees, mainly living in the South of\nChad continue to ably access bank accounts, mainly saving accounts, based on the prior recognition of their\nidentity cards by certain financial institutions.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Mobile network operators still accept refugee identity cards issued by CNARR as valid proof of identity.\nRefugees without an identity card must still be vouched for by those who possess one to enable SIM card\nregistration. Through their phone numbers, refugees have access to payment and money transfer facilities\noffered by telecommunications operators. Asylum-seekers still do not have the right to acquire a SIM card\nwith the asylum-seeker certificate.\n\n10 R E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "4. Access to National Public Services\n\nIn practical terms, a formal State led system for recognizing foreign diplomas of refugees and foreigners does\nexist through an authentication mechanism led by the National Office for Higher Education Examinations\nand Competitions (Office National des Examens et Concours du Supérieur - ONECS). Regarding driving\nlicenses, only Chadian driving licenses can be obtained through ANATS after fulfilling ANATS requirements.\n\n### **4. Access to National Public Services**\n\nThe new asylum legislation guarantees the right to health, education, public assistance and public relief for\nrefugees and asylum-seekers in possession of their individual documentation on par with nationals of Chad.\n\n**4.1** **Education**\n\nArticle 21 of the [2020 Asylum Law](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf) specifies that every refugee or asylum-seeker in possession of individual\ndocumentation enjoys the right to education and vocational training under the same conditions as nationals.\n[The same is reiterated by Article 65 of the 2023 Decree. Despite not explicitly mentioning asylum-seekers](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf)\nin its wording, Article 32 of the 2020 Law and Article 75 of the 2023 Decree should be interpreted in the\nspirit of Article 21 of the law and Article 65 of the Decree that asylum-seekers are equally entitled to the\nsame treatment as nationals regarding education.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The government of Chad has committed to providing refugees with access to quality education by integrating\nthem into the national education system. This integration allows all refugees and asylum-seekers to enrol in\nprimary, secondary, and higher education institutions, follow the Chadian curriculum, and obtain recognized\ndiplomas. The process of integrating refugees into the national education system has seen significant\nprogress since 27 November 2020, including the adoption of the ten-year refugee education strategy by\nthe government. In line with this, since 2018, the government has recognized and integrated 94 schools\nin camps and reception sites into the national system and opened seven examination centres in camps to\nensure that all students can take national exams under proper conditions.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "During the 2022-2023 school year, a total of 105,295 refugee children were enrolled in schools. This number\nincludes 7,520 in pre-primary, 75,205 in primary, 24,999 in secondary, and 571 in higher education and\nvocational training. The gross enrolment rates for refugees were as follows: 71 per cent in primary, 28 per\ncent in secondary, and less than 1 per cent in tertiary education. Refugee enrolment rates at the primary and\ntertiary levels remain below those of national students (71 per cent and 1 per cent compared to 91 percent\nand 3 per cent respectively), while they are higher than those of national students at the secondary level (28\npercent compared to 22 per cent).\n\nChad continues to have a government-led system to support the integration of refugee children coming\nfrom a different education system (Nigerian and Sudanese refugees) but funded by UNHCR. Local education\nauthorities organize placement tests and catch-up classes before integrating refugee children into the most\nappropriate Chadian school year according to their level, age and other criteria. With new arrivals from\nSudan since April 2023, Sudanese refugee children have been integrated into the existing programme with\nongoing recruitment of new teachers in the main refugee hosting areas.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**4.2** **Health care**\n\n[Article 21 of the Asylum Law](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf) stipulates that every refugee or asylum seeker is entitled to the right to health\nunder the same conditions as nationals.\n\nDuring the prescribed period, Chad has developed a new [National Health Development Plan for 2022-](https://www.afro.who.int/fr/countries/chad/publication/plan-national-de-developpement-sanitaire-pnds-4-2022-2030#:~:text=PLAN%20NATIONAL%20DE%20DEVELOPPEMENT%20SANITAIRE%20(PNDS%204)%202022%2D2030,-Le%20Minist%C3%A8re%20de&text=Le%20PNDS4%20est%20le%20dernier,des%20Objectifs%20de%20D%C3%A9veloppement%20Durable.)\n[2030, aiming to establish an integrated, efficient, resilient, and person-centered health system. This plan](https://www.afro.who.int/fr/countries/chad/publication/plan-national-de-developpement-sanitaire-pnds-4-2022-2030#:~:text=PLAN%20NATIONAL%20DE%20DEVELOPPEMENT%20SANITAIRE%20(PNDS%204)%202022%2D2030,-Le%20Minist%C3%A8re%20de&text=Le%20PNDS4%20est%20le%20dernier,des%20Objectifs%20de%20D%C3%A9veloppement%20Durable.)\nincludes refugees and asylum seekers, ensuring their access to public health services on an equal basis with\nnationals. Refugees and asylum-seekers are also incorporated into national, provincial, and departmental\n\nR E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D** 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "4. Access to National Public Services\n\nhealth planning documents.\n\nThe 2019 agreement between the UN Refugee Agency (UNHCR) and the Ministry of Health, regarding the\nprogressive integration of camp health centres into the national health system, has facilitated the inclusion\nof all camp health centres in the national system, with the assignment of a healthcare worker to each of\nthese centres, who is included on the Government payroll. The remaining personnel are still supported\nby UNHCR through its partners. This support from UNHCR remains necessary because access to quality\nGovernment led healthcare is still limited due to a shortage of healthcare professionals, medication, health\ninfrastructure, and equipment, as well as high costs.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In 2023, the national utilization rate was 0.31 new consultations per inhabitant per year, while in the camps, it\nstood at 1.2 new consultations per refugee per year. This indicates that healthcare access is more favourable\nin the camps than host populations. The disparity can be attributed partially to improved geographical access\nin the camps, but more significantly, it is driven by financial factors. Notably, healthcare is provided free of\ncharge in refugee camps, whereas state facilities require fees for healthcare services. For refugees living\noutside the camps, UNHCR, through its health partner, has signed agreements with state health centers for\nfree access, with costs covered by partners under UNHCR’s budget.\n\nSexual and reproductive healthcare is integrated into the package of services offered in refugee camps\ncovered by UNHCR and partners’ budget. As of June 2023, the rate of assisted childbirth was 91% in the\nrefugee camps.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Moreover, Chad developed a national strategic plan for the progressive implementation of universal health\ncoverage from 2017 to 2019, starting with vulnerable populations. Eventually, it should cover all individuals\nresiding in Chad, including refugees. However, the implementation of this plan was disrupted by the\nCOVID-19 pandemic. As of June 2023, its implementation was not yet effective.\n\n**4.3** **Social protection**\n\nState social protection programmes are guaranteed to refugees and asylum-seekers under Article 21 of the\n[2020 Asylum Law. Article 33 of the Law specifies that refugees and asylum-seekers are entitled to the same](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf)\n[treatment as nationals regarding assistance and public relief. Article 77 of the 2023 Decree reiterates the](https://www.ecoi.net/en/file/local/2091861/645b938a4.pdf)\nsame.\n\nThe 2016-2020 National Social Protection Strategy (SNPS) faced implementation challenges. Subsequently,\na new SNPS for 2022-2026, developed with support from UNICEF, the World Bank, UNFPA, WFP and FAO,\nis now awaiting validation and government approval. This strategy, distinct from its predecessor, prioritizes\naddressing financial and livelihood security for the most vulnerable, managing risks, reducing vulnerability\nand ensuring access to basic social services for all residents in Chad, including refugees. Notably, it aligns\nwith the Global Compact on Refugees (GCR). The forthcoming strategy will be accompanied by a priority\naction plan.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "School feeding programs are largely implemented by the World Food Programme, reaching a total of 13,000\nrefugees in 14 schools in the provinces of Lake Chad and Logone Oriental.\n\nThe Unified Social Registry (RSU), launched in 2019, has made limited progress in capacity, governance and\nfinancing, despite continuous support from WFP and NGOs.\n\n**4.4** **Protection for vulnerable groups**\n\nIn practical terms, the national protection system for the most vulnerable has not undergone any changes\nand the measures in place to support victims of human trafficking are still insufficient. This holds true for\nvictims of Gender-Based Violence (GBV) and at-risk children. The national protection services aimed at\nthese vulnerable groups continue to be underdeveloped and underfunded.\n\n12 R E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D**", "output": {"entities": {"named_data": ["Unified Social Registry"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "5. Cross Sectors\n\n### **5. Cross Sectors**\n\n**5.1** **Gender**\n\nGender considerations can generally be enhanced in many sub-dimensions of overall policies, with the\nmost relevant being the national institutional framework for the management and coordination of refugees.\nAdditionally, the limited participation of refugee women in advisory committees often results in inadequate\nconsideration of the concerns and needs of women and girls. This hinders their inclusion in national plans\nand programs.\n\nThe most consequential policy sub-dimensions in terms of socioeconomic development remain therefore\nas follows:\n\n**a.** **Justice and Security:** Challenges remain in preventing and responding to gender-based violence.\n\n**b.** **Land, Housing, and Property:** Refugees in general still face difficulties in accessing and owning land.\n\n**c.** **Education:** Limited access to education for refugee girls, particularly due to high dropout rates. In\n\nthe 2022-2023 school year, girls’ enrolment rates were 47 per cent in primary, 17 per cent in middle\nschool and only 6 percent in high school nationwide. The drop in middle and high school attendance is\nlinked to socio-cultural pressures, leading to issues like child marriages, early marriages, child labour.\n\n**d.** **Healthcare:** Insufficient prioritization of the specific needs of women and girls, including refugee women", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "and girls. Maternal and reproductive health services remain underdeveloped in some areas, struggling\nto be fully effective due to a lack of material and financial support.\n\n**5.2** **Social inclusion**\n\nThe most significant differences or restrictions in terms of socio-economic development affecting refugees\nwith particular characteristics are:\n\n**a.** **Access to biometric identity cards and unique identifier number:** The continued lack of access to\n\nlegally recognized identity cards for refugees issued by the competent authority in Chad, ANATS, and\nnational identification numbers, challenges their inclusion into national system and limits their socioeconomic integration in the country.\n\n**b.** **Access to civil registry civil status documents:** The low percentage of registered births, due to\n\nsignificant deficiencies in the Civil Registration and Vital Statistics (CRVS) system in Chad, especially in\nrural areas, exposes refugees born in Chad to the risk of statelessness.\n\nR E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D** 13", "output": {"entities": {"named_data": ["Civil Registration and Vital Statistics"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "5. Cross Sectors\n\n**c.** **Land:** Access to land for sustainable agricultural activities, which is crucial for fostering refugee’s self\nreliance remains challenging.\n\n14 R E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "# _Jordan Medical Referrals at a Glance_ _Year End Report_ _January –December, 2016_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Table of Contents:**\n\n**Background** ....................................................................................................................................... 3\n\n**Overview of UNHCR’s Referral Guidelines** .......................................................................... 3\n\n**Data Collection and Analysis** ...................................................................................................... 3\n\n**Summary of Findings** .................................................................................................................... 4\n\n**i.** **Demographic Characteristics of Medical Referrals in January – December**\n\n**2016 (n= 34,571)** ............................................................................................................................... 5\n\nFigure 1. Medical referrals by admission category and final outcome on discharge .......... 5\n\nFigure 2. Medical referrals per month; January – December 2016 (n= 34,571) ................. 5\n\nFigure 3. Frequency of referrals per unique patient; January – December 2016 (n=\n23,296) ........................................................................................................................................... 6\n\nFigure 4. Proportion of referrals by gender and age group (n= 34,571) ............................... 6\n\nFigure 5. Proportion of referrals by nationality (n= 34,571) .................................................. 7\n\nFigure 6. Proportion of referrals by referral hospital (n= 34,571) ......................................... 7\n\nFigure 7. Proportion of referrals by referring clinic (n= 34,571) ........................................... 8\n\nFigure 8. Number of JHAS Madina clinic referrals by nationality (n= 9,488) ................... 8\n\nFigure 9. Zaatri and Azraq referrals per month; January – December 2016 (n= 19,473) . 9\n\nFigure 10. Proportion of referrals by diagnosis at discharge ............................................... 10\n\n**ii.** **Mortality** ................................................................................................................................... 11\n\nFigure 11. Number and proportion of mortalities by age group (n= 94) ............................ 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Figure 12. Top causes of mortality by age group .................................................................... 11\n\n**iii.  Costs** ........................................................................................................................................... 12\n\nFigure 14. Costs by nationality (n= 6,294,786 JOD) ............................................................. 12\n\nFigure 15. Costs by referral hospital (n= 6,294,786 JOD) .................................................... 13\n\nFigure 16. Costs by diagnosis at discharge (n= 6,294,786 JOD) ......................................... 14\n\n**2 |** P a g e", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Background**\n\nJordan hosts asylum seekers and refugees from different neighbouring countries. As of 31 [st]\nDecember 2016, 655,344 (514,274 and 141,070 in urban and camps respectively) Syrian\nrefugees have been registered with UNHCR Jordan office since the onset of crisis in 2011. In\naddition, Jordan hosts asylum seekers and refugees from Iraq, Sudan, Yemen and Somalia.\n\nAccess to healthcare services for refugees varies according to the country of origin. Syrian\nrefugees living in the urban setting have access to all levels of healthcare services (primary,\nsecondary and tertiary) at governmental health facilities at the non-insured Jordanian rate,\ngiven that they hold a valid UNHCR asylum seeker certificate and a valid security card.\nSyrian refugees residing in the camps and non-Syrian need to pay foreigners’ rate when\naccessing any level of healthcare at governmental health facilities. This renders access to\nessential and life-saving healthcare services unaffordable without support.\n\n**Overview of UNHCR’s Referral Guidelines**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNHCR has adapted a policy of structured provision of health services for different\nnationalities in order to maintain affordable access to secondary and tertiary referral\nservices. Essential secondary and tertiary referrals are available to eligible refugees of all\nnationalities based on a pre-defined set of criteria at governmental hospitals and other\nprivate affiliated hospitals (though UNHCR’s implementing partner Jordan Health Aid\nSociety; JHAS).\n\nIn order to facilitate referrals, UNHCR has established two levels of authority with the\nimplementing partner in order to facilitate and control the referral process. If the estimated\ntreatment cost is less than JODs 750 per person per year then the UNHCR partner will\nmanage the referral directly, while if the referral cost is more than JODs 750 per person per\nyear, the case has to be approved by the UNHCR health unit (for emergency cases) and/or\nExceptional Care Committee (ECC) for non-emergency cases before the referral takes place.\n\n**Data Collection and Analysis**\n\nReferral care is considered an essential part of access to comprehensive health services, thus\nUNHCR since 2014 has maintained a medical referral database in order to monitor trends in\nurban and camp settings in Jordan.", "output": {"entities": {"named_data": ["medical referral database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- This report includes all referrals data for the period January to December 2016\n\n- Data was collected from 11 sites; 6 Urban (Amman, Zarqa, Mafraq, Irbid, Ramtha,\nand south Mobile Medical Unit), 4 camps (Zaatri, Azraq, Cyber City and King\nAbdullah Park), and Ruwaishid.\n\n- Data was captured on-site daily then compiled and shared on a monthly basis with\nJHAS referral hub, where the initial data compilation and cleaning was done.\nCompiled and cleaned data was then shared with the UNHCR Public Health Unit,\nwhere secondary data cleaning and analysis was carried out.\n\n- Descriptive analysis carried out using Microsoft Excel 2013.\n\n**3 |** P a g e", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Summary of Findings**\n\n- In 2016, **34,571** referrals for secondary and tertiary healthcare were conducted for\n\n**23,296** refugees, 78% of which were referred only once\n\n- Of the 34,571 referrals, **71%** (24,565) were elective and **29%** (10,006) were\n\nemergency referrals\n\n- The average number of monthly referrals was **2,881**\n\n- The number of referrals per month varied throughout 2016; mild decline from April\n\nto September was due to budget restrictions and sharp increase over last quarter was\n\ndue to budget supplementation\n\n- Referrals for females accounted for **55%** of total referrals\n\n- Referrals for children under the age of 5 and patients 60 years and older were **19.2%**\n\nand **9.4%** respectively\n\n- Majority of referrals were for Syrian ( **80%** ), followed by Iraqi, Sudanese and Yemeni\n\nrespectively\n\n- Approximately, **70%** of referrals were to private affiliated hospitals\n\n- Zaatri and Azraq referrals accounted for **56.3%** of total referrals\n\n- Madina referrals were the highest among urban sites accounting for **27.4%** of total\n\nreferrals\n\n- The most prevalent disease diagnosis was “diseases of the genitourinary system;\n\n**17.4%** ” followed by “pregnancy, childbirth and puerperium; **14.6%** ”\n\n- Overall expenditure on referrals was **6,294,786** Jordanian Dinar (JOD)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Largest proportion of cost was incurred by “diseases of the genitourinary system”;\n\n**11%**\n\n- Mortalities occurred primarily among 60 years and older ( **32%** )\n\n**4 |** P a g e", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**i.** **Demographic Characteristics of Medical Referrals in January –**\n\n**December 2016 (n= 34,571)**\n\nFigure 1. **Medical referrals by admission category and final outcome on discharge**\n\n**Discharged Alive** **Discharged Alive**\n\n9,944 (99.4%) 24,526 (99.8%)\n\n**Deceased** **Deceased**\n\n55* 39 (0.2%)\n\nFigure 2. **Medical referrals per month; January – December 2016 (n= 34,571)**\n\n**Discharged Alive**\n\n**Discharged Alive**\n\n9,944 (99.4%)\n\n24,526 (99.8%)\n\n**Deceased**\n\n**Deceased**\n\n55*\n\n39 (0.2%)\n\n**5 |** P a g e", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Figure 3. **Frequency of referrals per unique patient; January – December 2016**\n\n**(n= 23,296)**\n\nFigure 4. **Proportion of referrals by gender and age group (n= 34,571)**\n\n**6 |** P a g e", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Figure 5. **Proportion of referrals by nationality (n= 34,571)**\n\nFigure 6. **Proportion of referrals by referral hospital (n= 34,571)**\n\n**7 |** P a g e", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Figure 7. **Proportion of referrals by referring clinic (n= 34,571)**\n\nFigure 8. **Number of JHAS Madina clinic referrals by nationality (n= 9,488)**\n\n**8 |** P a g e", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Figure 9. **Zaatri and Azraq referrals per month; January – December 2016 (n=**\n\n**19,473)**\n\n**9 |** P a g e", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Figure 10. **Proportion of referrals by diagnosis at discharge**\n\n**10 |** P a g e", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**ii.** **Mortality**\n\nFigure 11. **Number and proportion of mortalities by age group (n= 94)**\n\nFigure 12. **Top causes of mortality by age group**\n\n**11 |** P a g e", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**iii.** **Costs**\n\nFigure 13. **Costs by referring clinic (n= 6,294,786 JOD)** [1]\n\nFigure 14. **Costs by nationality (n= 6,294,786 JOD)**\n\n---\n[1] One Jordanian Dinar (JOD) = 1,412 US Dollar", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Figure 15. **Costs by referral hospital** **(n= 6,294,786 JOD)**\n\n**13 |** P a g e", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Figure 16. **Costs by diagnosis at discharge (n= 6,294,786 JOD)**\n\n**14 |** P a g e", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "# **NEW ISSUES IN REFUGEE RESEARCH**\n\n**Research Paper No. 127**\n\n# **Land conflicts and their impact on refugee women’s** **livelihoods in southwestern Uganda**\n\n**Kalyango Ronald Sebba**\n\nDepartment of Women and Gender Studies\nMakerere University\nUganda\n\nE-mail kalyango@infocom.co.ug\n\nJuly 2006\n\n**Policy Development and Evaluation Service**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Policy Development and Evaluation Service**\n\n**United Nations High Commissioner for Refugees**\n\n**CP 2500, 1211 Geneva 2**\n\n**Switzerland**\n\n**E-mail: hqep00@unhcr.org**\n\n**Web Site: www.unhcr.org**\n\nThese papers provide a means for UNHCR staff, consultants, interns and associates, as well\nas external researchers, to publish the preliminary results of their research on refugee-related\nissues. The papers do not represent the official views of UNHCR. They are also available\nonline under ‘publications’ at <www.unhcr.org>.\n\nISSN 1020-7473", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Introduction**\n\nThis paper presents the preliminary findings of a study on land conflicts between\nrefugees and host communities in southwestern Uganda and their impact on refugee\nwomen’s livelihoods. Uganda has a long history of hosting refugees that dates back to\nthe 1940s, when it hosted Polish refugees; Rwandese and Sudanese in the 1950s\n(Holborn 1975:1213-1225). Refugees were placed in gazetted areas in close proximity\nto the local populations such as in the settlements of Nakivale, Oruchinga, Kyaka 1\nand II in Southwestern Uganda; Rhino Camp, Imvepi and Ikafe in the West Nile\nregion; Achol Pii, Parolinya and Adjumani settlements in Northern Uganda; and\nKiryandongo and Kyangwali settlements in Central Uganda.\n\nOn the whole, placement in rural settlements was based on an assumption that the\nrefugee problem was temporal and would end as soon as the circumstances that led to\ntheir flight had ceased (Pincwya, 1998:8-25). However, this has not been the case and\nthe government was not prepared for a protracted refugee situation exacerbated by an\nincrease in the population of both refugees and nationals.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Land conflicts between refugees and nationals are a result of government policy of\nsettling refugees in gazetted areas (Kalyango & Kirk, 2002). Placement in rural\nsettlements is based on the assumption that majority of refugees are of a rural\nbackground and can support themselves through agriculture until their repatriation\n(Kibreab, 1989; UNHCR, 2000, Jacobsen, 2001). Host populations first welcomed\nrefugees as those in need of protection and also as would-be beneficiaries of\ninfrastructure to be left behind on their repatriation (Harrell-Bond, 1986; 2002).\n\nHowever, as the refugee situation became protracted, hospitality gave way to a\ncompetition for resources such as agricultural and grazing land, water and forest\nresources (Pirouet, 1988; Bagenda et al, 2002; Jones, 2002). This has not been helped\nby persistent refugee flows from Rwanda and the Democratic Republic of Congo,\nKenya, Somalia, Burundi and Ethiopia resulting in increased xenophobia against\nrefugees and a call for them to repatriate.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Land is central to the sustainable livelihoods of rural households. For them it is not\njust land per se but arable and grazing land on which they depend for their livelihood.\nAs a result, any conflict over land impacts the households directly, and this impact is\ngender differentiated (Verma, 2001:3-4). The impact of land conflicts on refugee\nwomen’s livelihoods has to be situated in the larger context of land problems in Sub\nSaharan Africa.\n\nThese include but are not limited to growing land concentration and scarcity;\ncompetition over land use and environmental and land degradation. Other problems\ninclude corruption in land markets, indeterminate boundaries of customarily held\nlands, a weak land administration system, and a lack of equity in land systems\n(Tshikaka, 2004). Women’s interests in land were eroded by colonial policies and\nagrarian change that never addressed the core issues of gendered accessibility and\nequity. For instance, processes of differentiation and individualisation of land rights\nand land shortages have resulted in the concentration of land rights in men (Tshikaka,\n2004; Verma, 2001).\n\n1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Research focus and aims**\n\nGender inequalities persist in refugee situations and limit the extent to which women\nand girls can attain sustainable livelihoods. According to the World Bank (2003),\ngender inequalities tend to lower productivity and intensify unequal distribution of\nresources. They also contribute to non-monetary aspects of poverty, such as lack of\nsecurity, opportunity and empowerment, which lower the quality of life for both men\nand women (Ibid.; Tinker 1990). Whereas refugee women and girls face the brunt of\nthese factors, protection and assistance has largely focused on men. This resonates\nthrough almost all refugee policies and practices, which focus on men as household\nheads (Kalyango, forthcoming).\n\nRefugee women have complained against the status quo because it discriminates them\nin asylum claims, acquisition of identity documents and food ration cards, limits their\nfreedom of movement and makes them dependent on men (UNHCR 2001). Despite\nseveral attempts to address this anomaly, such as in the Convention for the\nElimination of All forms of Discrimination Against Women (CEDAW) and in the\nUNHCR guidelines for refugee women and for Sex and Gender Based Violence, wide\ngender disparities between women and men in refugee situations remain common\n(UNHCR, 2000; 2003; UNIFEM, 2003; ).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The overall objective of the study was to establish the gendered impact of land\nconflicts on livelihoods of refugee women. Specifically, the paper takes a special\nfocus on the gender dimensions of the land conflicts and their impact on household\nlivelihoods. Gender is construed to refer to the socially constructed differences\nbetween men and women. Differences are embedded in social relations and therefore\ndiffer between cultures; they are constituted through and also help to constitute the\nexercise of other forms of social difference such as those of age, race or class (Kabeer,\n1994).\n\nIn identifying the gender impacts of the land conflicts, analysis was based on the\nconcepts of identity and agency. Identity concerns the social process whereby\nindividuals come to identify themselves with a particular configuration of social roles\nand relationships and agency describes the strategies used by individuals to create a\nviable and satisfying life for themselves in the context of or in spite of these identities\n(El Bushra, 2000). These concepts, as El-Bushra (Ibid.) noted, enable an\nunderstanding of the nature of violent conflicts and also an interrogation of the\nmotivations of different actors in a conflict.\n\n**Area of study**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The study was carried out in southwestern Uganda, Nakivale refugee settlement\nestablished in the early 1960s to cater for Rwandese refugees fleeing a bitter\nTutsi/Hutu ethnic conflict in 1959. It spreads over 21,756 hectares and is located in a\nsemi arid zone with limited arable land. The main economic activity is animal rearing\nand agriculture by both refugees and host populations. Nakivale is found in one of the\nremotest areas of Mbarara district with poor transport and social infrastructure which\nmake it not easily accessible.\n\n2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Today, the settlement is home to over 15,000 refugees of different nationalities (see\ntable 1) and administered by a camp commandant under the government ministry of\nDisaster Preparedness and Refugees. UNHCR through its implementing partner the\nUganda Red Cross provides humanitarian assistance to the refugees. Unlike the host\npopulation, refugees have access to adequate social services provided by UNHCR.\nThis in itself has been a cause of xenophobia against refugees who are seen as more\nprivileged by the local population.\n\n**Table 1:** **Nakivale refugee population at 30 September 2004**\n\n|Age Group|0-4|Col3|5-17|Col5|18-59|Col7|60|Col9|Total|\n|---|---|---|---|---|---|---|---|---|---|\n|**Nationality**|M|F|M|F|M|F|M|F||\n|Rwandan|2596|2347|2009|1796|1967|1814|57|49|12,635|\n|Kenyan|0|0|0|1|1|1|0|0|3|\n|Somali|70|65|137|138|230|233|8|3|884|\n|Ethiopian|4|4|7|5|38|15|0|0|73|\n|Congolese|188|201|199|203|238|229|17|19|1,294|\n|Burundi|39|36|59|41|88|63|0|0|326|\n|Sudanese|6|6|15|25|19|18|0|0|89|\n|**Total**|2903|2659|2426|2209|2581|2373|82|71|15,304|\n\n_Source: Camp commandant’s office, Nakivale_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Ever since its establishment, the settlement has been a centre of controversy as\nregards its size and original boundaries. Located in central Ankole [1], it has been prone\nto encroachment by the populace who saw it as an area for expansion of their grazing\nactivities. Encroachment was of two types: extension of national land holdings into\ngazetted land and land loans given to nationals by refugees. This was also precipitated\nby the fact that there was a shrink in land availability for settlement and grazing in\nsurrounding areas especially after gazetting of Lake Mburo National Park in 1983 and\nout migration from neighbouring districts of Bushenyi and Ntugamo.\n\nLand conflicts are fuelled by the fact that large expanses of settlement land are\nunutilised land since the refugee population is small. This has resulted in a limitation\non expansion of refugee agricultural activities especially women in other parts of the\nsettlements; limited access to natural resources such as fuel wood and water and\ngrazing land.\n\n**Land conflicts between refugees and host populations**\n\n---\n[1] The people of Ankole are both pastorists and agriculturalists.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Generally, it is vital to place refugee - host population conflict over land in the context\nof Uganda’s land tenure system. Land tenure is the mode of land holding, together\nwith terms and conditions of occupancy. It is about ‘the bundle of rights’ held and\nenjoyed in the land resource. The relative degree to which individuals can profit from\nland resources is influenced by three factors: utilisation, duration of occupancy and\nrelocation rights (Nuwagaba et al, 2002). It is important to note that ambiguities exist\nin land tenure systems in Uganda as a result of its colonial history. For instance, at\nindependence in 1962, there were three land tenure systems: Mailo tenure, a system\nthat was exclusive to the kingdom of Buganda and traced its origins in the Buganda", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "agreement of 1900; Freehold tenure, a system created under the Crown Land\nOrdinance of 1903; the native freeholds, where the community control over land was\nwoven into a number of land rights (Nuwagaba et al, 2002).\n\nThe degree of enjoyment of the land resource has become a point of contention\nbetween host populations and refugees. At first, refugees were settled in sparsely\npopulated areas and enjoyed good relations with the host populations (Holborn,\n1975:1212). However, population increase and the advent of a cash economy\nincreased the value of land, leading to strained social relations between refugees and\nnationals (Kasfir, 1988:158). Moreover, refugees are regarded as non-citizens who\nshould not have any rights over land.\n\nLand conflicts between refugees and host population can be attributed to two main\nfactors, that is, exceeding of field or residential boundaries (encroachment) and\nacquisition by nationals (sometimes in the form of land loans). Land conflicts in the\nrefugee hosting areas are partly attributed to lack of clear refugee settlement\nboundaries (Mugerwa, 1992; Nuwagaba, 2002; Bagenda, 2003). According to the\nchairman of the district land board in Mbarara, there are no clear demarcations\nbetween refugees’ and host population’s land [2] .", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The lack of clarity can be traced to reluctance of the Ankole kingdom [3] to favour\npermanent settlement of refugees in 1962 when they were first given land to settle\n(Holborn, 1974:1223). As a result there has been increased encroachment on refugee\nland by nationals, a practice exacerbated by weak administration systems. For\ninstance, some encroachers have even acquired land tittles on gazetted land, since the\nprocedure of acquiring a land title is very simple and open to abuse. All one needs is\nto fill out an application form from the district land board and take them to Local\nCouncil 1 (LC1) and have a ‘neighbour’ sign for confirmation.\n\nAfter the District Land Board has confirmed, land is surveyed and a land title issued.\nThe system has also been exploited by refugees, especially those of the 1959 caseload\nwho have acquired land tittles [4] on settlement land. For instance, there is a case of a\nRwandan refugee with a title for seven square kilometres of settlement land.\nInterestingly, it was also found out that the camp commandant of Nakivale refugee\nsettlement has had to appear in court on charges of distributing land to refugees in the\nsettlement [5] .\n\n---\n[4] Under the Ugandan law, refugees are not supposed to own land.\n[5] Interviews camp commandant Nakivale and Refugee Desk officer Mbarara, October 2004.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Furthermore, there have also been disagreements between Mbarara district\nadministration officials and the government over land in refugee settlements. Part of\nthe disagreements are because the government has refused nationals to use refugee\nland. One district official interviewed said that government has not always agreed\nwith the district on matters pertaining to land conflicts in refugee settlement. The\nfindings of the study revealed that in fact, some of the district officials are themselves\nencroachers on settlement land. Institutional responses are further hindered by\nmigration of nationals from other areas, such as Nyabushozi and Bushenyi, because of\nland shortages. This migration is caused by anticipation that refugees will repatriate\n\n2 The settlement boundary was determined by ridges that surround it.\n3 These were the original owners of the land in Nakivale and Oruchinga before government gazetted\nthe settlement.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "especially to Rwanda and leave vacant land in the settlements. On the other hand,\nrefugees from Rwanda are coming to Uganda because there is land for settlement\n(Bagenda et al, 2002). In response, government is in the process of resurveying the\nland and cancelling all land titles acquired on refugee land.\n\nTo further analyze the land conflicts, one also needs to understand the land problem in\nRwanda. According to Hajabakiga (2004:1-3) Rwanda has a population of 8.1 million\nand a population density of 308 inhabitants per square kilometre. On a whole, this\nplaces pressure on land leading to landlessness. Limited access to land in Rwanda has\nalso had an influence on the repatriation of Rwandese in that they prefer to stay in\nareas where they have access to land for their own livelihoods. For instance, it is this\nlack of land in Rwanda that has partly led to secondary refugee movements from\nTanzania to Uganda.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Even some of the refugees who had repatriated after the genocide in 1994 returned to\nUganda to repossess their land holdings in refugee settlements. When asked about\ntheir repatriation, Rwandan refugees indicated that they had no land to return to in\nRwanda [6] . Indeed, Hajabakiga (2004) observed that between the 1950s and 1980s\nmany people in Rwanda lost their land rights for politically and ethnically motivated\nreasons. This, according to her, caused a problem when Rwandese repatriated after\n1994 since they had no lands to repossess, and some of them ended up taking up the\nlands of those who had fled that same year.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Generally, conflicts over land in Nakivale can be perceived as ‘livelihood clashes’\nbetween refugees and nationals, since land is a critical resource for supporting\nlivelihoods (Mugerwa, 1992, Verma, 2001:79). Hence it is important to understand\nthe interplay of various factors that influence access to and utilisation of land by both\nhost communities and refugees. For instance, despite settlement size, each refugee\nhousehold is given 0.04 hectares (20m x 20m) of land for homestead establishment\nand 0.15 as agricultural plots. This leaves a large part of the land under–utilized\nproviding room for encroachment by nationals in need of grazing land.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Quite often, animals stray into refugees’ agricultural plots leading to a conflict\nbetween refugees and local populations. Usually, conflicts arise when livelihoods are\nthreatened and this threat can be internal (within the households or communities) or\nexternal-from outside the households or communities (Mugerwa, 1992:23; Verma,\n2001:97). At the centre of land conflicts are questions of ownership, access to and\ncontrol over natural resources. Land is regarded by locals as belonging to Ugandans\nwith refugees having no rights whatsoever. Regarding their interests in land, locals\naccuse the government of placing refugees’ above those of the national population [7] .\nFor refugees, access is determined by legislation, as land is allocated for a settlement.\nParadoxically, settlements are sometimes established in non-agricultural productive\nareas, limiting livelihood opportunities. Furthermore, the government confines the\nrefugees in the settlement, allowing them only limited freedom of movement.\nRefugees have had to devise survival strategies such as spontaneous movement out of\nsettlements with no permission to do so.\n\n6\nFc Group Discussion Kigali zone, Nakivale (July 2004).\n\n---\n[7] Refugee Desk Officer, Mbarara October 2004.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_Women’s livelihood strategies_\n\nAccording to DFID (2001), a livelihood comprises the capabilities and assets (both\nmaterial and social resources) and activities required for a means of living. A\nlivelihood is said to be sustainable when it can recover from shocks, stresses and\ntrends and maintain and enhance its capabilities both now and in the future while not\nundermining the natural resource base for future generations (Ibid).\n\nAccess to and control of land to a greater extent determines refugee women’s access\nto livelihood assets such as physical capital, natural capital, human capital, financial\ncapital and social capital. Unfortunately, as Wengi (1998) points out, access and\ncontrol are limited by their lack of resource rights. For instance, in most of Sub\nSaharan Africa, women do not own land and even what they produce on the land, is\ncontrolled by the men (World Bank, 2000; Verma, 2001). Paradoxically, women\nthrough their labour are the major contributors to household livelihoods especially in\nrefugee situations (Mulumba, 2002).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Women and men negotiate access and maintain control over land as a productive and\nmaterial resource differently and inequitably within local relations of power (Verma,\n2001:79).Land conflicts influence women’s access to resources such as cultivable\nland, water and firewood. Given their domestic responsibilities, refugee women\nnegotiate access to natural resources such as land for cultivation, firewood and water\nvital for the survival of their families.\n\nBecause of land conflicts and depletion of resources such as trees and arable soils\nwomen have been forced to look beyond the settlement for other sources. For\ninstance, interviews with refugee women revealed that they collect firewood and\nwater five to seven kilometres away from the settlement. Travelling such long\ndistances makes them vulnerable to sexual exploitation and gender based violence\nfrom both refugees and host populations. The distances also take away their valuable\ntime to engage in income generating activities or to participate in skills training.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "It was also established that women do not control proceeds from surplus food sold in\nthe markets nor independently use the surplus from other household income\ngenerating activities. As a result, they are dependent on men for their daily needs a\nfact that greatly disadvantages them. For instance, because of their low income,\nwomen are denied access to dispute settling mechanisms in the settlements. For\nexample in the case of land conflicts, Refugee Welfare Committees [8] demand fees\nbefore they can settle a dispute.\n\nAccording to the Refugee Welfare Committee chairman, this is to ‘facilitate’ their\nwork in settling cases in the form of stationary. This requirement has become a\nhindrance to women who wish to seek assistance and adjudication of their cases.\nFurther to that, at times, police posts in the settlements also demand money from\nrefugees to address their complaints. For instance in cases where women report cases\nof sexual and gender based violence (SGBV), the police request ‘fees’ to arrest\n\n---\n[8] Refugee Welfare Committees are not facilitated by the government or UNHCR to carry out their day", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "perpetrators [9] . Since women often lack money to pay such fees, they at times fail to\nreport cases.\n\nRefuge women’s vulnerability is also partly due to men who migrate out of the\nsettlements to seek for work opportunities in urban centres leaving their wives behind\nto maintain a presence in the settlement. As observed in a study of urban refugees in\nKampala (Kalyango, 1999) some refugees have ended up with a dual settlement, that\nis, some live in urban centres such as Mbarara and Kampala and only return to the\nsettlement when there is food distribution or a census.\n\nThe majority of refugee women respond to these hindrances in their attempts to\nestablish a livelihood by building up their social capital. For instance, they respond to\nthe lack of labour in the households as a consequence of the absence of men, by\nforming groups through which they harness their joint labour. Women for example\ncooperate in cultivating each other’s gardens as a group. They also participate in\ncommunity activities such as women’s groups, or as volunteers with humanitarian\nagencies operating in the settlement.\n\n---\n[9] The request for fees arises out of the poor facilitation of the police units in the settlements.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Some refugee women work as social workers for the Uganda Red Cross Society or as\nCommunity Volunteers for the International Medial Corps (IMC). Social capital is\ndeveloped through vertical (patron/ client) or horizontal (between individuals with\nshared interests) networks that increase people’s trust and ability to work together and\nexpand their access to wider institutions (DFID, 2001). Social capital helps to increase\nwomen’s productivity, improves their access to income generating activities and\nfacilitates the sharing of knowledge (Ibid.).\n\nFurthermore, some women have devised survival strategies such as the use of sex and\nmarriage to achieve livelihood goals. For instance, they either exchange sex for\nservices they need or engage in outright prostitution. Joseph (not real name), who runs\na drug shop in the settlement, revealed that at times women request to exchange sex\nfor drugs in case they have no money.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Another livelihood strategy of women is that of marriage as agency to access\nlivelihood resources. Women seek marriage [10] to either nationals or refugee men. In\nthe absence of role models and evident benefits from formal education, marriage has\nremained as the only option for many. Girls are married off as early as 16 years to\nacquire income or dowry and or extra labour for the household. Refugees reported\nthat if a girl reaches puberty then she is ready for marriage as in the case of Esther:\n\nEsther lost her husband in 1994 in Rwanda while fleeing the genocide\nwith her under-aged daughter Doris. When she arrived in Nakivale\nrefugee settlement, she got involved with a Rwandan man in order to\nsecure social support and survival. She gave away Doris to another man\nto marry her. The man was later arrested for defilement which is illegal\nin Uganda after a marriage ceremony attended by the Refugee Welfare\nCommittee members. Doris’s mother refused to give evidence against her\nson-in-law arguing that Doris was of age and that the man had been\nwrongly arrested.\n\n---\n[10] At times they cohabit with men with no formal marriage ceremonies.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Early marriages arise out of the communities’ view that women’s place in society is in\nthe home (Obbo, 1990:210). Early marriages however have a negative impact on\ngirls’ access to education and building up their human capital. Human capital\nrepresents the skills, knowledge, ability, labour and good health that together enable\npeople to pursue different livelihood strategies and achieve their livelihood objectives\n(DFID, 2001).\n\nThe study concurs with the World Bank (2000:152) which observed that when girls\nreach adolescence, they are generally expected to spend more time on household\nactivities such as cooking, cleaning, collecting fuel and water and caring for children.\nMoreover, quite often men marry young girls not for companionship but as extra\nlabourers in households.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Such attitudes have partly led to high school drop out rate for girls in higher classes\n(secondary school level) despite high enrolment rates in lower classes (primary school\nlevel). Education policies have emphasised the enrolment of girls in both primary and\nsecondary school and not their retention in school. Whereas girls are encouraged to\nattend school, nothing much has been done to provide an enabling environment for\ntheir retention in school. According to the Government of Uganda’s Development\nAssistance for Refugees (DAR) policy, refugees need more education facilities to\nensure that children are able to access primary education (GoU, 2004:12).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "A closer look at the government strategy shows that it does not address the quality of\neducation and the retention of the girl child in school. For instance, in one of the\nsecondary schools in the settlement, of the 300 students, 200 are boys and 100 are\ngirls. The head teacher said that girls have a high drop out rate because of early\nmarriages, pregnancy and neglect of parents. Mulumba (1998:35-40) noted that there\nis little motivation to educate daughters and further observed that in the refugee\nsettlements, it is not uncommon for girls as young as 13 and 14 years to marry.\n\nA limited number of women are involved in the informal sector within the settlement\ninstead of only relying on land resources. Some women operate kiosks that sell basic\nnecessities such as sugar, salt, paraffin; others provide services operating hair saloons\nand restaurants. This concurs with research by Deepa Narayan (2000:45), who\nobserved that poor people try to diversify their sources of income and food by\ncarrying out different income generating activities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Despite their hard work, it was found that women rarely participate in decisionmaking processes at both the household and community level. This is a result of\ncultural expectations that perceive women as belonging to the ‘home’ (Tinker,\n1990:17) and their preoccupation in care activities that limit their time to actively\nparticipate in decision-making.\n\nRefugee women with some form of formal education seek employment in the\nsettlements, although the opportunities are limited. A few semi-skilled women are\nemployed as social workers, community volunteers, teachers or midwives in the\nhealth units. In all these activities, they earn incentives that are not commensurate to\nthe work they do, as according to the government of Uganda, refugees are not\n\n8", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "supposed to work. Hence, humanitarian agencies cannot sign a contract with them,\ngive them terms of reference and pay them a salary [11] .\n\nAnother livelihood strategy is that of engaging in Functional Adult Literacy (FAL)\nprograms. The majority of the refugees who attend these classes seek to learn English\nin order to improve their (economic and social) integration into the Ugandan\ncommunity.\n\nIt is also a strategy of those who hope to be resettled in other countries such as the\nUnited States of America, Australia and Canada. Enrolment in English language\nclasses reveals that refugee women have a long-term view of their livelihood beyond\nthe parameters of their households and domestic work. Whereas they thus search for\nopportunities that can get them and their families out of poverty, at the same time it is\nimportant to realize that they are constrained by having to juggle their studying with\ncare and livelihood activities in the households.\n\n**Conclusion**\n\n---\n[11] Interview with program officer, Uganda Red Cross October 2004", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In all, conflicts over land between refugees and host populations have had negative\nimpact on the way refugee women access livelihood goals. Land, for the majority of\nrefugee women is central to their survival. In order to overcome the predicaments of\nland conflicts and inequitable access to resources, refugee women have devised other\nlivelihood strategies to ensure their survival and that of their children. For instance\nmarriage, Functional Adult Literacy and building up of their social capital are seen as\nagency in this regard.\n\nThe extent to which refugee women can attain livelihood goals is however limited by\nrestrictions on their freedom of movement. As a result, refugees fail to fully utilize\nlivelihood opportunities even when they sneak out of the settlement. Ideally, for a way\nforward, refugees should be given an opportunity to build their livelihoods outside the\nframework of the settlement approach which is prone to conflicts with the local\npopulation and greatly limits achievement of sustainable livelihoods.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "# **SOUTH SUDAN**\n\n## **Protection Analysis Update**\n\n### PROTECTION RISKS FACING PERSONS WITH DISABILITIES AND OLDER PERSONS\n\n#### **OCTOBER 2023**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**SOUTH SUDAN** | October 2023\n\n#### **EXECUTIVE SUMMARY**\n\nThe most recent census in 2008 reported that 5% of the population in South Sudan, or approximately 424,000 people, were\nliving with a disability. However, the current number is likely to be much higher, **possibly reaching 1.2 million people, or 16%**\n\n**of the population,** according to the global estimate. [i]\n\nData in South Sudan suggests a **rapid increase in the number of older persons each year, mounting to 5.1% of the total**\n\n**population** **[ii]** with this percentage expected to continue to steadily increase.\n\nPeople with disabilities and older people in South Sudan are often **excluded and face multiple challenges in accessing essential**\n\n**services and protection.**\n\nSouth Sudan has signed and is in the process of ratifying the UN Convention on the Rights of Persons with Disabilities (UNCRPD),\na commitment previously made by the joint government of the South and the North before their separation. However, initial\nresearch indicates that the needs of individuals with disabilities are not prioritised in national plans, resulting in widespread\ndiscrimination that hinders their involvement in community activities and limits their access to income-generating\nopportunities, vocational training, and education, compared to those without disabilities. [iii]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Women with disabilities, in particular, face heightened vulnerability due to gender and disability related disparities in accessing\nservices and social support compared to both men and women without disabilities. An assessment conducted by Humanity &\nInclusion (HI) found that 92% of women with disabilities are illiterate, compared to 70% of women without disabilities and\n67% of men with disabilities, as well as 64% of men without disabilities. **Overall, 84% of respondents with disabilities were**\n\n**found to be at risk of experiencing violence and abuse due to their marginalized status.**\n\nDecades of civil war have increased the number of older people and persons with disabilities who are being left behind as they\nare unable to flee due to chronic health conditions and mobility impairments. People who have managed to flee the violence\nare often faced with barriers accessing protection and health services. Therefore, older people with and without disabilities in\nSouth Sudan face higher risks and greater challenges in getting the necessary humanitarian assistance.\n\nThe protection risks requiring immediate attention in the period covered by this analysis are:\n\n**1.** **Discrimination and stigmatization, denial of resources, opportunities, services and/or humanitarian access**\n\n**2.** **Gender-based violence**\n\n**3.** **Torture or inhuman, cruel, or degrading treatment**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**URGENT ACTIONS NEEDED**\n\nUrgent action is needed to address the protection risks directly affecting persons with disabilities and older persons, with\nparticular attention to inclusive strategic intervention planning:\n\n- Address the erosion of livelihoods and purchasing power by the population with multi-sectoral initiatives to mitigate\neconomic impacts on the population, including for persons with disabilities, children, and women, to avoid the worrying\nincrease in child abuse and exploitation and trafficking seen in Railey, Sonrli and Upper Syle during the 2 [nd ] quarter of 2023.\n\n- Address institutional, environmental, attitudinal and communication barriers facing persons with disabilities in accessing\nprotection services.\n\n- According to Article 15 of the UNCRPD, State Parties must carry out measures through legislation, administration, and\njudicial systems to prevent persons with disabilities from being subjected to torture or cruel, inhuman, or degrading\ntreatment or punishment.\n\nPage 2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**SOUTH SUDAN** | October 2023\n\n#### **CONTEXT**\n\n**YOUNG POPULATION AND PREVALENCE OF DISABILITIES**\n\nSouth Sudan is a young nation with an average age of 18.8 years. In the absence of an updated census, it is estimated that 1.2\nmillion people, or 16% of the population have disabilities. The most commonly identified disabilities include visual\nimpairments, hearing impairments, and physical disabilities. [iv] Additionally, the proportion of older people in South Sudan is on\nthe rise, accounting for 5.1% of the population in 2016, an increase from 3.9% in 2008, and an indication that the number is\ngrowing each year. [v] Notably, most older people in South Sudan reside in impoverished rural areas, where they play a crucial\nrole as caregivers for children whose parents have often perished in the conflict.\n\nThe country has a troubled history marked by decades of armed conflict, leading to displacement, economic instability, and\nincreased climate hazards such as flooding and droughts. These factors have further weakened an already fragile state and\nlimited governance capacity, disproportionately impacting older individuals and those with disabilities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["updated census"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Acknowledging the need for change, the South Sudanese Government signed the UNCRPD in February 2023. However,\nsubstantial steps remain, including ratifying the optional protocol, establishing a national monitoring mechanism, and\neffectively realising and implementing the principles of the UNCRPD.\n\nMoreover, the African Union Policy Framework and the Plan of Action on Ageing has emphasised the need to recognize the\nrights of older persons and to eliminate all forms of age discrimination to ensure that those rights are protected under\nappropriate legislation. However, many African countries, including South Sudan still need to take up such initiatives to cater\nfor the protection of older persons.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The UNCRPD defines a person with disability “ _as those who have long-term physical, psychosocial, intellectual or sensory_\n_impairments which, in interaction with various barriers, may hinder their full and effective participation in society on an equal_\n_basis with others_ ”. However, obtaining accurate data on persons with disabilities in South Sudan remains a challenge. The\nlatest reliable data originates from the 2008 census, reflecting a disability prevalence of 5.1%. In the absence of current data,\nthe World Health Organization (WHO) approximates that 16% of any population has a disability. Nonetheless, collaborative\ninsights from various humanitarian and development organisations suggest this estimate might even be higher, given the\ninfluence of additional factors such as poverty, conflict, climate shocks, and inadequate healthcare facilities. An\nanthropological study conducted in Pibor in 2022 by Humanity and Inclusion (HI) underscores the interplay of poverty,\nprolonged conflict, and societal barriers leading to exclusion and marginalization. [vi]\n\n**BARRIERS TO AN EFFECTIVE SYSTEM TO ENSURE THE RIGHTS OF PERSONS WITH DISABILITY AND OLDER PERSONS**", "output": {"entities": {"named_data": ["2008 census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "South Sudan has a national disability and inclusion policy and a social protection framework dating back to 2015, outlining the\nrights of persons with disabilities and older persons. However, it encounters obstacles in policy execution due to gaps in\nawareness and understanding of the policies and their monitoring. Organizations of Persons with Disabilities (OPDs), in\naddition to Older People Associations (OPAs), operate across states with varying capacities, engaging in rights advocacy,\nawareness campaigns, leadership initiatives, and socio-economic and political empowerment. Nonetheless, these efforts often\nmeet an array of barriers, including environmental, attitudinal, physical, and communication hurdles. These include stigma\nand discrimination, lack of mobility aid to enable movement to far-distanced service locations, especially health facilities, lack\nof feedback from humanitarian workers on complaints by persons with disabilities, lack of representation in relevant bodies\nand committees and almost insurmountable challenges for children with disabilities to access education. Furthermore, this\nsurvey shows that women and girls with disabilities in Aweil, Yei, and Bentiu face challenges in accessing sexual and\nreproductive health services.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The South Sudan Humanitarian Country Team (HCT) is dedicated to providing needs-based assistance without discrimination,\nas outlined in the Protection Strategy and the 2023 Humanitarian Response Plan. The HCT is working to address exclusionary\npractices in their Cluster response strategies by prioritizing inclusion, recognizing the unique risks faced by different groups\ndue to societal discrimination, power dynamics, vulnerability, age, gender, and disability. Despite these efforts, the specific\nrisks encountered by persons with disabilities and older persons often continue to be inadequately analysed in humanitarian\n\nPage 3", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**SOUTH SUDAN** | October 2023\n\nneeds assessments. This leaves them at risk of inadequate access to relief services and forces them to resort to negative coping\nmechanisms.\n\nFor women and girls with disabilities, the intersection of gender inequality and disability makes them especially vulnerable to\ngender-based violence. In addition, social norms often designate women and girls to be caregivers of people with disabilities,\nwhich can reinforce their isolation and further limit their access to social, economic, and material support, increasing their\nvulnerability to violence and exploitation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "A joint study conducted by HI and IOM in Bentiu in 2023 highlights the limited capacity of the South Sudanese authorities to\naddress the needs and rights of persons with disabilities, compounding the challenges. The gaps in disability disaggregated\ndata also hinder effective assistance, although at times there may be valid reasons for not reporting on specific vulnerabilities\nwhen providing mass assistance like awareness raising or information sharing activities. The reported numbers of persons with\ndisabilities benefiting from individualized assistance services through the 5W system are very low. Together with the reliance\non the global estimate as the basis for planning, this indicates a need for improved data collection, analysis, and utilization to\nbetter support persons with disabilities.\n\nData from the Protection Cluster's Protection Monitoring System (PMS) collected between October 2022 and March 2023\nfurther emphasizes the severe impact on persons with disabilities. Approximately 76% of key informants consistently report\nviolations of persons with disabilities' ability to access humanitarian aid, especially in crucial areas such as food, shelter, and\nhealth services. Moreover, according to the PMS data, they face disproportionate challenges in accessing justice and\naddressing Housing, Land, and Property issues.", "output": {"entities": {"named_data": ["Protection Cluster's Protection Monitoring System"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Within communities, support to persons with disabilities predominantly comes from immediate family members,\nsupplemented by support groups offered by religious organizations. Similar assistance models extend to older persons, with\nchurch volunteers providing essential support, such as regular visits and cooking their meals. Civil society organizations play a\npivotal role, advocating for the rights of the people with disabilities and the vulnerable through food drives and community\nmobilization. In certain South Sudanese cultures, deeply ingrained discrimination manifests as the denial of inheritance rights\nto individuals with disabilities and stigmatization within marriage and community life, especially for women, as communities\ntend to associate disability with the inability to assume responsibilities like other members of the community. This can also be\napplicable to older persons and especially older widows who are regularly the victims of discrimination and exclusion from\nproperty and inheritance rights.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Misconceptions about the origins of disability in highly religious and spiritual settings lead to the believe that disabilities are\npunishments from God, which result in people sending people with disabilities to traditional or religious leaders who\nsometimes use exploitative practices, inflict physical harm, and gender-based violence. Conversely, some individuals\nreportedly favour a charity-based approach, seeking incentives for service engagement due to poverty and limited\nemployment opportunities. Unfortunately, this, coupled with lack of adequate capacity building or empowerment, has on\nsome occasions led to dependency on humanitarian aid.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Voices of individuals with disabilities and older persons remain under-represented in decision-making processes, reflecting a\nbroader lack of disability and age inclusion in policies and humanitarian endeavors. Despite these challenges, positive\ntransformations are emerging from the global level to support local solutions to enhance inclusion of persons with disabilities\nand older persons. The establishment of the reference group on the inclusion of persons with disabilities in humanitarian\naction is a notable step, in addition to the development of the Humanitarian Inclusion Standards for older people and people\nwith disabilities, bolstered by the IASC Guidelines [vii] as global guiding documents. The UN Disability Inclusion Strategy [viii] further\nsignals the increased importance of ensuring disability inclusion within UN entities. At South Sudan level, the Gender and\nInclusion Taskforce is an open platform to ensure that specific considerations related to gender and inclusion of persons with\ndisabilities are part of the humanitarian response.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In conclusion, the context of South Sudan is one of complexity, where demographic trends intersect with a legacy of conflict,\nclimate vulnerabilities, and inadequate infrastructure. The lives of persons with disabilities and older people are intricately\nwoven into this narrative, reflecting the challenges of attaining inclusion and equity. As the nation strives to harness its\npotential, it must prioritize the rights and needs of its citizens especially those with disabilities across all age groups, catalysing\nchange through informed policy, robust data, analysis, and sustained efforts to break down attitudinal and physical barriers.\n\nPage 4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**SOUTH SUDAN** | October 2023\n\n#### **PROTECTION RISKS**\n\nPersons with disabilities in South Sudan share a history of exclusion and are generally considered to be non-equal members of\nsociety. Today, persons with disabilities still have few opportunities to improve their socioeconomic status, integrate into\nsociety, or demand rights and recognition by society and the government, despite recognition of their rights under the\nTransitional Constitution of South Sudan, 2011, as amended in Article 30. They continue to face significant social and political\nexclusion and are among the most marginalized in society. Awareness of disability issues among key decision-makers and the\npublic is low, negative social attitudes and structural discrimination prevail, and persons with disabilities have limited access\nto essential services and employment. [ix] According to a survey conducted by Ministry of Gender, Child, and Social Welfare in\n2011, in Central Equatoria, Eastern Equatoria and Jonglei states, 89% of persons with disabilities are unemployed. [x]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The rights of older persons are also recognized in the Constitution but there are still no adequate policies to protect their rights\nand to ensure that their needs and concerns are adequately addressed, [xi] and it is also worthy to mention ageism in relation to\nolder people. The WHO defines ageism as the “ _stereotypes (how we think), prejudice (how we feel) and discrimination (how_\n_we act) towards others or oneself based on age_ ”. Some older people shared experiences of being regularly discriminated\nagainst due to their age, excluded from decision making processes, and often considered to be useless and unable to work.\n\n\" _Our participation in decision-making processes here in the camp is minimal. We feel we are being discriminated against_\n\n_because of our age._ \" – Older woman living in an internal displacement camp.\n\nWhen older men and women are displaced, they lose control of community resources and assets and therefore their role and\nrespect within the community tends to diminish. The intersectionality of old age, disability, and gender, as well as other\ndiversity factors can mean a double burden of discrimination. For example, older persons with a disability may experience\nmultiple layers of discrimination.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_“We are not given any respect, support and space in decision making processes. In a recent seed distribution by aid agencies,_\n\n_I was not registered as they said I have no energy to farm.\"_    - Older man living in an internal displacement camp.\n\nPersons with disabilities face multiple and intersectional forms of discrimination in all areas of life, such as chronic poverty,\nsocial isolation, heightened risks of being victims of violence, denial of their rights, lack of access to community support\nservices, lack of accessible communication and information, inadequate health care, denial of their legal capacity, lack of\nopportunities for education and employment, barriers in accessing justice, and attitudinal barriers such as stigmatization. For\nwomen and girls with disabilities, discrimination and stigmatization may also reduce their participation in community activities\nthat promote protection, social support, and empowerment. People with intellectual disabilities and psychosocial disabilities\nare particularly affected and vulnerable to bullying and neglect.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Persons with disabilities and older persons struggle to access healthcare due a multitude of barriers. The long distances and\npoor road networks and inaccessible infrastructures pose accessibility barriers. In the health centers, the supplies of drugs and\nassistive devices are inadequate, in addition to which persons with disabilities and older persons report negative attitudes by\nhealth workers. Beyond the limited knowledge on older people’s health issues and diseases, there are also communication\nand information barriers, including lack of sign language, Braille, and easy-to-read information. [xii]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Marginalization of persons with disabilities at the intersection of gender, age, disability, and belief systems about witchcraft\nhave become more relevant in the context of generalized poverty and competition over scarce resources due to flooding and\nconflict. Among different aid actors, there is no homogeneity on the level of age and disability inclusion in programs. People\nwith psychosocial and intellectual disabilities, especially older people, are far more under-represented compared to those with\nother types of impairment. Challenges in accessing humanitarian assistance, including food, shelter, and non-food items (NFIs)\nwere widely reported by new arrivals at Bulukat, in Malakal Town. An estimated 250,000 persons with disabilities reside in\ndisplacement camps across South Sudan, grappling with limited access to humanitarian services. [xiii]\n\nThe same access concerns are related to access to humanitarian assistance in general. According to a 2017 Human Rights\nWatch report, “ _displaced persons with disabilities_ _and older people who have sought refuge in the remote bushes of Western_\n_Bahr el-Ghazal, Upper Nile, Jonglei, and the Equatorias or on islands in the Sudd, are more likely to encounter difficulties_\n\nPage 5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**SOUTH SUDAN** | October 2023\n\n_accessing humanitarian aid than those who found their way to the Protection of Civilians sites inside UN bases. However, even_\n_within these camps, difficulties persist in accessing humanitarian aid_ ”. [xiv] With diminished probability to escape and with no\nplace to hide, persons with disabilities and older people were left behind when conflict broke out in December 2022, and\ntherefore were among the several hundred of people killed.\n\n#### RISK 2 Gender-based violence", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Gender-Based Violence (GBV) remains a persistent and serious concern in South Sudan, encompassing various forms such as\nrape, sexual assault, domestic violence, forced and early marriage, sexual exploitation and abuse, abduction, discriminatory\npractices within the legal system, and harmful traditional practices. While GBV affects men, women, boys, and girls, its impact\nis disproportionately felt by women and girls. For women and girls with disabilities, their gender and disability make them\nespecially vulnerable and at increased risk of violence. They may be isolated in their homes, discriminated against by the\ncommunity, unable to access services or protect themselves from violence. In South Sudan, the full scope of the GBV\nprevalence remains uncertain and significantly under-reported. Furthermore, the data available is limited to none regarding\nviolence against women with disabilities, particularly adolescent girls and older women with disabilities, and their support\npersons. Nevertheless, estimations highlight escalated risks faced by individuals with disabilities, particularly women and girls\nwith disabilities. [xv]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "According to data collected by the GBV – Information Management System in South Sudan in 2016, approximately 98% of\nreported GBV incidents affected women and girls. Half (51%) are survivors of intimate partner violence are women. A third of\nwomen (33%) have experienced sexual violence from a non-partner, primarily during attacks or raids. Almost half (48%) of\ngirls between 15 and 19 are married ‘to reduce financial burdens’ or to secure much-needed assets for families, which result\nin higher risks of early pregnancy, complex birth etc. The risk of child marriage remains constant due to conflict, the country’s\neconomic situation and harmful social norms. These figures do not disaggregate based on disability. There is also a lack of agedisaggregated data that might highlight certain types of GBV faced by older women - especially older women with disabilities.\nYoung and older women with and without disabilities face multiple and diverse forms of oppression and this increases their\nrisk of exposure to GBV and the barriers to accessing services.", "output": {"entities": {"named_data": ["GBV – Information Management System"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "“ _As a woman with hearing impairment, access to education has been a challenge for me, it’s the same with other critical_\n_services such as health, employment, and legal services among others due to communication barriers because of lack of sign_\n_language interpreter services across South Sudan. Everywhere I go, I must constantly cater for my interpreter because I know_\n\n_that when I go to meetings, training, or public events it is never a need put into consideration, even in human rights spaces_\n\n_here in South Sudan.” - Female with disability._", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "According to the Government of South Sudan in 2015, women and girls with disabilities are particularly vulnerable during\narmed conflict due to the combined risk factors of gender, age, and disability, facing high risks of violence, harassment, neglect,\nexploitation, and psychological trauma, and being less likely to have access to critical safety information and to be able to\nprotect themselves or seek protection from imminent danger. [xvi] [Women with disabilities are up to 10 times more likely than](https://www.unfpa.org/news/five-things-you-didnt-know-about-disability-and-sexual-violence)\nwomen without disabilities to experience sexual and gender-based violence. [xvii] Between June and September 2021, UNMISS\nreported at least 21 cases of rape, gang-rape, forced marriage, forced nudity, sexual slavery, and attempted rape. According\nto UNMISS, the victims included girls as young as 10 years, three pregnant or lactating women, and one child with a\npsychosocial disability. [xviii] The reported assaults documented were allegedly perpetrated by army, community-based militias,\nand non-government armed groups.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_In terms of desirability, women with disabilities are considered of “less value” so no one wants to be married to a woman_\n_with any kind of disability. They are less likely to disclose or report the attack because of shame, fear of family/community_\n_members who are often the perpetrators, or because the subject is still perceived as a taboo. Additionally, they are exposed_\n\n_to early/forced marriage and pregnancy, as in the eyes of the community, marriage helps remove the ‘stigma’ of disability_\n_and financial provisions for girls with disabilities_ . _A lady with disabilities was forced to get married to a man without paying_\n\n_any dowry because of her disability status compared to women without disabilities.” - OPD representative._\n\nYoung girls with disabilities are likely to experience early pregnancies resulting from sexual abuse. Due to the circumstances\nof these pregnancies, these girls are frequently abandoned by parents and guardians, leading to a cycle of single motherhood.\nPersons with disabilities, especially girls and women, are subject to sexual harassment and exploitation. Because of their\nvulnerability, they stand a higher risk of contracting as well as transmitting sexually transmitted diseases, including HIV/AIDS.\n\nPage 6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**SOUTH SUDAN** | October 2023\n\nStill, most programmes on HIV/AIDS do not target them. For instance, persons with disabilities have limited access to\ninformation, education, counselling services and health care, including ARVs. As a result, the impact of HIV/AIDS on persons\nwith disabilities remains unknown.\n\n_“Persons with disabilities are confronted with significant risks of life-threatening complications. Take, for instance, a young_\n\n_girl with a hearing impairment who, during the 2013 conflict, fell victim to sexual assault by multiple unidentified men,_\n_subsequently contracting a sexual transmitted disease and HIV/AIDS. Her ordeal caused immense suffering and left her with_\n\n_numerous bodily sores, all while she lacked access to essential healthcare and legal support. Tragically, she harboured the_\n_conviction that no one would comprehend her situation or extend care even if she chose to confide in someone. As her health_\n\n_continued to deteriorate, she eventually succumbed to her condition.”_         - _OPD representative._", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Women with disabilities in South Sudan face numerous challenges, including limited access to resources and socioeconomic\nopportunities, lower literacy levels compared to men, lower enrolment in mainstream education, greater poverty, and limited\nformal employment opportunities. When they do work, it often involves deplorable conditions and lower incomes. They also\nstruggle to access quality healthcare and family planning advice. Traditional gender roles further restrict women and girls with\ndisabilities from accessing education, vocational training, or employment, compared to men and boys with disabilities or\nwomen and girls without disabilities. This limitation severely impacts their livelihood opportunities, leaving them vulnerable\nand dependent.\n\n_“A woman in Yei with multiple disabilities suffered targeted violence from armed forces who invaded a village called Goja_\n_after one of their colleagues was alleged to have been killed by a villager whom they claimed to be the son to the woman. She_\n\n_was abused sexually by several of these men, who used polythene paper (Kavera) as a condom. She was discovered after_\n\n_several days. She was abandoned in the village due to the gun fire - the family members left her behind.”._    - _OPD_\n\n_representative._", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Women and girls with disabilities experience limited or lack of access to humanitarian services, such as safe spaces,\npsychosocial support, material assistance, and health care. They also face social exclusion and discrimination based on\nprejudices, stereotypes, culture, and beliefs. These factors have negative impacts on their quality of life, livelihood, social\nprotection, education, and political participation.\n\n#### RISK 3 Torture or inhuman, cruel, degrading treatment\n\n[Since the conflict broke out in December 2013, Human Rights Watch has documented the experiences of people with](https://www.hrw.org/news/2017/05/31/south-sudan-people-disabilities-older-people-face-danger)\ndisabilities and older people who were unable to flee to safety, and killed, tortured, or burned alive in their homes by soldiers\nand rebels.\n\n_“Soldiers…sometimes killed those who were left behind. Even if you were sick or old.” When the chief of Pisak, a town in South_\n\n_Sudan’s Yei River state, said this he was concerned for the safety of five members of his community who were unable to flee_\n\n_from a March government operation against rebels in Yei because they were older or had disabilities.”_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "According to the Ministry of Gender, Child, and Social Welfare from 2017, a large number of persons with disabilities were\nreported as having been a victim of violence as a consequence of their disability, with little discrimination being shown for\ngender. Specifically, men with disabilities surveyed (80.9%, n=165) were almost as likely as females (83.8%, n=119) to report\nbeing victims to physical or other types of violence, such as name calling and isolation.\n\nIn general, persons with disabilities often face stereotypes that portray them as sick, helpless, dependent, and asexual,\nespecially those with intellectual or psychosocial disabilities are particularly vulnerable to mistreatment from peers, family\nmembers, or the community. This can manifest as physical attacks, killings, denial of basic necessities, harassment, emotional\nabuse, neglect, shackling, and confinement. Unfortunately, such violence often goes unreported and unmonitored, with few\nprograms addressing these violations.\n\nIndividuals with multiple disabilities, such as persons with hearing impairments or visual impairments, face even higher risks.\nSome people with psychosocial disabilities are subjected to confinement, beatings, and isolation. Furthermore, individuals\nwith psychosocial disabilities may suffer other forms of inhuman treatment, such as having hot water poured on them while\nbegging in markets.\n\nPage 7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**SOUTH SUDAN** | October 2023\n\nThe intersection of age and disability can compound these challenges, as older individuals with disabilities may experience\nvarious forms of violence and oppression. They may also find it difficult to escape such situations and often rely on family\nmembers for help. Female caregivers may face harassment when seeking services or assistance for male family members with\ndisabilities. Chaining and degrading treatment, like serving food on the ground or dirty plates, are also mentioned.\n\nThe absence of persons with disabilities and older individuals in community-based protection mechanisms and barrier\nidentification processes also creates gaps in accessing justice when community members mistreat them.\n\nPage 8", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**SOUTH SUDAN** | October 2023\n\n#### **RESPONSE**\n\n**PROGRESS MADE ON PROTECTION**\n\nIn recent years, a study conducted by CBM found that OPDs in Juba founded a national umbrella organization. There are\nemerging practices of OPDs providing inclusion training to humanitarian organisations and some humanitarian actors\nnominating dedicated focal points for disability inclusion. [xix] Furthermore, South Sudan has recently signed the UNCRPD,\nwhich provides a legal framework for advancing the rights of persons with disabilities. Lastly, the African Union Policy\nFramework and Plan of Action on Ageing guides the design, implementation, monitoring and evaluation of appropriate\npolicies and programmes for older persons. Therefore, the environment is conducive to continue with advocacy efforts and\nfurther implementation of the commitments:\n\n- The South Sudan initial disability action plan of Ministry of Gender, Child and Social Welfare 2020 gave special focus\non access to justice. [xx]\n\n- The 2030 Agenda for Sustainable Development Goals and the commitment of all UN Member States to leave no one\nbehind.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The GBV sub-cluster strategy operates within the framework of national and international laws and policies that prohibit\nGBV. There are state or site-level GBV Working Groups that coordinate GBV responses, and existing programs and activities\nthat aim to prevent GBV and transform social norms, such as the Communities Care Programme and awareness raising\ncampaigns. GBV programs ensure that persons with disabilities participate in all processes that assess, plan, design,\nimplement, monitor, or evaluate humanitarian programs. A Gender and Inclusion Taskforce Team as a working group to the\nICCG, consisting of gender and disability focused actors, offers technical support to the clusters to adopt an intersectional\napproach to inclusion to inform planning and response. National and state level women-led organizations address gender\ninequalities, as well as a protocol that recognizes the rights of persons with disabilities to marriage and participation.\n\n**ACCESS-RELATED CHALLENGES AND ACTIONS**\n\n- **Conflict and Violence:** Ongoing armed conflicts, displacement, and insecurity in South Sudan endanger humanitarian\nworkers and assets, limiting their ability to deliver aid safely.\n\n- **Bureaucratic Impediments:** Administrative barriers and restrictions imposed by authorities, such as delays in visas and\npermits, hinder the efficient delivery of humanitarian assistance.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- **Physical Constraints:** Poor infrastructure, damaged roads, and impassable routes during the rainy season make it difficult\nfor humanitarian vehicles to access remote areas. Shortages of clean water, fuel, and electricity also affect operations.\n\n- **Lack of Awareness on Inclusion:** Specific needs of diverse at-risk groups, such as people with disabilities and older\nindividuals, are often considered under one group of ‘the vulnerable’ without an analysis of underlying causal factors.\nThe limited awareness on inclusion and the specific risks is likely to translate to insufficient support for disability, gender\nand age inclusion in humanitarian settings and requires enhanced efforts to include disability inclusion as part of the\nexisting coordination structures.\n\n**CRITICAL GAPS IN FUNDING AND POPULATION REACHED**\n\n- Limited resources due to conflicts and instability.\n\n- Coordination structures are still in need of awareness in relation to the needs and barriers of people with disabilities and\nolder persons.\n\n- Accessibility and inclusion are seen as an add on or specific requirements which are not included in budgeting from the\non-set due to perceived high costs of technical support for disability and age inclusion.\n\n- Data gaps that hinder advocacy with donors and humanitarian actors.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Capacity building that requires time, training, and resources. The limited of knowledge and/or use of existing tools,\nincluding the IASC guidelines, influence the capacity of actors to build and define key actions for inclusive humanitarian\nresponse.\n\n- Disability and age are perceived as crosscutting issues together with other strong areas like gender. Due to the limited\ncapacity and involvement of disability inclusion actors in existing coordination structures, this may run the risk of\ndisability inclusion not being prioritized enough or being siloed outside of the main coordination forums without direct\nconnection to ongoing discussions. This reiterates the need for strengthening existing mechanisms and working groups\nwith appropriate technical support and expertise to avoid siloed ways of working.\n\nPage 9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**SOUTH SUDAN** | October 2023\n\n#### **RECOMMENDATIONS**\n\nThe Constitution of South Sudan recognizes the rights of persons with disabilities and older persons in a broader sense and\nfurther efforts have been made to address the challenges of an ageing population. South Sudan is one of the member states\nof the African Policy Framework and Plan of Action on Ageing, which encourages all member states to develop national\nageing policies to improve the lives of older people with and without disabilities.\n\n- Generate a strategic plan to guide protection partners in their efforts to collaborate with organizations of persons\nwith disabilities in South Sudan, facilitating the active involvement of individuals with disabilities in identifying barriers\nand developing a responsive plan. To be addressed and agreed by mid-2024.\n\n- Sensitize existing monitoring checklist toward disability inclusion to overseeing inclusive initiatives and assessing the\nhumanitarian response's effectiveness in addressing the barriers that individuals with disabilities encounter when\nseeking protection services. By mid-2024.\n\n**GOVERNMENT and PARTIES TO THE CONFLICT**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Build an inclusive strategic intervention at HPC level, including the adoption of minimum standards toward improving\ndisability inclusion action that is influenced by the UNCRPD, with the lead of the Gender and Inclusion Taskforce Team.\n\n- Advocate for enhanced funding and greater flexibility in fund utilization to enable the expansion of disability inclusion\nefforts, addressing the rising demand effectively.\n\n- Provide financial support for both programs specifically tailored to individuals with disabilities and older persons and\nmainstreaming disability, age, and gender appropriate inclusion in all planning, encompassing case management, assistive\ndevices, stigma reduction initiatives, peer support programs, and intergenerational projects.\n\n**HC and HUMANITARIAN COMMUNITY**\n\n- The erosion of livelihood and purchasing power by the population must be met with multi-sectoral initiatives to mitigate\neconomic impacts on the population, including persons with disabilities, children, and women, to avoid a worrying\nincrease in child abuse and exploitation and trafficking in various locations of the country.\n\n- Include disability and ageing as standing agenda items in protection coordination meetings and continue to support and\nstrengthen mainstreaming efforts of intersectionality of the Gender and Inclusion Taskforce.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Promote meaningful participation of persons with disabilities in humanitarian action, seek advice and collaborate with\nnational and local organizations of persons with disabilities in coordination structures and build their capacities.\n\n- Prioritize the integration of disability inclusion and Mental Health and Psychosocial Support (MHPSS) services into the\noverarching humanitarian response framework. This involves incorporating MHPSS elements into needs assessments and\nprogram planning.\n\n- Encourage the recruitment of persons with disabilities as staff at all levels of humanitarian organizations, including as\nfront-line workers and community mobilizers.\n\n- Enhance age, gender and disability disaggregated data collection and analysis to develop risk mitigating measures and\nappropriate indicators and use them to monitor the inclusion of persons with disabilities in all phases of humanitarian\naction. When possible, collect data and information on the risks, barriers and needs of persons with disabilities,\nparticularly in remote regions that are difficult to access.\n\n- Address the attitudinal and other barriers within the humanitarian community and perceptions of disability as an\n‘additional complexity’ in an already complex context, and include it from the onset as part of human diversity.\n\n#### RISK 2 Gender-based violence\n\n**PROTECTION SECTOR AND PARTNERS**", "output": {"entities": {"named_data": [], "descriptive_data": ["age, gender and disability disaggregated data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Address institutional, environmental, attitudinal, and communication barriers facing persons with disabilities in all their\ndiversities to access protection responses.\n\nPage 10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**SOUTH SUDAN** | October 2023\n\n- Adapt modalities of GBV services to address and response to needs of persons with disabilities and GBV actors know how\nto access support services, for example for interpretation. Local OPDs, in particular women led OPDs, are trained in how\nto safely identify and refer GBV survivors.\n\n- Ensure that Child Safeguarding Policy training is systematically provided to as many humanitarian workers as possible especially those in close contact with children - and communities to ensuring that all humanitarian actions are properly\nimplemented to protect all children including children with disabilities from the increasing deliberate or unintentional acts\nof abuse and exploitation registered in the last quarter.\n\n- Ensure the meaningful participation of older women and persons with disabilities in awareness raising campaigns and\nother community-based activities.\n\n- Adopt strategies to prevent and address discrimination against older women and support the full inclusion of women from\nall age groups in empowerment activities and interventions.\n\n- Continue collection of GBV data that is age and disability disaggregated to ensure that data is inclusive of older women\nwith and without disabilities and use the data to inform responses.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["GBV data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Take into consideration women and girls, men and boys with disabilities and place distribution sites in locations that are\naccessible to everyone. As necessary, deliver food and non-food items to the homes of persons with disabilities who are\nunable to reach distribution sites.\n\n- Take into consideration the placing of communal latrine blocks to ensure accessibility to persons with disabilities, ensuring\nthat they are physically accessible and provide clear signage.\n\n#### RISK 3 Torture or inhuman, cruel, degrading treatment.\n\n**Article 15 UNCRPD: State Parties must carry out measures through legislation, administration, and judicial systems to**\n\n**prevent persons with disabilities from being subjected to torture or cruel, inhuman, or degrading treatment or punishment.**\n\n**HUMANITARIAN ACTORS**\n\n- Communicate information on protection, and complaint and feedback mechanisms, in multiple and accessible formats.\nInvolve national and local organizations of persons with disabilities in the designing and dissemination of information\namong other community-based organizations and volunteers. Take steps to include individuals who are isolated in their\nhomes or in institutions or who rely on support persons for communication.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Establish a mechanism for monitoring and reporting cases of torture and other cruel, inhuman, or degrading treatment or\npunishment of persons with disabilities in coordination with health actors. The mechanism should involve persons with\ndisabilities themselves and actors representing them, and should provide legal, medical, and psychosocial assistance to\nthe victims and ensure accountability for the perpetrators.\n\n- Train and sensitize the security forces, health workers, and humanitarian actors on the rights and needs of persons with\ndisabilities, especially women and girls, and on the prohibition and prevention of torture or ill-treatment. This training\nshould include practical guidance on how to communicate with and accommodate persons with different types of\ndisabilities, how to identify and address their protection risks and barriers, and how to refer them to appropriate services.\n\n- Promote the participation and empowerment of persons with disabilities, especially women and girls, in the design,\nimplementation, and evaluation of humanitarian interventions. This can be done by involving them in consultations,\nassessments, feedback mechanisms, decision-making processes, and advocacy activities. This can also be done by\nsupporting their organizations and networks to raise awareness and influence policies and practices on disability inclusion.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Ensure that humanitarian assistance is accessible, inclusive, and responsive to the needs and capacities of persons with\ndisabilities, especially women and girls. This can be done by collecting and analyzing sex, age, and disability-disaggregated\ndata, by using inclusive methods and tools for data collection and analysis, such as the Washington Group to the delivery\nof services and facilities, by providing support to persons with disabilities according to their preferences, by ensuring that\ninformation is available in accessible and languages, and by addressing the specific risks and barriers that persons with\ndisabilities face in accessing humanitarian assistance, especially those with intellectual or psychosocial disabilities.\n\nPage 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**SOUTH SUDAN** | October 2023\n\n**Endnotes**\n\ni\n[WHO, 2022, https://www.who.int/publications/i/item/9789240063600)](https://www.who.int/publications/i/item/9789240063600)\n\nii UNDESA, 2016\niii Protection of Rights of Older Persons in South Sudan: Towards Enactment of Legislative Framework, 2019,\n[https://www.suddinstitute.org/assets/Publications/5cd019ad53e07_ProtectionOfRightsOfOlderPersonsInSouth_Full.pdf](https://www.suddinstitute.org/assets/Publications/5cd019ad53e07_ProtectionOfRightsOfOlderPersonsInSouth_Full.pdf)\niv https://www.wfp.org/stories/empowering-people-disabilities-south-sudan\nv Protection of Rights of Older Persons in South Sudan: Towards Enactment of Legislative Framework:\n[https://www.suddinstitute.org/assets/Publications/5cd019ad53e07_ProtectionOfRightsOfOlderPersonsInSouth_Full.pdf](https://www.suddinstitute.org/assets/Publications/5cd019ad53e07_ProtectionOfRightsOfOlderPersonsInSouth_Full.pdf)\nvi Social Anthropological Study on Disability in South Sudan: Pibor County, October 2022.\nvii https://interagencystandingcommittee.org/iasc-guidelines-on-inclusion-of-persons-with-disabilities-in-humanitarian-action-2019\n\nviii https://www.un.org/en/content/disabilitystrategy/\nix Forcier et al, 2016, p. 5; CARE, 2016, p. 15; Rieser, 2014, p. 3; MoGCSW 2013, p. 6; MoGCSWHADM, 2013, p. 18\n\nx Forcier et al, 2016, p. 4; Legge, 2017, p. 1\nxi https://www.helpage.org/blog/when-older-people-flee-their-homes-from-danger-ageism-is-a-barrier-to-accessing-help/\nxii MoGCSWHADM, 2013, p. 10\nxiii Faehnders, 2018; HRW, 2017\n\nxiv HRW, 2017\nxv https://publications.iom.int/system/files/pdf/south-sudan-gender-based-kap.pdf\nxvi GoSS, 2015, p. 10\nxvii MoGCSWHADM, 2013, p. 11\n\nxviii UNMISS - Attacks on Civilians in Tambura Country June, September 2021.\n[xix Funke, Carolin and Dijkzeul, Dennis, From Commitment to Action: Towards a Disability-Inclusive Humanitarian Response in South Sudan? 2021](https://www.cbm.org/fileadmin/user_upload/towards-a-disability-inclusive-humanitarian-response-in-south-sudan_web.pdf)\nxx [https://dr.211check.org/wp-content/uploads/2021/07/South-Sudan-National-Disability-Action-Plan-2020.pdf](https://dr.211check.org/wp-content/uploads/2021/07/South-Sudan-National-Disability-Action-Plan-2020.pdf)\n\n**Methodology**\n\nUnder the coordination of a Protection Analysis Update task force the methodological approach included:\n\n- Consultation with Organizations of People with Disabilities, NGOs, and UN agencies (see below).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Desk reviews of reports from various sources, such as protection monitoring reports, reports on GBV, reports from\ndisability actors, as well as the HNO and HRP. Identifying of gaps and challenges in addressing data and information\navailable linked to the protection needs of persons with disabilities, especially women and girls.\n\n**Participating partners: Humanity & Inclusion, HelpAge International, Universal Network for Knowledge Empowerment**\n\n**Agency, Women for Justice and Equality, Hope Restoration South Sudan, Community Humanitarian Inter livelihood and**\n\n**Emergency Focus, Africa Humanitarian Organisation, South Sudan Association of Visually Impaired, Disabled Action Group,**\n\n**South Sudan Women with Disability Network, Agency for Women and Children Development, Aid Link Organization,**\n\n**Community Aid for Refugees and IDPS Empowerment Network.**\n\n**Limitations**\nThe report does not have precise data on the prevalence of disability in South Sudan due to lack of recent assessments.\n\nFor further information please contact: **David Hattar**    - [hattar@unhcr.org](mailto:hattar@unhcr.org) | **Dorijan Klasnic** [- klasnic@unhcr.org](mailto:klasnic@unhcr.org)\n\nPage 12", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "# **BRIEF** **BRAZIL**\n\n### Operational Context & Analysis\n\nBrazil has in place a progressive and inclusive protection and solutions framework for refugees\nand other forcibly displaced individuals, ensuring equal access to rights and services alongside its\nnational citizens. With an open-border policy, Brazil guarantees the admission, registration, and\ndocumentation of those in need of international protection. Brazil hosts the largest number of\nVenezuelans recognized as refugees in Latin America and the Caribbean.\n\nAs of July 2024, Brazil has recognized 144.463 refugees and provided alternative protection\npathways to 572.877 persons in need of international protection, the majority of whom are\nVenezuelan (474.217) and Haitian (89.455) nationals. Additionally, there are 75,998 pending\nasylum applications, primarily from individuals originating from Cuba (26.225), Venezuela (15.065)\nand Angola (8.696).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Brazil stands as a regional leader, implementing _prima facie_ simplified refugee status determination\nprocedures under the regional refugee definition of the 1984 Cartagena Declaration\n(contemplated in its national legislation as those fleeing serious human rights violations) for\nindividuals from Venezuela, Burkina Faso, Iraq, Mali and Syria. The National Committee for\nRefugees (CONARE) has also granted refugee protection to individuals persecuted due to their\ndiverse sexual orientation and gender identity, as well as women and girls at risk of female genital\nmutilation.\n\nFurthermore, Brazil has established a humanitarian visa and temporary residence permit policy for\nnationals of Afghanistan, Haiti, Syria, and Ukraine. Temporary residence permits are also available\nfor Venezuelans who choose not to apply for asylum, ensuring access to protection and legal status\nfor a wide range of individuals in need.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In Brazil, refugees can choose between accessing the asylum system or applying for a residency\nas a complementary form of protection. Both options ensure freedom of movement, access to\nformal employment, education, healthcare, and social assistance. However, only those who apply\nfor asylum are explicitly safeguarded against refoulement, eligible to obtain travel documents,\ngranted expedite access to naturalization, and exempt from presenting documents from their\ncountry of origin in various civil procedures. These additional safeguards significantly ease their\nintegration into Brazilian society.\n\nDespite an overall favorable environment, the proportion of those living below the national\npoverty line is still considerable, especially if compared with host communities (40% vs 30%). [1]\nResearch conducted by UNHCR and other institutions indicates that refugees in Brazil experience\n\n_1_ _[https://unstats.un.org/sdgs/metadata/?Text=&Goal=10&Target=10.7](https://unstats.un.org/sdgs/metadata/?Text=&Goal=10&Target=10.7)_\n\nUNHCR / October, 2024 2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Over 11,000 indigenous Venezuelans currently residing in Brazil have been identified by UNHCR\nand partners. Compared to the overall Venezuelan population, they face compounded challenges\naccessing basic rights and services, including higher rates of food insecurity (58% vs 52%), health\ncare needs (75% vs 59%) and out of school children (21% vs 15%). [3] Language barriers and limited\nformal education of adults (indigenous refugees are 5 times more likely to have no formal\neducation when compared to the general Venezuelan population in Brazil), significantly affect their\nprospects for successful integration. [4]\n\n### Protection brief in graphics\n\nPopulation category Population category\n\nBreakdown by nationality\n\n_2 World Bank and UNHCR (2021), Integration of Venezuelan Refugees and Migrants in Brazil,_\n\n_[https://documents1.worldbank.org/curated/en/498351617118028819/pdf/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf;](https://documents1.worldbank.org/curated/en/498351617118028819/pdf/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf)_\n_ACNUR & Ministerio do Trabalho e Emprego (2024), Informe sobre o mercado de trabalho formal para Haitianos no Brasil,_\n_[https://www.acnur.org/br/sites/br/files/2024-11/informe-mercado-trabalho-formal-haitianos-brasil-jun-2024.pdf](https://www.acnur.org/br/sites/br/files/2024-11/informe-mercado-trabalho-formal-haitianos-brasil-jun-2024.pdf)_\n_ACNUR & Ministerio do Trabalho e Emprego (2024), Informe sobre o mercado de trabalho formal para pessoas refugiadas afegãs no Brasil_\nhttps://www.acnur.org/br/sites/br/files/2024-11/informe-mercado-trabalho-formal-pessoas-afegas-no-brasil-junho-2024.pdf\n\n---\n[3] R4V (2023), Refugee and Migrant Needs Analysis, https://rmrp.r4v.info/rmna2023/](https://rmrp.r4v.info/rmna2023/) p. 85", "output": {"entities": {"named_data": ["Refugee and Migrant Needs Analysis"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Total of Incidents** **[5]**\n\n### Protection risks\n\n##### Protection Risk I\n\n**Discrimination and stigmatization.** Racism and xenophobia are mentioned by refugees and other\npeople in need of international protection as serious obstacles to their local integration into the\nBrazilian society. Adults report that discrimination hinders their access to dignified housing and\nthe formal labor market, [6] while children refer to being bullied in school because of their origin. [7] In\nRoraima state, the primary entry point for Venezuelans into Brazil, 44% of refugees and migrants\nreported experiencing discrimination due to their nationality. This was most prevalent in the\nworkplace (37%), during job searches (30%), and while attempting to access healthcare (24%),\neducation (21%) and also while looking for housing (20%). [8 ]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "As per a recent assessment conducted in 21 Brazilian states, 15% of Venezuelan refugees and\nmigrants reported having suffered discrimination and stigmatization in the previous 12 months.\nOut of these, 80.4% were discriminated because of their nationality, 8.2% for their ethnicity, 5.2%\nfor their age, 3.5% for having a disability and 2.4% due to their sexual orientation and gender\nidentity. [9] On the other hand, according to the same source, 33% of indigenous Venezuelans\nexperienced discrimination and stigmatization, mainly due to their nationality (51.1%) but also for\ntheir ethnicity (31.9%), and to a smaller degree because of their age (6.3%). [10] This overlapping of\ndiscrimination causes not only affects the ability of these populations to access dignified\nlivelihoods, but also to exercise their fundamental rights, such as access to health and education.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_5_ For the purpose of this analysis, the category that refers to “basic and essential services” regards denial of, or unequal access to basic\nservices, the category “family life” is linked to family separation or inability to exercise family unit, and “life incidents” are linked to\nenforced disappearance.\n_6_ [ACNUR (2023), Diagnosticos Participativos, https://www.acnur.org/portugues/wp-content/uploads/2023/12/Diagnosticos-](https://www.acnur.org/portugues/wp-content/uploads/2023/12/Diagnosticos-Participativos-2023-.pdf)\n[Participativos-2023-.pdf](https://www.acnur.org/portugues/wp-content/uploads/2023/12/Diagnosticos-Participativos-2023-.pdf) pp. 7; 9.\n_7_ Idem, p. 13\n_8_ [Cáritas-REACH (2022), Avaliação Baseada em Área (ABA) em Boa Vista, Roraima, https://caritas.org.br/storage/arquivo-de-](https://caritas.org.br/storage/arquivo-de-biblioteca/March2023/Ir3dz5ULhRAzNV4wqkwi.pdf)\n[biblioteca/March2023/Ir3dz5ULhRAzNV4wqkwi.pdf, p.14](https://caritas.org.br/storage/arquivo-de-biblioteca/March2023/Ir3dz5ULhRAzNV4wqkwi.pdf)\n_9_ [R4V (2024), Microsoft Power BI](https://app.powerbi.com/view?r=eyJrIjoiMWJhN2UzOWMtZWFlZC00MzZiLWI0OWQtNGZlMDIzN2Y3Zjg5IiwidCI6ImU1YzM3OTgxLTY2NjQtNDEzNC04YTBjLTY1NDNkMmFmODBiZSIsImMiOjh9&pageName=f271143c36cbd9a6b0ee)\n\n_10_ Idem.\n\nUNHCR / October, 2024 4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "instance, report significantly more instances of racism compared to other South American refugees\nand migrants. [12] This exacerbates barriers to accessing a broad range of rights and services [13] and\nleads to greater vulnerability to both low skilled and temporary jobs, particularly when compared\nto Venezuelans who arrived in Brazil under similar circumstances. [14]\n\n##### Protection Risk II\n\n**Access to dignified housing.** In Brazil, a significant proportion of refugees and other forcibly\ndisplaced people can only afford precarious and overcrowded dwellings, located in impoverished\nand marginalized communities, often controlled by organized crime. [15] According to UNHCR´s\ndata, only 15% of refugees have secured tenure right to housing and\\or land, 27% live in physically\nsafe and secure settlements with access to basic facilities, and 54% feel safe walking alone in their\nneighborhood after dark. [16] In comparison, 65% of the Brazilian population owns the house in\nwhich they live - although 14% lacks the documentation to prove tenure rights [17] - 72% lives in\nphysically safe and secure settlements, [18] and 52% feels safe walking alone in their neighborhood\nafter dark. [19]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In these vulnerable conditions, any shock to the household economy -such as job loss, illness, or\nclimate related events- significantly impacts on refugees’ ability to cover the rent and utility costs\n(water, electricity), leaving them at heightened risk of eviction. [20] Climate related incidents are\nbecoming recurrent in the Brazilian context, such as floods and landslides in the south, as well as\ndroughts and wildfires in the center and the north of the country. [21] For instance, southern Brazil\nhas seen an increase of up to 30% in average rainfall over the last three decades [22] and, as of\nSeptember 2024, 59% of the Brazilian territory was affected by the most severe drought since\nnationwide measurements. [23]\n\n---\n[22] Presidência da Republica, « Southern Brazil has seen an increas of up to 30% in average annual rainfall over the last three decades »,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In this scenario, 19% of Venezuelan households reported being at risk of evictions from rented\nhousing in the three months prior to their interview, with a higher incidence in Roraima (26%) and\nAmazonas (29%), the two states hosting the highest proportion of refugees and other forcibly\ndisplaced people in Brazil. As per the same study, 10% of the indigenous households surveyed\nwere evicted during the three months prior to the interview, which makes them almost five times\nmore likely to face actual eviction when compared to the total surveyed population (2%). [24] The\nrisk of eviction has been also systematically reported by refugees also of other nationalities,\nincluding Haitians, Colombians, and Cubans. [25]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_11_ [Estou Refugiado, Qualifest (2021) Refugiados no Brasil, https://www.institutoqualibest.com/wp-](https://www.institutoqualibest.com/wp-content/uploads/2022/04/Estudo_Perfil-Refugiados-Brasil_Relatorio.pdf)\n[content/uploads/2022/04/Estudo_Perfil-Refugiados-Brasil_Relatorio.pdf](https://www.institutoqualibest.com/wp-content/uploads/2022/04/Estudo_Perfil-Refugiados-Brasil_Relatorio.pdf) p. 30\n_12_ ACNUR (2024), cit. p. 18\n_13_ Reinaldo Venâncio da Cruz Neto (2017) No Brasil, xenofobia tem cor e alvo: A realidade do deslocamento humano de haitianos ao\n[Brasil, atravès do Estado do Acre, pós-catastrofe natural no Haiti em 2010, https://acervodigital.ufpr.br/handle/1884/64891](https://acervodigital.ufpr.br/handle/1884/64891)\n_14_ ACNUR & Ministerio do Trabalho e Emprego (2024) cit.\n_15_ ACNUR (2023), cit. p. 8\n_16_ [UNHCR (2024). Brazil: Results Monitoring Survey (RMS) – UNHCR. (2024). Annual Results Report 2023: Brazil. (link)](https://reporting.unhcr.org/sites/default/files/2024-06/AME%20-%20Brazil%20ARR%202023_0.pdf)\n_17_ Agência IBGE Noticias, « Domicílios próprios predominam, mas 13,5% deles não tem documentação »\n\n[https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/38544-domicilios-proprios-predominam-](https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/38544-domicilios-proprios-predominam-mas-13-5-deles-nao-tem-documentacao#:%7E:text=A%20maior%20parte%20da%20popula%C3%A7%C3%A3o,2016%20(67%2C8%25).&text=A%20condi%C3%A7%C3%A3o%20de%20domic%C3%ADlio%20alugado,20%2C2%25%20em%202022)\nmas-13-5-deles-nao-tem[documentacao#:~:text=A%20maior%20parte%20da%20popula%C3%A7%C3%A3o,2016%20(67%2C8%25).&text=A%20condi%C3%A](https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/38544-domicilios-proprios-predominam-mas-13-5-deles-nao-tem-documentacao#:%7E:text=A%20maior%20parte%20da%20popula%C3%A7%C3%A3o,2016%20(67%2C8%25).&text=A%20condi%C3%A7%C3%A3o%20de%20domic%C3%ADlio%20alugado,20%2C2%25%20em%202022)\n[7%C3%A3o%20de%20domic%C3%ADlio%20alugado,20%2C2%25%20em%202022.](https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/38544-domicilios-proprios-predominam-mas-13-5-deles-nao-tem-documentacao#:%7E:text=A%20maior%20parte%20da%20popula%C3%A7%C3%A3o,2016%20(67%2C8%25).&text=A%20condi%C3%A7%C3%A3o%20de%20domic%C3%ADlio%20alugado,20%2C2%25%20em%202022)\n_18_ IBGE (2021) Pesquisa Nacional por Amostra de Domicílios Contínua, - Vitimização: Sensação de segurança,\n\n[https://biblioteca.ibge.gov.br/visualizacao/livros/liv101984_informativo.pdf, p.2](https://biblioteca.ibge.gov.br/visualizacao/livros/liv101984_informativo.pdf)\n_19_ Idem, p.3\n_20_ ACNUR (2023), cit. p. 8\n_21_ [INPE, “Dangerous climate change in Brazil” https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.ccst.inpe.br%2Fwp-](https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.ccst.inpe.br%2Fwp-content%2Fuploads%2Frelatorio%2FClimate_Change_in_Brazil_relatorio_ingl.pdf&psig=AOvVaw0sQlGsQl9kCmL1D4ZrNAle&ust=1729802339880000&source=images&cd=vfe&opi=89978449&ved=0CAQQn5wMahcKEwiAubWTrqWJAxUAAAAAHQAAAAAQBA)\n[content%2Fuploads%2Frelatorio%2FClimate_Change_in_Brazil_relatorio_ingl.pdf&psig=AOvVaw0sQlGsQl9kCmL1D4ZrNAle&ust=172](https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.ccst.inpe.br%2Fwp-content%2Fuploads%2Frelatorio%2FClimate_Change_in_Brazil_relatorio_ingl.pdf&psig=AOvVaw0sQlGsQl9kCmL1D4ZrNAle&ust=1729802339880000&source=images&cd=vfe&opi=89978449&ved=0CAQQn5wMahcKEwiAubWTrqWJAxUAAAAAHQAAAAAQBA)\n[9802339880000&source=images&cd=vfe&opi=89978449&ved=0CAQQn5wMahcKEwiAubWTrqWJAxUAAAAAHQAAAAAQBA](https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.ccst.inpe.br%2Fwp-content%2Fuploads%2Frelatorio%2FClimate_Change_in_Brazil_relatorio_ingl.pdf&psig=AOvVaw0sQlGsQl9kCmL1D4ZrNAle&ust=1729802339880000&source=images&cd=vfe&opi=89978449&ved=0CAQQn5wMahcKEwiAubWTrqWJAxUAAAAAHQAAAAAQBA)", "output": {"entities": {"named_data": ["Results Monitoring Survey (RMS)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_Sona Tahery, a young Afghan woman, found safety in Brazil after fleeing severe restrictions on women’s rights and access to work_\n_in her home country. After spending a year as a refugee in Iran, she arrived with her sister and brother-in-law, seeking a new_\n_beginning. Now, at Todos Irmãos Shelter in Guarulhos, she is rebuilding her life with dignity and hope._ © _UNHCR// Diego Baravelli_\n\n##### Protection Risk III\n\n**Gender equality and access to rights for refugee women and girls.** Refugee women and girls face\nsignificant barriers to the realization of their rights due to gender roles and power disbalances.\nRegardless of their nationality, women and girls face higher unemployment rates, less access to\neducation opportunities, greater exposure to gender-based violence and additional caregiving\nresponsibilities. [26] Members of the LGTBQIA+ community face discrimination and stigmatization\ndue to their sexual orientation and gender identity, and experience barriers in their access to\ndignified livelihoods with some of them, especially transgender women, resorting to survival sex\nas one of the few viable means to earn a living in the country. [27]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In terms of economic violence, only 21% of Venezuelan women of working age access the formal\njob market, compared with 42% of men, and the situation is even worst for young women aged\n18 to 26 who are single head of household (13%). [28] Moreover, women in the formal job market\nearn an average salary that is 7% lower than men, a gap that widens up to 32% at higher and post\ngraduate levels. [29]\n\nAs far as physical and sexual violence is concerned, 18% of Venezuelan women and girls do not\nfeel safe in their communities, a proportion that is three times higher in Roraima and Amazonas\nstates, compared with the rest of the country. [30] Among the places that are perceived as most\ninsecure, respondent mentioned: the way to school (44%), community and religious spaces (44%),\nshelters (35%) and their own homes (28%). [31] A recent study also found that in the state of Roraima,\n9% of key informants witnessed some form of sexual violence against children. [32]\n\n_26_ ACNUR (2023), cit. p. 14\n\n_27_ Idem, p.17\n\n_28_ R4V (2024), cit.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_29_ [Informe sobre o mercado de trabalho formal para venezuelanos refugiados e migrantes no Brasil [Informe sobre o mercado de trabalho](https://www.acnur.org/portugues/wp-content/uploads/2024/06/Informe-sobre-o-mercado-de-trabalho-formal-para-venezuelanos-refugiados-e-migrantes-no-Brasil-Marco.2024.pdf)\n[formal para venezuelanos refugiados e migrantes no Brasil (Março.2024) (acnur.org)]](https://www.acnur.org/portugues/wp-content/uploads/2024/06/Informe-sobre-o-mercado-de-trabalho-formal-para-venezuelanos-refugiados-e-migrantes-no-Brasil-Marco.2024.pdf)\n\n_30_ R4V (2024), cit.\n\n_31_ Idem\n\n_32_ UNICEF (2024), Inter-sectoral Multi-partner Rapid Needs Assessment with a focus on Children, publication forthcoming\n\nUNHCR / October, 2024 6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "emotional well-being, as well as local integration prospects. In this sense, some Afghan women\nhave reported a strict control exerted by male family members, which translates into their\nconfinement in the domestic realm.\n\n##### Protection Risk IV\n\n**Trafficking in persons, forced labor or slavery-like practices.** Although asylum seekers, refugees,\nand other forcibly displaced persons have the same labor rights as nationals in Brazil, they\nencounter several obstacles to their economic integration. The experience of displacement,\ncoupled with high levels of unemployment, informal labor, and poverty, heighten the risk of this\npopulation to fall prey of human traffickers. According to official data, between 2021 and 2023,\n355 refugees and migrants of all nationalities were rescued from forced labor or slavery-like\npractices in Brazil from various economic sectors, primarily in timber trade, cassava cultivation,\nclothing manufacturing, road transportation, and tobacco. [33 ]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Two per cent of surveyed Venezuelan households reported that at least one member had been\ndeceived, manipulated, coerced into debt, or received false promises intended to force them to\ntravel or migrate. [34] Additionally, one per cent of households reported at least one of its members\nbeing held against his\\her will by someone other than the country´s authorities, which may\nsuggest the possibility of human trafficking. [35]\n\nWomen and girls are particularly vulnerable to exploitation for various purposes of human\ntrafficking, including sexual exploitation, domestic forced labor, illegal adoption, and organs\nremoval. [36] Refugee women and girls in Brazil sometimes have to resort to survival sex, or cash-inhand domestic labor to make a living in Brazil, which increases their exposure to human trafficking\nnetworks. [37] In addition to these risks, domestic workers face numerous safety and health hazards\nrelated to their tasks and the environment they work in. These include chemical and ergonomical\nhazards, but also psychosocial risks, including violence and harassment, which are pervasive in the\nsector. The impact of these risks is amplified when domestic workers provide their services in the\ninformal economy, which is the case for most refugees and other forcibly displaced populations in\nBrazil. [38]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_33_ General Coordination of Inspection for the Eradication of Slave Labor and Human Trafficking (CGTRAE) of the Undersecretariat of Labor\nInspection (Ministry of Labor and Employment).\n_34_ Idem\n_35_ Idem\n_36_ Ministério da Justiça e Segurança Pública, Escritório das Nações Unidas sobre Drogas e Crimes (2024).Relatório Nacional de Dados\n\n2021-2023, Publication forthcoming.\n_37_ Relatório Situacional sobre Tráfico de Pessoas e contrabando de Migrantes, Organização Internacional para as Migrações, 2024\n\n(Publication forthcoming)\n_38_ ILO (2022), Guidance on occupational safety and health for domestic workers and employers to prevent and mitigate COVID-19,\n\n[https://www.ilo.org/media/376556/download](https://www.ilo.org/media/376556/download)\n\nUNHCR / October, 2024 7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_On World Refugee Day, refugees and local families in Porto Alegre's Sarandi neighborhood shared a meal and received support._\n_The event, organized by UNHCR partners, acknowledged the solidarity shown during recent floods and celebrated the rebuilding_\n_of lives by refugees from Venezuela, Haiti, Colombia, and Afghanistan, among others._ © _UNHCR/Ricardo Ara._\n\n### Challenges & Opportunities\n\nSince 2018, Brazil has supported refugees and migrants from Venezuela through a comprehensive\nhumanitarian federal initiative known as “Operacao Acolhida” . The Operation primarily focuses\non the northern State of Roraima, the main entry point for Venezuelans into Brazil, where\nreception, documentation, shelter, and other forms of humanitarian assistance are provided.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "From Roraima, the Government implements a voluntarily internal relocation program\n(interiorização) to facilitate the socio-economic integration of refugees and migrants. As of\nOctober 2024, over 141,000 Venezuelans have been relocated to some 1,000 municipalities\nacross other Brazilian states. The continued influx of Venezuelans into Brazil – which have been\non the rise since 2023 – has required the Government and the humanitarian community to\ncontinue focusing on the humanitarian response in Roraima, while moving toward the integration\nof “Operacao Acolhida” within the regular national protection system, reducing duplications and\nensuring the sustainability of the response.\n\nIn addition to Venezuelans refugees and migrants, Brazil hosts individuals from various countries,\nincluding from Cuba, Haiti, India, Sri Lanka, Vietnam, Nepal and Bangladesh; which places\nadditional strain on local protection networks, particularly in border states and cities with\ninternational airports, in the absence of a national response mechanism beyond “Operacao\nAcolhida”.\n\nUNHCR / October, 2024 8", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "reach the north of the continent through the Darien jungle or Europe through the French Guyana.\nIn this context, Brazil plays a crucial regional role and has the potential to offer effective legal and\nsocio-economic integration opportunities for displaced populations, which will contribute to the\nreduction of secondary movements.\n\nTo this end, the Government is set to launch Brazil's first National Policy on Migration, Asylum and\nStatelessness. Developed through an extensive participatory process involving concerned\npopulations, the policy aims to streamline access to rights and services at federal, state, and\nmunicipal levels. This will support the effective reception and integration of these populations into\nthe Brazilian society. Additionally, the Government has also adopted a Plan of Action to address\nthe protection and integration challenges faced by the Haitian population.\n\nMoreover, Brazil is launching a Resettlement, Admission, and Humanitarian Reception Program\nfor Afghan refugees through Complementary Means and Community Sponsorship. Authorities,\nwith the active support of UNHCR, are now developing Standard Operating Procedures for its\nimplementation, and planning ways to identify and train the civil society organizations who will be\nresponsible to receive these refugees in Brazil and support them in their local integration journey.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The government of Brazil has taken steps towards the recognition of the central role played by\nlocal authorities in protecting and promoting local integration of forcibly displaced persons. In\nNovember 2023 the Ministry of Justice and Public Security has launched the \"National Network\nof Welcoming Cities - Red Nacional de Cidades Acolhedoras\" with the objective of strengthening\ndialogue and actions around public policies and programs for migrants, refugees and stateless\npeople. UNHCR has since 2020 being supporting the Cities of Solidarity initiative in Brazil helping\nsharing experiences among the network of cities forming part of initiative and has recognized best\npractices from 17 municipalities.\n\nIn recent year, Brazil has experienced an increasing number of climate disasters, which have been\nmore frequent and severe and have been affecting a growing number of people, including\npopulations already displaced in Brazil. The Government is developing a Climate Plan as well as\nrisk reduction, adaptation, and resilience plans, which takes into account displacement impact;\nwhile the legislative branch is discussing bills to regulate and respond to internal displacement.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Substantial progress has been made by Brazil toward implementing the 2019 GRF pledges which\nfocused on the improvement of the asylum system and the provision of complementary forms of\nprotection, In 2023, the Government made 10 additional pledges at the GRF, covering the\nfollowing topics: improvement and facilitation of the right to family reunion; participation of\npeople in need of international protection in decision making and consultation processes; creation\nof resettlement and complementary pathways programs; strengthening of asylum systems and\nimprovement of refugee access to health, amongst others.\n\nIFC and UNHCR have been working together since 2018 to create innovative solutions to\novercome the challenges faced by refugees and migrants in Brazil. The two organizations sought\nto explore potential ways to engage the private sector in providing solutions with a primary focus\non employment, affordable housing and financial inclusion. IFC and UNHCR, in partnership with\nthe Brazilian Banking Association (FEBRABAN) and the Brazilian Central Bank, prepared a guide\nto inform financial institutions about the profiles and specific documentation of refugees and\nmigrants and their financial needs.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Additionally, 22 private companies pledged to hire 1,200 refugees and to support 15,000 refugees\nwith trainings and job placement by 2027. Currently, Brazil formally employs around 200,000\nrefugees and people in need of international protection. Of these, more than 12 thousand are\nemployed by 55 companies and organizations that are part of the Companies with Refugees\nForum, an initiative started in 2021 by UNHCR and the UN Global Compact – Brazil Chapter. The\n\nUNHCR / October, 2024 9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### Call(s) to Action\n\nThe Brazilian state is commendably recognized for its efforts in providing protection to refugees\nand other forcibly displaced populations, in line with international, regional, and national legal\nframeworks. Brazil stands as a beacon of hope for refugees in the region, demonstrating exemplary\ncommitment and leadership in humanitarian response, and striving to go ensure the successful\nlocal integration of displaced individuals.\n\nUNHCR calls upon the international community to boost the support for Brazilian authorities by\nincreasing financial and technical assistance to UNHCR and other humanitarian actors. This\nsupport is crucial for sustaining and expanding Brazil’s protection and assistance programs,\nensuring the rights and needs of refugees and other forcibly displaced people are met, and Brazil’s\nexemplary efforts in this field are fully supported and sustained.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "## **Protection Analysis Update**\n\n### **May 2022**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "#### 1. REPORT SUMMARY\n\nThe formation of Sudan’s Transitional Government in 2019, following\nthe dismissal of President Omar al-Bashir, opened up the protection\nspace in Sudan. It allowed for discussions on issues such as GBV,\nhuman rights and child protection. In 2020, several armed actors\nsigned the Juba Peace Agreement (DPA), allowing them to join the\ntransitional government. Nevertheless, these positive developments\ndid not ultimately endure. By 2021, the protection landscape\nchanged significantly with the departure of UNAMID and the military\ncoup d’état on 25 [th] October 2021. In the same year, IOM DTM\nreported the displacement of 450,000 IDPs in the country. The\ncurrent levels of violence in Darfur have been unseen since the mid2000s. There is also a marked increase in violence in the South\nKordofan and, to some extent, in the Blue Nile States.\n\nWithin this landscape, the Sudan Protection Sector responds to\narmed conflict settings, intercommunal violence, forced\ndisplacement, loss of property, human rights violations, grave child\nrights violations, climate change, sexual violence and criminal activity\namid political fragility and uncertainty. Significant protection risks\nfaced by civilians include the right to life, displacement, secondary\ndisplacement, GBV and denial and impediments to access to services.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "This Protection Analysis Update analyses priority protection risks and\nrecommendations. The findings are based on the analysis of\nprotection monitoring actors and assessments by the Protection\nSector, its AoRs, protection partners and reports produced by other\nagencies. The document uses both qualitative and quantitative\nanalysis. Humanitarian access issues and limited coverage of\nprotection actors contribute to information gaps such as data on civil\ndocumentation, GBV and housing, land and property.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "#### 2. CONTEXT OVERVIEW\n\nAfter the coup, a political agreement signed on 21 November 2021\nresulted in the dissolution of the civilian component of the\nTransitional Government. Though the political agreement allowed\nfor the reinstatement of Prime Minister Hamdok, other pollical\nactors, including the Sudanese Professionals Association (SPA), the\nForces for Freedom and Change (FFC), the National Umma Party and\nthe Sudanese Congress Party, condemned it. On 22 November, 12\nFFC government ministers also presented their resignations. [1]\n\nIn the absence of a political agreement, increased violence and\npolitical fragmentation, Prime Minister Hamdock publicly announced\nhis resignation on January 2, 2022.. In response to the end of civilian\nrule and the Prime Minister’s resignation, the Sudanese people\ncontinue to protest and engage in civil disobedience. In some\nlocations, protests and civil unrest reportedly were responded to\nwith arbitrary arrest and detention of civilians, journalists and\nactivists, further compounding the overall security environment [2] .\nThese political developments ultimately derail the achievements\nmade during the transition, including the peace process\ncompromising the future of the Sudanese transition.\n\n---\n[2] Report of the Secretary-General, 3 December 2021", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "On 8 January 2022, the United Nations Integrated Transition\nAssistance Mission in Sudan (UNITAMS) announced the launch of an\nUN-facilitated intra-Sudanese political process to design a way out of\nthe political crisis and forge a sustainable path forward towards\ndemocracy and peace.\n\n1 Report of the Secretary-General “Situation in the Sudan and the activities of the\nUnited Nations Integrated Transition Assistance Mission in the Sudan”, 2 March\n2022.\n\n##### **Severe levels of insecurity and violence**\n\nIn conflict-affected areas, civilians are\nwidely exposed to insecurity and\nviolence. The capacity constraints of\nState institutions and law\nenforcement entities create an\nenvironment of impunity and lawlessness.  Over the course of 2021,\nthe number of hotspot localities identified by the Protection Sector\nincreased from 43 to 73. In 2021, the Protection Sector recorded the\nkilling of over 1,256 individuals and injuries to 1,153 individuals. In\nthe first quarter of 2022, the number of hotspot localities remained\nthe same, and 156 killings and 132 injuries have been recorded.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The Protection Sector recorded 296 intercommunal violence and\nfactional fighting incidents in all Darfur states, South Kordofan and\nBlue Nile throughout 2021, which resulted in the displacement or\nsecondary displacement of 440,000 civilians and IDPs, bringing the\ntotal number of IDPs in Sudan to over three million.\n\n##### **Multidimensional impact on population vulnerabilities** **and coping capacities**\n\nDisputes over power-sharing, land ownership, competition over\nresources and criminality are fueling continuous intercommunal\nclashes and factional fighting. The Central, North and West and\nDarfur states and the South Kordofan have witnessed the most\nescalation in conflict in 2021. Though not to the same extent as other", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "states, Blue and White Nile states also witnessed violence in 2021. In\nDarfur, the departure of UNAMID in 2020 created a security vacuum.\nDespite some efforts by the Government of Sudan, clashes between\nherders and farmers continue with impunity. Following outbreaks of\nviolence, nomadic tribes or unidentified armed groups forcefully\nevict IDPs and vulnerable local populations from their land. This\nfragile situation has been exacerbated by the gradual return of Libyabased Darfuri fighters and the deployment of armed forces of parties\nto the Juba Peace Agreement.\n\nOngoing clashes and conflict, compounded by the political and\neconomic crisis, are having dire effects on the Sudanese population’s\ncapacity to cope and earn basic livelihoods. The situation interrupts\naccess to markets and income-earning opportunities. Market\nactivities and trade flows have been reduced. While slightly\ndecreasing from November 2021, inflation is still high at 259.8 per\ncent in January 2022. Due to the increased demand for United States\ndollars, the Sudanese pound decreased its value by more than 5%t\non the black market (January 2022). Staple food prices surged across\nSudan (at least 200 % compared to 2021, 400 % compared to five\nyears ago).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The high prices of cereals are being driven additionally by lower-thanexpected harvests together with high production and an increase in\ntransportation costs. [3] Livestock prices remain stable but are still\naround 200 % above prices in 2021. Electricity tariffs increased up to\n600 per cent in January 2022. [4]\n\nHigher prices and supply shortages of basic goods, including\nmedicine, wheat, fuel, and agricultural inputs, are thus drastically\nreducing purchasing power, increasing food insecurity, and\ndeepening the population's overall vulnerability. Poor pastoral and\nurban households are amongst the most impacted in terms of food\naccess. [5]\n\n3 Famine Early Warning Systems Network Key Message Update February 2022\n4 Report of the Secretary-General, 2 March 2022\n\n---\n[5] Report of the Secretary-General, 2 March 2022", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Amidst this complex environment, heavy rains and floods affected\n314,500 persons, with 15,000 houses destroyed and 46,000\ndamaged. COVID-19 cases continue to affect the population with\nover 3,300 associated deaths, and as of 17 April 2022, there are 4,494\nactive cases. COVID-19 cases are highly underreported due to the\nweak surveillance system. By 15 October 2021, the Federal Ministry\nof Health had reported 1,822,868 cases of malaria compared with\n1,456,413 during the same period in 2020. The number of cases\nexceeded threshold levels in many States. Over 1,860 cases of\nhepatitis E were also registered in Sudan between June and\nDecember 2021.\n\n##### **Access and security challenges**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The recurring incidents of inter-communal violence and the inability\nof the authorities to protect civilians pose a severe challenge to the\neffectiveness and sustainability of protection responses. The intense\nlevels of insecurity and violence impede humanitarian access\nthroughout Darfur, South Kordofan and conflict-affected areas of\nWhite and Blue Nile states. Local authorities also cannot provide\nsecurity escort in a timely manner due to their engagement with\nother activities and limited capacity on the ground. Humanitarian\nactors are also concerned about the varying amounts of the payment\nrequested by security actors per soldier per day. Some protection\nNGOs operate in these areas, but they do not have any working\nrelationship with the Sudan Protection Sector.\n\nFrom 28 to 30 December 2021, looters entered three WFP\nwarehouses in El Fasher town (North Darfur) with over 5,000 metric\ntons of food and dismantled warehouse structures. The looting of\nWFP warehouses deprived nearly two million people of food and\nnutrition support. From 10 to 12 January 2022, looters targeted the\nformer UNAMID log base, stealing the remaining 123 vehicles and\n300 containers.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_Figure 1 Hotspot Localities as of 2022_\n\n#### 3. PRIORITY PROTECTION RISKS\n\nThe events before and following October 2021directly impacts the\npopulation’s capacity to cope and increases the myriad protection\nthreats to the population. The threats include unlawful killings,\narbitrary detention, abduction, torture, and other forms of illtreatment; GBV; separated families and children; grave child rights\nviolations, including recruitment of children; the prevalence of\nExplosive Remnants of War (ERW); inter-communal conflict and\nforced displacement. These rights violations occur in an environment\nof general impunity where police do not have the capacity to\nrespond. In the conflict-affected areas, IDPs and the host community\ndo not have confidence in local authorities, armed forces and/or JPA\nsignatories.\n\n##### **RISK 1:  Right to Life, Attacks on Civilians and Civilian** **Infrastructure**\n\nCivilians, including IDPs and returnees, are continuously facing\nthreats to their right to life in conflict-affected areas. Allegedly,\narmed nomadic groups and other unidentified armed groups\ndeliberately kill and injure civilians. Motivated by a combination of\ndifferent factors, including political and economic, these actors\nbenefit from a limited presence of a judiciary and police system.\n\n_Figure 2 Number of Protection of Civilian Incidents in 2021_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The presence of landmines and ERW in urban and rural areas also\nhave a devastating impact on local communities, IDPs, and returnees\nin all five Darfur states and South Kordofan, West Kordofan and Blue\nNile states. Per UNMAS, as of 31 December 2021, 136.7 km2 (84%)\nout of recorded 162 km2 of contaminated land has been released.\n\nBelow are figures from 2021. From November 2021 to January 2022,\nUNITAMS documented 161 alleged incidents of human rights\nviolations and abuses involving 778 victims, including 22 children.\nViolations of the right to life accounted for 368 victims (295 men, 63\nwomen and ten children), violations of physical integrity accounted", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "for 366 victims (340 men and 26 women), and abductions accounted\nfor 14 victims (including ten women). [6]\n\n_Figure 3 Number of Causalities Per State in 2021_\n\n##### **RISK 2: Displacement and Secondary Displacement due** **to Violence and Conflict**\n\nThe total number of internally displaced persons in Sudan is over 3.08\nmillion [7], with over 89,000 newly displaced persons in Darfur from\nOctober 2021 to January 2022. In South Kordofan, renewed intercommunal conflict resulted in the displacement of 40,000 individuals\nto Abu Jubaiha in December 2021. All other hotspot localities\nwitnessed small numbers of new displacement in 2021.\n\n6 Report of the Secretary-General, 2 March 2022\n\n_Figure 4 Map IDP Concentrations in Sudan_\n\n---\n[7] According to third round of IOM DTM report, January 2022.", "output": {"entities": {"named_data": ["third round of IOM DTM report"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The majority of the IDP caseload in Sudan is protracted, with over 1.7\nmillion displaced between 2003 to 2010 and an additional 1.07\nmillion displaced between 2011 and 2017. Secondary displacement\nof the protracted and newer caseloads is an issue. In 2021, systemic\nviolence displaced at least 440,000 persons, primarily IDPs, to 200\nlocations. Additionally, around 18,000 individuals fled to Chad. Of the\n180,000, 104,432 remain secondarily displaced in West Darfur.\nSecondary displacement weakens existing coping mechanisms and\nincreases the risk of adverse coping mechanisms such as child\nmarriage and survival sex.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Presence of IDPs per State - January 2022**\n\n**1006690**\n\n**41054**\n\nSouth Darfur North Darfur Central Darfur West Darfur\n\nSouth Kordofan Blue Nile West Kordofan East Darfur\n\nNorth Kordofan Total\n\nThe continued presence of armed actors forces many IDPs, especially\nwomen and girls, to limit their movement outside camps, where\nsecurity is more stable and predictable.\n\nEfforts to support durable solutions for IDPs, their local integration,\nand the reintegration of returnees, do not bring concrete results due\nto recurring violence. IDP returnees face challenges with respect to\naccessing land, firewood collection points and water.\n\nCertain locations are difficult to reach due to insecurity associated,\nnotably due to the widespread proliferation of arms and constant\nthreats of violence.\n\n##### **RISK 3: Denial and impediments to access to services**\n\nThe civilian population face the brunt of the macro-economic effect\nof the political instability and conflict in Sudan in forms of the\nincreased cost of living (200% increase in staple food prices, 318%\ninflation, 600% in electricity tariffs, etc..), reduction of livelihood and\n\n8 IPC\n9 Famine Early Warning Systems Network Key Message Update February\n2022", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "economic capacities to cope (disruption of harvesting, increased\ninput costs, etc.), limited access to services, disruption of general\nsocial cohesion and the consequent severing of social ties. Civilians,\nincluding IDPs in the conflict-affected areas, are severely restricted in\nthe realisation of their rights to assistance and the rights to work,\nsocial security and adequate standards of living, housing, health and\neducation. The protracted nature of the crisis has left the population\nwith limited means of livelihood or social or economic opportunities\nto address their own situation.\n\nAbout 9.8 million people in Sudan may experience Crisis—IPC 3—or\nworse levels of acute food insecurity in 2022 due to below-average\ncrop yields, continued conflict and displacement, increasing food\nprices, and reduced household purchasing power. [8]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "As of mid-January 2022, the 2021/22 harvest is complete. However,\nthe outbreak of conflict in parts of Darfur and Kordofan regions,\nshortages and the high cost of resources delayed the harvest. Overall,\nthe harvest is expected to be lower than that of 2021. Fuel shortages\nand high electricity costs have negatively impacted wheat planting.\nFarmers are limited in hiring enough labour due to increased wages\n(around 300% compared to 2020). The area cultivated is expected to\nbe lower than last year and the five-year average [9] .\n\nIntercommunal fighting between the local population, IDPs and\nreturnees is rising due to the tensions on land use and several cases\nof crop destruction (around 1000 farms were destroyed in the\nconflict between farmers and pastoralists). [10]\n\nAccess to services is severely compromised by the widespread\nviolence, conflict, and lack of access to remedies. Health always\nremains a challenge for IDPs and returnees in remote areas due to a\nlack of health centres or shortages of medicines and staff. Health\n\n---\n[10] Report of the Secretary-General, 2 March 2022", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "facilities are recently subject to deliberate attacks and looting,\nthereby restricting health care access for civilians, exacerbating\nhealth needs amid an ongoing surge in coronavirus disease (COVID19) cases. WHO had distributed more than 850 rapid response kits to\ncover approximately 1.1 million people for three months. [11]\n\nThe cost of education remains an obstacle for IDP children\nspecifically. Schools lack feeding programs and classrooms and\ngenerally do not offer a safe environment.\n\nThe World Bank launched a family support program (cash-transfer)\nlaunched in February 2021 and is expanding in the states of Western,\nNorthern and Southern Kordofan, Blue Nile, Eastern Darfur and\nCentral Darfur. Despite eight million people registered, challenges to\nthe registration and payment systems have delayed roll-outs. The\nprogram has now suspended registration after the World Bank\npaused its activities in Sudan due to the political situation.\n\nNotwithstanding the collective efforts of humanitarian and political\nactors to bring the attention of authorities and institutions to the\nurgent need to reverse these downward and negative trends, no\nsubstantial progress has been observed. The population of Sudan is\nfacing the day-to-day risk of being denied services, opportunities and\nresources.\n\n##### **RISK 4: Gender-based violence**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "While the Government made a landmark decision to outlaw Female\nGenital Mutilation (FGM) in July 2020, legislation in Sudan continues\nto have significant shortcomings concerning the prevention of GBV.\nRape legislation was amended in 2015, removing previous legislation\nthat equated crimes of rape and adultery. While Article 149 of the\nCriminal Act provides a more substantive definition of rape, a lack of\nclarity remains about the age of consent due to the existence of\n\n11 “In a statement on January 11, WHO Regional Director for the Eastern\nMediterranean Dr. Ahmed Al-Mandhari condemned the threats against", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "conflicting laws, including the Criminal Act of 1991, wherein\nadulthood is defined in reference to puberty, and the Child Act of\n2010, which defines a child as any person under 18 years of age. In\naddition, existing legislation does not outlaw marital rape, and Sudan\ndoes not have domestic violence legislation. Furthermore, traditional\nsocial norms often center blame for abuse on survivors themselves,\nproviding impunity for perpetrators. Due to a lack of resources,\nunderfunded police forces and judicial actors are often unable to\nimplement investigations or conduct criminal proceedings to bring\nperpetrators to justice. Creating safe services to ease GBV survivors’\naccess to justice or medical attention remains challenging for both\nnational and international service providers.\n\nPervasive Conflict-Related Sexual Violence (CRSV) continues to be\nregularly reported by the Protection Sector partners. Women and\ngirls suffer disproportionally from GBV while collecting firewood,\nengaging in farm work, fetching water, or travelling. The widespread\nsocial stigma associated with GBV can create barriers for survivors in\naccessing services, leading to social exclusion, isolation and selfblaming. The underreporting of GBV is also attributed to this social\nstigma and the inability of authorities to bring perpetrators to justice\nand provide legal remedies and recovery services.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "#### 4. RESPONSE\n\nThe Protection Sector in Sudan comprises General Protection, Child\nProtection, GBV and Mine action AoRs. There is also active Housing\nLand and Property and Durable Solutions Working groups that work\nclosely with Protection Cluster. These structures meet every month\nat the national and state level (five Darfur, South Kordofan and Blue\nNile). The Protection Sector developed its strategy and work plan.\nThe Sector developed the Protection of Civilians’ Incidents Tracking\ntool in consultation with other agencies. It also publishes maps of\n\nhealth care providers and called on Sudanese authorities to enforce\ninternational”, Report of the Secretary-General, 2 March 2022", "output": {"entities": {"named_data": [], "descriptive_data": ["maps of\n\nhealth care providers"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "hotspot areas and regularly issues protection of civilians’ advocacy\nbriefs. The Sector also disseminates monthly updates and daily and\nweekly updates in times of crisis.\n\nFunding situation permitted, the UN protection agencies, Protection\nCluster, its AoRs, and their partners implemented activities\nmentioned under funding data.\n\nWhile most of these activities were conducted in response to several\nemergencies in Darfur, South, West Kordofan and Blue Nile states,\nsome activities were also implemented in the areas of protracted\ndisplacement.\n\nThe Protection Sector and its AoRs also actively supported the\ndevelopment of the One UN Protection of Civilians strategy and\nsupport plan for the National Plan for the Protection of Civilians\n(NPPOC). Before the military coup of 25 October 2021, the Sector was\npreparing to organise a joint national and several workshops at the\nstate level to develop further and prioritise proposed activities. The\nProtection Sector and AoRs also actively advocated for establishing\nthe state-level protection of civilians’ committees and working\nrelationships with the National Mechanism on the Protection of\nCivilians.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Furthermore, these actors and concerned government counterparts\nprovided their inputs and contributed to the development of a\ndurable solutions strategy for Sudanese IDPs and refugees and\nsupported the works of the HLP and DSWGs.\n\n##### 4.1- Funding data\n\nThe total funding requirement for **HRP 2021** of Sudan was **USD**\n\n**1.94bn,** out of which **USD 149,928,919 was planned for protection**\n\n**activities.** However **, only 20%** (USD 30,559,006 **) was provided,** and\nUSD 116,369,913 remained unmet. **The same amount of USD**\n\n**1.94bn is the funding requirement for HRP 2022, with USD**\n\n**161,917,433 for protection activities that incudes General**\n\n**Protection, Child Protection and GBV. However, as of 05 April 2022,**\n\n**only 1,705,350 in funding has been received** **.**\n\nIf the funding situation continues as such, and the government does\nnot take concrete steps towards the implementation of the NPPOC,\nprotection agencies and actors will not be able to provide effective\nassistance, including under:\n\n**a) protection** : conduct protection assessments, protection\nmonitoring by presence and remotely, referrals to specialised\nservices, provide legal assistance, psycho-social support, emergency\ncash assistance, awareness-raising, implement community support\nprojects, support community-based protection networks, capacity\nbuilding of service providers, conduct advocacy, protective\naccompaniment under.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Funding data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**b)** **child protection:** individuals with awareness-raising and\ncommunity engagement activities on child protection issues,\nreaching girls and boys with structured and sustained child\nprotection and psychosocial support activities, providing girls and\nboys with specialised child protection services, such as case\nmanagement and support to national child protection actors, and\nfacilitating leadership in coordination such as technical task forces,\nplace and support Unaccompanied and Separated Children in\nAlternative care, provide family tracing and reunification services,\nprovide rehabilitation and reintegration activities of children\nreleased from armed groups and armed forces, legal assistance\n(detention representation), on civil documentation including birth\ncertificates and other documents, conduct capacity building\nactivities, including mentoring and coaching, for government, NGO\nstaff and community based child protection structures, child\nprotection assessments or child protection monitoring missions,\nassistance to children with Disabilities, support community based\nprotection networks **.**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**c) GBV** : establish, rehabilitate women centres, support their\noperational costs, provide Case Management and referral services,\nestablish or construct semi-permanent safe spaces/multi-purpose\n(child and adolescent), procure and distribute tents for emergency\nChild-Friendly Spaces, provide their running cost, furniture,\nstationaries and equipment (Solar Power System, computer), procure\nand distribute recreation PSS and dignity kits tents for emergency\nresponse, support women & girls with startup capital for Income\nGenerating Activities and vocational skills, conduct training for\ncommunity members involved in GBV prevention and response,\ntraining on GBV for Non-GBV service providers, trainings on GBV for\nspecialised GBV service providers, provide dignity kits, specialized\nGBV response services and conduct GBV assessments.\n\n##### 5. RECOMMENDATIONS\n\n**The Protection Sector seeks advocacy support for the following**\n\n**points required for the protection of civilians:**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**1)** It is critical to implement the NPPOC fully, considered an official\ndocument by the UNSC. Apart from the formation of joint security\nforces, the plan includes activities in the following nine thematic\nareas: 1) addressing the issues of displaced persons and refugees; 2)\nthe rule of law and human rights; 3) disarmament, demobilisation\nand reintegration; 4) combating violence against women and\nchildren; 5) humanitarian action; 6) strengthening conflict avoidance\nand resolution mechanisms; 7) issues involving nomads and\nherdsmen; 8) reconstruction, development and basic services; and 9)\nwater and sanitation. To ensure the implementation of the NPPOC,\nthe GoS has to re-establish the National Mechanism and state-level\nProtection of Civilians Committees with clear ToRs and National-level\ncivil and military support and strong advice to coordinate closely with\nthe humanitarian and development actors.\n\n**2)** Safeguarding the civilian character of the IDP camps, villages of\ndisplacement and return is essential as there are reports on the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "presence of armed groups in and around such areas by SAF, armed\nnomads, and other armed movements signatory to JPA, violating the\nfundamental humanitarian principles and increasing the potential for\nlarge-scale violence in these areas as well as possible child\nrecruitment. It is also vital to implement the disarmament,\ndemobilisation, and reintegration process of the Juba Peace\nAgreement, which will allow protection actors and partners to\nmaintain critical and life-saving activities based on the international\nhumanitarian principles of ‘impartiality’, ‘neutrality’, and\n‘independence’.\n\n**3)** The ability of humanitarian agencies to deliver services is directly\nlinked to the ability and willingness of the authorities to assure the\nsafety and security of humanitarian personnel and assets, including\nthrough the provision of security escort to unsafe areas. Any acts of\nviolence against humanitarian organisations and their personnel\nconstitute a direct attack on the most vulnerable populations.\nWithout the guarantee of security, humanitarian service provision is\nat risk of halting.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**4)** Immediate stepping-up of efforts by regional and national\nauthorities and the law enforcement to prevent and investigate the\nupsurging security incidents, crimes, and human rights abuses,\nincluding gender-based violence, is essential to de-escalate\nintercommunal tensions, prevent displacement and control the\nsecurity situations by bringing perpetrators to justice ensuring law\nand order. Strengthened governance and state protection are\nessential to regain the trust and confidence in government\ninstitutions at the state level.\n\n**5)** The Protection Sector also draws attention to the funding situation\nof humanitarian actors, protection in particular, and strongly\nrecommends supporting the implementation of planned activities\nunder the 2022 HRP. The Sector also encourages the Humanitarian\nCountry Team to consider adding protection as a standing agenda\nitem of their meetings.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Return intention survey**\n\n**Shalozan Tangi, Kurrum agency**\n\n**June 2014**\n\n**I.** **Background**\n\nKurrum Agency is the only tribal region in the country’s semi-autonomous seven tribal\nterritories which has a large number of Shiites - the rest of the six tribal agencies are\noverwhelmingly inhabited by Sunni Muslims. According to official figures, its total\npopulation is 500,000, with 58 percent Sunni and 42 percent Shiite. The majority of the\nShiites live in the upper part of the Kurrum Agency, while Sunnis inhabit lower and central\nKurrum. The population of Kurrum valley consists of a number of tribes, namely Turi,\nBangash, Parachamkani, Massozai, Alisherzai, Zaimusht, Mangal, Kharotai, Ghalgi and\nHazara. There was also a sizeable Sikh population but most of them have left the valley.\nSectarian violence is not a new phenomenon in Kurrum Agency where well over 4000\npeople have been killed in clashes between the Sunni and Shia tribes since the decade of\n1980s. Kurrum Agency is divided into three tehsils: upper Kurrum, lower Kurrum and central\nKurrum.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Return intention survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In April and November 2007, the worst sectarian clashes started in the history of Kurrum\nagency. These clashes started in upper Kurrum agency and soon spread over both upper and\nlower sub-divisions of the agency and families started fleeing to down districts, Hangu,\nKohat Peshawar etc.\n\nThe government notified the entire Kurrum agency as a conflict zone and requested the\nhumanitarian agency for registration and assistance. UNHCR established registration desks\nthrough Social Welfare department in Hangu and Kohat for the IDP families but after the\nbombing incident at the Kacha Paka registration centre in Kohat on 2nd May 2010 claiming\n42 lives, the registration centres were closed and re-established in Peshawar with proper\nsecurity measures.\n\nIn August 2010, the military operation started against the militants in central Kurrum\ndisplacing three out of four main tribes (Ali Sherzai, Zamousht and Masozai while\nParachamkani was not displaced in 2010). In close coordination with FATA disaster\n\n1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Management Authority (FDMA) the registration desks was established in Sadda, lower\nKurrum. An IDP camp for the most vulnerable families was established in New Durrani,\nlower Kurrum. Parachamkani is the fourth main tribe of central Kurrum and was displaced\nlast year in May and returned after three months when the area was cleared by the military\nand political administration.\n\nShalozan Tangi is located in upper Kurrum. A protection cluster mission was conducted from\n22 to 24 April 2014, composed of FDMA, protection cluster, UNHCR and WFP staff. On 22\nand 24 April villages in lower Kurrum were visited while on 23 April upper Kurrum villages\nwere assessed along with visit of New Durrani camp. [1] Shalozan Tangi area was visited as\nwell.\n\n_Map 1: Kurrum agency_\n\nFATA Secretariat through FDMA informed the humanitarian community that 19 villages had\nbeen de-notified in 2012. The return to Shalozan Tangi area was discussed in the Return\nTask Force (RTF) held on 21 May 2014. FDMA requested for facilitation from the\nhumanitarian community to enable approximately 600 IDPs (including unregistered families)\naffected by conflict and sectarian violence respectively to return to Shalozan Tangi areas in\nKurrum agency.\n\n---\n[1] See Protection cluster report on mission to Kurrum 22- 26 April 2014 available at", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Responding to the evolving situation, in line with the Return SOP endorsed by the\nHumanitarian Country Team (HCT) in February 2012, but also in accordance with the\n“Return Policy Framework for IDP from FATA” endorsed by the FATA authorities in 2010, the\nProtection Cluster agreed to conduct a series of consultations with the Shalozan Tangi\npopulation to capture their intentions and position vis-à-vis the return process.\n\n**II.** **Methodology**\n\nAfter the crosscheck of UNHCR data on Shalozan Tangi IDPs, 359 records were found. Out of\nthose, telephone numbers were available for 171 families. The return intention survey was\nconducted through IVAP call centre (enumerators trained by protection cluster on 20 May\n2014 on the return intention survey form) and 137 of IDPs responded to the calls. The\nquantitative data collection was facilitated by the use of Personal Data Assistants (PDAs)\nprogrammed with ODK system software. The RIS was conducted by 6 enumerators who\ncontacted displaced families from a call centre using the contact information available\nthrough the IVAP records. This strategy was chosen due to limited time constraint before\nthe start of returns.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR data on Shalozan Tangi IDPs", "return intention survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "From 23 to 25 May 2014, 137 interviews were conducted with the Shalozan Tangi displaced\npopulation. The Return Intention Survey (RIS) was conducted using a specific tool/\nquestionnaire developed in 2013 for previous consultations and slightly adapted to the\ncurrent situation.\n\nFurthermore, during an inter cluster mission from 26 to 28 May, protection cluster\nrepresentative conducted additional consultations with populations displaced from\nShalozan Tangi (12 male key informants) and in areas of return in Shalozan Tangi (two male\nkey informants from amongst the population already returned to the area of origin in 2012).\n\n**III.** **Profile of respondents**\n\nOut of the 137 respondents, one was women (a female headed household) and 126 men. 69\n% of the respondents were heads of households (including one female respondent) and also\nincluded three community leaders.", "output": {"entities": {"named_data": ["Return Intention Survey (RIS)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In regards to the age of respondents, most were 30- 60 years old (55%), followed by\nrespondents aged between 18- 29 (24 %) and 4 % of elderly above 60 years old. Most of the\nfamilies interviewed have 10 family members (51 %), followed by families with 9 family\nmembers (39 %). 61 % of interviewed families have two children below five in the family\ncurrently, followed by families with 3 children below five years old in the family,\nrepresenting 24 % of families interviewed.\n\n3", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In 42 % of interviewed families, two children were attending school in displacement,\nfollowed by families with three kids attending schools (35 %) and four kids attending school\n(29 %). 16 % of families have one child at school, while in 10 % of families five children\nattend the school in displacement.\n\nFive families have one elderly in their families, and eight families have two. 11 families were\nhaving at the time of interview a pregnant or lactating women present in their family.\n\nMost of the interviewed IDPs currently live in Peshawar district (38 %), whole breakdown of\nlocations is provided below.\n\n_Graph 1: Current location of IDPs originating from Shalozan Tangi_\n\n**IV.** **Main findings of the Return Intention Survey**\n\n**A.** **Displacement timing and trends**\n\nOnly two of the interviewed persons came from areas of origin in less than 18 months,\nothers were in displacement for a longer period. For most of the interviewed persons (62 %)\nthe **reason for displacement** was sectarian violence, followed by reason of military\noperations (29 %) and lack of livelihood opportunities due to the conflict (3 %).\n\n4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "68 % of respondents **arrived at the current place of displacement** more than 18 months\nago, 15 % between 12- 18 months, followed by families who moved in last 6- 12 months\n(12%) and 5 % of families who moved to their current area of displacement in last 6 months.\n\n**B.** **Informed and voluntary nature of return**\n\nA series of queries were addressed to the Shalozan Tangi IDPs to ascertain the level of\ninformation that they possessed regarding their areas of origin/return, the need for\nadditional information and the decision-making process on which the decision was based.\n\n**Only 7 out of 137 respondents indicated that they do not have information about the**\n\n**situation in area of origin** . Most of the respondents feel informed [2] about water, health and\neducation facilities available in the areas of origin (36 %), followed by information on safety\nand security in the areas (23 %) and then with 20 % informed both on damage level of their\nhouses and crops.\n\n---\n[2] Multiple responses were possible", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In regards to the **sources of information on areas of origin**, 34 % of respondents visited\npersonally the area or other family members visited the return areas (18 %) as well as other\nmembers of the community who reported to the family about the situation (19 %), followed\nby media (14 %), 9 % from other people who visited the areas, 6 % from humanitarian\nworkers and 4 % from religious leaders.\n\n_Graph 2: Sources of information on areas of origin as reported by interviewed IDPs_\n_originating from Shalozan Tangi_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "14 % of respondents think that a **go and see visit** prior to the return would be useful. From\nthose who responded positively, 63 % would like to send community leaders for the go and\nsee visit, while remaining 37 % male family members.\n\n9 % were aware about some kind of information campaign on the return. It is to be noted\nthat at the time of the interviewed conducted, the information campaign on return has not\nyet been officially launched.\n\nIn 64 % of interviewed families, the response on **who decides on the return** was political\nadministration **.** This percentage is relatively high compared to other return intention\nsurveys conducted by protection cluster. In 23 % it is the community elders who reportedly\nmake the decision on return, followed by family members in 13 % of cases (which is usually\nthe highest category reported). Only 49 % of respondents felt that they **participate in the**\n\n**decision making process** on the return.\n\n**Despite the fact that IDPs wish to return to their area of origin, 26 % indicated that not**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["return intention\nsurveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**now.** On overall, 74 % of respondents feel ready to return right now, 10 % between 1- 3\nmonths, 7 % within one month, 4 % between 3- 6 months and 2 % of respondents after\nmore than 6 months.\n\nFor those who responded that they wish to return immediately, the main reason provided is\nthat the life in displacement is worst that in areas of return (61%), 28 % it is good time to\nstart cultivation, good time to rebuild house (9 %), it is safe now (2 %).\n\n_Graph 3: Factors influencing IDPs’ will to return immediately to areas of origin_\n\n6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Challenges to return** identified were mainly [3] house destruction (21 %), a family member not\nphysically fit to travel (14 %), lack of health facilities in areas of return (13 %), destroyed land\n(12 %), lack of education facilities in areas of return as well as lack of specialized services for\npersons with disabilities (both 11 %), no means of transport to return (10 %), no means for\ntransport facilities (9 %), absence of markets (7 %), occupation of the house and nonpossession of land in the area of origin as well as not physically fit for travel (all 4 %), more\nlivelihood opportunities for the family in displacement than in return area ( 2%).\n\n**C.** **Safety and security**\n\n**The main concern of IDPs related to security** originating from Shalozan Tangi was that the\nsituation is not yet stable in areas of origin (32 %), 14 % of respondents stated that there is\nstill conflict in the nearby areas and areas of origin as well as restrictions of movement\n(both 10 %). Detailed overview of IDPs’ concerns is provided in the graph below.\n\n---\n[3] Multiple answers were possible", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_Graph 4: Main concerns related to security in the areas of origin as reported by interviewed_\n_IDPs originating from Shalozan Tangi_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In 16 % of the families interviewed a family member visited the area of return. When asked\nif they had any security problem, 46 respondents provided answer, out of which only one\nstated that the persons visiting area of return had security problem.\n\n9 % of respondents indicated some concerns after the return. Those being lack of women\nhealth facilities and transport of persons with disabilities, as well as lack of education\nopportunities for girls.\n\n88 % of the IDPs originating from Shalozan Tangi indicated that their **house is completely**\n\n**damaged**, while 9 % partially damaged and 3 % do not know the status of their houses in\nareas of return. 93 % of respondent’s never heard about house compensations. 3 families\nout of 137 interviewed stated that their house is occupied in areas of return.\n\n_Graph 5: Damage of houses in area of return as reported by IDPs_\n\n**D.** **Humanitarian needs and return/reintegration assistance**\n\n9 % of respondents were not collecting food ration, while 91% were benefiting from food\nassistance (including one female headed household).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Only 10 % of interviewed families were aware about the **return assistance package** . Of\nthose who are informed, 47 % received information on return package from community\nelders, 28 % from family members and 15 % from political authorities. To be noted that at\nthe time of interviews, information campaign on return has not yet started.\n\n8", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "25 % of respondents were aware about some food assistance being provided upon return,\nfollowed by housing assistance (23 %), NFIs (21 %), house and land compensation (16 %) and\ntransport (15 %).\n\nWhen asked **what assistance would the IDPs mainly need related to return**, mostly\nrequested is the shelter assistance (19%), followed by transport and food (14 %),\nregistration and improved services in areas of return (13 %), house compensations and\nmore economic opportunities (10 %), assistance with land and house disputes (4 %) and\nspecial assistance to children and persons with disabilities (3 %).\n\n_Graph 6: Assistance needed related to return_\n\n**V.** **Recommendations**\n\n- Information should be made available to IDPs before the return process, in particular\n\non the security situation in areas of return, on the reconstruction/ rehabilitation\nplans of the authorities, on the housing compensation process, on the process of\nreturn and the assistance offered, and on the foreseen initial return and\nreintegration assistance [FATA authorities in cooperation with the humanitarian\ncommunity/ HRT and actors with expertise in mass communication]\n\n9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Consultations with the returning population and with returnees in the initial phases\n\nof the return and reintegration process should continue, in line with the HCTendorsed SOPs on Return and with the 2010 “Policy Framework for IDP Return to\nFATA”, in order to inform the plans and interventions of the humanitarian\ncommunity if and when supporting the authorities to organise the return of the IDP\npopulation. [Protection Cluster]\n\n- When the conditions of voluntary and safe character of the return process are\n\nsatisfactorily assessed, the humanitarian community should continue to support the\nreturn process as the most preferred durable solution, including with transport,\ngender-sensitive reception facilities and initial reintegration packages (food and\nNFIs). Specific attention should be devoted to those sectors highlighted as major\nconcerns by the returning IDPs during the monitoring and consultation process. This\nincludes commonly prioritised assistance needs such as housing, livelihood, health\nand education services, but also interventions to improve the situation of persons\nwith specific needs (children and women in psychological distress, persons with\ndisabilities) [HRT, HCT, Clusters]\n\n- Efforts should be addressed to support the initial post-return reintegration", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "assistance as well as the broader early recovery process in FATA, with Government\ninvestments and through generous donor support. These efforts should be combined\nwith a concrete possibility for humanitarian/ early recovery actors to directly carry\nout and directly monitor project implementation, through facilitated access by the\ncivil and military authorities to areas of return [donors, humanitarian community,\nUNDP, Early Recovery Working Group, FATA authorities].\n\n10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\n# **Supporting \r Durable \r Solutions** **in \r South-­‐East \r Myanmar**\n\n## **A \r framework \r for \r UNHCR \r engagement**\n\n**1.** **INTRODUCTION**\n\nMyanmar \r is \r currently \r undergoing \r a \r process \r of \r significant \r and \r rapid \r change, \r which \r has\nalready \r generated \r a \r series \r of \r political, \r social \r and \r economic \r reforms \r affecting \r all \r aspects\nof \r life \r in \r the \r country. \r The \r reforms \r launched \r by \r the \r president \r Thein \r Sein \r and \r largely\nsupported \r by \r the \r opposition \r leader \r Aung \r San \r Suu \r Kyi \r have \r received \r strong \r and \r positive\nencouragements \r from \r abroad \r with \r the \r most \r immediate \r outcome \r reflected \r in \r increased\nforeign \r aid \r and \r the \r temporary \r suspension \r of \r a \r number \r of \r economic \r sanctions. \r Key \r legal\namendments \r have \r been \r adopted \r to \r ease \r restrictions \r on \r foreign \r investments, \r national\nmedia \r and \r political \r parties \r while, \r in \r addition, \r hundreds \r of \r political \r prisoners \r have \r been\nreleased \r from \r detention.\n\nIn \r late \r 2011, \r President \r Thein \r Sein’s \r Government \r pledged \r to \r make \r the \r ethnic \r issue \r a\nnational \r priority, \r offering \r dialogue \r with \r all \r armed \r groups \r and \r dropping \r preconditions\nfor \r talks. \r By \r early \r 2012, \r cease-­‐fires \r were \r agreed \r between \r the \r Government \r and \r a \r number\nof \r non-­‐state \r armed \r opposition \r groups, \r including \r the \r Democratic \r Karen \r Buddhist \r Army\n(DKBA), \r the \r Karen \r National \r Union \r (KNU), \r and \r the \r Karenni \r National \r Progressive \r Party\n(KNPP).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "However, \r two \r serious \r internal \r conflicts \r still \r remain. \r Following \r two \r waves \r of \r inter-­‐\ncommunal \r violence \r in \r the \r western \r state \r of \r Rakhine \r in \r 2012, \r over \r 150 \r persons \r were\nkilled \r and \r over \r 115,000 \r people \r were \r displaced. \r In \r the \r northern \r states \r of \r Kachin \r and\nShan, \r the \r conflict \r with \r the \r Kachin \r Independence \r Army \r continues; \r fighting \r flared \r up \r in\nJune \r 2011 \r breaking \r a \r cease-­‐fire \r that \r held \r for \r 17 \r years. \r Despite \r repeated \r attempts \r to\nnegotiate \r a \r new \r cease-­‐fire \r agreement, \r no \r peace \r solution \r has \r been \r reached \r between \r the\nparties \r and \r the \r fighting \r has \r so \r far \r resulted \r in \r the \r displacement \r of \r an \r estimated \r of \r about\n85,000 \r persons \r scattered \r in \r more \r than \r 194 \r locations.\n\nAlthough \r the \r cease-­‐fire \r agreements \r have \r not \r led \r yet \r to \r durable \r peace \r accords, \r the\nsituation \r in \r the \r South-­‐East, \r where \r the \r number \r of \r Internally \r Displaced \r People \r (IDPs) \r in\nUNHCR’s \r area \r of \r operation [1] is \r estimated \r to \r be \r about \r 230,400 \r people, \r has \r started \r seeing\nchanging \r dynamics. \r From \r the \r moment \r when \r the \r new \r civilian \r Government \r started \r to\n\n---\n[1] UNHCR’s \r area \r of \r operation \r in \r the \r South-­‐East \r currently \r consists \r of \r Kayin, \r Kayah, \r and \r Mon \r States, \r and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\nmake \r efforts \r towards \r an \r internal \r peace \r process \r with \r armed \r groups \r in \r the \r border \r areas,\nparallel \r debates \r emerged \r about \r the \r opportunity \r to \r return \r for \r over \r a \r hundred \r thousand\nrefugees \r from \r the \r “temporary \r shelters” [2] in \r Thailand.\n\nWhile \r the \r present \r and \r immediately \r expected \r environment \r in \r Myanmar \r does \r not \r meet \r all\nthe \r conditions \r or \r safeguards \r for \r an \r organized \r return, \r the \r changing \r environment \r makes\nit \r prudent \r that \r measures \r are \r initiated \r to \r prepare \r for \r any \r possible \r voluntary \r repatriation\nof \r refugees \r as \r well \r as \r the \r return, \r local \r integration \r or \r voluntary \r resettlement \r to \r another\npart \r of \r the \r country \r of \r IDPs \r in \r Myanmar. \r Any \r preparations, \r however, \r should \r be \r initiated\nwith \r due \r caution \r so \r as \r not \r to \r send \r the \r wrong \r “message” \r to \r the \r Government, \r non-­‐state\narmed \r groups, \r IDPs, \r refugees \r or \r partners \r that \r return \r is \r being \r encouraged \r or \r promoted\nat \r this \r stage.\n\nThis \r Discussion \r Paper \r seeks \r to \r articulate \r a \r broad \r framework \r which \r should \r guide\nUNHCR’s \r engagement \r in \r the \r South-­‐East \r in \r 2013-­‐2015, \r and \r in \r particular, \r to \r define \r the\nparameters \r of \r UNHCR’s \r role \r in \r supporting:\n\ni. durable \r solutions \r for \r IDPs; \r and\nii. the \r sustainable \r reintegration \r of \r returning \r refugees.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "It \r builds \r upon \r a \r separate \r discussion \r paper \r dated \r 1 \r October \r 2012, \r which \r sets \r out \r a\nframework \r for \r voluntary \r repatriation \r for \r Myanmar \r refugees \r from \r Thailand. \r The \r papers\nshould \r form \r the \r basis \r of \r a \r more \r detailed \r operational \r strategy \r for \r 2013-­‐2015, \r and \r should\nalso \r serve \r as \r the \r first \r step \r towards \r a \r consultative \r multi-­‐stakeholder \r process, \r engaging\nGovernment, \r civil \r society, \r UN \r and \r NGO \r partners, \r donors \r and \r refugees \r and \r IDPs\nthemselves, \r to \r elaborate \r a \r shared \r vision \r and \r strategy \r for \r support \r to \r durable \r solutions \r in\nthe \r South-­‐East. \r The \r document \r outlines \r UNHCR’s \r general \r principles \r and \r standards \r as\nwell \r as \r context-­‐specific \r objectives \r and \r activities.\n\n**2.** **CONTEXT**\n\n**2.1. \r Political \r environment**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Despite \r the \r positive \r developments \r outlined \r above, \r considerable \r uncertainty \r remains\naround \r whether \r the \r cease-­‐fire \r agreements \r will \r lead \r to \r durable \r peace \r accords. \r There\nwere \r a \r number \r of \r clashes \r between \r armed \r groups \r and \r the \r Myanmar \r army \r reported \r in\n2012 \r in \r Shan, \r Kayah \r and \r Kayin. \r Trust \r in \r the \r Government \r is \r yet \r to \r be \r built \r in \r cease-­‐fire\nareas, \r after \r a \r long \r history \r of \r human \r rights \r abuses. \r Developments \r in \r relation \r to \r the\nconflict \r in \r Kachin \r will \r also \r play \r a \r role \r in \r influencing \r the \r prospects \r for \r peace \r elsewhere.\nPeace \r agreements \r will \r involve \r decisions \r on \r how \r best \r to \r share \r revenues \r from \r the \r natural\nresources \r in \r cease-­‐fire \r areas, \r how \r much \r to \r devolve \r political \r and \r economic \r authority \r to\nthe \r regional \r level, \r as \r well \r as \r how \r to \r maintain \r ethnic \r culture \r and \r language. \r Negotiations\nhave \r been \r and \r will \r continue \r to \r be \r extremely \r complex \r and \r the \r trajectory \r of \r the \r process\ntherefore \r remains \r uncertain.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "While \r all \r the \r conditions \r and \r safeguards \r for \r an \r organized \r voluntary \r return \r to \r South-­‐East\nMyanmar \r are \r not \r yet \r in \r place, \r positive \r political \r and \r economic \r reforms \r and \r security\ndevelopments \r have \r increased \r momentum \r for \r preparing \r for \r a \r possible \r voluntary \r return\nof \r refugees \r from \r Thailand \r and \r durable \r solutions \r for \r people \r internally \r displaced \r as \r a\nresult \r of \r protracted \r conflict. \r A \r small \r number \r of \r refugees \r are \r reported \r to \r have \r returned\n\n2 ‘Temporary \r shelters’ \r is \r the \r Royal \r Thai \r Government \r term \r used \r to \r identify \r the \r refugee \r camps \r along \r the\nborder.\n\n2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\nfrom \r the \r temporary \r shelters \r in \r Thailand \r to \r their \r place \r of \r origin, \r the \r majority \r of \r whom\nare \r single \r males \r who \r stated \r that \r they \r had \r returned \r to \r assess \r the \r security \r situation \r and\nstart \r re-­‐establishing \r their \r livelihood \r before \r the \r return \r of \r other \r family \r members. \r The\ndimensions \r of \r this \r spontaneous \r return \r movement \r have \r been, \r so \r far, \r negligible.\n\nWhile \r the \r dimensions \r of \r IDP \r return \r movements \r remain \r extremely \r difficult \r to \r assess,\nsome \r 37,000 \r IDPs \r are \r estimated \r by \r the \r The \r Border \r Consortium \r (TBC) \r to \r have \r returned\nhome \r or \r resettled \r in \r surrounding \r areas \r between \r August \r 2011 \r and \r July \r 2012 [3] .\n\nIf \r the \r current \r trend \r of \r political \r and \r socio-­‐economic \r reforms \r continues \r and \r as \r larger\npolitical \r events \r draw \r closer, \r such \r as \r the \r ASEAN/AEC \r agenda \r with \r Myanmar \r as \r Chair \r in\n2014, \r a \r national \r census \r in \r 2014, \r and \r national \r elections \r in \r 2015, \r then \r the \r momentum \r to\ntranslate \r cease-­‐fire \r negotiations \r into \r peace \r agreements \r may \r increase. \r This \r may \r lead \r to\nan \r increase \r in \r the \r number \r of \r spontaneous \r returns \r and \r the \r possibility \r of \r sudden\ndemands \r upon \r UNHCR \r to \r facilitate \r the \r voluntary \r repatriation \r of \r refugees.\n\n**2.2. \r Protection \r environment**", "output": {"entities": {"named_data": [], "descriptive_data": ["national \r census"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Displaced \r people \r and \r their \r host \r communities \r in \r South-­‐East \r Myanmar \r currently \r face \r a\nnumber \r of \r protection-­‐related \r risks \r and \r problems \r such \r as \r the \r lack \r of \r civil \r documentation\n(including \r birth \r registration, \r family \r lists \r and \r identity \r documentation \r in \r remote \r areas),\nlandmines, \r access \r to \r land \r and \r livelihoods, \r forced \r labour, \r forced \r recruitment, \r forced\ncontribution \r or \r taxation, \r trafficking, \r gender-­‐based \r violence \r and, \r in \r some \r cases,\nrestrictions \r of \r movement. \r The \r protection \r concerns \r are \r often \r exacerbated \r for \r IDPs \r and,\npotentially, \r refugees \r upon \r return.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Many \r people \r do \r not \r possess \r a \r Citizenship \r Scrutiny \r Card \r (or \r “CSC”) \r either \r because \r they\nlive \r or \r used \r to \r live \r in \r conflict \r areas \r controlled \r by \r ethnic \r armed \r groups \r with \r no\nGovernment \r representation, \r or \r simply \r because \r they \r do \r not \r have \r access \r to \r the\nadministrative \r mechanisms \r that \r issue \r these \r documents. \r Furthermore, \r the \r Government\nin \r the \r past \r limited \r distribution \r of \r CSCs \r in \r border \r areas \r to \r contain \r movement \r of \r people\nlinked \r to \r ethnic \r armed \r groups. \r Very \r few \r IDPs \r held \r CSCs \r before \r their \r displacement, \r and \r it\nmay \r be \r extremely \r difficult \r for \r people \r who \r have \r been \r displaced \r as \r refugees \r or \r IDPs,\nparticularly \r from \r areas \r controlled \r by \r ethnic \r armed \r groups, \r to \r provide \r the \r necessary\ndocumentation \r to \r obtain \r CSCs. \r Additionally, \r persons \r not \r belonging \r to \r one \r of \r the\nofficially \r recognized \r “ethnic \r groups” \r face \r difficulty \r in \r acquiring \r CSCs, \r despite \r the \r fact\nthat \r there \r are \r relevant \r provisions \r available \r for \r them \r under \r the \r Myanmar \r Citizenship\nLaw. \r In \r July \r 2011, \r the \r Immigration \r and \r National \r Registration \r Department \r of \r the\nMinistry \r of \r Immigration \r and \r Population \r initiated \r the \r Moe \r Pwint \r Operation, \r which \r is \r an\naccelerated \r procedure \r to \r issue \r CSCs, \r especially \r in \r areas \r that \r were \r remote \r and/or\ndifficult \r to \r access \r in \r the \r past \r because \r of \r the \r presence \r of \r non-­‐state \r armed \r groups.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "There \r are \r no \r mine \r maps \r currently \r available \r and \r the \r extent \r of \r the \r threat \r is \r impossible \r to\naccurately \r assess. \r However, \r it \r is \r suspected \r that \r Myanmar \r is \r one \r of \r the \r most \r highly\nlandmine-­‐contaminated \r countries \r in \r the \r world, \r with \r these \r devices \r continuing \r to \r claim\nseveral \r hundred \r civilian \r victims \r each \r year. \r Myanmar \r has \r not \r acceded \r to \r the \r Mine \r Ban\nTreaty \r but \r has \r recently \r set \r up \r a \r Myanmar \r Mine \r Action \r Centre, \r under \r the \r Myanmar\nPeace \r Centre \r (MPC) [4] to \r co-­‐ordinate \r and \r oversee \r the \r implementation \r of \r a \r national\n\n3 _Changing \r Realities, \r Poverty \r and \r Displacement \r In \r South \r East \r Burma/Myanmar_, \r The \r Border \r Consortium, \r 31\nOctober \r 2012, http://www.tbbc.org/resources).\n\n---\n[4] The \r Myanmar \r Peace \r Centre \r (MPC) \r was \r established \r in \r October \r 2012 \r by \r a \r Presidential \r Decree \r to \r serve \r as", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\nhumanitarian \r mine \r action \r programme \r with \r local \r and \r international \r humanitarian\nagencies. \r Despite \r these \r positive \r developments \r there \r are \r still \r no \r activities \r related \r to\nsurvey \r and \r clearance, \r marking, \r or \r fencing \r being \r undertaken. \r Mine \r risk \r education\nprogrammes \r and \r assistance \r to \r mine \r victims \r remain \r still \r limited \r in \r scope.\n\nLand \r registration \r documents \r are \r held \r by \r township \r authorities \r in \r Myanmar. \r Land \r tenure\ndocuments \r and \r deeds \r are \r not \r always \r recorded \r or \r respected \r and \r there \r are \r frequent\nreports \r of \r land \r expropriation \r (or \r “land \r grabbing”) \r by \r the \r Government, \r the \r Myanmar\nArmy, \r non-­‐state \r armed \r groups, \r and \r private \r companies, \r often \r resulting \r in \r internal\ndisplacement \r without \r appropriate \r guarantees \r of \r compensation. \r Although \r the \r reforms\nintroduced \r by \r the \r Government \r in \r 2008 \r provide \r some \r additional \r security \r of \r land \r tenure,\nthey \r still \r fail \r to \r adequately \r recognise \r widely \r used \r customary \r rights.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Land \r registration \r documents"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The \r ongoing \r presence \r of \r armed \r actors \r in \r places \r of \r displacement \r and \r potential \r return\nremains \r a \r key \r concern. \r Communities \r have \r been \r subject \r to \r protection \r risks \r associated\nwith \r the \r presence \r of \r military \r units \r including \r forced \r labour, \r forced \r recruitment \r and \r the\npayment \r of \r “taxes.” \r In \r addition, \r Myanmar \r is \r believed \r to \r have \r a \r large \r number \r of \r children\nin \r armed \r conflict, \r including \r child \r soldiers, \r with \r both \r the \r Government \r and \r various \r non-­‐\nstate \r armed \r actors \r having \r been \r responsible \r for \r the \r recruitment \r of \r minors. \r On \r 27 [th] June\n2012, \r the \r United \r Nations \r Country \r Task \r Force \r on \r Monitoring \r and \r Reporting \r (CTFMR)\nand \r the \r Government \r signed \r a \r Plan \r of \r Action \r with \r regards \r to \r underage \r recruitment \r in \r the\nMyanmar \r Army. \r Procedures \r are \r underway \r for \r the \r systematic \r identification \r and\ndischarge \r of \r verified \r minors \r from \r the \r national \r forces. \r However, \r plans \r of \r action \r with\nnon-­‐state \r armed \r groups \r in \r the \r South-­‐East \r have \r not \r yet \r been \r agreed \r and \r it \r remains \r to \r be\nseen \r whether \r the \r Plan \r of \r Action \r with \r the \r Government \r will \r be \r comprehensively\nimplemented.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Individuals \r and \r families \r often \r have \r no \r means \r to \r retain \r legal \r services \r and \r are \r often\nunaware \r of \r their \r rights \r to \r seek \r legal \r remedies \r under \r Myanmar \r law. \r Services, \r security\nand \r the \r rule \r of \r law \r are \r weak, \r and \r effective \r protection \r and \r response \r mechanisms \r to\naddress \r sexual \r and \r gender-­‐based \r violence \r (SGBV) \r or \r the \r specific \r needs \r of \r extremely\nvulnerable \r individuals \r (EVIs), \r are \r still \r rudimentary \r with \r a \r lack \r of \r appropriate \r core\nmechanisms \r and \r services \r available.\n\nCases \r of \r arbitrary \r arrest \r and \r detention \r in \r Myanmar \r continue \r to \r be \r reported. \r Pervasive\nproblems \r with \r the \r rule \r of \r law \r in \r Myanmar \r are \r well \r documented \r and \r detention \r and\nprison \r conditions \r remain \r extremely \r problematic.\n\nIn \r addition, \r under \r the \r 1947 \r Immigration \r (Emergency \r Provisions) \r Act \r (as \r amended),\nillegal \r departure \r or \r entry \r is \r punishable \r by \r a \r fine \r or \r imprisonment \r of \r up \r to \r five \r years.\nUNHCR \r is \r not \r aware \r of \r any \r prosecutions \r brought \r under \r this \r law, \r and \r the \r President \r has\nalso \r stated \r publically \r that \r he \r welcomes \r back \r Myanmar \r people \r who \r ‘for \r various \r reasons’\nleft \r the \r country. \r However \r there \r have \r not \r been \r any \r changes \r or \r amnesties \r pronounced \r in\nrelation \r to \r this \r law, \r and \r this \r would \r therefore \r need \r to \r be \r addressed \r prior \r to \r any \r organised\nvoluntary \r repatriation \r of \r refugees.\n\n**2.3. \r Socio-­‐economic \r environment**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The \r health \r infrastructure \r in \r the \r South-­‐East \r remains \r substantially \r underserved \r with \r a\n\nCommittee. \r The \r Myanmar \r Peace \r Centre \r is \r tasked \r to \r provide \r policy \r advice \r and \r strategic \r guidance \r as \r well \r as\nco-­‐ordinating \r Government \r activities \r in \r the \r key \r areas \r of: \r cease-­‐fire \r negotiations \r and \r implementation; \r peace\nnegotiations \r and \r political \r dialogue; \r co-­‐ordination \r of \r assistance \r in \r conflict \r affected \r areas \r and \r outreach \r and\npublic \r diplomacy.\n\n4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\nlack \r of \r skilled \r personnel, \r facilities, \r basic \r equipment \r and \r supplies, \r including \r in \r terms \r of\npotentially \r life-­‐saving \r reproductive \r health, \r malaria \r prevention \r and \r control \r and \r HIV\nservices.\n\nThe \r education \r sector \r is \r also \r substantially \r underserved \r and \r not \r of \r adequate \r standards,\nwith \r a \r shortage \r of \r teachers \r and \r an \r inadequate \r number \r of \r primary \r schools \r within\nreasonable \r distance \r of \r many \r communities. \r Regular \r school \r attendance \r is \r hampered \r by\neducation \r costs, \r distances, \r illness, \r work \r requirements, \r insecurity \r in \r conflict-­‐affected\nareas \r and, \r for \r ethnic \r minority \r children, \r “language \r barrier”. \r Most \r children \r have \r limited\nopportunity \r to \r continue \r their \r education \r beyond \r primary \r school.\n\nAccess \r to \r safe \r drinking \r water, \r particularly \r in \r rural \r areas \r and \r during \r the \r dry \r season \r is\nuneven \r and \r in \r many \r locations \r insufficient, \r with \r those \r water \r sources \r available \r during \r the\ndry \r season \r located \r far \r away \r from \r human \r dwellings. \r Standards \r of \r sanitation \r are \r very \r low,\nwith \r open \r defecation \r common \r and \r household \r latrines \r less \r than \r international \r standards.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The \r sustainability \r of \r IDP \r (and \r potentially, \r refugee) \r returns \r is \r also \r still \r hampered \r by\nlimited \r access \r to \r livelihood \r opportunities. \r At \r present, \r access \r to \r livelihood \r resources \r and\ntraining \r opportunities \r are \r scarce. \r Returning \r IDPs \r and \r refugees \r may \r have \r lost \r the\nproductive \r assets \r needed \r to \r restart \r agriculture, \r while \r homes \r have \r been \r destroyed \r by\nthe \r conflict \r or \r have \r fallen \r into \r disrepair.\n\nAdditionally, \r distances \r are \r substantial \r between \r the \r temporary \r shelters \r in \r Thailand \r and\npotential \r return \r areas. \r Many \r of \r the \r places \r of \r origin \r of \r registered \r refugees \r in \r Thailand \r are\nparticularly \r isolated \r and \r have \r received \r little \r investment \r in \r infrastructure. \r Investment \r in\nrepatriation \r infrastructure, \r including \r transit \r centres \r or \r way \r stations, \r will \r be \r required \r in\ncase \r of \r an \r organized \r repatriation \r movement.\n\n**3.** **POPULATION \r OF \r CONCERN \r IN \r SOUTH-­‐EAST \r MYANMAR**\n\n**3.1. \r Refugees \r in \r Thailand**\n\nUNHCR’s \r current \r area \r of \r operations \r in \r South-­‐East \r Myanmar \r covers \r Kayin, \r Kayah, \r and\nMon \r States, \r and \r Taninthayri \r Regions. \r These \r are \r the \r primary \r places \r of \r origin \r of \r the\nestimated \r 128,876 \r Myanmar \r refugees \r residing \r in \r the \r temporary \r shelters \r along \r the\nborder \r in \r Thailand, \r and \r are \r assumed \r to \r be \r the \r primary \r potential \r areas \r of \r return.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNHCR \r ProGres \r data \r on \r the \r 83,044 \r registered \r refugees \r in \r Thailand \r indicates \r an\nestimated \r 84 \r per \r cent \r are \r ethnic \r Karen \r and \r 12 \r per \r cent \r are \r ethnic \r Karenni. \r The\nremaining \r 4 \r per \r cent \r are \r of \r Burman, \r Shan, \r and \r Mon \r descent, \r and \r other \r groups. \r The\nmajority \r of \r registered \r refugees \r come \r from \r Kayin \r State \r (65.3 \r per \r cent), \r followed \r by\nKayah \r (14.6 \r per \r cent), \r Tanintharyi \r (7.3 \r per \r cent), \r Bago \r (5.2 \r per \r cent) \r and \r Mon \r (5 \r per\ncent). \r (Annex: \r Myanmar \r Thailand \r Border \r – \r Refugee \r Overview, \r as \r of \r end \r of \r March \r 2013)", "output": {"entities": {"named_data": ["UNHCR \r ProGres \r data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "A \r profiling \r exercise \r covering \r both \r the \r registered \r and \r unregistered \r refugee \r population\nresiding \r in \r the \r temporary \r shelters \r in \r Thailand, \r will \r be \r carried \r out \r by \r the \r Mae \r Fah \r Luang\nFoundation \r on \r behalf \r of \r UNHCR \r in \r 2013-­‐2014. \r The \r exercise \r will \r permit \r updating \r of \r data\non \r areas \r of \r origin \r (Regions/States, \r districts \r and \r townships, \r and \r village \r tracts/villages)\nand \r will \r assess \r the \r intentions \r of \r refugees, \r whether \r that \r would \r be \r for \r eventual \r voluntary\nreturn, \r resettlement \r to \r a \r third \r country \r or \r other \r durable \r solution \r possibilities. \r For \r those\nrefugees \r that \r intend \r to \r return, \r information \r about \r their \r desired \r or \r intended \r destination\nwill \r be \r captured \r in \r the \r survey \r (this \r may \r include \r places \r of \r origin \r or \r prior \r habitual\nresidence, \r or \r other \r locations \r in \r Myanmar). \r The \r major \r focus \r of \r the \r survey \r is \r about \r future\n\n5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\nlivelihoods, \r household \r and \r family \r security \r issues, \r as \r well \r as \r about \r past, \r present \r and\npossibly \r future \r skill-­‐sets \r that \r will \r help \r the \r refugees \r to \r return \r to \r a \r normal \r life \r outside \r the\ncamp. \r This \r information \r will \r also \r help \r in \r identifying \r both \r the \r major \r return \r areas \r and \r all\nother \r locations \r along \r with \r indications \r of \r the \r possible \r number \r of \r refugees \r intending \r to\nreturn \r to \r those \r areas, \r and \r required \r programmatic \r interventions \r to \r help \r in \r preparing \r the\nconditions \r that \r would \r support \r a \r sustainable \r voluntary \r return \r and \r reintegration.\n\n**3.2. \r Internally \r Displaced \r Persons**\n\nIn \r its \r October \r 2012 \r report, \r TBC \r estimated \r that \r a \r total \r of \r about \r 400,000 \r individuals \r are\nstill \r internally \r displaced \r in \r the \r rural \r areas \r of \r 36 \r townships \r in \r South-­‐East \r Myanmar \r in\nKayin, \r Kayah, \r South \r and \r East \r Shan \r and \r Mon \r States, \r and \r Bago \r and \r Tanintharyi \r Regions.\nOf \r these, \r the \r number \r located \r in \r Kayin, \r Kayah, \r Tanintharyi \r and \r Mon \r combined \r is\nestimated \r at \r 230,400 \r individuals \r (89,150 \r or \r 38.7% \r in \r Kayin; \r 71,650 \r or \r 31.1% \r in\nTanintharyi; \r 35,000 \r or \r 15.1% \r in \r Mon \r and \r 34,600 \r or \r 15% \r in \r Kayah) [ \r 5] .", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Due \r to \r the \r size \r and \r remoteness \r of \r the \r operational \r area, \r compounded \r by \r access\nlimitation \r and \r sensitivities, \r reliable \r and \r disaggregated \r information \r on \r the \r profile \r and\nneeds \r of \r displaced \r populations \r remains \r scarce. \r UNHCR \r has \r recently \r strengthened \r its\ninformation \r management \r capacities \r with \r a \r view \r to \r working \r towards \r obtaining \r a \r more\nsystematic \r understanding \r of \r the \r locations \r and \r characteristics \r of \r populations \r of \r concern,\nand \r the \r protection \r risks \r affecting \r them. \r Additionally, \r the \r Joint \r IDP \r Profiling \r Service\n(JIPS), \r has \r recently \r completed \r a \r scoping \r mission \r aiming \r at \r assessing \r whether \r it \r is\nfeasible \r and \r desirable \r to \r conduct \r an \r IDP \r profiling \r that \r would \r inform \r the \r design \r of \r an\neffective \r strategy \r for \r support \r to \r IDPs \r durable \r solutions, \r as \r well \r as \r targeting, \r improved\nadvocacy \r and \r fundraising.\n\n**4.** **UNHCR’S \r CURRENT \r OPERATIONS \r IN \r THE \r SOUTH-­‐EAST**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNHCR \r first \r established \r an \r operational \r presence \r in \r South-­‐Eastern \r Myanmar \r in \r 2004\nwith \r the \r objective \r of \r assisting \r communities \r affected \r by \r armed \r conflict \r in \r Kayin \r and \r Mon\nStates \r and \r in \r Tanintaryi \r Region. \r Between \r 2004 \r and \r 2012, \r UNHCR, \r in \r partnership \r with\nlocal \r and \r international \r organizations, \r has \r delivered \r over \r 3,000 \r humanitarian \r projects \r to\nensure \r that \r displaced \r and \r host \r families \r in \r affected \r areas \r have \r access \r to \r proper\nsanitation, \r primary \r health \r care \r facilities, \r safe \r and \r clean \r water, \r livelihood \r activities \r and\nother \r training \r including \r technical \r support \r to \r community \r management \r capacities,\nparticularly \r in \r relation \r to \r small \r infrastructure \r management.\n\nUNHCR \r also \r undertakes \r protection \r assessment, \r legal \r awareness \r training, \r and \r provides\nassistance \r to \r extremely \r vulnerable \r individuals \r (including \r land \r mine \r victims \r and\nsurvivors \r of \r sexual \r and \r gender-­‐based \r violence). \r The \r extent \r and \r capacity \r of \r protection\nmonitoring \r and \r referral \r networks \r in \r the \r South-­‐East \r nonetheless \r remain \r extremely\nlimited.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In \r 2012 \r the \r programme \r expanded \r to \r cover \r capacity \r building \r of \r government \r officials \r and\nother \r partners \r in \r relation \r to \r IDP \r and \r refugee \r protection \r and \r the \r role \r and \r mandate \r of\nUNHCR. \r Additionally, \r support \r has \r been \r provided \r by \r UNHCR \r to \r the \r Moe \r Pwint \r Operation\nthrough \r a \r pilot \r project \r as \r well \r as \r the \r production \r of \r leaflets \r in \r the \r local \r language \r to\nincrease \r the \r Ministry \r of \r Immigration \r efforts \r in \r enhancing \r awareness \r of \r the \r Operation\nwithin \r the \r communities.\n\n5 \r TBC \r 2012, \r see \r note \r 3 \r above.\n\n6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\nAlso \r in \r 2012 \r UNHCR \r received \r authorization \r to \r open \r offices \r in \r Hpa-­‐an \r in \r Kayin \r State \r and\nLoikaw \r in \r Kayah \r State, \r although \r this \r is \r still \r to \r be \r formalised. \r Access \r remains \r restricted\nin \r certain \r areas \r and \r is \r dependent \r on \r Government \r authorization \r and \r restrictive \r advance\nclearance \r procedures, \r especially \r for \r international \r staff. \r However, \r there \r have \r been\nincremental \r improvements \r in \r access \r in \r areas \r close \r to \r the \r Thai \r border, \r particularly \r as \r the\ncease-­‐fire \r process \r has \r become \r embedded. \r Nonetheless, \r humanitarian \r access, \r and \r the\ndegree \r to \r which \r UNHCR \r is \r free \r to \r assess \r needs \r and \r to \r effectively \r target \r its \r support \r to \r the\nmost \r vulnerable \r displaced \r and \r host \r communities \r varies \r considerably \r from \r state \r to \r state.\n\n**5.** **LEGAL, \r INSTITUTIONAL \r AND \r POLICY \r FRAMEWORKS, \r AND \r KEY**\n\n**PRINCIPLES**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "International \r Refugee \r Law \r governs \r the \r protection \r of \r refugees, \r including \r the \r realisation\nof \r solutions \r to \r their \r plight. \r UNHCR’s \r mandate \r is \r to \r provide \r protection \r to \r refugees \r and\nseek \r durable \r solutions \r for \r them \r and \r UNHCR’s \r Statute \r and \r numerous \r Executive\nCommittee \r Conclusions \r provide \r the \r legal \r framework \r for \r its \r lead \r role \r in \r voluntary\nrepatriation \r operations [6] . \r UNHCR’s \r Statute \r provides \r for \r key \r activities \r in \r this \r regard,\nincluding \r promoting \r with \r Governments \r measures \r to \r improve \r the \r situation \r of \r refugees\nand \r to \r assist \r Governmental \r and \r other \r efforts \r to \r promote \r voluntary \r repatriation.\n\nIn \r its \r involvement \r with \r durable \r solutions \r for \r IDPs \r UNHCR \r is \r committed \r to \r contributing\nto \r an \r inter-­‐agency \r approach \r framed \r by \r the \r standards \r set \r out \r in \r the \r Guiding \r Principles\non \r Internal \r Displacement \r and \r the \r Framework \r for \r Durable \r Solutions [ \r 7] .", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Voluntary \r return \r is \r guided \r by \r internationally \r recognized \r standards, \r which \r inter \r alia\nprovide \r that \r refugees \r have \r the \r right \r to \r return \r voluntarily, \r in \r safety \r and \r dignity, \r to \r their\nown \r countries. \r A \r freely \r expressed \r wish \r to \r return \r by \r those \r displaced \r is \r a \r pre-­‐condition \r to\nany \r voluntary \r return. \r Recognized \r principles \r also \r include: \r taking \r measures \r to \r address\nany \r root \r causes \r of \r displacement \r (cross-­‐border \r and \r internal) \r by \r all \r parties \r to \r the \r past\nconflict; \r that \r refugees \r and \r IDPs \r have \r the \r right \r to \r freedom \r of \r movement \r and \r the \r right \r to\nreturn \r to \r their \r place \r of \r former \r residence \r or \r another \r place \r of \r their \r choice \r in \r the \r country\nof \r origin; \r and \r that \r UNHCR \r and \r partners \r have \r access \r to \r returnee \r areas \r in \r order \r to\nmonitor \r their \r return.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "While \r UNHCR \r has \r been \r present \r and \r operational \r in \r South-­‐East \r Myanmar \r since \r 2004, \r the\nUNHCR \r framework \r for \r co-­‐operation \r with \r the \r Government \r of \r the \r Union \r of \r Myanmar \r in\nthe \r South-­‐East \r is \r defined \r by \r a \r draft \r Letter \r of \r Understanding \r (LOU) \r with \r its \r line \r Ministry,\nthe \r Ministry \r of \r Border \r Affairs \r (NaTaLa). \r The \r LOU \r articulates \r UNHCR \r and \r NaTaLa’s\nresponsibilities \r in \r regard \r to \r the \r implementation \r of \r programmes \r aimed \r at \r improving\nlivelihoods \r in \r communities \r affected \r by \r displacement, \r reducing \r the \r risk \r of \r further\ndisplacement, \r and \r specifically \r creating \r the \r appropriate \r conditions \r for \r return \r for \r IDPs\nand \r refugees. \r The \r current \r LOU, \r which \r includes \r Kayin, \r Mon, \r Kayah, \r Shan \r and \r Chin \r States,\nand \r Bago \r and \r Tanintharyi \r Regions, \r was \r signed \r on \r 10 \r June \r 2013.\n\nOnce \r the \r required \r safeguards \r are \r in \r place \r for \r the \r return \r of \r refugees \r and \r an \r organized\nprogramme \r is \r to \r be \r launched, \r a \r tripartite \r agreement \r between \r Myanmar, \r Thailand \r and\nUNCHR \r should \r form \r the \r main \r legal \r framework \r for \r the \r voluntary \r repatriation \r and\n\n6 \r Executive \r Committee \r Conclusion \r 29 \r of \r 1983 \r calls \r upon \r Governments \r to \r facilitate \r the \r work \r of \r UNHCR \r “in\ncreating \r conditions \r favourable \r to \r and \r promoting \r voluntary \r repatriation, \r which \r whenever \r appropriate \r and\nfeasible \r is \r the \r most \r desirable \r solution \r for \r refugee \r problems.” \r Also \r refer \r to \r Executive \r Committee \r Conclusions", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\nreintegration \r of \r refugees \r as \r is \r the \r norm \r in \r repatriation \r operations \r across \r the \r world. \r The\nagreement \r would \r govern \r the \r procedures \r for \r entry \r into \r Myanmar \r and \r modalities \r for\nreception, \r registration, \r immediate \r humanitarian \r assistance, \r and \r travel \r to \r places \r of\ndestination. \r Another \r key \r aspect \r will \r be \r access \r and \r monitoring \r of \r returnee \r areas, \r as \r well\nas \r the \r immediately \r needed \r support \r to \r the \r reintegration \r process.\n\nUNHCR’s \r 2008 \r Policy \r on \r Return \r and \r Reintegration \r defines \r reintegration \r as \r the\nreduction \r and \r ultimately \r the \r disappearance \r of \r any \r factors \r that \r differentiate \r returnees\nfrom \r other \r members \r of \r their \r community \r in \r terms \r of \r both \r their \r legal \r and \r socio-­‐economic\nstatus. \r Therefore, \r reintegration \r is \r a \r comprehensive, \r gradual \r and \r dynamic \r effort \r that\ninvolves \r the \r establishment \r of \r conditions \r which \r enable \r returnees \r and \r their \r communities\nto \r exercise \r their \r social, \r economic, \r civil, \r political \r and \r cultural \r rights, \r and \r on \r that \r basis \r to\nenjoy \r peaceful, \r productive \r and \r dignified \r lives.\n\nThe \r definition \r of \r a \r comprehensive, \r multi-­‐year \r strategy \r for \r return \r and \r reintegration\nshould \r therefore \r be \r framed \r by \r and \r support \r development \r plans. \r Strategic \r plans \r should\ncover \r all \r key \r relevant \r sectors: \r legal, \r economic, \r social, \r and \r environmental, \r and \r guide \r the\ndivision \r of \r roles \r and \r responsibilities \r between \r relief, \r development, \r public \r and \r private\nstakeholders.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Securing \r durable \r solutions \r for \r displaced \r populations \r should \r be \r an \r integral \r component\nof \r the \r peace \r process, \r and \r the \r engagement \r of \r UNHCR \r and \r other \r actors \r in \r support \r to\ndurable \r solutions \r should \r be \r located \r within \r a \r broader \r peace-­‐building \r framework. \r In\naddition \r to \r advocating \r for \r the \r participation \r of \r IDPs \r and \r refugees \r in \r the \r peace\nnegotiations, \r activities \r in \r support \r of \r durable \r solutions \r should \r be \r grounded \r in \r sound\nconflict \r analysis. \r The \r provision \r of \r assistance \r should \r be \r conflict-­‐sensitive, \r minimizing\nunintended \r negative \r impact \r while \r maximising \r its \r peace-­‐building \r impact, \r and \r use \r “Do \r No\nHarm” \r approaches \r to \r contribute \r at \r building \r trust \r and \r supporting \r the \r peace-­‐building\nprocess. \r Co-­‐existence \r projects \r and \r other \r activities \r should \r be \r prioritised \r in \r support \r of\nreintegration. \r Activities \r such \r as \r peace \r education \r and \r conflict \r resolution \r training \r may \r be\nundertaken \r even \r prior \r to \r an \r organised \r voluntary \r return.\n\nRefugees \r and \r IDPs \r will \r frequently \r opt \r to \r spread \r risk \r and \r to \r cushion \r the \r impact \r of \r return\nby \r having \r some \r family \r members \r remaining \r outside \r the \r country, \r or \r move \r elsewhere\nwithin \r the \r country \r to \r pursue \r migration \r strategies. \r This \r should \r be \r facilitated \r and \r not\nviewed \r as \r a \r failure \r of \r the \r reintegration \r process.\n\n**6.** **NATIONAL \r OWNERSHIP \r AND \r CO-­‐ORDINATION**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Securing \r durable \r solutions \r is \r fundamentally \r linked \r to \r the \r restoration \r of \r national\ncapacity \r to \r provide \r for \r the \r protection \r and \r welfare \r of \r formerly \r displaced \r communities,\nand \r UNHCR’s \r role \r should \r be \r designed \r to \r support \r such \r an \r outcome, \r through \r systematic\nengagement \r with \r key \r Government \r counterparts, \r civil \r society \r and \r other \r national\nstakeholders.\n\nNational \r authorities \r have \r primary \r responsibility \r to \r secure \r durable \r solutions \r for \r those\nwho \r have \r been \r formerly \r displaced, \r while \r UNHCR \r and \r other \r humanitarian \r and\ndevelopment \r actors \r have \r a \r complementary \r role.\n\nCo-­‐ordination \r and \r multi-­‐sector \r engagement \r is \r also \r critical \r to \r the \r sustainable\nreintegration \r of \r refugees \r and \r durable \r solutions \r for \r IDPs [8] . \r Therefore, \r mobilising \r the\n\n---\n[8] See \r UNHCR’s \r 2008 \r Policy \r on \r the \r Return \r and \r Reintegration \r of \r Refugees \r and \r IDPs, \r and \r the \r 2011 \r Secretary-­‐", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\nengagement \r of \r key \r partners, \r including \r relevant \r development \r actors \r and \r the \r private\nsector \r since \r the \r early \r stage \r will \r be \r a \r crucial \r component \r of \r UNHCR’s \r strategy.\n\nCurrent \r co-­‐ordination \r fora \r include \r the \r South-­‐East \r Consultation \r Platform, \r led \r by \r UNHCR,\nconsisting \r of \r UN, \r NGO, \r donors, \r Government \r and \r other \r partners, \r including \r the \r Myanmar\nPeace \r Centre \r (MPC) [9] . \r This \r currently \r meets \r every \r 2-­‐3 \r months \r in \r Yangon. \r The \r objective \r of\nthe \r Platform \r is \r to \r maintain \r a \r common \r understanding \r of \r the \r operational \r environment\nand \r challenges \r in \r the \r South-­‐East \r and \r to \r forge \r strategic \r partnerships. \r UNHCR \r also \r chairs\nthe \r National \r Protection \r Working \r Group.\n\nMonthly \r inter-­‐agency \r co-­‐ordination \r meetings, \r chaired \r by \r UNHCR \r and \r attended \r by\nhumanitarian \r partners, \r are \r currently \r held \r in \r Mawlamyine \r (Mon \r State), \r Taungoo \r (Bago\nRegion), \r and \r Myeik \r and \r Dawei \r (Tanintharyi \r Region). \r Inter-­‐agency \r meetings \r also \r take\nplace \r in \r Loikaw \r (Kayah \r State), \r chaired \r on \r a \r rotational \r basis \r amongst \r agencies.\n\nIn \r January \r 2013, \r UNHCR \r also \r initiated \r cross-­‐border \r meetings \r between \r UNHCR\nMyanmar \r and \r UNHCR \r Thailand.\n\n---\n[9] The \r Myanmar \r Government \r draft \r Framework \r for \r Economic \r and \r Social \r Reform \r 2012-­‐2015 \r tasks \r the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The \r number \r of \r UN \r organisations \r and \r international \r and \r national \r NGOs \r as \r well \r as \r the\nscale \r of \r humanitarian \r and \r development \r activities \r in \r the \r South-­‐East \r are \r currently\nexpanding. \r At \r present \r UNHCR \r is \r reviewing, \r together \r with \r partners, \r how \r leadership \r and\nco-­‐ordination \r in \r support \r of \r durable \r solutions \r in \r the \r South-­‐East \r can \r be \r strengthened \r and\ncentred \r around \r a \r shared \r vision \r and \r strategy \r located \r within \r a \r broader \r protection\nframework \r incorporating \r key \r standards. \r UNHCR, \r in \r accordance \r with \r its \r mandate\nobligations, \r will \r play \r an \r active \r role \r in \r this \r process \r and \r in \r enhancing \r inter-­‐agency \r co-­‐\nordination \r on \r durable \r solutions \r at \r regional \r and \r State/Region \r levels.\n\nOne \r mechanism \r might \r be \r the \r establishment \r of \r a \r Working \r Group \r on \r Durable \r Solutions\nfor \r IDPs \r and \r Refugees \r to \r serve \r as \r a \r platform \r to \r co-­‐ordinate \r durable \r solutions \r related\nactivities \r from \r the \r preparatory \r phase. \r The \r working \r group \r could \r be \r co-­‐led \r by\nrepresentatives \r from \r the \r Government \r and \r UNHCR, \r and \r might \r consist \r of \r relevant \r UN\nagencies, \r NGOs, \r donors, \r civil \r society \r and \r representatives \r of \r parties \r involved \r in \r the \r peace\nprocess. \r In \r cease-­‐fire \r areas, \r all \r activities \r will \r have \r to \r be \r undertaken \r in \r close \r consultation\nwith \r non-­‐state \r armed \r groups \r operating \r in \r those \r areas.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Systematic \r engagement \r with \r key \r actors \r facilitating \r and \r supporting \r the \r peace \r process,\nsuch \r as \r MPC, \r is \r also \r required. \r This \r will \r ensure \r that \r issues \r affecting \r the \r rights \r and\nwelfare \r of \r IDPs \r and \r returning \r refugees \r are \r appropriately \r addressed \r in \r peace\nnegotiations \r and \r peace-­‐building \r programmes \r and \r initiatives \r such \r as \r the \r MPC-­‐led \r Joint\nPeace-­‐building \r Need \r Assessment \r (JPNA), \r and \r that \r displaced \r populations, \r including\nwomen, \r have \r the \r opportunity \r to \r participate \r in \r these \r processes.\n\nEfforts \r will \r also \r be \r required \r to \r ensure \r that \r durable \r solutions \r are \r mainstreamed \r in \r the\nimplementation \r of \r the \r UN \r Strategic \r Framework \r for \r Myanmar \r for \r 2013 \r to \r 2015, \r and\nnational \r development \r plans \r and \r programmes \r as \r means \r of \r catalysing \r interventions \r by\ndevelopment \r agencies. \r Relevant \r funding \r opportunities, \r such \r as \r the \r Peace \r Building \r Fund\n(PBF), \r should \r also \r be \r pursued.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\n**7.** **OPERATIONAL \r FRAMEWORK**\n\nWhile \r positive \r developments \r already \r undertaken \r by \r the \r Government \r point \r to \r an\neventual \r return \r of \r refugees \r from \r Thailand, \r the \r timing \r of \r the \r return \r remains \r difficult \r to\npredict. \r  \r The \r Government \r of \r Myanmar \r has \r indicated \r that \r its \r initial \r priority \r will \r be \r to\ncommence \r facilitating \r solutions \r for \r IDPs, \r followed \r by \r the \r voluntary \r repatriation \r of\nrefugees. \r The \r strategy \r for \r the \r South-­‐East \r shall \r therefore \r be \r phased \r in \r its \r approach\nstarting \r by \r strengthening \r involvement \r in \r IDP \r operations \r to \r be \r better \r positioned \r to\nreceive \r returning \r refugees \r under \r organized \r repatriation \r in \r the \r future. \r Beyond \r the\nplanned \r phases \r UNHCR \r and \r partners \r shall \r however \r remain \r alert \r to \r sudden \r changes \r in\nthe \r political \r environment \r and \r being \r ready \r to \r respond.\n\nThe \r decision \r to \r shift \r between \r phases \r will \r be \r dictated \r by \r the \r operational \r context,\nparticularly \r changes \r in \r peace \r and \r security, \r access, \r conditions \r in \r return \r areas, \r and\nindications \r of \r readiness \r of \r IDPs \r and \r refugees \r to \r return. \r It \r is \r expected \r that \r UNHCR \r and\npartners’ \r presence \r and \r activities \r in \r one \r phase \r could \r also \r contribute, \r for \r instance, \r to\nimprovements \r in \r return \r conditions, \r thereby \r catalysing \r a \r shift \r to \r the \r next \r phase.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The \r proposed \r five \r phases \r reflect \r different \r stages \r in \r IDP \r operations, \r and \r in \r the \r refugee\nrepatriation \r and \r reintegration \r process, \r with \r each \r corresponding \r to \r incrementally\nincreasing \r resource \r requirements \r for \r UNHCR \r and \r its \r operational \r partners.\n\n- Phase \r 1: Strengthening \r of \r IDP \r operations \r and \r preparation \r for \r refugee \r return\nwhile \r providing \r integrated \r assistance \r to \r spontaneous \r returnees;\n\n- Phase \r 2: Expansion \r of \r integrated \r support \r to \r spontaneous \r returnees \r and \r their\ncommunities \r while \r enhancing \r preparedness;\n\n- Phase \r 3: Refugee \r repatriation \r and \r initial \r reintegration \r operations;\n\n- Phase \r 4: Consolidation \r of \r reintegration \r operations; \r and\n\n- Phase \r 5: Reintegration \r operations \r are \r scaled \r down \r and \r phased \r out.\n\n**8.** **OBJECTIVES**\n\nGeneral \r operational \r objectives \r are:\n\n1. Refugees \r are \r empowered \r to \r make \r an \r informed \r choice \r on \r whether \r to \r return, \r and\n\nif \r so, \r when \r and \r to \r where, \r including \r through \r the \r provision \r of \r accurate \r and \r up \r to\ndate \r information \r on \r the \r situation \r in \r areas \r of \r potential \r return.\n2. Refugees \r and \r IDPs \r have \r the \r opportunity \r to \r determine \r which \r solution(s) \r are\n\nmost \r appropriate \r for \r them, \r and \r to \r participate \r fully \r in \r the \r assessment, \r design \r and\ndelivery \r of \r durable \r solutions \r programmes, \r using \r an \r age, \r gender \r and \r diversity\nmainstreaming \r approach.\n3. Legal \r frameworks \r are \r in \r place \r that \r guarantee \r the \r rights \r of \r IDPs \r and \r returning", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "refugees, \r in \r particular \r in \r relation \r to \r civil \r documentation, \r land \r tenure \r and \r the\nneed \r for \r amnesties.\n4. The \r physical \r safety \r of \r IDPs \r and \r returnees \r is \r ensured, \r in \r particular \r through \r the\n\nimplementation \r of \r a \r humanitarian \r landmine \r action \r strategy, \r and \r effective\nsystems \r for \r prevention \r and \r response \r to \r sexual \r and \r gender-­‐based \r violence.\n\n10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\n5. Returning \r refugees \r and \r IDPs \r and \r receiving/hosting \r communities \r have\n\nopportunities \r to \r (re-­‐)establish \r meaningful \r and \r productive \r lives, \r through \r access\nto \r livelihoods \r opportunities.\n6. IDPs \r and \r returned \r refugee, \r together \r with \r the \r communities \r receiving \r or \r hosting\n\nthem \r enjoy \r key \r socio-­‐economic \r rights, \r including \r access \r to \r shelter, \r education \r and\nhealth \r and \r other \r services \r and \r infrastructure.\n\n**9.** **KEY \r ASSUMPTIONS \r AND \r RISKS**\n\nWith \r the \r understanding \r that \r a \r more \r in-­‐depth \r analysis \r is \r needed, \r the \r following \r is \r a \r list \r of\nkey \r assumptions \r and \r risks \r that \r need \r to \r be \r taken \r into \r consideration \r prior \r to \r planning\ndurable \r solutions \r programming \r in \r the \r South-­‐East:\n\n- The \r peace \r process \r remains \r one \r of \r the \r Government’s \r main \r priorities \r including\nadequately \r addressing \r the \r current \r situations \r in \r Kachin \r and \r Rakhine. \r Much \r will\nalso \r depend \r on \r the \r agreements \r that \r are \r reached \r with \r the \r different \r armed \r groups\nand \r the \r level \r of \r political, \r economic \r and \r cultural \r autonomy \r they \r could \r retain. \r Any\non-­‐going \r armed \r clashes \r and/or \r protection \r incidents \r could \r derail \r progress \r made\nso \r far.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- The \r Government \r will \r abide \r by \r international \r standards \r for \r return \r namely, \r that\npeople \r have \r a \r right \r to \r return \r voluntarily \r to \r places \r of \r choice, \r based \r on \r their\nindividual \r and \r freely-­‐expressed \r wish \r to \r return, \r and \r that \r return \r and\nreintegration \r is \r carried \r out \r in \r conditions \r of \r physical, \r legal \r and \r material \r safety\nand \r dignity.\n\n- The \r Government \r and \r non-­‐state \r armed \r groups \r engage \r in \r addressing \r the \r problem\nof \r landmines \r as \r an \r early \r priority. \r Landmines \r are \r a \r particular \r threat \r in \r the \r South-­‐\nEast \r and \r a \r serious \r impediment \r to \r any \r possible \r return.\n\n- Land \r reform, \r including \r restoration \r of \r land \r rights \r and \r compensation, \r remains \r at\nthe \r centre \r of \r the \r country’s \r political \r debate \r and \r reform \r agenda. \r The \r current \r laws\nallow \r expropriation \r of \r land \r not \r utilised \r for \r a \r specific \r period \r of \r time. \r In \r addition,\nabandonment, \r widespread \r destruction \r and \r “land \r grabbing” \r may \r mean \r that \r some\nvillages \r of \r origin \r may \r not \r exist \r anymore, \r or \r may \r not \r be \r accessible.\n\n- Humanitarian \r and \r development \r actors \r are \r granted \r full \r access \r to \r return \r areas,\nparticularly \r allowing \r the \r opening \r of \r offices \r and \r regular \r monitoring \r capacity.\nDirect \r access \r to \r key \r areas \r of \r origin \r is \r still \r limited \r for \r many \r humanitarian \r and\ndevelopment \r actors \r in \r particular \r in \r conflict-­‐affected \r areas.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Refugees \r and \r IDPs \r are \r provided \r with \r accurate \r and \r up-­‐to-­‐date \r information \r that \r is\nobjectively \r presented \r and \r consistent \r with \r humanitarian \r protection \r and \r human\nrights \r principles \r and \r a \r wide \r range \r of \r representatives \r are \r allowed \r to \r visit \r places\nof \r origin \r or \r intended \r return.\n\n- All \r stakeholders \r at \r all \r level \r have \r a \r clear \r understanding \r and \r commitment \r to \r the\nprinciples \r and \r standards \r underpinning \r durable \r solutions.\n\n- The \r Government \r includes \r local \r integration/reintegration \r needs \r of \r IDPs \r and\nreturning \r refugees \r in \r its \r long \r term \r development \r plans.\n\n- Sufficient \r resources \r are \r available \r to \r increase \r UNHCR \r and \r partners’ \r operational\ncapacity \r in \r the \r immediate \r to \r longer \r term. \r Investments \r are \r made \r by \r development\nactors \r in \r long-­‐term \r reconstruction \r and \r development \r projects. \r Many \r places \r of\norigin \r of \r refugees \r are \r isolated \r and \r have \r received \r little \r investment \r in\ninfrastructures \r or \r in \r the \r creation \r of \r livelihood \r opportunities \r that \r can \r support \r the\nsustainability \r of \r the \r return \r process.\n\n11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\n- Assistance \r is \r co-­‐ordinated \r and \r complies \r with \r principles \r of \r neutrality \r and\nimpartiality \r as \r well \r as \r humanitarian \r standards. \r  \r Donor \r support \r also \r converges\nwith \r co-­‐ordination \r efforts.\n\n- A \r valid \r Letter \r of \r Understanding \r (LOU) \r between \r UNHCR \r and \r appropriate \r line\nministries \r will \r remain \r in \r effect \r for \r the \r duration \r of \r UNHCR’s \r activities \r and \r could\nbe \r amended \r as \r necessary \r should \r an \r organized \r voluntary \r repatriation \r operation\nbe \r launched.\n\n**10. \r PROPOSED \r ACTIVITIES**\n\nRecognizing \r and \r emphasizing \r the \r need \r for \r all \r durable \r solutions \r decisions \r to \r be \r designed\nand \r implemented \r with \r affected \r communities, \r and \r in \r consultation \r with \r relevant\nauthorities \r and \r non-­‐state \r actors, \r UNHCR \r will \r ensure \r that \r displaced \r populations \r as \r well\nas \r communities \r of \r origin \r and \r return \r are \r equipped \r to \r participate \r fully \r in \r the \r assessment,\ndesign \r and \r implementation \r of \r all \r interventions \r in \r support \r of \r durable \r solutions, \r using \r an\nage, \r gender \r and \r diversity \r mainstreaming \r approach.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Activities \r – \r whether \r delivered \r by \r UNHCR \r or \r partners-­‐ \r should \r be \r conflict-­‐sensitive \r and\naddress \r key \r issues \r of \r concern \r of \r both \r returning \r refugees \r and \r IDPs \r and \r their\ncommunities. \r A \r community-­‐based \r programme \r that \r builds \r on \r existing \r systems \r and\nstructures \r and \r supports \r local \r development \r plans \r should \r be \r planned \r and \r implemented\nbased \r upon \r comprehensive \r needs \r assessments \r and \r consultations \r with \r the \r populations\nconcerned \r as \r well \r as \r government \r and \r non-­‐government \r actors.\n\nActivities \r and \r project \r delivery \r should \r be \r flexible \r and \r context-­‐specific \r so \r as \r to\naccommodate \r complexities \r and \r respond \r to \r any \r changes \r that \r may \r occur \r throughout \r the\nprocess. \r Activities \r range \r from \r food \r assistance \r to \r essential \r health \r care \r facilities \r and\nservices, \r primary \r and \r secondary \r education \r support, \r and \r community-­‐level \r infrastructure\nrehabilitation \r or \r construction \r and \r livelihoods. \r Livelihood \r opportunities \r will \r be \r a \r key\ncomponent \r in \r ensuring \r the \r sustainability \r of \r the \r reintegration \r process \r and \r cash \r and\nvoucher \r modalities \r to \r boost \r the \r local \r economy \r should \r be \r actively \r considered \r where\nthese \r are \r appropriate \r and \r effective \r ways \r of \r meeting \r identified \r needs.\n\nUNHCR \r will \r seek \r innovative \r solutions, \r in \r particular \r for \r self-­‐reliance, \r education, \r shelter,\nand \r domestic \r energy \r solutions. \r Poverty \r graduation \r models \r will \r be \r explored, \r combining\nrelief \r assistance \r with \r capacity \r building \r measures, \r to \r enable \r the \r poorest \r individuals \r and\nhouseholds \r to \r move \r out \r of \r poverty.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Considering \r that \r decades \r of \r conflict, \r displacement \r and \r poverty \r have \r weakened\ntraditional \r community \r support \r and \r leadership \r structures \r will \r be \r important \r that \r the\ncapacities \r of \r communities, \r as \r well \r as \r local \r partners \r such \r as \r local \r NGOs, \r CBOs \r and\nborder-­‐based \r groups \r working \r out \r of \r Thailand, \r are \r effectively \r harnessed \r in \r support \r of\ndurable \r solutions. \r  \r In \r particular \r border-­‐based \r organisations \r represent \r critical \r assets\nthat \r are \r complementary \r to \r the \r traditional \r agencies. \r The \r networks \r and \r working\nrelationships \r that \r Thai-­‐based \r organisations, \r which \r have \r been \r working \r with \r the \r refugee\nand \r IDP \r communities \r for \r about \r 30 \r years, \r have \r developed \r with \r displaced \r communities\nwill \r be \r invaluable \r in \r building \r trust \r and \r providing \r services.\n\nThe \r scope \r of \r activities \r required \r is \r likely \r to \r be \r significant \r and \r will \r require \r prioritized\nattention \r and \r significant \r donor \r support. \r Early \r support \r to \r kick-­‐start \r the \r reintegration\nprocess \r that \r will \r address \r immediate \r needs \r (initial \r phases) \r should \r transition \r to \r medium\nand \r longer \r term \r development \r projects \r (consolidation \r phase) \r to \r ensure \r the \r achievement\nof \r sustainable \r reintegration \r and \r eventually \r lead \r to \r a \r measurable \r UNHCR \r disengagement.\n\n12", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\nA \r set \r of \r standards \r and \r qualitative \r and \r quantitative \r indicators \r should \r be \r developed \r to\nmeasure \r progress \r toward \r the \r achievement \r of \r the \r reintegration \r objectives.\n\nThis \r section \r proposes \r various \r activities \r that \r should \r be \r considered \r to \r address \r immediate\nas \r well \r as \r longer-­‐term \r needs. \r UNHCR \r could \r take \r the \r lead \r on \r these \r activities \r or \r provide \r a\nco-­‐ordination \r or \r advocacy \r role, \r depending \r on \r the \r specific \r activity.\n\n**10.1.** **Phase \r 1 \r (current \r phase): \r Strengthening \r of \r IDP \r operations \r and**\n\n**preparation \r for \r refugee \r return \r while \r providing \r integrated**\n\n**assistance \r to \r spontaneous \r returnees**\n\n- Build \r on \r previously \r established \r co-­‐ordination \r mechanisms \r -­‐ \r such \r as \r monthly\ninter-­‐agency \r meetings \r at \r state \r level, \r the \r South-­‐East \r Consultation \r and \r cross-­‐\nborder \r meetings \r -­‐ \r to \r strengthen \r co-­‐ordination \r on \r durable \r solutions. \r Co-­‐\nordination \r systems \r shall \r facilitate \r respect \r for \r key \r protection \r and \r assistance\nstandards \r and \r enable \r the \r development \r of \r a \r strategic \r vision \r for \r durable \r solutions.\n\n- Strengthen \r information \r management \r and \r analysis \r capacities, \r including \r the\ndevelopment \r of \r tools \r to \r capture \r and \r analyse \r patterns \r of \r spontaneous \r returns\nand \r carry \r out \r protection \r assessments \r and \r monitoring. \r  \r  \r  \r Information \r products\nwill \r also \r aim \r at \r supporting \r informed \r decision-­‐making \r by \r refugees \r in \r Thailand.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Enhance \r awareness, \r developing \r trust \r among \r stakeholders \r and \r building\ncapacities \r of \r central \r and \r local \r authorities, \r implementing \r and \r operational\npartners, \r civil \r society \r and \r local \r communities \r to \r support \r and \r deliver \r protection\nand \r assistance, \r including \r investing \r in \r support \r to \r community \r self-­‐management\nstructures \r and \r community \r mobilisation \r through \r training, \r awareness \r campaigns,\nand \r support \r to \r community \r committees.\n\n- Follow \r up \r and \r advocate \r with \r Immigration \r and \r National \r Registration \r Department\non \r expedited \r procedures \r for \r the \r issuance \r of \r CSCs \r for \r IDPs \r and \r returnees,\nincluding \r measures \r to \r prevent \r statelessness \r which \r properly \r address \r the \r specific\nsituation \r of \r refugees. \r Support \r authorities \r to \r ensure \r that \r internally \r displaced\npersons \r and \r returning \r refugees \r as \r well \r as \r former \r child \r soldiers \r do \r not \r miss \r the\nopportunity \r of \r obtaining \r CSCs.\n\n- Initiate \r negotiations \r for \r the \r removal \r of \r administrative \r and \r legal \r impediments\nwhich \r might \r inhibit \r sustainable \r return, \r such \r as \r taxation-­‐related \r issues.\n\n- Support \r a \r comprehensive \r evaluation \r of \r property \r laws \r and \r provide \r concrete\nproposals \r for \r revisions \r that \r will \r ensure \r the \r restoration \r or \r repossession \r of \r land\nrights \r by \r returnees.\n\n- Provide \r access \r to \r legal \r services \r through \r advocacy \r interventions \r and \r training \r in\nserving \r the \r legal \r needs \r of \r returnees \r and \r offering \r appropriate \r legal \r assistance.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Provide \r assistance \r to \r mine \r victims \r and \r support \r Mine \r Risk \r Education \r (MRE)\nwhile \r following \r up \r and \r advocate \r for \r the \r implementation \r of \r humanitarian \r mine\naction \r including \r survey, \r mapping, \r marking \r and \r clearance \r of \r contaminated \r areas,\nespecially \r in \r areas \r of \r potential \r and \r current \r return. \r Develop \r key \r advocacy\nmessages \r and \r co-­‐ordination \r mechanisms \r to \r ensure \r that \r displaced \r communities\nare \r aware \r of \r any \r landmine \r contamination \r in \r places \r of \r origin \r and \r transit.\n\n- Advocate \r for \r the \r recognition \r of \r education \r certificates \r received \r in \r country \r of\nasylum \r and \r the \r development \r of \r bi-­‐lingual \r educational \r programmes.\n\n- Conducting \r awareness-­‐raising \r on \r SGBV \r prevention \r and \r response, \r assisting \r SGBV\nvictims \r through \r established \r referral \r mechanisms \r for \r treatment \r and \r psycho-­‐\nsocial \r support, \r and \r supporting \r community-­‐based \r protection \r solutions.\n\n- Establish \r community \r based \r mechanisms \r for \r identifying \r EVIs \r and \r provide\ntailored \r assistance.\n\n13", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\n- Contribute \r to \r the \r CTFMR \r efforts \r in \r documenting, \r verifying, \r reporting \r and\nresponding \r to \r grave \r child \r rights \r violations, \r including \r recruiting \r and \r using\nchildren \r in \r armed \r conflict.\n\n- Conduct \r multi-­‐sector \r baseline \r assessments \r and \r identify \r proxy \r indicators \r for \r self-­‐\nreliance; \r establish \r monitoring \r and \r evaluation \r system \r in \r order \r to \r track \r progress\nmade \r on \r key \r indicators \r over \r the \r framework \r period.\n\n- Support \r and \r participate \r to \r multi-­‐sector \r need \r assessments \r and \r planning \r process,\nin \r particular \r in \r areas \r of \r current \r or \r potential \r return.\n\n- Provide \r an \r initial \r light \r rapid \r response \r to \r spontaneous \r IDP \r and \r refugee\nreturnees, \r identified \r through \r returnee \r monitoring, \r by \r providing \r NFIs \r and\nrelevant \r protection \r interventions \r on \r a \r needs \r basis, \r as \r well \r as \r community-­‐based\nassistance \r to \r receiving \r communities, \r with \r a \r view \r to \r building \r confidence \r in \r the\nreturn \r process.\n\n- Advocate \r with \r Government, \r donors \r and \r private \r sector \r in \r regard \r to \r rehabilitation\nof \r main \r roads \r to \r key \r potential \r returnee \r areas \r and \r establishment \r of \r facilities \r that\ncould \r eventually \r support \r an \r organised \r return.\n\n**10.2.** **Phase \r 2: \r Expansion \r of \r integrated \r support \r to \r spontaneous \r returnees**\n\n**and \r their \r communities**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Preparedness \r activities \r will \r continue \r and \r will \r be \r strengthened, \r including \r assistance \r to\nspontaneous \r return. \r As \r patterns \r of \r spontaneous \r returns \r to \r specific \r locations \r emerge, \r in\nparticular \r with \r the \r return \r of \r entire \r households, \r UNHCR \r will \r enhance \r its \r assistance \r to\nreturnees \r and \r their \r communities \r though \r an \r integrated \r multi-­‐sector \r package \r of\ninterventions \r with \r the \r aim \r of \r building \r confidence \r without \r creating \r an \r artificial \r ‘pull’\nfactor. \r UNHCR \r will \r aim \r to \r play \r a \r leading \r role \r in \r bringing \r partners \r together \r to \r address\nthe \r identified \r needs, \r in \r the \r area \r of \r legal, \r physical \r and \r material \r safety, \r collectively \r and \r in\nan \r integrated \r manner.\n\n**10.3.** **Phase \r 3: \r Refugee \r repatriation \r and \r initial \r reintegration \r operations**\n\nPrevious \r phases \r activities \r will \r be \r further \r strengthened \r and \r operationalized \r with \r a\nfocus \r on \r providing \r immediate \r relief \r assistance \r to \r newly \r returned \r refugees \r while\nenhancing \r early \r recovery \r and \r development \r partnerships. \r Activities \r specific \r to \r this\nphase \r will \r include:\n\n- Provision \r of \r immediate \r relief \r assistance \r such \r as \r food, \r non-­‐food \r items \r and\nshelter \r in \r accordance \r with \r international \r standards \r and \r any \r nationally \r agreed\nhumanitarian \r standards. \r Particular \r support \r will \r be \r provided \r to \r the \r most\nvulnerable \r previously \r identified \r through \r a \r referral \r mechanism \r with \r the \r country\nof \r asylum.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Co-­‐existence \r activities \r in \r areas \r of \r return, \r including \r through \r working \r with \r CBOs\nfor \r the \r identification \r of \r key \r points \r of \r current/potential \r tensions, \r designing \r of\njoint \r reintegration \r projects, \r and \r conducting \r cultural \r activities \r that \r foster\ntraditional \r values \r of \r consideration \r and \r peace.\n\n- Constructing/rehabilitating \r basic \r facilities, \r such \r as \r health, \r safe \r drinking \r water\nsystems \r and \r sanitation \r as \r well \r as \r improving/expanding \r services.\n\n14", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\n- Contribute \r towards \r a \r comprehensive \r livelihood \r support \r programme. \r As \r per\nlocal \r needs \r and \r opportunities, \r this \r may \r include \r skills \r and \r employment\npromotion; \r support \r to \r productive \r assets \r and \r community \r infrastructure;\nenterprise \r development; \r microfinance \r promotion.\n\n- Ensure \r the \r provision \r of \r mine \r risk \r education \r before \r return \r and \r upon \r arrival,\nensure \r the \r prioritization \r of \r mine \r marking \r of \r contaminated \r areas \r in \r areas \r of\nreturn \r and \r advocate \r and \r support, \r if \r required, \r punctual \r clearance \r of \r critical \r areas\nsuch \r as \r access \r roads \r to \r basic \r services \r (schools, \r hospitals, \r etc).\n\n- Capitalize \r on \r the \r experience \r and \r expertise \r developed \r by \r committees \r in \r the\ncamps, \r such \r as \r the \r GBV \r committees, \r and \r re-­‐mobilize \r their \r members \r in \r places \r of\nreturn \r to \r strengthen \r prevention \r and \r response \r networks.\n\n**10.4.** **Phase \r 4: \r Consolidation \r of \r reintegration \r operations**\n\nUNHCR \r will \r facilitate \r and \r support \r the \r reintegration \r process \r increasingly \r involving\nspecialized development organisations implementing long-­‐term area-­‐based\nprogrammes \r in \r support \r to \r the \r Government \r and \r in \r the \r framework \r of \r national\ndevelopment \r plans, \r ensuring \r appropriate \r delegation \r and \r follow \r up \r on \r commitments.\n\n**10.5.** **Phase \r 5: \r Reintegration \r operations \r are \r scaled \r down \r and \r phased \r out**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNHCR \r will \r gradually \r phase \r down \r its \r direct \r assistance \r reviewing, \r together \r with\nstakeholders \r progress \r achieved \r on \r repatriation \r and \r reintegration, \r including \r analysing\nprospects \r for \r durable \r solutions \r and \r efforts \r to \r mainstream \r reintegration \r needs \r in\nnational \r policies \r and \r programmes. \r As \r key \r benchmarks \r are \r met, \r UNHCR \r will \r review \r and\nrefine \r its \r disengagement \r plan, \r including \r the \r timing \r and \r degree \r of \r phasing \r out \r from\nspecific \r projects \r and \r activities. \r A \r communication \r strategy \r will \r be \r designed \r to \r inform\nstakeholders \r of \r UNHCR \r progressive \r disengagement. \r The \r reintegration \r operation \r will \r be\nregularly \r reviewed \r and \r evaluated \r and \r lessons \r gathered \r for \r future \r application \r in \r other\noperations.\n\nUNHCR \r Myanmar\n15 \r June \r 2013\n\n15", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNHCR ETHIOPIA **PROTECTION BRIEF |** AUGUST 2024\n\n# **Gender-Based** **Violence in the** **Sudan Refugee** **Response – Ethiopia**\n\nThis August 2024 protection brief\nanalyzes gender-based violence (GBV) as\nfaced by refugees fleeing the Sudan\nsituation into Ethiopia. The brief is\nintended to be actionable and to take\nstock of the effectiveness of the current\nGBV interventions that include a\ncoordinated approach prioritizing timely\naccess to appropriate psychosocial,\nmedical, safety and legal services. It will\nalso provide recommendations for areas\nwhere immediate improvement is\nneeded. Relevant UNHCR guidance on\nbest practices is used as a benchmark.\nInterventions in (1) coordination and\nresponse (2) prevention (3) risk mitigation\nare considered.\n\nThe situation of both new arrivals and\npersons who arrived before April 2023 is\n\nconsidered. The brief covers the\nresponse in the Amhara and Benishangul\nGumuz regions, and also looks at some\nactivities in Gambella and Addis Ababa.\n\nFurthermore, the brief provides\ninformation on the funding constrains that\nrequire immediate attention from donors\nand partners. Based on the gaps\nidentified below, the overall\nrecommendation is that the **GBV**\n\n**response interventions in the Amhara**\n\n**and Benishangul Gumuz regions must**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**be immediately expanded** . The current\nresponse capacity which survivors can\naccess is not sufficient. While UNHCR\nhas an important role to play, other actors\nalso need to strengthen their\ninterventions.\n\n1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**PROTECTION BRIEF |** Gender-Based Violence in the Sudan Refugee Response – Ethiopia | AUGUST 2024\n\n## **I. Understanding GBV risks** **among the Sudanese** **refugees in Ethiopia**\n\nAs related to the new Sudan situation,\nrecorded incidents of GBV have included\nconflict related sexual violence, Intimate\nPartner Violence (IPV), child or forced\nmarriage, and Female Genital Mutilation\n(FGM). Additionally, in older refugee camps,\nreported incidents of GBV included rape and\nsexual assault, often linked to violence within\nthe camps or in relation to the collection of\nfirewood and water outside the camps,\nfollowed by IPV, denial of resources or\nrestriction of access to services and\ninformation, as well as psychological and\nemotional abuse.\n\nCultural norms from Sudan, including\nentrenched gender inequalities and harmful\npractices, persist and are exercised\ndiscreetly, significantly contributing to a\nculture of silence. Deep-seated patriarchal\nnorms and traditional practices also\nperpetuate GBV and often discourage\nsurvivors from reporting their experiences or\nseeking help. These norms frequently lead to\nweak or poorly enforced legal protections,\nfurther contributing to the persistence of\nGBV.\n\n**Benishangul Gumuz Region**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "There are 74,210 Sudanese refugees living in\nthe Benishangul Gumuz region out of which\n22,649 arrived during the recent conflict in\nSudan. Long-staying refugees are located at\nthe Sherkole (established in 1996), Bambasi\n(established in 2012), and Tsore (established\nin 2015) camps. Most new arrivals cross from\nSudan into Ethiopia at Kurmuk. A refugee\nsettlement has been established in Ura for\nthe delivery of protection and assistance.\n\ni. Kurmuk Transit Centre and Ura\nRefugee Settlement in\nBenishangul Gumuz (Emergency)\n\nAs per statistical data from Kurmuk Transit\nCentre and Ura Refugee Settlement, 39% of\nthe reported incidents involved rape and\nsexual assault, 47% were physical abuse, and\n14% were psychological and emotional\nabuse. Among the survivors, 89% are women,\nand 11% are girls.", "output": {"entities": {"named_data": [], "descriptive_data": ["statistical data from Kurmuk Transit\nCentre"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Regarding the location of the incidents, 39%\noccurred in Sudan or during the journey from\nSudan to Ethiopia, 61% of the incidents\noccurred within the settlement or transit\ncenter, with the rest happening in the\nsurrounding areas or outside the settlement\nor transit centre. Among the GBV cases, 46%\nwere related to intimate partner violence\n(IPV), and 3% were associated with firewood\nor water collection. Others were reported as\nconflict-related sexual violence in Sudan and\nharmful traditional practices such as FGM and\nchild marriage.\n\nii. Tsore, Sherkole, and Bambasi\nRefugee Settlements in Assosa\n\nIn Tsore, Sherkole, and Bambasi Settlements,\nout of the total reported GBV incidents, 33%\ninvolved rape and sexual assault, 44% were\nphysical abuse, 21% were psychological and\nemotional abuse, and 2% were related to\ndenial of resources. Among the survivors,\n86% are women and 14% are girls.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Regarding the location of the incidents, 21%\noccurred in Sudan or during the journey to\nEthiopia, while the remaining incidents took\nplace within Ethiopia. Specifically, 79% of the\nincidents happened within the camps, with\nthe rest occurring in the surrounding areas or\noutside the camps. Among the reported GBV\ncases, 40% were related to intimate partner\nviolence (IPV), and 9% were associated with\nactivities such as firewood or water\ncollection.\n\n**Amhara Region**\n\nThere are 12,657 registered Sudanese\nrefugees living in the Amhara region. All are\nnew arrivals arriving after April 2023. Most\n\n2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**PROTECTION BRIEF |** Gender-Based Violence in the Sudan Refugee Response – Ethiopia | AUGUST 2024\n\ncross from Sudan into Ethiopia at Metema.\nAfter a series of security incidents in the\nKumer and Awlala Settlements originally\nestablished to receive the refugees have\nbeen decommissioned and a new settlement\nhas been established in Aftit, where refugees\nhave access to protection and assistance\nunder safer conditions.\n\nWith regards to reported incidents in\nlocations within the Amhara region, 29% of\nthe incidents reported involved rape and\nsexual assault, 21% physical abuse, 1% forced\nmarriage, 29 % denial of resources, services,\nand opportunities, and 20%\npsychosocial/emotional abuse. 43.75% of\ncases were reported to be intimate partner\nviolence (IPV) while 50% were related to\nfirewood and water collection. Others were\nreported as conflict-related sexual violence\nand harmful traditional practices. Among the\nsurvivors, 60 % are women, 33% are girls,\nwhile 2% and 5% are men and boys\nrespectively.\n\nFurthermore, 15.63 % of the reported\nincidents happened in Sudan or on the way\nfrom Sudan to Ethiopia while others\nhappened in Ethiopia. While 84.37% of the\nincidents happened within the settlements or\nthe transit center, others happened in the\nsurroundings or outside the settlements.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "## **ii. GBV Risk Mitigation [1]** **Mainstreaming for the** **Sudanese refugees in** **Ethiopia**\n\nIn Ethiopia, effort has been undertaken to\nmainstream GBV risk mitigation across all\nlocations. UNHCR continues to build the\ncapacity of different stakeholders including\n\n---\n[1] Risk mitigation refers to a process and specific", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**PROTECTION BRIEF |** Gender-Based Violence in the Sudan Refugee Response – Ethiopia | AUGUST 2024\n\nunaccompanied and separated children\n(UASC), and young women and girls leading\nthem to opt for working in dangerous areas of\ngold mining, further exposing them to sexual\nexploitation and abuse etc.\n\nAdditionally, the food-aid pause in Ethiopia,\nhad severe implications for refugees,\nexacerbating the risks of GBV. With dwindling\nresources, many women and girls faced\nincreased vulnerability as they struggled to\nsecure necessities, leading to heightened\ninstances of exploitation and abuse. The\nscarcity of food not only intensified economic\npressures but also contributed to a\nbreakdown in protective community\nstructures, further exposing refugees to GBV.\nurgent need for comprehensive support\nsystems to safeguard vulnerable populations\nin crisis settings.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "This year, services were provided to refugees\ncritically in need and at risks of GBV. In\nKurmuk Transit Center and Ura Settlement,\ncash assistance was provided to 301 women\nand girls at risk in Ura and 100 women and\ngirls at risk living in the host community, while\ndignity kits and sanitary items were provided\nto 200 women and girls at risk. In Tsore,\nSherkole and Bambasi settlements,\ndistribution of dignity kits for 200 women and\ngirls of reproductive age was conducted to\nreduce GBV risks and avoid harmful coping\nmechanisms. Material support was provided\nfor the 200 asylum-seekers and 350 refugee\nsurvivors of GBV or at heightened risk,\nmaking a cumulative total of 550 women and\ngirls. In Metema and Kumer, dignity kits were\nprovided to 3,995 women and girls, 691\nreceived other material assistance. 130\nwomen and girls received sanitary napkins\nupon arrival in Aftit. 540 solar lanterns were\nalso distributed.\n\n## **iii. GBV Prevention** **Activities underway for** **the Sudanese refugees in** **Ethiopia**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "To effectively prevent GBV, this year UNHCR\nand its partners have focused on addressing\nthe root causes, such as entrenched gender\ninequality, systemic discrimination, and\nunequal power dynamics between women\nand men. By tackling these underlying issues,\nUNHCR strives to create a safer and more\nequitable environment where GBV is less\nlikely to occur.\n\nIn Assosa, UNHCR and partners ensured the\nprevention against GBV through mass\nawareness-raising campaigns, door-to-door\nvisits, community consultations, and\nawareness-raising sessions in Women and\nGirls’ Safe Spaces (WGSS) focusing on\ndifferent forms of GBV including Sexual\nExploitation and Abuse and harmful\ntraditional practices, such as FGM and early\nmarriages. The campaigns were mainly\ncarried out in public spaces such as\nfood/non-food items distribution areas and\nwaterpoints. The campaigns reached a total\nof 7,842 individuals (4,335 women, 3,507\nmen), and the activities in WGSS reached a\ntotal of 6316 (3,652 women, 2652 girls), while\nhome-to-home visits reached a total of 1,732\nindividuals (948 women, 784 men).\nFurthermore, International Women’s Day was\ncelebrated with the dissemination of various\nmessages on gender equality and human\nrights.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In Tsore, Sherkole and Bambasi Settlements,\nUNHCR and partners continued to provide\nGBV awareness-raising sessions. During this\nperiod 300 girls shine trainees, and 300\nfemale caregivers/parents completed the life\nskill programme and graduated during the\nWorld Refugee Day celebration. Around\n11,263 refugees (6,107 women and 5,156 men)\nwere engaged in GBV prevention sessions.\nDuring the International Women’s Day\n\n4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Celebration, an estimated 3,400 individuals\n(1500 women and 1900 men) were reached\nthrough awareness-raising sessions.\n\nIn Kumer, Women and Girls’ Safe Spaces\n(WGSS) were established in 2023. With\nrefugees now being moved to the new\nsettlement in Aftit, a new WGSS is being\nestablished there. A total of 383 women and\ngirls were reached with empowerment\nprogrammes such as life-skills training and\nawareness-raising around women’s rights.\n\nThe GBV partners work together with\ncommunity incentive workers who lead the\nprevention and awareness activities at Aftit.\nAwareness-raising interventions and\ninformation-sharing on GBV response\nservices reached 6,185 refugees and 1,258\nmembers of the host community.\n\n## **iv. GBV Response [2]** **interventions underway** **for the Sudanese** **refugees in Ethiopia**\n\nIn Ethiopia, there is a significant gap in the\nGBV response capacity. To address this issue\neffectively, it is crucial to allocate additional\nresources to GBV programmes and enhance\nthe responsiveness of service providers.\n\nDue to limited funding in 2024, case\nmanagement services for GBV survivors in\nKurmuk were provided by UNHCR partner\nIRC only until 31 May. All identified survivors\nduring the given period received basic/nonspecialized psycho-social counseling and\nmaterial support, including hygiene materials,\nclothes, and other core relief items. 50% of\n\n---\n[2] GBV response refers to immediate interventions that", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**PROTECTION BRIEF |** Gender-Based Violence in the Sudan Refugee Response – Ethiopia | AUGUST 2024\n\nthey had improved their sleep patterns and\nhad a reduction in intrusive or suicide\nideation. Suicide attempts were also\nmanaged.\n\nAs part of the capacity building of partners\ninvolved in the response, GBV trainings\nundertaken in 2024 include (1) case\nmanagement and referral pathways, (2)\npromoting safe disclosure and access to\nservices, and (3) Mental Health and\nPsychosocial Support (MHPSS) involving\ndifferent stakeholders. Both community\nvolunteers, government officials, UNHCR and\npartners staff have participated in these\ntrainings.\n\nRegarding coordination, there are monthly\nmeetings of the protection working group led\nby UNHCR and RRS, supported by GBV and\nCP partners and service providers such as\nInnovative Humanitarian Solutions (HIS),\nRehabilitation and Development\nOrganization (RADO), Development and\nInter-Church Aid Commission (DICAC),\nInternational Rescue Committee (IRC) and\nPlan International Ethiopia (PIE) in\nBenishangul Gumuz and Amhara regions.\nThe working group allows for regular\ninformation sharing, aligning strategies, and\npooling resources to address gaps and avoid\nduplication, and thereby enhance the overall\nGBV response.\n\n6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**PROTECTION BRIEF |** Gender-Based Violence in the Sudan Refugee Response – Ethiopia | AUGUST 2024\n\n## **v. Critical Gaps and** **Recommendations**\n\nDespite ongoing efforts, several critical gaps\nin GBV services require urgent attention to\nensure effective support and protection for\nsurvivors. These gaps include:\n\n1. **Lack of GBV Programming in Ura**\n\n**Settlement and Kurmuk Transit**\n\n**Centre:** As of mid-year 2024, both\nKurmuk Transit Center and Ura\nRefugee Site are facing a critical\nfunding gap with no partner operating\nfor GBV prevention and response.\n\n2. **Lack of Safe Houses:** There are no\nsafe houses available for GBV\nsurvivors, which limits immediate\nprotection and safe accommodation\noptions for those escaping violence.\n\n3. **Lack of Specialized Support for Child**\n\n**Survivors of GBV:** There is a shortage\nof tailored support services for child\nsurvivors of GBV, impacting their\nability to receive appropriate care and\nrecovery services.\n\n4. **Lack of Access to Legal Assistance**\n\n**Services,** **Especially** **Due** **to**\n\n**Language Barriers:** Survivors face\ndifficulties accessing legal assistance\ndue to language barriers, hindering\ntheir ability to seek justice and\nprotection.\n\n5. **Staff Turnover and Limited Capacity**\n\n**of Current Staff:** High staff turnover\nand insufficient training limit the ability\nof current personnel to provide\neffective GBV response services.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "6. **Women and Girls’ Safe Space**\n\n**(WGSS)** **Established** **but** **Not**\n\n**Operationalized:** A WGSS was set up\nby the partner MTI in Ura Settlement,\nbut it has not become operational due\nto a lack of funding, preventing it from\nproviding necessary services.\n\n7. **Limited** **Engagement** **with** **the**\n\n**Community Through Dialogues and**\n\n**Male** **Engagement:** There is\ninsufficient community engagement\nand involvement of men in GBV\nprevention efforts, reducing the\neffectiveness of community-based\ninterventions.\n\n8. **Lack of an Information Management**\n\n**System for GBV Case Management:**\nThe absence of a robust information\nmanagement system leads to data\nprotection issues and challenges in\ntracking and managing GBV cases.\n\n**Based on the above, key recommendations**\n\n**to the humanitarian community are:**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**1.** **Risk Mitigation:** Conduct regular\nsafety audits to identify GBV risks and\nintegrate risk mitigation measures\nacross all sectors by applying the\ninsights and recommendations from\nsafety audit exercises, ensuring that\npotential risks are proactively\naddressed and managed in every\naspect of program implementation.\nSpecific areas to review are\nexpanding the camp sizes, the\nnumber of shelters, and WASH\nfacilities to reduce the risks\nassociated with overcrowding,\nimproving the transit center or\nsettlement security by increasing the\nnumber of security personnel,\ncommunity policing and patrolling at\nnight, increasing the number of\nstreetlights, provision of sufficient\nfood, core relief items and dignity kits,\namong others.\n\n**2.** **Safe House:** Establish safe houses in\nGende-Wuha and Ura Woreda to offer\nimmediate shelter and protection for\nGBV survivors.\n\n**3.** **Support Services:** Enhance legal\nassistance and mental health and\npsychosocial support (MHPSS) for\nGBV survivors, utilizing Women and\n\n7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**PROTECTION BRIEF |** Gender-Based Violence in the Sudan Refugee Response – Ethiopia | AUGUST 2024\n\nGirls’ Safe Spaces (WGSS) among\nother resources. Ensure a survivor\ncentered approach for all survivors\nand child-friendly services to child\nsurvivors of GBV.\n4. **Community** **Engagement:** Boost\ncommunity dialogues with refugee\nleaders and engage men and boys to\nchallenge and change harmful\ncultural norms and stereotypes\nrelated to GBV, promote positive\nmasculinity, and foster a culture of\nrespect and equality.\n\n**5.** **Women** **Empowerment:** Provide\ntraining and livelihood programs for\nwomen and girls at risk to promote\nsustainability and self-reliance.\n\n**6.** **Information** **Management:**\nImplement a GBV Information\nManagement System (GBVIMS) to\nimprove data management and\nensure better protection and\nresponse.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**7.** **Training:** Provide comprehensive\ntraining on GBV prevention and\nresponse for aid workers, refugees,\nand local authorities. Develop\nawareness programs to provide\nrefugees with information on\navailable GBV services and reporting\nmechanisms. Train security personnel\nto effectively support and protect\nrefugees.\n8. **MHPSS:** Given the profound and\ndevastating impacts on the lives of\nGBV survivors, it is evident that there\nis a critical need to strengthen MHPSS\nsupport. The trauma survivors endure\ncan be crippling, making it essential to\nenhance and expand the range of\nmental health and psychosocial\nservices available to help them\nrebuild resilience, regain stability, and\nfoster long-term recovery.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**PROTECTION BRIEF |** Gender-Based Violence in the Sudan Refugee Response – Ethiopia | AUGUST 2024\n\n**Annex I – Results of the GBV Safety Audit undertaken in Benishangul Gumuz and Amhara**\n\n**region**\n\n**Benishangul Gumuz Region:**\n\n**i.** **Kurmuk TC and Ura Refugee Site (emergency)**\n\nIn June 2024, UNHCR in collaboration with International Committee for the Development of People\n(CISP) conducted a GBV Safety Audit in Ura to identify GBV risks in the refugee locations. Findings\nof the assessment are:\n\n**a.** **Camp layout and shelter:** Participants noted that Ura camp lacks adequate night lighting,\nwith some refugees using personal generators and batteries. Many women and girls do not\nhave alternative solutions such as flashlights or torches, which compromises their safety while\nstaying in the shelters and while using communal facilities at night. Additionally, while access\nroads and walkways in the camp are sufficiently wide for safe movement, overcrowding is a\nsignificant issue, with multiple families often sharing a single household that further\nexacerbates safety concerns, particularly for women and girls.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**b.** **Water, Sanitation, and Hygiene (WASH):** Water points in Ura refugee camp are centrally\nlocated and accessible, providing sufficient water for the current population. However,\nadditional water points will be needed if the camp's population increases, as current resources\nmay become inadequate. The community has reported that the water quality is not ideal and\nis primarily used for washing rather than drinking. Regarding sanitation, some latrines lack\ngender segregation, with both men and women using the same facilities. Additionally, the\nlatrines have internal and external locks.\n\n**c.** **Camps safety and security:** Ura camp currently lacks formal security personnel. The security\nis managed by the RRS protection team and community leaders, who act as the camp's\nprotection force. Participants in the safety audit noted a lack of visible services for GBV\nsurvivors, with existing centers located in unsafe, inaccessible areas, particularly challenging\nfor women, girls, and individuals with special needs.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**d.** **Energy:** The National Rural Development Programme (NRDP) organization provides firewood\nto the refugee community, but it is insufficient, forcing women and girls to travel long distances\ninto the bush to collect firewood, which poses significant protection risks. Additionally, the lack\nof a market within the camp requires women and girls to venture outside for supplies, exposing\nthem to further safety concerns.\n\nii. **Tsore, Sherkole, and Bambasi camps in Assosa**\n\nIn 2024 Safety audit exercise were conducted in Tsore, Sherkole, and Bambasi Refugee camps.\nKey findings include:\n\n**a.** **Shelter and Infrastructure:** In Sherkole camp, significant overcrowding was noticed in Zone\nC and new sites in Tsore camp with multiple families living in inadequate shelters. Femaleheaded and child-headed households face increased vulnerability due to lack of privacy and\nprotection. Unsafe, narrow roads in Zone F and dark areas of Tsore camp increase risks for\nwomen and girls traveling within the camp.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**b.** **Security and Protection:** In Sherkole, abandoned houses and lack of security lights, limited\npresence of security forces and local police are among the major challenges that contribute\nto unsafe living conditions. Women and children working in gold mining areas face significant\nGBV risks, including exploitation and abuse. In Tsore, absence of formal law enforcement with\n\n9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**PROTECTION BRIEF |** Gender-Based Violence in the Sudan Refugee Response – Ethiopia | AUGUST 2024\n\nreliance on local community police limits effective protection against GBV was noted. Limited\nsafe spaces for children and youth, with the need for more youth centers and safe spaces in\nnewly established sites were highlighted by Children and youth. In Bambasi, limited security\nand legal protection services are available. The detention center in Zone A was reported to\nhave inadequate fencing and sex-segregated spaces, affecting safety and protection.\n\n**c.** **Water, Sanitation, and Hygiene (WASH):** In Tsore camp, limited latrines in several zones\ncreates unsafe conditions while the existing ones lack sex-segregation and proper locks,\nimpacting privacy and safety for women and girls. Water points are inadequately fenced,\nraising safety and cleanliness concerns. Overcrowding at water sources cause tensions\namong the refugees and host communities in and around Sherkole camp. In Tsore, centralized\nwater points are present but may need expansion if the population increases. Insufficient\nmenstrual hygiene materials for adolescent girls are a concern in all the three camps.\n\n**d.** **Energy Supply:** In Bambasi camp, insufficient provision of firewood forces women and girls to\ncollect firewood from surrounding areas, increasing their risk of GBV.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**e.** **Health and Nutrition:** In Sherkole, limited availability of essential medicines and frequent\nclosure of health services affect care for GBV survivors. Support for individuals with disabilities\nis inadequate. In Tsore Health facilities are not fully stocked, and there are issues with\nproviding timely and adequate care, including the lack of emergency contraception and family\nplanning services. In Bambasi, the health center operates 24/7 but lacks separate spaces for\nmale and female patients, emergency contraception, and family planning services. Medication\nshortages and power issues affect service quality.\n\n**f.** **Food/CRI/NFI Distribution Practices:** The distribution points do not consider the needs of\npeople with specific needs. Separate ques are not considered, or priority is given to pregnant\nwomen, people with disabilities and elderly during distributions, impacting their safety and\naccess to resources.\n\n**Amhara Region:**\n\n**i.** **Metema border, Kumer TC (Emergency)**\n\nBelow are the key findings from the assessment conducted in Metema and Kumer safety and needs\nassessment in 2023 while a similar exercise is planned to take place before the end of September\nin Aftit.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**a.** **Shelter and Infrastructure and WASH:** The entry points and transit centers are considered\nunsafe due to overcrowding, poor infrastructure, and environmental hazards like snakes,\nscorpions, and exposure to harsh weather. Participants of the assessment consistently\nreported feeling unsafe due to inadequate security, threats from local communities, and the\nlack of secure places, such as proper lighting and fenced areas. Specific risks highlighted by\nthe refugees were, attacks from local men, harassment, and the presence of dangerous\nwildlife. Shelters are reported to be overcrowded and poorly managed, leading to a lack of\nprivacy and increased risks of GBV. Reports also indicate insufficient or damaged facilities like\ntoilets and shelters that lack proper locks or sex-segregation. Conditions at entry points and\ntransit centers are particularly dire, with issues such as lack of proper food, shelter, and\nelectricity. Relations with the host community vary, with some reports of friendly interactions\nand others noting issues such as harassment and lack of support.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**b.** **GBV Awareness and Services:** There is a significant lack of information about GBV and\navailable support services among refugees. Many participants are unaware of where to report\nGBV incidents or receive help. Some refugees rely on informal methods of addressing GBV,\n\n10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**PROTECTION BRIEF |** Gender-Based Violence in the Sudan Refugee Response – Ethiopia | AUGUST 2024\n\nsuch as talking to family members or community members, rather than accessing formal\nreporting mechanisms or services. Services for GBV survivors are reported as limited or nonexistent in several areas. There is a lack of accessible, gender-sensitive services, and\ninadequate medical and psychological support. There is also a need for more trained female\nstaff and case workers to handle GBV cases and psychological support and counseling\nservices to address mental health issues. The psychological impact of living in unsafe and\novercrowded conditions is significant, with reports of severe stress, anxiety, and depression.\n\n**c.** **Training and Awareness:** There is a need for enhanced training for both aid workers and\nrefugees on GBV issues and available resources. Creating awareness among refugees about\nGBV services and reporting mechanisms is crucial. Training should also extend to local\nauthorities and security personnel to ensure they can effectively support and protect refugees.\n\n11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "# Protection and Solutions Strategy\n\n## _UNHCR Afghanistan 2025 - 2027_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2025-2027\n\n### I. Introduction\n\nWith the end of major hostilities in Afghanistan, after more than 40 years and the consolidation of\ncontrol by the Taliban led _de facto_ authorities (DfA) in August 2021, conflict is no longer the primary\ndriver of displacement. However, 3.25 million Afghans remain displaced because of conflict within\nthe country and over 5.53 million Afghans are registered refugees or in refugee-like situations in\nthe region, hosted mainly in Iran and Pakistan. An estimated 34,840 refugees are living in\nAfghanistan’s Khost and Paktika regions.\n\nSince the takeover of power, the DfA have systematically dismantled human rights in Afghanistan,\nespecially with regards to the rights of women and girls. Decrees restricting the right to work for\nAfghan women employed by NGOs and UN organisations have significantly constrained access to\nvulnerable women and girls and impacted the ability to provide services.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The needs in Afghanistan are vast. Approximately 85 percent of Afghans live on less than one dollar\na day. The humanitarian country team estimates that 23.7 million people - more than half of\nAfghanistan´s population - require humanitarian assistance in 2024. Afghan people experience\ndrastic rises in poverty, hunger and malnutrition, a near collapse of the national public health\nsystem as well as reduced resilience, coping and adaptation abilities to climate change shocks and\nnatural disasters. UNDP’s report “Two Years in Review: Changes in Afghan Economy, Households\nand Cross Cutting Sectors” shows that the Afghan economy is struggling to recover after a 27\npercent contraction since 2020. Two years following the change in regime, seven out of ten\nAfghans do not have access to the most basic items such as cooking items, winter clothing and\nbasic healthcare. 15.2 million people are categorized as severely food insecure. Women are\ndisproportionately affected by the socio-economic crises, in that their share of employment has\nnearly halved, decreasing from 11 percent in 2022 to 6 percent in 2023.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2024-2027 T\n\nWhile the change in power in Afghanistan in August 2021 is the most important development of\nthe recent past, large-scale returns from Pakistan since September 2023, the June 2022 and\nOctober 2023 earthquakes and other natural and climate disasters have contributed to a\nsignificantly changed environment.\n\nHigh levels of returns of Afghans, many of them forced, are expected to continue. Projections\nindicate over 1.46 million Afghans from Pakistan and Iran will return in 2024. While predictions for\n2025 are difficult at the time of drafting this strategy, the escalating rhetoric vis-à-vis Afghans in\nboth Pakistan and Iran is of great concern. While Pakistan is reflecting on implementing the next\nphase of its plans to repatriate “illegal foreigners”, Iran announced in September 2024 that Afghans\nmust leave the country before the end of the 1403 Persian year (March 2025), referring to up to 2\nmillion people.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Finding durable solutions for forcibly displaced people, including returnees, is core to the mandate\nUNHCR was given by the UN General Assembly. Above events have fundamentally changed the\nlandscape within which UNHCR is pursuing this mandate and demand new directions for UNHCR´s\nengagement in Afghanistan. A specific focus on refugee returnees is therefore indicated.\n\nThe purpose of this document is to provide such strategic directions for UNHCR Afghanistan’s\nprotection and solutions activities going forward. Key strategic focus areas are:\n\n1. Ensure greater access to rights and services for all forcibly displaced, returnees and\n\nstateless people, including through empowering communities to become agents of\ntheir own protection.\n2. Provide and facilitate greater access to legal protection services and civil\n\ndocumentation for all forcibly displaced, returnees and stateless people.\n3. Reinforce resilience, (economic) inclusion, and solutions for all forcibly displaced,\n\nreturnees and stateless people.\n4. Empower women and girls.\n5. Facilitate greater protection for refugees, asylum-seekers and stateless persons and\n\npathways towards durable solutions.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "An evidence-based approach will shape the implementation of these priorities to protect, assist\nand empower vulnerable populations in Afghanistan. Integrated data and protection analysis will\nserve as the foundation for UNHCR’s planning, interventions, monitoring and evaluation. Through\nage, gender and diversity-disaggregated data and protection analysis, UNHCR will inform\nprogrammes, including in the context of large-scale displacements or returns, with the aim to focus\non reaching those who are furthest behind, especially women and girls.\n\n2 UNHCR / MAY 2024", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2025-2027\n\n### II. The current protection situation\n\n**International legal framework**\n\nAfghanistan is a state party to seven United Nations human rights treaties [1] and remains bound by\nvarious international legal obligations and principles outlined in international human rights law,\ninternational humanitarian law, international refugee law and international customary law. As such,\nthe DfA must uphold, promote and fulfil the human rights of all individuals within the territory\nunder their control, including forcibly displaced persons.\n\nAfghanistan is also a state party to four Geneva Conventions and two Additional Protocols [2] as well\nas a state party the Convention Relating to the Status of Refugees and the Protocol Relating to the\nStatus of Refugees [3] . Afghanistan has not acceded to the Convention relating to the Status of\nStateless Persons [4,] nor to the Convention on the Reduction of Statelessness [5] .\n\nAfghanistan became the first state in Asia to endorse the Comprehensive Refugee Response\nFramework [6] . This endorsement entails implementing the overarching structure of the Solution\nStrategy for Afghan Refugees, particularly focusing on efforts to create conditions conducive to\nthe sustainable return and reintegration of Afghan refugees.\n\n**National legal and policy frameworks**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "After assuming controls in August 2021, the DfA suspended the 2004 Constitution of Afghanistan,\nlaws and rules concerning legal procedures, judicial appointments and procedures for a fair trial\nimplemented by the Government of Afghanistan under the republic.\n\nAlthough Afghanistan acceded to the 1951 Convention relating to the Status of Refugees and its\n1967 Protocol in August 2005, the National Law on Asylum remains in draft form. No progress has\nbeen observed under the rule of the DfA.\n\nAfghanistan has not acceded to the 1954 Convention relating to the Status of Stateless Persons,\nnor to the 1961 Convention on the Reduction of Statelessness. However, the 2004 Afghan\nConstitution guarantees the right to nationality for all Afghans, while the 2014 Law on Registration\n\n_1 Afghanistan is a state party to the International Covenant on Economic, Social and Cultural Rights, the International Covenant on Civil and Political Rights, the International_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_Convention on the Elimination of All Forms of Racial Discrimination, the Convention on the Elimination of Discrimination Against Women, the Convention Against Torture,_\n_Convention against Torture and Other Cruel Inhuman or Degrading Treatment or Punishment and the Optional Protocol to the Convention against Torture and other Cruel,_\n_Inhuman or Degrading Treatment or Punishment, the Convention on Rights of Child and Optional Protocol to the Convention on the Rights of the Child on the involvement of_\n_children in armed conflict and the Optional Protocol to the Convention on the Rights of the Child on the sale of children child prostitution and child pornography, and the Convention_\n_on the Rights of Persons with Disabilities._\n\n_2 Afghanistan is a state party to the Convention for the Amelioration of the Condition of the Wounded and Sick in Armed Forces in the Field, the Convention for the Amelioration_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_of the Condition of Wounded, Sick and Shipwrecked Members of Armed Forces at Sea, the Convention relative to the Treatment of Prisoners of War, the Convention relative to_\n_the Protection of Civilian Persons in Time of War, the Protocol Additional to the Geneva Conventions of 12 August 1949, and relating to the Protection of Victims of International_\n_Armed Conflicts and the Protocol Additional to the Geneva Conventions of 12 August 1949, and relating to the Protection of Victims of Non-International Armed Conflicts._\n\n_3 The United Nations Treaty Collection, Convention relating to the status of refugees and Protocol relating to the status of refugees._\n\n_4 The United Nations, Treaty Collection, Status of Treaties, Convention relating to the Status of Stateless Persons, as at 12.2.2024._\n\n_5 The United Nations, Treaty Collection, Status of Treaties, Convention on the Reduction of Statelessness, as at 12.2.2024._\n\n_6 The United Nations General Assembly, New York Declaration for Refugees and Migrants, A/RES/71/1, 3.10.2016._", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2024-2027 T\n\nof Population Records underscores the entitlement of every Afghan individual to obtain a Tazkira\nthrough application, regardless of age, gender, ethnicity, or place of residence. The citizenship\nregime operates under a _jus sanguinis_ structure, granting automatic citizenship to children born\nwithin or outside of the state to two citizen parents. It also offers pathways for children born to\none citizen parent and a foreign national to acquire citizenship. In light of the suspension of the\n2004 Afghan Constitution and related legislation, the current state of citizenship and legal identity\nregimes remains however uncertain.\n\nIn 2014, the government of the Islamic Republic of Afghanistan (GoIRA) addressed the pressing\nissue of long-term and large-scale displacements by adopting a comprehensive national policy on\ndisplacement, recognizing the legitimate rights of internally displaced persons (IDPs) as Afghan\ncitizens and aligning with international human rights and humanitarian law, including the 1998\nUnited Nations Guiding Principles on Internal Displacement. The policy ultimately failed to bring\nabout significant changes in the lives of IDPs.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "On 21 August 2024 the de facto authorities announced the ratification of a Law on the Promotion\nof Virtue and the Prevention of Vice, which further and dramatically restricts the rights of women\nin the country. The document is the first formal declaration of the vice and virtue laws under the\ndfa's strict interpretation of Sharia law, and – unlike the previous ‘edicts’ – was published in the\nOfficial Gazette. It gives the Ministry of Vice and Virtue a mandate to enforce the law; in other\nwords, a legal basis for infringement of human rights, with the intention to further restrict women’s\nrights in the public sphere. In addition, the law defined the responsibilities of an inspector\nresponsible for enforcing the prohibitions with discretionary power over arbitrary arrests and\ndetentions.\n\n**Forcibly displaced, returnee and persons at risk of statelessness in Afghanistan**\n\n3.25 million Afghans remain displaced by conflict within the country and over 5.53 million are\nregistered refugees or Afghans in refugee-like situations in the region. An estimated 52,000\nrefugees are living in Afghanistan’s Khost and Paktika regions.\n\n_Refugees and asylum-seekers_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "About 34,800 refugees are living in Khost and Paktika regions and 165 registered refugees as well\nas 324 registered asylum seekers in urban settings. Additionally, there are about 250 asylum\nseekers of Baluchi origin that are not registered with UNHCR. While no significant changes related\nto these numbers are expected, refugees and asylum seekers are among the most vulnerable\ngroups of people in Afghanistan in the absence of refugee laws, national policies and governmental\nprograms. This despite Afghanistan having signed the 1951 Convention relating to the Status of\nRefugees and its 1967 Protocol. Refugees and asylum-seekers in Afghanistan have limited access\nto basic rights, including economic rights, documentation, education and protection from\n_refoulement_ . Refugees and asylum-seekers are subjected to hate crimes and risks of arrest,\ndetention, intimidation, and harassment are frequently reported. As a result, it is difficult for this\n\n4 UNHCR / MAY 2024", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2025-2027\n\ngroup to secure livelihoods and sustain their families. UNHCR will continue to advocate for the\nrights of refugees and asylum seekers in Afghanistan, facilitate their access to documentation as\nwell as support with seasonal financial assistance to the most vulnerable. UNHCR will also continue\nto advocate for support of refugees and asylum seekers by international organizations.\n\n_Refugee returnees_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "While UNHCR has been facilitating voluntary repatriation of Afghans from Pakistan and Iran for\nsome years, Pakistan’s “Illegal Foreigners’ Repatriation Plan” (IFRP), with a focus on the repatriation\nof undocumented individuals, has driven return movements in 2023 and 2024. However, status\nand documentation in Pakistan are not representative of peoples’ international protection needs\ngiven that access to registration is restricted and many Afghans never had an opportunity to apply\nfor asylum and formally seek protection, including those that fled Afghanistan after the events in\n2021. The majority of those returning cited the use of police force and harassment in Pakistan as\none of the drivers to return. As a result, some 712,000 Afghans have returned since the start of\nthe IFRP in mid-September 2023 and 31 August 2024, including 23,658 Registration (PoR) card\nholders. The number of deportations has been progressively increasing during that period, with\nnearly 34,400 deportations recorded. Documentation/legal assistance, protection services for\nchildren, and protection services for girls and women are the top three protection services required\nby returnees. Loss of social support networks, assets and property and the need to restart lives\nand livelihoods in unfamiliar locations with few resources present additional challenges for\nreturnees. At the same time, deportations from Iran have been consistently high over the past\nyears, with several hundred thousand people returned in 2023. This must be considered the\nbaseline, with potentially significantly more deportations taking place by the end of 2024 and the\nyears to come. Given these significant pressures from both Pakistan and Iran and related Protection\nrisks and the need for Solutions, refugee returnees are a prioritised population group under this\nstrategy.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_Internally displaced persons (IDPs)_\n\nAs of December 2023, an estimated 3.2 million persons remained internally displaced due to\nconflict and another 3.3 million because of natural disasters. This makes Afghanistan the country\nwith the largest IDP population in South Asia and the second largest globally, following Syria.\nFollowing the withdrawal of international armed forces and the collapse of the Afghanistan\nDefense and Security Forces, armed conflict ceased. Since August 2021, it is believed that around\n1.5 million conflict-induced displaced persons have returned to their previous habitual residences.\nLack of documentation is reported by IDPs as a key protection issue - an estimated 41 percent of\nIDPs lack proper documentation. Among them, women were the most affected at 88 percent,\nfollowed by girls at 86 percent and boys at 78 percent. The lack of documentation hinders IDPs to\naccess basic services such as health and education and restricts their freedom of movement. But\nlegal services were also found to be inaccessible to 38 percent of IDPs, with women being the most\naffected at 93 percent.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2024-2027 T\n\nFurther studies are necessary to determine whether the absence of clear legal provisions and\narbitrary actions by authorities could exacerbate barriers for specific groups in accessing\ndocumentation. Nonetheless, there has been scant research conducted on populations at increased\nrisk of statelessness in Afghanistan. Among these vulnerable groups are nomadic communities like\nthe Bangriwala, as well as members of the Jat community, including the Jogi, Chori Frosh, and\nGorbat communities. The intergenerational lack of documentation and the nomadic lifestyle are\nkey factors contributing to the administrative hurdles these communities encounter in accessing\ndocumentation, thus affecting their ability to obtain or confirm citizenship. Other factors\ncontributing to a risk of statelessness include challenges related to documentation, gender, age,\ndisplacement, and historical obstacles in establishing citizenship.\n\n6 UNHCR / MAY 2024", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2025-2027\n\n**The situation of women and girls in Afghanistan**\n\nThe most significant development in the recent past\naffecting the protection situation in Afghanistan of all\npopulation groups is the change in power in\nAfghanistan in August 2021. Restrictions on the rights\nof Afghans to freedom of opinion, freedom of speech,\nand freedom of assembly were imposed and there is\ngrowing curtailment by the DfA of the human rights of\nAfghan women and girls.\n\nSince August 2021, there have been more than 50\ndecrees that directly curtail the rights and dignity of\nwomen, including those banning them from public\nspaces and affairs, for example, the exclusion of girls\nfrom secondary school, women from universities,\npublic parks, community baths and gymnasiums.\nWomen spaces have also been closed and the Ministry\nof Women Affairs abolished. Women and girls have\nbeen ordered to travel only with a mahram (male\nrelative chaperone) beyond a certain distance.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The situation has been exacerbated by decrees limiting\nthe employment of Afghan women by NGOs and UN\norganisations, restricting access to vulnerable women\nand girls. Currently, there are no signs that the\nsystematic discrimination against women and girls will\ncease or that their quality of life will improve. On the\ncontrary, on 21 August 2024, the de facto authorities announced the ratification of a Law on the\nPromotion of Virtue and the Prevention of Vice, which dramatically restricts the rights of women\nin the country even further. The document is the first formal declaration of the vice and virtue\nlaws under the dfa's strict interpretation of Sharia law, and – unlike the previous edicts – was\npublished in the Official Gazette. It gives the Ministry of Vice and Virtue a mandate to enforce\nthe law; in other words, a legal basis for infringement of human rights, further restricting women’s\nrights. In addition, the law defines the responsibilities of an inspector responsible for enforcing\nthe prohibitions with discretionary power over arbitrary arrests and detentions.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The ban on girls’ education beyond grade 6 and the ban on women attending secondary schooling\nand university is devastating for the individuals, but also the future of Afghanistan. Despite a\ndecision by the Ministry of Public Health to allow female high school graduates in Afghanistan to\nenrol in state-run medical institutes for the 2024 academic year, the future of secondary and\ntertiary education for girls is uncertain.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2024-2027 T\n\nFurthermore, the lack of land and property rights for women constitutes a major cause of genderbased inequality, particularly given that land is often a household’s most important asset.\n\nViolations are happening in the context of long-standing gender inequalities in Afghanistan, with\nhigh rates of intimate partner violence and of early and forced marriage. UNAMA’s recent thematic\nreport “Divergence of practice: The handling of complaints of gender-based violence against\nwomen and girls by Afghanistan’s de facto authorities” observes that the already high prevalence\nof gender-based violence against Afghan women and girls, including domestic and intimate partner\nviolence given their relegation to their homes, is even higher after the takeover by the DfA.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Bans on women working with NGOs and UN organisations have significantly reduced access to\ndisplaced women and girls and impacted the ability to provide assistance and services for women\nand girls, including the provision of safe spaces and lifesaving response services to gender-based\nviolence services. With less female aid workers on the ground, the ability to ensure safe disclosure\nof violence, including cases of sexual exploitation and abuse, is reduced, as well as the ability to\ncapture the views and needs of women in humanitarian assessments, and to support local womenled organisations.\n\nWhile exemptions in relation to emergency situations have brought some respite in this regard, for\nexample allowing women UN and NGO staff to receive Afghan women at the border or directly\nengaging with them in the response to the earthquakes in Herat, the overall policies remain in\nplace. Female aid workers who have resumed work have also reported the need to be accompanied\nby a mahram and to wear specific clothing, as well as instances of intimidation and harassment.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In view of the situation in Afghanistan, the empowerment of women, in line with the Sustainable\nDevelopment Goal No. 5 to achieve gender equality and empower all women and girls, is the cross-cutting\ntheme in all UNHCR interventions in the country. This is done by recognizing the unique challenges faced\nby women through an age, gender and diversity (AGD) approach. Further to mainstreaming, UNHCR has\nprioritized the design and delivery of programming by women and for women and the prioritization of\nwomen in beneficiary selection, financial inclusion, technical and vocational education, all of which\ngrounded in the accountability to affected people (AAP) framework.\n\nUNHCR’s existing strength in community-based protection and Communication with Communities (CwC) is\nkey to maintaining close contact with women and girls. Through this approach, support is delivered directly\nto the communities and individuals.\n\n**Ongoing large-scale returns and deportations to Afghanistan**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "It was estimated that some 1.3 million undocumented Afghans were residing in Pakistan at the\ntime the Government of Pakistan announced the “Illegal Foreigners’ Repatriation Plan” (IFRP),\nsetting a 1 November 2023 deadline for the “voluntary return” of all undocumented individuals in\nPakistan to their country of origin. The sudden surge in returns due to this plan has put additional\npressure on already strained resources in receiving communities in Afghanistan, including for\nshelter and basic services.\n\n8 UNHCR / MAY 2024", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2025-2027\n\nDespite their own serious humanitarian needs, Afghan communities - serving as first responders are hosting displaced people and welcoming returnees from Pakistan, Iran and elsewhere.\nSupporting receiving communities directly is therefore imperative in view of the ongoing crises in\nthe country and the high numbers of returns.\n\nPhase II of the IFRP is expected to be initiated by Pakistan in early 2024, and initiate the\nrepatriation of Afghan Citizens Card (ACC) holders that are currently residing in the country. It is\nestimated that some 840,000 ACC holders are presently residing in Pakistan. Considering the\nsituation of mixed-status households and the generalized risk of harassment and arrests faced by\ndocumented Afghans, it is estimated that, in addition to the remaining undocumented Afghans in\nPakistan, Proof of Registration (PoR) card holders and UNHCR slip holders may also be compelled\nto return due to the deteriorating environment in Pakistan.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Large scale returns to Afghanistan, many of which are forced and/or effectively deportations, may\nadditionally lead to a weakening of protection of Afghan refugees globally due to view of returns\nas a migratory movement by some - disregarding international protection risks of those forced to\nreturn, including many who left after August 2021 with risk profiles as per the Guidance Note on\nthe International Protection Needs of People Fleeing Afghanistan (Update I).\n\nFollowing the deterioration of the political context in Afghanistan in 2021 which produced large\nscale outflow movements, UNHCR expanded its border monitoring activities at eight official and\nsome 40 unofficial crossing points with Iran, Pakistan, Tajikistan, Turkmenistan and Uzbekistan.\nUNHCR border monitoring is a protection centric exercise and seeks to understand the triggers,\nintentions and reasons for Afghan cross-border movements, assess access to territory and the right\nto seek asylum as well as the barriers which hinder the movement of people who may need\ninternational protection.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Finding durable solutions for forcibly displaced people, including refugee returnees, is core to the\nmandate of UNHCR. This is why UNHCR established the Priority Areas of Return and Reintegration\n(PARRs) programme in Afghanistan, which was subsequently expanded to include IDPs and IDP\nreturnees. The PARRs model is an area-based approach and designed to adapt to an evolving\nlandscape of displacement and return movements. The emphasis on fostering sustainable\nlivelihoods is fundamental, with interventions designed to enhance market engagement, promote\nvocational training and small and medium enterprise development, and facilitate financial inclusion,\nall aimed at strengthening economic independence and community stability. Recent developments,\nincluding funding limitations, call for a re-assessment and potentially sharpening up of PARRs\nactivities. Maintaining the area based PARRs approach is however crucial as it increases absorption\ncapacity of host communities and stabilises populations.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2024-2027 T\n\n### III. Planning scenario\n\nThere is no indication that the systematic discrimination of vulnerable groups, and women and girls\nspecifically, will cease in Afghanistan and their lives improve. Sporadic internal conflicts could lead to\ndisplacement, whereas urbanization and related population movement may lead to an increase in evictions.\nRisks for impactful natural and climate disasters remain significant in Afghanistan.\n\nContinued high levels of returns and deportations from Pakistan and Iran can be expected, possibly with a\nchanging profile of deportees more likely to need international protection. Pressure on returns may increase\nalso from other parties. High number of returns to unsustainable situations may increase the risk of onward\nmovements. Returnees generally have civil documentation issues leading to an increased risk of\nstatelessness. Increased return may furthermore lead to more Housing, Land and Property issues.\n\nIn view of the humanitarian situation in the country and unchanged positions by the DfA, the protection\nenvironment is unlikely to improve in Afghanistan. The humanitarian space is likely to shrink further.\nReduced funding for UNHCR and the international humanitarian community risks further speeding up this\ndevelopment.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "However, since the DfA took power, there is greater geographical access to all parts of Afghanistan. This\npresents some opportunities. UNHCR, with its PARRs is well positioned to deliver protection services at\nkey locations across Afghanistan and focus interventions on those furthest behind. At the same time,\nwithout a nationwide lifting of restrictions, operational uncertainty in Afghanistan will remain a key\nchallenge.\n\nSecurity Council Resolution 2721 (2023), encouraging increased international engagement, may lead to\nsome opportunities. A joined-up multi-stakeholder approach for engagement in Afghanistan will be critical\nfor UNHCR to effectively implement its mandate and for the international community to assist the Afghan\npeople. Closer collaboration and coordination between stakeholders such as the RC/HC, the Durable\nSolutions Working Group, the Clusters, INGOs and NGOs as well as Member States and donors could\nimprove the operational, protection and humanitarian spaces.\n\n10 UNHCR / MAY 2024", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2025-2027\n\n**1.** **Ensure greater access to rights and services for all forcibly displaced, returnees and stateless**\n\n**people, including through empowering communities to become agents of their own**\n\n**protection.**\n\n- Protection monitoring tools will be strengthened and streamlined to ensure a continued\nunderstanding of the protection risks and needs of displaced populations to inform\nprotection programming. This includes a heightened protection response at the border and\nthe gathering of information on population movements, in particular related to refugee\nreturns. Protection monitoring tools will also be used to monitor the impact of UNHCR\ninterventions on the host community.\n\n- The operation will ensure preparedness and response capacity to assist returnees from\nPakistan and Iran. Follow-up on protection cases identified among the returnees, in close\ncoordination with protection partners, remains a key priority.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- The operation will continue to pursue an area-based approach that extends beyond\nprotection monitoring, encompassing the provision of civil documentation, and legal\nsupport, continued identification, prevention of and response to violence against women\nand girls, child protection activities, advocating for the rights of IDPs, returnees and\nrefugees, and fostering social cohesion. All are key to ensure the safety, dignity and rights\nof all forcibly displaced populations during their (re)integration process.\n\n- A focus will be on community-based protection, including through support to select\ncommunity structures, legal clinics, grassroots campaigns, and community-led initiatives.\n\n- The operation will focus on a collaborative, multi-dimensional approach to child protection\nthat addresses both the immediate and underlying factors contributing to child protection\nrisks and the vulnerability of children. Recognizing that children, families, and communities\nare central to the protection of forcibly displaced and stateless children, UNHCR will\nintegrate child protection within community-based protection programming.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Recognizing the often-overlooked nature of mental health and psychosocial support\n(MHPSS) services in displacement contexts, the operation will place significant emphasis\non integrating and making accessible MHPSS in protection, public health, and education\ninitiatives. Particularly in a setting like Afghanistan, where resources are limited and\ncommunities face numerous challenges, MHPSS plays a vital role in enhancing resilience\nand well-being.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2024-2027 T\n\n**2.** **Provide and facilitate greater access to legal protection services and civil documentation for**\n\n**all forcibly displaced, returnees and stateless people.**\n\n- The operation will strengthen legal assistance services for refugees, asylum-seekers,\nreturnees, stateless people and IDPs. Access to civil documentation through legal\nassistance is not only vital for enabling individuals to assert their rights but also essential\nfor accessing public services and humanitarian aid, which is integral to attaining greater\nfreedom of movement and durable solutions.\n\n- The operation will have a heightened focus on returned refugees and IDPs related to the\nprovision of legal documents such as birth certificates, national identity card (Tazkira) and\nland title deed. Ensuring access to documentation is a critical element to prevent\nstatelessness.\n\n**3.** **Reinforce resilience, (economic) inclusion, and solutions for all forcibly displaced, returnees**\n\n**and persons at risk of statelessness.**\n\n- In the face of significant challenges in Afghanistan, the operation will pursue an approach\nthat goes beyond short-term humanitarian relief but emphasizes both immediate needs and\nlong-term sustainable solutions (Nexus) to foster resilience and increased self-reliance\namong forcibly displaced, returnees, stateless people, refugees, as well as the host\ncommunities they live among.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- UNHCR Afghanistan will enhance women’s access to economic opportunities, build\nresilient livelihoods and foster socio-economic inclusion and self-reliance, with a view\ntowards the realization of protection and solutions outcomes.\n\n- Additionally, efforts will be directed towards empowering Afghan youth economically.\n\n- Recognizing shelter as more than just physical structures, the operation will integrate it\nwithin a broader solutions framework that includes livelihoods and access to services. This\nperspective ensures that when providing shelter, the multifaceted needs of beneficiaries,\nsuch as economic stability, are also considered.\n\n- Education related activities will be pursued in close partnership with with UNICEF,\nUNESCO, and other stakeholders, leveraging collective expertise and resources to address\neducations challenges.\n\n- UNHCR Afghanistan will engage with organizations led by or working with persons with\ndisabilities and ensure UNHCR and partner premises are accessible for persons with\ndisabilities, persons with disabilities participate in the humanitarian response, have equal\naccess to information and complaints and feedback mechanisms.\n\n12 UNHCR / MAY 2024", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2025-2027\n\n- The operation will adopt a multi-faceted approach that integrates climate action across\nactivities, focusing on collaboration with both humanitarian and development partners to\nensure the holistic well-being of displaced communities and ensure activities strengthen\npreparedness and resilience against the impacts of climate change and natural disasters,\ne.g., through the building of durable, earthquake secure shelters.\n\n**4.** **Empower women and girls.**\n\n- Empowerment of women and girls is at the core of all protection and solutions\ninterventions in Afghanistan, by recognizing the unique challenges faced by women in the\ncountry through an age, gender and diversity (AGD) approach.\n\n- The operation will facilitate design and delivery of programmes by women and for women\nand the prioritization of women in beneficiary selection, financial inclusion, technical and\nvocational education, and training is informed by UNHCR’s accountability to affected\npeople (AAP) framework.\n\n- UNHCR Afghanistan will facilitate and actively advocate for the rights of women and girls\nwith stakeholders in Afghanistan and rally advocacy support with external partners. A focus\non women’s and girls’ access to rights is paramount for their future and the country itself.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- The operation will seek to expand its response capacity by developing a dedicated case\nmanagement program for women and girls, that consider the specific needs and heightened\nrisks for survivors in Afghanistan as well as sensitivities for partners.\n\n**5.** **Facilitate greater protection for refugees, asylum-seekers and stateless persons and pathways**\n\n**towards durable solutions.**\n\n- UNHCR will continue its advocacy to regularize the legal status of asylum-seekers and\nrefugees, with the aim to provide them with greater legal protection and social rights and\nto facilitate solutions by ensuring access to education, healthcare, documentation,\nlivelihood and employment. Particular focus will be given to access to birth registration for\nrefugee children born in Afghanistan.\n\n- The operation will ramp up case-processing for the urban refugee population with the aim\nto increase resettlement submissions. Complementary pathways will also be explored, to\nincrease the opportunities of third countries solutions for refugees and asylum-seekers.\n\n- Related to the Khost-Paktika refugee population, the operation will continue implementing\nan area-based approach, by assisting all individuals in the target area based on\nvulnerabilities and needs, irrespective of their status. As the population approaches a\ndecade of displacement in 2024, UNHCR will re-double its efforts to enhance economic", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2024-2027 T\n\nself-reliance, ensuring that the refugee population becomes a part of the broader\ncommunity, and identify durable solutions.\n\n- Related to the stateless population in Afghanistan, the operation aims to establish whether\nthe absence of clear legal provisions and arbitrary actions by authorities exacerbates\nbarriers for specific groups to access documentation. Simultaneously, in its protection\nmonitoring, legal documentation and aid work, a special focus will be on those at risk of\nstatelessness or stateless.\n\n**Kabul, May 2024**\n\n14 UNHCR / MAY 2024", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION AND SOLUTIONS STRATEGY 2025-2027\n\nUNHCR\n\nwww.unhcr.org", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "PROTECTION  \nBRIEF \nINDONESIA \n©UNHCR/Amanda Jufrian", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "As of September 2024, Indonesia is host to 11,735 refugees and asylum-seekers (6,548 families)\nresiding primarily in urban areas throughout the Indonesian archipelago. Almost half of all refugees\nand asylum seekers are from Afghanistan, followed by Myanmar, Somalia, and 49 other countries.\nUNHCR has a country office in Jakarta and field presence in Aceh, Medan, Makassar, Pekanbaru, and\nTanjung Pinang.\n\nWhile Indonesia is not signatory to the 1951 Refugee Convention, there are provisions for refugee\nprotection embedded within domestic law (including the 1945 Constitution, the 1999 Human Rights\nLaw, and the 2016 Presidential Regulation on the Handling of Refugees) and Indonesia generally\nrespects the right to seek asylum and the principle of non-refoulement. UNHCR continues to work to\nadvance opportunities for refugee inclusion and participation in Indonesia, including by seeking to\nimprove access to self-reliance and to national education, health, civil registration, and social systems.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNHCR [1] supports the Government of Indonesia by undertaking core refugee protection functions,\nincluding registration, refugee status determination, gender-based violence prevention and response,\nchild protection, legal protection, and the pursuit of durable solutions. Approximately 48% of the\nrefugee population in Indonesia is residing in IOM-managed accommodation centers, established as\npart of the Regional Cooperation Agreement (RCA) between Indonesia, the Government of Australia,\nand IOM in 2000. 42% of the refugee population – largely those who arrived in Indonesia after 2017\n\n- are living independently and, with the exception of the most vulnerable, do not receive financial\nsupport for food, rent, and basic needs. Approximately 10% of the refugee population is comprised of\nRohingya refugees who arrived by boat in recent years and who are residing primarily in temporary\nshelters in Aceh and North Sumatra.\n\n---\n[1] The Government of Indonesia-UNHCR host country agreement has been in place since 1979.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Indonesia’s National Refugee Task Force, established in 2019, remains an important Government entity\nin the management of refugee affairs. Deputy V in POLHUKAM is the head of the National Refugee\nTask Force and members include the Ministry of Foreign Affairs and a wide range of security-focused\nactors. Several local refugee task forces operate in provinces hosting refugees and remain active and\nimportant counterparts. UNHCR also works with a range of partners in Indonesia, particularly IOM,\nYCWS, and YKMI in several areas of protection and assistance. In addition, UNHCR works with nongovernmental, civil society and refugee-led organizations, as well as other UN agencies working with\nand advocating for the rights of the refugee population in Indonesia.\n\n**Locations of Persons Registered with UNHCR**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Total Population**\n\n**11,735 Individuals**\n\n**(6,548 Cases)**\n\n**Vulnerabilities***\n\nUnaccompanied or\n\nseparated child\n\nWoman at risk\n\nSingle parent\n\nChild at Risk\n\nDisability\n\nChronic Illness\n\n**136**\n\n_*One individual may have multiple specific needs_\n\n**2,288**\n\n2020 2021 2022 2023 2024\n\n_*Pre-registration data refers to a headcount upon arrival or at disembarkation sites. Some individuals departed prior to registration with UNHCR._\n\nUNHCR / 1 November 2024 3", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Pre-registration data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### **Registration and Documentation**\n\nUNHCR Indonesia undertakes registration of persons seeking asylum in Indonesia on behalf of the\nGovernment of Indonesia and issues UNHCR identity documentation. Registration interviews are\nprimarily conducted in-person for verification of biometric data and relevant records. Most\nregistration interviews take place at the Reception Center in Jakarta or during accommodation\nvisits/missions by the respective field team for individuals residing outside the greater Jakarta area. In\n2023, 2,547 individuals (1,324 cases) were registered by UNHCR Indonesia, which includes 1,225\nRohingya refugees registered during emergency registration missions following boat disembarkations\nin Aceh in November and December 2023. UNHCR Indonesia provides continuous registration services\nto registered refugees and asylum-seekers and maintains updated personal data in our internal\ndatabase to ensure vulnerable refugees and asylum-seekers are identified and assisted with\nprotection interventions and solutions.\n\n### **Refugee Status Determination**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["internal\ndatabase"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In the absence of comprehensive national refugee laws and national RSD processes, UNHCR\nundertakes refugee status determination (RSD) in Indonesia. As asylum-seekers have access to the\nsame rights and services as refugees in Indonesia, UNHCR uses RSD strategically for individuals with\nan immediate third country solution which requires refugee recognition (resettlement and some\ncomplementary pathways), cases with heightened protection concerns, and cases presenting with\nissues that need to be clarified through the RSD process. UNHCR also conducts RSD as part of its\nemergency response in the context of boat arrivals to identify those in need of international\nprotection.\n\n### **Child Protection**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNHCR and partners provide case management assistance to support children who are\nunaccompanied or separated from traditional caregivers or who are suffering from violence,\nexploitation, and neglect. UNHCR works with partners, including government partners, to assist\nchildren with heightened protection needs by providing legal assistance, safety and security, and\npsychological and health interventions as needed. UNHCR works with YCWS, our implementing\npartner, to provide assistance to unaccompanied children (UAC) in a Semi-Independent Living Care\nArrangement (SILCA) through which UAC are accommodated in a rented room and their basic needs\nare covered. In response to the emergency situation in Aceh, UNHCR is undertaking Best Interest\nAssessments (BIA) for the large number of UAC, child spouses, and other children at risk to better\nunderstand their needs and determine a protection approach that best addresses those needs.\n\n### **Gender-Based Violence (GBV)**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNHCR conducts case management for survivors of gender-based violence and refers them to support\nservices with their consent. Assistance may include medical care, psychosocial support, temporary\nsafe accommodation, assistance to report to police and in any legal process, and interventions to\nprotect impacted children, if required. UNHCR and YCWS run a GBV hotline that can be accessed by\nsurvivors of GBV 24 hours/day.  Since July 2023, UNHCR has conducted a GBV prevention program\nthat seeks to increase awareness of the root causes of GBV and to build the capacity of the community\nto prevent and respond to GBV by Engaging Men in Accountable Practices (EMAP).\n\n### **Legal Support**\n\nUNHCR provides support to refugees and asylum seekers experiencing legal protection concerns,\nincluding individuals seeking international protection at air and sea borders who are unable to access\n\nUNHCR / 1 November 2024 4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "particularly when they are at risk of refoulement, arrest, and detention.\n\n### **Financial Assistance**\n\nUNHCR assists a small number of extremely vulnerable refugees and asylum seekers with cash support\nto help meet basic needs. Together with partners, UNHCR conducts a socio-economic assessment of\ncases that have been identified as requiring financial assistance. Those cases that have heightened\nneeds are presented to a Socio-Economic and Health Panel (comprised of YCWS, JRS, and UNHCR) for\na determination of inclusion in the cash program. Those who qualify receive up to six months of cash\nassistance, following which a re-assessment is required to determine continued needs.\n\n### **Economic Empowerment**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Refugees are not legally permitted to work in Indonesia. UNHCR advocates for refugees to be given\naccess to livelihoods opportunities and works with partners to create community empowerment and\nself-reliance programs that benefit Indonesians and refugees. These programs include vocational\ntrainings and entrepreneurships that also promote economic development in Indonesia. At the first\nGlobal Refugee Forum (GRF) in December 2019, the Indonesian Government pledged support to\nrefugee productivity and empowerment activities. This commitment was confirmed in the second\nGlobal Refugee Forum in December 2023. In September 2023, the Ministry of Manpower issued a\nCircular Note allowing refugee participation in skills training programs at government training centers.\nThe Circular and GRF commitment serve as pivotal entry points to advance economic empowerment.\n\n### **Education**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The Indonesian Ministry of Education, Culture, Research and Technology issued a Circular Note in\n2019, with amendments in 2022, allowing refugees and asylum seekers to access primary and\nsecondary formal and informal education in the national education system. Enrollment in local schools\nrequires a valid UNHCR document and competency in Indonesian language. UNHCR and our partners\nprovide preparation classes, covering Indonesian language and basic skills (reading, writing, and\nmath), as well as additional support (tuition fees, transport allowance and school supplies) to assist\nchildren to access local schools. IOM similarly supports refugee children living in IOM\naccommodations. Challenges to increase enrolment rates among refugee children include limited\ninterest on the part of refugee children to learn Indonesian, financial barriers, resettlement\nexpectations, the inability to obtain official documentation certifying completion of school (due to the\nlack of an Indonesian identification number), and inadequate physical space within classrooms to\naccommodate non-Indonesian children. As a result of these and other challenges, as of September\n2024, only 769 refugee children (of approximately 3,000 school age children) are enrolled in\naccredited national schools.\n\n### **Health**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNHCR is committed to ensuring that refugees and asylum seekers have access to lifesaving and lifesustaining health services. To achieve this, UNHCR continues to advocate with the Ministry of Health\nfor the inclusion of refugees in the national health system, including enabling access to national health\ninsurance, working towards the achievement of SDG 3 (Good Health and Well-Being). All registered\nasylum seekers and refugees have access to low-cost primary health care at the local Community\nHealth Centers (PUSKESMAS), managed by the Government of Indonesia. Individuals requiring\nemergency or advanced health treatment may be financially supported by UNHCR through our health\npartner YCWS, within the parameters of established guidelines and budgetary restrictions. UNHCR\ncoverage is limited to critical interventions, mental health services, immunization, natal care, and\n\nUNHCR / 1 November 2024 5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "requests.\n\n### **Solutions - Resettlement**\n\nUNHCR Indonesia continues to identify and submit refugees for resettlement consideration. In 2024,\n950 resettlement spaces have been made available for refugees in Indonesia. UNHCR seeks to ensure\nthe most vulnerable refugees are prioritized for resettlement consideration, assessing heightened\nspecific needs and protection concerns, as well as length of stay in the country, when identifying cases\nfor submission.\n\n### **Solutions - Complementary Pathways**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNHCR Indonesia is working to expand access to complementary pathways for refugees in the country\nand provides information and guidance to refugees who may be eligible for these programs. Solutions\nthrough complementary pathways have increased in recent years, from no individual departures on\npathways in 2020 to over 300 departures in 2023. UNHCR recognizes and amplifies the link between\naccess to education, skills building, and empowerment activities in Indonesia with improved access to\neducation and labour mobility pathways in third countries. In this way, continued success with these\npathways also serves to reinforce and enhance advocacy with Indonesian authorities on the\nimportance of access to opportunities while in Indonesia. Since mid-2021, UNHCR has collaborated\nwith Talent Beyond Boundaries (TBB) on a project that matches refugee candidates in Indonesia with\nemployers in Australia and Canada to offer employment and a pathway to residency through labour\nmobility. In 2023, 328 individuals departed on a sponsorship pathway, eight refugees departed to a\nthird country on family reunification, and seven refugees departed to Canada and Australia on labour\nmobility programs.\n\n### **Solutions - Voluntary Repatriation**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNHCR facilitates voluntary repatriation for refugees and asylum seekers who request to return to\ntheir countries of origin, with arrangements often made in close cooperation with IOM through its\nAssisted Voluntary Return (AVR) program. During the course of 2023, 95 refugees voluntarily\nrepatriated from Indonesia, primarily to Sri Lanka and Iraq. Voluntary repatriation is impacted by\nongoing conflicts and human rights violations in countries of origin, which renders many unable to\nsafely return home and limits interest in repatriation.\n\nUNHCR / 1 November 2024 6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### **Access to Territory**\n\nIn 2023 and 2024, boats carrying nearly 3,000 Rohingya refugees (the majority of whom are women\nand children) have landed in Aceh and North Sumatra, Indonesia. According to information provided\nto UNHCR by the new arrivals, the increase in the number of individuals undertaking the sea journey\nhas been driven by a number of factors, including increasing insecurity in the camps in Bangladesh; a\ndecrease in the cost of the sea journey; continued instability in Myanmar; and a lack of progress in\ncreating conditions that would enable return to Myanmar, including addressing the root causes of\nRohingya displacement.\n\nWhile many Indonesians remain supportive of and sympathetic to the challenges facing Rohingya\nrefugees, the most recent boat arrivals have met unprecedented resistance in Indonesia. Some boats\nwere initially prevented from disembarking, many refugees were forced to relocate several times due\nrejection from local communities, and many of those who have disembarked have yet to be allocated\nan adequate shelter by Indonesian authorities. As a result, hundreds of refugees who have\ndisembarked in recent months are currently living in extremely perilous, overcrowded, and\nsubstandard conditions in which the protection and assistance response remained a challenge.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "With partners, UNHCR continues to advocate with the Indonesian government at all levels and works\nto strengthen collaboration with coastal communities to ensure boats in distress at sea are rescued\nand able to disembark in Indonesia with adequate sites designated to host those who arrive by sea.\nIn addition to arrivals by sea, UNHCR continues to advocate with competent authorities when\ncontacted by individuals arriving by air if their right to seek asylum was denied at the airport.\n\n### **Resettlement Expectations**\n\nRefugees and authorities consider Indonesia to be a “transit country” prior to the realization of a\nsolution in a third country. The refugee assistance models established by the Comprehensive Plan of\nAction for Indochinese Refugees and the Regional Cooperation Agreement, as well as\ndisproportionately high resettlement opportunities from Indonesia when compared to opportunities\nfrom many other host countries, have perpetuated this narrative. The pervasive and persistent\nmisconception regarding the extent to which refugees are entitled to resettlement and the belief that,\nwith time, resettlement will be accessible for all refugees in the country has a negative impact on the\nrefugee population and the protection space in Indonesia.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "While there have been some important advancements in refugee inclusion in Indonesia, the\ncharacterization of the refugee experience in Indonesia as a transient one creates little impetus for\npolicy makers to develop comprehensive and inclusive protection policies. For refugees, resettlement\nexpectations have discouraged engagement in constructive experiences in Indonesia, including\neducation, training, skills building, and self-empowerment opportunities; fostered a sense of\nunfairness, anxiety, and frustration; led to a deterioration in mental health; and eroded trust in UNHCR\nand partners.  UNHCR’s efforts to work with and for the refugee community are also undermined by\nthe unique focus on resettlement in the country, impacting outreach and communication efforts, the\nprioritization of human and financial resources, staff security, and programming.\n\n### **Lack of Access to Opportunities for Economic Empowerment**\n\nRefugees are unable to work legally in Indonesia, creating economic vulnerabilities and compounding\nprotection risks for the refugee population. In addition to economic insecurity, lack of access to work\nimpacts refugees in a number of ways: informal work experiences may render refugees vulnerable to\n\nUNHCR / 1 November 2024 7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "including physical and sexual abuse and neglect; children are kept from school due to difficulties\npaying for incidental school expenses; refugees have limited opportunities to productively engage in\nIndonesia by developing and utilizing skills and capacities in an employment setting, impacting both\nthe quality of their experience in the host country and opportunities to access a third country solution\nthrough labor mobility; barriers to accessing the workplace limit opportunities to meaningfully\ninteract with Indonesians, impacting social cohesion between refugees and their hosts; refugees are\nunable to make positive contributions to the Indonesian economy while residing in the country; etc.\n\n### **Smuggling and Trafficking**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Refugees access Indonesia through the air and sea. Many refugees in Indonesia, particularly Rohingya\ntraveling by boats, are facilitated in their travel and arrive in the country with the assistance of agents.\nIn previous years, many Rohingya who arrived in Indonesia by sea – approximately 80% - departed\nsoon after arrival to Malaysia, where a large Rohingya community resides and more robust\nemployment opportunities, albeit informal opportunities, exist. UNHCR is extremely concerned about\ncredible reports that refugees in Indonesia – particularly Rohingya in Aceh and North Sumatra – are\nbeing victimized by traffickers during their journey to Indonesia or during onward movement after\narrival in the country.  Refugees have reported numerous protection incidents that took place during\ntheir movement to, through, and from Indonesia, including gender-based violence, physical abuse,\nexploitation, harassment, intimidation, forced movement, and extortion. UNHCR monitors the\nmovement of refugees to assess trends and identify protection risks, to engage with Indonesian\nauthorities to respond to reports of trafficking, and to counsel on the risks of onward movement.\n\n### **Ensuring Protection with Resource Limitations**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Indonesia is host to a relatively small number of refugees and asylum seekers, but they reside over a\nwide geographical area in a country of over 17,000 islands. While approximately half of the refugee\npopulation lives in Jakarta and the surrounding areas, the other half is spread throughout the\narchipelago. The UNHCR operation in Indonesia is a small one, both in terms of staffing and resources,\nand the distribution of refugees across this large territory, coupled with complex protection concerns\nand emergency boat arrivals, creates unique operational challenges. UNHCR continually strives to\nprioritize efforts and resources to be as impactful as possible in supporting refugees who are most in\nneed and in advancing a sustainable protection environment in the country.\n\nUNHCR / 1 November 2024 8", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### **Improved Protection Environment**\n\nWhile it is not a signatory to the Refugee Convention, Indonesia generally respects international and\ndomestic refugee law, particularly the principle of non-refoulement, and has demonstrated critical\nhumanitarian leadership in the region by consistently stepping forward to disembark boats carrying\nRohingya refugees. 2024 and 2025 present important opportunities to further advance the protection\nenvironment in the country, including the planned revision of a key domestic refugee law and the new\npolicies and priorities of recently elected national leaders.\n\nThe National Refugee Taskforce has initiated inter-ministry discussions on the revision of Presidential\nRegulation 125 of 2016. This Regulation provides the foundation for ensuring access to asylum and\nassigns UNHCR a key role in the management of asylum claims and solutions. UNHCR’s\nrecommendations for consideration in the revision have focused on the need for clarifying and\nexpanding the decree’s scope to ensure a strong refugee protection framework in Indonesia, including\nprotection safeguards that ensure entry to safe territory through channels other than the sea;\nincreasing joint activities with the Government, particularly in registration and documentation; and\nadvancing inclusion efforts in the country.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Indonesia’s new president and vice president were inaugurated in October 2024. This new\ngovernment offers an opportunity to renew and expand Indonesia’s humanitarian leadership on\nrefugee issues. The country is a regional and global power – the fourth largest country by population\nin the world, the largest majority Muslim country in the world, the largest economy in southeast Asia,\nand recent leadership roles as the President of the G20 and the Chair of ASEAN. Advocacy efforts will\nseek to ensure Indonesia does not solely embrace political, economic, and military leadership, but that\nthe country also showcases independent humanitarian leadership – particularly on refugee issues.\n\n### **UN Common Pledge**\n\nThe UN Common Pledge offers new opportunities to strengthen the protection environment in\nIndonesia. At the 2023 Global Refugee Forum, several agencies in the UN Country Team (UNHCR, IOM,\nUNFPA, WHO, UNDP, ILO, UNICEF, FAO, and UNESCO) pledged to provide guidance, technical support,\nand advocacy to enable refugee inclusion in Indonesia in four key areas: education, health, selfreliance, and birth registration. UNHCR will work closely with these agencies in 2024 and beyond to\nsecure inclusive policies and an enabling protection environment in the country.\n\n### **Enhancing Engagement with Refugees**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "UNHCR will continue to expand the ways in which we engage with refugees in 2024 and 2025. A\ncornerstone of these activities is the rollout of the Digital Gateway, a corporate self-service tool being\npiloted in Indonesia that will address many of the concerns of the refugee community, as well as the\ngeographic challenges in Indonesia, by providing a platform through which refugees can remotely\ncommunicate with UNHCR, view their bio-data, update contact information, book appointments for\nservices, and obtain updates on case processing status.\n\nIn addition to the Digital Gateway, UNHCR is strengthening tools of engagement and communication\nwith the refugee community following several years of more limited contact resulting from COVID\nrestrictions and security challenges in the field. These initiatives include re-instituting walk-in\ncounseling at the UNHCR reception center; revitalizing the UNHCR HELP website; expanding refugee\nTown Halls to respond to queries and share information; undertaking regular outreach missions to\n\nUNHCR / 1 November 2024 9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### **Verification Exercise**\n\nUNHCR is undertaking a country-wide verification exercise in 2024. The project enables UNHCR staff\nto systematically connect with and update registration data for every refugee and asylum seeker in\nIndonesia.  UNHCR protection/registration teams are updating family composition and recording\ninformation on specific needs and vulnerabilities, skills, work experience, education levels, and family\nconnections outside Indonesia. The information collected will provide a clean and accurate set of data\nthat will enable UNHCR to better develop and target programming for the refugee population and will\nassist in identifying individuals who may qualify for solutions outside Indonesia.\n\n©UNHCR/Amanda Jufrian\n\nUNHCR / 1 November 2024 10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "## **PROTECTION CLUSTER** **VENEZUELA**\n\n#### **SEPTEMBER/OCTOBER 2023 ACTIVITY** **REPORT**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "##### **September/ October 2023 report**\n\n###### Venezuela\n\n**Protection Cluster September/ October 2023 activity report**\n\nThis report has been prepared by the Protection Cluster in Venezuela\n\n_Cover photo: © UNHCR / Kimberly Sarkis (2023)_\n\nThe Protection Cluster is a broad-based participatory forum of protection partners which brings\ntogether United Nations agencies, human rights and development organisations and actors, as well\nas local and international non-governmental organisations. The Protection Cluster is led by UNHCR.\n\nAll our information products, including reports, maps and factsheets are available on the Venezuela\n\nProtection Cluster website:\n[https://ven.protectioncluster.org](https://ven.protectioncluster.org)\n\nContacts:\n\nProtection Cluster Coordinator, **Alice Contini**, continia@unhcr.org\n\nProtection Associate, **Patricia Bosco**, boscoleo@unhcr.org\nCounter-Trafficking in Crisis Specialist, **Norma Ferrer**, noferrer@iom.int\n\nProtection Assistant, **Kimberly Sarkis**, sarkisne@unhcr.org\nProtection Specialist, **Giuliana Inturrisi**, giuliana.inturrisi@drc.ngo\n\n**2**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### TABLE OF CONTENTS September/ October 2023 report Venezuela\n\n**Operational highlights**\n\n4 Humanitarian Program Cycle 2024: Humanitarian Needs Overview (HNO) Process\n\n4 Coordination with other platforms, subnational Clusters, ICCG, and Partners\n\n5 Accountability for Affected Populations (AAP) WG\n\n5 GPC Townhall\n\n5 La Carnada Play\n\n6 Annual TiP Bulletin 2022-2023\n\n6 Food Security and Livelihoods (FSL) and Trafficking in Persons (TiP)\n\n6 Monthly Meetings of the WGTiP\n\n6 Protection Mainstreaming Training\n\n8 Training on Mixed Migration and Durable Solutions\n\n8 Reporting Transition to 345W\n\n8 Focal Group Discussions with LGBTIQ+ Organizations for\n\nThematic Protection Analysis Update (PAU)\n\n10 Mental Health and Psychosocial Support (MHPSS)\n\nCoaching and Training of Trainers (ToT)\n\n10 Impact Stories of Series: September and October\n\n11 Protection Service Mapping Revisions and Edits\n\n11 Participation in the Gender ToT for Government Officials\n\n11     September’s Monthly Meeting with Partners\n\n12 Second Allocation for Venezuela Humanitarian Fund (VHF)\n\n12 Protection Monitoring Tool (PMT)\n\n**14** **Protection response: performance and funding**\n\nHumanitarian Response\n\n**3**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### **OPERATIONAL HIGHLIGHT**\n\n##### **September/ October 2023 report**\n\n###### Venezuela\n\n**Some key achievements**\n\n- Humanitarian Needs Overview (HNO)\n\nProcess\n\n- Coordination with other platforms,\n\nsubnational Clusters, ICCG, and Partners\n\n- Accountability for Affected Populations\n\n(AAP) WG\n\n- GPC Townhall\n\n- La Carnada Play\n\n- Annual TiP Bulletin 2022-2023\n\n- Food Security and Livelihoods (FSL) and\n\nTrafficking in Persons (TiP)\n\n- Monthly Meetings of the WGTiP\n\n- Protection Mainstreaming Training\n\n- Training on Mixed Migration and Durable\n\nSolutions\n\n- Reporting Transition to 345W\n\n- Focal Group Discussions with LGBTIQ+\n\nOrganizations for Thematic Protection\n\nAnalysis Update (PAU)\n\n- Mental Health and Psychosocial Support\n\n(MHPSS) Coaching and Training of Trainers\n\n(ToT)\n\n- Participation in the Gender ToT for\n\nGovernment Officials\n\n**4**\n\n**I. Humanitarian Program Cycle**\n\n**2024: Humanitarian Needs**\n\n**Overview (HNO) Process**\n\n**September was dedicated to identi-**\n\n**fying and define indicators and the**\n\n**local adjustment of severity scales**\n\n**for People in Need (PIN) and Needs**\n\n**Severity Calculation as part of the**\n\n**Humanitarian Needs Overview (HNO)**\n\n**process.** The indicators defined for the\n\nProtection Cluster (PC) Severity and PIN\n\ncalculation are Protection Risks, Protec\ntion Risks linked to trafficking in person,\n\nAccess to Documentation, Access to\n\nthe Justice System, Negative Coping\n\nMechanisms in Protection, Violent", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Death Rates, Adolescent Mothers, and\n\nChildren and Adolescents out of school\n\nusing primary data from the MSNA and\n\nsecondary data sources.\n\nCalculations were refined using qualita\ntive information obtained from experts\n\nduring needs assessments validation\n\nworkshops conducted in Miranda, Zulia,\n\nFalcón, Bolívar, Delta Amacuro, Sucre,\n\nAmazonas, Apure, Táchira, Lara, and\n\nthe Capital District. The overall PIN for\n\nthe Protection Cluster is 4,425,466.00,\n\nrepresenting a 5% increase compared\n\nto the calculation for the Humanitarian\n\nResponse Plan (HRP) 2022-2023.\n\nFurther, throughout October, the national\n\nProtection Cluster participated in the\n\nfacilitation of extended OCHA Local\n\nCoordination Fora meetings in the states\n\nof Miranda, Sucre, Bolivar, Delta Ama\ncuro, Apure, Amazonas, Táchira, Zulia,\n\nFalcón, and Lara. The primary objective\n\nof these workshops was to validate data\n\ncollected from the MNSA on the human\nitarian situation of the respective state\n\nand, to gather complementary informa\n\ntion to contribute to the narrative of the\n\nHNO. Finally, it helped to validate the\n\nprioritization of municipalities in collabo\nration with Cluster partners and OCHA.\n\n**II.** **Coordination** **with** **other**\n\n**platforms, subnational Clusters,**\n\n**ICCG, and Partners**\n\n**Coordination with the Response for**\n\n**Venezuela (R4V) continued through-**\n\n**out September and October through**\n\n**bilateral meetings and common work-**", "output": {"entities": {"named_data": ["MSNA"], "descriptive_data": [], "vague_data": ["secondary data sources"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**ing grounds for the next months**\n\n**have been set.** Further, meetings have\n\nbeen held with the 6 subnational Pro\ntection Clusters in Zulia, Táchira, Apure,\n\nAmazonas, Barinas, and Bolivar. Coor\ndination with these coordinators will be\n\nstrengthened to assure decision taking\n\nis guaranteed also from the field, and\n\ntheir support in the implementation of\n\nthe extended OCHA Local Coordination", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "##### **September/ October 2023 report**\n\n###### Venezuela\n\nedits to some of the objectives and to the\n\ntimeline stipulated.\n\n**IV. GPC Townhall**\n\n**On October 18th a Townhall was**\n\n**organized by the Global Protection**\n\n**Cluster (GPC) with Field Operations.**\n\nHRP 2024 key messages/instructions\n\nwere discussed and a calendar of\n\nbi-monthly GPC Townhalls with field\n\noperations with planned key topics were\n\npresented. The meeting had the aim to\n\nunderstand the different timelines and\n\nongoing HPC processes in each field\n\noperation as well as provide support to\n\nwhom needed it.\n\n**V. La Carnada Play**\n\n**The Working Group on the Prevention**\n\n**and** **Response** **to** **Trafficking** **in**\n\n**Persons** **(WGTiP)** **organized** **and**\n\nFora for the HNO process are in line with\n\nProtection needs in their area.\n\nThe Protection Cluster assured partic\nipation in the weekly ICCG meetings\n\nwhere HPC ongoing activities have been\n\ndiscussed and planned.\n\nFinally, bilateral meetings have been\n\norganized with PC’s partners NRC and\n\nOHCHR. With both partners their protec\ntion programming has been discuss and\n\npossible coordination in the Cluster.\n\n**III. Accountability for Affected**\n\n**Populations (AAP) WG**\n\n**The PC has participated actively in**\n\n**the monthly meetings conducted by**\n\n**the AAP working group, which aimed**\n\n**at increasing the inputs of affected**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**populations in responding to and**\n\n**shaping the assistance following a**\n\n**disaster in the Venezuelan context.**\n\nThe cluster’s participation in this space\n\nguarantees that affected populations are\n\nplaced at the heart of the emergency\n\nresponse. Additionally, the different\n\nneeds and capacities of all these groups\n\nwill shape the response plan and how\n\ncluster partners and affected populations\n\ninteract through all the phases of the\n\nHumanitarian Programme Cycle (HPC),\n\nguiding the necessary workplans.\n\nDuring the meeting of October, the new\n\nframework of AAP was presented, and it\n\nwas revised jointly by its members. This\n\nframework was created with the support\n\nand accompaniment of the Humanitarian\n\nCountry Team and the Resident and\n\nHumanitarian Coordinator. The WG\n\ncoordinator shared the minimum\n\ncommitments of the framework with\n\nthe members and opened the floor for\n\ndiscussions. The PC suggested some\n\n**coordinated the \"La Carnada\" play**\n\n**in Petare in collaboration with the**\n\n**Taller Experimental de Teatro/ Center**\n\n**for Artistic Creation (TET), IOM, and**\n\n**UNHCR on September 23rd and 24th.**\n\nThis The TET is a theatral group based\n\nin Caracas, Venezuela that was founded\n\non December 11, 1972, by Eduardo Gil\n\nunder the heart of the Central University\n\nof Venezuela (UCV) with the purpose", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "of establishing a theater group with an\n\nexperimental line of work.\n\nLa Carnada, an immersive and\n\ndocumentary-style theatre production,\n\naims to raise awareness about victims\n\nof Trafficking in Persons (TiP), focusing\n\non sexual and labour exploitation. It\n\nsheds a light on contexts where digital\n\nmedia, domestic violence, and deceptive\n\nopportunities pose significant risks,\n\nparticularly for women and girls. As\n\npart of the French-Venezuelan Festival\n\n**5**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "##### **September/ October 2023 report**\n\n###### Venezuela\n\n**trafficking networks.** After bilateral\n\nmeeting between the Protection Cluster,\n\nthe WGTiP and the FSL Cluster, a focal\n\ngroup was organized on October 10th.\n\nA total of 7 participants from UN Agen\ncies, AoRs, and local NGOs participated.\n\nWG members actively participated by\n\nsharing field experiences and proposing\n\nrecommendations to address challenges\n\nraised during the discussion.\n\n**VIII. Monthly Meetings of the**\n\n**WGTiP**\n\n**The WGTiP convenes monthly to**\n\n**review its working plan and provide**\n\n**a platform for members to discuss**\n\n**trends and activities related to TiP. In**\n\n**the last meeting, the WG collectively**\n\n**decided to establish a Task Force to**\n\n**draft a guide on security for orga-**\n\n**nizations working with Victims of**\n\n**Trafficking (VoT).** Additionally, members\n\nhave developed standardized training\n\nand sensitization activities/workshops to\n\nimplement in communities on the topic of\n\nTiP. These trainings will be beneficial to\n\nother organizations addressing human\n\ntrafficking issues. In October’s monthly\n\nmeeting, the WG hosted the office of UN\n\nSpecial Rapporteur for Trafficking in Per\nsons. The meeting involved discussions\n\non potential collaboration with WG mem\nbers and how the UN Rapporteur's office\n\ncan support advocacy efforts.\n\nMember organizations also shared infor\nmation on available services for victims", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "of human trafficking in different states of\n\nVenezuela through a service mapping\n\nexercise that was implemented.\n\n**IX. Protection Mainstreaming**\n\n**Training**\n\n**The PC in collaboration with the**\n\nof Performing Arts, over 140 people\n\nattended the play, including community\n\nmembers, representatives from NGOs\n\nand INGOs, and agency representatives.\n\nDrawing on testimonies and real\n\ndata, and with the technical support\n\nof UNHCR, IOM, and the Venezuela\n\nProtection Cluster, the authors of the\n\nplay have crafted an original and avant\ngarde dramaturgy that highlights the\n\nissue of TiP and its associated risks. Two\n\nadditional performances are scheduled\n\nfor the last week of November (in\n\nPetare) and another in December in\n\nChichiriviche de La Costa, La Guaira,\n\nin commemoration of the 16 days of\n\nactivism for the International Day for the\n\nEradication of Violence against Women.\n\n**VI. Annual TiP Bulletin 2022-2023**\n\n**After one year of the creation of the**\n\n**6**\n\n**WGTiP, an annual bulletin was pub-**\n\n**lished in October.** This document\n\nconsolidates information from the quar\nterly bulletins of 2022 and 2023 and\n\nprovides a context analysis of TiP in\n\nVenezuela, raising awareness on new\n\ninformation and dynamics and highlights\n\ncounter-trafficking efforts of members\n\nwith a specific focus on activities for the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "World Day Against Trafficking in Persons\n\n2023. The document can be downloaded\n\n[here link.](https://www.globalprotectioncluster.org/publications/1586/reports/annual-report/boletin-anual-trata-de-personas-agosto-2022-agosto-2o23)\n\n**VII. Food Security and Liveli-**\n\n**hoods (FSL) and Trafficking in**\n\n**Persons (TiP)**\n\n**Under the Food Security and Live-**\n\n**lihoods (FSL) Cluster consultancy,**\n\n**research has been done to under-**\n\n**stand the connection between food**\n\n**insecurity, lack of livelihoods and**\n\n**the likelihood of being recruited into**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_© FPM/ Franco Chramosta (2023)_\n\n##### **September/ October 2023 report**\n\n###### Venezuela\n\n**7**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "##### **September/ October 2023 report**\n\n###### Venezuela\n\n**ProCap Senior Protection Advisor**\n\n**have provided the WASH, Shelter,**\n\n**Logistics, Health, Education, Food**\n\n**Security, Nutrition Cluster as well as**\n\n**Venezuelan Humanitarian Fund (VHF)**\n\n**partners a Protection Mainstreaming**\n\n**in-person training.** The training com\nprised a theoretical component covering i)\n\nhumanitarian and protection principles, ii)\n\nfundamental protection concepts and the\n\nrisk equation, iii) the distinction between\n\nintegrated protection and protection\n\nmainstreaming and their definitions. The\n\nsecond part involved a practical exercise\n\nwhere participants selected an activity\n\nwithin their sector, analyzed the context,\n\nidentified three primary protection risks,\n\nand applied the protection equation.\n\nThis dynamic and participative exercise\n\nallowed participants to present their\n\nactivities and benefit from constructive\n\nfeedback. Overall, the positive feedback\n\ngiven by participants shows the need to\n\n**8**\n\nstrengthen humanitarian organizations'\n\nunderstanding on Protection Main\nstreaming to guarantee the Centrality of\n\nProtection is in place in the humanitarian\n\nresponse.\n\n**X. Training on Mixed Migration**\n\n**and Durable Solutions**\n\n**A full-day training session on Mixed**\n\n**Migration and Durable Solutions took**\n\n**place on October 19th. The session**\n\n**began with a review of basic protec-**\n\n**tion concepts, followed by an analysis**\n\n**of strategies to mitigate threats and**\n\n**vulnerabilities, and enhance capabil-**\n\n**ities through practical exercises with**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**PC partners.**\n\nThe morning session focused on explain\ning the concept of mixed migration and\n\nvarious migratory profiles, emphasiz\ning the distinctions between the causes\n\nand consequences of migration through\n\ngroup dynamics. In the latter part of the\n\nsession, the meaning of durable solu\ntions and the criteria for achieving them\n\nwere presented in relation to migratory\n\nprofiles and the Venezuelan context.\n\nFinally, together with IOM, a session on\n\nreturns and reintegration was included\n\nto discuss practical implementation\n\nof durable solutions. A total of 20 local\n\nand international organizations, part of\n\nthe PC, participated, and the feedback\n\nreceived was positive. Finally, to assure\n\nthat this information reaches colleagues\n\nin the field, an online session was orga\nnized on November 7th. Despite the\n\nchallenges posed by virtual sessions,\n\n28 people attended the session, showed\n\ninterest in the topic, and actively par\nticipated during the organized group\n\ndynamics.\n\n**XI. Reporting Transition to 345W**\n\n**During September and October, two**\n\n**training sessions were conducted**\n\n**with Protection Cluster Partners to**\n\n**facilitate the transition from the 5W**\n\n**system to the 345W reporting plat-**\n\n**form.** Adjustments were made in the\n\nplatform to reinforce quality controls, and\n\nAreas of Improvement were identified", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "by Information Management (IM) per\nsonnel in the Protection Cluster and the\n\nAreas of Responsibility. The cluster's IM\n\nautomated data transformations to con\nsolidate information for the year 2023.\n\nThe pilot phase for the migration to 345W\n\nhas concluded, and final refinements are\n\nunderway. These refinements aim to\n\nestablish a more automated process for\n\nreporting, validation, and consolidation\n\nof partners' responses.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_© IOM/ Norma Ferrer (2023)_\n\n##### **September/ October 2023 report**\n\n###### Venezuela\n\n**9**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "##### **September/ October 2023 report**\n\n###### Venezuela\n\n**XII. Focal Group Discussions**\n\n**with LGBTIQ+ Organizations for**\n\n**Thematic Protection Analysis**\n\n**Update (PAU)**\n\n**In view of the preparation of a the-**\n\n**matic Protection Analysis Update**\n\n**(PAU) on the protection situation of**\n\n**LGBTQI+ persons, as part of the PC’s**\n\n**reporting efforts, with support from**\n\n**the Areas of Responsibility (AoRs)**\n\n**and the GenCap Gender Advisor,**\n\na hybrid focus group discussion was\n\norganized with LGBTIQ+ organizations\n\nand relevant actors on September 7th,\n\ncombining face-to-face and remote par\nticipation.\n\nThe focal group aimed to collect infor\nmation from key informants on the\n\nhumanitarian needs and protection\n\nrisks of LGBTIQ+ people, with a focus\n\non changes perceived in the last year.\n\n**10**\n\nThis information supported the drafting\n\nof the document that outlines the current\n\ncontext, main protection risks, affected\n\ngroups by geographical areas, and key\n\nrecommendations for humanitarian\n\nactors, government entities, donors, and\n\ncivil society organizations in Venezu\nela. The report's analytical conclusions\n\naim to provide an evidence base for\n\nprogramming, advocacy, and dialogue\n\nto influence behaviors and policies,\n\nfostering a more favorable protection\n\nenvironment.\n\n**XIII. Mental Health and Psychoso-**\n\n**cial Support (MHPSS) Coaching**\n\n**and Training of Trainers (ToT)**\n\n**The PC through the Protection Assis-**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**tant participated in a two-day MHPSS**\n\n**coaching organized by PAHO and led**\n\n**by an international IASC consultant.**\n\nThis coaching, held during the second\n\nweek of October, aimed to strengthen the\n\nMental Health and Psychosocial Support\n\nTechnical Working Group’s role by eval\nuating its work plan, achievements, and\n\nthe next steps. The coaching agenda\n\nintended to bring accompaniment and\n\nadvice to people with coordination\n\nfunctions for the implementation of an\n\nMHPSS strategy in their organizations,\n\nagencies, and clusters.\n\nThese efforts represent a significant\n\ninter-institutional initiative to empower\n\nprofessionals in the field, providing\n\nknowledge and tools essential for effi\ncient organizational functionality during\n\nhumanitarian emergencies. Following\n\nthese activities, the working group plans\n\nto implement its work plan, emphasizing\n\nMHPSS as a cross-cutting topic in the\n\nhumanitarian architecture.\n\n**XIV. Impact Stories of Series:**\n\n**September and October**\n\n_a. Premiere Urgence Internationale (PUI)_\n\n_(September):_ The subnational cluster of\n\nBolívar, in coordination with the national\n\nPC, visited PUI's activities in Casaco\nima, Delta Amacuro state. PUI's project\n\nfocuses on improving living conditions\n\nthrough mental health, physical health,\n\nand WASH. Beneficiaries of the project\n\nwere interviewed for a better under\nstanding of the projects’ impact. The\n\ninterviews confirmed how mental and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "physical health of the members of the\n\ncommunity was improved thanks to the\n\nwork of PUI, which translates in the need\n\nof the PC to keep supporting partners\n\nimplementing such relevant projects in\n\ncommunities of difficult access.\n\n[Full access to the story through this link.](https://www.globalprotectioncluster.org/publications/1658/reports/report/historias-de-impacto-cluster-de-proteccion-venezuela-septiembre)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "##### **September/ October 2023 report**\n\n###### Venezuela\n\nand Gender-Based Violence for officials\n\nof the Women's Secretariat of the State\n\nof Miranda. Conducted from September\n\n28th to October 11, 2023, the PA facil\nitated the first virtual session on key\n\nconcepts, receiving positive feedback\n\nfrom the 35 participating officials.\n\n**XVII.** **September’s** **Monthly**\n\n**Meeting with Partners**\n\n**On Thursday, September 28th, the PC**\n\n**convened its monthly meeting with**\n\n**53 members representing 41 partner**\n\n**organizations.** The agenda covered an\n\ninformation exchange between partners\n\nwhich expressed concerns on the way\n\nnational IDs are processed in light of the\n\nsoon elections; saw a presentation of\n\nthe New Accountability Framework for\n\nAffected Populations (AAP), its objectives\n\nand work plan; updates from the national\n\nsubcluster of Ciudad Guayana on their\n\n**11**\n\n**October.** The focus was on ensuring\n\nthe accuracy of registered services and\n\ncollaborating with partner organizations\n\nto edit or confirm the removal of inactive\n\nservices. The revised database is under\ngoing verification for coherence by the\n\nIM team before being uploaded onto the\n\nplatform. Considerations regarding the\n\nplatform itself and the inclusion of other\n\nsectors are also under review, and once\n\nfinalized, the PC will implement its com\nmunication strategy on Service Mapping.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**XVI. Participation in the Gender**\n\n**ToT for Government Officials**\n\n**As part of the efforts of the Gender**\n\n**Technical Working Group of Miranda**\n\n**(“Mesa** **de** **Genero”)** **to** **support**\n\n**strengthening of local government,**\n\n**the Protection Cluster through the**\n\nProtection Assistant, supported the\n\ndelivery of a ToT on Gender Equality\n\n_b.Fundación Proyecto Maniapure (FPM)_\n\n_(October):_ FPM, a PC partner, imple\nments integrated protection, and WASH\n\nstrategies in Amazonas and Bolivar. The\n\nPC, in coordination with FPM staff, vis\nited the state of Amazonas to showcase\n\nFPM's impactful work in the monthly\n\nimpact stories series.\n\nThe main axis of the organization\n\nrevolves around a community model that\n\ncenters the mental and physical health\n\nas a human right. Their work has been\n\ndistinctive and their projects of great\n\nimpact to the most vulnerable groups,\n\nsuch as the indigenous communities of\n\nthe Venezuelan Amazonia.\n\nThe PA visited the community of Valle\n\nLindo in the municipality of Atures in the\n\nstate of Amazonas, where he was able to\n\ninterview several persons of interest who\n\nparticipate in the foundation's programs,\n\nand in particular those people who were\n\nparticipating in community sensitizations\n\nand trainings on the prevention of TiP", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "and other forms of violence. Beneficia\nries mentioned that they feel grateful for\n\nall the work of FPM and wish that more\n\ncommunities could benefit from such\n\nsensitizations and trainings, which are\n\nrelevant to the daily realities they expe\nrience.\n\n[Full access to the story through this link.](https://www.globalprotectioncluster.org/publications/1689/reports/report/historias-de-impacto-de-socios-cluster-de-proteccion-octubre-2023)\n\n**XV. Protection Service Mapping**\n\n**Revisions and Edits**\n\n**In line with the Protection Cluster’s**\n\n**efforts to guarantee a functioning and**\n\n**updated service mapping, together**\n\n**with the UNHCR IM Unit, the Pro-**\n\n**tection Service Mapping database**\n\n**underwent a comprehensive review**\n\n**and** **update** **in** **September** **and**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "##### **September/ October 2023 report**\n\n###### Venezuela\n\nReproductive Health), reinforcing com\nmunity resilience through livelihoods\n\nand access to adequate food, women's\n\nempowerment, gender equality, and\n\nprovision of assistance and protection\n\nservices (including Gender-Based Vio\nlence (GBV), children and adolescents,\n\nand human trafficking), and supporting\n\nemergency education activities with a\n\nsocio-productive approach.\n\n**XIX. Protection Monitoring Tool**\n\n**(PMT)**\n\n**The outcomes of the Protection Mon-**\n\n**itoring Tool (PMT) were shared with**\n\n**cluster partners. The PMT contin-**\n\n**ues to measure primary protection**\n\n**risks, population needs, access to**\n\n**rights, and humanitarian assistance**\n\n**at the community level.** For the period\n\nof March to August 2023, the PMT\n\nrecorded a total of 674 interviews con\n\nprotection work and the advancements\n\nof their workplan along with areas of\n\nresponsibility (AoRs), and the response\n\nmonitoring for August, showing a total of\n\n385,000 people reached by 95 partner\n\norganizations.\n\nAdditionally, the Service Mapping tool\n\nwas shared with partners, and it was\n\ninformed that together with the UNHCR\n\nInformation Management Unit and the\n\nAoRs, a complete update of the services\n\nregistered in the tool was being made.\n\nOrganizations were invited to always be\n\non the lookout to update services and to\n\nbe able to support the dissemination of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "this tool. This meeting marked the first\n\nunder the leadership of the new cluster\n\ncoordinator.\n\nFor more information request the\n\nmeeting minutes for any cluster member.\n\n**12**\n\n**XVIII. Second Allocation for Ven-**\n\n**ezuela Humanitarian Fund (VHF)**\n\n**The call for proposals for the second**\n\n**allocation of the Venezuela Human-**\n\n**itarian Fund (VHF) was opened on**\n\n**October 13th closing on November**\n\n**6th.** This allocation focuses on \"Multisec\ntoral assistance for the improvement of\n\nessential health services, strengthening\n\ncommunity resilience through livelihood\n\nsupport, gender equality and women's\n\nempowerment, supporting emergency\n\neducation activities, and the provision\n\nof assistance and protection services\n\nin complementarity with CERF funds in\n\n13 municipalities across 5 border states:\n\nBolivar, Delta Amacuro, Falcon, Sucre,\n\nand Zulia.\" The three key prioritized\n\nareas encompass enhancing essential\n\nhealth services (Mental Health and Psy\nchosocial Support (MHPSS), Women's\n\nand Adolescents Health, and Sexual &\n\nducted in 16 states, with the collaborative\n\nefforts of 9 partner organizations. This tool\n\nprovides valuable insights into the evolving\n\nprotection landscape and aids in tailoring\n\nresponses to the specific needs of affected\n\npopulations.\n\n_© FPM/ Franco Chramosta (2023)_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_© UNHCR / Kelvin Toissant (2023)_\n\n##### **September/ October 2023 report**\n\n###### Venezuela\n\n**13**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "##### **September/ October 2023 report**\n\n### **MONITORING THE PROTECTION RESPONSE**\n\n###### Venezuela\n\n### **PERFORMANCE AND FUNDING**\n\nThe PC continues to reinforce the impor\ntance of reporting, which allows it to\n\ntrack funding and conduct advocacy.\n\nThroughout the last years the number of\n\npartners that regularly report 5Ws (cur\nrenly 345ws), including the implementing\n\npartners of the lead agencies of the Clus\nter and AoRs, increased notoriously.\n\nThe activity monitoring dashboard co\nvering latest data collection in October\n\n2023 and other information products\n\n[can be accessed in the protection clus-](https://app.powerbi.com/view?r=eyJrIjoiYzg3NGFkM2EtYjZhMS00YWNkLWE1NjItMjg0OThlMTAzMDM1IiwidCI6ImU1YzM3OTgxLTY2NjQtNDEzNC04YTBjLTY1NDNkMmFmODBiZSIsImMiOjh9)\n\n[ter website, and currently 105 partners](https://app.powerbi.com/view?r=eyJrIjoiYzg3NGFkM2EtYjZhMS00YWNkLWE1NjItMjg0OThlMTAzMDM1IiwidCI6ImU1YzM3OTgxLTY2NjQtNDEzNC04YTBjLTY1NDNkMmFmODBiZSIsImMiOjh9)\n\nare supporting the 345W reports. Please\n\nnote the list of Specific Objectives in\n\nFigure 1 are aligned to the HRP 2022.\n\nThe percentage (%) in each chart (see\n\nfigure 1) represent the number of benefi\nciaries (individuals) reached against the\n\ntarget set in the HRP 2022 - 2023.\n\n**14**\n\n353, 037 million USD were received\n\nby the Venezuelan Republic so far in\n\n2023 through the HRP plan, accord\ning to the Financial Tracking Service\n\n(FTS). Requested funding for the Protec\ntion Cluster was 101.2 million USD and\n\n33,385,8929 million USD were granted.\n\nAccording to FTS, the total coverage has", "output": {"entities": {"named_data": [], "descriptive_data": ["activity monitoring dashboard"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "been of 33 % of the funding requested.\n\nAccording to the 345W reports, 478K\n\npeople were impacted until october, which\n\n65% are women and 35% men. 6,479\n\npeople with disabilities were reached\n\nduring the last period.\n\n**violence**\n\nThe states with the higher response\n\ninclude Tachira, Zulia, Bolivar, and\n\nMiranda. Some of the organizations with\n\nthe higher reach include ACNUR, DIOCE\nSIS SC, CODEHCIU, HIAS, ALIADAS EN\n\nCADENA A.C, TINTA VIOLETA and CAR\nITAS.\n\n**Figure 1. Summary of HRP 2022 - 2023 results, by Specific Objetive.**\n\n**Responding**\n\n**to protection**\n\n47% **risks associated** 45%\n\n**with violence of**\n\n**GbV**\n\n**children**\n\n# 47 4\n\n**Prevention,**\n\n**mitigation, and**\n\n13%\n\n# 13\n\n99%\n\n# 99\n\n**Figure 1. Summary of HRP 2022 - 2023 results, by Specific Objetive.**\n\n**Responding to**\n\n**protection risks**\n\n**associated with**\n\n47%\n\n# 47\n\n47%\n\n**Responding**\n\n**to protection**\n\n**risks associated**\n\n**with violence of**\n\n45%\n\n99%\n\n**Access to legal**\n\n**documentation***\n\n45%\n\n77% **Access to**\n\n**livelihoods**\n\n# 77\n\n**children**\n\n**Specialised**\n\n**protection services**\n\n_*Target does not consider birth certificates (EV-25)._\n\n77%\n\n**to people affected**\n\n**Access to** 13%\n\n**livelihoods**\n\n**response to**\n\n**protection risks**\n\n**by all forms of**\n\n36%\n\n36%\n\n# 3", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "# **Post distribution monitoring** **for Winter Cash Assistance** **for Syrian Refugees (2020-2021)**\n\n#### February 2021", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### **I. Table of contents**\n\nI. Table of contents\n\nII. Introduction\n\nIII. Methodology\n\nIV. Key findings\n\nV. Demographics\n\nVI. Shelter and Household Assets\n\nVII. Income Sources and Debt\n\na) Income\n\nb) Debt\n\nVIII. Accessing Cash assistance\n\na) Amount of assistance received\n\nb) Spending cash assistance\n\nc) Card Distribution\n\nd) Withdrawing assistance at ATMs\n\nIX. Risks and problems related to the cash assistance\n\nX. Markets and shops\n\nXI. Expenditure\n\nXII. Outcomes\n\nXIII. Well being\n\nXIV. Coping mechanisms\n\na) Livelihoods coping strategies\n\nb) Food coping strategies\n\nXV. Accountability\n\nXVI. Difference between food assisted and non-assisted\n\n1\n\n2\n\n2\n\n2\n\n4\n\n5\n\n7\n\n7\n\n7\n\n8\n\n8\n\n8\n\n8\n\n8\n\n9\n\n10\n\n10\n\n10\n\n11\n\n12\n\n12\n\n13\n\n14\n\n15", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### **II. Introduction**\n\nThis report presents the Post Distribution Monitoring (PDM) results for the winter cash assistance programs targeting\nSyrian refugees in Lebanon.\n\nDuring the 2020-2021 winter season, UNHCR assisted close to 200,000 Syrian refugee families. UNHCR aimed at\nsupporting vulnerable families who are faced with increased stress due to extreme weather conditions, coupled with\nalready limited resources. In the winter season of 2020/2021, families in Lebanon not only faced challenges of the\nwinter season but also additional challenges due to the deteriorating national economy and COVID19 pandemic.\nAdditionally, strains on the economy and the banking sector made the use of ATMs in the country more restricted.\nInflation and increased prices made it more difficult for families to meet their most basic needs.\n\nUNHCR provided assistance to refugees through an unconditional, unrestricted cash transfer as part of the winter\ncash assistance program (WinCAP). Previously conducted research has shown that refugee families in Lebanon have\nincreased expenditure during the winter months linked to additional needs. This is coupled with a decrease in the\navailability of income generating activities, which are usually more accessible in warmer months.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Starting November 2020, and through the season, UNHCR provided a one-off cash payment of LBP 954,000 to\nrefugee families in an effort to help them meet the additional basic needs brought about by the winter season.\n\nThe cash transfer was redeemable through an ATM card from ATMs and/or through direct payment in shops equipped\nwith POS across the country in the local currency.\n\n### **III. Methodology**\n\nThere were 531 valid survey responses in this data collection exercise. A simple random sample was selected from the\nlist of beneficiaries who received winter cash assistance during the month of December 2020 representing all of\nLebanon. Data collection was administered by phone and took place between the 3rd and the 17th of February 2021.\nData collection occurred through trained partner staff  by phone.\n\n### **IV. Key findings**\n\n##### **Process**\n\n- Only 46.7% of households mentioned that the amount of cash they received was the amount expected\n\n- Half of the interviewees mentioned that the male head of household was the decision-maker on how to spend the\ncash\n\n- About 51.6% of interviewed households attended a distribution of red cards in the last three months.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- The average time that the card distribution attendees took to arrive at the distribution site was 28.6 minutes. The\naverage transportation cost to the distribution site cost was 7,803 LBP. Most families were satisfied with the\ndistribution process (94.6%).\n\n- The vast majority of households who received assistance (98.7%) withdrew it from ATM. The average cost of\ntransportation to the ATM was 5,270 LBP.\n\n- Most households (98%) reported not facing any safety risk related to receiving, keeping, or spending the cash.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "##### **Outcomes**\n\n- More than 63 % of families had enough winter items such as blankets, heaters, mattresses, and cloths\n\n- Most of the households (95.2%) mentioned that they were able to find the items and services needed in the markets\nand shops\n\n- Most of the respondents (87.1%) mentioned that they had spent the full amount received from UNHCR by the time\nof the interview.\n\n- The top three expenditures as rated by respondents were food (1st), rent (2nd), firewood, or fuel for cooking or\nheating (3rd).\n\n- Most of the respondents (98.7%) agreed that the cash assistance improved their living conditions, reduced their\nfinancial burden (98.95%), and reduced feelings of stress (98.7%).\n\n- Most households (72%) were able to meet half or less than half of their basic needs\n\n##### **Socioeconomic conditions and well being**\n\n- The top three sources of income were cash assistance through ATM from humanitarian organizations, credit debts,\nand income for work, which was similar to 2019-2020 PDM results\n\n- The majority of families (87.9%) had borrowed money in the last three months, which was similar to 2019-2020 PDM\nresults", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- The average overall debt amount that has not been paid back was 1,846,817 LBP which was higher than the average\ndebt for WINCAP only assisted group (1,475,605) in 2019-2020 PDM results\n\n- The majority of interviewed households (63.9%) indicated being dissatisfied or very dissatisfied with their lives, that\nthey feel their standard of living is getting worse (71.7%), and that they worry about the money always or most of the\ntime (88.4%).\n\n- About 99.2% of households had at least one food coping strategy, 77.6% had at least one stress coping strategy,\n74.3% had at least one crisis coping strategy, 1.7% of households had at least one emergency coping strategy\n\n- The top four coping strategies were: 1. Reduce food expenditure; 2. Reduce expenditure on hygiene items, water,\nbaby items, health, or education; 3. Take out new loans or borrowed money; 4. Skip paying rent/debt repayments\n\n- The percentage of households reporting no increase in prices of key items/services over the last four weeks was\n7.9%.\n\n### **V. Demographics**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The sample was randomly distributed across field offices, including 32.6% of households in Beirut and Mount Lebanon,\n28.2% in the North, 26.2% in Bekaa, and 13% in the South. About 30.3% of the interviewees were females, whereas\n66.7% were males. The interviewees' age was mainly between 18 and 35 years old (58.4%) and 36 to 59 years old\n(36.7%). The majority of those interviewed were the heads of households (81%). The remaining 19% were mainly\nspouses of the head of household (77.2%) or had another type of relationship such as mother/ father (8.9%),\ndaughter/son (0.4%), and other family relation (11.9%).\n\nMost household heads were males (81%), while the remaining 19% were female-headed households. The age of heads\nof households was mainly between 18 and 35 years old (52.4%) and between 36 and 59 years old (51.60%), while only\na few were 60 years old and above (6%)—the average number of individuals per household is 4.4 individuals.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Table 1: Age and Gender groups of interviewed households**\n\n|Male|Female|Total|\n|---|---|---|\n|**246**|**213**|**459**|\n|**384**|**333**|**717**|\n|**528**|**570**|**1098**|\n|**25**|**35**|**60**|\n\n**1,151**\n\n**< 5**\n\n**5-17**\n\n**18-59**\n\n**60 and above**\n\n**Total**\n\n**1,183**\n\n**2,334**\n\n**Figure 1: Age and gender distribution of interviewed households**\n\n**19.7%**\n\n**30.7%**\n\n**47.0%**\n\n**2.6%**\n\n**100.0%**\n\n**60 and** ~~**ab**~~ **ove**\n\n**18-59**\n\n**5-17**\n\n**<5**\n\n**60**\n\n**40** **20** **0** **20** **40** **60**\n\nAround 30.5% of the households had pregnant or lactating women, 10% of the respondent households had a person\nwith a disability, 31.8% had individuals with chronic illness, 17.9% had temporary illness or injury, 1.3% of households\nhad individuals with serious medical conditions, and 2.3% had elderly who are unable to take care of themselves.\n\n**Figure 2: Households with individuals with specific needs**\n\n**30.5%**\n\n**10%**\n\n**Pregnant/** **Disability (physical,**\n\n**Lactating** **sensorial,**\n\n**mental/intellectual)**\n\n**31.8%**\n\n**17.9%**\n\n**2.3%**\n\n**1.3%**\n\n**Chronic** **Temporary illness** **Serious medical** **Older person**\n\n**illness** **and/or injury** **condition** **unable to care for**\n\n**self**\n\n### **VI. Shelter and Household Assets**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Most of the refugees lived in apartments or houses (70 .8%), followed by tents (14.5%), and the remaining lived in other\ntypes of housing. The majority of refugee households lived in rented apartments/places (86.8%), 7.3% were hosted for\nfree, and 3.2% rented in exchange for work. The average rent per month among households who paid rent was 328,917\nLBP, while the median rent was 300,000LBP. The average rent was the highest in Beirut and Mount Lebanon and the\nlowest in Bekaa.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Table 2: Average rent per month**\n\n**Area** **Average rent per month in LBP**\n\n**Bekaa**\n\n**230,390**\n\n**Beirut and Mount Lebanon                 441,957**\n\n**North**\n\n**South**\n\n**National**\n\n**282,522**\n\n**316,641**\n\n**328,917**\n\nRegarding the households’ satisfaction with their shelters, 17.5% were dissatisfied, 3.4% were very dissatisfied, 33%\nwere satisfied, and 1.7% were very satisfied. The remaining 44.4% were neither satisfied nor dissatisfied. When it\ncomes to the relationship with the landlords, 50.3% of households stated that the relationship with the landlord was\npositive or very positive, whereas 43.2% stated that the relationship with landlords was neither negative nor positive,\n6.5% indicated having negative or very negative relationships with their landlords.\n\nMost beneficiary households (70.8%) indicated their landlords didn't know that they received winter cash assistance\nfrom UNHCR, whereas 20.9% indicated their landlords knew about receiving assistance, while 8.4% of households\nmentioned they don't know if their landlords knew about the assistance.\n\nAbout 29.2% of households mentioned that their shelter was affected by adverse weather conditions, 2.4% of\nhouseholds said they needed to procure additional shelter materials to secure or reinforce shelter for winter.\n\nAbout 63.8% of respondent households had enough winter clothes, 69.9% had enough mattress, 67.6% had enough\nblankets, and 66% indicated having enough heaters.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Figure 3: Households reporting on winter items availability**\n\n**63.8%**\n\n**HH**\n\n**Assets/Winter**\n\n**Clothes**\n\n**69.9%** **67.6%** **66.1%**\n\n**17.9%**\n\n**10%**\n\n**HH** **HH** **HH**\n\n**Assets/Mattresses** **Assets/Blankets** **Assets/Heater**\n\nMost of the families indicated not receiving core relief items during the winter (94.4%). About 4.3% of families\nindicated receiving blankets, 2.6% indicated receiving quilts, 1.7% indicated receiving winter clothes for adults or\nchildren, and 0.2% indicated receiving heaters.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### **VII. Income Sources and Debt**\n\n##### **a) Income**\n\nRegarding income, the top three income sources were cash assistance through ATM from humanitarian organizations,\ncredit debts (informal) from shops, friends hosts, and income for work (formal and informal).\n\nAbout 44.3% mentioned cash assistance as their first choice of income, followed by income for work (former or\ninformal) (26.4%) and 16.6% for Ecards used in WFP food shops. Regarding the second choice for income households,\nmainly mentioned credit/debts (informal) shops, friends hosts (38.5%), followed by cash assistance through ATM\n(30.2%), and  Ecards used in WFP food shops (15.3%). The third choice for income participant households mainly\nmentioned credit/debts (informal) shops, friends hosts (33.0%), while 43.3% of families had no third income source.\n\n##### **b) Debt**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The majority of families (87.9%) had borrowed money in the last three months. The primary reasons for debt were to\nbuy food (91.2%), to pay rent (67.7%), to buy medicine (39%), and to pay for health care such as a doctor or hospital visit\n(17.1%).\nAbout 89% of HH are on debt. For this group of people, the average national debt amount that has not been paid back,\nthe average was 2,082,081 LBP, while the median value was 1,600,000 LBP. The average amount of new debt in the last\n30 days was 700,822 LBP, while the median value was 600,000 LBP. The highest amount of total debt was in the south,\nwhile the average new debt was the highest in Beirut and Mount Lebanon.\n\n**Figure 4: Average debt in field offices**\n\n**National**\n\n**South**\n\n**North**\n\n**Beirut & Mount Lebanon**\n\n**Bekaa**\n\n**2,422,500**\n\n**500,000** **1,000,000** **1,500,000** **2,000,000** **2,500,000** **3,000,000**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### **VIII. Accessing Cash assistance**\n\n##### **a) Amount of assistance received**\n\nRegarding the amount of assistance, most the beneficiaries (98.6%) indicated receiving an amount that is 940,000 LBP\nor above. About 46.7% mentioned that the amount they received was the expected, 35.6% mentioned that it wasn't\nthe amount expected, whereas 17.7% said they don't know.\n\n##### **b) Spending cash assistance**\n\nRespondents mainly mentioned spending cash assistance in the supermarket (61.8%), in local markets (45.2%), and\nlocal shops (42.4%).\n\nMost families (99.8%) had no disagreement related to decisions on how to use cash assistance. About half of the\ninterviewees mentioned that the male head of household was the decision-maker on spending the cash, whereas\n39.5% of the family mentioned a joint decision between husband and wife. Only 10% mentioned that it was the woman\nhead of household was taking the decision.\n\n##### **c) Card Distribution**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "About 51.6% of interviewed households had a member who attended a red card distribution in the last three months.\nDistributions were mainly attended by the heads of households (76.3%), followed by the wife of the head of household\n(19%), and the remaining were other family members. About 87.6% of households who attended distribution mentioned\nthat the person who attended the distribution was available to answer the distributions' questions. Most of the distribution\nattendees (99.6%) indicated that the information received about the distribution was clear. Only one person mentioned\nthat the information received was unclear, and it was stated that they received the card without any information.\n\nAt the national level, the average time that the families took to arrive at the distribution site was 28.59 minutes. The\naverage transportation cost among those who had paid for transportation was 9,515 LBP. The average time spent at\nthe distribution site was around 56 minutes.\n\nMost families were satisfied with the distribution process (94.6%), while only 5% mentioned that they were neither\nsatisfied nor dissatisfied and only one family mentioned that they were not satisfied with the process. All families\nindicated that the distribution process was safe.\n\n##### **d) Withdrawing assistance at ATMs**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The vast majority of households who received assistance (98.7%) withdrew it from ATM; The remaining consisted of\nthose who didn’t withdraw through ATM including 0.4% of families made purchases directly at the supermarket with\nthe card, whereas 0.9% of households could not withdraw cash.\n\nRegarding withdrawal from the ATM, 24.1% of households indicated withdrawing the assistance on the same day of\nreceiving SMS, 33.9% withdrew the money the day after receiving the SMS, 19.6% withdrew money two days after\nreceiving the SMS, and 20.2% withdrew money more than three days after receiving the SMS, and 1.3% went before\nreceiving the SMS.\n\nMost of the respondents (81.7%) were the people who went to the ATM to withdraw money. Most of those who went\nto withdraw the cash were heads of households (80.6%), followed by the spouse of the head of household (9.5%), other\nhousehold member (4.3%), and not a household member (4.4%). Only two families reported that the person who\nwithdrew the money asked for a fee in return, and one family only reported having issues with the person who\nwithdrew the money for them.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Regarding transportation to the ATMs, households mainly relied on taxi (36.1%), bus (25.4%), and walking (25.6%).\nAbout 67% of the households paid transportation cost. The average transportation cost for those who paid for\ntransportation to the ATM was 8,502 LBP. This cost varied among field offices; the mean cost was the lowest in Beirut\nand Mount Lebanon (5267 LBP) and the highest in the South (16,667 LBP). The mean time to get to the ATM was 21.75\nmin. Time varied across regions: it was the highest in the South at 27.6 minutes, followed by the North at 26.2 minutes.\n\n**Table 3: Average ATM transportation Cost and Time to Reach per Area**\n\n**Area** **Average transportation cost (LBP )** **Average Length in time to reach ATM (min)**\n\n**Bekaa**\n\n**Beirut and Mount Lebanon**\n\n**North**\n\n**South**\n\n**National**\n\n**5684**\n\n**5267**\n\n**8494**\n\n**16679**\n\n**8502**\n\n**20.74**\n\n**16.63**\n\n**26.24**\n\n**27.62**\n\n**21.75**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "About 37.8% of the households mentioned that they had to wait in line before using the ATM. The average waiting time\nat the ATM was 54 minutes, while the median time was 30 minutes. About 24.5% of households mentioned facing very\nlong waiting times at ATMs, whereas 5% mentioned that they went and found no cash available at the ATM. Around\n1.5% said they faced mistreatment at ATM. However, 1.1% had their card blocked after several attempts, and one\nfamily only (0.2%) mentioned they needed to pay money to use the ATM. Most families (98.7%) withdrew the full\namount from the first time.\n\n### **IX. Risks and problems related to the cash assistance**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The majority of households reported not facing any problems while going to withdraw or get the money (98.1%) when\nkeeping money at home (99.1%) or going to spend money (99.1%). Most families indicated not having problems such as\nthe registered person is not being available to withdraw money (99.4%), having wrong pin code (99.4%), or issues such\nas poor service at the bank (99.8%), or markets or shops refusing to serve them (99.2%). All families confirmed not\nneeding to pay additional favors to spend or withdraw money. Only 1.6% of the families expressed being worried about\nsafety issues, such as COVID 19 and theft.\n\nRegarding COVID-19 related restrictions, 15.9% of the families indicated that they had movement restrictions when\nwithdrawing cash assistance, 13.8% of families had movement restrictions when spending the money, and only 1.7%\nhad issues when withdrawing or spending money due to a household member having contracted COVID-19.\n\nIn summary, 2 % of households reported feeling at risk (unsafe) receiving, keeping, or spending the cash assistance, and\n18% of households reported having one or more problems receiving, keeping, or spending the cash assistance,\nincluding COVID related restrictions.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Most households (99.6%) mentioned that they had faced no issues with the refugees who did not receive cash\nassistance. Only two families mentioned having tension with other refugees who didn't receive assistance. All families\ninterviewed indicated that they didn't face any problems with the host community related to receiving cash assistance.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### **X. Markets and shops**\n\nMost of the households (95.2%) mentioned that they could find the items and services needed in the markets and shops\nwhile88.6% said that they were able to find the right quality items and services in the market. However, 82.9%\nreported an increase in the price of the items and services provided. These included 55.9% of households reporting an\nincrease in food prices, 6.8% claiming an increase in the baby product's prices, 3% mentioned increase in the cost of\nfuel and heating, 8% on hygiene and cleaning items, and 41.6% noted an increase in all items’ prices.\n\n### **XI. Expenditure**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Most of the respondents (87.1%) mentioned that they had spent the full amount received from UNHCR, 4.8% spent\nmore than half, 5.1% spent half, and 2.7% spent less than half. The top three expenditures as rated by respondents were\nfood (1st), rent (2nd), firewood, or fuel for cooking or heating (3rd). Cash was mainly spent on food (83.5% of\nhouseholds) with an average of 416,212 LBP spent, followed by rent (45.5% of households) with an average of 357,393\nLBP spent, debt repayment (33.1% of households) with an average of 428,259 LBP spent, firewood or fuel for cooking\nor heating (32.7%) with an average of 349,645 LBP spent, health costs (29.8%) with an average of 260,490 LBP,\nhygiene items (21.3%) with an average of 106,464 LBP spent.\n\n### **XII. Outcomes**\n\nThe majority of respondents (98.3%) mentioned that the assistance improved their living conditions and reduced their\nfeelings of stress. Also, 98.9% indicated that the assistance contributed to reducing their financial burden. Table 4\nshows the detailed responses\n\n**Table 4: Households outcomes**\n\n**1.3%**\n\n**27.4%**\n\n**55.9%**\n\n**15.4%**\n\n**100.0**\n\n**Not at all**\n\n**Slightly**\n\n**Moderately**\n\n**Significantly**\n\n**Total**\n\n|Improved your living conditions|Reduced the financial burden of their household|\n|---|---|\n|**1.3%**|**1.1%**|\n|**26.8%**|**28.9%**|\n|**51.3%**|**49.4%**|\n|**20.5%**|**20.5%**|\n\n**100.0**\n\n**100.0**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Only 7.4% of refugee households mentioned they met all their basic needs, 18.3% met more than half but not all their\nneeds. The majority of families (71.7%) indicated they met half or less than half of their needs, while 2.7% did not meet\ntheir needs at all.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Table 4: Households outcomes**\n\n**Area** **Average rent per month in LBP**\n\n**Bekaa**\n\n**230,390**\n\n**Beirut and Mount Lebanon                 441,957**\n\n**North**\n\n**South**\n\n**National**\n\n**282,522**\n\n**316,641**\n\n**328,917**\n\n**Figure 5: Extent to which needs are met**\n\n**All**\n\n**More half (but not all)**\n\n**Half**\n\n**Less than half**\n\n**Not at all**\n\n**41%**\n\nThe primary cited unmet needs that were not affordable were food (59.1% of households), rent (55.4% of households),\nand debt repayment (39.9% of households). Many families mentioned that they had other unmet needs that they could\nnot afford, such as hygiene items (30%), clothes and shoes (28.5%), and health costs, including medicines (27.7%).\n\n### **XIII. Well being**\n\nThe majority of interviewed households (63.9%) indicated being dissatisfied or very dissatisfied with their lives, 27.2%\nwere neither satisfied nor dissatisfied, and only 8.9% were satisfied. The majority of respondents (71.7%) mentioned that\nthey feel their standard of living is getting worse and that they worry about the money always or most of the time (88.4%).\n\n**Figure 6: Feeling about the standard of living** **Figure 7: Worrying about money**\n\n**1%**\n\n**Getting better** **Getting worse** **The same** **Always** **Most of the time** **Sometimes**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "### **XIV. Coping mechanisms**\n\n##### **a) Livelihoods coping strategies**\n\nAbout 99.2% of households had at least one food coping strategy, 77.6% had at least one stress coping strategy, 74.3%\nhad at least one crisis coping strategy, and 1.7% had at least one emergency coping strategy. The main cited coping\nstrategies were reducing expenditure on food (78.5%), reduce expenditure on hygiene items, water, baby items, health,\nor education (72.8%), taking out new loans (66.9%), and skip paying rent or debt repayment (57.6%).\n\n**Figure 8: Livelihoods coping mechanisms**\n\n**Stop a child from attending school?**\n\n**Sell livelihood/productive assets in order to buy food or basic goods?**\n\n**(e.g. sold items such as a car, motorbike, plough, sewing machine, tools)**\n\n**Ask for money from strangers (begging)?**\n\n**Move to a poorer quality shelter**\n\n**Send household members under the age of 16 to work?**\n\n**Send a member of the household to work far away?**\n\n**Engage in activites for money or items that you feel puts you or other**\n\n**members of your household at risk of harm?**\n\n**Skip paying rent/debt repayments to meet other needs?**\n\n**Take out new loans or borrowed money?**\n\n**Reduce expenditure hygiene items, water, baby items, health, or education**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**in order to meet housegold food needs?**\n\n**Sell HH assets/goods (radio, furniture, jewellery)**\n\n**Reduce food expenditure**\n\n**Spent household savings**\n\n**Sold house/land**\n\n**Accept degrading unsuitable high risk dangerous or exploitative work**\n\n**Some members return to homeland**\n\n##### **b) Food coping strategies**\n\n**79%**\n\nThe reduced Coping Strategies Index (rCSI) includes the five most commonly used food-related coping strategies and\ntheir order of severity as a proxy indicator to measure access to food. The higher the rCSI, the more coping strategies\nhouseholds had to endure. The reduced food coping index score was the highest in the North with a value of 24.66,\nfollowed by BML 21.4, South 16.58, and Bekaa was the lowest with the value of 10.25.\n\n**Figure 9: Food coping reduced index score per area**\n\n**24.66**\n\n**21.41**\n\n**18.74**\n\n**16.58**\n\n**10.25**\n\n**Bekaa** **BML** **North** **South** **All**", "output": {"entities": {"named_data": ["reduced Coping Strategies Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Regarding the average number of days using different coping strategies, the most used coping strategies were relying\non less expensive or preferred food with an average of 5.39 days, followed by reducing the numbers of meals eaten per\nday with an average of 3.73 days, reducing the portion size of meals with an average of 3.45, and then restricted\nconsumptions of adults so that children can eat with 1.45 days.\n\n**Figure 10: Average numbers of days per week for food coping strategies**\n\n**Restrict consumption of female household members**\n\n**Sent HH members to eat elsewhere**\n\n**Restricted consumption of adults so that children could eat**\n\n**Spent days without eating**\n\n**Reduced portion size of meals**\n\n**Reduced the number of meals eaten per day**\n\n**Borrowed food and/or relied on help from friends/relatives**\n\n**Relied on less expensive/less preferred food**\n\n### **XV. Accountability**\n\n**5.39**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The majority of households (87.4%) indicated that they knew how to report on complaints and feedback regarding cash\nassistance. The main channels mentioned were a hotline (97.2%), complaints desk (6.9%), and complaints and suggestion\nbox (1.5%). Only 9% of the respondents have previously registered a complaint related to cash assistance. Complaints\nwere mainly about missing an upload of a particular month (45.85%), request to receive cash or food assistance (33.3%),\nlost, damaged or stolen card or pin and (10.4%), and card swallowed by ATM (6.3%). Complaints were mainly reported\nthrough the hotline (81.3%).\n\n### **XVI. Difference between food assisted and non-assisted**\n\nThe majority of the sample households (507; 95.4%) were either non-assisted or food-assisted [1] . The non-assisted\nconstitute 56% of this group, while the food assisted constitutes 44% of this group. In this section, outcomes such as\ndebt, expenditures, meeting needs, and coping mechanisms will be compared between these two groups.\nThe average current dept (unpaid) and the average new dept (last 30 days) were significantly lower (p <0.05) for the\nfood assisted group.\n\n**Table 5: Unpaid debt and new debt across groups**\n\n**Average Current Debt**\n\n**700,822**\n\n**740,246**\n\n**669,083**\n\n**18809.5; p = 0.014**\n\n**Overall sample**\n\n**Non-Assisted**\n\n**Food assisted**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Mann–Whitney U test (Food assisted vs non assisted)**\n\n**Average Current Debt**\n\n**2,082,081**\n\n**2,151,206**\n\n**1,959,067**\n\n**21975; p=0.038**\n\n_1One family was MCAP assisted, while the assistance status for the remaining files was not available in the database. The analysis in this section will focus on_\n_food assisted and non-assisted groups._", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Regarding the top 3 expenditures, the first two choices were identical among both groups. However, the third choice\nwas debt repayment for non-assisted and firewood/fuel for the assisted group.\n\n**Table 6: Top 3 expenditure across groups**\n\n**Order**\n\n**First**\n\n**Second**\n\n**Third**\n\n|Overall|Non-Assisted|\n|---|---|\n|**Food**|**Food**|\n|**Rent**|**Rent**|\n\n**Firewood/Fuel**\n\n**Debt repayment**\n\n**Assisted**\n\n**Food**\n\n**Rent**\n\n**Firewood/Fuel**\n\nThe results in table 6 show that the percentage of households meeting all or more than half of basic needs was higher\namong the food assisted group (28.1%) vs. the non-assisted group (25.2%). The same pattern was observed among\nhouseholds who met half of their needs. It was 35.3% among food assisted vs. 24.9% among non-assisted. However,\nthe percentage of households not meeting their needs at all or less than half of the needs was higher among\nnon-assisted (49.8%) vs. food assisted (36.7%). Thus, those who are food assisted were more likely to meet a higher\nportion of their needs.\n\n**Table 7 Meeting needs across groups**\n\n**Food Assisted**\n\n**7.7%**\n\n**20.4%**\n\n**35.3%**\n\n**36.2%**\n\n**0.5%**\n\n|Overall|Non-Assisted|\n|---|---|\n|**7.4%**|**7.8%**|\n|**18%**|**17.4%**|\n|**31%**|**24.9%**|\n|**41%**|**45.2%**|\n\n**4.6%**\n\n**Coverage of Basic needs**\n\n**All**\n\n**More than half but not all**\n\n**Half**\n\n**Less than half**\n\n**Not at all**\n\n**3%**\n\n**Table 8: Reduced food coping index across groups**\n\n**Overall sample**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Non assisted**\n\n**Food assisted**\n\n**ManWittney U test (Food assisted vs non assisted)**\n\n**Reduced food coping index score**\n\n**18.74**\n\n**19.52**\n\n**16.70**\n\n**26514.5; p=0.05**\n\nThe food coping index was significantly lower within the food-assisted group (16.7 vs. 19.52 within the non-assisted),\nindicating less food coping severity. Thus, those who are not assisted had a more severe food coping than their food\nassisted counterparts.\n\nTable 5 shows the differences in different coping strategies between non assisted and food-assisted groups. The\npercentage of households with at least one stress coping strategy was higher among non-assisted (79%) than those\nwho were food-assisted (73%). However, Chi-square test results confirm no significant differences among\nfood-assisted and non-assisted groups regarding the three coping strategies listed below.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Table 9: Coping strategies across groups**\n\n**Coping strategy**\n\n**HH with at least one Stress Coping strategy**\n\n**HH with at least one Crisis Coping Strategy**\n\n**HH with at least one Emergency coping strategy**\n\n|Overall|Non-Assisted|\n|---|---|\n|**77.6%**|**79%**|\n|**74.3%**|**75%**|\n\n**1.7%**\n\n**1%**\n\n**73%**\n\n**76%**\n\n**2%**\n\nRegarding wellbeing, a higher percentage of non-assisted (77%) indicated that their standard of living is getting worse\nas shown in table 10. In addition, a higher percentage of non-assisted were dissatisfied of very dissatisfied about their\nlives as shown in table 10.\n\n**Table 10: Wellbeing difference between assisted and non-assisted**\n\n**Standard of living**\n\n**Getting better**\n\n**Getting worse**\n\n**The same**\n\n**Satisfaction about life**\n\n**Very dissatisfied**\n\n**Dissatisfied**\n\n**Neutral**\n\n**Satisfied**\n\n|Overall|Not Assisted|\n|---|---|\n|**1%**|**1%**|\n|**71%**|**77%**|\n|**28%**|**23%**|\n|||\n|**22%**|**25%**|\n|**42%**|**46%**|\n|**27%**|**23%**|\n\n**9%**\n\n**7%**\n\n**2%**\n\n**63%**\n\n**35%**\n\n**18%**\n\n**37%**\n\n**33%**\n\n**12%**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "# **NEW ISSUES IN REFUGEE RESEARCH**\n\n**Research Paper No. 125**\n\n# **The Refugee Convention as a rights blueprint** **for persons in need of international protection**\n\n**Jane McAdam**\n\nFaculty of Law\nUniversity of Sydney\nAustralia\n\nE-mail : janem@law.usyd.edu.au\n\nJuly 2006\n\n**Policy Development and Evaluation Service**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Policy Development and Evaluation Service**\n\n**United Nations High Commissioner for Refugees**\n\n**CP 2500, 1211 Geneva 2**\n\n**Switzerland**\n\n**E-mail: hqep00@unhcr.org**\n\n**Web Site: www.unhcr.org**\n\nThese papers provide a means for UNHCR staff, consultants, interns and associates, as well\nas external researchers, to publish the preliminary results of their research on refugee-related\nissues. The papers do not represent the official views of UNHCR. They are also available\nonline under ‘publications’ at <www.unhcr.org>.\n\nISSN 1020-7473", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "**Introduction**\n\nSince coming into force in 1954, the Refugee Convention [1] has been the central\ninternational instrument on refugee status, supplemented by the 1967 Protocol [2] which\nextended its temporal and (with respect to some States) geographical application. In\nthe half-century since the Convention’s inception, international human rights law has\nevolved as a sophisticated system of rights and duties between the individual and the\nState, which has affected traditional notions of State sovereignty and behaviour in an\nunprecedented manner. [3] Yet, despite the influence of ‘international human rights law’\non the regulation of State behaviour, there has been a general reluctance by States,\nacademics and institutions to view human rights law, refugee law and humanitarian\nlaw as branches of an interconnected, holistic regime, [4] particularly when it comes to\ntriggering eligibility for protection beyond the scope of article 1A(2) of the\nConvention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Complementary protection is largely about this intersection. A feature of most\nwestern protection regimes, it describes protection granted by States to individuals\nwith international protection needs falling outside the 1951 Convention framework. [5] It\nmay be based on human rights treaties, such as the prohibition on _refoulement_\nexpressly in article 3 of the CAT [6] and impliedly in article 7 ICCPR, [7] or on more\ngeneral humanitarian principles, such as providing assistance to persons fleeing from\n\nA revised version of this paper is forthcoming in an edited collection to be published by Berghahn\nBooks, edited by the present author.\n\n---\n[7] International Covenant on Civil and Political Rights (adopted 16 Dec 1966, entered into force 23", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "1 Convention relating to the Status of Refugees (adopted 28 July 1951, entered into force 22 April\n1954) 189 UNTS 137.\n2 Protocol relating to the Status of Refugees (adopted 31 January 1967, entered into force 4 October\n1967) 606 UNTS 267.\n3 eg Human Rights Act 1998 c 42 (UK).\n4 It is refreshing to note, however, that the 2006 International Law Association (British Branch)\nconference considered these issues under the general conference theme: ‘Tower of Babel: International\nLaw in the 21 [st] Century—Coherent or Compartmentalised?’.\n5 Australia is exceptional in having no formal system of complementary protection. The only means for\nan asylum seeker to have a non-Convention protection claim considered in Australia is if, following a\nnegative primary decision and an unsuccessful appeal to the Refugee Review Tribunal, he or she seeks\nto invoke the non-compellable, non-delegable and non-reviewable discretion of the Minister for\nImmigration and Multicultural and Indigenous Affairs under section 417 of the Migration Act 1958\n(Cth). For a critique of this section and suggested alternatives to it, see Senate Select Committee on\nMinisterial Discretion in Migration Matters: Senate Select Committee on Ministerial Discretion in\nMigration Matters _Report_ (Commonwealth of Australia March 2004)\n<www.aph.gov.au/senate/committee/minmig_ctte/report/index.htm> (8 January 2005) esp Ch 8. For a\ndiscussion of how complementary protection might operate in the Australian context, see eg Refugee\nCouncil of Australia, National Council of Churches in Australia and Amnesty International Australia\n‘Complementary Protection: The Way Ahead’ (April 2004)\n<www.refugeecouncil.org.au/docs/current_issues/Complementary_Protection_model_files/Complemen\ntary%20Protection%20Model-.doc> (2 November 2005); UNHCR Australia ‘Discussion Paper:\nComplementary Protection’ (No 2, 2005) <www.unhcr.org.au/pdfs/Discussion22005.pdf> (2\nNovember 2005). New Zealand does not at present have a complementary protection regime, but it is\nhighly likely that one will be introduced into the Immigration Act as part of current reforms: Dept of\nLabour ‘Immigration Act Review: Discussion Paper’ (April 2006) section 14.\n6 Convention against Torture and Other Cruel, Inhuman or Degrading Treatment or Punishment\n(adopted 10 December 1984, entered into force 26 June 1987) 1465 UNTS 85.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "generalized violence. [8] (It is on this latter basis that temporary protection in mass\ninflux situations is premised.) Importantly, complementary protection derives from\nlegal obligations preventing return, rather than from compassionate reasons or\npractical obstacles to removal. Even though these latter instances of ‘protection’ may\nbe humanitarian in nature, they are not based on international protection obligations\nper se and therefore do not fall within the legal domain of ‘complementary\nprotection’.\n\nAt first glance, it appears that international law has little to say about the relatively\namorphous concept of complementary protection. Although there is longstanding\nState practice of protecting extra-Convention refugees, encompassed by such terms as\n‘de facto refugees’, ‘B status refugees’, ‘OAU and Cartagena-type refugees’ and\n‘humanitarian refugees’, the term ‘complementary protection’ appears in no\ninternational treaty and has no singular connotation in State practice. [9] An EXCOM\nConclusion adopted in October 2005 specifically refers to ‘complementary\nprotection’, but does not define it. [10]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The first binding, supranational instrument on complementary protection was\nconcluded in April 2004 by the European Union, but it adopts the term ‘subsidiary\nprotection’ instead. Beneficiaries of subsidiary protection are defined as those facing a\nreal risk of the death penalty or execution, torture or inhuman or degrading treatment\nor punishment in the country of origin, or a serious and individual threat to their life\nor person by reason of indiscriminate violence in situations of international or internal\narmed conflict, [11] and who do not meet the Convention definition of a refugee.\nSignificantly, the Qualification Directive also sets out the rights to which beneficiaries\nare entitled. This is a considerable step forward for some EU States, which previously\nsimply ‘tolerated’ the presence of non-removable persons but did not grant them a\nformal legal status. There are well-documented cases of the financial, social and\n\n---\n[11] Council Directive 2004/83/EC of 29 April 2004 on Minimum Standards for the Qualification and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "8 See D Perluss and JF Hartman ‘Temporary Refuge: Emergence of a Customary Norm’ (1986) 26\nVirginia Journal of International Law 551; GS Goodwin-Gill ‘ _Non-Refoulement_ and the New Asylum\nSeekers’ (1986) 26 Virginia Journal of International Law 897; cf K Hailbronner ‘ _Non-Refoulement_ and\n“Humanitarian” Refugees: Customary International Law or Wishful Legal Thinking?’ (1986) 26\nVirginia Journal of International Law 857.\n9 See survey of State practice in R Mandal ‘Protection Mechanisms outside of the 1951 Convention\n(“Complementary Protection”)’ UNHCR Legal and Protection Policy Research Series, PPLA/2005/02\n(June 2005). For discussion of OAU Convention refugees, see text to n72. Refugees under the\nCartagena Declaration include ‘persons who have fled their country because their lives, safety or\nfreedom have been threatened by generalized violence, foreign aggression, internal conflicts, massive\nviolation of human rights or other circumstances which have seriously disturbed public order’:\nCartagena Declaration on Refugees (22 November 1984) in Annual Report of the Inter-American\nCommission on Human Rights OAS Doc OEA/Ser.L/V/II.66/doc.10, rev.1, 190–93 (1984–85)\nConclusion 3.\n10 ExCom Conclusion No 103 (LVI) ‘The Provision of International Protection including through\nComplementary Forms of Protection’ (2005). For background discussion paper: Mandal (n9); ExCom\nStanding Committee 33 [rd] Meeting ‘Providing International Protection including through\nComplementary Forms of Protection’ UN Doc EC/55/SC/CRP.16 (2 June 2005); on original\nrecommendation for an ExCom Conclusion: UNHCR _Agenda for Protection_ (2 [nd] edn March 2000) 34;\nfor text of preliminary draft: Global Consultations on International Protection ‘Complementary Forms\nof Protection’ UN Doc EC/GC/01/18 (4 September 2001) [11].\n\n---\n[11] Council Directive 2004/83/EC of 29 April 2004 on Minimum Standards for the Qualification and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "psychological hardship suffered by persons left in legal limbo. [12] However, rather than\nrecognizing the _need_ for protection as triggering protection entitlements equivalent to\nthose of Convention refugees, part of the political compromise reached in drafting the\nQualification Directive was the dilution of standards for beneficiaries of _subsidiary_\nprotection. While certain delegations sought to justify a secondary status on the\nground that subsidiary protection needs are of a more temporary nature—an assertion\nnot supported by empirical evidence—ultimately no legal justification was offered to\nsupport the establishment of a protection hierarchy. [13] In addition to the unjustified\ndilution of subsidiary protection beneficiaries’ rights, differentiation in treatment may\nlead to States favouring subsidiary protection by ‘defining out’ categories of persons\nwho legitimately fall within article 1A(2), so as to avoid the more stringent\nobligations required for Refugee Convention refugees. Procedurally, it may also\ncreate an incentive for appeal by beneficiaries of subsidiary protection, attempting to\n‘upgrade’ their status. [14]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "This paper seeks to establish the fundamental conceptual connections between\ninternational refugee law and human rights law in order to argue that under\n_international_ law, beneficiaries of protection, whether as Convention refugees or\notherwise, are entitled to an identical status. While there are clear policy reasons why\nthis should be the case, there are also cogent legal arguments that support the\nextension of Convention status to extra-Convention refugees. These are based on a\nconceptualization of international law as a body of interrelated norms that must be\ninterpreted in relation to, and be informed by, each other.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The discussion begins by reflecting on the inadequacy of human rights law in\nproviding a legal _status_ for beneficiaries of complementary protection. I argue that\nwhile human rights attach to all persons in principle—irrespective of their nationality\nor formal legal status [15] —in practice such characteristics can significantly affect the\nextent of rights an individual is actually accorded. In reality, States _do_ differentiate\nbetween the rights of citizens and the rights of aliens (and even between different\ncategories of aliens), premising this on their sovereign right to determine who remains\nin their territories and under what conditions. While the rights set out in the Refugee\nConvention are not inherently superior to those in the universal human rights treaties,\n\n---\n[15] Although certain exceptions exist with respect to political rights reserved for citizens (art 25 ICCPR);", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "12 See eg _Ahmed v Austria_ (1997) 24 EHRR 278, and discussion in O Andrysek ‘Gaps in International\nProtection and the Potential for Redress through Individual Complaints Procedures’ (1997) 9\nInternational Journal of Refugee Law 392.\n13 GS Goodwin-Gill and A Hurwitz ‘Memorandum’ in Minutes of Evidence Taken before the EU\nCommittee (Sub-Committee E) (10 April 2002) [19], in House of Lords Select Committee on the EU\n_Defining Refugee Status and Those in Need of International Protection_ (The Stationery Office London\n2002) Oral Evidence 2–3. This is contrasted to Canadian practice relating to ‘protected persons’:\nImmigration and Nationality Act 2001 ss 95–97.\n14 House of Lords Select Committee (n13) [102], [111]. The Minister (Angela Eagle MP Parliamentary\nUnder Secretary of State at the Home Office) acknowledged that this already happens. For a discussion\nof appeal processes, see J McAdam ‘Complementary Protection and Beyond: How States Deal with\nHuman Rights Protection’ UNHCR _New Issues in Refugee Research_ Working Paper No 118 (Geneva\nAugust 2005).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "being largely based on the latter, [16] they are applied in a different way. Whereas a\ngrant of Convention status entitles the recipient to the full gamut of Convention rights,\nno comparable status arises from recognition of an individual’s protection need under\na human rights instrument. The Refugee Convention alone creates a status recognized\nin domestic law. [17]\n\nThus, although I would like to be able to point to human rights law as offering a\ncomplementary and, in part, more generous set of rights than the Refugee Convention,\nthe generality and vagueness of those rights, combined with a lack of implementing\nmechanisms at the domestic level, make them in practice comparatively weak.\nAlthough the universal human rights instruments grant a comprehensive set of rights\nto all persons within a State’s jurisdiction, [18] international human rights law is strong\non principle but weak on delivery. [19]\n\n---\n[18] With the exception of certain rights granted to citizens only: see n15.\n[19] Thanks to Prof Chris McCrudden (Lincoln College, University of Oxford) for this description.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "It is for this reason that the paper then seeks to demonstrate, through historical\nanalysis, why the status set out in the Refugee Convention should attach to all those\nwhom the principle of _non-refoulement_ protects. This does not have to be viewed as\nan attempt to broaden the scope of article 1A(2), but rather as recognition that the\nwidening of _non-refoulement_ under customary international law and treaty requires a\nconcomitant consideration of the status which beneficiaries acquire. Though a\nspecialist treaty, the Refugee Convention nevertheless forms part of the corpus of\nhuman rights law, both informing and informed by it. Accordingly, with respect to the\nstatus it confers on protected persons, [20] the Convention acts as a type of _lex specialis_ .\nIt does not seek to displace the _lex generalis_ of international human rights law, but\nrather complements and strengthens its application.\n\n**The inadequacy of** _**non-refoulement**_ **plus human rights law alone**\n\nBeyond providing a widened threshold for claiming protection, international human\nrights law alone is an inadequate alternative source of substantive protection. Many\n\n---\n[20] Complications arise with respect to persons whom States cannot remove under human rights law, but", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "16 Many of the provisions of the Convention were based on the UDHR and the draft ICCPR and\nICESCR: see nn 47 and 48 below.\n17 H Lambert ‘Protection against _Refoulement_ from Europe: Human Rights Law Comes to the Rescue’\n(1999) 48 International and Comparative Law Quarterly 515, 519; _Ahmed v Austria_ (1997) 24 EHRR\n278; _BB v France_ App No 30930/96 (9 March 1998). It is not surprising that treaties such as the CAT\ndo not articulate a resultant status for those who benefit from human rights-based _non-refoulement_ . For\nexample, the purpose of the CAT was not to enumerate the rights of persons protected from\n_refoulement_, but rather to strengthen the existing prohibition of torture and other cruel, inhuman or\ndegrading treatment or punishment under international law through a number of supportive measures.\nSee JH Burgers and H Danelius _The United Nations Convention against Torture: A Handbook on the_\n_Convention against Torture and Other Cruel, Inhuman or Degrading Treatment or Punishment_\n(Martinus Nijhoff Publishers Dordrecht 1988). Hathaway argues that refugee rights consist of ‘an\namalgam of principles drawn from both refugee law and the [human rights] Covenants’, and that\nrefugee ‘status’ should now be understood as comprising a combination of these: JC Hathaway _The_\n_Rights of Refugees under International Law_ (CUP Cambridge 2005) 9. Certainly, where this is the case,\nthat more comprehensive status should also be accorded to beneficiaries of complementary protection\nfor the same reasons as advanced above.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "States undertake human rights obligations at the formal level, but do not ensure that\nthe rights subscribed to can actually be claimed. [21] Unless special measures are taken\nto ensure that such provisions are translated into national law, then certain benefits\nmay be inaccessible. [22] Even where individuals may not be barred from enjoyment of a\nright, ‘they are in practice often deprived of it inasmuch as it is dependent on the\nfulfilment of certain formalities, such as production of documents, intervention of\nconsular or other authorities, with which … they are not in a position to comply.’ [23]\nWhile human rights law requires States to respect the rights it sets out in relation to _all_\npersons within its jurisdiction or territory, the quality of each right may vary\ndepending on the individual’s legal position vis-à-vis the State. Thus, while the\n_standard_ of compliance with human rights law is international, the State retains\ndiscretion in its choice of _implementation_ [24] _-_ whether and how to incorporate treaty\nprovisions into domestic law.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "There is therefore a gap between the theory of human rights and the ability to enjoy\nthose rights. [25] As Hathaway notes, ‘[t]he divergence between the theory and the\nreality of international human rights law is strikingly apparent.’ [26] At the international\nlevel, the content of rights is very broad and ill-defined, and it may be possible for\nStates to ‘guarantee’ such rights without doing much towards their positive\nimplementation. A common problem is that State constitutions often guarantee rights\nonly to ‘citizens’, [27] making enforcement for non-citizens’ rights difficult. In 1967,\nWeis described international measures for safeguarding human rights as ‘modest’, [28]\nand nearly 25 years later Hathaway still characterized them as ‘generally sluggish and\nonly occasionally effective.’ [29] As Goodwin-Gill observes, the test of whether a treaty\nis effectively implemented domestically depends not on form alone, but on an overall\nassessment of practice. [30]\n\n---\n[29] Hathaway (n26) 113.\n[30] Goodwin-Gill and Kumin (n21) 4.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "It is this that makes reliance on human rights law, either alone or in combination with\n_non-refoulement_ under customary international law, [31] a precarious option. Even\nthough the Refugee Convention repeats many of the same rights as the universal\ntreaties, its retention as a specialist refugee instrument is not redundant. As Hathaway\nargues, refugee law has its own legitimacy, and coordination between refugee and\nhuman rights law should not lead to a downgrading of protection for persons in need\n\n---\n[31] For its scope, see E Lauterpacht and D Bethlehem ‘The Scope and Content of the Principle of _Non-_", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "21 GS Goodwin-Gill and J Kumin ‘Refugees in Limbo and Canada’s International Obligations’\n(Caledon Institute of Social Policy September 2000) 4.\n22 ibid 5.\n23 Ad Hoc Committee on Statelessness and Related Problems ‘A Study of Statelessness’ UN Doc\nE/1112, E/1112.Add.1 (NY August 1949); Andrysek (n12) 411.\n24 GS Goodwin-Gill _The Refugee in International_ Law (2nd edn OUP Oxford 1996) 237.\n25 UNHCR ‘Note on International Protection’ A/AC.96/898 (3 July 1998) [45].\n26 JC Hathaway ‘Reconceiving Refugee Law as Human Rights Protection’ (1991) 4 Journal of Refugee\nStudies 113, 113.\n27 ECOSOC Commission on Human Rights ‘Prevention of Discrimination: The Rights of NonCitizens’ (26 May 2003) E/CN.4/Sub.2/2003/23 [24]; ‘General Comment 15’ (n15) [3].\n28 P Weis ‘Human Rights and Refugees’ Lecture, Yale University Law School (7 November 1967) 13\n(Refugee Studies Centre (Oxford) Archive WEIS A21.6 WEI).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "of international protection. [32] Furthermore, if the substantive rights of beneficiaries of\ncomplementary protection were dependent on human rights law alone, the quality of\nprotection would be contingent on the combination of treaties ratified (and\nimplemented) by the State and status would consequently be very inconsistent. [33] For\nexample, while the ICESCR and many of the ILO Conventions cover similar rights to\narticles 17 to 24 of the Convention, certain parties to the Convention have not ratified\nthose instruments and hence they would not apply. [34]\n\n**The Refugee Convention as a human rights treaty** [35]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Given this state of affairs, it is important to inquire into whether there is a means of\nextending the protection which States recognize for Convention refugees to others in\nneed of international protection. Accordingly, this part of the paper considers the\nhistorical context in which the Refugee Convention arose, and takes a dynamic\napproach towards interpreting how that may have a bearing on the expansion of the\nprinciple of _non-refoulement_ to those falling outside the terms of article 1A(2). It does\nthis in light of the Convention’s humanitarian object and purpose [36] to ensure to\n‘refugees the widest possible exercise of … fundamental rights and freedoms’, [37] and\nby deriving or inferring [38] subsequent agreement between the contracting States and\nState practice bearing on the Convention’s interpretation. [39] Relevant examples include\nthe regional OAU Convention and Cartagena Declaration, the 2005 ExCom\nConclusion on complementary protection, and the various domestic regimes States\nhave consistently implemented in response to flows of extra-Convention refugees.\n\n---\n[39] Vienna Convention art 31(3).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The drafting of the 1951 Convention represented a ‘profound re-orientation’ in\nrefugee organizations, agreements and agendas, but it was ‘ _evolution_, not\n_revolution_ ’. [40] In 1947, the Commission on Human Rights adopted a resolution that\n‘early consideration be given by the United Nations to the legal status of persons who\ndo not enjoy the protection of any Government, in particular the acquisition of\n\n---\n[40] GS Goodwin-Gill ‘Editorial: The International Protection of Refugees: What Future?’ (2000) 12", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "32 Hathaway (n26) 117. States that are not party to the Convention do not have superior protection\nregimes: see eg BS Chimni ‘The Legal Condition of Refugees in India’ (1994) 7 Journal of Refugee\nStudies 378, 398; A Helton ‘What is Refugee Protection?’ (1990) Spec Issue International Journal of\nRefugee Law 119, 125.\n33 In monist States, international treaties have direct effect, whereas in dualist States, they must be\nimplemented domestically following ratification to be justiciable.\n34 W Kälin ‘The Legal Condition of Refugees in Switzerland’ (1994) 7 Journal of Refugee Studies 82,\n95.\n35 IC Jackson _The Refugee Concept in Group Situations_ (Martinus Nijhoff Publishers The Hague\n1999); UNHCR ‘Note on International Protection’ UN Doc A/AC.96/975 (2 July 2003) [49]–[52]\nemphasizes relevance of human rights law to refugee issues.\n36 Vienna Convention on the Law of Treaties (adopted 23 May 1969, entered into force 27 January\n1980) 1155 UNTS 331 art 31(1). Even though the Vienna Convention was adopted after the conclusion\nof the Refugee Convention, it is codifies principles of customary international law and is therefore\napplicable. ExCom has noted that refugee law is a dynamic body of law which is ‘informed by the\nobject and purpose’ of the Convention and Protocol ‘and by developments in related areas of\ninternational law, such as human rights and international humanitarian law’: ExCom Conclusion on\nComplementary Protection (n10) para (c).\n37 Refugee Convention Preamble.\n38 Goodwin-Gill (n24) 367, discussing the Vienna Convention rules in relation to interpretation of the\nRefugee Convention. .", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "nationality, as regards their legal status and social protection and their\ndocumentation.’ [41] At the request of ECOSOC, the Ad Hoc Committee on\nStatelessness and Related Problems was asked to draft a binding legal instrument to\nimplement articles 14 and 15 of the UDHR, [42] firmly cementing the Convention’s\nfoundations in human rights law. Its purpose was to ‘consolidate existing agreements\nand conventions and, further, to determine the status of those refugees who had so far\nenjoyed no protection under any of the existing instruments.’ [43] Although the\nConvention took as its departure point human rights principles contained in the\nUDHR, it revised, consolidated and substantially extended earlier agreements to\ncreate a new protection regime. [44] Many substantive provisions were based on\nprinciples of the UDHR [45] and the embryonic ICCPR and ICESCR, known then as the\ndraft Covenant on Human Rights. [46]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The Convention was to establish practical but universal standards [47] for the rights of\nrefugees that went beyond the lowest common denominator, ‘since a convention\nwould hardly be useful if it contained only the minimum acceptable to everyone.’ [48]\nEarly UNGA resolutions support its underlying human rights basis, with an emphasis\non assisting the most needy, [49] affirming basic principles relating to solutions, [50] and\nrecommending increased protection activities. [51]\n\nThe result is a specialist human rights treaty that reflects the tenets of the UDHR,\nICCPR and ICESCR in such provisions as the acquisition of property, the right to\nwork, housing, public education, public relief, labour legislation, social security, and\n\n---\n[49] UNGA Res 639 (VI) of 20 December 1952; UNGA Res 728 (VIII) of 23 October 1953.\n[50] UNGA Res 1166 (XII) of 26 November 1957; ECOSOC Res 686 (XXVI) B of 21 July 1958.\n[51] UNGA Res 1284 (XIII) of 5 December 1958 [1], in GS Goodwin-Gill ‘The Language of Protection’", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "41 Commission on Human Rights Report to ECOSOC on the 2nd Session of the Commission Held at\nGeneva from 2 to 17 December 1947 (1948) UN Doc E/600 [46], in P Weis ‘Human Rights and\nRefugees’ (1971) 1 Israel Yearbook on Human Rights 35, 37.\n42 Universal Declaration of Human Rights (adopted 10 December 1948) UNGA Res 217A (III); Ad\nHoc Committee on Statelessness and Related Problems, First Session ‘Summary Record of the 1 [st]\nMeeting’ (NY 16 January 1950) UN Doc E/AC.32/SR.1 (23 January 1950) [4] (Secretariat).\n43 Conference of Plenipotentiaries on the Status of Refugees and Stateless Persons ‘Summary Record of\nthe 2 [nd] Meeting’ (Geneva 2 July 1951) UN Doc A/CONF.2/SR.2 (20 July 1951) 9 (High\nCommissioner).\n44 Refugee Convention Preamble.\n45 ‘Comments on the Draft Convention and Protocol: General Observations’ Annex II to Ad Hoc\nCommittee on Statelessness and Related Problems ‘Draft Report of the Ad Hoc Committee on\nStatelessness and Related Problems’ (16 January–February 1950) UN Doc E/AC.32/L.38 (15 February\n1950) 36 (art 3 non-discrimination), 46 (art 26 education); Ad Hoc Committee on Statelessness and\nRelated Problems ‘Refugees and Stateless Persons: Compilation of the Comments of Governments and\nSpecialized Agencies on the Report of the Ad Hoc Committee on Statelessness and Related Problems’\n(Document E/1618) UN Doc E/AC.32/L.40 (10 August 1950) 31 (France on UDHR art 29(1)).\n46 ‘Comments on the Draft Convention and Protocol: General Observations’ (n45) 58; see UN Doc\nE/1572, 12 (art 32 (then art 27) expulsion).\n47 Conference of Plenipotentiaries on the Status of Refugees and Stateless Persons ‘Summary Record of\nthe 2 [nd] Meeting’ (Geneva 2 July 1951) UN Doc A/CONF.2/SR.2 (20 July 1951) 18 (High\nCommissioner); Conference of Plenipotentiaries on the Status of Refugees and Stateless Persons\n‘Summary Record of the 3 [rd] Meeting’ (3 July 1951) UN Doc A/CONF.2/SR.3 (19 November 1951) 10\n(France).\n48 Ad Hoc Committee on Statelessness and Related Problems, First Session ‘Summary Record of the\n25 [th] Meeting’ (NY 10 February 1950) UN Doc E/AC.32/SR.25 (17 February 1950) [68].", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "freedom of movement. [52] Moreover, it reinforces States’ protection of refugees as an\ninternational legal duty, arising from article 14 of the UDHR and embodied in binding\nform by the principle of _non-refoulement_ in article 33 of the Convention. As one\ncommentator remarks: ‘The framers’ unambiguous reference in the Preamble of the\n1951 Convention to the Universal Declaration of Human Rights indicates a desire for\nthe refugee definition to evolve in tandem with human rights principles.’ [53]\nLauterpacht and Bethlehem stress that the law on human rights that has emerged since\nthe Convention’s conclusion is ‘an essential part of [its] framework … that must, by\nreference to the ICJ’s observations in the _Namibia_ case, be taken into account for\npurposes of interpretation.’ [54] UNHCR has also emphasized that:", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The human rights base of the Convention roots it quite directly in the\nbroader framework of human rights instruments of which it is an integral\npart, albeit with a very particular focus. The various human rights treaty\nmonitoring bodies and the jurisprudence developed by regional bodies\nsuch as the European Court of Human Rights and the Inter-American\nCourt of Human Rights are an important complement in this regard, not\nleast since they recognize that refugees and asylum-seekers benefit both\nfrom specific Convention-based protection and from the range of general\nhuman rights protections as they apply to all people, regardless of\nstatus. [55]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "While developments in human rights law may shape interpretations of ‘persecution’, [56]\nthey may also _independently_ form grounds for non-removal. Article 3 CAT, article 7\nICCPR and article 3 ECHR [57] are recognized sources of human rights _non-refoulement_\n(or complementary protection) which prohibit removal in circumstances additional to\n(and sometimes overlapping with) article 1A(2). External to and independent of the\nConvention, [58] the instruments provide only a trigger for protection and do not\nelaborate a resultant legal status. The main problem with the EU Qualification\nDirective, and one which has characterized many ad hoc complementary protection\nschemes, is that beneficiaries do not receive the same level of rights as Convention\nrefugees. In so far as there is no legal justification for distinguishing between the\nstatus granted to Convention or extra-Convention refugees, [59] it makes sense that the\n\n---\n[58] Although some States may procedurally determine the order in which protection may be invoked.\n[59] UNHCR’s Observations on the European Commission’s Proposal for a Council Directive on", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "52 J Patrnogic ‘International Protection of Refugees in Armed Conflicts’ (reprinted by UNHCR\nProtection Division from Annales de Droit International Médical (July 1981)) section 4.\n53 MR von Sternberg The Grounds of Refugee Protection in the Context of International Human Rights\nand Humanitarian Law: Canadian and United States Case Law Compared (Martinus Nijhoff The Hague\n2002) 314.\n54 Lauterpacht and Bethlehem (n31) [75].\n55 UNHCR ‘Note on International Protection’ UN Doc A/AC.96/951 (13 September 2001) [4].\n56 See JC Hathaway The Law of Refugee Status (Butterworths Canada 1991) 112, approved in Horvath\nv Sect’y of State for the Home Dept [2001] 1 AC 489 (HL) 495 (Lord Hope of Craighead); Sepet v\nSect’y of State for the Home Dept [2002] 1 WLR 856 (HL) [7] (Lord Bingham); Ullah v Sect’y of\nState for the Home Dept [2004] UKHL 26 [32] (Lord Steyn); International Association of Refugee\nLaw Judges Human Rights Nexus Working Party ‘Rapporteur’s Report’ (1998 Annual Conference\nOttawa 12–17 October 1998) 8. See eg gender-related persecution.\n57 Convention for the Protection of Human Rights and Fundamental Freedoms (European Convention\non Human Rights, as amended) (4 November 1950).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Convention, as a ‘Magna Carta for the persecuted’, [60] applies to both. It is argued that\nsince the Convention is itself a specialist human rights instrument, the protection\nconceptualization it embodies is necessarily extended by developments in human\nrights law, rather than via the conventional means of a protocol. It therefore acts as a\nform of _lex specialis_ which applies to persons encompassed by that extended concept\nof protection.\n\n**‘Humanitarian refugees’: Article 1A(1)**\n\nAnalysis of the Convention’s conceptualization of ‘protection’ invariably focuses on\nthe refugee definition in article 1A(2), since an individual must satisfy its\nrequirements to trigger Convention status. Article 1A(1), which extends the benefits\nof the 1951 Convention to any person who\n\n[h]as been considered a refugee under the Arrangements of 12 May 1926\nand 30 June 1928 or under the Conventions of 28 October 1933 and 10\nFebruary 1938, the Protocol of 14 September 1939 or the Constitution of\nthe International Refugee Organization", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "is generally overlooked as an historical remnant. Although eligibility under article\n1A(1) is retrospective, the fact that the Convention recognizes all previous refugee\ndefinitions as giving rise to Convention status is significant, since they typically\nprotected victims of armed conflict or communal violence. The incorporation of these\ndefinitions necessarily broadens the Convention’s conceptual basis of protection,\nmaking it difficult to sustain the argument that, conceptually, the Convention does not\nsupport the grant of its international legal status to persons fleeing situations of armed\nconflict or communal violence. [61] This has particular significance for persons seeking\ncomplementary protection on the basis of civil war, and challenges the EU’s current\napproach of creating a new and separate protection status for such persons.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Furthermore, even though an applicant today cannot invoke an article 1A(1)\ninstrument as the basis of an asylum claim, the fact that Convention status flows from\nthe definitions contained in those instruments, which embody what Melander has\ntermed the ‘humanitarian refugee’ concept, [62] makes it more difficult to justify\ndifferential treatment for persons seeking complementary protection on similar\ngrounds. Not only has State practice continued to recognize both ‘humanitarian’ and\nConvention refugees, but the dominant legal refugee instrument implicitly retains the\nhumanitarian concept of protection within its definitional provision, further\nilluminating the Convention’s object and purpose. [63]\n\n---\n[63] Vienna Convention art 31(1).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "November 2001) [46]; UNHCR ‘Note on Key Issues of Concern to UNHCR on the Draft Qualification\nDirective’ (March 2004) 2.\n60 Conference of Plenipotentiaries on the Status of Refugees and Stateless Persons ‘Summary Record of\nthe 19 [th] Meeting’ (Geneva 13 July 1951) UN Doc A/CONF.2/SR.19 (26 November 1951) 27\n(International Association of Penal Law).\n61 Of course, many of those fleeing such circumstances may qualify for protection under article 1A(2).\nFor discussion of this, see Mandal (n9) [21]–[24].\n62 G Melander ‘Refugee Policy Options—Protection or Assistance’ in G Rystad (ed) _The Uprooted:_\n_Forced Migration as an International Problem in the Post-War Era_ (Lund University Press Lund\n1990) 146–47.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Thus, while the text of article 1A(1) does not support an argument that the provision\nitself gives rise to additional grounds for claiming protection under the Convention, its\nimplicit incorporation of earlier legal definitions of ‘refugee’ (and the concepts of\nprotection which those definitions embody) supports the view that the Convention\ntolerates a broader protection concept than article 1A(2) might suggest, and that\nConvention status is the appropriate status for persons in need of international\nprotection for humanitarian reasons.\n\n**Recommendation E of the Final Act**\n\nRecommendation E of the Final Act of the Conference of Plenipotentiaries, which is\nappended to the Refugee Convention, expresses ‘the hope that the Convention relating\nto the Status of Refugees will have value as an example exceeding its contractual\nscope and that all nations will be guided by it in granting so far as possible to persons", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "in their territory as refugees, and who would not be covered by the terms of the\nConvention, the treatment for which it provides.’ This was a UK initiative, prompted\nby the deletion of a former article which would have allowed the Contracting States to\nadd to the definition of the term ‘refugee’. [64] The UK representative explained that his\ndelegation had felt that a general recommendation was called for to cover those\nclasses of refugees who were altogether outside the scope of article 1A. [65]\n\nRecommendation E of reveals that the drafters of the 1951 Convention to some extent\n‘envisaged a complementary protection system’. [66] This statement needs further\nexplanation to avoid any suggestion that the drafters envisaged a separate\ncomplementary protection system operating outside the Convention’s parameters,\nwhich is not sustained when one considers the phrasing of the Recommendation.\nCertainly the Recommendation envisages the expansion of the Convention to\nencompass additional categories of refugees not provided for by the terms of article\n1A(2) of the Convention. [67] Its wording makes clear that what is imagined is not a\n\n---\n[67] Conference of Plenipotentiaries on the Status of Refugees and Stateless Persons ‘35th Meeting’ (n65)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "64 Conference of Plenipotentiaries on the Status of Refugees and Stateless Persons ‘Texts of the Draft\nConvention and the Draft Protocol to be Considered by the Conference’ UN Doc A/CONF.2/1 (12\nMarch 1951) 5 (citations omitted). A Final Act to a treaty provides a formal summary of the conference\nproceedings, and may also seek to establish political, rather than legal, agreement on particular issues\nor set out matters for future discussion. It may also provide a useful aid for interpretation of the treaty,\nand at times the treaty text may even be incorporated into the Final Act: see I Brownlie _Principles of_\n_Public International Law_ (5 [th] edn OUP Oxford 1998) 610; A Aust _Modern Treaty Law and Practice_\n(CUP Cambridge 200) 73–74.\n65 Conference of Plenipotentiaries on the Status of Refugees and Stateless Persons ‘Summary Record of\nthe 35 [th] Meeting’ (Geneva 25 July 1951) UN Doc A/CONF.2/SR.35 (3 December 1951) 44.\n66 H Storey and others ‘Complementary Protection: Should There Be a Common Approach to\nProviding Protection to Persons Who Are Not Covered by the 1951 Geneva Convention?’ (Joint\nILPA/IARLJ Symposium 6 December 1999) (copy with author) 4.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "complementary status for such categories, but rather that the terms of the Convention\nitself would be extended by the General Assembly [68] :\n\nEXPRESSES the hope that the Convention relating to the Status of\nRefugees will have value as an example _exceeding its contractual scope_\nand that all nations will be guided by it in _granting_ so far as possible to\npersons in their territory _as refugees_ and who would not be covered by\nthe terms of the Convention, _the treatment for which it provides_ .\n(emphasis added)\n\nRead in this way, the Recommendation is a most useful guiding principle in the\ncomplementary protection debate. Though aspirational rather than a firm legal duty,\nthe Recommendation helps to counter claims that the Convention is too restrictive to\nabsorb the additional groups of refugees covered by complementary protection\nsources, or that the Convention was not intended to apply to additional groups. This\ninterpretation is reinforced by an earlier version of the text, which was originally\nproposed as part of the Preamble to the Convention:", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Expressing the hope finally that this Convention will be regarded as\nhaving value as an example exceeding its contractual scope, and that\nwithout prejudice to any recommendations the General Assembly may be\nled to make in order to invite the High Contracting Parties to extend to\nother categories of persons the benefits of this Convention, all nations\nwill be guided by it in granting to persons who might come to be present\nin their territory in the capacity of refugees and who would not be\ncovered by the following provisions, _treatment affording the same rights_\n_and advantages_ . [69] (emphasis added)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Recommendation E is important in two respects. First, with respect to eligibility, it\nencourages the extension of protection to individuals not encompassed by the\nConvention definition of a refugee. Secondly, with respect to substantive rights, it\nenvisages the application of the Convention framework to persons covered by\nextended eligibility, tacitly recognizing that the source of the harm causing flight is\nirrelevant for the purposes of status. This is in fact the position adopted in the 1969\nOAU Convention, which, as a regional complement to the Convention, applies\nConvention rights to persons fleeing external aggression, occupation, foreign\ndomination or events seriously disturbing public order in part or the whole of the\ncountry of origin. [70] This is very significant in light of EU developments, where\nsubsidiary protection status instead results in a lower form of rights than Convention\nstatus. The Recommendation supports the argument that there is no justification for\ncreating two levels of rights simply by distinguishing between the source of harm (or\nthe legal basis for protection).\n\nThe Hungarian refugee crisis of 1956 provided the first real challenge to the article\n1A(2) definition, and reflects the first example of widespread Refugee Convention\n\n---\n[70] Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "68 ‘Comments on the Draft Convention and Protocol: General Observations’ (n45) 34.\n69 Conference of Plenipotentiaries on the Status of Refugees and Stateless Persons ‘Texts of the Draft\nConvention and the Draft Protocol to be Considered by the Conference’ UN Doc A/CONF.2/1 (12\nMarch 1951) 2–3.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "related complementary protection. [71] The refugees did not strictly fall within the\ntemporal requirements of the Convention definition, however the High Commissioner\ndetermined that since the flight of Hungarian refugees was related to recent events and\npolitical changes resulting from the end of the Second World War, they should be\nconsidered as falling within the Convention’s scope. [72] Austria followed this\ninterpretation when it granted asylum to 180,000 Hungarian refugees. [73] It issued them\nwith normal refugee eligibility certificates as soon as technically possible, unless\nindividual status determination showed that a person was not entitled to the\nConvention’s benefits.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Most other States granted protection on a prima facie basis, at least initially. [74] Norway\ngranted all Hungarian citizens a residence permit for one year that included\npermission to work, renewed automatically on request. After two years, they could\nrequest a permanent residence permit, which was mostly granted. It was only at this\npoint that individual status determination took place. [75] The distinction between\nHungarian refugees and Convention refugees in Norway lay in the grant of travel\ndocuments. If an individual had not left Hungary for an article 1A(2) reason, then he\nor she was not entitled to a Convention travel document but to an alien’s passport. In\nreality, this did not have a substantial impact on the rights received.\n\nThe UK did not have a special eligibility procedure for Hungarian refugees but\ngranted them the same rights as Convention refugees. As in Norway, the only\ndistinction was with respect to travel documents. In Germany, they were subject to a\nsimplified eligibility procedure for recognition as Convention refugees and received\nConvention rights, including Convention travel documents.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "A 1956 Resolution on Hungarian Refugees of the Consultative Assembly of the\nCouncil of Europe requested all Member States ‘to accord to all of them who are able\nto work the facilities available under the system established by the Statute relating to\nrefugees and provided for under the Geneva Convention of 1951.’ [76] A memo by Paul\nWeis the following year revealed that:\n\nOn the whole … no Government has, as far as we know, raised any\nobjection to the application of the Convention to Hungarian refugees who\notherwise fulfill the conditions of Article 1 of the Convention and it can,\ntherefore, be assumed that the interpretation of the dateline of 1 January\n\n71 Earlier instances of complementary protection can be found in relation to League of Nations\ninstruments on refugee protection.\n72 UNHCR ‘The Problem of Hungarian Refugees in Austria’ UN Doc A/AC.79/49 (17 January 1957)\nAnnex IV [4].\n73 ibid.\n74 ibid [5].\n75 Letter from A Fjellbu (Norwegian Refugee Council) to P Weis (1 July 1959), in UNHCR Archives\nFonds 11 Sub-fonds 1, 6/1/HUN.\n\n---\n[76] Resolution adopted by the Committee on Population and Refugees (Vienna 15 October 1956) COE", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "1951 contained in Document A/AC.79/49 Annex IV is accepted by\nGovernments parties to the Convention. [77]\n\nOf course, it cannot be overlooked that the policy of declaring every Hungarian to be\na refugee ‘suited the ideological and racial preferences of western powers’ [78] Europeans fleeing Communism. Yet, in a sense, Recommendation E reflects an\noptimal system of complementary protection, operating more as a theoretical concept\nguiding the expansion of international protection within a broadened refugee law\nframework, than a separately defined system of protection (as has been created in the\nEU). Although from a pragmatic perspective, some form of codified complementary\nprotection would seem necessary for States to acknowledge and fulfil their\ninternational obligations, [79] the international law regime in principle already contains\nsufficient safeguards. [80]\n\n**‘Complementary’ versus ‘subsidiary’: a final word**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Though the term ‘subsidiary protection’ is largely descriptive, it may also have some\nweak normative significance. UNHCR has criticized States’ increasing use of\nsubsidiary forms of protection as a means of restricting asylum ‘on their own terms’,\narguing that subsidiary protection implies less binding obligations on States than their\nobligations under international law. [81] It can be seen as an attempt to remove the\nentitlements of protected persons beyond the reach of international scrutiny. There is a\ndanger of soft law edging out hard law obligations by ‘diluting principles and fudging\nstandards.’ [82]\n\nIn December 2001, representatives of the Contracting States to the Convention\nadopted a Declaration ‘[r]ecognizing the enduring importance of the 1951\nConvention, as the primary refugee protection instrument which, as amended by its\n1967 Protocol, sets out rights, including human rights, and minimum standards of\ntreatment that apply to persons falling within its scope’. [83] UNHCR has repeatedly\n\n---\n[82] Goodwin-Gill (n8) 914.\n[83] Declaration of States Parties to the 1951 Convention and/or its 1967 Protocol relating to the Status of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "77 Memo from P Weis to Mr J Mersch, UNHCR Branch Office in Luxembourg ‘Application of 1951\nConvention to Hungarian Refugees’ (28 May 1957) Ref.G.XV.7/1/8, 6/1/HUN [3], in UNHCR\nArchives Fonds 11 Sub-fonds 1, 6/1/HUN.\n78 Independent Commission on International Humanitarian Issues _Refugees: The Dynamics of_\n_Displacement_ (London 1986) 33, in G Melander ‘The Two Refugee Definitions’ Raoul Wallenberg\nInstitute of Human Rights and Humanitarian Law _Report No 4_ (Lund 1987) 14.\n79 This is the view expressed in Storey and others (n66) 14.\n80 For subsequent State practice, see eg Perluss and Hartman (n8); Goodwin-Gill (n8).\n81 ExCom ‘Summary Record of the 540th Meeting’ (Geneva 7 October 1999) UN Doc A/AC.96/SR.540\n(12 October 1999) [44]. The Nordic States’ relatively generous complementary protection is\ncounterbalanced by very low recognition rates of Convention refugees. Domestic complementary\nprotection effectively takes refugee protection outside international law. In Denmark, the ratio was\napproximately one-third Convention refugees to two-thirds de facto refugees: KU Kjær ‘The Abolition\nof the Danish _De Facto_ Concept’ (2003) 15 International Journal of Refugee Law 254, 258.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "called for States to respect the primacy of the Convention. [84] In 1994 and 1995, the\nGeneral Assembly passed two resolutions reiterating\n\nthe importance of ensuring access, for all persons seeking international\nprotection, to fair and efficient procedures for the determination of\nrefugee status _or, as appropriate, to other mechanisms to ensure that_\n_persons in need of international protection are identified and granted_\n_such protection,_ while not diminishing the protection afforded to\nrefugees under the terms of the 1951 Convention, the 1967 Protocol and\nrelevant regional instruments. [85]\n\nCreating a protection hierarchy reflects a very literal interpretation of respecting the\nConvention’s primacy. Simply entrenching the Convention as the pinnacle of\nprotection does not engage with the underlying protection principles it reflects, and\nmay in fact undermine its primacy by siphoning refugees into complementary\ncategories. Conceptually, the affirmation of the Convention’s primacy is, in effect, a\ncommitment to respect its protection principles and refrain from diluting its scope by\ndeveloping the law _outside_ its boundaries. The Convention’s primacy would be better\nobserved if it were recognized as the source of international protection status for all\npersons protected by _non-refoulement_ .", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "To provide maximum protection, international human rights treaties must not be\nviewed as discrete, unrelated documents, [86] but as interconnected instruments which\ntogether constitute the international obligations to which States have agreed. In effect,\ntherefore, this paper argues for a reconsideration of international law as a holistic and\nintegrated system. Compartmentalizing international law into parallel but autonomous\nand non-intersecting branches leads not only to stultification, but to ineffectual\nimplementation of the interlocking duties which States have undertaken to respect.\nViewing the Convention as a discrete instrument implies that refugee law ‘possesse[s]\nits own special purposes and principles which [are] determined essentially by its own\nconstituent instruments and which [are] thus independent of those of human rights\nlaw.’ [87] But human rights law contains principles that are explicitly or implicitly\napplicable to the refugee context, [88] having both influenced and been influenced by it.\nHuman rights law not only provides an additional source of protection for persons\nwith an international protection need, but also strengthens the status accorded to _all_\nrefugees through its universal application. Accordingly, while human rights law\nwidens threshold eligibility for protection, the Convention remains the blueprint for\nrights and legal status.\n\n---\n[87] GJL Coles ‘Refugees and Human Rights’ (1992) 91 Bulletin of Human Rights 63, 63.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "If international law already accommodates complementary protection within its\nexisting framework, then why is there no discernable universal system of\n\n84 eg ExCom ‘Global Consultations on International Protection: Report of the Meetings within the\nFramework of the Standing Committee: Report of the First Meeting in the Third Track (8–9 March\n2001) UN Doc A/AC.96/961 (27 June 2002) [14].\n85 UNGA Res 49/169 of 23 December 1994 [5]; UNGA Res 50/152 of 21 December 1995 [5]\n(emphasis added).\n86 On the fragmentation of international law: International Law Commission Study Group on\nFragmentation (Koskenniemi) ‘Fragmentation of International Law’ (2003)\n<http://www.un.org/law/ilc/sessions/55/fragmentation_outline.pdf> (30 November 2005) esp 1.3; 3\n(self-contained regimes).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "complementary protection? The problem lies not in international law itself, but rather\nin States’ failure to adequately implement their international legal obligations in a\nholistic and bona fide manner, combined with a lack of enforcement mechanisms. A\nbenefit of codifying States’ complementary protection obligations in a new\ninternational instrument would be to clearly elucidate the source and (non-exhaustive)\ncontent of those obligations—explicitly drawing the links between States’ general\nhuman rights obligations and their specific relevance to the protection context—and to\nexpressly describe the legal status that results from recognition of a protection need on\nthose grounds.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Yet, the dangers of codifying complementary protection have been amply illustrated\nby the negotiations on the Qualification Directive. They demonstrate that States may\nseek to dilute their obligations to a minimum level, extrapolating some aspects of\nexisting law but not others, and closing off potential avenues for future protection\nneeds. [89] In the context of setting out fundamental standards of humanity, the\nCommission on Human Rights has noted that any new instrument may be seen to\n‘undermine existing international standards … or pose a risk to existing treaty law’, [90]\neven where such standards are largely a ‘repackaging’ of existing international law.\nAs such, it is imperative to identify the international legal basis of obligations in any\ncodified complementary protection regime, so that ‘soft law’ is not used to fudge\nstandards or replace treaty-based obligations.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Although an EXCOM Conclusion on complementary protection was adopted in\nOctober 2005, it does not explicitly address the question of beneficiaries’ status.\nInstead, it contains important but relatively elusive statements calling upon States to\n‘provide for the highest degree stability and certainty by ensuring the human rights\nand fundamental freedoms of [beneficiaries of complementary protection] without\ndiscrimination’, [91] and affirming that complementary protection should be applied ‘in a\nmanner that strengthens, rather than undermines, the existing international refugee\nprotection regime’. [92] Further, it emphasizes the importance of applying and\ndeveloping international protection in a manner that avoids the creation or\ncontinuation of protection gaps. [93] However, the Conclusion does not go so far as to\nexpressly call for the equal treatment of Convention refugees and beneficiaries of\ncomplementary protection. [94] While this is perhaps not surprising, given the political\nclimate and the results of the EU’s recent deliberations about the Qualification\nDirective, it perpetuates at the international level an approach tied closely to _domestic_\npolitical concerns about asylum seekers, that require national governments to be\n‘seen’ to be distinguishing between ‘genuine’ (Convention) refugees and ‘others’.\nYet, as one commentator has poignantly observed:\n\n---\n[94] NGO delegations sought to have a statement to this effect included: Draft Conclusion on the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "89 For a comprehensive analysis of the drafting process, see J McAdam ‘The European Union\nQualification Directive: The Creation of a Subsidiary Protection Regime’ (2005) 17 International\nJournal of Refugee Law 461.\n90 Minimum Humanitarian Standards: Analytical Report of the Secretary-General submitted pursuant to\nCommission on Human Rights Resolution 1997/21 UN Doc E/CN.4/1998/87 (5 January 1998) [94].\n91 ExCom Conclusion on Complementary Protection (n10) para (n).\n92 ibid para (k).\n93 ibid para (s).\n\n---\n[94] NGO delegations sought to have a statement to this effect included: Draft Conclusion on the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In the past forty years the rich first world countries have received so\nmany _de facto_ refugees that it would not have made any difference if they\nhad agreed to an expanded international definition … . In fact, it would\nhere have helped clarify and identify those circumstances which were\ninsufficiently clear-cut to merit recognition as refugee-like situations. [95]\n\nBy retaining the political discretion to determine to whom, and when, protection will\nbe granted, States have in fact complicated the protection regime. Diverging statuses,\ndifferent eligibility thresholds and variations from State-to-State have created\nincentives for asylum-seekers to forum-shop and appeal decisions granting subsidiary\nstatus. It is arguably in States’ own interests to grant a single legal status based on the\nConvention to all persons in need of international protection. In this way, they\nacknowledge complementary protection as the natural extraterritorial response to their\ncommitment to uphold and promote respect for human rights. A creative use of\nhuman rights law can thus enhance the legal status of refugees and asylum-seekers, [96]\nbasing international protection on the individual’s _need_, rather than on which treaty\nprovides the legal source of the obligation.\n\n---\n[96] Goodwin-Gill (n51) 16.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "95 P Nobel ‘Blurred Vision in the Rich World and Violations of Human Rights—A Critical Assessment\nof the Human Rights and Refugee Linkage’ (1992) 91 Bulletin of Human Rights 74, 80.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "## Table of Contents\n\n**Overview** ................................................................................................................................................... 2\n\n**Community-Based Protection and Urban Outreach Strategy 2017-2019** .................................................. 2\n\n**Methodology and Geographical Focus** ...................................................................................................... 3\n\n**Multifunctional Teams** .............................................................................................................................. 3\n\n**Key Findings** .............................................................................................................................................. 4\n\nTheme 1: Community Self-Management ............................................................................................ 4\n\nTheme 2: Positive Coping Mechanisms / Self-Reliance / Decision-Making........................................... 6\n\nTheme 3: Access to Basic Services ...................................................................................................... 8\n\nTheme 4: Safety and Security ............................................................................................................. 9\n\nTheme 5: Education / Health / Persons with Specific Needs ............................................................... 9\n\nTheme 6: Information Needs / Communication with Communities ....................................................11\n\n**Key Recommendations for Community-Based Protection Activities** ........................................................12\n\nTheme 1: Community Self-Management ...........................................................................................12\n\nTheme 2: Positive Coping Mechanisms / Self-Reliance / Decision-Making..........................................13\n\nTheme 3: Access to Basic Services .....................................................................................................14\n\nTheme 4: Safety and Security ............................................................................................................14\n\nTheme 5: Education / Health / Persons with Specific Needs ..............................................................15\n\nTheme 6: Information Needs / Communication with Communities ....................................................15\n\n**Conclusions** ..............................................................................................................................................16\n\nPage | 1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 2\n\nOverview\n\nFollowing a strategic shift in focus from individual case management to increased urban outreach and \ncommunity-based protection, UNHCR Pakistan, its Sub-Offices and Field Unit (Peshawar, Quetta, and \nIslamabad) conducted a targeted Participatory Assessment (PA) with a focus on Community-Based \nProtection (CBP) concepts and areas of implementation to gauge impact and/or areas that require further \nfocus. Guided by UNHCR’s CBP and Urban Outreach Strategy 2017-2019, the PA focused on selected \nAfghan communities in urban areas. Below are the key objectives of the 2017 Participatory Assessment:\n\n \nTo gather information on the specific protection concerns faced by refuges, the underlying causes, \nas well as community capacities and proposed solutions. \n \nTo analyze information on community capabilities including existing and future resources that \ncontribute to self-reliance. \n \nTo incorporate the community’s input into the implementation phases of UNHCR’s CBP and \nOutreach Strategy 2017-2019.   \n \nTo inform 2018 Country Operation Planning process.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The thematic areas covered in this PA include: (1) Community Self-Management; (2) Positive Coping \nMechanisms / Self-Reliance / Decision Making; (3) Access to Basic Services; (4) Safety and Security; (5) \nEducation / Health / Persons with Specific Needs and Persons with Disabilities; and (6) Information Needs / \nCommunication with Communities.\n\nCommunity-Based Protection and Urban Outreach Strategy 2017-2019", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The overall goal of the Community-Based Protection and Urban Outreach Strategy is for Afghan refugees \nliving in Pakistan to be empowered and their resilient capacity strengthened, enabling them to minimize \ntheir exposure to protection risks. The strategy includes the following four strategic priorities:  \n \nPriority 1: Capacity Development in Community-Based Protection and Outreach through provision of \nNational and Provincial level Training of Trainers on community-based protection and outreach in order \nto build the knowledge and skills of UNHCR and partner staff in theory, practice and methods of CBP.  \n \nPriority 2: Building Efficient and Effective Community-Based Outreach and Referral Pathways through \nOutreach Volunteers at the community level, whom will act as a key source for information on services \nand assistance provided by UNHCR, partners and other service providers.  \n \nPriority 3: Establishing a Referral Network of Protection Services through an online interagency directory \nof service providers and referral partners. Focal persons for Afghan Refugees identified to help facilitate \naccess of individual cases to services. Information about services is widely disseminated to the refugees \nby OVs. Accountability mechanisms installed to solicit views and suggestions to ensure accountability of \nUNHCR and partners.  \n \nPriority 4: Enabling Afghan Refugee to Prevent and Respond to Protection Risks through strengthening \nthe capacities of community structures to be more fair and inclusive. Social, protection and assets \nmapping of communities to map patterns and trends of protection issues and refugees most at risk. \nCommunity safety action plans to prioritize the key protection risks, issues, and forms of violence and \nabuse, for the community to work on communally. Change Makers to support communities to work on \nbehavior change and address harmful social practices. Community centers as a safe meeting place. \nCommunity pooled funding to finance public goods and services for the benefit of their community.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 3\n\nMethodology and Geographical Focus\n\nMethodology: Focus Group Discussion (FGDs) were used as the primary data collection tool, followed by \nKey Informant Interviews (KIIs) and Observations. The Age, Gender and Diversity approach was \nmainstreamed in the methodology by involving children (10-14 years), youth (15-24 years), adults (25-59 \nyears), and older persons (60+ years). Each FGD was comprised of 10-15 persons and was conducted \nseparately for girls, boys, men, and women. Child-friendly participatory methods were used for FGDs with \nchildren, including drawing and mapping protection risks, a simplified questionnaire and shorter sessions. \nAdditionally, key Informant interviews were arranged to obtain views of Extremely Vulnerable Individuals \n(EVIs) that were not able to physically participate in the FGDs. The data collected was primarily qualitative.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Geographical Focus: Provincial offices selected four to six locations for the PA within the areas that have \nbeen selected for CBP in 2017 or plan to have CBP interventions in 2018. The targeted number of location \ntook into consideration that communities previously reported they were tired and frustrated with UNHCR \napproaching them once a year to ask a series of questions but not addressing the issues raised, as well as \nour desire to ‘take the pulse’ on the first six months of implementation of our shift in focus to increased \nurban outreach and community-based protection. The following locations were selected by each office:\n\n \nKhyber Pakhtunkhwa: Taj Abad, Danish Abad, Haji Camp in Peshawar District and Kheshgi Refugee \nVillage in Nowshera District. A total of 32 FGDs (12 women, 12 men, 4 girls, 4 boys) and 8 KIIs (3 \nwomen, 3 men, 1 girl, 1 boy) were conducted.\n\n \nBaluchistan: Ghous Abad, Hazara Town, Killi Landi Kuchlak, Killi Samali Kuchlak, Pashtoon Bagh, Qadri \nAbad in Quetta District. A total of 48 FGDs (18 women, 18 men, 6 girls, 6 boys) and 48 KIIs (18 women, \n18 men, 6 girls, 6 boys) were conducted.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": " \nPunjab: People’s Colony and Dar ul Salam in Attock District, Khyaban-e-Sir Syed and Afghan Ghari in \nRawalpindi District. A total of 28 FGDs (10 women, 11 men, 4 girls, 3 boys) and 5 KIIs (3 women, 1 man, \n1 girl) were conducted.\n\nA total of 108 Focus Group Discussions (FGDs) and 61 Key Informant Interviews (KIIs) were conducted with \napproximately 1411 Afghan refugees from Pashtun, Hazara, Tajik, Turkmen and Uzbek ethnicity.\n\nMultifunctional Teams\n\nTraining: A one day National workshop was organized to agree upon the questionnaire, methodology, \nreporting responsibilities and overall planning dates and deadlines. A one day Provincial training was held \nfor the MFT members involved in conducting the PA in the respective provinces. COI CBP colleagues \nassisted with the training at the provincial level.\n\nTeam Members:  Multifunctional teams (MFTs) were comprised of UNHCR staff, partner staff and Outreach \nVolunteers. UNHCR PA focal points at the Provincial level were responsible for training MFT members, \noverseeing the process and providing the necessary support and direction for quality data collection and \nanalysis. FGDs with men and boys were facilitated by male MFT team members and FGDs for women and \ngirls were facilitated by female MFT members.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Outreach Volunteers:  Outreach Volunteers (OVs) were key to ensuring the success of the PA, by mobilizing \nthe community to participate in the FGDs and supporting staff to select venues and arrange refreshments \nlocally. Some OVs participated in the FGDs and provided valuable insights and contributions. OVs were \ninstrumental in ensuring females participated in the FGDs, as movement restrictions for females can create \na barrier to their participation in activities, including the PA. Additionally, OVs assisted with language \nbarriers that occurred due to specific dialects spoken by participants.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 4\n\nThe findings support the \nestablishment of inclusive \ncommunity level decision making \nmechanisms, as outlined in \nPriority 4 of the CBP and \nOutreach strategy \nData Analysis and Reporting: An online data management tool was developed by Information Management \ncolleagues to streamline the data entry and analysis process. Each Provincial office conducted the initial \nreview and analysis of data and prepared an overview of key findings, recommendations and challenges. It \nwas imperative that Provincial staff were involved in the analysis as the data was primarily qualitative. The \nCOI CBP team was responsible for preparing the National PA report, which includes findings and \nrecommendations that are common across the operation and relevant for 2018 Country Operation \nPlanning. \nKey Findings\n\nTheme 1: Community Self-Management", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Community Structures [Questions about the existence of community structures / committees, types and places meetings are held, \nhow the structures / committees are helpful and tasks or activities these committees should undertake].   \nFormal community structures do not exist in urban areas.  Rather, informal community structures, such \nas jirgas, shuras and tribal elder councils, are usually in place in urban areas1. Communities report they \nare willing and interested to set-up committee structures as they see value in a communal means to \ndiscuss and resolve community level issues as well as raise \nawareness on certain issues. In general, communities do not \nhave designated communal venues to hold meetings, rather \nthey utilize the homes of elders or schools, and have requested \nUNHCR to provide support in allocating a space for community \nactivities and meetings, such as a community center.  \nIn Kheshgi Refugee Village (RV), reference was made to \ncommittees that were set-up in the past, but are no longer functioning, such as: grand shura committee, \nsocial welfare committee, child committee, youth committee, elder committee and health committee. It \nwas noted that repatriation has negatively affected the committees in Kheshgi RV, as influential \nmembers have returned to Afghanistan.  \nCommunity Participation [Questions about participation in community structures / committees and reasons for non-participation].  \nThe majority of community level decisions are made by male community elders with leadership roles in \nthe community. Female participation in community structures or committees is generally not allowed as \nculturally males have the decision making role in the family and \ncommunity. Similarly, it is culturally not widely accepted or \nallowed for children to be involved in the decision making \nprocess. Additionally, lack of culturally appropriate places for \nwomen to gather is a hindrance to participation as well as \ndifficulty for women to find time to meet due to their daily \nhousehold duties.  \nOverall, value is not placed on involving women, children, \nPersons with Specific Needs (PWSN) or Persons with Disabilities (PWD) in community level decision \nmaking. Additionally, older men and women in the community that are not actively involved in \ncommunity dialogues and decision making are at-risk for social isolation and neglect due to the lack of \nactivities that welcome their participation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "1 An exception is the settlement of Khyaban-e-Sir Syed in Rawalpindi, a Hazara Ismaili community, which is well organized and \nstructured with male, female and youth committees.  \nThe findings support the \nestablishment of community \ncommittees and community \ncenters, as outlined in Priority 4 \nof the CBP and Outreach strategy", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 5\n\nThe findings support the \nestablishment of inclusive \ncommunity conflict resolution \nmechanisms, as outlined in \nPriority 4 of the CBP and \nOutreach strategy \nThe findings support the \nestablishment of a network of \nOutreach Volunteers to provide \ninformation and facilitate access to \nservices and assistance, as outlined \nin Priority 2 of the CBP and Outreach \nstrategy  \nConflict Resolution [Questions about where people go for support during times of conflict within the family and community, how they \nparticipate in conflict resolution mechanisms at the community level, how conflict resolution mechanisms at the community level can be \nfair and inclusive]. \nThe situation for conflict resolution varies from community to community. In some communities, there \nare traditional structures (shuras, jirgas) of influential Afghan elders and religious leaders for the \ncommunity to seek support and advice to resolve conflict. In these communities, the majority of \nrespondents view committees as effective conflict resolution mechanisms. In other communities, there is \nnot a traditional structure but elders are called upon to intervene \nas a last resort, if the conflict is not resolved within the family or \nthrough intervention of close relatives. When needed, local police \nor legal authorities may also be called upon.  \nOf adult respondents, 89% view the traditional system of conflict \nresolution through local elders as fair and inclusive. The 11% of \nrespondents that reported the traditional system is not fair or \ninclusive was overwhelmingly female (83%). Some female \nrespondents noted that conflict resolution for issues related to domestic violence, abuse of women and \nchildren is generally not a fair process. Women usually keep their family conflict within the family for fear \nof shame or the cultural norm of not exposing family issues to the wider community. In some \ncircumstances, elderly females intervene to help resolve a situation that has resulted in conflict amongst \nwomen. It is noted that children and youth are not directly involved in the conflict resolution process.  \nVolunteerism [Questions about the culture of volunteerism in the Afghan community, if persons are interested in volunteerism, and \nwhat can be done to encourage volunteerism in the community].  \nAfghan communities have a positive view and long tradition of supporting each other informally as part \nof their culture and tribal associations. The vast majority of respondents, 86%, reported that people in \ntheir community volunteer to solve problems in the community, such as elders and youth in the \ncommunity that are willing to support when there is a need. The \nmajority of community members voiced an interest, specifically \nmale and female youth, to volunteer in order to support their \ncommunity.  Females shared concerns they may not be able to \nvolunteer due to mobility restrictions, unless they had support of a \nmale relative, and requested UNHCR to support in this regard. It \nwas also noted that persons who are employed will have less time \nto dedicate towards volunteering.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The overwhelming positive response was surprising as prior efforts \nto encourage volunteerism have had mixed results in Pakistan, primarily due to expectation of some \nform of payment or recognition.  This is something that UNHCR will keep in mind as the Outreach \nVolunteers begin working with the communities and different incentives are provided over time. \nCoexistence [Questions about the social interaction between refugees and the host communities, where they interact socially and if \nthey help one another]. \nThe majority of communities (non-Hazara Ismaili) report positive social interactions and relationships \nwith the host community. Women report that interactions during weddings, funerals or at water points, \nhas led to learning from one another and sharing of views. It is also common for Afghan women to work \nas house help for host community families. Generally, there is neighborly support and acceptance as well \nas cooperation to resolve common neighborhood issues such as repair of electricity, sanitation, streets, \netc. Positive examples of how the host community supports the refugee community include: lending \nmoney in cases of emergency, helping with admittance to schools and hospitals, and acting as guarantors \nor witnesses in court in cases of arrest. However, concerns were raised regarding the potential for social \nisolation due to growing security concerns by the host community towards Afghans.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 6\n\nThe findings support promoting \ncommunity engagement and \ncommunity-led support \nmechanisms, as outlined in \nPriority 2 and Priority 3 of the \nCBP and Outreach strategy \nThe findings support the idea that \ncommunity-pooled funds can \nprovide financial support for \nprotection specific interventions \nand assistance to the most \nvulnerable community members, \nas outlined in Priority 4 of the CBP \nand Outreach strategy \nThe findings support building \nharmony with the Pakistani host \ncommunity in order to improve \nthe protection environment, as \noutlined in Priority 4 of the CBP \nand Outreach strategy \nIn the Hazara Ismaili community, males do not report positive \nrelations with the host community and attribute this to religious \nand linguistic differences. However, women and children have \nlinks and interact with the host community during certain \noccasions such as funerals, wedding, sports and other social \nevents. Mosques and markets are common places for meeting \nand interaction.  \nTheme 2: Positive Coping Mechanisms / Self-Reliance / Decision-Making \nCommunity Strengths [Questions about community strengths, community support mechanisms, community groups, associations]. \nUrban communities do not have organized community support mechanisms to take care of persons \nwithout familial support. On an ad-hoc basis, community members do support others in need by sharing \nfood, clothing or monetary support to vulnerable community members. The community generally \nbelieves that groups and associations can bring positive behavior change in the community, as they \nprovide opportunities for discussion of common issues which leads to information sharing and increased \nawareness. In some urban areas, youth have small sports groups and gatherings of friends.  \nMore formal community groups or associations reportedly do not \nexist due to lack of communal meeting / recreational space and \nsupport from the community or NGOs. In one community (Taj \nAbad), an elder rented a house (2000 PKR per month) to provide \na place for male youth to gather during their free time, instead of \nloitering on the streets. Such opportunities generally are not \navailable for females. Madrassas and water collection points are \nthe only place of community gatherings reported by Afghan \nfemales.  \nCommunity strengths noted or observed include: a solid work ethic, unity, hospitality, resiliency and \nsupport to one another in difficult times. Many have skills they learned in Afghanistan such as carpet \nweaving, carpet designing, handicrafts, art work and traditional embroidery. The community has shown \ndetermination to earn their living with dignity rather than begging on the streets. They state that they \nare willing to provide labor voluntarily to demonstrate support for their community. Additionally, elders \nand tribal affiliations were viewed as community strengths.  \nCommunity Resources and Self-Reliance [Questions about resources, community pooled funds, how resources are shared]. \nCommunity members understand self-reliance as being hard working and earning a living in order to \ncover basic needs. Community members, in particular youth and children, want to develop knowledge \nand skills that will afford them better employment opportunities \nto support themselves and their families. Afghan communities \nhave various skills brought from Afghanistan, such as carpet \nweaving, embroidery and handicrafts and passed through \ngenerations, which have helped them generate incomes. The \ncommunities report strong social networks and ties with the host \ncommunities, which can be better utilized to link existing skills \nand products to the markets in order to earn fair wages. \nEducation and vocational training opportunities are also viewed \nas a resource that will pay off in the future. \nIn urban areas, community-pooled funds do not formally exist. \nHowever, in some communities there are informal means to collect funds to assist persons in need, \nprimarily for health, food, funerals, education, electricity or other related needs2. In Kheshgi RV, the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "2 Examples of such practice provided from participants living in settlements in Chiltan Town Tehsil, Quetta district in Baluchistan \nand Rawalpindi and Attock districts in Punjab.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 7\n\nPriority 4 of the CBP and \nOutreach strategy aims to \ncontribute to prevention of \nviolence through Community \nsafety action plans to reduce \nviolence in the community \ncommunity makes contributions to pay the salary of the Imam and may pool together funds to provide \nsupport to persons living in poverty, vulnerable persons (i.e. widows, orphans, PWSN), and persons \nseeking financial assistance for medical treatment or to pursue educational opportunities.  \nPositive Coping Mechanisms [Questions about how people cope with stress and what activities in the community help persons \novercome traumatic events].  \nFor stress relief, males referenced different coping mechanisms such as: discussing their issues with \nelders and/or religious scholars, engaging in prayer to seek help and guidance from their faith, and \ngathering with their friends or relatives to discuss issues (peer counseling). Those who can afford it, \nengage in sports, go to recreational spaces or restaurants with \nfriends, or gather in common places (Hujras) to play cards or \nother games. Negative coping mechanisms reported by males \ninclude domestic violence against women and children, or \n“unusual religious activities” such as going beyond the required \nnumber of prayers and excessive citation of the Qur’an.  \nDue to movement restrictions, females do not have the same \navailability of stress relief opportunities outside the home. As a \nresult, females reported that to cope with stress they may isolate themselves in the home and cry, seek \nmedication, and talk to relatives to find support and solutions (peer counseling). Females also mentioned \nthat due to stress, a negative coping mechanism is emotional and physical abuse of children. The findings \nreinforce previous PA findings and lend strong support for Community safety plans as a means to \nempower the community to address the negative coping mechanisms.  \nVoluntary Repatriation [Questions about the decision making process of return to Afghanistan, how persons receive information on \nreturn to Afghanistan and how return to Afghanistan has affected community dynamics].  \nThe majority of community members report that they have relatives, friends or know of community \nmembers that have returned to Afghanistan, for the following reasons3: \n \nPolice harassment in Pakistan \n \nUncertainty of POR card extension in Pakistan \n \nPoverty and lack of employment opportunities in Pakistan \n \nStrict border crossing control between Pakistan and Afghanistan \n \nDesire to join relatives and/or communities back in Afghanistan  \nCommunity members report they receive information on repatriation from4: \n \nUNHCR/partner helplines \n \nPrint and electronic media \n \nContact with friends and relatives back in Afghanistan via phone and internet \n \nInformation from persons that have recently traveled to Pakistan from Afghanistan \nAs a result of return, the community reports a decrease in livelihoods5 opportunities and increased \nexposure to security issues as community numbers reduce and government authorities actively support \nreturns. Furthermore, loneliness and sadness due to friends and family departing Pakistan is reported as a \nnegative impact as well as decreased educational opportunities for children due to teachers returning.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR/partner helplines"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "3 UNHCR Pakistan Monthly Protection Trends Report, May-Aug 2017, cites similar reasons for return to Afghanistan including the \nfollowing pull factors: reunion with family in Afghanistan, employment / livelihoods, no longer fear of persecution, UNHCR \nassistance, happy to return and the following push factors: strict border entry requirements, uncertainty of POR card extension, no \noverall protection value of POR cards, overall deterioration of security situation in Pakistan, arrest and detentions, denial of access \nto services.  \n4 UNHCR Pakistan Monthly Protection Trends Report, July 2017, reflects similar means for persons to receive information on \nrepatriation, collected during VolRep Exit Interviews and Encatchment Center reports with Afghan returnees, stating they receive \ninformation on Afghanistan from the Afghan community in Pakistan, UNHCR, and visits to Afghanistan.  \n5 Example provided of carpet weaving businesses returning to Afghanistan and thus resulting in a number of carpet weavers out of \nwork in Khyber Pakhtunkhwa.", "output": {"entities": {"named_data": ["UNHCR Pakistan Monthly Protection Trends Report"], "descriptive_data": ["Encatchment Center reports with Afghan returnees"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 8\n\nFindings support the \nestablishment of Outreach \nVolunteer networks to increase \nprovision of information at the \ncommunity level as well as a \nservice directory and referral \npathways, as outlined in Priority 2 \nand Priority 3 in the CBP and \nOutreach strategy  \nThe majority of the community members are not aware of persons planning to return to Afghanistan in \nthe near future. Generally, persons want to remain in Pakistan (98% of adult respondents and 88% of child \nrespondents) due to a better security environment and greater availability of services in Pakistan.   \nTheme 3: Access to Basic Services\n\nAvailability [Questions about the availability of basic services in their community, UNHCR services and partners].  \nAdult participants were asked if certain basic services were available and accessible. The graph below \nprovides an average rate of availability of services reported across all the communities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The individual information collected from each community should be utilized by UNHCR CBP teams to \ncreate a community profile to understand the challenges in access to services that exist in each \ncommunity as well as the need to increase awareness on services available. When asked how the \ncommunity can contribute to increased access and availability of \nservices, the main response included raising awareness on the \navailability of services.  \nA total of 73% of adults and 52% of children reported they know \nabout UNHCR and partner services. The most common means of \nobtaining this information was through shura meetings or family \nmembers. This is significant improvement from PA 2016 findings6 \nand can likely be attributed to the CBP interventions, such as \nsocial, protection and asset mapping, an activity outlined in \nPriority 4 of the CBP and Outreach strategy, and initiated in \nselected communities in 2017.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "6 Excerpt from UNHCR Pakistan PA 2016: “Most respondents (male and female) living in informal settlements in urban contexts, \nacross the country, appear to have little awareness of the available procedures to access UNHCR directly. Of the total respondents \nconsulted on Legal & Physical Protection PA, only 9% female and 12% male respondents confirmed having some information.”", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 9\n\nThe findings support the establishment of \ncommunity safety action plans to prioritize \nthe key protection risks, issues, and forms of \nviolence and abuse, which the community \nwill address together, as outlined in Priority \n4 of the CBP and Outreach strategy \nTheme 4: Safety and Security \nSafety and Security [Questions about the major security risks experienced by the community this year, specific safety and security \nconcerns for females, the role of the community in making places within the community safer].  \nCommunity members have reported that safety and security has improved and no major security \nincidents in 2017 were reported by 56% of all adult respondents. Police harassment was listed as the \nmost common safety and security concern in 2017 by 20% of all adult respondents, followed by arrest \nand detention at 6%, which is also an improvement from 20167. Community members are generally \naware of the local police station and have built positive relationships with the local police officers, with \nthe exception of the communities that report police harassment as a main security threat8. Community \nmembers recognize they have a role to play to improve the security environment within their \ncommunity, especially to address security incidents within the home, such as domestic violence.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Females report harassment by male community \nmembers and domestic violence in the home. The \nHazara community reports ethnic and religious \ndiscrimination that has marginalized them from the \nlarger Afghan and host community. Targeted killings \nof Hazara in Baluchistan, although isolated events, are \nstill a cause for concern.  \nTheme 5: Education / Health / Persons with Specific Needs \nEducation [Questions about the value of education, educational opportunities provided in the community, if parents were satisfied with \nthe quality of education, community-led education initiatives, highest level of education provided in the community, rates of enrollment \nand participation in School Management Committees (SMC) and Parent Teacher Associations (PTA)].  \nEducation is a top priority amongst communities as all value the importance of education and the \npositive impact education has on a person’s ability to be employed and earn a living. Regarding the \ngeneral school enrollment of children in community, 42% of adults and 55% of children report the \nmajority of children do attend school9. The results demonstrate an overall improvement when compared", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "7 UNHCR Pakistan PA 2016 participants reported regularly experienced arrest, detention, threats of deportation and extortion \n(Punjab: 24% female / 17% male; KP: 4% female / 5% male; Sindh: female 7% /male 10%; Baluchistan: 7% female /  4% male) \n8 Locations that reported Police Harassment to be the most common safety and security concern, include Peshawar in KP, Attock \nand Rawalpindi in Punjab, and Chiltan Town (Tehsil) in Baluchistan. However, male respondents from Chiltan Town Tehsil (Killi Landi \nKuchlak, Qadri Abad, Ghous Abad) reported a significant improvement in the overall security situation compared to last year.", "output": {"entities": {"named_data": ["UNHCR Pakistan PA 2016"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 10\n\nThe findings support the \nestablishment of community \ncenters, which can be used for \ncommunity-led education \ninitiatives, as outlined in Priority 4 \nof the CBP and Outreach strategy \nThe findings support the need to \nbuild capacity of existing support \nmechanisms at the community-\nlevel, as outlined in Priority 4 of the \nCBP and Outreach strategy \nto the PA 2016 in which an average of 30% of females and an average of 50% of males reportedly were \nattending school10.  \nBoth male and females are encouraged to attend religious schools (madrassas), however there is a \npreference for females to only attend informal religious schools. Overall, it is clear that communities put \nless importance on education for girls, which is exacerbated when separate schools for girls, as well as \nfemale teachers, are not available. Additionally, if schools are located far away from the residential area \nof the community, females will not be able to attend due to \ntraditional movement restrictions. There was an expressed \ninterest to enroll out of school children, particularly girls, \nand a few examples of community-led education initiatives \nsuch as community members buying books and stationary \nfor impoverished children11, opening a school to provide \nfree education for female and male students12, and \nprovision of free computer and language classes13 at the \ncommunity level.  \nGrades 10-12 are the highest levels of education readily available to Afghan students. Adequate support \nto enroll in higher education is reportedly not available. The majority of youth not attending school are \ninvolved in daily labor type activities to support their families. Generally, child labor is considered a \ncommon practice. For the Hazara community, language abilities are a barrier to accessing government \nschools. No formal Parent Teacher Association14 (PTA) or School Management Committee (SMC) is \navailable in the community, however individual parent teacher meetings are conducted in some schools. \nMost community members are not satisfied with the quality of education, teachers are not generally \nregarded to be well-qualified or professional, and effective monitoring mechanisms are not in place in \nmost public schools.  \nHealth [Questions about the most prevalent health conditions in the community, if it was common practice to immunize children, the \nuse of public verses private hospitals and clinics, existing community support mechanisms for persons with health concerns that do not \nhave familial support]. \nGenerally, urban communities are in close proximity to a government hospitals, private clinics and other \nhealth facilities. Those that can afford to pay, prefer to access health care from private doctors and \nclinics. Those that cannot afford to pay access government hospitals. Afghan doctor clinics are also \ncommonly utilized by all community members as they are trusted and charge a minimal fee. \nImmunizations are provided in the nearest government hospitals and Basic Health Units (BHUs). 95% of \nadults reported they are immunizing their children.  \nFor birth practices, 71% give birth in health facilities, \nfollowed by 29% at home. In cases of emergency, the nearest \nhospital is approached. In Khyber Pakhtunkhwa, dengue \nfever was the most frequently mentioned health concern15, \nfollowed by depression and other psychological issues. In \nBaluchistan, hepatitis was the most frequently mentioned \nhealth concern. For community support mechanisms for", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "10 It is noted that communities varied from PA 2016 to PA 2017. \n11 Example of wealthier community members paying for tuition, books and stationary for impoverished children in Killi Samali \nKuchlak Settlement in Chiltan Town Tehsil in Quetta district.  \n12 Example of a community member, with assistance of an organization, opened a free school for female and male students in \nQadri Abad Settlement, Chiltan Town Tehsil in Quetta district. Participants in Qadri Abad also reference a community-led initiate to \nopen a community center.   \n13 Reference to the free English language and computer classes provided in Killi Landi Kuchlak Settlement in Chiltan Town Tehsil in \nQuetta district.  \n14 Exception is Kheshgi RV, participants referenced the parents of the PTA organized an awareness campaign on health and hygiene \nand the importance of school attendance.  \n15 The provincial health department of Khyber Pakhtunkhwa reported a total of 74,820 cases of suspected dengue fever since the \noutbreak was first reported on 19 July 2017, including 15,828 laboratory-confirmed cases and 54 deaths, WHO (October 2017).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 11\n\nThe findings support the need to \nestablish mechanisms at the \ncommunity-level to support the most \nvulnerable such as PWSN and PWD, \nas outlined in Priority 4 of the CBP \nand Outreach strategy \npersons without relatives to care for them, 34% of adult respondents report there is community level \nsupport, such as individuals giving financial assistance and community members helping others in times \nof need.  \nPersons with Specific Needs and Persons with Disabilities [Questions about how PWSN and PWD are self-reliant, how PWSN \nand PWD are supported in the community and suggestions on how support mechanisms can be strengthened].  \nThe majority of community members are not acutely aware of PWSN and PWDs living in their \ncommunity, reportedly because they are kept inside the homes and not generally spoken about, \nespecially female PWSN and PWDs. In Kheshgi RV, there is more awareness due to the history of \ncommunity committees and NGO support in the community.  \nGeneral information on services and resources for PWSN and \nPWDs is unknown by the community. PWSN and PWDs are \nconsidered the primary responsibility of the families, \nhowever charity mechanisms like Zakat and Sadqa are \ncommon practices16 for vulnerable persons. Certain \ncommunities expressed a willingness and interest to help \nPWSN and PWD, through “moral support”, charity and \nlearning opportunities as PWDs and PWSNs are living as the \n“poorest of the poor among the community”. Children unanimously responded that PWSN and PWD do \nnot attend school.  \nTheme 6: Information Needs / Communication with Communities  \nSharing and Receiving Information [Questions about how people share information in the community, preferred methods to \nreceive information, topics of most interest to receive information] \nThe most common means of sharing information is through community gatherings, announcements at \nmosques and schools, and phone calls to friends and relatives. The preferred methods of receiving \ninformation reported by adult participants is through face to face communication (39%), community \nleaders and community meetings (22%) and mobile phones (17%). Children, similarly prefer to receive \ninformation in-person, from parents, teachers and friends.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 12\n\nPriority 2 of the CBP and \nOutreach strategy which outlines \nthe establishment of Outreach \nVolunteer is designed to address \nthe gap in information sharing to \nfemales and other vulnerable \ngroups that do not regularly \nreceive information at the \ncommunity level \nFemales are the least likely community members to receive \ncommunity level information and reported males do not \nregularly share information with them. During FGDs, females \nwould at times state they did not know and recommend they \nspeak to male family members for the information, which \ndemonstrates a lack of engagement and participation of \nwomen at the family and community level. House to house \ndissemination of information as well as phone calls (so females \ndo not need to leave the house) will be best to target females.  \nAdult participants are most interested in receiving information \non how to access medical services (17%) followed by \ninformation on how to obtain or replace identity documents (16.5%). How to access primary, secondary \nand tertiary education (14%) and news about the community (13%) are also of high interest. The \nmajority of child participants expressed an interest in receiving information on education.\n\nKey Recommendations for Community-Based Protection Activities", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Theme 1: Community Self-Management \nCommunity Structures / Participation / Conflict Resolution \n \nCommunity Committees can play an effective role in the positive development of a community in a \nvariety of areas such as awareness raising, sharing of \ninformation, creating a forum for discussion, consultation and \nsolution identification for community-based responses to \nprotection related issues and concerns.  \n \nUNHCR and partner staff should work with communities to \nestablish committees and link them with Outreach Volunteers \nas well as service providers, including local authorities and \nrelevant contacts in the host community. If community \ncommittees are already in place, efforts should be made to \nbuild capacity of the committees to ensure they are accessible \nto all and provide a venue for equal participation and remain fair and impartial in order to resolve day \nto day issues in the community.  \nCapacity building of \ncommunity committees \non fair and inclusive \ndecision making is \noutlined in Priority 4 of \nthe CBP and Outreach \nstrategy", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 13\n\n \nCommittees function as decision making and conflict resolution structures. Members of the \ncommittees should be trained on fair and inclusive conflict resolution skills as well as local relevant \nlaws and applicable punishments. Awareness and sensitization is needed on the importance of \nincluding women, children, PWSN and PWD in conflict resolution and decision-making mechanisms at \nthe community level.  \n \nTo increase inclusiveness, community-led meetings and conflict resolution discussions should be held \nin neutral locations, in order to ensure access for a wide range of persons in the community. \nVolunteerism \n \nThe Afghan community’s history and acceptance of volunteerism is an ideal environment to initiate an \nOutreach Volunteer program. The volunteers will be supported through \ntraining and supervision.  \n \nAn awareness campaign on volunteerism should be conducted at the \ncommunity level to sensitize the community on the role of the \nOutreach Volunteers and objectives of the program as well as set \npractical and reachable expectations.    \n \nThe community should be engaged to create an environment for which \ngirls and women are allowed to volunteer within their communities, \ndespite cultural and traditional restrictions of their movement.  \n \nUNHCR and partners to support development of accountability \nstructures, such as community feedback and complaints mechanisms, in order to ensure \naccountability at the community level, including accountability of volunteer networks and other \ncommunity-based protection interventions. \nCoexistence \n \nRecreational activities for children and youth to be organized with the host community, in order to \nbuild bridges, support mutual understanding and reduce tension to increase safety and security. \n \nCommunity-based protection interventions to work closely with UNHCR RAHA initiatives, in order to \ncreate joint efforts that build social cohesion and positive co-existence amongst refugee and host \ncommunities. \nTheme 2: Positive Coping Mechanisms / Self-Reliance / Decision-Making \nCommunity Strengths / Resources / Self-Reliance \n \nCommunity centers to be identified and utilized by the community (and host community) for various \nactivities, including community meetings and dialogues, community \nevents, community learning opportunities such as carpet weaving and \nhandicraft courses, computer and language classes.  \n \nWomen, PWSN and PWD, to be involved in the decision making of \ncommunity funds to ensure identification and distribution of funds to \npersons most in need.  \n \nUNHCR livelihood opportunities to be linked with community-based \nprotection initiatives in order to enhance self-reliance.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Accountability of \nvolunteer networks and \nother community-based \nprotection interventions is \noutlined in Priority 4 of \nthe CBP and Outreach \nstrategy \nThe establishment of \ncommunity centers and \ninclusive community \ndecision making \nmechanisms is outlined in \nPriority 4 of the CBP and \nOutreach Strategy", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 14\n\nPositive Coping Mechanisms \n \nCommunity-based protection activities to include more opportunities for community dialogues and \npositive social gatherings at the community level, including those that \ncan be freely attended by women and children.  \n \nPeer support is a positive coping mechanism reported by the \ncommunity. Provision of supportive counseling services, such as \nPsychological First Aid (PFA) and/or Psychosocial Support (PSS).  \n \nOutreach Volunteers should be trained on positive self-care in order for \nthem to take care of themselves while also serving persons in their \ncommunity.  \nVoluntary Repatriation \n \nUpdated Information regarding country of origin conditions and Voluntary Repatriation to be \ndisseminated through Outreach Volunteer networks and community \ncommittees.  \nTheme 3: Access to Basic Services \nAccessibility and Availability  \n \nOutreach Volunteers act as a bridge between service providers and the \ncommunity. Awareness sessions to be held at community level that \nexplain the availability of services and procedures for accessing such \nservices. \n \nAdvocacy to increase accessibility of refugees to access basic services \nthat are provided by government and non-governmental actors, an \nactivity also linked to Sustainable Development Goals 3 and 4 on health \nand education, which aim to ensure access of Afghan refugees to \nexisting public-sector services. \n \nOutreach Volunteers should provide information on how to contact UNHCR, UNHCR services and \nUNHCR partner’s services reaches persons of all ages and genders within the community.   \nTheme 4: Safety and Security \nCommunity Safety and Security \n \nOutreach Volunteers to engage with community elders, community members, host community and \nlocal authorities to develop community safety action plans, to combat \nissues such as violence against women, including domestic violence and \nstreet harassment of women and girls.   \n \nCommunity watch networks, which include youth, should be establish in \nurban communities. \n \nUNHCR and partners to conduct more frequent sensitization workshops \nfor police in locations with higher instances of police harassment and \nextortion.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Provision of supportive \ncounseling services is \noutlined in Priority 2 of \nthe CBP and Outreach \nStrategy \nInformation dissemination \nby Outreach Volunteers \non availability of services \nis outlined in Priority 2 of \nthe CBP and Outreach and \nadvocacy to increase \navailability of assistance is \nPriority 3 of the CBP and \nOutreach strategy   \nCommunity safety action \nplans, to combat issues \nsuch as violence against \nwomen, including \ndomestic violence and \nstreet harassment of \nwomen and girls is \noutlined in Priority 4 of \nthe CBP and Outreach \nstrategy", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 15\n\nTheme 5: Education / Health / Persons with Specific Needs \nEducation \n \nOutreach Volunteer to engage the community to set-up a community of volunteer teachers to provide \nprimary education classes for a few hours per day. Efforts to be made \nto include children with physical and mental disabilities in such type of \ncommunity-based alternative educational opportunities.   \n \nCommunity to organize local language opportunities in English, Pashto \nand Urdu, in order to increase the ability of Afghan students to be \nadmitted to local education institutes.  \n \nProvide awareness sessions on rights of the child and the importance \nof education, particularly education for females, to all community \nleaders and develop community-led initiative to overcome the \nchallenges and barriers to enrollment of out of school children.   \n \nParent Teacher Associations and School Management Committees \nshould be engaged / formed and supported by Outreach Volunteers to visit schools and provide \nfeedback, as a means to ensure accountability of the quality of education provided in the community.  \nHealth \n \nOutreach Volunteers, in coordination with local health professionals, to assist with community \nawareness sessions on hygiene and sanitation, predominant health related concerns (i.e. dengue fever \nand hepatitis), preventative measures and treatment as well as the importance of immunizations.   \n \nMapping of health facilities for inclusion in the Interagency Service \nDirectory and information disseminated to the community by \nOutreach Volunteers. \nPersons with Specific Needs / Persons with Disabilities \n \nOutreach Volunteers to be engaged in awareness campaigns on the \nrights of PWSN and PWD with the aim to reduce stigma of PWSN and \nPWD and increase understanding and acceptance. Include high \nfunctioning PWSN and PWDs in the awareness sessions.  \n \nFor PWSN and PWD that are high functioning in the community, if not \nalready OVs, then include as focal points for OVs on PWSN and PWD \nissues.  \n \nOutreach Volunteers to assist the community to set-up networks of family members with PWSN and \nPWD to help one another in care responsibilities and support.    \n \nMapping of services for PWSN and PWD for inclusion in the Interagency Service Directory and \ninformation disseminated to the community by Outreach Volunteers.   \nTheme 6: Information Needs / Communication with Communities \nInformation \n \nIn coordination with communities and Outreach Volunteers, \ninformation sharing campaigns on priority topics outlined in this \nassessment should be designed and implemented. UNHCR Public \nInformation, Information Management, Protection, Health and \nEducation colleagues should be involved in designing the messaging. \nThe preferred means of receiving information should be utilized. \nHealth, Education and \nServices for PWSN and \nPWD should be mapped \nand included in the \nInteragency Service \nDirectory, as outlined in \nPriority 3 of the CBP and \nOutreach strategy \nCBP and Outreach strategy \nwill hinge on how well it is \nsynergized, mainstreamed \nand integrated across \nother priorities of UNHCR \nin Pakistan, particularly \nHealth, Education and \nLivelihoods \nA key component of the \nOutreach Volunteer role is \nto disseminate information, \nas outlined in Priority 2 and \n3 of the CBP and Outreach \nstrategy", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Page | 16\n\n \nThe use of social media is becoming increasingly more common in the refugee community, therefore \nsocial media to be utilized in community-based protection interventions as a means to share \ninformation and receive feedback.  \nConclusions\n\nIn 2017, UNHCR Pakistan operation has made a concerted efforts to build capacity of UNHCR staff, \npartners and government counterparts in community-based protection theory, practice and methods. A \nnumber of activities outlined in the CBP and Outreach strategy have taken place, such as the Training of \nTrainers (ToT) at National and Provincial levels; launch of the Online Interagency Service Directory; social, \nprotection and asset mapping of selected communities for CBP interventions; selection and training of \nOutreach Volunteers. Even with all of the improvements that have been made in 2017, resources and \nsupport from staff are still needed to operationalize the objectives and outputs outlined in the CBP and \nOutreach strategy. The PA results support the direction the operation is moving and, coupled with the \nstrategy, provide a roadmap for key interventions and activities and should continue to be actively utilized \nthroughout the coming years.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "The involvement of Outreach Volunteers in the PA 2017 is a major accomplishment and a best practice. As \nthe PA is used in 2018 Country Operation Planning, Outreach Volunteers and community members should \nbe briefed on how the information was used and the relevant outcomes to close the feedback loop and \ndemonstrate the UNHCR uses the information collected from refugees to inform planning decisions. \nRecommendations for the PA 2018 were also collected and include the following: questions should be \nkept to a minimum; the questionnaire should be piloted prior to implementation to ensure questions are \ntranslatable; staff require a training on qualitative data analysis; involvement of social media in the PA \nshould be explored.\n\nUNHCR Pakistan would like to thank all the staff, partners and community members that contributed to the \nPA 2017, a critical component to ensure an effective operation that serves, assists and protects persons of \nconcern to UNHCR.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Post-distribution Monitoring (PDM) is an exercise performed by UNHCR and its partner agencies for all its CashBased interventions, based on an institutional-defined methodology and tools, that has also been adapted to the\nCOVID-19 context. This exercise covers various aspects of the programme, including process, implementation,\noutcomes of cash usage, survival strategies, protection aspects and communication and feedback mechanisms. The\nresults are analysed by a multi-functional team to adjust and improve the programme where necessary.\n\n### EXECUTIVE SUMMARY\n\nThis report presents the results of the PDM exercise carried out by UNHCR for refugees receiving cash assistance\n\nin the second semester of 2021. Through Multi-Purpose Grants (MPG) delivered via prepaid cards, UNHCR and 11\n\npartners disbursed a total of BRL 6,759,342 (USD 1,300,000) to 2,615 vulnerable households (7,779 persons) in 21\n\nstates across the country in 2021. The majority of the population is Venezuelan. Assistance is designed to\n\ncomplement public social protection programs in Brazil (for example, over 53,000 Venezuelans benefitted from\n\nAuxilio Brasil in November 2021) and allow refugee families to meet their basic needs and reduce the protection\n\nrisks associated with survival strategies. UNHCR also uses cash assistance to support Operation Welcome’s", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "voluntary internal relocation strategy for those relocated through the employment-based modality as well as a shelter\nexit strategy to facilitate local integration.\n\nThe results of this monitoring exercise indicate that, as intended, almost all respondents use cash to meet their\n\nrunning essential household needs. The most prioritised expenses included rent, food, utilities, clothing, but also\n\nhealth-related expenditures. The percentage of persons of concern’s (POCs) households who have a bank or mobile\n\nmoney account increased from 37% in 2020 to 67% in 2021. Higher inflation rates required UNHCR to review and\n\nincrease the MPG values in July 2021. Moreover, 64% of respondents indicated that cash assistance received\n\nsignificantly reduced the urgency to generate income to meet their basic needs. When asked about who decides\n\nhow to spend the assistance, respondents reported that women make the decision in 65% of the cases, underlining\n\nthat having the cards registered to women enhances their control over resources and boosts their self-confidence\n\nand decision-making power, while benefitting the entire family. In 2021, cash assistance was delivered to a total of\n\n2,039 female-headed families.\n\nAnother achievement is the reduction in the use of negative coping strategies. Though the majority of respondents", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "(61%) reported using at least one negative coping strategy, this represents a 25% reduction when compared to 2020\n\nresults (86%). Furthermore, most people (72%) are more likely to buy less expensive food, and more than half of the\n\nrespondents also mentioned reducing food portions (60%) or number of meals in one day (56%) as common\n\nstrategies. Other prominent negative coping strategies not related to food consumption include reducing essential\n\nexpenditures on hygiene, water, baby items, health, or education, in order to meet food needs (73%). Nevertheless,\n\nthe positive psychosocial effects of cash assistance continue to be emphasized by the respondents, with many\n\nrespondents (61%) indicating that cash assistance had significantly reduced their feelings of stress and allowed them\n\nto improve their living conditions.\n\nThe feedback on service delivery is generally positive, with 83% of the respondents receiving the assistance on time.\n\nMost respondents (82%) reported feeling safe receiving, keeping, or spending the cash assistance. Feelings of\n\ninsecurity mainly relate to general levels of criminality and violence.\n\nwww.unhcr.org 1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "|Col1|Baseline|Actual|\n|---|---|---|\n|**Key Question: How many persons of concern have been assisted with CBI?**1|**Key Question: How many persons of concern have been assisted with CBI?**1|**Key Question: How many persons of concern have been assisted with CBI?**1|\n|Indicator 1.1: # of persons of concern assisted with CBI|8,045|7,779|\n|Indicator 1.2: # cash transfers made|6,636|6,794|\n|Indicator 1.3: Total monetary value of cash transferred/ distributed|BRL 6,046,937|BRL 6,759,342|\n|**Key question: How efficient was the distribution process?**|**Key question: How efficient was the distribution process?**|**Key question: How efficient was the distribution process?**|\n|Indicator 2.1: % of persons of concern who received correct<br>transfer value delivered on time|100%|90%|\n|**Key question: Accountability: Is the CBI intervention accountable to persons of concern? (What**<br>**preferences do people have over how assistance is delivered?)**|**Key question: Accountability: Is the CBI intervention accountable to persons of concern? (What**<br>**preferences do people have over how assistance is delivered?)**|**Key question: Accountability: Is the CBI intervention accountable to persons of concern? (What**<br>**preferences do people have over how assistance is delivered?)**|\n|Indicator 3.1: % of persons of concern who are able to correctly<br>identify at least one of the locally available channels for raising<br>complaints or feedback with UNHCR about the cash assistance.|36.6%|21%|\n|Indicator 3.2: # of complaints received about CBI|30|20|\n|Indicator 3.3: % of persons of concern who rate CBI as their<br>preferred modality for assistance|59.3%|58%|\n|**Key question: Risks and problems: Did persons of concern face any problems with the CBI? Did the**<br>**CBI put persons of concern at additional risk?**|**Key question: Risks and problems: Did persons of concern face any problems with the CBI? Did the**<br>**CBI put persons of concern at additional risk?**|**Key question: Risks and problems: Did persons of concern face any problems with the CBI? Did the**<br>**CBI put persons of concern at additional risk?**|\n|Indicator 4.1: % of persons of concern who report feeling at risk<br>(unsafe) receiving, keeping or spending the cash assistance|17.2%|18%|\n|Indicator 4.2: % persons of concern who report facing one or<br>more problem receiving, keeping or spending the cash assistance|33.3%|39%|\n|**Key question: Markets and prices: Can persons of concern find what they need in the markets, at a**<br>**price they can afford?**|**Key question: Markets and prices: Can persons of concern find what they need in the markets, at a**<br>**price they can afford?**|**Key question: Markets and prices: Can persons of concern find what they need in the markets, at a**<br>**price they can afford?**|\n|Indicator 5.1: % of persons of concern who report being able to<br>find key items / services in the market when needed|92.6%|92.5%|\n|Indicator 5.2: % of persons of concern who report being able to<br>find key items / services of sufficient quality in shops/markets|94.6%|92%|\n|Indicator 5.3: % of persons of concern who report no increase in<br>prices of key items/services over the last 4 weeks|13.2%|11%|\n|**Key question: Outcomes: What changes is the cash assistance contributing to in persons of**<br>**concern households?**|**Key question: Outcomes: What changes is the cash assistance contributing to in persons of**<br>**concern households?**|**Key question: Outcomes: What changes is the cash assistance contributing to in persons of**<br>**concern households?**|\n|Indicator 7.1: % of persons of concern who report improved living<br>conditions|72.06%|73%|\n|Indicator 7.2: % of persons of concern who report reduced<br>feelings of stress|69.12%|61%|\n|Indicator 7.3: % of persons of concern who report being able to<br>meet all or more than half of the basic needs of their households|49.01%|42%|\n|**Key question: Has the cash assistance helped put persons of concern on the pathway to sustainable**<br>**solutions?**|**Key question: Has the cash assistance helped put persons of concern on the pathway to sustainable**<br>**solutions?**|**Key question: Has the cash assistance helped put persons of concern on the pathway to sustainable**<br>**solutions?**|\n|Indicator 8.1 % of persons of concern households who have a<br>bank account or mobile money account or other official account|37.25%|67%|\n|Indicator 8.2: % of persons of concern households who are on a<br>pathway to sustainable solutions|54.41%|75%|", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "_1 This question considers information up to 31 December 2021._\n\nwww.unhcr.org 2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Venezuela, with Haiti, Cuba, Syrian Arab Republic and Democratic Republic of Congo as other top countries of\norigin. Most refugees live in the northern states of Roraima and Amazonas, as well as in urban and rural areas in\nstates such as São Paulo, Rio de Janeiro, and Paraná. A study conducted by UNHCR and the World Bank found\nthat Venezuelans registered in the Unified Registry for Social Programs of the Brazilian government ( _Cadastro Único)_\nare on average poorer than their Brazilian peers and that 72.3% of Venezuelans registered in _Cadastro Único_ live in\nextreme poverty, compared to 48% of registered Brazilian nationals. [3] In this context, cash assistance plays a\nfundamental role to support refugees meet their basic needs, while also empowering them to determine their own\nneeds and the best way of meeting them.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In Brazil, the Government implements various social assistance programmes which target the local population,\nincluding refugees and migrants. For example, over 53,000 Venezuelans benefit from the conditional cash transfer\nprogram (known as Auxilio Brasil) in November 2021. [4] In addition, cash assistance has already been provided by\nUNHCR and partners for many years to support the most vulnerable refugees cover their basic needs and\ncomplement the benefits available under the Brazilian social assistance programs. Since June 2019, UNHCR’s multipurpose grants are paid through prepaid cards with a financial service provider selected through a nationwide\ntendering process, providing persons of concern flexibility to use these cards at ATMs and a wide range of\ncommercial establishments.\n\nUNHCR provides MPGs to vulnerable Venezuelan and non-Venezuelan PoCs in need of humanitarian assistance\nand to support those to be relocated through the government’s voluntary internal relocation strategy (interiorização).\nIn 2021, UNHCR together with 11 partners assisted a total of 2,615 households (7,779 persons) in 21 states.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Cash assistance is provided after a thorough assessment process. Following a family’s registration in UNHCR’s\nregistration and case management system (ProGres V4), partner organizations carry out an evaluation to assess\nthe applicants’ socio-economic situation and prioritize assistance requests based on pre-set vulnerability criteria,\nincluding persons at risk, with disabilities or serious medical conditions.\n\nAssistance is provided for up to 3 months, which can be extended for an additional three months after supplementary\nevaluations. The MPG value depends on family composition, and ranges from BRL 839 for one person and BRL\n1,284 for a family of 6 or more. The amount of assistance provided is standardized based on socio-economic publicly\navailable data through an annual costing survey (minimum expenditure basket). Beneficiaries receive the cash\ntransfer immediately after it is approved by UNHCR. Prepaid cards are distributed by partners and cash is transferred\nto the cards directly by UNHCR. During the COVID-19 emergency, cash assistance has been delivered following\nsecurity and preventive measures, reducing personal interactions with remote evaluations and registration.\n\n### 2.PDM METHODOLOGY\n\n- Details about the PDM:", "output": {"entities": {"named_data": ["UNHCR’s\nregistration and case management system"], "descriptive_data": [], "vague_data": ["annual costing survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "`o` Data was collected through phone calls made in November 2021 focusing on the families that received\nassistance during the second semester of 2021.\n\n`o` 15 enumerators supported this exercise (10 women and 5 men).\n\n_[2 UNHCR Global Focus, available at https://reporting.unhcr.org/brazil#toc-populations](https://reporting.unhcr.org/brazil#toc-populations)_\n\n_[3 Full text of this research is available at: https://openknowledge.worldbank.org/bitstream/handle/10986/35358/Integration-of-Venezuelan-Refugees-and-Migrants-in-](https://openknowledge.worldbank.org/bitstream/handle/10986/35358/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf?sequence=1&isAllowed=y)_\n_[Brazil.pdf?sequence=1&isAllowed=y](https://openknowledge.worldbank.org/bitstream/handle/10986/35358/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf?sequence=1&isAllowed=y)_\n\n_4 For more information, see_\n_[https://app.powerbi.com/view?r=eyJrIjoiMmVmNGNkOWEtZjQ2Yi00ZjFlLWExMzQtMjAxNjg2YjMxMzM3IiwidCI6IjE1ODgyNjJkLTIzZmItNDNiNC1iZDZlLWJjZTQ5Y](https://app.powerbi.com/view?r=eyJrIjoiMmVmNGNkOWEtZjQ2Yi00ZjFlLWExMzQtMjAxNjg2YjMxMzM3IiwidCI6IjE1ODgyNjJkLTIzZmItNDNiNC1iZDZlLWJjZTQ5YzhlNjE4NiIsImMiOjh9)_\n_[zhlNjE4NiIsImMiOjh9](https://app.powerbi.com/view?r=eyJrIjoiMmVmNGNkOWEtZjQ2Yi00ZjFlLWExMzQtMjAxNjg2YjMxMzM3IiwidCI6IjE1ODgyNjJkLTIzZmItNDNiNC1iZDZlLWJjZTQ5YzhlNjE4NiIsImMiOjh9)_\n\nwww.unhcr.org 3", "output": {"entities": {"named_data": ["UNHCR Global Focus"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "`o` Respondents were selected randomly from the ProGres database out of the total pool of beneficiaries\nwho received the assistance from August to November 2021.\n\n`o` With a 95% confidence level, and 7% for confidence interval, the sample size needed for the total of\nhouseholds (HH) assisted was 182. Nevertheless, a total of 400 HH were selected to address the high\nmobility and lack of phone numbers on record.\n\n`o` A total of 174 households answered, out of which 141 were headed by women.\n\n- Limitations and challenges faced:\n\n`o` PoCs are usually registered at the border state of Roraima; however, many individuals continue their\njourney to the country resulting, on most occasions, in a change of phone number. As data is collected\nusing phone calls, this represents a real challenge. An alternative was to reach respondents through\nWhatsApp for those that were not initially answering.\n\n`o` This monitoring exercise was carried out during the COVID-19 pandemic, under the impact of restrictive\nmeasures to combat the spread of the disease.\n\n### 3.KEY FINDINGS\n\n**Household demographics**\n\n- Average household size 3.8\n\n- Nationalities: 97% Venezuelans and 3% other\n\nnationalities\n\n- Number of people by age group who live in the", "output": {"entities": {"named_data": ["ProGres database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "same household:\n\n`o` 46% of families are composed of adults (18 to\n\n59 years old).\n\n`o` 33% include children between 5 and 17 years\n\nold.\n\n|Number of respondents disaggregated by age<br>and gender|Col2|Col3|Col4|\n|---|---|---|---|\n|**Age**|**Age**|**Gender**|**Gender**|\n|**# Cases**|**Age range**|**# Cases**|**Gender**|\n|104|18-35 yrs|141|female|\n|62|36-59 yrs|31|male|\n|8|60 yrs +|2|other|\n|**174**||**174**||\n\n`o` 17% of household have children from 0 to 4 years old\n\n`o` 4% of households have people over 60 years old.\n\n- Women and children represented 50 % and 33 % of the sample respectively. 81% of the families\n\ninterviewed were female headed households.\n\n- Breakdown of respondents by State: São Paulo (33,3%); Federal District (17,2%); Amazonas (11,5%);\n\nRio de Janeiro (10,9%); Rio Grande do Sul (8%); Santa Catarina (6,3%); Roraima (3,4%); Minas\n\nGerais (2.3%); Goiás (0.6%), Mato Grosso (0.6%); Paraná (0,6%).\n\n- The average amount given to beneficiaries in each disbursement was BRL 1,053.\n\n#### **3.1. RECEIVING AND SPENDING CASH ASSISTANCE**\n\n50% of the respondents indicated receiving 3 allotments, 20% 2 allotments, 20% 1 allotment and 10%\nhave received between 4 and 6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- On the use of the assistance, participants reported that in 65% of the cases the decision was made\nby a woman; in 15% of the cases, it was taken by the couple together; in 9% the entire family unit\n\nwww.unhcr.org 4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "amount and 19% answered that they received different values than expected.\n\n- 83% of the participants interviewed reported having received the amount on the expected date and\n16% reported a delay.\n\n- The following chart shows the main source of income identified by the respondents to complement the cash\n\nassistance provided by UNHCR:\n\n#### **Risks and Problems**\n\n- Most of the participants reported feeling safe (82%) receiving, keeping, or spending the cash\nassistance. When asking about facing one or more problems receiving, keeping or spending the cash\nassistance, (18%) participants indicated the main problems were of a practical nature, namely the\nregistered person is not available to withdraw the money, receiving a wrong pin code or forgetting the\none received, and/or not been able to enter the PIN code by themselves. When asked in the focus\ngroups some participants recall asking the bank directly for support or contacting the partner\norganization.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "|Key question: Risks and problems: Did persons of concern<br>face any problems with the CBI? Did the CBI put persons of<br>concern at additional risk?|Baseline|Actual|\n|---|---|---|\n|Indicator 4.1: % of persons of concern who report feeling at risk<br>(unsafe) receiving, keeping or spending the cash assistance|17.2%|18%|\n|Indicator 4.2: % persons of concern who report facing one or more<br>problems receiving, keeping or spending the cash assistance|33.3%|39%|\n\nwww.unhcr.org 5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "the inflation rate in 2020 (4.52%). In July 2021, the multipurpose values were reviewed and\nincreased, and for 2022 an analysis of the percentage that the MPG represents in the minimum\nexpenditure basked will be performed.\n\n|Key question: Markets and prices: Can persons of concern<br>find what they need in the markets, at a price they can afford?|Baseline|Actual|\n|---|---|---|\n|Indicator 5.1: % of persons of concern who report being able to find<br>key items / services in the market when needed|92.6%|93%|\n|Indicator 5.2: % of persons of concern who report being able to find<br>key items / services of sufficient quality in shops/markets|94.6%|92%|\n|Indicator 5.3: % of persons of concern who report no increased in<br>prices of key items/services over the last 4 weeks|13.2%|11%|\n\n#### **3.3. Expenditures**\n\n- On the use of the cash assistance, 84% of the participants indicated that they had already spent all\nthe money received; 16% saved part of the value received.\n\n- More than 91% of the participants reported, as intended, spending their cash on rent and food. The\nfollowing table provides an overview on the amount spent by respondents on various items.\n\n_[5 Instituto Brasileiro Geografia e Estatística (IBGE)](https://www.ibge.gov.br/indicadores)_\n\nwww.unhcr.org 6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "urgency to generate income, as they previously used a lot of resources to purchase the items they\nbought with the assistance; 18% that there was a moderate reduction; and 17% that there was\nonly a slight reduction.\n\n- 61% of the respondents indicated that there was a significant reduction of the stress situation; 21%\nthat there was a moderate reduction; 14% that there was a slight reduction; and 4% pointed out\nthat there was no reduction in stress.\n\n- When asked about their ability to provide basic needs for their homes at the time of the interview,\n42% manage to supply all or more than half of basic needs; 24% reported that they are able to\nsupply half of the needs; 26% manage to supply less than half of the needs; and 7% cannot meet\nbasic needs.\n\n**Coping Mechanisms**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- 61% of respondents reported still using at least one negative coping strategy, which is a reduction\nfrom 86% in the previous reporting period. The following strategies related to food consumption\nwere recorded: (a) buying less expensive and less preferred foods (72%); (b) borrow food or help\nfrom friends or relatives (43%); (c) limit the portion size of the food (60%); (d) restrict the\nconsumption of adults for children to eat (40%); and (e) reduce the number of meals in one day\n(56%).\n\n- The chart below reflects coping strategies not related to food consumption which respondents used\nto purchase food. Compared to previous year these percentages have reduced.\n\nwww.unhcr.org 7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "|Key question: Outcomes: What changes is the cash<br>assistance contributing to in persons of concern households?|Baseline|Actual|\n|---|---|---|\n|Indicator 7.1: % of persons of concern who report improved living<br>conditions|72.06%|73%|\n|Indicator 7.2: % of persons of concern who report reduced feelings<br>of stress|69.12%|61%|\n|Indicator 7.3: % of persons of concern who report being able to<br>meet all or more than half of the basic needs of their households|49.01%|42%|\n|Indicator 7.4: % persons of concern households reporting using<br>one or more negative coping strategy in the last 4 weeks.|86.27%|61%|\n\n#### **3.5. LONGER-TERM OUTCOMES**\n\n- Cash assistance in Brazil is used by the operation as a key tool to help the most vulnerable to\ncover their survival needs and bridge gaps until inclusion in sustainable pathways, including the\nnational social protection system and labour market, is attained. Cash assistance is generally\nprovided up to 3 months for PoCs with specific needs and critical levels of vulnerabilities, though\nit can be extended for up to 6 months. A one-off MPG is also given in the employment-based\nmodality of interiorization to support the first month upon arrival at the destination, until the\nbeneficiary receives the first salary.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- 32,8% of the respondents reported having productive assets, in the form of small street vendor\nbusinesses. However, two out of three respondents indicated not to have the means to guarantee\ntheir families' subsistence. For those with productive assets, this may bring some loss risks due to\nthe potential confiscation of the products by the authorities, or even security risks for having their\nassets stolen in the streets. Health risks are also present when restrictions imposed by the COVID19 situation are not observed.\n\n- 67% of participants indicated to have an account with a bank or other financial institution, _**which**_\n_**represents an increase of more than 100% from last year)**_ . 33% of respondents do not have\nan account.\n\n- 7% of the respondents indicated having access to microcredit, but the large majority (90%)\nmentioned not to have such access.\n\n|Key question: Has the cash assistance helped put persons of<br>concern on the pathway to sustainable solutions?|Baseline|Actual|\n|---|---|---|\n|Indicator 8.1 % of persons of concern households who have a bank<br>account or mobile money account or other official account|37.25%|67%|\n|Indicator 8.2: % of persons of concern households who are on a<br>pathway to sustainable solutions|54.41%|75%|\n\n#### **3.6. ACCOUNTABILITY TO AFFECTED PERSONS**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "In focus groups all the participants reported receiving information on how UNHCR’s cash assistance\nworks, in line with the SOP requiring partners to distribute an informative brochure and send the CBI\ninformative video with the delivery of the prepaid card. Respondents feel the material shared by UNHCR\npartners contains important and useful information. All respondents indicated receiving the informative\nbrochure but didn’t remember the information included and did not know how to raise complaints or ask\nquestions.\n\nwww.unhcr.org 8", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "|Key question: Accountability: Is the CBI intervention<br>accountable to persons of concern? (What preferences do<br>people have over how assistance is delivered?)|Baseline|Actual|\n|---|---|---|\n|Indicator 3.1: % of persons of concern who are able to correctly<br>identify at least one of the locally available channels for raising<br>complaints or feedback with UNHCR about the cash assistance.|36.6%|21%|\n|Indicator 3.2: # of complaints received about CBI|30|20|\n|Indicator 3.3: % of persons of concern who rate CBI as their<br>preferred modality for assistance|59.31%|58%|\n\n### 4.CONCLUSIONS AND RECOMMENDATIONS\n\n#### **4.1. Conclusions**\n\n- The results of this PDM show that cash assistance works, it has a positive impact on the\nimprovement of the refugees’ short-term, emergency living conditions and on the local economy.\nBrazil presents many opportunities and potential to expand the use of cash in refugee response,\ndue to the strong local markets and the robust delivery mechanisms and financial service\nproviders.\n\n- This post distribution monitoring exercise also shows that, in line with the Office priorities to reach\nmost at-risk refugee households, most recipients of UNHCR’s cash assistance are highly\nvulnerable households and not able to meet their basic food needs taking into consideration their\neconomic capacities, livelihoods resilience and food consumption score.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- Considering that 2021 ended with a two-digit inflation rate, a revision of the grant values is needed,\ntogether with an analysis of the duration of the assistance, to potentially contribute to a greater\nreduction in the use of negative coping mechanisms.\n\n- In addition, there is a need to further strengthen links between cash assistance and complementary\nprotection support services, including the use of cash in the areas of GBV and child protection\ncase management.\n\n- Upon delivery of the prepaid card, PoCs are provided with general information about the use of\nthis modality, including feedback mechanisms, how to use the card, costs of withdrawals, how to\ncheck their balance and report any incident. However, during the focus groups, respondents\nevidenced a lack of familiarity with basic issues related to the use of the prepaid card and\nhighlighted not recalling the content of the information they had previously received. In this sense,\nthere is a need to work with partners and PoCs to simplify the messages, broaden the channels of\ncommunication and deliver better tailored information campaigns to address this gap.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "- The positive results attained through cash assistance, as for instance evidenced by the significant\nincrease in the number of households who have a bank account, demonstrate the effectiveness of\nembedding cash in livelihood activities such as skills training, budget management/prioritization\nand access to financial services. These actions have successfully strengthened the beneficiaries’\nself-reliance.\n\nwww.unhcr.org 9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "together, and what combinations of assistance can create greater impact.\n\n- As more organizations are starting using cash assistance, there is a need to develop a coordinated\nand standardized package of MPGs within the R4V Cash Working Group, complemented by sector\nspecific assistance.\n\n- A revision of the Minimum Expenditure Basket at Cash working group is needed in order to improve\nthe complementarity of assistance and enhance impact, given the limited resources.\n\n- Together with protection and livelihood units, discuss a possible evaluation on other sectoral\ninterventions (in-kind) or referral mechanisms, and how they have impacted the wellbeing of\npersons of concern together with the strategic use of cash assistance.\n\n- Within the Cash Working Group, continue to promote the implementation of data-sharing\narrangements among organizations providing cash assistance with a view to reduce the risk of\noverlap of beneficiaries.\n\n- Continue to elaborate Communication with Communities (CwC) materials on identified topics,\nincluding the good use of cash, complaint, and feedback mechanisms.\n\n- Continue to monitor and analyse in more depth the interplay between UNHCR’s cash assistance\nprogram and the national social protection programs.\n\nwww.unhcr.org 10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "reliefweb"}
-{"input": "Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized © 2024 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW, Washington, DC 20433 Telephone: 202-473- 1000; Internet: www.worldbank.org Some rights reserved This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Nothing herein shall constitute or be considered to be a limitation upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved. Rights and Permissions This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) http:// creativecommons.org/licenses/by/3.0/igo. Under the Creative Commons Attribution license, you are free to copy,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved. Rights and Permissions This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) http:// creativecommons.org/licenses/by/3.0/igo. Under the Creative Commons Attribution license, you are free to copy, distribute, transmit, and adapt this work, including for commercial purposes, under the following conditions: Attribution—Please cite the work as follows: World Bank (2024). Expanding Development Approaches to Refugees and Their Hosts in Ethiopia. Washington DC. © World Bank. Translations—If you create a translation of this work, please add the following disclaimer along with the attribution: This translation was not created by The World Bank and should not be considered an official World Bank translation. The World Bank shall not be liable for any content or error in this translation. Adaptations—If you create an adaptation of this work, please add the following disclaimer along with the attribution: This is an adaptation of an original work by The World Bank. Views and opinions expressed in the adaptation are the sole responsibility of the author or authors of the adaptation and are not endorsed by the World Bank. Third-party content—The World Bank does", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "following disclaimer along with the attribution: This is an adaptation of an original work by The World Bank. Views and opinions expressed in the adaptation are the sole responsibility of the author or authors of the adaptation and are not endorsed by the World Bank. Third-party content—The World Bank does not necessarily own each component of the content contained within the work. The World Bank therefore does not warrant that the use of any third-party owned individual component or part contained in the work will not infringe on the rights of those third parties. The risk of claims resulting from such infringement rests solely with you. If you wish to re-use a component of the work, it is your responsibility to determine whether permission is needed for that re-use and to obtain permission from the copyright owner. Examples of components can include, but are not limited to, tables, figures, or images. All queries on rights and licenses should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; e-mail: pubrights@worldbank.org. Photos: © World Bank Abbreviations........................................................................................................................................................................................................................................................................................ i Acknowledgements....................................................................................................................................................................................................................................................................... ii Executive Summary......................................................................................................................................................................................................................................................................... vi 1. Introduction............................................................................................................................................................................................................................................................................... 1 1.1 How can we achieve better development outcomes", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "images. All queries on rights and licenses should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; e-mail: pubrights@worldbank.org. Photos: © World Bank Abbreviations........................................................................................................................................................................................................................................................................................ i Acknowledgements....................................................................................................................................................................................................................................................................... ii Executive Summary......................................................................................................................................................................................................................................................................... vi 1. Introduction............................................................................................................................................................................................................................................................................... 1 1.1 How can we achieve better development outcomes for all?.................................................................................................................................................... 5 1.2 How does Socio-Economic Survey of Refugees in Ethiopia (SESRE) contribute to the debate on policies?....................................... 6 2. Sociodemographic Profile ........................................................................................................................................................................................................................................... 9 2.1 Demographic characteristics .............................................................................................................................................................................................................................. 9 2.2 Education .......................................................................................................................................................................................................................................................................... 12 2.3 Health and nutrition.................................................................................................................................................................................................................................................. 17 2.4 Living conditions.......................................................................................................................................................................................................................................................... 21 3. Jobs and Livelihoods ....................................................................................................................................................................................................................................................... 24 3.1 Labor market outcomes of in-camp refugees and their hosts................................................................................................................................................ 27 3.2 Labor market outcomes of OCP refugees and their hosts.......................................................................................................................................................... 34 3.3 Refugee youth................................................................................................................................................................................................................................................................ 37 4. Refugees’ Aspirations....................................................................................................................................................................................................................................................... 39 5. Welfare and Equity.............................................................................................................................................................................................................................................................. 42 5.1 Welfare dimensions.................................................................................................................................................................................................................................................... 42 5.1.1 Monetary poverty and inequality .............................................................................................................................................................................................. 42 5.1.2 Expenditure patterns ......................................................................................................................................................................................................................... 44 5.1.3 Multidimensional poverty ............................................................................................................................................................................................................. 46 5.1.4 Food security........................................................................................................................................................................................................................................... 47 5.1.5 Shocks and coping strategies....................................................................................................................................................................................................... 48 5.2 Determinants of welfare......................................................................................................................................................................................................................................... 49 5.3 Cost of basic needs for refugees ..................................................................................................................................................................................................................... 53 6. Markets and Opportunities ....................................................................................................................................................................................................................................... 56 6.1 Spatial disparities in refugees labor market access and outcomes...................................................................................................................................... 57 6.2 Effects of local", "output": {"entities": {"named_data": ["Socio-Economic Survey of Refugees in Ethiopia"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "5.1.3 Multidimensional poverty ............................................................................................................................................................................................................. 46 5.1.4 Food security........................................................................................................................................................................................................................................... 47 5.1.5 Shocks and coping strategies....................................................................................................................................................................................................... 48 5.2 Determinants of welfare......................................................................................................................................................................................................................................... 49 5.3 Cost of basic needs for refugees ..................................................................................................................................................................................................................... 53 6. Markets and Opportunities ....................................................................................................................................................................................................................................... 56 6.1 Spatial disparities in refugees labor market access and outcomes...................................................................................................................................... 57 6.2 Effects of local factors on refugees’ labor market outcomes...................................................................................................................................................... 61 7. Social Cohesion...................................................................................................................................................................................................................................................................... 63 7.1 Attitudes between refugees and hosts...................................................................................................................................................................................................... 64 7.2 Social interactions ...................................................................................................................................................................................................................................................... 68 8. Policy Recommendations ........................................................................................................................................................................................................................................... 72 References ............................................................................................................................................................................................................................. 77 Annexes ................................................................................................................................................................................................................................. 82 Annex A: Description of Refugees by Country of Origin.............................................................................................................................................. 83 Annex B: Refugee Policies in Ethiopia.............................................................................................................................................................................. 85 Annex C: Survey Design and Methodology.................................................................................................................................................................... 92 Annex D: Descriptive Statistics and Regression Results............................................................................................................................................. 97 Annex E: Robustness Checks of Refugees’ Consumption.......................................................................................................................................... 124 Annex F: Comparison of Results from Skills Profile Survey and SESRE................................................................................................................. 128 TABLE OF CONTENTS Figure ES.1: Desired location in three years ����������������������������������������������������� iv Figure ES.2: Expected location in three years ��������������������������������������������������� iv Figure ES.3: Poverty incidence........................................................................... v Figure ES.4: Food insecurity scale...................................................................... v Figure ES.5: Refugee employment and proximity to resource hubs .................. vi Figure ES.6: Host response to “Refugees are good people” ............................ vii Figure ES.7: Host response to “Would you feel comfortable having", "output": {"entities": {"named_data": ["Skills Profile Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Expected location in three years ��������������������������������������������������� iv Figure ES.3: Poverty incidence........................................................................... v Figure ES.4: Food insecurity scale...................................................................... v Figure ES.5: Refugee employment and proximity to resource hubs .................. vi Figure ES.6: Host response to “Refugees are good people” ............................ vii Figure ES.7: Host response to “Would you feel comfortable having a refugee as a neighbor?” ��������������������������������������������������������������� vii Figure 1.1: Refugees and asylum seekers in Ethiopia by country of origin, 1984-2023.................................................................................... 1 Figure 2.1: Refugees by survey domain ���������������������������������������������������������� 10 Figure 2.2: Country of birth............................................................................. 10 Figure 2.3: Refugees arrival in Ethiopia (15 years and above) ..................... 10 Figure 2.4: Age structure................................................................................. 11 Figure 2.5: Gender composition....................................................................... 11 Figure 2.6: Marital status (18 years and above) ��������������������������������������������� 11 Figure 2.7: Education level (18 years and above) ������������������������������������������ 13 Figure 2.8: Youth (15 to 24) education level �������������������������������������������������� 13 Figure 2.9: Refugees’ education outside of Ethiopia (18 years and above) .... 15 Figure 2.10: Children currently attending school ����������������������������������������������� 15 Figure 2.11: Gross Enrollment Rate (GER) �������������������������������������������������������� 16 Figure 2.12: Net Enrollment Rate (NER) ������������������������������������������������������������ 16 Figure 2.13: Share of children and youth above school age in education ....... 16 Figure 2.14: Reasons for not currently attending school ���������������������������������� 17 Figure 2.15:", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Figure 2.10: Children currently attending school ����������������������������������������������� 15 Figure 2.11: Gross Enrollment Rate (GER) �������������������������������������������������������� 16 Figure 2.12: Net Enrollment Rate (NER) ������������������������������������������������������������ 16 Figure 2.13: Share of children and youth above school age in education ....... 16 Figure 2.14: Reasons for not currently attending school ���������������������������������� 17 Figure 2.15: Faced any health problem ������������������������������������������������������������� 18 Figure 2.16: Received medical assistance when faced with health problem .... 18 Figure 2.17: Use of the national healthcare system when faced with health problems............................................................................. 19 Figure 2.18: Child nutritional indicators ������������������������������������������������������������� 21 Figure 2.19: Presence of any disability ������������������������������������������������������������� 21 Figure 2.20: Dwelling type.................................................................................. 22 Figure 2.21: Housing quality.............................................................................. 22 Figure 2.22: Access to drinking water and hygiene .......................................... 23 Figure 2.23: Access to toilet facility and waste disposal ������������������������������������ 23 Figure 2.24: Source of lighting........................................................................... 23 Figure 3.1: Top 3 difficulties with being a refugee ������������������������������������������ 25 Figure 3.2: Work status.................................................................................... 28 Figure 3.3: Work type....................................................................................... 28 Figure 3.4: Occupation..................................................................................... 28 Figure 3.5: Work status by gender................................................................... 29 Figure 3.6: In-camp refugee share employed by age ������������������������������������� 29 Figure 3.7: Female work type........................................................................... 30 Figure 3.8: Female occupations....................................................................... 30 Figure 3.9: Refugee work location.................................................................... 30 Figure 3.10: Hours per week.............................................................................. 31 Figure", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Figure 3.3: Work type....................................................................................... 28 Figure 3.4: Occupation..................................................................................... 28 Figure 3.5: Work status by gender................................................................... 29 Figure 3.6: In-camp refugee share employed by age ������������������������������������� 29 Figure 3.7: Female work type........................................................................... 30 Figure 3.8: Female occupations....................................................................... 30 Figure 3.9: Refugee work location.................................................................... 30 Figure 3.10: Hours per week.............................................................................. 31 Figure 3.11: Hourly earnings............................................................................. 31 Figure 3.12: Share employed by age – camp refugees ������������������������������������ 31 Figure 3.13: Share employed by age – camp hosts ����������������������������������������� 31 Figure 3.14: Household owns crops................................................................... 32 Figure 3.15: Household owns livestock ������������������������������������������������������������� 32 Figure 3.16: Total value of livestock.................................................................. 32 Figure 3.17: Value per tropical livestock unit ����������������������������������������������������� 32 Figure 3.18: Household has non-farm business ������������������������������������������������ 33 Figure 3.19: Value of productive assets among households with non-farm business......................................................................... 33 Figure 3.20: Household primary income source ������������������������������������������������ 33 Figure 3.21: Household primary income source ������������������������������������������������ 35 Figure 3.22: Work status by gender .................................................................. 36 Figure 3.23: Work type by gender...................................................................... 36 Figure 3.24: Occupation..................................................................................... 36 Figure 3.25: Occupation among completed secondary or more ....................... 36 Figure 3.26: Refugee occupation concentration ������������������������������������������������ 37 Figure 3.27: Youth work status........................................................................... 38 Figure 3.28: Male youth work status.................................................................. 38 Figure 3.29: Female youth work status �������������������������������������������������������������� 38 Figure 4.1:", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "3.23: Work type by gender...................................................................... 36 Figure 3.24: Occupation..................................................................................... 36 Figure 3.25: Occupation among completed secondary or more ....................... 36 Figure 3.26: Refugee occupation concentration ������������������������������������������������ 37 Figure 3.27: Youth work status........................................................................... 38 Figure 3.28: Male youth work status.................................................................. 38 Figure 3.29: Female youth work status �������������������������������������������������������������� 38 Figure 4.1: Desired location in three years ����������������������������������������������������� 39 Figure 4.2: Expected location in three years ��������������������������������������������������� 39 Figure 4.3: Locus of control............................................................................. 41 Figure 4.4: Locus of control by type of control ����������������������������������������������� 41 Figure 5.1: Poverty incidence........................................................................... 43 Figure 5.2: Income inequality, Gini index ���������������������������������������������������������� 43 Figure 5.3: Expenditure components (in birr) ������������������������������������������������� 44 Figure 5.4: Shares of food expenditure ����������������������������������������������������������� 44 Figure 5.5: Food and non-food expenditures shares by sources ................... 45 Figure 5.6: Food expenditure shares by food groups ������������������������������������ 46 Figure 5.7: Multidimensional poverty incidence, severity, and vulnerability ... 46 Figure 5.8: Perceived changes in household living standards .......................... 47 Figure 5.9: Food insecurity scale for refugees and hosts 48 Figure 5.10: Dietary diversity and food consumption status ............................ 48 Figure 5.11: Type of shocks experienced ���������������������������������������������������������� 49 Figure 5.12: Shock coping strategies ���������������������������������������������������������������� 49 LIST OF FIGURES Figure 5.13: Household composition by quintiles ����������������������������������������� 50 Figure", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "standards .......................... 47 Figure 5.9: Food insecurity scale for refugees and hosts 48 Figure 5.10: Dietary diversity and food consumption status ............................ 48 Figure 5.11: Type of shocks experienced ���������������������������������������������������������� 49 Figure 5.12: Shock coping strategies ���������������������������������������������������������������� 49 LIST OF FIGURES Figure 5.13: Household composition by quintiles ����������������������������������������� 50 Figure 5.14: Demographic characteristics by quintile ���������������������������������� 50 Figure 5.15: Poverty headcount rate for in-camp refugees and their hosts, by domain .............................................................. 50 Figure 5.16: Poverty incidence decreases with education of the household head ........................................................................ 51 Figure 5.17: Household wealth indicators by expenditure quintiles ............. 52 Figure 5.18: Labor market outcomes by expenditure quintiles .................. 52 Figure 5.19: Poverty rates and employment for refugees and hosts ........... 53 Figure 5.20: Share of consumption provided in-kind or for free by consumption per capita quintiles among in-camp refugees 54 Figure 5.21: Poverty incidence at consumption and pre-assistance consumption levels ................................................................... 54 Figure 5.22: Costs of basic needs per refugee per year under different scenarios .................................................................... 54 Figure 6.1: Refugee incidence...................................................................... 58 Figure 6.2: Labor force participation rate by proximity to resource hub, market accessibility.................................................................... 59 Figure 6.3: Refugees’ labor market outcomes �������������������������������������������� 59 Figure 6.4: Sectoral employment.................................................................. 60 Figure 6.5: The share of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Costs of basic needs per refugee per year under different scenarios .................................................................... 54 Figure 6.1: Refugee incidence...................................................................... 58 Figure 6.2: Labor force participation rate by proximity to resource hub, market accessibility.................................................................... 59 Figure 6.3: Refugees’ labor market outcomes �������������������������������������������� 59 Figure 6.4: Sectoral employment.................................................................. 60 Figure 6.5: The share of refugee youth who are NEET ������������������������������� 60 Figure 6.6: Local labor supply effect of refugee’s odds of employment 61 Figure 6.7: Local unemployment level matters to obtain jobs ................... 61 Figure 6.8: Distance to the nearest city and the chance of obtaining a job for refugees ������������������������������������������������������ 62 Figure 6.9: Employment and proximity to resource hubs .......................... 62 Figure 7.1: Host response to “Refugees are good people” ....................... 64 Figure 7.2: Host response to “Would you feel comfortable having a refugee as a neighbor?”......................................................................... 64 Figure 7.3: Host response to “Refugees are good people” by gender ..... 65 Figure 7.4: Host response to “Would you feel comfortable having a refugee as a neighbor?” by gender ��������������������������������������� 65 Figure 7.5: Share of hosts who agree refugees should have access to... 66 Figure 7.6: Host beliefs about refugee impact in Ethiopia ......................... 66 Figure 7.7: Negative experiences due to refugees �������������������������������������� 66", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Host response to “Would you feel comfortable having a refugee as a neighbor?” by gender ��������������������������������������� 65 Figure 7.5: Share of hosts who agree refugees should have access to... 66 Figure 7.6: Host beliefs about refugee impact in Ethiopia ......................... 66 Figure 7.7: Negative experiences due to refugees �������������������������������������� 66 Figure 7.8: Positive experience due to refugees ������������������������������������������� 66 Figure 7.9: Are most Ethiopians/refugees in Ethiopia trustworthy? .......... 67 Figure 7.10: Host attitudes index................................................................... 67 Figure 7.11: Host attitudes index by gender ������������������������������������������������� 67 Figure 7.12: Share with family or friends in Ethiopia ������������������������������������� 68 Figure 7.13: Share with friends in Ethiopia by demographic group ............. 68 Figure 7.14: Share of refugees who think interactions with hosts are “easy to do” ........................................................................... 69 Figure 7.15: Who do refugees rely on in times of need �������������������������������� 69 Figure 7.16: Share of refugees who agree they are “culturally similar to hosts”....................................................................... 70 Figure 7.17: Share or refugees involved in a community representative body 70 Figure 7.18: Share or refugees engaged in a community representative body by demographic group ���������������������������������������������������� 70 Figure 7.19: Discrimination and harassment ������������������������������������������������ 71 Figure 7.20: Discrimination and harassment by demographic group ....... 71 Figure D.1: Age group by", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Figure 7.17: Share or refugees involved in a community representative body 70 Figure 7.18: Share or refugees engaged in a community representative body by demographic group ���������������������������������������������������� 70 Figure 7.19: Discrimination and harassment ������������������������������������������������ 71 Figure 7.20: Discrimination and harassment by demographic group ....... 71 Figure D.1: Age group by gender................................................................ 100 Figure D.2: Refugees’ education document ����������������������������������������������� 100 Figure D.3: Share of school-age children in education per household ..... 100 Figure D.4: School-age children currently attending school by gender .... 101 Figure D.5: Reasons for not currently attending school by gender ........... 101 Figure D.6: Average annual household education expenditure (in ETB) ... 101 Figure D.7: Type of health institutions......................................................... 102 Figure D.8: Problems faced in health institutions ��������������������������������������� 102 Figure D.9: Stunting by gender of children ������������������������������������������������� 102 Figure D.10: Childbirth in health institutions (children under five years) 103 Figure D.11: No birth evidence available (children under five years) ......... 103 Figure D.12: Average annual per capita health expenditure ....................... 103 Figure D.13: Types of disability..................................................................... 104 Figure D.14: Rent expenditure (Refugees and hosts in Addis Ababa) ........ 104 Figure D.15: Hand washing facility................................................................. 104 Figure D.16: Top 3 difficulties with being a refugee by survey domains .....", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "years) ......... 103 Figure D.12: Average annual per capita health expenditure ....................... 103 Figure D.13: Types of disability..................................................................... 104 Figure D.14: Rent expenditure (Refugees and hosts in Addis Ababa) ........ 104 Figure D.15: Hand washing facility................................................................. 104 Figure D.16: Top 3 difficulties with being a refugee by survey domains ..... 108 Figure D.17: Work status by survey domains ������������������������������������������������ 108 Figure D.18: Type of work by survey domains ���������������������������������������������� 108 Figure D.19: Occupation by survey domains ������������������������������������������������� 108 Figure D.20: Work location by survey domains ��������������������������������������������� 108 Figure D.21: Hours per week by survey domains ����������������������������������������� 108 Figure D.22: Hourly earnings by survey domains ����������������������������������������� 109 Figure D.23: Household owns crops ............................................................. 109 Figure D.24: Household owns livestock......................................................... 109 Figure D.25: Total value of livestock ............................................................ 109 Figure D.26: Value per tropical livestock unit ������������������������������������������������ 109 Figure D.27: Household has non-farm business ����������������������������������������� 109 Figure D.28: Value of productive assets in households with business ........ 110 Figure D.29: Primary source of income pre-post migration by survey domains......................................................................... 110 Figure D.30: Youth work status by survey domains �������������������������������������� 110 Figure D.31: In-camp refugee locations by ecological Zone ........................ 115 Figure D.32: Refugee’s labor market performance ��������������������������������������� 115 Figure D.33: Economic sector........................................................................", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "in households with business ........ 110 Figure D.29: Primary source of income pre-post migration by survey domains......................................................................... 110 Figure D.30: Youth work status by survey domains �������������������������������������� 110 Figure D.31: In-camp refugee locations by ecological Zone ........................ 115 Figure D.32: Refugee’s labor market performance ��������������������������������������� 115 Figure D.33: Economic sector........................................................................ 115 Box 1.1: Comparison of SPS 2017 and SESRE 2023.................................................................................................................................................. 7 Box 2.1: Education system for refugees in Ethiopia................................................................................................................................................. 13 Box 2.2: Refugees under the Out-of-Camp Policy (OCP).......................................................................................................................................... 14 Box 3.1: Eritrean refugee sample in the SESRE......................................................................................................................................................... 26 Box 3.2: OCP Refugees under the Amnesty Program............................................................................................................................................... 27 Box 3.3: Refugee Vocational Training and Cooperatives......................................................................................................................................... 34 Box 5.1: Consumption aggregation and poverty measurement............................................................................................................................. 43 Box 5.2: Disparity between refugee ration aid and reported consumption quantities........................................................................................ 45 Box 5.3: MPI methodology.......................................................................................................................................................................................... 47 Box 5.4: Estimation of the cost of basic needs for refugees..................................................................................................................................... 55 Box 6.1: Measurement of proximity and market access index in Ethiopia............................................................................................................. 58 Box 7.1: Socio-political tensions in the Gambella Region....................................................................................................................................... 65 Box B.1: Employment pathways of refugees............................................................................................................................................................. 87 LIST OF TABLES LIST OF BOXES Table 2.1: Household characteristics............................................................................................................................................................................ 11 Table 3.1: Labor force statistics..................................................................................................................................................................................... 28 Table 3.2: Labor force statistics..................................................................................................................................................................................... 35 Table 3.3: Youth labor force statistics (age", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "index in Ethiopia............................................................................................................. 58 Box 7.1: Socio-political tensions in the Gambella Region....................................................................................................................................... 65 Box B.1: Employment pathways of refugees............................................................................................................................................................. 87 LIST OF TABLES LIST OF BOXES Table 2.1: Household characteristics............................................................................................................................................................................ 11 Table 3.1: Labor force statistics..................................................................................................................................................................................... 28 Table 3.2: Labor force statistics..................................................................................................................................................................................... 35 Table 3.3: Youth labor force statistics (age 15-24)........................................................................................................................................................ 38 Table B.1: Pledges made at 2016 UN Leaders’ Summit and progress....................................................................................................................... 88 Table B.2: GRF pledges and implementation progress............................................................................................................................................... 90 Table C.1: The distribution of sampled and surveyed households by domains...................................................................................................... 94 Table D.1: Demographic characteristics by survey domains...................................................................................................................................... 97 Table D.2: Education outcomes by survey domains.................................................................................................................................................... 98 Table D.3: Health outcomes by survey domains.......................................................................................................................................................... 99 Table D.4: Living conditions by survey domains.......................................................................................................................................................... 99 Table D.5: Labor force statistics by survey domains.................................................................................................................................................... 105 Table D.6: Determinants of refugee-host earnings gap............................................................................................................................................... 105 Table D.7: Determinants of employment outcomes.................................................................................................................................................... 106 Table D.8: Refugee Household Reliance on NGOs/Donations ................................................................................................................................... 107 Table D.9: Determinants of refugee-host earnings gap............................................................................................................................................... 107 Table D.10: Refugee intention to migrate abroad.......................................................................................................................................................... 111 Table D.11: Poverty headcount rate by subgroups........................................................................................................................................................ 112 Table D.12: Determinants of welfare (total expenditure per capita)............................................................................................................................ 113 Table D.13: Determinants of welfare for in-camp refugees........................................................................................................................................... 114 Table D.14: Variables used to estimate employment outcomes..................................................................................................................................", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "D.9: Determinants of refugee-host earnings gap............................................................................................................................................... 107 Table D.10: Refugee intention to migrate abroad.......................................................................................................................................................... 111 Table D.11: Poverty headcount rate by subgroups........................................................................................................................................................ 112 Table D.12: Determinants of welfare (total expenditure per capita)............................................................................................................................ 113 Table D.13: Determinants of welfare for in-camp refugees........................................................................................................................................... 114 Table D.14: Variables used to estimate employment outcomes.................................................................................................................................. 116 Table D.15: Factors determining the odds of obtaining a job for refugees: logit model............................................................................................ 117 Table D.16: Proximity and market accessibility effects on engagement in agriculture activity: logit model.......................................................... 119 Table D.17: Proximity and market accessibility effects on engagement in service sector: logit model................................................................... 120 Table D.18: Regression analysis of host and refugee attitudes..................................................................................................................................... 121 Table D.19: Regression analysis of social integration outcomes.................................................................................................................................. 122 Table D.20: Regression analysis of social integration and labor market outcomes................................................................................................... 123 Table E.1: Food aid data/information received from UNHCR..................................................................................................................................... 124 Table E.2: Food aid and consumption comparisons................................................................................................................................................... 125 Table E.3: Aggregate food expenditures....................................................................................................................................................................... 126 Table E.4: Food aid data/information received from WFP.......................................................................................................................................... 126 Table E.5: Food quantity and expenditure comparisons............................................................................................................................................ 127 Table E.6: Aggregate food expenditures....................................................................................................................................................................... 128 Table F.1: Results on common indicators from SPS 2017 and SESRE 2023.............................................................................................................. 129 ABBREVIATIONS ARRA Administration for Refugee and Returnee Affairs CBHI Community Based Health Insurance CPI", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Table E.4: Food aid data/information received from WFP.......................................................................................................................................... 126 Table E.5: Food quantity and expenditure comparisons............................................................................................................................................ 127 Table E.6: Aggregate food expenditures....................................................................................................................................................................... 128 Table F.1: Results on common indicators from SPS 2017 and SESRE 2023.............................................................................................................. 129 ABBREVIATIONS ARRA Administration for Refugee and Returnee Affairs CBHI Community Based Health Insurance CPI Consumer Price Index CRRF Comprehensive Refugee Response Framework CSB Corn Soy Blend DICAC Development and Inter-Church Aid Commission EA Enumeration Area EMIS Education Management Information System EOP Economic Opportunities Program ESDP Education Sector Development Programme ESS Ethiopian Statistical Service EUAA European Union Agency for Asylum FDRE Federal Democratic Republic of Ethiopia GCR Global Compact on Refugees GDP Gross Domestic Product GER Gross Enrollment Rate GIZ German Agency for International Cooperation GoE Government of Ethiopia GRF Global Refugee Forum HoWStat Household Welfare Statistics Survey IOM International Organization for Migration IPCC Intergovernmental Panel on Climate Change ISCO International Standard Classification of Occupations JDC Joint Data Center LFPR Labor Force Participation Rate LFS Labor Force Survey LoC Locus of Control LSMS Living Standards Measurement Study MoE Ministry of Education MoLS Ministry of Labor and Skills MoLSA Ministry of Labor and Social Affairs MoR Ministry of Revenue MoTRI Ministry of Trade and Regional Integration", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "JDC Joint Data Center LFPR Labor Force Participation Rate LFS Labor Force Survey LoC Locus of Control LSMS Living Standards Measurement Study MoE Ministry of Education MoLS Ministry of Labor and Skills MoLSA Ministry of Labor and Social Affairs MoR Ministry of Revenue MoTRI Ministry of Trade and Regional Integration MoU Memorandum of Understanding MPI Multidimensional Poverty Index NEET Not in Employment, Education or Training NER Net Enrollment Rate NGO Non-governmental Organization OAU Organization of African Unity OCP Out-of-Camp Policy PPP Purchasing Power Parity RRS Refugees and Returnees Service SESRE Socio-economic Study of Refugees in Ethiopia TVET Technical and Vocational Education and Training UNHCR The United Nations Refugee Agency UNICEF The United Nations International Children’s Emergency Fund UPSNJP Urban Safety Net and Jobs Project WFP World Food Programme WHO World Health Organization i The report titled \"Expanding development approaches to refugees and their hosts in Ethiopia\" was prepared by a team of the Poverty and Equity Global Practice at the World Bank led by Christina Wieser (Senior Economist, World Bank), including Wondimagegn Mesfin Tesfaye (Economist, World Bank), Fikirte Girmachew (Consultant, World Bank), Jeremey Aaron Lebow (Young Professional, World Bank), and Manex Bule Yonis (Economist, World Bank), under the adept guidance", "output": {"entities": {"named_data": ["LFS Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "team of the Poverty and Equity Global Practice at the World Bank led by Christina Wieser (Senior Economist, World Bank), including Wondimagegn Mesfin Tesfaye (Economist, World Bank), Fikirte Girmachew (Consultant, World Bank), Jeremey Aaron Lebow (Young Professional, World Bank), and Manex Bule Yonis (Economist, World Bank), under the adept guidance of Pierella Paci (Practice Manager, World Bank). The report is an output of a collaboration effort between the World Bank, the Ethiopian Statistical Service (ESS), the Ethiopia Refugees and Returnees Service (RRS), and UNHCR with generous financial support from the World Bank and UNHCR Joint Data Center on Forced Displacement (JDC). Our appreciation extends to the UNHCR and RRS colleagues for their unwavering support. We offer our special thanks to Mulualem Desta (Deputy Director General, RRS) and Ashenafi Demeke (Education Team leader, RRS) for their critical guidance and support throughout the survey design, implementation, and report development phases. Moreover, we would like to thank colleagues from RRS who provided invaluable comments on the various draft stages of this report: Bruhtesfa Mulugeta, Zewdu Bedada, Daniel Adefires, Anteneh Mekasha, Anteneh Gorfu, Yewulsew Nigussie, Fantaw Kabtamu, Biruk Kebede, Dr. Goitom Ademnur, Dr. Tagay Kelil, Daniel Darcha. Additionally, we acknowledge the Household Expenditure and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "would like to thank colleagues from RRS who provided invaluable comments on the various draft stages of this report: Bruhtesfa Mulugeta, Zewdu Bedada, Daniel Adefires, Anteneh Mekasha, Anteneh Gorfu, Yewulsew Nigussie, Fantaw Kabtamu, Biruk Kebede, Dr. Goitom Ademnur, Dr. Tagay Kelil, Daniel Darcha. Additionally, we acknowledge the Household Expenditure and Welfare Statistics core team at ESS for their exceptional work on the data collection, from the survey's inception to the report's culmination: Amare Legesse, Alemayehu Teferi, Efrem Belachew, Salah Yusuf, Seid Jemal, Hagos Haile, Zenaselase Siyum, Tsigab Halefom, Yirga Nigussie, Kassu Gebeyehu, Zemecha Abdella, Mengistu Abebe, Aklilu Fikre, and Sisay Guta. Our gratitude goes to Leslie Velez (Assistant Representative (Protection, UNHCR) for her unwavering support and encouragement throughout the whole SESRE process. We are particularly thankful to UNHCR colleagues Yonas Lemma, Yonatan Assefa, Michel Uwamahoro, Millicent Lusigi, and Mekdes Aschalew for enabling access to administrative refugee data and their support during the sampling and data collection stages. Moreover, our thanks go to the following UNHCR colleagues for their invaluable comments on the various draft stages of this report: Emily Lugano, Annick-Laure Tchuendem, Jed Fix, Theresa Beltramo, Alessio Baldaccini, Anna Gaunt, Asaad Kadhum, Benoit d’Ansembourg, Berhanu Geneti, Campbell Macknight, Daniel Gebrekidan,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["administrative refugee data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "during the sampling and data collection stages. Moreover, our thanks go to the following UNHCR colleagues for their invaluable comments on the various draft stages of this report: Emily Lugano, Annick-Laure Tchuendem, Jed Fix, Theresa Beltramo, Alessio Baldaccini, Anna Gaunt, Asaad Kadhum, Benoit d’Ansembourg, Berhanu Geneti, Campbell Macknight, Daniel Gebrekidan, Florah Bukania, Florence Nimoh, Johannes Abate, Joyce Wahome, Katie Ogwang, Nada Omeira, Nathalie Bussien, Robert Nyambaka and Yukta Kumar. Our thanks also go to our JDC colleagues Felix Schmieding (Senior Statistician, UNHCR) and Harriet Kasidi Mugera (Senior Data Scientist, World Bank) for their tireless support and advice during the preparation of SESRE. The report benefited from the insights of peer reviewers: Leslie Velez, Mulualem Desta, Takaaki Masaki (Senior Economist, World Bank), and Precious Zikhali (Senior Economist, World Bank). We are indebted to Nistha Sinha (Senior Economist, World Bank) for her constructive comments that significantly enhanced the report. We also recognize Aldo Morri for his excellent editorial support. Finally, we extend our deepest gratitude to the survey respondents for their willingness to share their experiences which has been instrumental in deepening our understanding of the challenges and needs faced by both refugees and host households. ACKNOWLEDGEMENTS ii Introduction E thiopia, with", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Aldo Morri for his excellent editorial support. Finally, we extend our deepest gratitude to the survey respondents for their willingness to share their experiences which has been instrumental in deepening our understanding of the challenges and needs faced by both refugees and host households. ACKNOWLEDGEMENTS ii Introduction E thiopia, with its long history of hosting refugees, is grappling with the complex challenges of accommodating close to 1 million refugees and asylum seekers. These come primarily from neighboring countries like South Sudan, Somalia, Eritrea, and Sudan housed in camps in mostly rural areas spread around the country near border areas. While Ethiopia has adopted progressive refugee policies, including the Comprehensive Refugee Response Framework (CRRF), challenges persist in translating these policies into tangible socioeconomic outcomes for refugees. Despite Ethiopia’s efforts to shift from a camp-based approach to a more inclusive model promoting self-reliance and integration, refugees live largely in camps, are reliant on humanitarian aid, and face barriers to accessing employment and education. The country’s new Refugee Proclamation grants refugees the right to basic services, work, and freedom of movement, but implementation delays hinder their realization. To address these challenges and achieve better development outcomes for both refugees and host communities, a", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "on humanitarian aid, and face barriers to accessing employment and education. The country’s new Refugee Proclamation grants refugees the right to basic services, work, and freedom of movement, but implementation delays hinder their realization. To address these challenges and achieve better development outcomes for both refugees and host communities, a shift towards supporting refugees’ self-reliance and economic integration is essential. This involves enabling refugees to move toward economic opportunities, facilitating their access to the labor market through self-employment, wage- employment, and special projects, and integrating refugee children into the education system. Though refugees in Ethiopia still face significant barriers to accessing employment and education, hampering their long-term integration and exacerbating their vulnerability, initiatives are on the way to improve socioeconomic outcomes. The Socio-Economic Survey of Refugees in Ethiopia (SESRE) plays a crucial role in informing policy decisions by providing comprehensive data on the socioeconomic dimensions of refugees and host communities. By highlighting socioeconomic interactions and outcomes, SESRE aims to guide development interventions and facilitate refugee integration. The survey covers various aspects, including demographic profiles, livelihoods, welfare patterns, and social cohesion, offering valuable insights for policymakers and humanitarian actors. SESRE is a separate but integrated survey alongside the Ethiopian Household Welfare", "output": {"entities": {"named_data": ["Ethiopian Household Welfare", "Socio-Economic Survey of Refugees in Ethiopia"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "By highlighting socioeconomic interactions and outcomes, SESRE aims to guide development interventions and facilitate refugee integration. The survey covers various aspects, including demographic profiles, livelihoods, welfare patterns, and social cohesion, offering valuable insights for policymakers and humanitarian actors. SESRE is a separate but integrated survey alongside the Ethiopian Household Welfare Statistics Survey (HoWStat),1 the national household survey to measure poverty and other socio-economic outcomes. Like most national poverty surveys, HoWStat excludes displaced populations—Internally Displaced People (IDPs) or refugees—including in Ethiopia. To have up-to-date information on the socio-economic outcomes and poverty levels of refugees and to allow comparison to Ethiopian host communities, the SESRE applied the same questionnaire and data collection methods as the HoWStat, with some modifications. The World Bank, Ethiopia’s RRS, Ethiopia’s Statistical Service, and UNHCR collaborated to implement SESRE and was the first of its kind. This report uses data from the SESRE extensively to analyze the Ethiopian refugee situation and to devise policy directions. The SESRE covers three types of groups: (i) refugees in camps; (ii) refugees out-of-camps in Addis Ababa; and (iii) host communities; all of which require a distinct sampling procedure. The sampling frame for refugee camps is based on UNHCR’s proGRES database. SESRE is", "output": {"entities": {"named_data": ["Ethiopian Household Welfare Statistics Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "refugee situation and to devise policy directions. The SESRE covers three types of groups: (i) refugees in camps; (ii) refugees out-of-camps in Addis Ababa; and (iii) host communities; all of which require a distinct sampling procedure. The sampling frame for refugee camps is based on UNHCR’s proGRES database. SESRE is a representative survey of the refugee population 1 Formerly the Household Consumption and Expenditure Survey and Welfare Monitoring Survey. EXECUTIVE SUMMARY iii of Eritrean, South Sudanese, and Somali origin living in camps in Ethiopia, refugees living in Addis Ababa, and their respective host communities. Host communities are defined as Ethiopian non-displaced households living enumeration areas adjacent to the refugee camps. SESRE data was collected from November 2022 to January 2023, from a nationally representative sample of 3,452. The following represents a summary of findings stemming from the SESRE data and associated statistical regression work using this data. Sociodemographic Ethiopia is a second home for close to one million refugees who predominantly originate from South Sudan, Somalia, and Eritrea. Around 88 percent of refugees live in camps, and the rest reside in urban areas under the Out-of-Camp Policy (OCP) regime. Refugees fled from their country mainly due to conflict and violence.", "output": {"entities": {"named_data": ["UNHCR’s proGRES database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "a second home for close to one million refugees who predominantly originate from South Sudan, Somalia, and Eritrea. Around 88 percent of refugees live in camps, and the rest reside in urban areas under the Out-of-Camp Policy (OCP) regime. Refugees fled from their country mainly due to conflict and violence. After they arrive in Ethiopia, refugees stay, on average, 15 years. Refugees and hosts share similar demographic characteristics regarding age and gender. However, in-camp refugees have a higher share of children and youth, with a significantly higher number of second-generation born in Ethiopia compared to OCP refugees, the majority being within a working age group. The refugee policy granted refugees the right to access basic services, including primary education and healthcare services in camps and secondary education and health services under the national system. Education: Educational attainment is low among refugees and hosts, but the majority of refugees have no education or attend below primary education. This is worse for in-camp refugees. OCP refugees (Eritreans in Addis Ababa) have better education before they arrive in Ethiopia. School attendance and primary school enrollment rates are similar between refugees and hosts, but secondary school enrollment rates are much lower among refugees. Inadequate", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "education or attend below primary education. This is worse for in-camp refugees. OCP refugees (Eritreans in Addis Ababa) have better education before they arrive in Ethiopia. School attendance and primary school enrollment rates are similar between refugees and hosts, but secondary school enrollment rates are much lower among refugees. Inadequate school infrastructure, the need to support family income, and families unwilling to send children to school are some main reasons for the low secondary school enrollment. Refugee children are also much more likely to not attend education at the appropriate age. Providing sufficient, appropriate, and sustainable support from all responsible actors can overcome some of these challenges. Health: The prevalence of illness and getting medical assistance are similar between refugees and hosts, with child nutritional problems of stunting, underweight, and wasting challenging for both refugee and host children. Basic infrastructure: Refugees and hosts have similar access to WASH facilities and access to electricity. However, housing conditions are worse for in-camp refugees, who mainly live in shelters, whereas OCP refugees live in rented housing of better quality. Jobs and Livelihoods In-camp refugees mainly rely on humanitarian aid as they have low employment rates and few opportunities to generate income. Labor market", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "access to electricity. However, housing conditions are worse for in-camp refugees, who mainly live in shelters, whereas OCP refugees live in rented housing of better quality. Jobs and Livelihoods In-camp refugees mainly rely on humanitarian aid as they have low employment rates and few opportunities to generate income. Labor market outcomes show high inactivity and unemployment rates for in-camp refugees. If refugees earn income, they are less likely than hosts to earn from agriculture, livestock, and non-farm business. Given low education, employed refugees tend to work in low- skill jobs, though there is a disparity in the occupation types among refugees by country of origin: Eritrean refugees work in crafts and related trades, while South Sudanese refugees are engaged in elementary occupations, and Somali refugees work in a mix of services, sales, and skilled agriculture. Besides low employment, refugees’ ownership of assets such as agricultural land, livestock, and productive assets is lower than that of hosts. Executive Summary iv Executive Summary Working outside of camps helps improves refugees’ livelihoods. A significant proportion of in-camp refugees work outside camps despite not having work permits, earning more than those working inside camps. For employed in-camp refugees, hourly and monthly earnings are lower", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "is lower than that of hosts. Executive Summary iv Executive Summary Working outside of camps helps improves refugees’ livelihoods. A significant proportion of in-camp refugees work outside camps despite not having work permits, earning more than those working inside camps. For employed in-camp refugees, hourly and monthly earnings are lower than for hosts. However, having lower wages is not associated with education level or experience. Having higher educational attainment and experience increases the likelihood of employment and income levels for hosts, not for refugees. Refugees receive returns from education and experience only when working outside camps. Likewise, reliance on assistance for in-camp refugees declines when a household member works outside the camp. Similar to in-camp refugees, OCP refugees heavily rely on remittances. Selection criteria for OCP refugees allow refugees to get OCP permits based on self-reliance or support from others. Hence, Eritrean refugees in Addis Ababa also had better educational attainment, indicating that they were relatively well-off before displacement and continued having support from their families after displacement. Few OCP refugees work but if they work, they face occupational downgrading regardless of demographic characteristics, and all refugees are less likely to be employed in high-skill jobs than hosts despite completing", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "attainment, indicating that they were relatively well-off before displacement and continued having support from their families after displacement. Few OCP refugees work but if they work, they face occupational downgrading regardless of demographic characteristics, and all refugees are less likely to be employed in high-skill jobs than hosts despite completing secondary education. Female refugees have high employment rates—as high as men’s—and their high work participation rate makes a critical contribution to refugee household incomes. On average, in-camp refugee women and men are equally likely to be employed (around 25 percent), while among hosts, men are twice as likely to be employed (62 percent compared to 37 percent for women). Like men, refugee women are more likely than host counterparts to be self-employed and less likely to be in high-skill occupations. Refugee Aspirations Despite low resettlement rates, most refugees unrealistically aspire to go to a Western country in the next three years (Figure ES.1). Even when asked where they would realistically be in the next three years, one-third of refugees believe that they will live in a Western country (Figure ES.2). The intention to migrate abroad is higher for youth. Refugees also perceive they have less control over their lives than", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "years (Figure ES.1). Even when asked where they would realistically be in the next three years, one-third of refugees believe that they will live in a Western country (Figure ES.2). The intention to migrate abroad is higher for youth. Refugees also perceive they have less control over their lives than hosts, a result driven by South Sudanese refugees. These intentions to migrate combined with low “locus of control” (LOC) may limit refugees’ investment in improving their livelihoods or to integrate. Welfare and Equity In-camp refugees are poorer than their hosts. While monetary poverty appears to be high in refugee- concentrated areas, it is more prevalent among in-camp refugees than their hosts or OCP refugees (Figure ES.3). Welfare varies significantly over the different groups of refugees in Ethiopia, with Eritrean refugees having the lowest poverty incidence and South Sudanese refugees the highest. Although poverty incidence is higher for refugees, the high 0 20 40 60 80 Percent 100 Eritrean (camps) Somali South Sudanese OCP All Refugees Ethiopian refugee camp Ethiopian city Country of birth Other African country Western country Figure ES.1: Desired location in three years Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 Percent 100", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "0 20 40 60 80 Percent 100 Eritrean (camps) Somali South Sudanese OCP All Refugees Ethiopian refugee camp Ethiopian city Country of birth Other African country Western country Figure ES.1: Desired location in three years Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 Percent 100 Eritrean (camps) Somali South Sudanese OCP All Refugees Ethiopian refugee camp Ethiopian city Country of birth Other African country Western country Figure ES.2: Expected location in three years Source: World Bank Staff based on SESRE 2023. v poverty rates among hosts imply that refugee host communities are themselves severely resource- constrained; this calls for the urgent need of place- based developmental investment in the area to benefit both refugees and host communities. Besides losses refugees have endured, welfare and economic disparities between refugees and host communities in Ethiopia are due to limited access refugees have to livelihood opportunities and legal restrictions on their employment. Legal restrictions (i.e., not having work permits) and location often prevent refugees from working, which limits their ability to generate income and improve their economic situation. As a result, many refugees rely on food aid and have limited access to necessities such as housing and electricity.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "restrictions on their employment. Legal restrictions (i.e., not having work permits) and location often prevent refugees from working, which limits their ability to generate income and improve their economic situation. As a result, many refugees rely on food aid and have limited access to necessities such as housing and electricity. Multidimensional poverty tends to be high among refugees. Low living standards and low education primarily drive multidimensional poverty. Standard of living indicators, low-quality cooking fuel, inadequate housing and low asset ownership, contribute half to non-monetary poverty. Moreover, deprivation in education and child malnutrition also contribute most to multidimensional poverty among refugees. Refugee households tend to have worse food security than hosts. In-camp refugees have less diverse diets, suffer food insecurity, and have low consumption status compared to hosts (Figure ES.4). Broadly, there is a need to enhance the economic self-sufficiency and food security of in-camp refugees and host communities by improving their livelihood opportunities. For in-camp refugees, consumption (expenditures) tends to increase with certain characteristics. These include higher education, access to mobile phones, owing a non-farm business, possessing a bank account, and being closer to a market town or and Woreda capitals. Education (of the household head) and employment strongly", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "improving their livelihood opportunities. For in-camp refugees, consumption (expenditures) tends to increase with certain characteristics. These include higher education, access to mobile phones, owing a non-farm business, possessing a bank account, and being closer to a market town or and Woreda capitals. Education (of the household head) and employment strongly correlate with higher household expenditures, providing evidence again that improved access to education and labor markets would reduce poverty among refugees. Policies that limit formal employment and mobility of in-camp refugees contribute to their economic exclusion. We analyzed economic aid needed for each refugee under three scenarios: (i) no economic opportunities, (ii) current level of integration, and (iii) full integration. Under the hypothetical scenario of “no economic opportunities”—where refugees are not allowed to work but solely depend on aid or assistance—the annual cost of basic needs per refugee would be approximately US$378. The “current” level of economic integration scenario, where refugees can find opportunities to earn money or work—assuming that assistance is the gap between the consumption of refugees to the poverty line— reduces the cost by 44 percent to an annual US$221 per person. Further, the cost of basic needs could decrease to an estimated US$78 per year if", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "refugees can find opportunities to earn money or work—assuming that assistance is the gap between the consumption of refugees to the poverty line— reduces the cost by 44 percent to an annual US$221 per person. Further, the cost of basic needs could decrease to an estimated US$78 per year if the country adopts a “full inclusion” scenario 32% 18% 25% 84% 7% 75% In camp Addis Ababa Total Poverty headcount rate (%) Hosts Refugees Figure ES.3: Poverty incidence Source: World Bank staff based on SESRE 2023. 4.0 2.1 2.9 8.1 3.4 7.0 In Camp Addis Ababa Total Hosts Refugees Figure ES.4: Food insecurity scale Source: World Bank Staff based on SESRE 2023. Executive Summary vi where in-camp refugees have equal opportunities as hosts. The results show that refugee integration has considerable potential to save money, creating an “economic-inclusion dividend” that could be allocated to other interventions. Markets and Opportunities Ethiopia’s 24 refugee camps2 are spatially dispersed, and location matters significantly in terms of refugee’s ability to work. About 88 percent of refugees in Ethiopia remain in camps (based on SESRE data). The different camp areas have different geographic, social, and economic contexts, and are in different ecological zones, with different", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "24 refugee camps2 are spatially dispersed, and location matters significantly in terms of refugee’s ability to work. About 88 percent of refugees in Ethiopia remain in camps (based on SESRE data). The different camp areas have different geographic, social, and economic contexts, and are in different ecological zones, with different ethnic and language linkages between the refugees and local host communities. Refugees overall have lower employment rates and incomes and are more likely to engage in the informal sector than their hosts, but spatial disparity in labor market access and outcomes among refugees exists. The local labor market structure, proximity to resource hubs (Zone capitals, Woreda cities), and market connectivity significantly explain the differences in refugee labor market outcomes, highlighting the importance of refugees’ locations in terms of providing opportunities for self- reliance (Figure ES.5). The local labor market structure affects the possibility of refugees finding jobs. Naturally, the better the local labor market, the easier for refugees to find employment. High local unemployment reduces refugees’ job prospects, regardless of the gender of the refugee. The structure of sectoral employment in the local market also affects the odds of refugee employment; the higher the share of employment in the trade", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "the local labor market, the easier for refugees to find employment. High local unemployment reduces refugees’ job prospects, regardless of the gender of the refugee. The structure of sectoral employment in the local market also affects the odds of refugee employment; the higher the share of employment in the trade and services, the better the likelihood of employment for refugees. Refugees are more likely to work where most land is used for non-agriculture (built-up and shops). Overall, results indicate the importance of agglomeration effects, as refugees perform better in labor markets with urban characteristics. Proximity to resource hubs and connectivity help refugees to work, regardless of gender. Refugees in well-connected areas have better prospects of being employed. The gender gap persists at any level of market access but is more pronounced with decreased accessibility. Refugees are also more likely to work in agriculture in areas with poor market access, while more connectivity encourages service sector work. Social Cohesion Hosts display a generally positive attitude towards refugees (Figure ES.6 and Figure ES.7). Cultural and linguistic proximity and perception of improvement of local infrastructure are related to hosts’ positive attitude and trust towards refugees. Positive attitudes are stronger among Somali refugees and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "more connectivity encourages service sector work. Social Cohesion Hosts display a generally positive attitude towards refugees (Figure ES.6 and Figure ES.7). Cultural and linguistic proximity and perception of improvement of local infrastructure are related to hosts’ positive attitude and trust towards refugees. Positive attitudes are stronger among Somali refugees and hosts and weaker between South Sudanese refugees and hosts. Even though both Somali and South Sudanese refugees are culturally similar to their hosts, the socio-political tension in the Gambella region weakens host attitudes toward South Sudanese refugees of Nuer ethnicity. Executive Summary 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Nearest to zone Nearest to Woreda Nearest to border Remote Probability of being employed Male Female Figure ES.5: Refugee employment and proximity to resource hubs Source: World Bank Staff based on SESRE 2023. Note: Predicted marginal probabilities of being employed based on proximity to resource, tabulated by gender. 2 Although there are approximately 30 refugee camps in Ethiopia, this report refers to the 24 camps included in SESRE. vii Hosts tend to support refugees’ right to work and move to locations with better economic opportunities. Hosts support increasing refugees’ economic opportunities in Ethiopia, but some perceive that refugees increase insecurity", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "there are approximately 30 refugee camps in Ethiopia, this report refers to the 24 camps included in SESRE. vii Hosts tend to support refugees’ right to work and move to locations with better economic opportunities. Hosts support increasing refugees’ economic opportunities in Ethiopia, but some perceive that refugees increase insecurity and are taking their land. Hosts’ perceptions of adverse effects from refugees are low, but they indicate the impact on economic competition, price increases, deforestation, and security issues. Trust between refugees and hosts is similar, with refugees being more trusting. Refugees are more likely to trust a host if they are culturally similar. Cultural proximity and positive perceptions of economic benefits improve the co-existence of refugees and hosts. Still, additional effort is required to improve the social integration of refugees for enhanced economic integration. Policy Recommendations Addressing challenges refugees face in Ethiopia requires a concerted effort to promote their self- reliance, economic integration, and access to education and health. By leveraging data from initiatives like SESRE and adopting a comprehensive approach that considers the needs of both refugees and host communities, Ethiopia can maximize the benefits from hosting refugees while minimizing associated costs. The Government of Ethiopia has committed to", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "reliance, economic integration, and access to education and health. By leveraging data from initiatives like SESRE and adopting a comprehensive approach that considers the needs of both refugees and host communities, Ethiopia can maximize the benefits from hosting refugees while minimizing associated costs. The Government of Ethiopia has committed to a significant shift in its refugee management policies and most recently in its pledges and commitments made at the 2023 Global Refugee Forum to improve the socio and economic opportunities for refugees through an agenda to transform camps to human settlements as well as for inclusion into national services for education, including secondary education as well as health (UNHCR, 2024). The recommendations below advance these commitments backed by the findings in this survey. Key policy recommendations stemming from this analysis are: Promote refugee self-reliance: ◆ Enable mobility for refugees to access areas with higher economic opportunities. ◆ Facilitate labor market access for refugees by easing restrictions and providing work permits. ◆ Integrate refugee children into national education system to improve their long-term prospects. ◆ Strengthen inclusive healthcare systems to address the health needs of refugees. Focus on place-based interventions: ◆ Invest in refugee hosting areas to benefit both refugees and", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "access for refugees by easing restrictions and providing work permits. ◆ Integrate refugee children into national education system to improve their long-term prospects. ◆ Strengthen inclusive healthcare systems to address the health needs of refugees. Focus on place-based interventions: ◆ Invest in refugee hosting areas to benefit both refugees and host communities. ◆ Direct additional educational resources to districts hosting refugees to support integration. ◆ Expand access to social safety nets for vulnerable refugees and hosts. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Hosts Comfortable Neutral Not comfortable Percent Figure ES.7: Host response to “Would you feel comfortable having a refugee as a neighbor?” Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Hosts Strongly agree Agree Disagree Strongly disagree Percent Figure ES.6: Host response to “Refugees are good people” Source: World Bank Staff based on SESRE 2023. Executive Summary viii Continue implementation of progressive policies: ◆ Implement concrete actions to fulfill Government pledges and proclamations to move away from encampment toward mobility based on economic opportunities. ◆ Harmonize national and sub-national", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "ES.6: Host response to “Refugees are good people” Source: World Bank Staff based on SESRE 2023. Executive Summary viii Continue implementation of progressive policies: ◆ Implement concrete actions to fulfill Government pledges and proclamations to move away from encampment toward mobility based on economic opportunities. ◆ Harmonize national and sub-national laws to support the full implementation of refugee protection. ◆ Coordinate efforts among stakeholders to track progress and share best practices. ◆ Redesign the out-of-camp policy (OCP) to encourage mobility to realize greater socioeconomic opportunities for refugees while accelerating and automating issuance of work authorizations to enable sustainable improvements in refugees’ lives. ◆ Address challenges in accessing business licenses for refugee self-employment, including access to finance. Improve cooperation and coordination ◆ Invest in and accelerate inclusive approaches to economic opportunities and self-reliance to support the GoE in implementing the Refugee Proclamation of 2019. ◆ Define better coordination and engage line ministries to achieve better outcomes for refugees and their hosts. ◆ Improve the coverage, accuracy, reliability, quality, and comparability of data to provide the analytical underpinning for policy decisions. Executive Summary 1 C onflict, political unrest, environmental disruption, and economic instability has forcibly displaced millions of people globally (Ferris, 2010;", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "ministries to achieve better outcomes for refugees and their hosts. ◆ Improve the coverage, accuracy, reliability, quality, and comparability of data to provide the analytical underpinning for policy decisions. Executive Summary 1 C onflict, political unrest, environmental disruption, and economic instability has forcibly displaced millions of people globally (Ferris, 2010; Black, 2001). Over the last decade, the number of forcibly displaced persons has continuously increased. In mid-2023, there were 36.4 million refugees worldwide (UNHCR, 2023d). As development reduces global poverty, extreme poverty is increasingly concentrated among vulnerable groups; refugees are among these vulnerable groups (World Bank, 2017). Therefore, the plight of the forcibly displaced poses significant challenges to broad development efforts to eradicate extreme poverty and achieve the Sustainable Development Goals (SDGs). Ethiopia has a long history of hosting refugees and has one of the largest refugee populations in Africa. The refugee situation in Ethiopia is characterized by both complex humanitarian emergencies and protracted refugee status. Forced displacement is a pressing issue in the country, a result of conflict, drought, flood, economic instability, and political instability in neighboring countries (Martin, 2010; UNHCR, 2020d; IPCC, 2019). As of year-end 2023, more than 922,000 refugees and asylum seekers 1. Introduction 0 200,000", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "humanitarian emergencies and protracted refugee status. Forced displacement is a pressing issue in the country, a result of conflict, drought, flood, economic instability, and political instability in neighboring countries (Martin, 2010; UNHCR, 2020d; IPCC, 2019). As of year-end 2023, more than 922,000 refugees and asylum seekers 1. Introduction 0 200,000 400,000 600,000 800,000 1,000,000 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Population Years Sudan Somalia South Sudan Eritrea Kenya Other countries Figure 1.1: Refugees and asylum seekers in Ethiopia by country of origin, 1984-2023 Source: UNHCR Refugee Data Finder 2024. Introduction 2 were seeking refuge in Ethiopia, with the majority originating from South Sudan (420,000), Somalia (280,000), Eritrea (170,000), and Sudan (49,000). Ethiopia is a signatory to the 1951 UN Convention on the Status of Refugees and its 1967 Protocol, with an obligation to protect refugees and asylum seekers. Most refugees (92 percent) are living in approximately 30 camps and sites located in Afar, Amhara, Benishangul-Gumuz, Gambella, Somali, and Tigray regions, with an increasing number of refugees", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "the 1951 UN Convention on the Status of Refugees and its 1967 Protocol, with an obligation to protect refugees and asylum seekers. Most refugees (92 percent) are living in approximately 30 camps and sites located in Afar, Amhara, Benishangul-Gumuz, Gambella, Somali, and Tigray regions, with an increasing number of refugees living in the capital city of Addis Ababa (70,000) (UNHCR, 2023e). The camps are in different locations with ethnic and language linkages between the refugees and the host community. They are spatially dispersed, have different geographic, social, and economic contexts, and are in different ecological zones. For example, about 38 percent of refugees live in drought-prone lowland and pastoralist areas, whereas 60 percent of refugees are in humid reliable lowland areas. Ethiopia made significant progress articulating a more progressive and comprehensive refugee response.3 The Government of Ethiopia (GoE) made a groundbreaking shift in its refugee policies over the last few years, especially since 2016, shifting its refugee policies from an encampment approach toward greater socio-economic inclusion. The GoE has adopted several national laws and policies to protect refugees and ensure respect for their rights. The Refugee Proclamation—the primary legal framework that governs refugee protection and management in Ethiopia—was enacted in", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "years, especially since 2016, shifting its refugee policies from an encampment approach toward greater socio-economic inclusion. The GoE has adopted several national laws and policies to protect refugees and ensure respect for their rights. The Refugee Proclamation—the primary legal framework that governs refugee protection and management in Ethiopia—was enacted in 20044. In 2017, Ethiopia became the first country to fully adopt the Comprehensive Refugee Response Framework (CRRF), a global framework for a comprehensive response to refugee situations. After endorsing the Global Compact on Refugees (GCR) in 2018, Ethiopia continued its commitment by adopting a new, progressive Refugee Proclamation in January 2019.5 Despite these groundbreaking legal and policy actions, many refugees in Ethiopia remain poor and depend heavily on humanitarian aid. This report highlights that Ethiopia’s progressive policy framework has not yet translated into tangible socioeconomic outcomes for refugees. Refugees are mainly living in camps, and few refugees benefit from the progressive policy framework. Delays in implementing policies makes it difficult for refugees to encourage mobility to access better economic opportunities or to access land or finance, or get a work permit to work in the labor market outside of refugee camps. The GoE has a clear long-term vision to address", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "progressive policy framework. Delays in implementing policies makes it difficult for refugees to encourage mobility to access better economic opportunities or to access land or finance, or get a work permit to work in the labor market outside of refugee camps. The GoE has a clear long-term vision to address the refugee situation in Ethiopia through gradual transformation of the existing refugee response model into a more comprehensive approach. Until recently, Ethiopia’s refugee response model has focused on protecting and assisting them in camps, where services are delivered through parallel systems typically financed externally. In many refugee- hosting areas in Ethiopia, except for a few areas such as Addis Ababa, refugees and host communities share cross-border cultural and economic connections, common ties of kinship, language, and ethnicity, and relatively fluid attachments to national identity (see Annex A for more details). The refugee camps and sites in the country span a broad 3 Annex B summarizes the evolution of refugee policies in Ethiopia. 4 In 2004, the country enacted its first national refugee proclamation that granted restricted rights to refugees. The Refugee Proclamation #409/2004 was not comprehensive enough to improve protection and assistance, promote sustainable solutions for refugees, and support host", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "broad 3 Annex B summarizes the evolution of refugee policies in Ethiopia. 4 In 2004, the country enacted its first national refugee proclamation that granted restricted rights to refugees. The Refugee Proclamation #409/2004 was not comprehensive enough to improve protection and assistance, promote sustainable solutions for refugees, and support host communities per international standards. The previous refugee law did not reflect the recent policy commitments of the Government and did not confer legal standing to their implementation. For a long period of time, it has had a limitation, particularly in terms of the various privileges that are newly accorded by the revised refugee law to both asylum seekers and refugees, whether as equal to that of foreigners residing in the country or the same as Ethiopian nationals. These include rights to access services, work, move freely, and locally integrate. 5 To complement the Proclamation three directives came into effect on 30 December 2019 namely: Directive to Determine the Conditions for Movement and Residence of Refugees Outside of Camps, Directive No.01/2019; Directive to Determine the Procedure for Refugees Right to Work, Directive No. 02/2019; and Refugees and Returnees Grievances and Appeals Handling Directive, Directive 03/2019. These secondary legislations will have huge", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "30 December 2019 namely: Directive to Determine the Conditions for Movement and Residence of Refugees Outside of Camps, Directive No.01/2019; Directive to Determine the Procedure for Refugees Right to Work, Directive No. 02/2019; and Refugees and Returnees Grievances and Appeals Handling Directive, Directive 03/2019. These secondary legislations will have huge contribution to the proper interpretation and implementation of the country’s refugee law. Introduction 3 range of protractedness6 and some camps are in locations with few economic opportunities, making refugees dependent on humanitarian assistance for years and often decades. As one of the champions of the CRRF, GoE aims to enhance the self-reliance and resilience of refugees and host communities and prepare them for durable solutions by supporting their socio-economic integration and strengthening their contribution to the country’s socio-economic development.7 Across the world, including Ethiopia, refugees tend to be poorer than most host populations. In Uganda, for example, 46 percent of refugees lived in poverty, compared to 17 percent of hosts, in 2018 (World Bank, 2019). In Kalobeyei Settlement in Turkana County in Kenya, more than half of refugees are poor (58 percent), higher than the national poverty rate of 37 percent, lower than the poverty rate in Turkana County but", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "refugees lived in poverty, compared to 17 percent of hosts, in 2018 (World Bank, 2019). In Kalobeyei Settlement in Turkana County in Kenya, more than half of refugees are poor (58 percent), higher than the national poverty rate of 37 percent, lower than the poverty rate in Turkana County but comparable to the average poverty rate of the 15 poorest counties in Kenya (UNHCR and World Bank Group, 2020). About 72 percent of registered Venezuelans in Brazil live in extreme poverty, compared to 48 percent of Brazilians (Shamsuddin et al., 2021). Similarly, this report finds that poverty rates in Ethiopia’s refugees in camps are much higher than for host communities. Yet, refugee inflows can significantly affect host communities. Governments have been preoccupied with whether the arrival of large numbers of people in specific locations creates risks or opportunities for decades. Experience has shown that opportunities typically result if the influx of refugees is managed well and brings benefits to host communities similar to those of voluntary migrants. A recent review of the literature on refugee effects on host communities showed that most studies find a positive or non- significant effect of forced displacement on hosts’ employment, wages, and household well-being.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "of refugees is managed well and brings benefits to host communities similar to those of voluntary migrants. A recent review of the literature on refugee effects on host communities showed that most studies find a positive or non- significant effect of forced displacement on hosts’ employment, wages, and household well-being. This finding is contrary to popular perceptions (Verme and Schuettler, 2021). In some exceptional cases, a refugee influx creates challenges for host communities, typically related to exacerbating existing imbalances, negative outcomes for specific groups who directly compete with refugees in the labor market, and overburdening public infrastructure or services (Hanafi et al., 2021). Deteriorating economic conditions and soaring inflation rates exacerbate the already challenging conditions for refugees and host communities. The country is grappling with a complex array of emergencies, with increasing needs for solutions for refugees. These challenges were intensified by an economic downturn marked by persistent inflation, which has consistently outpaced the Sub-Saharan African regional average over the past decade. The persistent inflation is driven by various factors, including supply-demand imbalances, unrest, high global commodity prices, and relaxed monetary and fiscal policies. In 2022, the average inflation rate stood at 34 percent. The surge in prices has rendered", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "has consistently outpaced the Sub-Saharan African regional average over the past decade. The persistent inflation is driven by various factors, including supply-demand imbalances, unrest, high global commodity prices, and relaxed monetary and fiscal policies. In 2022, the average inflation rate stood at 34 percent. The surge in prices has rendered necessities unaffordable for many, hitting marginalized populations the hardest, including refugees. The economic turmoil is further fueled by ongoing conflict and instability, adding complexity and uncertainty. 6 The oldest camp has operated for 31 years (Kebribeyah refugee camp in the Somali region), the newest (Alemwach in the Amhara Region) was established only in as a response to the war in Tigray. 7 The Ethiopian Refugees and Returnees Service (RRS, formerly the Administration for Refugee and Returnee Affairs (ARRA)) is responsible for implementing this long-term strategy and coordinating country-level refugee assistance and protection programs. ARRA was first established in 1992 as the main government department responsible for refugee affairs, housed within the former National Intelligence and Security Services (NISS). The former ARRA was elevated to an agency level in accordance with Proclamation No. 1097/2018, which defines the Powers and Duties of the Executive Organs of the Government and established the Agency", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "the main government department responsible for refugee affairs, housed within the former National Intelligence and Security Services (NISS). The former ARRA was elevated to an agency level in accordance with Proclamation No. 1097/2018, which defines the Powers and Duties of the Executive Organs of the Government and established the Agency for Refugees and Returnees Affairs (ARRA) under the Ministry of Peace in 2018. In 2021, a new government announced through the Definition of Powers and Duties of the Executive Organs Proclamation No. 1263/2021, during which it reestablished ARRA as Refugees and Returnees Service (RRS). The RRS became one of the executive organs accountable to the NISS which is accountable to the Prime Minister’s Office and oversees the Immigration and Citizenship Service other than RRS. Introduction 4 Refugees in Ethiopia have been severely affected by ongoing conflict and unrest across Ethiopia. The country has dealt with multiple crises, including rampant inflation, a devastating war, and frequent droughts and floods. SESRE data collection was carried out between November 2022 and January 2023, marked by drought, inflation, and insecurity, posing significant threats to the livelihoods of refugees and host communities in an already fragile economy. The conflict in Northern Ethiopia continued until 2022", "output": {"entities": {"named_data": [], "descriptive_data": ["SESRE data collection"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "a devastating war, and frequent droughts and floods. SESRE data collection was carried out between November 2022 and January 2023, marked by drought, inflation, and insecurity, posing significant threats to the livelihoods of refugees and host communities in an already fragile economy. The conflict in Northern Ethiopia continued until 2022 and disrupted the socio- economic conditions in the North Gondar Zone, home to over 20,000 refugees. The November 2022 peace agreement between the federal government and Tigrayan authorities offered a glimmer of hope for ending the war in Northern Ethiopia. Still, tensions in other parts of Ethiopia continued. Additionally, following the outbreak of fighting in the Amhara region in 2023, refugees in the Alemwach camp in the Amhara region faced attacks by unidentified armed groups. Moreover, food assistance for refugees has been unstable due to funding shortfalls, resulting in reduced food rations for hundreds of thousands of refugees for several months in 2022 and 2023. Insecurity in the Gambella and Benishangul-Gumuz regions not only undermined refugee and host community livelihoods but also heightened tensions between them. The intensification of conflict in Western Oromia further disrupted humanitarian operations in Eastern Benishangul-Gumuz, blocking the transport of relief and commercial supplies and affecting", "output": {"entities": {"named_data": [], "descriptive_data": ["SESRE data collection"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "months in 2022 and 2023. Insecurity in the Gambella and Benishangul-Gumuz regions not only undermined refugee and host community livelihoods but also heightened tensions between them. The intensification of conflict in Western Oromia further disrupted humanitarian operations in Eastern Benishangul-Gumuz, blocking the transport of relief and commercial supplies and affecting 76,000 refugees. The concerning economic outlook in Ethiopia is exacerbated by the prolonged and recurrent drought the country has seen in decades, affecting vast swathes of the southern and eastern regions, including refugee-hosting areas. This prolonged drought has heightened vulnerabilities, leading to widespread food insecurity and increased exposure to diseases, including multiple outbreaks of waterborne diseases. The impact of these drought and flood events on food security is particularly acute among refugee-hosting communities, notably in the Somali and Afar regions. The drought has destroyed agricultural production, leading to severe food shortages for refugees and host communities. In the Somali region, the Fafan and Siti Zones have been hit hard by drought events. Amhara region— the most severely affected North Gondar Zone— has also suffered from below-average rainfall, causing crop failures, livestock deaths, and worsening food insecurity. Similarly, the drought has impacted around 250,000 people across five districts in the Central", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "the Fafan and Siti Zones have been hit hard by drought events. Amhara region— the most severely affected North Gondar Zone— has also suffered from below-average rainfall, causing crop failures, livestock deaths, and worsening food insecurity. Similarly, the drought has impacted around 250,000 people across five districts in the Central Gondar Zone, resulting in a significant decrease in water availability with dire consequences for the health and nutrition of the population. Overall, the effects of the drought have been particularly pronounced in regions that host refugees. Challenges related to hosting refugees can be overcome through national development strategies that keep both host communities and refugees in mind. Host communities have their own development priorities and needs. Supporting them in managing these new circumstances to facilitate their poverty reduction can create a more accepting environment for refugees. In Ethiopia, among the major refugee-hosting regions, four— Afar, Benishangul-Gumuz, Gambella, and Somali— are designated as “emerging regions,” and Tigray is considered post-conflict. These regions are the least developed regions in the country, characterized by harsh weather conditions, poor infrastructure, low administrative capacity, high poverty, and poor development outcomes. The arid environment in the Afar and Somali regions, and the small and scattered nomadic", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "designated as “emerging regions,” and Tigray is considered post-conflict. These regions are the least developed regions in the country, characterized by harsh weather conditions, poor infrastructure, low administrative capacity, high poverty, and poor development outcomes. The arid environment in the Afar and Somali regions, and the small and scattered nomadic populations, make it more challenging to provide services. Focusing on development interventions that benefit both communities can foster improved socioeconomic outcomes and social cohesion. Introduction 5 1.1 How can we achieve better development outcomes for all? The GoE has recently bolstered development- oriented initiatives to complement humanitarian interventions and improve the lives of refugees and host communities. Yet, most refugee responses in the country remain humanitarian-focused. Better integration and increased attention to easing the pressure on host communities can further support refugees and their hosts. But how can this be achieved? Development approaches are most successful when they focus on building self-reliance—including offering refugees secure terms of stay, mobility to access better economic opportunities, and access to the labor market— and supporting refugees’ pursuit of economic opportunities while simultaneously supporting refugee-hosting communities (Betts et al., 2014; Clements et al., 2016; Krause and Schmidt 2020). This not only can improve refugees’", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "building self-reliance—including offering refugees secure terms of stay, mobility to access better economic opportunities, and access to the labor market— and supporting refugees’ pursuit of economic opportunities while simultaneously supporting refugee-hosting communities (Betts et al., 2014; Clements et al., 2016; Krause and Schmidt 2020). This not only can improve refugees’ outcomes, it can also reduce the burden on host communities by reaping economic benefits from refugees’ presence. The path of self-reliance includes, at a minimum: (i) encouraging mobility within the host country to access better economic opportunities; (ii) enabling and incentivizing labor market participation; and (iii) providing access to education for refugee children. Many refugees do not enjoy mobility in their host territories, including choice of residence.8 Globally, one-third of refugees are prevented from moving freely (UNHCR, 2022e). About 27 percent of the refugee population is contained in camps, leading to a situation in which they cannot be self-reliant and thereby improve their economic opportunities to reduce their dependence on support from their hosts and the international community (World Bank, 2017). In Ethiopia, 88 percent of refugees live in camps. Denying refugees mobility to settle where they would like comes at a cost, as the choice of location within the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "thereby improve their economic opportunities to reduce their dependence on support from their hosts and the international community (World Bank, 2017). In Ethiopia, 88 percent of refugees live in camps. Denying refugees mobility to settle where they would like comes at a cost, as the choice of location within the host country matters for refugees’ labor market outcomes. Therefore, development approaches centering around mobility within the host country enable refugees to move where economic opportunities are highest and allow them to contribute to the local economy more productively. While refugees in Ethiopia face challenges accessing the labor market, the GoE has taken positive steps to support their economic integration and self- reliance. Restrictions on the right to work affect refugees in many countries. Only 75 out of the 145 signatories to the Refugee Convention grant the right to work (Zetter and Ruaudel, 2016). Even countries that grant access to the formal labor market in the same way as nationals—Burkina Faso, Cameroon, Democratic Republic of the Congo, Djibouti, Mauritania, Niger, and Rwanda— often restrict access in practice by requiring certain identification documents, the country employers are reluctant to hire refugees (World Bank Group, 2021), or restrictions exist, such as wait periods,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "formal labor market in the same way as nationals—Burkina Faso, Cameroon, Democratic Republic of the Congo, Djibouti, Mauritania, Niger, and Rwanda— often restrict access in practice by requiring certain identification documents, the country employers are reluctant to hire refugees (World Bank Group, 2021), or restrictions exist, such as wait periods, limited mobility, owning property, or accessing finance. In other countries—Burundi, Chad, Uganda, and Ethiopia—access to the labor market is limited by regulations, such as requiring work permits, caping the percentage of foreign workers, or restricting work to certain sectors of employment (World Bank Group, 2021). In addition to wage employment, self-employment can be an important avenue, but in many countries, access to self-employment is restricted for refugees, including in Ethiopia where refugees require business licenses. Overall, while the GoE has made progress in creating a legal framework for refugees to obtain work permits, refugees still face significant challenges accessing employment, contributing to their overall vulnerability and lack of self-reliance. As this report shows, few refugees in Ethiopia work, and those who do work mostly inside camps. Research indicates that extended periods of forced unemployment negatively affect refugees’ longer- 8 As granted under Article 26 of the 1951 Geneva Refugee Convention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "contributing to their overall vulnerability and lack of self-reliance. As this report shows, few refugees in Ethiopia work, and those who do work mostly inside camps. Research indicates that extended periods of forced unemployment negatively affect refugees’ longer- 8 As granted under Article 26 of the 1951 Geneva Refugee Convention. Introduction 6 term labor market participation (Hainmueller et al., 2016; Hvidtfeldt et al., 2018; Brell et al., 2020), thereby also hampering the host society through larger expenditure on assistance and forgone taxes (Marbach et al., 2018; Fasani et al., 2022). Enabling refugees’ labor market participation outside of refugee camps as early as possible is key to achieving their integration (Fasani et al., 2022; Slotwinski et al., 2019) by limiting long-term scarring effects, such as long-term unemployment or inactivity. Integrating refugee children into education soon after arrival avoids loss of valuable years of education and harm to human capital accumulation that hinders future prospects. Globally in 2019, almost half of all refugee children were out of school (UNHCR, 2020d). Of those attending school, most do not make it past basic education; gross enrolment in primary education stood at 77 percent in 2019. Yet, the contrast between primary and secondary school enrolment", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "hinders future prospects. Globally in 2019, almost half of all refugee children were out of school (UNHCR, 2020d). Of those attending school, most do not make it past basic education; gross enrolment in primary education stood at 77 percent in 2019. Yet, the contrast between primary and secondary school enrolment remains stark, and only 31 percent of refugee children were enrolled in secondary school, much below the global average of secondary school enrolment. A recent study of refugee children in Kakuma refugee camp in Kenya, for example, found that literacy and learning outcomes for refugee children were significantly lower than in immediate host community or the rest of Kenya (Piper et al., 2020). This report shows that education outcomes for children in Ethiopia are low across all population groups and ages but particularly for refugee children. COVID-19 exacerbated this situation for many refugee children (Wieser, 2020). 1.2 How does Socio-Economic Survey of Refugees in Ethiopia (SESRE) contribute to the debate on policies? The Socio-Economic Survey of Refugees in Ethiopia (SESRE) is a representative survey of the refugee population in Ethiopia and their host communities, the first of its kind.9 Ethiopia made significant progress over the past few years in articulating", "output": {"entities": {"named_data": ["Socio-Economic Survey of Refugees in Ethiopia"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Survey of Refugees in Ethiopia (SESRE) contribute to the debate on policies? The Socio-Economic Survey of Refugees in Ethiopia (SESRE) is a representative survey of the refugee population in Ethiopia and their host communities, the first of its kind.9 Ethiopia made significant progress over the past few years in articulating more progressive and comprehensive refugee responses. In 2023, the GoE included new pledges and commitments made in the 2023 Global Refugee Forum, including a significant shift in its refugee management policies. This includes improving the socio and economic opportunities for refugees through an agenda to transform camps into human settlements and including refugees in national services for education, including secondary education and health (UNHCR, 2024). Systematically collecting high-quality data on refugees and their hosts in one survey is pertinent to inform the GoE’s roadmap and programs to address the development needs of refugees and hosts. The national household survey of Ethiopia–Household Welfare Statistics Survey (HoWStat)—excludes the majority of displaced populations (Internally Displaced People [IDPs] or refugees) from its sample of households. Thus, we have limited in-depth information on the socio-economic outcomes—including on poverty— for refugees across all camps in Ethiopia to compare with Ethiopian hosts. SESRE collected data from November", "output": {"entities": {"named_data": ["Socio-Economic Survey of Refugees in Ethiopia"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Ethiopia–Household Welfare Statistics Survey (HoWStat)—excludes the majority of displaced populations (Internally Displaced People [IDPs] or refugees) from its sample of households. Thus, we have limited in-depth information on the socio-economic outcomes—including on poverty— for refugees across all camps in Ethiopia to compare with Ethiopian hosts. SESRE collected data from November 2022 to January 2023, from a nationally representative sample of 3,452 refugee households and their hosts. The SESRE covers all currently operating refugee camps of major refugee groups: Eritreans, South Sudanese, and Somalis, as well as the out-of- camp refugees of Addis Ababa and their respective host communities. The survey was aligned with the HoWStat methodology, allowing comparability between refugees and their host communities. The World Bank, Ethiopia’s RRS, Ethiopia’s Statistical Service, and UNHCR collaborated to implement SESRE10 and was the first of its kind, building on the “Skills Profile Survey 2017, A Refugee and Host Community Survey” conducted in Ethiopia in 2017. The SPS 2017 was conducted in refugee camps and host communities in four regions in Ethiopia. The survey was used to draw a profile for skills and potential opportunities for refugees and host 9 See Annex C for detailed information on survey design and methodology. 10 Financial", "output": {"entities": {"named_data": ["Skills Profile Survey 2017", "Ethiopia–Household Welfare Statistics Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Ethiopia in 2017. The SPS 2017 was conducted in refugee camps and host communities in four regions in Ethiopia. The survey was used to draw a profile for skills and potential opportunities for refugees and host 9 See Annex C for detailed information on survey design and methodology. 10 Financial support was provided by the World Bank and UNHCR Joint Data Center on Forced Displacement. Introduction 7 communities to design a better mix of approaches that could help the government in designing livelihood opportunities for these communities. Due to differences in scope, sampling design, and methodology, results based on the SPS cannot be directly compared with those of SESRE. Box 1.1 summarizes the similarities and differences between SPS 2017 and SESRE 2023. The Skills Profile Survey (SPS), conducted in 2017, is a household survey focused on collecting data on refugees from South Sudan, Somali, Eritrea, and Sudan living in camps in Ethiopia, as well as from host communities. The sample frame for the survey was derived from the list of all refugee camps, sites, and locations provided by UNHCR-Ethiopia as of January 2017, covering the four main regions that host refugees: Tigray, Afar, Gambella, Benishangul-Gumuz, and Somali. The SPS specifically", "output": {"entities": {"named_data": ["SPS 2017"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "in Ethiopia, as well as from host communities. The sample frame for the survey was derived from the list of all refugee camps, sites, and locations provided by UNHCR-Ethiopia as of January 2017, covering the four main regions that host refugees: Tigray, Afar, Gambella, Benishangul-Gumuz, and Somali. The SPS specifically excludes refugee households living out of camp, thereby making it representative of the refugee population residing in camps in Ethiopia. In contrast, SESRE, carried out in 2023, expanded its data collection to include out-of-camp refugees living in Addis Ababa. The SPS and SESRE both utilized stratified sampling designs but with different methodologies and definitions of the host households. The SPS employed a multi-stage stratified random sampling approach, dividing refugee camps into EAs of 150 by 150 meters using GIS technology. The number of EAs selected from each camp was proportional to the size of the camp, ensuring all camps in the sample frame were surveyed. In this way, all the camps in the sample frame were selected in the sample and were surveyed. For host households, areas within 5-kilometer radius of the camps were divided into EAs of 300 by 300 meters, with only residential EAs as per Open Street", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "the sample frame were surveyed. In this way, all the camps in the sample frame were selected in the sample and were surveyed. For host households, areas within 5-kilometer radius of the camps were divided into EAs of 300 by 300 meters, with only residential EAs as per Open Street Maps included in the sample frame. SESRE, on the other hand, used a stratified, two-stage cluster sample design. Initially, camps were divided into EAs, and pseudo EAs were created from the proGRES database by grouping 150-200 households consecutively. EAs and households within those EAs were then selected. For host households, EAs adjacent to refugee camps were used as the sampling frame. While the definition of host households differed between SPS and SESRE, both surveys shared similarities in the selection of EAs and the random sampling of households within those EAs. In SPS, all households within the selected EAs for host community sampling were listed, and 12 households were randomly chosen and surveyed per EA. SESRE also selected 12 refugee and host households per EA, treating EAs as the Primary Sampling Unit and households as the Secondary Sampling Unit. (i) The distinct sampling designs and objectives of the two surveys render", "output": {"entities": {"named_data": ["proGRES database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "community sampling were listed, and 12 households were randomly chosen and surveyed per EA. SESRE also selected 12 refugee and host households per EA, treating EAs as the Primary Sampling Unit and households as the Secondary Sampling Unit. (i) The distinct sampling designs and objectives of the two surveys render challenges in comparing findings from the two surveys. Moreover, the two surveys are not comparable in other ways, including: (ii) Surveyed population: SESRE includes out-of-camp refugees living in Addis Ababa, while SPS does not. (iii) Survey scope: The methodology to sample and definitions of host communities varied between the two surveys. (iv) Survey design: SESRE’s questionnaire aimed at comparability with the national poverty survey, while SPS aimed at comparability across countries. This rendered differences in the contents of the surveys, where the SESRE employed the same survey instrument as the national poverty survey, while the SPS used an instrument specific to the survey. (v) Differences in consumption: SESRE includes a full consumption module while SPS relied on the Rapid Consumption methodology. Moreover, there are differences in the recall period for food consumption data collection. While SPS used the past 7 days recall period, SESRE collects food consumption data through two", "output": {"entities": {"named_data": [], "descriptive_data": ["national poverty survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "to the survey. (v) Differences in consumption: SESRE includes a full consumption module while SPS relied on the Rapid Consumption methodology. Moreover, there are differences in the recall period for food consumption data collection. While SPS used the past 7 days recall period, SESRE collects food consumption data through two visits—last 3 days and past 4 days—that leads to considerable difference in consumption aggregates. Box 1.1: Comparison of SPS 2017 and SESRE 2023 Introduction 8 This report uses SESRE data to describe the socioeconomic dimensions of refugees and their host communities. By highlighting socioeconomic outcomes of refugees and hosts in Ethiopia, we aim to provide analytical underpinnings for development interventions in Ethiopia. The analysis focuses on aspects such as economic activity, livelihoods, welfare patterns, as well as on social dynamics and longer-term socioeconomic viability of refugee host areas. Focusing on socioeconomic interaction, social inclusion, and relations among refugees and between refugees and their host communities, this work aims to inform policies and operations (humanitarian actors, development partners, and government) to facilitate refugee integration and their lives, along with hosting communities. This report’s eight chapters aim to comprehensively provide an overview of SESRE results, with the final chapter highlighting policy implications.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "refugees and their host communities, this work aims to inform policies and operations (humanitarian actors, development partners, and government) to facilitate refugee integration and their lives, along with hosting communities. This report’s eight chapters aim to comprehensively provide an overview of SESRE results, with the final chapter highlighting policy implications. This Chapter 1 introduces the refugee situation in Ethiopia. Chapter 2 presents the sociodemographic profile of refugees and their hosts, including demographic characteristics, education, health, and living conditions. Chapter 3 provides an in-depth profile of jobs and livelihoods of refugees and their hosts, covering labor market outcomes for refugees inside and outside of camps and those of hosts living in the vicinity of refugees, as well as a subsection on labor market outcomes of youth. Chapter 4 dives deeper into refugees’ future aspirations and their feeling of personal control over their lives. Chapter 5 describes the welfare situation of refugees and their hosts by: (i) understanding different dimensions of welfare, such as monetary poverty, inequality, multidimensional poverty, food security, and shocks; and (ii) understanding determinants of welfare and estimating the cost to meet basic needs through a combination of assistance and some economic inclusion of refugees into national systems. Chapter", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "and their hosts by: (i) understanding different dimensions of welfare, such as monetary poverty, inequality, multidimensional poverty, food security, and shocks; and (ii) understanding determinants of welfare and estimating the cost to meet basic needs through a combination of assistance and some economic inclusion of refugees into national systems. Chapter 6 aims to understand how the location of camps determines labor market outcomes, highlighting the importance of refugees’ location as part of the development strategy for refugees in Ethiopia. Chapter 7 looks at social cohesion by showcasing attitudes between refugees and hosts and the level of social integration of refugees. Chapter 8 highlights policy directions based on the conclusions of the report to maximize the benefits of hosting refugees while minimizing the costs. (vi) Differences in poverty estimation: The poverty measurement methodology is distinct for each survey with SESRE applying the same methodology as HoWStat. Despite the difference in methodology used in the Skills Profile Survey (SPS) and SESRE, we find similar patterns in some of the indicators common in both surveys among in camp refugees such as demographic composition, primary and secondary net enrollments, housing condition, access to basic infrastructures, employment and attitude of hosts toward refugees. Moreover, both", "output": {"entities": {"named_data": ["Skills Profile Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "used in the Skills Profile Survey (SPS) and SESRE, we find similar patterns in some of the indicators common in both surveys among in camp refugees such as demographic composition, primary and secondary net enrollments, housing condition, access to basic infrastructures, employment and attitude of hosts toward refugees. Moreover, both surveys find that in camp refugees in Ethiopia are much poorer than host community households and poverty rates are heterogenous across refugee groups: South Sudanese refugees have the highest incidence of poverty, while Eritrean refugees have the lowest poverty incidence amongst the refugees. However, different poverty estimation methodologies and different poverty lines are used. Likewise, both surveys indicate that refugees are more food insecure than the host community. Annex F, Table F.1 shows a summary of these findings. Source: Pape, U. J., Petrini, B., and Iqbal, S. A. (2018). Informing Durable Solutions by Micro-Data: A Skills Survey for Refugees in Ethiopia. Box 1.1: Comparison of SPS 2017 and SESRE 2023 Sociodemographic Profile 9 2. Sociodemographic Profile T his chapter presents results on sociodemographic outcomes of refugees and hosts. It provides the context for refugees and their hosts, covering demographic characteristics, human capital, living conditions, and displacement experience, which are crucial", "output": {"entities": {"named_data": ["SESRE", "Skills Profile Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Ethiopia. Box 1.1: Comparison of SPS 2017 and SESRE 2023 Sociodemographic Profile 9 2. Sociodemographic Profile T his chapter presents results on sociodemographic outcomes of refugees and hosts. It provides the context for refugees and their hosts, covering demographic characteristics, human capital, living conditions, and displacement experience, which are crucial to understand refugees’ context in Ethiopia. These results are presented across the eight domains: Eritrean, Somali, South Sudanese, and refugees in Addis Ababa and their hosts, as well as broad categories between in-camp and out-of-camp refugees and hosts. 2.1 Demographic characteristics In the SESRE sample, most refugees are from South Sudan, accounting for 53 percent of all refugees.11 South Sudanese refugees reside in camps in Gambella and Benishangul-Gumuz regions. Somali refugees living in camps in the Somali region constitute 30 percent of the refugee sample. Eritrean refugees who reside in camps in the Amhara and Afar regions and Addis Ababa under the Out-of-Camp Policy (OCP) constitute 5 and 12 percent of the sample, respectively (Figure 2.1). More than 30 percent of refugees in camps are born in Ethiopia (Figure 2.2). Somali refugees have a higher share of refugees born in Ethiopia (38 percent). According to UNHCR estimates (2023)12, around 1.9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "under the Out-of-Camp Policy (OCP) constitute 5 and 12 percent of the sample, respectively (Figure 2.1). More than 30 percent of refugees in camps are born in Ethiopia (Figure 2.2). Somali refugees have a higher share of refugees born in Ethiopia (38 percent). According to UNHCR estimates (2023)12, around 1.9 million children were born as refugees between 2018 and 2022 globally. Overall, Ethiopia’s refugee situation is protracted; refugees have been in Ethiopia for an average of about 14 years. Refugees differ by country of origin. For example, Eritrean refugees have been in Ethiopia for average of slightly more than 16 years, Somalis for just under 16 years, and South Sudanese for roughly 15 years. On the other hand, refugees in Addis Ababa arrived nine years ago, on average (Figure 2.3a). Globally, the number of refugees in protracted situations—at least 25,000 refugees from the same country who lived in exile for more than five consecutive years—increased over time, accounting for 40 percent of all refugees in 2021 (World Bank, 2023). In Ethiopia, 95 percent of all refugees live in a protracted situation, according to SESRE data. 11 According to UNHCR mid-2022 statistics, South Sundanese, Somali and Eritrean refugees constitute 46, 29", "output": {"entities": {"named_data": ["UNHCR mid-2022 statistics", "SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "more than five consecutive years—increased over time, accounting for 40 percent of all refugees in 2021 (World Bank, 2023). In Ethiopia, 95 percent of all refugees live in a protracted situation, according to SESRE data. 11 According to UNHCR mid-2022 statistics, South Sundanese, Somali and Eritrean refugees constitute 46, 29 and 18 percent of the total refugee populations in Ethiopia. (https://www.unhcr.org/refugee-statistics/download/?url=2bxU2f) 12 https://www.unhcr.org/refugee-statistics/insights/explainers/children-born-into-refugee-life.html Sociodemographic Profile 10 53% 30% 5% 12% South Sudanese Somali Eritrean Addis Refugees Figure 2.1: Refugees by survey domain Source: World Bank Staff based on SESRE 2023. 68% 62% 63% 88% 31% 38% 32% 11% 0 10 20 30 40 50 60 70 80 90 100 Percent Eritrean Somali South Sudanese Addis Refugees Country of origin Ethiopia Other Figure 2.2: Country of birth Source: World Bank Staff based on SESRE 2023. Refugees fled from their country of birth mainly due to conflict and violence. Almost all South Sudanese refugees, or 99 percent, left their country because of conflict and violence, as did 91 percent of Somalis, and 73 percent of Eritrean refugees. On the other hand, OCP13 refugees came to Ethiopia with the hope of going to a Western country (35 percent), to escape from conflict and", "output": {"entities": {"named_data": ["SESRE data", "UNHCR mid-2022 statistics"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Sudanese refugees, or 99 percent, left their country because of conflict and violence, as did 91 percent of Somalis, and 73 percent of Eritrean refugees. On the other hand, OCP13 refugees came to Ethiopia with the hope of going to a Western country (35 percent), to escape from conflict and violence (28 percent), and for social and economic reasons (22 percent) such as education, health problem, marriage or family reunification, and employment (Figure 2.3b). Eritrean refugees live in Addis Ababa under OCP, which requires refugees to cover their cost of living without support from the international community. Age structure is similar between hosts and refugees, but we see differences in age structure for in-camp and OCP refugees. While the majority of in-camp refugees are children (below age 15) and youth (ages 15 to 24), most OCP refugees are between the ages of 15 and 44 (Figure 2.4). In camps, about 53 percent of refugees are children under age 15. This is slightly higher than the 47 percent of hosts under age 15. In Addis Ababa, however, refugees are less likely to be children, with roughly 70 percent of refugees and 59 percent of hosts between the ages of 15 and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "53 percent of refugees are children under age 15. This is slightly higher than the 47 percent of hosts under age 15. In Addis Ababa, however, refugees are less likely to be children, with roughly 70 percent of refugees and 59 percent of hosts between the ages of 15 and 44. There is no major difference between hosts and refugees in gender composition. The share of female hosts and refugees is higher for both in- camp and OCP refugees and their hosts. Moreover, the gap between the percentage of females and males is larger among hosts and refugees in Addis Ababa compared to in-camp counterparts. Across refugees, South Sudanese (54 percent) and the OCP (55 percent) have a higher share of female refugees (Annex D, Table D.1). The proportion of married individuals aged 18 and above is higher among refugees than hosts, except for refugees in Addis Ababa. Hosts have a relatively higher percentage of married individuals. In Addis Ababa, 60 percent of refugees are unmarried. 16.2 15.7 14.9 8.8 14.1 Eritrean Somali South Sudanese Addis refugees All 0 20 40 Percent 60 80 100 Eritrean Somali South Sudanese Addis refugees Conflict and violence Natural/man-made disaster Personal threat and political", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "a relatively higher percentage of married individuals. In Addis Ababa, 60 percent of refugees are unmarried. 16.2 15.7 14.9 8.8 14.1 Eritrean Somali South Sudanese Addis refugees All 0 20 40 Percent 60 80 100 Eritrean Somali South Sudanese Addis refugees Conflict and violence Natural/man-made disaster Personal threat and political persecution Social and economic reasons Hope to go to a Western country Other reasons Figure 2.3: Refugees arrival in Ethiopia (15 years and above) Source: World Bank Staff based on SESRE 2023. a. Years since arrival b. Reasons for leaving the country of birth 13 For details on OCP refugees, please see Box 2.2. Sociodemographic Profile 11 Refugees have larger households, younger heads, and a higher proportion of female heads than hosts. Household size is higher among in-camp refugees compared to hosts. Across in-camp refugees, South Sudanese refugees have the highest number of household members, averaging roughly seven members per household. The dependency ratio— the ratio of dependents of those under age 15 and above age 64—to working members in a household, is also higher for in-camp refugees relative to hosts and highest among South Sudanese refugees. OCP refugees have the smallest average household size and the lowest dependency ratio.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "household. The dependency ratio— the ratio of dependents of those under age 15 and above age 64—to working members in a household, is also higher for in-camp refugees relative to hosts and highest among South Sudanese refugees. OCP refugees have the smallest average household size and the lowest dependency ratio. Refugee households are more likely to be headed by women. The share is exceptionally high for South Sudanese refugees, where 84 percent are female-headed (Annex D, Table D.1). This reflects a large share of women (71 percent) aged 25 to 44 among South Sudanese refugees (Annex D, Figure D.1). Refugees have younger household heads than hosts, except for Somali refugees, with the youngest household heads in Addis Ababa. -60 -40 -20 0 20 40 60 -60 -40 -20 0 20 40 60 <15 15 to 24 25 to 44 45 to 64 >=65 In camp refugees In camp hosts Percent Percent <15 15 to 24 25 to 44 45 to 64 >=65 Addis refugees Addis hosts Figure 2.4: Age structure Source: World Bank Staff based on SESRE 2023. 49% 48% 45% 45% 47% 47% 51% 52% 55% 55% 53% 53% 0 20 40 60 80 Percent 100 Hosts Refugees Hosts", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Percent <15 15 to 24 25 to 44 45 to 64 >=65 Addis refugees Addis hosts Figure 2.4: Age structure Source: World Bank Staff based on SESRE 2023. 49% 48% 45% 45% 47% 47% 51% 52% 55% 55% 53% 53% 0 20 40 60 80 Percent 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Male Female Figure 2.5: Gender composition Source: World Bank Staff based on SESRE 2023. 22% 27% 36% 60% 30% 33% 66% 55% 54% 28% 59% 50% 12% 18% 9% 12% 11% 17% 0 20 40 60 80 Percent 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Never Married Married Other Figure 2.6: Marital status (18 years and above) Source: World Bank Staff based on SESRE 2023. Table 2.1: Household characteristics In camp Addis Ababa Total Hosts Refugees Hosts Refugees Hosts Refugees Household size 5.2 6.2 3.5 2.7 4.2 5.4 Dependency ratio 1.1 1.5 0.5 0.4 0.7 1.3 Female-headed 44% 73% 45% 58% 44% 69% Head’s age 42.3 39.6 42.0 30.9 42.1 37.6 Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 12 2.2 Education Integrating refugee children into educational programs soon after arrival14 avoids the loss of", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "5.4 Dependency ratio 1.1 1.5 0.5 0.4 0.7 1.3 Female-headed 44% 73% 45% 58% 44% 69% Head’s age 42.3 39.6 42.0 30.9 42.1 37.6 Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 12 2.2 Education Integrating refugee children into educational programs soon after arrival14 avoids the loss of valuable years of education and human capital accumulation that can hinder future prospects. Integrating refugees into national education systems can improve future outcomes for refugee children and their hosts (UNHCR, 2020; Piper et al., 2020; Abu-Ghaida and Silva, 2020; Crawford et al., 2015; Bilgili et al., 2019). Investment in human capital development can enable refugees to contribute to local economies to benefit refugees and hosts alike, and it can contribute to the recovery of countries of origin and the hosting communities. Despite the positive externalities of integrating refugees into public-school systems, a large influx of refugee children can exacerbate existing inefficiencies. Where there are large inflows, the national education system might require additional human and financial resources to integrate newly arrived children. Increasing the supply and improving the quality of schools in affected areas, supported by external assistance and financing, can avoid tension between refugees and host community populations over", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "inefficiencies. Where there are large inflows, the national education system might require additional human and financial resources to integrate newly arrived children. Increasing the supply and improving the quality of schools in affected areas, supported by external assistance and financing, can avoid tension between refugees and host community populations over competition for access to education.15 Support is particularly needed in remote areas—where refugees are often hosted—where educational services are already strained for local children (Abu- Ghaida and Silva, 2020). An inclusive education system can benefit both refugees and native children. Research has shown that an inclusive education system has positive externalities for host community children (Abu- Ghaida and Silva, 2020). A Rwanda study showed that local Rwandan children’s school attendance is higher among communities within a 10-kilometer radius of a refugee camp. Other countries have also integrated refugee children into the national education system (Bilgili et al., 2019). In Colombia, about 333,000 Venezuelan children were enrolled in government schools in 2020 (about 3.4 percent of the total student population in Colombia) (UNHCR, 2021a). In Turkey, the government is supporting the transition of Syrian refugee children into the national school system, redirecting resources to locations with high concentrations of refugees (Abu-Ghaida", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "about 333,000 Venezuelan children were enrolled in government schools in 2020 (about 3.4 percent of the total student population in Colombia) (UNHCR, 2021a). In Turkey, the government is supporting the transition of Syrian refugee children into the national school system, redirecting resources to locations with high concentrations of refugees (Abu-Ghaida and Silva, 2020), resulting in nearly 80 percent of Syrian primary school-aged refugee children being enrolled in education programs by 2020/2021 (UNHCR, 2021). In addition to benefiting refugee children, high school enrolment and learning outcomes increased for local Turkish students (Tumen, 2019; 2021). Educational attainment is low among both hosts and refugees, especially in camps. About 73 percent of in-camp adult refugees and 59 percent of adult hosts (aged 18 and above) either did not attend school or did not complete primary education. South Sudanese refugees have the worst educational attainment. Refugees in Addis Ababa have relatively better educational attainment compared to their hosts, with a higher percentage of refugees in Addis Ababa completing primary (45 percent) and secondary (27 percent) education (Figure 2.7). Although more educated, even youth’s (age 15 to 24) educational attainment is low, with large differences by survey domains. Many youth refugees and hosts have not", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "compared to their hosts, with a higher percentage of refugees in Addis Ababa completing primary (45 percent) and secondary (27 percent) education (Figure 2.7). Although more educated, even youth’s (age 15 to 24) educational attainment is low, with large differences by survey domains. Many youth refugees and hosts have not completed primary education (Figure 2.8). While the percentage of youth who completed primary education is close to 50 percent for hosts, it is only 35 percent of in-camp refugee youth. Moreover, there are large differences by location, with only 22 percent of Eritrean refugee youths in camps having completed primary school compared to 37 percent of in-camp Somali youth. On the other hand, refugee youth in Addis Ababa have similar levels of education compared to their hosts (Annex D, Table D.2). 14 In an emergency setting, it is recommend to provide refugees with access to educational programmes within the first 3 months of arrival in the hosting country. 15 Moreover, access to education services is often tied to having identification documents. Enabling refugees to receive identification documents is critical, not only for accessing education but also other services such as health or financial services. Sociodemographic Profile 13 16 In January", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "3 months of arrival in the hosting country. 15 Moreover, access to education services is often tied to having identification documents. Enabling refugees to receive identification documents is critical, not only for accessing education but also other services such as health or financial services. Sociodemographic Profile 13 16 In January 2024, RRS handed over the management of refugee primary education to DICAC, Plan International, and EDUKANS in a transition process to eventually transfer responsibility of refugee primary education to the Ministry of Education and the Regional Education Bureaus. 17 Incentive teachers are refugees who teach in return for a small stipend. They may be qualified teachers but at a minimum received training. According to Ethiopia’s Refugee Proclamation 2019, refugees are granted access to pre-primary and primary education in the same way as nationals. Whereas, secondary education, higher education, technical and vocational education and training, and adult and non-adult formal education is provided with available resources. At all levels, refugees receive the same education as nationals in terms of curriculum, access to national examinations, and accredited certificates. Yet, the administration of primary schools in camp settings follows a parallel system. For refugees, the RRS administers primary education for in-camp refugees in", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "is provided with available resources. At all levels, refugees receive the same education as nationals in terms of curriculum, access to national examinations, and accredited certificates. Yet, the administration of primary schools in camp settings follows a parallel system. For refugees, the RRS administers primary education for in-camp refugees in partnership with UNHCR16. Primary education is also provided in partnership with an NGO (Plan International) in refugee camps in Gambella, Benishangul- Gumuz, and Amhara regions. For hosts, meanwhile, management of primary public schools falls under the responsibility of the Ministry of Education (MoE) and Regional Education Bureaus. Primary education is delivered by a combination of national and refugee incentive teachers.17 It is important to note that all national teachers are qualified, but some refugee incentive teachers receive training to build their capacity. Moreover, there are not enough female teachers in camps, with the majority of teachers being male. Refugee schools also face high turnover of teachers as they take on better-paid jobs (UNHCR, 2017). Secondary education, on the other hand, is provided for refugees in camp-based refugee schools and in government administered public schools, with support from UNHCR’s NGO partner (Development and Inter-Church Aid Commission, DICAC). This relies on qualified", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "also face high turnover of teachers as they take on better-paid jobs (UNHCR, 2017). Secondary education, on the other hand, is provided for refugees in camp-based refugee schools and in government administered public schools, with support from UNHCR’s NGO partner (Development and Inter-Church Aid Commission, DICAC). This relies on qualified national teachers exclusively. UNHCR and partners are working towards progressive transfer of secondary school administration from DICAC to Regional Education Bureaus. The MoE is responsible for managing refugee and national education for higher education. However, enrollment of refugees in higher institutions is low due to low absorption capacity of higher education institutions (UNHCR, 2020a). At the same time, the Ministry of Skills and Labor (MoLS) is responsible for managing Technical and Vocational Education and Training (TVET). OCP refugees receive education at the same levels as nationals through the national education system. Refugee education data is integrated into the MoE’s Education Management Information System (EMIS) (UNHCR, 2020), and a separate chapter on refugee education is included in the annual education statistics report of the MoE. The GoE included expanding primary and secondary education for refugees in the national five-year Education Sector Development Programme VI (ESDP), covering 2020 to 2025, but little", "output": {"entities": {"named_data": ["Education Management Information System"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Management Information System (EMIS) (UNHCR, 2020), and a separate chapter on refugee education is included in the annual education statistics report of the MoE. The GoE included expanding primary and secondary education for refugees in the national five-year Education Sector Development Programme VI (ESDP), covering 2020 to 2025, but little progress has been made. Box 2.1: Education system for refugees in Ethiopia 0 20 40 60 Percent 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total No education Incomplete primary Complete primary Complete secondary Complete post-secondary Figure 2.7: Education level (18 years and above) Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total No education Incomplete primary Complete primary Complete secondary Complete post-secondary Percent Figure 2.8: Youth (15 to 24) education level Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 14 OCP refugees attended education outside of Ethiopia and have relatively better educational attainment. One reason why OCP refugees have higher educational attainment compared to other refugees (especially other Eritreans) could be related to being relatively better off in their countries of origin (qualification for OCP requires having", "output": {"entities": {"named_data": ["SESRE 2023", "Management Information System"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Sociodemographic Profile 14 OCP refugees attended education outside of Ethiopia and have relatively better educational attainment. One reason why OCP refugees have higher educational attainment compared to other refugees (especially other Eritreans) could be related to being relatively better off in their countries of origin (qualification for OCP requires having some resources other than international aid); thus, they are more likely to have received education at home. A much higher proportion of adult refugees in Addis Ababa (89 percent) attended education outside of Ethiopia than refugees in camps (49 percent). Despite high school attendance rates outside of Ethiopia, only 23 percent of adult refugees in Addis Ababa have education documents, and only 15 percent of those with education documents were able to verify the documents through the responsible Ethiopian authority (Annex D, Figure D.2). Concerning educational level, most in-camp refugees had education below primary level. In contrast, most OCP refugees completed primary and secondary education before moving to Ethiopia, highlighting a systematic difference in the selection of OCP refugees. The percentage of primary school-age children attending primary education is similar for hosts and refugees. A more liberal education policy towards refugee education in Ethiopia increased the likelihood of attending school", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "completed primary and secondary education before moving to Ethiopia, highlighting a systematic difference in the selection of OCP refugees. The percentage of primary school-age children attending primary education is similar for hosts and refugees. A more liberal education policy towards refugee education in Ethiopia increased the likelihood of attending school (World Bank, 2023c). In-camp refugees and hosts are less likely to receive education than OCP refugees and hosts; in camps, 63 and 61 percent of refugee and host children attend school, while 80 and 82 percent of OCP refugee and host children attend school (Figure 2.10a). On the other hand, refugee children and youth are half as likely to go to secondary school (22 percent) compared to hosts (44 percent) (Figure 2.10b). There is no difference in primary education attendance between boys and girls in refugee and host communities, but refugee and host secondary school-age girls are less likely to attend secondary school than boys (Annex D, Figure D.4). The GoE introduced the Out-of-Camp Policy (OCP) in 2010 to give refugees opportunities to live in Addis Ababa and other non-camp locations of their choice (RRS, 2017). In 2019, the RRS introduced a directive for implementing the OCP, enabling refugees to", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "attend secondary school than boys (Annex D, Figure D.4). The GoE introduced the Out-of-Camp Policy (OCP) in 2010 to give refugees opportunities to live in Addis Ababa and other non-camp locations of their choice (RRS, 2017). In 2019, the RRS introduced a directive for implementing the OCP, enabling refugees to establish residence outside the camp to broaden employment opportunities and achieve self-reliance. Refugees who live for more than one month in a camp can apply for a regular OCP residency permit. To be eligible for OCP residency, a refugee should be able to prove they can cover the cost of living or provide a sponsor and receive a work permit. OCP residency permit rules exempt refugees with special conditions (orphaned children, with medical issues, single mothers, elderly, and with urgent overseas travel). Refugees who are no longer beneficiaries of the urban assistance program can also get the permit if they meet the requirements of the OCP residency permit (RRS, 2019). Arrival before and after November 2020 The Tigray region of Ethiopia used to host Eritrean refugees in four camps before the outbreak of conflict between the regional and the Federal government in November 2020.18 Consequently, refugees in the region fled", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "meet the requirements of the OCP residency permit (RRS, 2019). Arrival before and after November 2020 The Tigray region of Ethiopia used to host Eritrean refugees in four camps before the outbreak of conflict between the regional and the Federal government in November 2020.18 Consequently, refugees in the region fled to neighboring Afar and Amhara regions and Addis Ababa.19 Hence, the RRS granted out-of-camp residency for those refugees who arrived in Addis Ababa due to the conflict. As of April 2023, Eritrean refugees relocated from Tigray to Addis Ababa constitute 36 percent of the total Eritrean population in Addis Ababa (UNHCR, 2023a). OCP refugees who arrived before and after November 2020 have similar sociodemographic characteristics except age, education, and child health outcomes. Refugees after November 2020 are younger and less educated compared to refugees before November 2020. Moreover, child health problems in terms of nutritional indicators, underweight, stunting, and wasting are higher among refugees moved from camps relative to refugees who were in Addis Ababa for a longer time. Box 2.2: Refugees under the Out-of-Camp Policy (OCP) 18 https://www.unrefugees.org/news/ethiopias-tigray-refugee-crisis-explained/ 19 https://www.hrw.org/news/2021/09/16/ethiopia-­eritrean-­refugees-­targeted-­tigray Sociodemographic Profile 15 Primary and secondary school enrollment rates vary between hosts and refugees; refugee primary education rates are", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "are higher among refugees moved from camps relative to refugees who were in Addis Ababa for a longer time. Box 2.2: Refugees under the Out-of-Camp Policy (OCP) 18 https://www.unrefugees.org/news/ethiopias-tigray-refugee-crisis-explained/ 19 https://www.hrw.org/news/2021/09/16/ethiopia-­eritrean-­refugees-­targeted-­tigray Sociodemographic Profile 15 Primary and secondary school enrollment rates vary between hosts and refugees; refugee primary education rates are higher than secondary enrollment. The Primary Gross Enrollment Rate (GER)20—which shows the share of children going to school—is similar between hosts (98 percent) and refugees (97 percent). For South Sudanese and Somali refugees, primary GER are higher than their respective hosts. However, the primary Net Enrollment Rate (NER)21—which shows whether children attend education at the right age—is higher for hosts (76 percent) compared to refugees (70 percent), though the gap is small. Across camp- based refugees, primary NER is higher among South Sudanese refugees (80 percent) compared to Eritrean (65 percent) and Somali (62 percent) refugees. Despite similar enrollment rates in primary compared to their hosts, refugees struggle to attend secondary education. Secondary GER22 and NER23 for refugees are almost half those of hosts. For example, only 23 percent of secondary school-aged refugee children and youth attend secondary school. This share is higher (41 percent) for hosts. Refugee Secondary NER", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "in primary compared to their hosts, refugees struggle to attend secondary education. Secondary GER22 and NER23 for refugees are almost half those of hosts. For example, only 23 percent of secondary school-aged refugee children and youth attend secondary school. This share is higher (41 percent) for hosts. Refugee Secondary NER also is very high compared to the national 5 percent in 2021/22 (MoE, 2022). By country 0 20 40 60 80 100 Eritrean Somali South Sudanese Addis refugees South Sudanese Addis refugees 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali Incomplete primary Complete primary Complete secondary Complete post-secondary Percent Percent Figure 2.9: Refugees’ education outside of Ethiopia (18 years and above) Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Attending school Primary school Secondary school 0 10 20 30 40 50 60 70 80 90 100 Primary school (7 to 14 years) Secondary school (15 to 18 years) Percent Percent Figure 2.10: Children currently attending school Source: World Bank Staff based on SESRE", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Refugees In camp Addis Ababa Total Attending school Primary school Secondary school 0 10 20 30 40 50 60 70 80 90 100 Primary school (7 to 14 years) Secondary school (15 to 18 years) Percent Percent Figure 2.10: Children currently attending school Source: World Bank Staff based on SESRE 2023. a. Attended education b. Education level a. All children b. School-age children 20 The Primary Gross Enrollment Rate (GER) is the ratio of the number of children enrolled in primary school irrespective of age to the number of children of primary school age (age 7 to 14). 21 The Primary Net Enrollment Rate (NER) is the ratio of the number of children of primary school age enrolled in primary school to the number of children of primary school age (age 7 to 14). 22 The secondary Gross Enrollment Rate (GER) is the ratio of the number of children enrolled in secondary school irrespective of age to the number of children of secondary school age (age 15 to 18). 23 Secondary Net Enrollment Rate (NER) is the ratio of the number of children of secondary school age enrolled in secondary school to the number of children of secondary school age", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "in secondary school irrespective of age to the number of children of secondary school age (age 15 to 18). 23 Secondary Net Enrollment Rate (NER) is the ratio of the number of children of secondary school age enrolled in secondary school to the number of children of secondary school age (age 15 to 18). Sociodemographic Profile 16 of origin, Somali refugees have higher secondary NER (32 percent) compared to South Sudanese (17 percent) and Eritrean (14 percent) refugees. In Addis Ababa, 83 percent of primary-aged refugee children attend primary school, but only 31 percent of secondary-aged refugee children attend secondary school (Annex D, Figure D.3). Following the revision of the Refugee Proclamation in 2019, the Ministry of Education (MoE) and RRS have tried to provide primary education to refugee children in the same circumstances as nationals. However, transition from primary to secondary school remains very low among refugees due to, among other things, limited available schools, lack of adequate infrastructure, and examination bottlenecks24 (UNHCR, 2020a). Refugee children face challenges in attending education at the appropriate age. This is also demonstrated by the fact that many children and youth participate in primary or secondary education despite being much older. Among youth", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "among other things, limited available schools, lack of adequate infrastructure, and examination bottlenecks24 (UNHCR, 2020a). Refugee children face challenges in attending education at the appropriate age. This is also demonstrated by the fact that many children and youth participate in primary or secondary education despite being much older. Among youth aged 15 to 18, 61 percent of refugees (driven by South Sudanese refugees) and 31 percent of hosts attend primary education. In South Sudan, 70 percent of children are out of school (UNICEF, 2021). Hence, limited access to education in the country of origin contributes to late school entry among South Sudanese refugee children. In Somali refugee camps, parents want their children to attend Quranic schools before attending primary schools, which could contribute to delaying school attendance. Regarding secondary education, 27 and 18 percent of refugees (mainly South Sudanese and Somali refugees) and hosts aged 19 to 24 are still in secondary schools (Figure 2.13). Reasons for not attending school differ among children of primary and secondary school age. Most children who do not attend primary school do so because their families think they are too young or unwilling to send them to school. This is similar for hosts and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "in secondary schools (Figure 2.13). Reasons for not attending school differ among children of primary and secondary school age. Most children who do not attend primary school do so because their families think they are too young or unwilling to send them to school. This is similar for hosts and refugees. For secondary school-age children, the reasons for not attending school differ between refugees and hosts and across refugee domains. Reasons related to need to work is higher among hosts (47 percent) compared to refugees (17 percent), whereas family unwillingness is higher for refugees (33 percent) than hosts (16 percent). Being unable to attend school due to need to work is higher among host boys than girls, while 24 Grade 12 national examinations for refugees and host communities are administered in nearby government public universities—a long distance for refugees based in remote locations—impacting the performance of the refugee students. 0 20 40 60 80 100 120 In camp Addis Ababa Total In camp Addis Ababa Total Primary GER Secondary GER Hosts Refugees Percent Figure 2.11: Gross Enrollment Rate (GER) Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 120 In camp Addis Ababa Total In", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "20 40 60 80 100 120 In camp Addis Ababa Total In camp Addis Ababa Total Primary GER Secondary GER Hosts Refugees Percent Figure 2.11: Gross Enrollment Rate (GER) Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 120 In camp Addis Ababa Total In camp Addis Ababa Total Primary NER Secondary NER Hosts Refugees Percent Figure 2.12: Net Enrollment Rate (NER) Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Primary school (15 to 18 years) Secondary school (19 to 24 years) Percent Figure 2.13: Share of children and youth above school age in education Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 17 family unwillingness is higher among refugee girls compared to boys (Annex D, Figure D.5). Refugee parents are less likely to send their children to secondary school in part because the opportunity cost of schooling becomes higher since children going to school cannot help support the family (UNHCR, 2020a). Across refugees, Somali refugee children of secondary school age do not attend school due to family unwillingness (52 percent). On the other", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "send their children to secondary school in part because the opportunity cost of schooling becomes higher since children going to school cannot help support the family (UNHCR, 2020a). Across refugees, Somali refugee children of secondary school age do not attend school due to family unwillingness (52 percent). On the other hand, sickness/injury or natural or human- caused disasters, such as drought or violence, hinder children from going to school among South Sudanese and Eritrean refugees (Figure 2.14). Studies show that refugee children also face challenges to attend school due to a lack of academic records, mental health issues and (Thomas, 2016), and language barriers (Reddick and Chopra, 2021). Given that the international community provides education in camp settings, refugees spend less on education compared to hosts. The average annual expenditure on education per school-age child among in-camp refugees is much lower than among hosts (Annex D, Figure D.6). On the other hand, refugees in Addis Ababa spend less on education than their hosts, likely reflecting a much lower share of refugee children who attend private education. Almost half of all host children, and 27 percent of refugee children, attend private schools. 2.3 Health and nutrition Experiences before, during, and after", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "other hand, refugees in Addis Ababa spend less on education than their hosts, likely reflecting a much lower share of refugee children who attend private education. Almost half of all host children, and 27 percent of refugee children, attend private schools. 2.3 Health and nutrition Experiences before, during, and after displacement can have stark consequences on the health of refugees. Before displacement, refugees often live in countries experiencing economic turmoil and humanitarian crises. During their flight, refugees face harsh and uncertain conditions. Refugees often struggle to integrate and feel accepted when they arrive at their new destinations. All of this can severely affect their physical and psychological wellbeing. Yet, many refugees face barriers to accessing health services they need—including accessing health providers and getting medicines or medical supplies—due to distance, safety, language, policy, or financial constraints. Good health is an essential requirement to rebuild refugees’ lives after displacement. Refugees, like any other population, have varied health-related issues, including noncommunicable and communicable diseases and trauma from injuries and violence. Research shows that conflict inflicts extensive psychological harm on many refugees, particularly youth and children, which often remain unaddressed (Simpson, 2018; Bosqui and Marshoud, 2018; Dong, 2018). Refugee women are specifically vulnerable", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "any other population, have varied health-related issues, including noncommunicable and communicable diseases and trauma from injuries and violence. Research shows that conflict inflicts extensive psychological harm on many refugees, particularly youth and children, which often remain unaddressed (Simpson, 2018; Bosqui and Marshoud, 2018; Dong, 2018). Refugee women are specifically vulnerable to sexual and other forms of gender-based violence and require specialized care and access to sexual and reproductive healthcare. 0 10 20 30 40 50 Percent 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Eritrean Somali South Sudanese 8 to 14 years 15 to 18 years Need to work Unable to cover education expenses (fee and materials) School too far Too young Marriage or pregnancy Family not willing Sickness/injury or natural or human calamites Negative perception towards the benefit of education Other Figure 2.14: Reasons for not currently attending school Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 18 Access to essential health services when and where refugees need them is crucial for allowing them to restart their lives. Refugees need access to treatment and preventive care during health emergencies, the importance of which manifested", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "currently attending school Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 18 Access to essential health services when and where refugees need them is crucial for allowing them to restart their lives. Refugees need access to treatment and preventive care during health emergencies, the importance of which manifested during the COVID-19 pandemic. Aligned with the GCR, refugees should be able to access essential health services through the national health systems of the host countries at affordable costs and sufficient quality. Ethiopia provides access to healthcare for refugees through health centers in camps, with referrals to services outside of camps for complicated cases or for secondary and tertiary healthcare. UNHCR, in partnership with the RRS and other operational partners, provides primary healthcare services for in-camp refugees. For refugees in settlement sites,25 healthcare service is provided by government health centers, through Regional Health Bureaus, and through UNHCR partners. In the case of medical conditions that cannot be addressed by treatment received in health centers, refugees are referred to nearby zonal and regional hospitals for secondary care and to hospitals in Addis Ababa for tertiary care. Refugees with complicated health problems, or needing long-term regular checkups, are granted OCP residency. They", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "case of medical conditions that cannot be addressed by treatment received in health centers, refugees are referred to nearby zonal and regional hospitals for secondary care and to hospitals in Addis Ababa for tertiary care. Refugees with complicated health problems, or needing long-term regular checkups, are granted OCP residency. They receive assistance through the urban assistance program, which covers their medical expenses. Other OCP refugees can access public or private health services but must pay for their own medical costs. Receiving health care No significant difference exists between hosts and refugees regarding prevalence of illness and receiving medical assistance. The proportion of refugees facing health problems in Ethiopia is slightly higher for in-camp refugees (19 percent) compared to OCP refugees (14 percent). Yet, significant differences exist across survey domains. A much larger proportion of South Sudanese refugees (25 percent) and their hosts (29 percent) faced health problems in the two months before their survey interview compared to any other group. Of the South Sudanese refugees and hosts that had health issues, 65 percent of refugees and 59 percent of hosts were ill due to malaria. Somali refugees and their hosts have the lowest share of illness (Annex D, Table D.3).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "months before their survey interview compared to any other group. Of the South Sudanese refugees and hosts that had health issues, 65 percent of refugees and 59 percent of hosts were ill due to malaria. Somali refugees and their hosts have the lowest share of illness (Annex D, Table D.3). Of the ill, most hosts and refugees received the necessary treatment in health institutions. However, in-camp refugees (91 percent) are more likely to get treatment than OCP refugees (71 percent). Refugees access medical services in health institutions located inside and outside of camps. Most in-camp refugees get medical assistance in health centers and health institutions implemented by RRS or NGOs within and outside camps. Refugees in Addis Ababa—who, due to their OCP, have to access healthcare without support from the international community—get medical services from private sources (58 percent) and government 25 Currently, there is only one refugee settlement site in Ethiopia: Alemwach in the Amhara region. 0 5 10 15 20 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Percent Figure 2.15: Faced any health problem Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "in Ethiopia: Alemwach in the Amhara region. 0 5 10 15 20 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Percent Figure 2.15: Faced any health problem Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Percent Figure 2.16: Received medical assistance when faced with health problem Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 19 health institutions (40 percent) (health centers and hospitals) (Annex D, Figure D.7). Both refugees and hosts face problems concerning service delivery of health institutions, which is higher for in-camp refugees and hosts compared to OCP refugees and their hosts. Problems mainly relate to shortage or unavailability of medicines and long wait times to get services in camps (Annex D, Figure D.8). Refugees use health facilities outside of the camp, but 66 percent of Eritrean refugees26 and 41 percent of Somali refugees have better usage of out-of-camp healthcare services compared to South Sudanese (5 percent). Overall, in- camp refugees with chronic27 illnesses tend to receive treatment in health institutions located outside of camps compared to those with non-chronic diseases (Figure 2.17b). Refugees receive follow-up", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Eritrean refugees26 and 41 percent of Somali refugees have better usage of out-of-camp healthcare services compared to South Sudanese (5 percent). Overall, in- camp refugees with chronic27 illnesses tend to receive treatment in health institutions located outside of camps compared to those with non-chronic diseases (Figure 2.17b). Refugees receive follow-up treatment for tuberculosis and antiretroviral therapy (ART) for HIV/AIDS in camp health facilities and also get treatment for common illnesses such as asthma, diabetes, hypertension, epilepsy, and mental issues. OCP refugees spend more on health compared to hosts. OCP refugees can no longer rely on international aid sources for healthcare, and do not have health insurance. However, OCP refugees who get out-of-camp residency permits due to health problems receive medical services free of charge under the urban assistance program. UNHCR is working with the GoE tow include OCP refugees in the Community Based Health Insurance (CBHI) Scheme. Of the total refugee households living under OCP, 10 percent moved to Addis Ababa to access basic social services, such as education and health. Thus, some refugees from the above may receive medical services free of charge. Based on SESRE data, most OCP refugees (58 percent) rely primarily on private healthcare services, while", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "refugee households living under OCP, 10 percent moved to Addis Ababa to access basic social services, such as education and health. Thus, some refugees from the above may receive medical services free of charge. Based on SESRE data, most OCP refugees (58 percent) rely primarily on private healthcare services, while 40 percent access healthcare through the national system (Annex D, Figure D.7). This can help explain why OCP refugees’ average annual per capita expenditure on health is almost twice that of hosts. In-camp refugees have access to healthcare through the international community, so their out-of-pocket spending on health is thus very low and much lower than hosts’ out-of-pocket health expenditures. The difference in per capita health expenditure is large between Eritrean and South Sudanese refugees and their hosts but low among Somali refugees and their hosts (Annex D, Figure D.12). Child Nutrition and Health Outcomes Child nutrition and health represent a significant challenge for both hosts and refugees. The nutritional status of children under age five is based on anthropometry measures; that is, stunting, underweight, and wasting. A child is identified as 26 Driven by Eritrean refugees in Alemwach camp who do not have a health facility inside the refugee", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "a significant challenge for both hosts and refugees. The nutritional status of children under age five is based on anthropometry measures; that is, stunting, underweight, and wasting. A child is identified as 26 Driven by Eritrean refugees in Alemwach camp who do not have a health facility inside the refugee site and get medical services from government health facilities outside the camp. 27 Chronic illness: tuberculosis, hepatitis-B, asthma, uric acid, blood pressure, diabetes, HIV/AIDS, kidney problem, epilepsy, cancer, mental illness 0 10 20 30 40 50 60 70 Eritrean Somali South Sudanese All in camp Eritrean Somali South Sudanese All in camp 0 10 20 30 40 50 60 70 Chronic illness Non-chronic illness Percent Percent Figure 2.17: Use of the national healthcare system when faced with health problems Source: World Bank Staff based on SESRE 2023. a. Overall b. By type of illness Sociodemographic Profile 20 “stunted”, “underweight”, or “wasted” if height- for-age, weight-for-age, and weight-for-height “z-scores28” are more than two standard deviations below the 2006 World Health Organization (WHO) Child Growth Standard medians for these measures. Child stunting is a major child health problem for both hosts and refugees, but stunting rates are largest for refugee children. Stunting", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "“wasted” if height- for-age, weight-for-age, and weight-for-height “z-scores28” are more than two standard deviations below the 2006 World Health Organization (WHO) Child Growth Standard medians for these measures. Child stunting is a major child health problem for both hosts and refugees, but stunting rates are largest for refugee children. Stunting is the impaired growth and development children experience from poor nutrition, inadequate maternal health, and repeated infection during the critical first 1,000 days of a child’s life. Stunting has long- lasting consequences such as impaired cognitive development, health issues, increased mortality, and reduced earning potential in adulthood. SESRE results show high stunting rates for both hosts and refugees, with regional differences. The prevalence of stunting is higher among Eritrean refugees (52 percent) and their hosts (43 percent), followed by Somali refugees (47 percent) and their hosts (37 percent). Children in the South Sudanese domain show the lowest stunting rates, yet 26 percent of refugees and their host children are too short for their age. Children in Addis Ababa have lower shares of stunting, but stunting rates are still high for OCP refugees (27 percent) and their hosts (24 percent) (Annex D, Table D.3). Overall, stunting rates are higher for boys", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "26 percent of refugees and their host children are too short for their age. Children in Addis Ababa have lower shares of stunting, but stunting rates are still high for OCP refugees (27 percent) and their hosts (24 percent) (Annex D, Table D.3). Overall, stunting rates are higher for boys than girls among both refugees and hosts (Annex D, Figure D.9), which is also confirmed by studies using the demographic and health survey in Ethiopia (Tasic et al., 2020; Gebreegziabher and Regassa, 2019; Gebru et al., 2019). Being underweight is another large challenge related to nutrition among children under age five in Ethiopia. In-camp refugees and hosts have a higher percentage of underweight children (25 and 29 percent) compared to OCP refugees and hosts (2 and 11 percent). Across refugees, the proportion of underweight children is higher among Eritrean and Somali refugees compared to their hosts. At the same time, it is lower among South Sudanese and OCP refugees compared to hosts. In-camp refugee and host children also suffer from wasting. The percentage of wasted in-camp refugee children (14 percent) is higher compared to OCP refugee children (6 percent). Somali and OCP refugees have a higher proportion of wasted children", "output": {"entities": {"named_data": [], "descriptive_data": ["demographic and health survey in Ethiopia"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "it is lower among South Sudanese and OCP refugees compared to hosts. In-camp refugee and host children also suffer from wasting. The percentage of wasted in-camp refugee children (14 percent) is higher compared to OCP refugee children (6 percent). Somali and OCP refugees have a higher proportion of wasted children than their hosts, whereas the wasting rate is lower among South Sudanese refugees compared to their hosts (Annex D, Table D.3). Refugees in camps have better access to health institutions for child delivery than hosts. Access to healthcare during childbirth is crucial for the health of mothers and newborns. In camps, 87 percent of refugee mothers give birth to children in health institutions (health centers and hospitals), while 75 percent of host mothers give birth in health institutions. As a result, 91 percent of births among in-camp refugees are assisted by skilled health personnel, while the rate is only 77 percent among hosts. For OCP refugees and hosts, more than 90 percent of children are born in health institutions, and all births are attended by professional or trained health workers (Annex D, Figure D.10). A significant share of both in-camp refugees and hosts have no registration of births for their", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "percent among hosts. For OCP refugees and hosts, more than 90 percent of children are born in health institutions, and all births are attended by professional or trained health workers (Annex D, Figure D.10). A significant share of both in-camp refugees and hosts have no registration of births for their children. Birth registration provides legal identity for children and ensures protection and access to essential services such as health, education, and justice (UNICEF, 2019). Yet, 51 percent of refugees and 56 percent of host children have no birth evidence (either vaccination card or birth certificate). Availability of birth evidence is better for Somali refugees (67 percent) compared to other refugees and hosts (38 percent). In Addis Ababa, birth documentation is available for over 90 percent of refugee and host children born there (Annex D, Figure D.11). 28 z-scores are calculated as (X-m)/SD, where X is child height, weight or age, m and SD are the mean and standard deviation value of the distribution corresponding the reference population (2006 WHO Child Growth Standards). Sociodemographic Profile 21 Disability Disability rates are similar for hosts and refugees. The proportion of individuals with a disability29 is comparable between refugees and hosts, except for Eritrean", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "m and SD are the mean and standard deviation value of the distribution corresponding the reference population (2006 WHO Child Growth Standards). Sociodemographic Profile 21 Disability Disability rates are similar for hosts and refugees. The proportion of individuals with a disability29 is comparable between refugees and hosts, except for Eritrean refugees. The percentage of persons with disabilities is higher among Eritrean refugees (8 percent) compared to hosts (5 percent). At the national level, and estimated 9 percent of the population lives with at least one disability, according to 2016 national survey results (UNICEF, 2018). Disability is more prevalent among elderly refugees and hosts who above age 60 compared to adults and children. Refugees and hosts mainly face disabilities related to seeing, walking, or climbing steps (Annex D, Figure D.13). 2.4 Living conditions Housing differs drastically between refugees and their hosts, with in-camp refugees living in UN or NGO shelters, and refugees in Addis Ababa residing in rented houses. Most Eritrean refugee households (61 percent) live in temporary30 shelters provided by the UN or NGOs, whereas 50 percent of Somali refugee households and 71 percent of South Sudanese refugee households live in UN or NGO-provided permanent31 shelters. In Addis Ababa, 97", "output": {"entities": {"named_data": [], "descriptive_data": ["2016 national survey results"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "refugees in Addis Ababa residing in rented houses. Most Eritrean refugee households (61 percent) live in temporary30 shelters provided by the UN or NGOs, whereas 50 percent of Somali refugee households and 71 percent of South Sudanese refugee households live in UN or NGO-provided permanent31 shelters. In Addis Ababa, 97 percent of refugee households live in rented houses. SESRE data also show that OCP refugees pay higher rents than hosts; on average, refugees pay roughly ETB 31,600 per year per adult equivalent, while hosts pay slightly less than half of that (ETB 18,700) (Annex D, Figure D.14). Refugees do not qualify for government 29 At least having difficulty with seeing, hearing, walking, remembering, selfcare or communicating. 30 Temporary shelters have walls mainly made of tent, plastic cover, and irons sheet. 31 Permanent shelters have walls mainly made of wood, mud, non-plastered blocks. 0 5 10 15 20 25 30 35 40 In camp Addis Ababa Total In camp Addis Ababa Total In camp Addis Ababa Total Stunted Underweight Wasted Hosts Refugees Percent Figure 2.18: Child nutritional indicators Source: World Bank Staff based on SESRE 2023. 0 1 2 3 4 5 6 Hosts Refugees Hosts Refugees Hosts Refugees In camp", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": ["SESRE data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "40 In camp Addis Ababa Total In camp Addis Ababa Total In camp Addis Ababa Total Stunted Underweight Wasted Hosts Refugees Percent Figure 2.18: Child nutritional indicators Source: World Bank Staff based on SESRE 2023. 0 1 2 3 4 5 6 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total <=18 years In camp Hosts Refugees Hosts Refugees Hosts Refugees 40 30 20 10 0 Percent Addis Ababa Total >18 to 60 years >60 years Percent Figure 2.19: Presence of any disability Source: World Bank Staff based on SESRE 2023. a. Overall b. By age group Sociodemographic Profile 22 housing schemes (such as Kebele housing). OCP refugees thus tend to rent in the private housing market, typically more extensive and better quality, but which increases the cost of renting. High rents are a large challenge for OCP refugees, with rent expenditure taking the highest share of their total non-food expenditure (56 percent), compared to only 37 percent for host households in Addis Ababa. Housing quality varies across camps. Housing quality is measured using three indicators: overcrowding, quality of the wall, and roof construction materials. Both in camps and in Addis Ababa, refugee households live in more overcrowded32", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "total non-food expenditure (56 percent), compared to only 37 percent for host households in Addis Ababa. Housing quality varies across camps. Housing quality is measured using three indicators: overcrowding, quality of the wall, and roof construction materials. Both in camps and in Addis Ababa, refugee households live in more overcrowded32 conditions compared to hosts. Overcrowding is highest among Eritreans, with 66 percent of refugee households living in dwellings with more than three people per room. Most in-camp refugees and their respective host households live in homes with low-quality walls,33 with only 2 percent of in-camp refugee households live in dwellings with an improved wall. In Addis Ababa, the percentage of refugee households living in houses with good quality walls is 82 percent, even higher than hosts at 58 percent. This is related to the fact that refugees cannot access public housing schemes, such as Kebele housing, often of lower quality. Regarding the quality of roofing, more than half of refugee and host households live in dwellings with improved roofs,34 except for South Sudanese refugees which have 8 percent. Housing conditions in terms of wall and roof construction materials is worst for South Sudanese refugees, none of which live in a", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Regarding the quality of roofing, more than half of refugee and host households live in dwellings with improved roofs,34 except for South Sudanese refugees which have 8 percent. Housing conditions in terms of wall and roof construction materials is worst for South Sudanese refugees, none of which live in a house with an improved wall, with only 8 percent of households having an improved roof (Annex D, Table D.4). Refugees have better access to drinking water compared to hosts since the international community provides water, sanitation, and hygiene (WASH) services. The share of in-camp refugee households with access to safe drinking water35 is higher than for host households. This is not surprising considering that the international aid sources prioritize access to drinking water when setting up camps. South Sudanese hosts have relatively lower access to safe drinking water. Despite good access to drinking water in camps, the proportion of in- camp refugee households with improved bathing facilities36 is low, especially among Somali refugees and their hosts. In addition, few refugee and host households have a place or item designated for hand washing in their dwellings. Availability of water or detergent for hand washing is low, especially among refugees (Annex D,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "camp refugee households with improved bathing facilities36 is low, especially among Somali refugees and their hosts. In addition, few refugee and host households have a place or item designated for hand washing in their dwellings. Availability of water or detergent for hand washing is low, especially among refugees (Annex D, Figure D.15). Regarding rented homes, OCP refugee households live in houses with better bathing facilities (82 percent) than hosts (66 percent). Also, around 80 percent of refugee and host households in Addis Ababa have a place for hand washing, and more than half of refugees and hosts have water or soap. 32 Overcrowding occurs when if more than three people live per room (UN-Habitat). 33 Improved wall is made of stone & cement, blocks-plastered with cement or bricks. 34 Improved roof is made of corrugated iron sheet or concrete/cement. 35 Improved sources of drinking water are piped, bottled, sachet, or tanker water. 36 Improved bathing refers private or shared bathtub, shower, separate room for bathing. 0 20 40 60 80 100 120 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Overcrowded Improved wall Improved roof Percent Figure 2.21: Housing quality Source: World Bank Staff based on SESRE", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "tanker water. 36 Improved bathing refers private or shared bathtub, shower, separate room for bathing. 0 20 40 60 80 100 120 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Overcrowded Improved wall Improved roof Percent Figure 2.21: Housing quality Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Owned Rented UN/NGO temporary UN/NGO permanent Other Percent Figure 2.20: Dwelling type Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 23 Refugees’ access to improved sanitation facilities is similar to hosts. Refugees’ access to improved toilet facilities37 is the same as hosts, or even better in some cases. Eritrean, Somali, and OCP refugees and their hosts have similar toilet facilities. Even though the percentage of households with access to improved toilet facilities is lower among South Sudanese refugees than other refugees, it is higher compared to their hosts. Moreover, refugees have higher access to improved waste disposal methods38 than hosts. Both refugee and host households have low access to electricity, except for hosts of Eritrean refugees. Hosts around Eritrean refugees have better access to electricity (meter private or shared) for", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "refugees, it is higher compared to their hosts. Moreover, refugees have higher access to improved waste disposal methods38 than hosts. Both refugee and host households have low access to electricity, except for hosts of Eritrean refugees. Hosts around Eritrean refugees have better access to electricity (meter private or shared) for lighting (74 percent). The use of solar energy is common among Eritrean refugees, with 78 percent of Eritrean refugee households get lighting from solar energy. Almost all South Sudanese refugee households have no access to electricity either from meter or solar sources. All refugees in Addis Ababa use electricity for lighting, similar to their hosts. 0 20 40 60 80 100 120 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Improved toilet facility Improved waste disposal method Percent Figure 2.23: Access to toilet facility and waste disposal Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Electricity (meter) Electricity (meter, generator, solar) Percent Figure 2.24: Source of lighting Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Electricity (meter) Electricity (meter, generator, solar) Percent Figure 2.24: Source of lighting Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Improved source of drinking water Improved bathing facilities Percent Figure 2.22: Access to drinking water and hygiene Source: World Bank Staff based on SESRE 2023. 37 Improved toilet facility includes toilets flush to septic tank/pit latrine/piped sewer system, pit latrine with slab, or composting toilet. 38 Improved waste disposal refers waste not thrown to field or yard, into river and burnt. 24 T his chapter presents findings on labor market outcomes and livelihood choices of refugees and hosts. It discusses how sociodemographic characteristics such as age, gender, education level, and location of residence relate with labor market outcomes. Ethiopia has experienced steady economic growth for much of the last two decades, but even before the country’s concurrent crises, economic growth did not transform the labor market structure. Between 2004 and 2020, Ethiopia’s GDP annual growth averaged 10 percent, helping to reduce the poverty by about ten percentage points.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Ethiopia has experienced steady economic growth for much of the last two decades, but even before the country’s concurrent crises, economic growth did not transform the labor market structure. Between 2004 and 2020, Ethiopia’s GDP annual growth averaged 10 percent, helping to reduce the poverty by about ten percentage points. But during this period, the distribution of workers across sectors and geographies shifted very little. According to newly released 2021 Labor Force Survey (LFS) data, about 80 percent of Ethiopians live in rural areas, where roughly 75 percent work in agriculture. In urban areas, approximately 70 percent of people work in services. Nationally, self-employment accounts for about half of jobs, with unpaid family work second most common and wage work a distant third. Again, the urban context is different: wage employment is prevalent but often poorly paid. Over the last decade, negative repercussions from the concurrent overlapping crises have threatened this marginal progress. In 2020, the COVID pandemic closed markets, albeit relatively briefly, and this immediately decreased job opportunities. While most reentered the labor market, many changed their work situation, and some permanently exited the workforce or reduced working hours. At the same time, more and more people— rural women", "output": {"entities": {"named_data": ["2021 Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "this marginal progress. In 2020, the COVID pandemic closed markets, albeit relatively briefly, and this immediately decreased job opportunities. While most reentered the labor market, many changed their work situation, and some permanently exited the workforce or reduced working hours. At the same time, more and more people— rural women in particular—are unemployed or out of the labor market altogether. Nationally, unemployment doubled between 2013 and 2021, and it tripled in rural areas. Women and youth both saw particularly sharp increases in unemployment. There was also a decrease in the labor force participation rate (LFPR), from 86 percent in 2013 to 74 percent in 2021, following a long period of steady LFPR in the two previous LFS surveys. Like for unemployment, LFPR was much more affected in the rural labor market, and these trends are particularly striking for women. 3. Jobs and Livelihoods Jobs and Livelihoods 25 Within this challenging context, vulnerable populations—including refugees—face unique barriers to accessing quality work. On average, rural women, urban youth, people with disabilities, and rural-urban migrants are more likely to be inactive and less likely to have improved their livelihoods over the last two decades. In this context, it is unsurprising that refugees cite", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["LFS surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "context, vulnerable populations—including refugees—face unique barriers to accessing quality work. On average, rural women, urban youth, people with disabilities, and rural-urban migrants are more likely to be inactive and less likely to have improved their livelihoods over the last two decades. In this context, it is unsurprising that refugees cite “lack of economic opportunity” as one of their most critical challenges. In all domains, when refugees are asked to list the top three challenges they face as refugees in Ethiopia, the most common responses are lack of work or business opportunities and high cost of living. To refugees, these are much more important than poor services, lack of community networks, or insecurity and discrimination. This highlights the severity of the labor market challenges refugees face in Ethiopia. Inclusion, rather than marginalization, can benefit both refugees and host communities. Defining development approaches and better situating them within the agenda of international protection and national, regional, and local development plans can enable refugees and their hosts to fulfill their potential. Development approaches are most successful when they focus on building self-reliance—including offering refugees secure terms of stay, mobility to access better economic opportunities, and access to the labor market— and supporting refugees’", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "protection and national, regional, and local development plans can enable refugees and their hosts to fulfill their potential. Development approaches are most successful when they focus on building self-reliance—including offering refugees secure terms of stay, mobility to access better economic opportunities, and access to the labor market— and supporting refugees’ pursuit of economic opportunities while simultaneously supporting refugee-hosting communities (Betts et al., 2014; Clements et al., 2016; Krause and Schmidt, 2020). Refugees endure trauma and loss of assets and livelihoods resulting from their flight. Stabilizing their livelihoods, improving their economic opportunities, and placing them on a path of self- reliance can help refugees overcome these conditions and avoid short-term survival strategies that have negative long-term consequences, such as putting children to work, early marriage of children, or selling remaining assets (World Bank, 2017). Development approaches enabling and incentivizing refugees’ self-reliance can improve refugee outcomes and reduce the burden on host communities by reaping economic benefits from refugees’ presence. Refugees are forced to suddenly leave their countries and settle in foreign lands without necessarily selecting their destination or having favorable employment prospects. Compared to economic migrants, refugees often arrive without connections to employers or time to invest in applicable human capital,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "communities by reaping economic benefits from refugees’ presence. Refugees are forced to suddenly leave their countries and settle in foreign lands without necessarily selecting their destination or having favorable employment prospects. Compared to economic migrants, refugees often arrive without connections to employers or time to invest in applicable human capital, language, or other skills (Brell et al., 2020). Nonetheless, many refugees have valuable skills and experience to contribute to local economies (World Bank, 2023; Lebow, 2023). Strengthening refugees’ human capital during displacement— that is, strengthening their skills, knowledge, and experience and the ability to apply them in the host country setting—is essential to enable refugees to realize their potential, become productive members of society, and achieve self-reliance. Upon arriving in the host country, the first few years have an outsized effect on economic opportunities and wages. Enabling refugees’ labor market participation from a very early stage is critical to achieving positive long-term integration as it limits long-term scarring effects, such as long- term unemployment or inactivity (Fasani et al., 2022; Slotwinski et al., 2019). Refugee employment after arrival depends on policies in the host country concerning work permits and mobility (Fuller, 2015; World Bank, 2017). On the other hand, arrival", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "positive long-term integration as it limits long-term scarring effects, such as long- term unemployment or inactivity (Fasani et al., 2022; Slotwinski et al., 2019). Refugee employment after arrival depends on policies in the host country concerning work permits and mobility (Fuller, 2015; World Bank, 2017). On the other hand, arrival of refugees may have a complex range of positive and negative effects on local labor markets in host communities, including on sectoral employment, wages, and prices. Studies in Ethiopia show that 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Addis Refugees Camp Refugees Lack of work/business opportunities High cost of living Poor services or institutional support Lack of freedom or mobility Lack of community/family networks Insecurity or discrimination Figure 3.1: Top 3 difficulties with being a refugee Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 26 refugees may decrease employment among hosts in rural areas (Ayenew, 2021), while other studies find no effect on employment and increases in consumption (von der Goltz, 2023) and product diversification and livestock sales (Walelign et al., 2022) as refugees increase consumer demand for agricultural products, with variations in effects across the different regions of Ethiopia. Most refugees in Ethiopia do", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "2021), while other studies find no effect on employment and increases in consumption (von der Goltz, 2023) and product diversification and livestock sales (Walelign et al., 2022) as refugees increase consumer demand for agricultural products, with variations in effects across the different regions of Ethiopia. Most refugees in Ethiopia do not move to locations with better economic opportunities and live in camps. About 88 percent of refugees in Ethiopia are living in camps, where they cannot take advantage of economic opportunities to be self-reliant and thereby improve their economic opportunities to reduce their dependence on support from their hosts and the international community (World Bank, 2017). Denying refugees mobility to settle where they would like comes at a cost, as the choice of location within the host country affects refugees’ labor market outcomes. Placing refugees in areas of lower economic opportunity while unable to relocate to better areas makes it hard for them to work (Azlor et al., 2020; Eckert et al., 2020; Fasani et al., 2022). Therefore, development approaches promoting refugees’ mobility to where economic opportunities are highest are most likely to contribute to local economies. While the GoE has made commendable steps towards granting refugees the right to", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "hard for them to work (Azlor et al., 2020; Eckert et al., 2020; Fasani et al., 2022). Therefore, development approaches promoting refugees’ mobility to where economic opportunities are highest are most likely to contribute to local economies. While the GoE has made commendable steps towards granting refugees the right to work, most refugees do not have work permits for wage or self- employment. Technically, refugees in Ethiopia have a right to participate in the labor market. However, this has not been implemented in practice due to a lack of clarity on what ���most favorable treatment accorded to foreign nationals” means. Though progress is made by clarifying the legal framework and issuing work permits for different employment pathways, such as for joint projects, wage- employment, and self-employment (see Annex B for details on pathways of employment), few refugees have obtained work permits or business licenses. Instead, refugees typically work in the informal sector in surrounding communities or the camps., this may include selling aid rations on the local market, informal trade, and economic exchange, or working for local NGOs and UN agencies (ReDSS, 2018). Extensive research has shown that not being able to enter local labor markets legally is detrimental to", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "informal sector in surrounding communities or the camps., this may include selling aid rations on the local market, informal trade, and economic exchange, or working for local NGOs and UN agencies (ReDSS, 2018). Extensive research has shown that not being able to enter local labor markets legally is detrimental to refugees’ ability to earn for their families, and for them to find an occupational match that maximizes the benefits they contribute to Ethiopia (World Bank, 2023). Because of conflict, the SESRE data collection could not include refugees in the Tigray region in Ethiopia, where most Eritrean refugees were hosted before the conflict. Since outbreak of the conflict in November 2020, many Eritrean refugees moved to Addis Ababa, and many fled from the Mai Ani and Adi Harush refugee camps in Tigray to the newly established refugee hosting site of Alemwach, Dabat in the Amhara region. Between February and July 2022, over 15,000 refugees relocated from the Tigray camps to Alemwach, and an additional 7,000 refugees were resettled in November 2022 following the cessation of hostilities (UNHCR, 2022). Before the conflict, 64 percent of all Eritrean refugees were hosted in camps in Tigray and 36 percent in camps in Afar (UNHCR,", "output": {"entities": {"named_data": [], "descriptive_data": ["SESRE data collection"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "over 15,000 refugees relocated from the Tigray camps to Alemwach, and an additional 7,000 refugees were resettled in November 2022 following the cessation of hostilities (UNHCR, 2022). Before the conflict, 64 percent of all Eritrean refugees were hosted in camps in Tigray and 36 percent in camps in Afar (UNHCR, 2020b). Refugees going to either Tigray or Afar are distinct culturally and linguistically. Eritrean refugees in Afar are Muslim and speak Afar, as do the Ethiopian hosts in Afar. Many Eritrean refugees in Tigray—and thus the ones who moved to Amhara during the conflict—are Orthodox Christians and speak Tigrinya. The SESRE sample, therefore, includes in-camp Eritrean refugees in two regions: Afar (216 households) and Amhara (216 households). This means that Eritrean refugees in Amhara, representing half of the Eritrean refugee sample, were displaced from Tigray only a few months before the SESRE was implemented, thus have had less time to integrate into the surrounding community and labor market. Among Eritreans in Afar and Amhara, the share working is 43 percent and 9 percent, respectively. Among workers, Eritreans in Amhara are three times as likely to work for NGOs or RRS, and very few work outside the camp. Box 3.1: Eritrean", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "integrate into the surrounding community and labor market. Among Eritreans in Afar and Amhara, the share working is 43 percent and 9 percent, respectively. Among workers, Eritreans in Amhara are three times as likely to work for NGOs or RRS, and very few work outside the camp. Box 3.1: Eritrean refugee sample in the SESRE Jobs and Livelihoods 27 Ethiopia relies on a camp-based model, with 88 percent of refugees hosted in camps. As outlined, in-camp refugees generally do not have work permits or business licenses and largely depend on work inside the camp or informal work outside the camp. On the other hand, the GoE introduced an out-of- camp policy (OCP)39 in 2010 that provides refugees the opportunities to live in Addis Ababa and different non-camp locations of their choice. Roughly 71,000 Eritreans were under the OCP regime as of 2022, with over 90 percent living in Addis Ababa. In practice, most of those approved for the OCP have family and friends in Ethiopia who support them with remittances—fewer than 1,500 OCP work permits were issued by 2022. The permit allows refugees to freely move and establish residence in all areas of the country except restricted areas. OCP refugees", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "In practice, most of those approved for the OCP have family and friends in Ethiopia who support them with remittances—fewer than 1,500 OCP work permits were issued by 2022. The permit allows refugees to freely move and establish residence in all areas of the country except restricted areas. OCP refugees are systematically different from in-camp refugees, as seen in their livelihood strategies. Therefore, the remainder of this chapter divides the analysis between in-camp refugees and OCP refugees. 3.1 Labor market outcomes of in-camp refugees and their hosts In-camp refugees have high inactivity rates (not working or unemployed) and low labor force participation. Table 3.1 shows that only 31 percent of all in-camp refugees aged 15-64 “participated” in the workforce (in the week before the survey), meaning they were employed or available to work and actively searching (strict unemployment). This compares to 52 percent for hosts. This figure increases to 43 percent for refugees and 57 percent for hosts if you include all available to work regardless of whether they are searching (relaxed unemployment). The remaining 57 percent of refugees are inactive, and just over half are currently in school, leaving 23 percent of refugees neither working nor studying, compared to", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "percent for refugees and 57 percent for hosts if you include all available to work regardless of whether they are searching (relaxed unemployment). The remaining 57 percent of refugees are inactive, and just over half are currently in school, leaving 23 percent of refugees neither working nor studying, compared to 20 percent of hosts. In-camp refugees have high unemployment rates relative to hosts. Figure 3.2 shows that only 25 percent of in-camp refugees performed paid work in the week before the survey, compared to 48 percent for hosts. At the household level, only 54 percent of refugee households have any workers, relative to 86 percent for hosts. The strict unemployment rate is 21 percent for refugees and 7 percent for hosts, while the relaxed unemployment rate is significantly higher at 43 percent for refugees and 15 percent for hosts. Across camp domains, Eritreans have the highest rate of relaxed unemployment at 55 percent, while it is 45 percent and 40 percent for Somalis and South Sudanese, respectively. South Sudanese have the highest rates of inactive workers remaining in school, reflecting that they have a younger population and that more young adults stay in school (mainly primary) after age 15.40 During", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "at 55 percent, while it is 45 percent and 40 percent for Somalis and South Sudanese, respectively. South Sudanese have the highest rates of inactive workers remaining in school, reflecting that they have a younger population and that more young adults stay in school (mainly primary) after age 15.40 During the conflict in Ethiopia’s Tigray region, many Eritreans fled Tigray and moved to Addis Ababa without OCP documentation. Given the needs, Ethiopia’s Refugee and Returnee Service (RRS) implemented an “amnesty” program, providing OCP documents to all refugees who came to Addis Ababa after November 2020. Between November 2020 and 2022, approximately 43,000 Eritreans migrated to Addis Ababa. These represent 27 percent of Addis Ababa refugees in the SESRE sample. The Eritreans who migrated after November 2020 are slightly less educated and, given they have had less time to integrate into the Addis Ababa labor market, are less likely to be employed (10 percent relative to 19 percent for Eritreans who came before November 2020). These households are also more likely to rely on remittances as their primary source of income. Throughout this chapter, we will keep Eritreans who arrived to Addis Ababa before and after November 2020 combined for analysis.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "employed (10 percent relative to 19 percent for Eritreans who came before November 2020). These households are also more likely to rely on remittances as their primary source of income. Throughout this chapter, we will keep Eritreans who arrived to Addis Ababa before and after November 2020 combined for analysis. Box 3.2: OCP Refugees under the Amnesty Program 39 For a more detailed description of OCP see Box 2.2. 40 Refer to the Annex D Table D.5 for statistics broken down by survey domains. Jobs and Livelihoods 28 Working in-camp refugees tend to work in lower- skill jobs than hosts, and many rely on employment with NGOs, international organizations, and RRS. In-camp refugees are more likely than surrounding hosts to be self-employed (71 percent) or work in private households (6 percent), and 15 percent rely on work with NGOs, international organizations, or RRS.41 Refugees are less likely to be in high- skill occupations, which include managerial or professional jobs (based on ISCO-2008 classifications), and more likely to be in elementary occupations, crafts, and services. This largely reflects the fact that in-camp refugees have lower education than hosts. Notably, refugees are much less likely to work in agriculture, even though many", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "high- skill occupations, which include managerial or professional jobs (based on ISCO-2008 classifications), and more likely to be in elementary occupations, crafts, and services. This largely reflects the fact that in-camp refugees have lower education than hosts. Notably, refugees are much less likely to work in agriculture, even though many households previously relied on agriculture in their home country, reflecting refugees’ lack of access to land. These patterns mask important variations across refugee domains. While the share of in-camp working refugees does not vary by country of origin, Eritrean refugees are more likely to work in crafts and related trades (49 percent; see Annex D, Figure D.19). South Sudanese refugees are likelier to be in elementary occupations (77 percent). As a result, Eritreans concentrate more in the industrial sector and less in services. Somali refugees stand out because they work more in services and sales (29 percent) and skilled agricultural (24 percent) with higher livestock ownership relative to other domains. Somalis are also more likely to work for private households, including household services, construction, and agricultural work. 0 10 20 30 40 50 60 70 80 90 100 Camp Hosts Camp Refugees Employed Unemployed, searching Unemployed, not searching Inactive not", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "(24 percent) with higher livestock ownership relative to other domains. Somalis are also more likely to work for private households, including household services, construction, and agricultural work. 0 10 20 30 40 50 60 70 80 90 100 Camp Hosts Camp Refugees Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent Figure 3.2: Work status Source: World Bank Staff based on SESRE 2023. Table 3.1: Labor force statistics Camp- Hosts Camp- Refugees Labor force participation rate (strict) 52% 31% Unemployment rate (strict) 7% 21% Labor force participation rate (relaxed) 57% 43% Unemployment (relaxed) 15% 43% Employment-to-population ratio 48% 25% Source: World Bank Staff based on SESRE 2023. Note: Labor force participation ratio is the share of working-age people who are engaged in the labor market, either employed or unemployed. Unemployment is the share of people participating in the labor force who are not employed. The “relaxed” definition of labor force participation includes anyone who is available to work. The “strict” definition of labor force participation includes only those who are available to work and also actively searching for work. Employment-to- population ratio is the share of working-age people who are employed. 41 In Ethiopia, in-camp", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "“relaxed” definition of labor force participation includes anyone who is available to work. The “strict” definition of labor force participation includes only those who are available to work and also actively searching for work. Employment-to- population ratio is the share of working-age people who are employed. 41 In Ethiopia, in-camp refugees can work as incentive workers, with standardized pay scales according to their skills, in different organizations, including RRS, a government entity. Thus in-camp refugees who indicated that they work in the public sector were assumed to be incentive workers under RRS. 0 10 20 30 40 50 60 70 80 90 100 Camp Hosts Camp Refugees Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure 3.3: Work type Source: World Bank Staff based on SESRE 2023. Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals 0 10 20 30 40 50 60 70 80 90 100 Camp Hosts Camp Refugees Percent Figure 3.4: Occupation Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 29 Female refugees have high employment rates relative to male refugees of all ages and make a critical contribution to refugee household incomes. Figure 3.5", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "50 60 70 80 90 100 Camp Hosts Camp Refugees Percent Figure 3.4: Occupation Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 29 Female refugees have high employment rates relative to male refugees of all ages and make a critical contribution to refugee household incomes. Figure 3.5 shows that, on average, in-camp refugee women and men are equally likely to be employed (around 25 percent), while among hosts, men are twice as likely to be employed (62 percent compared to 37 percent for women). Like men, refugee women are more likely than host counterparts to be self-employed and less likely to be in high-skill occupations. Despite not having work permits, many in-camp refugees work outside the camps, which presents many more income-generating opportunities than working in camps. On average across refugee camps, 40 percent of working refugees work outside the camp. This rate is 42 and 44 percent in Somali and South Sudanese camps, respectively. It is only 10 percent for Eritrean refugees, but this low number is a result of the Amhara camps, where refugees were more recently displaced due to the conflict in Tigray; in Amhara, the rate is 5 percent relative to 34 percent", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "percent in Somali and South Sudanese camps, respectively. It is only 10 percent for Eritrean refugees, but this low number is a result of the Amhara camps, where refugees were more recently displaced due to the conflict in Tigray; in Amhara, the rate is 5 percent relative to 34 percent in Afar. Out-of-camp work is primarily a mix of elementary occupations, skilled agricultural work— especially among Somalis, for whom it accounts for 51 percent of work outside the camp—and, to a lesser extent, services and sales. As the next section shows, these workers earn much more than inside the camp, highlighting the greater income-generating opportunities outside the camp. This demonstrates the importance of allowing refugees to work outside of camps to support their self-reliance; access to the labor market outside of camps is a critical element of sustainability—both financially and socially—to reduce dependence on host government assistance (World Bank, 2023). Finally, Figure 3.9 shows that, while women are less likely than men to work outside the camps, they still do so at relatively high rates—35 percent of working women work outside of camps, compared to 47 percent of men. Not only are refugees less likely to work, but those who", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Finally, Figure 3.9 shows that, while women are less likely than men to work outside the camps, they still do so at relatively high rates—35 percent of working women work outside of camps, compared to 47 percent of men. Not only are refugees less likely to work, but those who do also have lower earnings than hosts. Figure 3.11 shows that, on average, camp-based refugees’ hourly earnings are 57 percent lower than hosts’ hourly earnings, and this gap is higher for women at 68 percent. For both men and women, the hourly earnings gap is largest in the South Sudanese domain (77 percent) and lowest in the Somali domain (52 percent). Because refugees work fewer hours on average, the average monthly earnings gap is even larger at 62 percent. Lower wages for refugees are not explained by differences in education, demographics, occupation, or sector. We can demonstrate this statistically by using a regression analysis to compare the earnings of refugees and hosts and how this wage gap changes after adjusting for the effects of demographic and job characteristics. Column 1, Annex D, Table D.6 shows that, after controlling for the domain, monthly earnings is 70 percent lower for refugees than", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "using a regression analysis to compare the earnings of refugees and hosts and how this wage gap changes after adjusting for the effects of demographic and job characteristics. Column 1, Annex D, Table D.6 shows that, after controlling for the domain, monthly earnings is 70 percent lower for refugees than for hosts. After controlling for age, gender, and education in Column 2, this earnings gap remains at 64 percent. This means 0 20 40 60 80 100 Camp Hosts Male Camp Refugees Male Camp Hosts Female Camp Refugees Female Employed Unemployed, searching Percent Unemployed, not searching Inactive not in school Inactive in school Figure 3.5: Work status by gender Source: World Bank Staff based on SESRE 2023. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 Female Male Figure 3.6: In-camp refugee share employed by age Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 30 that, after adjusting for any differences explained by age, gender, and education, refugees still earn significantly less than hosts. Column 3 indicates that refugees face this same earnings gap even after adjusting for differences explained by occupation and industry. Only after restricting the", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "on SESRE 2023. Jobs and Livelihoods 30 that, after adjusting for any differences explained by age, gender, and education, refugees still earn significantly less than hosts. Column 3 indicates that refugees face this same earnings gap even after adjusting for differences explained by occupation and industry. Only after restricting the analysis to refugees who work only outside the camp does this wage gap fall to 41 percent, indicating that policies to allow refugees to work outside the camp are crucial for improving refugees’ ability to generate income, though they still face significant disadvantages in the labor market even after adjusting for their age, gender, and education. Compared to hosts, camp-based refugees’ employment depends little on education and increases less with age. Among hosts, those who completed secondary are much more likely to work across all ages, while people who completed primary are more likely to work if they are older; among hosts who completed primary education, the share who are employed rises to 89 percent by age 45-54 compared to 69 percent for hosts without primary. However, for refugees, employment does not depend on education. Similarly, employment increases dramatically with age for hosts in all education groups, but this is", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "among hosts who completed primary education, the share who are employed rises to 89 percent by age 45-54 compared to 69 percent for hosts without primary. However, for refugees, employment does not depend on education. Similarly, employment increases dramatically with age for hosts in all education groups, but this is less the case for refugees. A regression analysis (Annex D, Table D.7) confirms these patterns after controlling for country of origin, gender, and years spent in Ethiopia – hosts enjoy greater increases in earnings as they age or if they are better educated compared to in-camp refugees. This may partly explain why many refugee households are unwilling to send their children to school, as highlighted in Chapter 2. Education and experience are also more associated with working in a high-skill occupation for hosts than for refugees, though there is a positive relationship between refugee education and working in a high-skill occupation. Regression results (see Annex D, Table D.7) also confirm that, for hosts, older and more educated people are, in addition to having higher monthly earnings, more likely to work in a high-skill occupation (managerial or professional occupations). For refugees, older and more educated people are no more likely to", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "occupation. Regression results (see Annex D, Table D.7) also confirm that, for hosts, older and more educated people are, in addition to having higher monthly earnings, more likely to work in a high-skill occupation (managerial or professional occupations). For refugees, older and more educated people are no more likely to work outside the camp. Yet, refugees with higher levels of education are more likely to be in a high-skill occupation when they are able to find work. 0 20 40 60 80 100 Camp Refugees Male Camp Refugees Female Inside the camp Outside the camp Percent Figure 3.9: Refugee work location Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Camp Hosts Female Camp Refugees Female Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure 3.7: Female work type Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Camp Hosts Female Camp Refugees Female Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals Percent Figure 3.8: Female occupations Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 31 0 10 20 30 40 50", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "60 80 100 Camp Hosts Female Camp Refugees Female Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals Percent Figure 3.8: Female occupations Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 31 0 10 20 30 40 50 60 Camp Hosts Male Camp Refugees Male Camp Hosts Female Camp Refugees Female Figure 3.11: Hourly earnings 0 5 10 15 20 25 30 35 40 45 Camp Hosts Male Camp Hosts Female Camp Refugees Male Camp Refugees Female Figure 3.10: Hours per week Source: World Bank Staff based on SESRE 2023. Note: Hourly earnings are past-month earnings in the main occupation in Birr divided by the typical hours worked in a month over the past year. Only refugees working outside of camps start to see earnings improve with schooling. Working outside the camp is associated with a 42 percent increase in earnings. Most importantly, the relationship between completing secondary and post-secondary schooling and wage earnings becomes significant only when the refugee sample is restricted to workers outside the camps (see Annex D, Table D.7). This indicates that refugees only benefit from education and are incentivized to invest in education", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "increase in earnings. Most importantly, the relationship between completing secondary and post-secondary schooling and wage earnings becomes significant only when the refugee sample is restricted to workers outside the camps (see Annex D, Table D.7). This indicates that refugees only benefit from education and are incentivized to invest in education if they can access work outside the camps. This highlights the importance of refugees’ access to the labor market without restrictions, particularly outside the camps, as a critical component to achieving positive long- term effects. Refugee outcomes in the labor market do not improve over time in Ethiopia. Annex D, Table D.7 also shows that, after adjusting for domain and demographic characteristics, there is a slight increase in the probability of working for each year that a refugee is in Ethiopia, by around one percentage point per year, but no change in the likelihood of being in a high-skill occupation, likelihood of working outside the camp, or in monthly earnings. Agriculture is an important source of livelihood for host households, but refugee households have low agricultural holdings, reflecting their inability to own land legally. Refugee households are less than half as likely as hosts to report an agricultural holding with", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "likelihood of working outside the camp, or in monthly earnings. Agriculture is an important source of livelihood for host households, but refugee households have low agricultural holdings, reflecting their inability to own land legally. Refugee households are less than half as likely as hosts to report an agricultural holding with crops (19 percent versus 41 percent of host households, Figure 3.14). Refugee livestock ownership is similarly low (22 percent of households own livestock versus 48 percent for host households) but average livestock ownership is higher for Somali households (41 percent) (Figure 3.15). 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 15-24 25-34 35-44 45-54 55-64 Less than Primary Primary Secondary Figure 3.12: Share employed by age – camp refugees Source: World Bank Staff based on SESRE 2023. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 15-24 25-34 35-44 45-54 55-64 Less than Primary Primary Secondary Figure 3.13: Share employed by age – camp hosts Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 32 When refugees own livestock, the value and flock size of this livestock is low. Somali refugees mostly own sheep, goats, and donkeys, and compared to Somali hosts, they", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Secondary Figure 3.13: Share employed by age – camp hosts Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 32 When refugees own livestock, the value and flock size of this livestock is low. Somali refugees mostly own sheep, goats, and donkeys, and compared to Somali hosts, they have smaller flock sizes and lag them in terms of cattle ownership. As Figure 3.17 shows, the lower value of livestock in refugee households also reflects lower reported monetary value of equivalent livestock (per Tropical Livestock Unit). For the most part, however, it reflects the lower value of the type and number of livestock refugees own. In Eritrean and Somali camps, refugees and hosts report a similar rate of non-farm business ownership; but the value of productive assets in refugee businesses is low, indicating they are primarily small-scale and low-income. The exception is Somali refugees, partially driven by ownership of animal-drawn carts. Across all domains, refugees have a lower value of productive assets such as farming tools and construction equipment. They are also less likely to own commercial cars, motorcycles, or Bajaj, a cause of a significant portion of the gap in total value of assets between refugees and hosts.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "animal-drawn carts. Across all domains, refugees have a lower value of productive assets such as farming tools and construction equipment. They are also less likely to own commercial cars, motorcycles, or Bajaj, a cause of a significant portion of the gap in total value of assets between refugees and hosts. With low employment rates, earnings, and value of household income-generating activities, in-camp refugee households rely heavily on aid. Expanding refugee access to agricultural land, livestock, and legal work outside of camps are vital for refugees to maintain their livelihoods without depending on donations. On average, 78 percent of in-camp refugee households report that NGOs or government donations are their primary source of income (Figure 3.20), increasing to 88 percent for South Sudanese households. On the other hand, their host counterparts rely most on employment, and 32 percent rely on agricultural income compared to only 3 percent of refugee households. This contrasts with refugees’ previous livelihoods in their country of birth, where they relied on traditional income sources, especially agriculture and remittances, along with a smaller amount of aid. 0 0.1 0.2 0.3 0.4 0.5 0.6 Camp Hosts Camp Refugees Figure 3.14: Household owns crops Source: World Bank Staff based on", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "contrasts with refugees’ previous livelihoods in their country of birth, where they relied on traditional income sources, especially agriculture and remittances, along with a smaller amount of aid. 0 0.1 0.2 0.3 0.4 0.5 0.6 Camp Hosts Camp Refugees Figure 3.14: Household owns crops Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Camp Hosts Camp Refugees Figure 3.15: Household owns livestock Source: World Bank Staff based on SESRE 2023. 0 10,000 20,000 30,000 40,000 50,000 60,000 Camp Hosts Camp Refugees Figure 3.16: Total value of livestock Source: World Bank Staff based on SESRE 2023. 0 5,000 10,000 15,000 20,000 25,000 Camp Hosts Camp Refugees Figure 3.17: Value per tropical livestock unit Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 33 Reliance on aid is even larger for female-headed households, which are larger and have more children. While female refugees have comparable employment rates to men, they are more likely to work in elementary occupations, more likely to work inside the camp, and earn substantially lower hourly earnings. Female-headed refugee households are half as likely to rely on salary as their primary source of income (11 versus 22 percent for male-headed", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "comparable employment rates to men, they are more likely to work in elementary occupations, more likely to work inside the camp, and earn substantially lower hourly earnings. Female-headed refugee households are half as likely to rely on salary as their primary source of income (11 versus 22 percent for male-headed households) and instead rely more on aid and donations (Figure 3.20b). Female- headed households are also larger, on average, and have more children under age 15, highlighting the importance of creating livelihoods opportunities for these households. As with labor market outcomes, household reliance on donations improves little over time in Ethiopia but improves once a household member works outside the camp. The regression in Annex D, Table D.8 shows that, after adjusting for household demographic characteristics and education, households with at least one member working outside the camp rely much less on donations as the primary source of income—specifically, 8 percentage points less overall and 34 and 18 percentage points less in Eritrean and Somali camps, respectively. There is little benefit to working outside the camp to reduce aid reliance in South Sudanese households. On the other hand, years spent in Ethiopia are only associated with a gradual decrease in", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "percentage points less overall and 34 and 18 percentage points less in Eritrean and Somali camps, respectively. There is little benefit to working outside the camp to reduce aid reliance in South Sudanese households. On the other hand, years spent in Ethiopia are only associated with a gradual decrease in dependence on donations, with only a 1 percentage point reduction for every year spent in the country. The lack of employment outcomes contrasts with other refugee-hosting countries in East Africa, highlighting the lack of labor market access for Ethiopian refugees. Evidence suggests that refugees 0 5,000 10,000 15,000 20,000 25,000 Camp Hosts Camp Refugees Figure 3.19: Value of productive assets among households with non-farm business 0 0.05 0.1 0.15 0.2 0.25 Camp Hosts Camp Refugees Figure 3.18: Household has non-farm business Source: World Bank Staff based on SESRE 2023. Productive assets include the subset of assets with production value, such as farm tools and water pumps, sewing and building equipment, and commercial cars. Outliers are treated, and values are adjusted for inflation. 0 20 40 60 80 100 Camp Hosts Camp Refugees Current Camp Refugees COB Other (rental income, PSNP, pension) Remittances (local/international) Donations (NGO/gov) Crops/livestock Salary (employment/casual labor) 0", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "as farm tools and water pumps, sewing and building equipment, and commercial cars. Outliers are treated, and values are adjusted for inflation. 0 20 40 60 80 100 Camp Hosts Camp Refugees Current Camp Refugees COB Other (rental income, PSNP, pension) Remittances (local/international) Donations (NGO/gov) Crops/livestock Salary (employment/casual labor) 0 20 40 60 80 100 Camps-Male-Headed Camps-Female-Headed Salary (employment/casual labor) Crops/livestock Donations(NGO/gov) Remittances (local/international) Other (rental income, PSNP, pension) Percent Percent Figure 3.20: Household primary income source Source: World Bank Staff based on SESRE 2023. Note: “COB” refers to livelihood strategies in their country of birth. a. Pre-post migration b. By gender of head Jobs and Livelihoods 34 arrive in Uganda with few assets, in a state of high poverty, and with similarly low employment rates. However, unlike Ethiopia, employment rates for refugees in Uganda improve over time, approximately doubling after five years or more (World Bank, 2023b). Uganda is also notable for providing work rights to refugees in practice (Ginn et al., 2022) (Box 3.3). 3.2 Labor market outcomes of OCP refugees and their hosts Refugee households in Addis Ababa rely heavily on remittances as their primary source of income. This reflects the fact that the Eritrean OCP refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "also notable for providing work rights to refugees in practice (Ginn et al., 2022) (Box 3.3). 3.2 Labor market outcomes of OCP refugees and their hosts Refugee households in Addis Ababa rely heavily on remittances as their primary source of income. This reflects the fact that the Eritrean OCP refugees who move to Addis Ababa must provide proof of formal employment or guarantor to support them. Figure 3.21a shows that only 19 percent of refugee households in Addis Ababa rely on employment income, compared to 79 percent of hosts. Almost all of the remaining 81 percent of refugees in Addis Ababa rely on remittances. As in the camp domains, remittance reliance is even higher for female- headed households (84 instead of 72 percent for male-headed households). The refugee households in Addis Ababa are also used to relying on remittances; 45 percent relied on remittances even before migrating to Ethiopia, and only 12 percent relied on crops or livestock. This contrasts with the Eritreans in camps, who previously relied primarily on agricultural and labor income, and demonstrates the large differences between Eritrean households that could and could not acquire OCP status. This, coupled with the finding that OCP refugees have much", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "only 12 percent relied on crops or livestock. This contrasts with the Eritreans in camps, who previously relied primarily on agricultural and labor income, and demonstrates the large differences between Eritrean households that could and could not acquire OCP status. This, coupled with the finding that OCP refugees have much higher levels of education compared to in- camp Eritrean refugees (as highlighted in Chapter 2), again indicates that OCP refugees were relatively well-off before displacement, and they still have family members or other support systems. Refugees across all regions of Ethiopia have benefited from livelihood training interventions provided by RRS, domestic and international NGOs, and humanitarian organizations. For example, the Ikea Foundation, through UNHCR, invested around US$100 million in the Dollo Ado camps in Somalia between 2012 and 2019. Much of this funding supported economic development and livelihood opportunities for refugees and the host community, including creating livelihood cooperatives in agriculture, livestock value chain, energy, firewood, and microfinance (Betts et al., 2020). In the north of the country, UNHCR worked with various partners to provide Eritrean refugees with vocational skills training, tools, and start-up capital for crafts, such as leather products, weaving, and tailoring. UNHCR records indicate that more than", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR records"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "agriculture, livestock value chain, energy, firewood, and microfinance (Betts et al., 2020). In the north of the country, UNHCR worked with various partners to provide Eritrean refugees with vocational skills training, tools, and start-up capital for crafts, such as leather products, weaving, and tailoring. UNHCR records indicate that more than 8,000 Eritrean refugees received such training, which could explain the high share of Eritreans working in crafts and related trades in the SESRE. As another example, the German Agency for International Cooperation (GIZ) has a multi-million-dollar program to improve quality and access to vocational training for refugees and Ethiopians across all refugee-hosting regions (Giordano et al., 2021). World Bank programs include the Economic Opportunities Program (EOP) and the Urban Safety Net and Jobs Project (UPSNJP), which provide economic opportunities for Ethiopians and refugees through various social protection and labor market interventions like public works employment and job search assistance. How effective have these livelihoods and vocational training programs for refugees been in Ethiopia? This question is difficult to answer due to lack of consolidated information on programs, the number of beneficiaries, and the economic outcomes of beneficiaries. Case-study evaluations indicate that vocational training programs have helped to increase incomes, diversify", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR records"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "effective have these livelihoods and vocational training programs for refugees been in Ethiopia? This question is difficult to answer due to lack of consolidated information on programs, the number of beneficiaries, and the economic outcomes of beneficiaries. Case-study evaluations indicate that vocational training programs have helped to increase incomes, diversify income sources, and, in some cases, promote local infrastructure development. However, these income increases are often modest, and trained individuals and cooperatives often fail to become self-sustainable in the long term and continue to rely on external inputs, especially in more remote camps with poor market linkages (Betts et al., 2020; Giordano et al., 2021; Holzaepfel, 2015). This is consistent with the results of the SESRE finding that, despite the scale of livelihood training investments across the country, most working-age refugees still do not work, and most refugee households rely on aid as a primary source of income. Given the protracted nature of refugee hosting in Ethiopia, it is essential to better understand how vocational training programs and cooperatives can become self-sustainable, especially in the more remote border regions with poor market linkages. Box 3.3: Refugee Vocational Training and Cooperatives Jobs and Livelihoods 35 In line with high reliance on", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "protracted nature of refugee hosting in Ethiopia, it is essential to better understand how vocational training programs and cooperatives can become self-sustainable, especially in the more remote border regions with poor market linkages. Box 3.3: Refugee Vocational Training and Cooperatives Jobs and Livelihoods 35 In line with high reliance on remittances, labor force participation and employment rates among OCP refugees are low, with rates similar for male and female refugees. Table 3.2 presents labor force participation (LFP) data; among those aged 15-64, LFP is 46 percent for refugees and 66 percent for hosts. This increases to 67 and 72 percent when you use the relaxed definition of unemployment, reflecting the large number of OCP refugees who say they are ready to work but are not actively searching. The unemployment rate is an astonishing 63 percent for refugees relative to 12 percent for hosts, and it increases to 75 percent under the relaxed definition. Only 17 percent of OCP refugees work, and 23 percent are inactive and not in school. Female refugees have similarly large rates of inactivity and unemployment, and the total share working is similar to male refugees. Female refugees also have high rates of self-employment relative to female", "output": {"entities": {"named_data": [], "descriptive_data": ["labor force participation (LFP) data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "the relaxed definition. Only 17 percent of OCP refugees work, and 23 percent are inactive and not in school. Female refugees have similarly large rates of inactivity and unemployment, and the total share working is similar to male refugees. Female refugees also have high rates of self-employment relative to female hosts. Among the 17 percent of working-age OCP refugees who are working, many have completed secondary schooling; yet, they face considerable occupational downgrading relative to hosts, and this is especially severe for female refugees. While refugees in Addis Ababa are less likely than hosts to have a post-secondary degree (4 percent versus 20 percent for hosts), many of them have completed secondary (25 percent versus 21 percent for hosts). Yet, Figure 3.24 shows that refugees are less likely than hosts to be in a high-skill occupation, which includes managers, professionals, and technical and associate professionals. This gap increases further when restricted to workers with completed secondary education. Among workers with completed secondary, 30 percent of refugees and 50 percent of hosts are in high-skill occupations (Figure 3.25). Among only women, these numbers are 20 percent and 50 percent, respectively. Instead, refugee men and women in Addis Ababa are over- represented", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "restricted to workers with completed secondary education. Among workers with completed secondary, 30 percent of refugees and 50 percent of hosts are in high-skill occupations (Figure 3.25). Among only women, these numbers are 20 percent and 50 percent, respectively. Instead, refugee men and women in Addis Ababa are over- represented in crafts and related trades (typically classified as medium-skill occupations). 0 20 40 60 80 100 Addis Hosts Addis Refugees Current Addis Refugees COB Salary (employment/casual labor) Crops/livestock Donations(NGO/gov) Remittances (local/international) Other (rental income, PSNP, pension) Percent 0 20 40 60 80 100 Percent Addis Male-Headed Addis Female-Headed Figure 3.21: Household primary income source Source: World Bank Staff based on SESRE 2023. Note: “COB” refers to livelihood strategies in their country of birth. a. Pre-post migration b. By gender of head Table 3.2: Labor force statistics Addis Hosts Addis Hosts Labor force participation rate (strict) 66% 46% Unemployment rate (strict) 12% 63% Labor force participation rate (relaxed) 72% 67% Unemployment (relaxed) 19% 75% Employment-to-population ratio 58% 17% Source: World Bank Staff based on SESRE 2023. Note: Labor force participation ratio is the share of working-age people who are engaged in the labor market, either employed or unemployed. Unemployment is the", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "12% 63% Labor force participation rate (relaxed) 72% 67% Unemployment (relaxed) 19% 75% Employment-to-population ratio 58% 17% Source: World Bank Staff based on SESRE 2023. Note: Labor force participation ratio is the share of working-age people who are engaged in the labor market, either employed or unemployed. Unemployment is the share of people participating in the labor force who are not employed. The “relaxed” definition of labor force participation includes anyone who is available to work. The “strict” definition of labor force participation includes only those who are available to work and also actively searching for work. Employment-to-population ratio is the share of working-age people who are employed. Jobs and Livelihoods 36 Occupational downgrading among the small share of OCP refugees who work is not explained by education, gender, and age. Another way to visualize the scale of occupational downgrading among refugees in Addis Ababa is to calculate how they should be distributed across occupations if they were in the same occupations as hosts within their education, gender, and age group. For example, suppose that among male hosts under age 25 with primary education, 70 percent work in elementary occupations and 30 percent in crafts and related trades. Imagine taking", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "should be distributed across occupations if they were in the same occupations as hosts within their education, gender, and age group. For example, suppose that among male hosts under age 25 with primary education, 70 percent work in elementary occupations and 30 percent in crafts and related trades. Imagine taking all male refugees under age 25 with primary education and assigning 70 percent of them to elementary occupations and 30 percent to crafts and related trades – to represent their occupational concentration if they worked the same occupations as hosts – and repeating this for all demographic groups. The final share of refugees in each occupation now represents the occupational concentration of refugees if they were in the same occupations as hosts with their same demographic characteristics. If the actual share of refugees in an occupation is different from this predicted share, then it is due to other factors not related to education, gender, and age. The results in Figure 3.26 show that refugees are under-represented in high-skill occupations and services and sales relative to what we would expect based on their age, gender, and education, and substantially over-represented in crafts and related trades. Among employee workers, OCP refugees also", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "related to education, gender, and age. The results in Figure 3.26 show that refugees are under-represented in high-skill occupations and services and sales relative to what we would expect based on their age, gender, and education, and substantially over-represented in crafts and related trades. Among employee workers, OCP refugees also earn less than hosts, though the wage gap is smaller than for in-camp refugees. Annex D, Table D.9 shows that refugees in Addis earn 25 percent less than their hosts. Even after adjusting for demographic characteristics, occupation, and sector of work, this wage gap remains at around 19 percent. 0 20 40 60 80 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female - Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent Figure 3.22: Work status by gender Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure 3.23: Work type by gender Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Addis Hosts Male", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "80 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure 3.23: Work type by gender Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals Percent Figure 3.24: Occupation Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals Percent Figure 3.25: Occupation among completed secondary or more Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 37 These results indicate that the OCP model is not ideal for refugees’ labor market inclusion. Few refugees primarily enroll through an existing formal employer; thus, the OCP is mainly open to refugees with networks that can support them with remittances, and this makes these households less likely to work", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "37 These results indicate that the OCP model is not ideal for refugees’ labor market inclusion. Few refugees primarily enroll through an existing formal employer; thus, the OCP is mainly open to refugees with networks that can support them with remittances, and this makes these households less likely to work in Addis Ababa. Therefore, refugee labor market outcomes in Addis Ababa would be very different if all refugees had the possibility to move in response to economic opportunities. In fact, households without an existing support system are precisely those who can benefit most from access to labor markets and will contribute the most economically. However, the occupational downgrading and lower wages among Ethiopia’s OCP refugees who work show that challenges persist even after refugees are granted access to urban labor markets. This has been well-documented in many other settings around the world (Lebow, 2023; World Bank, 2023). Solutions that integrate refugees and host communities throughout the displacement cycle have proven most promising for achieving good development outcomes (World Bank, 2023). This requires safeguarding refugees from harm while integrating them as workers, students, and neighbors. Despite Ethiopia’s goodwill towards refugees, and the global recognition that responses to forced displacement need humanitarian", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "refugees and host communities throughout the displacement cycle have proven most promising for achieving good development outcomes (World Bank, 2023). This requires safeguarding refugees from harm while integrating them as workers, students, and neighbors. Despite Ethiopia’s goodwill towards refugees, and the global recognition that responses to forced displacement need humanitarian and development responses, too few efforts exist to better integrate refugees within host communities. Instead of keeping refugees in camps, it is vital to allow them to realize their potential and thus benefit host communities as productive members of society. 3.3 Refugee youth Refugee inactivity and unemployment are high among youth (aged 15-24) in Addis Ababa. Table 3.3 shows that, in Addis Ababa, the youth participation rate is 60 percent for refugees, just higher than the 54 percent rate for hosts. However, this falls to 37 percent for refugees and 48 percent for hosts under the “strict” definition, reflecting the large number of refugee youth who are available to work but not actively searching. Of the 60 percent of refugee youth who are available to work, 82 percent are unemployed, compared to 21 percent for hosts. As a result, only 11 percent of refugee youth work relative to 43 percent", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "large number of refugee youth who are available to work but not actively searching. Of the 60 percent of refugee youth who are available to work, 82 percent are unemployed, compared to 21 percent for hosts. As a result, only 11 percent of refugee youth work relative to 43 percent of hosts. Also, while the inactivity rates are broadly similar, refugee youth in Addis are half as likely to be in school (21 percent relative to 40 percent among hosts). The lower schooling among refugees in Addis Ababa only emerges after age 18, indicating a lower probability of attending post-secondary when they instead enter inactivity or unemployment. Youth participation and employment rates are also lower for refugees than hosts in the camp domains, though this difference is starker in Addis Ababa. The share who are not in employment, education, or training (NEET) in Addis Ababa is 19 percent for refugees and 6 percent for hosts, and in the camps is 13 percent for refugees and 12 percent for hosts. 0.05 0 0.1 0.15 0.2 0.25 0.3 0.35 Elementary Occupations Craf/Related Trades Service/Sales Machine Operators Associate Prof. Clerical Support Professionals Share of Refugees Observed Predicted according to observables Figure 3.26: Refugee", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "and 6 percent for hosts, and in the camps is 13 percent for refugees and 12 percent for hosts. 0.05 0 0.1 0.15 0.2 0.25 0.3 0.35 Elementary Occupations Craf/Related Trades Service/Sales Machine Operators Associate Prof. Clerical Support Professionals Share of Refugees Observed Predicted according to observables Figure 3.26: Refugee occupation concentration Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 38 In Addis Ababa, refugee girls—like boys—are much less likely than host counterparts to work or attend school, and they are more likely to be unemployed or NEET. They are even less likely than refugee boys to be in school and more likely to be NEET (23 percent for girls relative to 15 percent for boys—Figure 3.28). In camps, refugee girls look more like their host counterparts regarding schooling and labor force participation. However, among those in the workforce, their relaxed unemployment rate is much higher (55 percent for female refugees and 36 percent for female hosts). Boys in camps have higher schooling rates (75 percent relative to 65 percent for hosts), reflecting their higher propensity to stay enrolled in primary or secondary schooling after the typical completion age, especially in South Sudanese camps where 84 percent", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "percent for female refugees and 36 percent for female hosts). Boys in camps have higher schooling rates (75 percent relative to 65 percent for hosts), reflecting their higher propensity to stay enrolled in primary or secondary schooling after the typical completion age, especially in South Sudanese camps where 84 percent of boys aged 15- 24 are in school. The relaxed unemployment rate is high among boys in the workforce, as it is for girls (60 percent for male refugees and 24 percent for male hosts). 0 20 40 60 80 100 Addis Hosts Addis Refugees Camp Hosts Camp Refugees Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent Figure 3.27: Youth work status Source: World Bank Staff based on SESRE 2023. Table 3.3: Youth labor force statistics (age 15-24) Camp Hosts Camp Refugees Addis Hosts Addis Refugees Participation (strict) 25% 12% 48% 37% Unemployment (strict) 15% 25% 12% 71% Participation (relaxed) 30% 22% 54% 60% Unemployment (relaxed) 30% 57% 21% 82% Employment-to-population ratio 21% 9% 43% 11% Source: World Bank Staff based on SESRE 2023. Note: Labor force participation ratio is the share of working-age people who are engaged in the labor market, either employed", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "15% 25% 12% 71% Participation (relaxed) 30% 22% 54% 60% Unemployment (relaxed) 30% 57% 21% 82% Employment-to-population ratio 21% 9% 43% 11% Source: World Bank Staff based on SESRE 2023. Note: Labor force participation ratio is the share of working-age people who are engaged in the labor market, either employed or unemployed. Unemployment is the share of people participating in the labor force who are not employed. The “relaxed” definition of labor force participation includes anyone who is available to work. The “strict” definition of labor force participation includes only those who are available to work and also actively searching for work. Employment-to-population ratio is the share of working-age people who are employed. 0 20 40 60 80 100 Camp Hosts Male Camp Refugees Male Addis Hosts Male Addis Refugees Male Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent Figure 3.28: Male youth work status Source: World Bank Staff based on SESRE 2023. Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent 0 20 40 60 80 100 Camp Hosts Female Camp Refugees Female Addis Hosts Female Addis Refugees Female Figure 3.29: Female youth work status Source: World Bank", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "status Source: World Bank Staff based on SESRE 2023. Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent 0 20 40 60 80 100 Camp Hosts Female Camp Refugees Female Addis Hosts Female Addis Refugees Female Figure 3.29: Female youth work status Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 39 T his chapter looks at how low sociodemographic and labor market outcomes shape how refugees perceive their future prospects and aspirations. While “resettlement” to a high-income country is an attractive solution, the share of refugees resettled globally is marginal. Resettlement is considered one of the three “durable solutions” for refugee protection under the 1951 Refugee Convention, alongside naturalization and return. Yet, the share of refugees resettled globally—including private sponsorship and other complementary pathways of refugee admission to third countries outside of UNHCR processes—was below 2 percent over the past twenty years (World Bank, 2023). According to government statistics, there has also been a downward global trend in the number of resettlement opportunities, fluctuating from 99,000 in 2010 to just 34,000 in 2020, even as the number of forcibly displaced persons increases globally. In Ethiopia, resettlement numbers are similarly low; in 2022,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "(World Bank, 2023). According to government statistics, there has also been a downward global trend in the number of resettlement opportunities, fluctuating from 99,000 in 2010 to just 34,000 in 2020, even as the number of forcibly displaced persons increases globally. In Ethiopia, resettlement numbers are similarly low; in 2022, only 309 refugees departed for resettlement (UNHCR, 2022). 4. Refugees’ Aspirations 0 10 20 30 40 50 60 70 80 90 100 Eritrean (camps) Somali South Sudanese Addis Ababa All Refugees Ethiopian refugee camp Ethiopian city Country of birth Other African country Western country Percent Figure 4.1: Desired location in three years Source: World Bank Staff based on SESRE 2023. Note: Household respondents’ responses to the questions “Where do you hope to be living in 3 years?” and “Realistically, where do you think you will be living in 3 years?” 0 10 20 30 40 50 60 70 80 90 100 Eritrean (camps) Somali South Sudanese Addis Ababa All Refugees Ethiopian refugee camp Ethiopian city Country of birth Other African country Western country Percent Figure 4.2: Expected location in three years Refugees’ Aspirations 40 Most refugees hope to go to a Western country in the next three years. When asked", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "100 Eritrean (camps) Somali South Sudanese Addis Ababa All Refugees Ethiopian refugee camp Ethiopian city Country of birth Other African country Western country Percent Figure 4.2: Expected location in three years Refugees’ Aspirations 40 Most refugees hope to go to a Western country in the next three years. When asked in the SESRE where they would like to live in three years, most refugees say they would like to live in a Western country. This rate is highest among OCP refugees (90 percent) and Eritreans in camps (83 percent), lower in Somali camps (66 percent), and lowest in South Sudanese camps (29 percent). More Somalis and South Sudanese hope to stay in Ethiopian refugee camps than Eritreans. South Sudanese refugees stand out in that almost 20 percent hope to return to their country of birth in the next three years, while this rate is meager for other groups. Despite the low probability of being resettled, refugees hold an unrealistically high belief that they will migrate to a Western country in the next three years. To distinguish between desires and expectations of reality, in addition to asking households where they hope to live in three years, the SESRE also asks where", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "low probability of being resettled, refugees hold an unrealistically high belief that they will migrate to a Western country in the next three years. To distinguish between desires and expectations of reality, in addition to asking households where they hope to live in three years, the SESRE also asks where they “realistically” think they will be living in three years. One-third of all refugees indicated they realistically believe they will be resettled to a Western country, which is in stark contrast to the low resettlement numbers worldwide and in Ethiopia. As with aspirations, the share who expect to be in a Western country is highest among OCP refugees (57 percent), similarly high for Eritreans in camps (53 percent), lower for Somalis (42 percent), and lowest for South Sudanese (18 percent). While these numbers may be higher than true beliefs if they reflect a response bias, they are so high that they strongly indicate an over-optimism about relocation. Most of those who do not believe they will be in a Western country believe they will remain where they are. Many South Sudanese (13 percent) also think they will return to South Sudan. While the intention to migrate abroad is lower for", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "they strongly indicate an over-optimism about relocation. Most of those who do not believe they will be in a Western country believe they will remain where they are. Many South Sudanese (13 percent) also think they will return to South Sudan. While the intention to migrate abroad is lower for older refugees, on average, it does not differ widely depending on gender or education, and persists across most subgroups, including youth. Intention to migrate abroad is lower by around 8 percentage points for refugees over age 45 (Annex D, Table D.10).42 However, it does not depend on gender. Only in Eritrean camps is intention to migrate abroad lower, by 5 percentage points, among those with completed secondary education. Intention to migrate abroad also does not vary with time in Ethiopia. Refugee aspirations and expectations for resettlement may be important determinants of how much they will invest in their skills and socio-economic integration. Evidence from various settings shows that when migrants expect to spend more time in a country, they invest more in their skills and socio-economic and labor market integration (Adda et al., 2022). For example, this could include investing in language proficiency, starting a business, acquiring legal work documents,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "socio-economic integration. Evidence from various settings shows that when migrants expect to spend more time in a country, they invest more in their skills and socio-economic and labor market integration (Adda et al., 2022). For example, this could include investing in language proficiency, starting a business, acquiring legal work documents, or studying for an occupational license. It could also mean building social networks in Ethiopia, which needs to improve considering, for example, the tiny share of refugees who report having an Ethiopian friend (as will be discussed in Chapter 7). Better alignment of refugees’ expectations for resettlement with reality could improve socio-economic outcomes. Based on evidence of resettlement over the past ten years, better- aligning expectations with reality could be essential to encourage refugees to make more significant investment in skills and socio-economic integration. Humanitarian organizations have long understood the importance of managing resettlement expectations (UNHCR, 2023b). Better understanding the reasons for unrealistic expectations in Ethiopia, and the role that policymakers and the international community play in this, could support refugees’ long-term trajectory. “Locus of control” (LOC)—a feeling of personal control over events in one’s life—is significantly related to willingness to invest in one’s future and has been shown to", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "reasons for unrealistic expectations in Ethiopia, and the role that policymakers and the international community play in this, could support refugees’ long-term trajectory. “Locus of control” (LOC)—a feeling of personal control over events in one’s life—is significantly related to willingness to invest in one’s future and has been shown to improve refugees’ socio- economic integration. LOC is a psychological concept 42 This analysis is based on an individual-level question regarding intention to migrate abroad, which has rates similar to the question to the household head discussed above. Refugees’ Aspirations 41 indicating the degree to which people believe that they, as opposed to external forces, have control over the outcomes of events in their lives (Rotter, 1966). For example, if a harvest is good or bad, a farmer with low LOC is likelier to attribute it to chance or external forces than their skill. Higher LOC has been shown to affect schooling decisions, occupational choice, and savings (Cobb-Clark et al., 2016; Heckman et al., 2006). In Ethiopia, higher LOC has been shown to predict farmer adoption of modern agricultural technologies (Taffesse and Tadesse, 2017). In refugee populations, low LOC also correlate with depression, anxiety, and psychological distress (Schlechter et al., 2023;", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "schooling decisions, occupational choice, and savings (Cobb-Clark et al., 2016; Heckman et al., 2006). In Ethiopia, higher LOC has been shown to predict farmer adoption of modern agricultural technologies (Taffesse and Tadesse, 2017). In refugee populations, low LOC also correlate with depression, anxiety, and psychological distress (Schlechter et al., 2023; Tsionis et al., 2022). Higher LOC has also been found to improve employment and socio-economic integration among immigrants and refugees in Germany (Hahn et al., 2019; Thum, 2014). Compared to hosts, South Sudanese refugees perceive less personal control over their lives and destinies. Based on SESRE data, the index used to construct a measure of personal control over one’s life is an unweighted average of 10 LOC- related questions. The index (Likert scale) ranges from 1—”little control over one’s life”—to 4—”more control over one’s life”. The index is 0.12 points (.25 standard deviations) lower for refugees than hosts indicating they feel they have lower control over their lives and destinies, and this difference is statistically significant. This difference, however, is driven by South Sudanese refugees. When comparing LOC by country of origin, we find that there is only for South Sudanese there is a significant difference between refugees and hosts", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "feel they have lower control over their lives and destinies, and this difference is statistically significant. This difference, however, is driven by South Sudanese refugees. When comparing LOC by country of origin, we find that there is only for South Sudanese there is a significant difference between refugees and hosts on perception of control over their lives. LOC for South Sudanese refugees is 0.15 points (0.33 standard deviations) lower than that of hosts, highlighting that they feel that they have less personal control over their lives and destinies than their hosts. When considering different dimensions, we find that refugees’ LOC is driven by a feeling of lower internal control over the future. LOC can be grouped into three categories: a sense of internal control, the role of chance or fate, and the role of “powerful others” (Levenson, 1981). Lower LOC among refugees is driven mainly by a feeling of lower internal control over fate, as opposed to a greater sense of chance or the role of other individuals (though in South Sudanese camps, the role of chance and powerful others is more important). LOC increases with higher levels of education for both hosts and refugees. Still, it has little relationship", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "internal control over fate, as opposed to a greater sense of chance or the role of other individuals (though in South Sudanese camps, the role of chance and powerful others is more important). LOC increases with higher levels of education for both hosts and refugees. Still, it has little relationship with age or gender, or how much time refugees have spent in Ethiopia, nor their aspirations to go abroad. 0 0.5 1 1.5 2 2.5 3 Internal Control Chance or Fate Powerful Others All Hosts All Refugees Figure 4.4: Locus of control by type of control 1.9 1.95 2 2.05 2.1 2.15 2.2 2.25 2.3 Eritrean Somali South Sudanese Addis All Hosts Refugees Figure 4.3: Locus of control Source: World Bank staff based on SESRE 2023. Note: This index is the unweighted average of 10 questions about feelings of control over one’s fate. The index ranges from 0 to 4, where more positive indicates greater control. The internal control index uses four questions regarding personal control over destiny. The chance index uses five questions regarding the role of chance or determinism. The role of the powerful others index is 1 question on whether others determine fate. Refugees’ Aspirations 42 T", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "where more positive indicates greater control. The internal control index uses four questions regarding personal control over destiny. The chance index uses five questions regarding the role of chance or determinism. The role of the powerful others index is 1 question on whether others determine fate. Refugees’ Aspirations 42 T his chapter examines the state of welfare and poverty levels of refugees and host communities in Ethiopia. The emphasis on refugees and host communities acknowledges both groups’ mutual—and sometimes interdependent— development needs. We assess multiple dimensions of welfare and poverty of refugees and hosts in Ethiopia using household-level consumption data. The data presents a comprehensive set of social and economic indicators to determine poverty incidence, food security, and standard of living. In addition to refugees overall, we look at welfare differences across refugee groups—Eritreans, Somalis, and South Sudanese—and compare differences in contexts and situations. In more detail, we analyze (i) poverty incidence and inequality, (ii) expenditure patterns, (iii) multidimensional poverty, and (iv) food security, perception of standard of living, and shocks. The chapter also provides a poverty profile and determinants of the welfare of refugees and host community households and estimate of the cost of basic needs for refugees. Insights", "output": {"entities": {"named_data": [], "descriptive_data": ["household-level consumption data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "poverty incidence and inequality, (ii) expenditure patterns, (iii) multidimensional poverty, and (iv) food security, perception of standard of living, and shocks. The chapter also provides a poverty profile and determinants of the welfare of refugees and host community households and estimate of the cost of basic needs for refugees. Insights on poverty drivers and living conditions contribute to deeper understanding of displacement dynamics and point to specific potential policies to help refugees and their hosts. 5.1 Welfare dimensions 5.1.1 Monetary poverty and inequality In-camp refugees have lower welfare outcomes than their hosts. In-camp refugees have significantly higher monetary poverty based on strikingly low average expenditures. A staggering 75 percent of refugees live below the international poverty line of US$2.15 in 2017 Purchasing Power Parity (PPP) per day per capita. Though still high, host communities have a relatively lower poverty rate of 25 percent (Figure 5.1). Considering in-camp refugees and their hosts only, we find higher poverty incidence; roughly 84 percent of in-camp refugees and 32 percent of hosts live in poverty. Although poverty incidence is higher for refugees, the high poverty rates among host communities also imply that they live in severely resource- constrained conditions. This calls for development approaches", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "hosts only, we find higher poverty incidence; roughly 84 percent of in-camp refugees and 32 percent of hosts live in poverty. Although poverty incidence is higher for refugees, the high poverty rates among host communities also imply that they live in severely resource- constrained conditions. This calls for development approaches that invest in refugee-hosting areas in a manner that benefits both refugees and hosts alike (Annex D, Table D.11 presents detailed poverty rates by refugee domains). 5. Welfare and Equity Refugees’ Aspirations 43 Refugees in Addis Ababa are less poor than their hosts, as well as in-camp refugees and their hosts. Poverty incidence in Addis Ababa for refugees living under the OCP is lower (7 percent) than their hosts (18 percent).43 This difference is driven primarily by the high rent expenditures of refugees since they cannot benefit from public housing schemes, increasing their overall consumption expenditure. As discussed in Chapter 2, about 97 percent of refugee and 39 percent of host households in Addis Ababa live in rented houses. The data show that Addis Ababa refugees pay higher rents (ETB 31,600 per year, per adult equivalent) than hosts (ETB 18,700 per year, per adult equivalent). Moreover, rent expenditures make up", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "2, about 97 percent of refugee and 39 percent of host households in Addis Ababa live in rented houses. The data show that Addis Ababa refugees pay higher rents (ETB 31,600 per year, per adult equivalent) than hosts (ETB 18,700 per year, per adult equivalent). Moreover, rent expenditures make up 56 percent of refugees’ non-food expenditure. In-camp refugees in Ethiopia are much poorer than their hosts, but because everyone suffers from similarly low expenditures, inequality is also low for refugees. As measured by the Gini 43 For details on the OCP policy, see Box 2.2. 32% 18% 25% 84% 7% 75% In camp Addis Ababa Total Poverty headcount rate (%) Hosts Refugees Figure 5.1: Poverty incidence Source: World Bank Staff based on SESRE 2023. 0 5 10 15 20 25 30 35 40 45 In Camp Addis Ababa Total Hosts Refugees Figure 5.2: Income inequality, Gini index Source: World Bank Staff based on SESRE 2023. Most of the analysis presented in this chapter is based on detailed consumption data from the Socioeconomic Survey of Refugees in Ethiopia (SESRE) conducted between October 2022 and February 2023. All consumption of food and non-food items is included, regardless of whether these items are", "output": {"entities": {"named_data": ["Socioeconomic Survey of Refugees in Ethiopia"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Staff based on SESRE 2023. Most of the analysis presented in this chapter is based on detailed consumption data from the Socioeconomic Survey of Refugees in Ethiopia (SESRE) conducted between October 2022 and February 2023. All consumption of food and non-food items is included, regardless of whether these items are purchased on the market, come from own production, or received as gifts. For own-consumption and gifts, the quantities consumed are valued at prevailing prices in the enumeration area. Although consumption is expressed annually, the reference period used during data collection varies based on the nature of the items. For example, questions related to information on food and food-related items was asked by visiting households twice a week using the “last three days” and “last four days” as reference periods. For house rent, durable goods, clothing, health and education expenditures, and some other categories, the survey questions used the “last three months” and “last 12 months” as references. Imputed rent for owner-occupied houses is calculated by the Ethiopian Statistical Service (ESS) team and is included in the consumption expenditure data shared with the Bank team. Spatial and temporal price deflators adjust for price variations across time and space. First, nominal consumption", "output": {"entities": {"named_data": ["Socioeconomic Survey of Refugees in Ethiopia"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "months” and “last 12 months” as references. Imputed rent for owner-occupied houses is calculated by the Ethiopian Statistical Service (ESS) team and is included in the consumption expenditure data shared with the Bank team. Spatial and temporal price deflators adjust for price variations across time and space. First, nominal consumption is adjusted for price differences across survey domains using spatial deflators calculated using the Household Welfare Statistics (HoWStat 2021) survey data. Second, spatially-deflated consumption levels are expressed in December 2022 prices using the food and non-food Consumer Price Indexes produced and provided by the ESS. Finally, to adjust for variations in household size and composition, the spatially and temporally adjusted consumption expenditure is divided by household size. This is because the poverty rates presented in this chapter are calculated using the international poverty line of USD 2.15 per capita in 2017 PPP. The US$2.15 poverty line was converted to local currency in 2017 using the PPP conversion factor, and then the value was inflated to December 2022 prices using the national CPI. Given that international poverty estimates reported at the global level are based on consumption aggregates not spatially deflated, the poverty reports presented in this report are not strictly", "output": {"entities": {"named_data": ["Household Welfare Statistics (HoWStat 2021) survey"], "descriptive_data": ["food and non-food Consumer Price Indexes"], "vague_data": ["consumption expenditure data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "currency in 2017 using the PPP conversion factor, and then the value was inflated to December 2022 prices using the national CPI. Given that international poverty estimates reported at the global level are based on consumption aggregates not spatially deflated, the poverty reports presented in this report are not strictly comparable to global poverty rates. Box 5.1: Consumption aggregation and poverty measurement Refugees’ Aspirations 44 index, income inequality averages 28.7 for in-camp refugees and 32.7 for their hosts (Figure 5.2). While welfare is generally unevenly distributed in Ethiopia, inequality tends to be lower among refugees than hosts, except in Addis Ababa. Yet, when looking at the whole sample (in-camp and OCP), inequality is very high among refugees (39.2), much higher than their hosts (33.2). This result is driven by the stark welfare disparity between in-camp and OCP refugees (Figure 5.3). Moreover, differences in employment opportunities, and mobility create an uneven playing field for in-camp refugees and OCP (see Chapter 3). 5.1.2 Expenditure patterns Average consumption expenditures for in-camp refugee households is nearly half of hosts. Average annual expenditure per capita is around 45,600 Birr for host households and 27,700 for refugees (Figure 5.3). The average food and non-food expenditure (such", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "playing field for in-camp refugees and OCP (see Chapter 3). 5.1.2 Expenditure patterns Average consumption expenditures for in-camp refugee households is nearly half of hosts. Average annual expenditure per capita is around 45,600 Birr for host households and 27,700 for refugees (Figure 5.3). The average food and non-food expenditure (such as utilities and supplies, clothing and footwear, and rent) for refugees is more than half that of their hosts, except for refugees in Addis Ababa, where the average expenditure of OCP refugees is considerably higher than that of hosts. The strikingly low average expenditure for refugees could be related to measurement errors (See Box 5.2 for additional information). Food expenditure shares are higher for refugees, indicating a high dependence on food associated with higher poverty. Except for Addis Ababa, the share of expenditures on food is slightly higher for refugees than hosts; about 68 percent of all expenditures of in-camp refugees are spent on food, while hosts spend 61 percent on food, consistent with lower poverty rates in host communities (Figure 5.4). Stark differences in food and non-food expenditures exist between refugees and hosts, with in-camp refugees receiving most of their food and non- food expenditures as transfers. Refugees rely", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "refugees are spent on food, while hosts spend 61 percent on food, consistent with lower poverty rates in host communities (Figure 5.4). Stark differences in food and non-food expenditures exist between refugees and hosts, with in-camp refugees receiving most of their food and non- food expenditures as transfers. Refugees rely on aid. While 83 percent of the in-camp refugees depend on transfers and gifts to cover their food consumption needs, more than two-thirds of the host community households depend on market purchases for their food consumption (Figure 5.5a). Similarly, most refugees depend on transfers or gifts for non-food consumption, while their hosts depend on market purchases (Figure 5.5b). A large share of refugees in Addis Ababa also rely on transfers or gifts driven by remittances. Expenditure patterns vary by refugee domain and in-camp and OCP refugees. Analysis of expenditure patterns helps to understand differences in dietary preferences that affect food poverty and well-being. With increasing income, more affluent households are more likely to spend a greater share of their budget on high-value food items such as animal- origin diets, processed food, and food away from home, as well as on non-food items. Except for Addis Ababa, food consumption patterns, as", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "poverty and well-being. With increasing income, more affluent households are more likely to spend a greater share of their budget on high-value food items such as animal- origin diets, processed food, and food away from home, as well as on non-food items. Except for Addis Ababa, food consumption patterns, as indicated by expenditure shares, differ by food groups (Figure 5.6). Overall, refugees expenditures are higher on cereals and less on animal-origin food items associated with the types of food aid provided. This could be because refugees receive assistance for cereals/grains, not animal-origin food items. Food away from home is lower for refugees than hosts, except in Addis Ababa. - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Food Non-food non-durables Durables Rent Figure 5.3: Expenditure components (in birr) Source: World Bank staff based on SESRE 2023. Note: The expenditures are in December 2022 values. 61% 58% 59% 68% 56% 65% In Camp Addis Ababa Total Hosts Refugees Figure 5.4: Shares of food expenditure Refugees’ Aspirations 45 Expenditures for in-camp refugees is almost half that of hosts, despite the sizeable food aid and cash transfers (in selected camps) the WFP and", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "in December 2022 values. 61% 58% 59% 68% 56% 65% In Camp Addis Ababa Total Hosts Refugees Figure 5.4: Shares of food expenditure Refugees’ Aspirations 45 Expenditures for in-camp refugees is almost half that of hosts, despite the sizeable food aid and cash transfers (in selected camps) the WFP and UNHCR provide. The significantly lower expenditures (food and non-food) among refugees compared to host populations led to higher poverty rates. The team cross-checked the food aid received by in-camp refugees based on administrative data from the UNHCR and WFP and food consumption data from SESRE. The information received from UNHCR on food aid provided to refugees in each camp includes quantities per food item per month and cash transfers per person per month for each camp and period. The food items include cereal, wheat, maize, rice, sorghum, CSB/famex (CSB+), pulse, biscuit, date biscuit, dates, oil, vegetable oil, salt, and cash (see Annex E for detailed information). Food aid information received from WFP includes five food items and their quantities distributed to refugees: cereal (mainly wheat but in some camps rice), pulses (mostly yellow split peas), CSB+, vegetable oil, and salt. We have computed the per person, per month in-kind aid", "output": {"entities": {"named_data": [], "descriptive_data": ["administrative data from the UNHCR", "food consumption data from SESRE"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "(see Annex E for detailed information). Food aid information received from WFP includes five food items and their quantities distributed to refugees: cereal (mainly wheat but in some camps rice), pulses (mostly yellow split peas), CSB+, vegetable oil, and salt. We have computed the per person, per month in-kind aid quantities into annual values using the same prices as other food items based on SESRE data, mapping them to the closest food item in SESRE (this was not straightforward as the items are different). We further considered the changes in quantities of food rations that took place across survey months due to funding shortages, which can significantly affect the overall wellbeing of refugees in Ethiopia. Based on this information, we compare items refugees should have received with what refugees reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food aid admin data for every item except biscuits. Refugee households still report lower quantities, even when valuing the food ration quantities indicated as sold in markets. Possible explanations for lower food quantities are that food rations are only received once a month, which may not coincide with the interview date. Moreover, SESRE asks what food people", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": ["UNHCR food aid admin data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "except biscuits. Refugee households still report lower quantities, even when valuing the food ration quantities indicated as sold in markets. Possible explanations for lower food quantities are that food rations are only received once a month, which may not coincide with the interview date. Moreover, SESRE asks what food people consumed (not based on a pre-set list of food items), not food received as aid. Refugees may sell more than indicated. Valuing quantities of food aid with prices from SESRE suggests that if UNHCR food aid quantities were received/reported by refugees, refugees’ food expenditures would be much more comparable to those of hosts. Using WFP food aid information, we found a picture similar to UNHCR’s. Quantities consumed in SESRE are lower than food aid, as reported by WFP, except for CSB+ and salt (See Annex E for details of the disparity in food aid between the admin data disparity SESRE report). Box 5.2: Disparity between refugee ration aid and reported consumption quantities 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Own production Market purchase Transfers/gifs Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total", "output": {"entities": {"named_data": [], "descriptive_data": ["WFP food aid information"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "report). Box 5.2: Disparity between refugee ration aid and reported consumption quantities 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Own production Market purchase Transfers/gifs Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Percent 0 10 20 30 40 50 60 70 80 90 100 Percent Own production Market purchase Transfers/gifs Figure 5.5: Food and non-food expenditures shares by sources Source: World Bank Staff based on SESRE 2023. a. Food expenditure b. Non-food expenditure Refugees’ Aspirations 46 5.1.3 Multidimensional poverty Refugees are more vulnerable to multidimensional poverty than hosts. The multidimensional poverty rate is relatively high among refugees, driven primarily by low living standards and poor access to education. Trends in monetary poverty are mirrored using the Multidimensional Poverty Index (MPI)—an index measuring deprivations across three dimensions of well-being: education, health, and standard of living (see Box 5.3). MPI provides a general picture of the extent of deprivation (Alkire et al., 2021). The results show that 50 percent of refugees and 23 percent of hosts are multidimensionally poor (Figure 5.7). Looking at in-camp refugees and their hosts, multidimensional poverty is 64 percent for", "output": {"entities": {"named_data": ["Multidimensional Poverty Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "of living (see Box 5.3). MPI provides a general picture of the extent of deprivation (Alkire et al., 2021). The results show that 50 percent of refugees and 23 percent of hosts are multidimensionally poor (Figure 5.7). Looking at in-camp refugees and their hosts, multidimensional poverty is 64 percent for refugees and 50 percent for hosts. Unlike monetary poverty, multidimensional poverty as measured here appears to be relatively lower for refugees. This reflects improvement and ease of providing public services in high-density areas with high-refugee concentrations. There is a considerable correlation between monetary and multidimensional poverty for in-camp refugees. About 56 percent of in-camp refugees and 24 percent of their hosts are both monetarily and multidimensionally poor. The picture differs for OCP refugees, less than 2 percent are poor in both monetary and non-monetary dimensions. The percentage of households who are multidimensionally poor but not monetarily poor stands at 10 percent for refugees living in camps and 32 percent for the communities hosting them. Low living standards and low educational attainment drive deprivation for all refugee groups. Low living standards due to low-quality cooking fuel, poor housing, and low asset holdings contribute more than 50 percent to non-monetary poverty, followed", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "percent for refugees living in camps and 32 percent for the communities hosting them. Low living standards and low educational attainment drive deprivation for all refugee groups. Low living standards due to low-quality cooking fuel, poor housing, and low asset holdings contribute more than 50 percent to non-monetary poverty, followed by education. For in-camp refugees, the contribution of education, health, and living standards to overall non-monetary poverty is 30, 18, and 52 percent, respectively. Few years of schooling and child malnutrition are the dimensions that contribute most to poverty (Figure 5.7). However, child mortality and access to improved water 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Cereals Other grains (pulses, oilseeds) Fruits, root crops, and vegetables Animal origin food Other food (spices, fats & oils, beverages) Food away from home Percent Figure 5.6: Food expenditure shares by food groups Source: World Bank Staff based on SESRE 2023. - 10 20 30 40 50 60 70 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Headcount Vulnerable to poverty Severe poverty 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "shares by food groups Source: World Bank Staff based on SESRE 2023. - 10 20 30 40 50 60 70 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Headcount Vulnerable to poverty Severe poverty 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Years of education School attendance Child mortality Nutrition Electricity Sanitation Water Housing Cooking fuel Assets Percent Figure 5.7: Multidimensional poverty incidence, severity, and vulnerability Source: World Bank Staff based on SESRE 2023. a. Poverty incidence b. Contributions by dimensions Refugees’ Aspirations 47 contribute less to multidimensional poverty across all refugee groups. There is a similar pattern for the host community around the refugee camps. Low education, together with limited access to electricity, housing, assets, sanitation facilities, and drinking water, mean that low living standards contribute more to overall poverty among refugees. 5.1.4 Food security Refugees and hosts perceive that household living standards have deteriorated over time. To capture subjective well-being, the survey asks if the living standard of the household or their community has improved or worsened in the past five years (Figure 5.8a) and in the past 1 year (Figure", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "5.1.4 Food security Refugees and hosts perceive that household living standards have deteriorated over time. To capture subjective well-being, the survey asks if the living standard of the household or their community has improved or worsened in the past five years (Figure 5.8a) and in the past 1 year (Figure 5.8b). Overall, most households feel that their living standards have deteriorated. While in-camp refugees are more pessimistic about the changes in their households’ living standards, there is no significant difference in perceptions among OCP refugees in Addis Ababa and their hosts. The considerably high negative perception about changes in household living standards indicates that well-being has been worsening for everyone over the past few years, but even more so for refugees in camps. Refugees, on average, have poor food and nutrition security outcomes compared to their hosts. The extent of food insecurity measured by the food insecurity scale is significantly higher for refugees than hosts, both for in-camp and for out-of-camp refugees (Figure 5.9). While the food insecurity Refugee and host communities could differ in multiple dimensions over and above consumption. The Multidimensional Poverty Index (MPI) explores this multiple deprivation, capturing differences across three dimensions of well-being: health, education, and", "output": {"entities": {"named_data": ["Multidimensional Poverty Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "higher for refugees than hosts, both for in-camp and for out-of-camp refugees (Figure 5.9). While the food insecurity Refugee and host communities could differ in multiple dimensions over and above consumption. The Multidimensional Poverty Index (MPI) explores this multiple deprivation, capturing differences across three dimensions of well-being: health, education, and living standards (Alkire et al., 2021). MPI provides a general picture of the extent of deprivation. In this context, deprivation in education is assessed using school attendance for school-age children and years of schooling among adults. Health is proxied by the presence in the household of a stunted child or death of a child in the last 12 months before the survey. Living standards are assessed by access to electricity, improved water, sanitation, cooking fuel source, housing, and economic assets. The MPI ranges from 0 to 1, with 1 representing a high level of deprivation. It is the product of two partial indices: the headcount ratio (H) and the intensity of poverty (A) i.e. (MPI = H*A). The headcount ratio is the share of poor people in the population, while the intensity shows how much deprivation poor people experience on average. A cut-off point of 0.33 is used for the", "output": {"entities": {"named_data": ["Multidimensional Poverty Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "two partial indices: the headcount ratio (H) and the intensity of poverty (A) i.e. (MPI = H*A). The headcount ratio is the share of poor people in the population, while the intensity shows how much deprivation poor people experience on average. A cut-off point of 0.33 is used for the multidimensional poverty headcount ratio; that is, a household is multidimensionally poor if the MPI is greater than 0.33. The population vulnerable to poverty is defined as those who experience 20-32.9 percent intensity of deprivation, and the population in severe poverty are those with an intensity of 50 percent or higher (that is, if the MPI is 0.50 or higher). Box 5.3: MPI methodology 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Worse Same Better Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Percent 0 10 20 30 40 50 60 70 80 90 100 Percent Worse Same Better Figure 5.8: Perceived changes in household living standards Source: World Bank Staff based on SESRE 2023. Note: The survey asks how the household living standard has changed compared to last year and the last five", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "0 10 20 30 40 50 60 70 80 90 100 Percent Worse Same Better Figure 5.8: Perceived changes in household living standards Source: World Bank Staff based on SESRE 2023. Note: The survey asks how the household living standard has changed compared to last year and the last five years. a. Last 5 years b. Last 1 year Refugees’ Aspirations 48 scale44 gap between refugees and hosts is higher for in-camp refugees, the gap is relatively narrower for Addis Ababa refugees. The average food insecurity scale for in-camp refugees is “8” and for their hosts it is “4” out of 10; that is, in- camp refugee households experienced about eight food insecurity events while host households experienced about 4 in the past year. Consistent with other welfare indicators discussed, food insecurity tends to be more severe among in-camp refugees than their hosts or OCP refugees. In-camp refugees have less diverse diets and poor food consumption status compared to their hosts. The average household dietary diversity score—the number of food groups consumed out of twelve—is 7.5 for hosts and 6.5 for refugees (Figure 5.10a). Overall, the average dietary diversity score is also lower for in-camp refugees than their hosts. The", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "diverse diets and poor food consumption status compared to their hosts. The average household dietary diversity score—the number of food groups consumed out of twelve—is 7.5 for hosts and 6.5 for refugees (Figure 5.10a). Overall, the average dietary diversity score is also lower for in-camp refugees than their hosts. The share of households with acceptable food consumption status—food consumption score of 35 or above—is considerably lower among in-camp refugees (49 percent) than their host (74 percent). The relatively lower dietary diversity could be due to refugees having limited access to diverse food as they depend on aid. Most Addis Ababa refugees and their hosts have an acceptable food consumption status (Figure 5.10b). 5.1.5 Shocks and coping strategies Market-related shocks are common, but refugees are exposed to more diverse shocks than their hosts. While Ethiopian households face a plethora of risks that affect their livelihoods—risks to assets, income, and food supply (Dercon et al 2005; Woldehanna et al 2008)—market shocks related to rising food prices, food shortage, and health shocks appear to be most prevalent (Figure 5.11). High food prices drive the market shocks. Food shortage seems to represent a crucial economic shock among refugees—roughly 31 percent of in-camp refugees are", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household dietary diversity score"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "(Dercon et al 2005; Woldehanna et al 2008)—market shocks related to rising food prices, food shortage, and health shocks appear to be most prevalent (Figure 5.11). High food prices drive the market shocks. Food shortage seems to represent a crucial economic shock among refugees—roughly 31 percent of in-camp refugees are affected by food shortage—but not for their host communities. 4.0 2.1 2.9 8.1 3.4 7.0 In Camp Addis Ababa Total Hosts Refugees Figure 5.9: Food insecurity scale for refugees and hosts Source: World Bank Staff based on SESRE 2023. 44 Food insecurity experience is measured based on a scale that ranges between 0 and 10 and calculated by adding household’s experience related to the following events in the past year: (i) worried about having enough food, (ii) unable to eat healthy/nutrition food, (iii) only ate a few kinds of food, (iv) had to skip a meal, (v) adults ate less, (vi) ran out of food, (vii) adults were hungry but did not eat, (viii) went without eating for a whole day, (ix) restricted consumption so kids could eat, and (x) borrowed food or relied on friend/relative for help. 7.0 8.4 7.8 6.0 8.2 6.5 In Camp Addis Ababa Total Hosts", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "(vi) ran out of food, (vii) adults were hungry but did not eat, (viii) went without eating for a whole day, (ix) restricted consumption so kids could eat, and (x) borrowed food or relied on friend/relative for help. 7.0 8.4 7.8 6.0 8.2 6.5 In Camp Addis Ababa Total Hosts Refugees 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Poor (0-21) Borderline (21-35) Acceptable ( > 35) Percent Figure 5.10: Dietary diversity and food consumption status Source: World Bank Staff based on SESRE 2023. Note: Dietary diversity score is calculated as the total number of food groups (out of 12) consumed by the household in the last seven days before the survey. The food groups are cereals, roots and tubers, vegetables, fruits, meat (including poultry and offal), eggs, fish and seafood, pulses and legumes and nuts, milk and milk products, oils and fats, sugar/honey, and others. Food consumption status is determined based on food consumption score. a. Dietary diversity score (out of 12 groups) b. Food consumption status Refugees’ Aspirations 49 Moreover, insecurity and displacement-related shocks are common for Eritrean refugees, with 14 percent having experienced", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "milk and milk products, oils and fats, sugar/honey, and others. Food consumption status is determined based on food consumption score. a. Dietary diversity score (out of 12 groups) b. Food consumption status Refugees’ Aspirations 49 Moreover, insecurity and displacement-related shocks are common for Eritrean refugees, with 14 percent having experienced a recent displacement event. This result is driven by refugees in the Alemwach refugee hosting site, all of whom moved to the refugee site within a few months before the survey as a result in the conflict in Tigray, and would have reported a recent displacement event. Both refugees and host communities use “consumption-smoothing” to cope with the various shocks they face. Households utilize a mix of coping strategies to mitigate harm to their welfare that shocks cause. “Consumption smoothing”, among the major risk coping strategies, mainly involves relying less on preferred food and more on less expensive food (diet changes) and reducing the number of meals eaten daily (negative food intake). Borrowing food or cash from friends and relatives and purchasing food on credit second represent the second and third most common coping strategies. Refugees in camps and in Addis Ababa are more likely to rely on these coping", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "changes) and reducing the number of meals eaten daily (negative food intake). Borrowing food or cash from friends and relatives and purchasing food on credit second represent the second and third most common coping strategies. Refugees in camps and in Addis Ababa are more likely to rely on these coping strategies than their hosts (Figure 5.12). The results further show that both refugee and host households do not engage in adverse coping strategies, such as the sale of (productive) assets that would make them vulnerable to poverty. This could be because either they do not have enough assets to sell or because the strategies they utilize are enough to cope with the effects of shocks. 5.2 Determinants of welfare The poverty profile in this section compares the characteristics of poor compared to non-poor people. The previous section presents refugees’ and host communities’ poverty and welfare patterns. This section substantiates the earlier discussions on poverty levels by describing the demographic, geographic, and socioeconomic characteristics by expenditure quintiles for each refuge and host group separately, along with the poverty headcount rate across grouping variables (see Annex D, Table D.11). The descriptive statistics are substantiated by results from a regression analysis examining correlates", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "earlier discussions on poverty levels by describing the demographic, geographic, and socioeconomic characteristics by expenditure quintiles for each refuge and host group separately, along with the poverty headcount rate across grouping variables (see Annex D, Table D.11). The descriptive statistics are substantiated by results from a regression analysis examining correlates of poverty while holding other things constant. The dependent variable is the natural logarithm of consumption per capita. That is, we compare level of consumption to other variables to identify characteristics that correlate to a household being poor. Table D.12 and Table D.13 in Annex D show the full results of the regressions on the determinants of consumption per capita separately for in-camp and out-of-camp refugees and hosts. Poor in-camp refugees have higher household sizes, dependency ratios, and male heads. The poorest refugee and host households have significantly larger household sizes and dependency ratios than the richest counterparts (Figure 5.13). The average household size of the poorest in-camp refugee and host households is more than double that of the richest households (Figure 5.13). Larger 0 5 10 15 20 25 30 35 40 45 50 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Health shock (death/illness) Market", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "The average household size of the poorest in-camp refugee and host households is more than double that of the richest households (Figure 5.13). Larger 0 5 10 15 20 25 30 35 40 45 50 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Health shock (death/illness) Market shock (output/food/input prices) Employment shock Production shock (drought, crop damage, livestock loss) Political shock (insecurity, loss of land, displacement) Food shortage Percent Figure 5.11: Type of shocks experienced Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Consumption smoothing Sale of household assets (incl. livestock) Borrow food or cash or purchase on credit Percent Figure 5.12: Shock coping strategies Source: World Bank Staff based on SESRE 2023. Refugees’ Aspirations 50 household sizes for the poor are mainly driven by a larger number of children (under age 15). The data further show that the poorest refugees and hosts are more likely to have married and older household heads compared with the richest counterparts (Figure 5.14). Richest in-camp and out of camp refugee households are more likely to have female- headed households compared to the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "children (under age 15). The data further show that the poorest refugees and hosts are more likely to have married and older household heads compared with the richest counterparts (Figure 5.14). Richest in-camp and out of camp refugee households are more likely to have female- headed households compared to the poorest. There is no difference in the gender of the household head among the poorest and richest host households (Annex D, Table D.11). Location is an essential determinant of monetary poverty. Monetary poverty is highest among South Sudanese refugees (89 percent) (Figure 5.15). There is a significant difference in poverty rates between in-camp refugees and their hosts, the gap being the highest in the Eritrean domain. As discussed in Chapter 2, refugee households have larger household sizes than hosts, except in Addis Ababa. In light of the discussion above, the highest poverty incidence among South Sudanese refugees could be associated with their high dependency ratio and high number of female-headed households. In-camp refugees working inside the camp tend to exhibit lower poverty incidence (81 percent) than those working outside the camp (88 percent). The poor tend to live in households headed by individuals with limited education. This trend is evident", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "with their high dependency ratio and high number of female-headed households. In-camp refugees working inside the camp tend to exhibit lower poverty incidence (81 percent) than those working outside the camp (88 percent). The poor tend to live in households headed by individuals with limited education. This trend is evident among both refugees and hosts, where a lower level of educational attainment by household heads and members correlates with increased poverty. While building human capital represents an essential pathway out of poverty, there appears to be low human capital among refugee and host households, as indicated by the household head and members’ low education. The data reveals that poverty incidence is more prevalent among households with no or minimal education (Annex D, Table D.11). Conversely, poverty incidence tends to decline with increased education level of the household head and members. These findings underscore the critical role of education as a means to alleviate poverty among refugees and host communities in Ethiopia. The regression results also indicate that increasing years of schooling of the household head is associated with increased household consumption (Annex D, Table D.12); average household expenditures linearly increase, and poverty headcount decreases, as the education 0.0 0.2 0.4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "means to alleviate poverty among refugees and host communities in Ethiopia. The regression results also indicate that increasing years of schooling of the household head is associated with increased household consumption (Annex D, Table D.12); average household expenditures linearly increase, and poverty headcount decreases, as the education 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 Household size Adult equivalent 0 10 20 30 40 50 60 70 80 90 Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Age of head (year) Percent Dependency ratio In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts Figure 5.13: Household composition by quintiles Source: World Bank Staff based on SESRE 2023. Note: Primary axis labels represent household size/adult equivalent. 0 5 10 15 20 25 30 35 40 45 50 0 10 20 30 40 50 60 70 80 90 Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Female headed Married head Age of head (year) Percent Age (years) In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts Figure 5.14: Demographic", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "60 70 80 90 Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Female headed Married head Age of head (year) Percent Age (years) In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts Figure 5.14: Demographic characteristics by quintile Source: World Bank Staff based on SESRE 2023. Note: Primary axis labels represent gender and marital status. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Hosts Refugees Percent Figure 5.15: Poverty headcount rate for in-camp refugees and their hosts, by domain Source: World Bank Staff based on SESRE 2023. Refugees’ Aspirations 51 level of the household head increases. This is only the case for Addis Ababa refugees and hosts (Figure 5.16). However, for in-camp refugees, there appears to be no response to expenditure on an additional level of education of the household head compared to other refugees. In-camp host community households are higher, on average, than for in-camp refugees, and returns to the education level of the household head for these households appears to be slowly increasing. Household welfare is linked to possession of certain assets or access to services.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "the household head compared to other refugees. In-camp host community households are higher, on average, than for in-camp refugees, and returns to the education level of the household head for these households appears to be slowly increasing. Household welfare is linked to possession of certain assets or access to services. We assessed a number of indicators, including whether the household has access to electricity; possesses livestock; has a mobile phone; owns agricultural land; runs a non-farm enterprise; or has bank accounts. These “wealth” indicators show stark differences between the poorest and richest in-camp refugees and their hosts. The poorest in-camp refugees and their hosts tend to have limited access to electricity, mobile phones, bank accounts, and non-farm enterprises (Figure 5.17). Livestock and agricultural holding do not show a clear pattern among the poorest and the richest. For Addis Ababa refugees and hosts, there tends to be increased access to electricity, ownership of mobile phones, bank accounts, and non-farm enterprises across expenditure quintiles. Regression results show that possessing a bank account, a mobile phone, and access to electricity positively correlate with consumption for in-camp refugees and host households (Annex D, Table D.12). Mobile phone ownership and ownership of a nonfarm enterprise", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "ownership of mobile phones, bank accounts, and non-farm enterprises across expenditure quintiles. Regression results show that possessing a bank account, a mobile phone, and access to electricity positively correlate with consumption for in-camp refugees and host households (Annex D, Table D.12). Mobile phone ownership and ownership of a nonfarm enterprise positively correlate with household welfare for out- of-camp refugees and hosts. Ownership of a non- farm enterprise also appears to correlate positively with welfare for in-camp refugees. The poorest refugees and hosts tend to have worse labor market outcomes than the richest. Figure 5.18 summarizes labor market outcomes by expenditure quintiles for in-camp and Addis Ababa refugees and their hosts. Labor force participation and employment-to-population rates increase with welfare, and unemployment rates fall with increasing welfare (Figure 5.18a). This underscores the critical role labor market participation or employment plays for poverty reduction among refugees and their hosts. Looking at the sectoral distribution of employment, the poorest refugees—in- and out-of- camp—tend to be employed in the service sector. While employment in the industry sector is low for refugees, the poorest are less likely to be employed in the industry sector than the richest. The poorest hosts of in-camp refugees are more", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "sectoral distribution of employment, the poorest refugees—in- and out-of- camp—tend to be employed in the service sector. While employment in the industry sector is low for refugees, the poorest are less likely to be employed in the industry sector than the richest. The poorest hosts of in-camp refugees are more likely to be employed in agriculture, and the richest appear to be employed in the industry or service sectors (Figure 5.18b). Not surprisingly, the poorest hosts of in-camp refugees and the poorest refugees work in low (or medium)-skilled occupations, while the richest are employed in high-skill occupations (Figure 5.18c). - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 No education Primary incomplete Primary complete Secondary incomplete Secondary complete Post- secondary No education Primary incomplete Primary complete Secondary incomplete Secondary complete Post- secondary In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts All Refugees All Hosts In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts All Refugees All Hosts - 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Predicted total expenditure Predicted poverty rate Figure 5.16: Poverty incidence decreases with education of the household head Source: World Bank Staff based on SESRE", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts All Refugees All Hosts - 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Predicted total expenditure Predicted poverty rate Figure 5.16: Poverty incidence decreases with education of the household head Source: World Bank Staff based on SESRE 2023. Refugees’ Aspirations 52 A larger proportion of refugees work inside the camp across the distribution. Yet, better-off refugees are more likely to work inside the camp. Regarding location, although working outside the camp is shown to have significant wage effects (see Chapter 3), the data show that the poorest in-camp refugees are more likely to work outside the camp than the richest (Figure 5.18d), an effect apparently driven by refugees from South Sudan and Somalia, who are poorer overall. Regression results show that an increase in the share of employed household members is associated with increased household expenditure for in-camp refugees, their hosts, and out-of-camp refugees (Table D.12 in Annex D). The predicted poverty rate decreases with the share of employed household members, indicating that employment is essential to lowering poverty for in- camp refugees (Figure 5.19). 0 10 20 30 40 50 60 70 80 90 100 Poorest", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "in-camp refugees, their hosts, and out-of-camp refugees (Table D.12 in Annex D). The predicted poverty rate decreases with the share of employed household members, indicating that employment is essential to lowering poverty for in- camp refugees (Figure 5.19). 0 10 20 30 40 50 60 70 80 90 100 Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts Electricity Mobile phone Any livestock Bank account Agricultural holding Nonfarm enterprise Percent Figure 5.17: Household wealth indicators by expenditure quintiles Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 Labor force participation Employment to working age population ratio Unemployment rate 0 10 20 30 40 50 60 70 80 90 100 Agriculture Industry Services 0 10 20 30 40 50 60 70 Poorest 2 3 4 Richest In Camp Refugees Inside the Camp Outside the Camp 0 10 20 30 40 50 60 70 80 Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Low-skill Medium-skill High-skill Percent Percent", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "60 70 Poorest 2 3 4 Richest In Camp Refugees Inside the Camp Outside the Camp 0 10 20 30 40 50 60 70 80 Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Low-skill Medium-skill High-skill Percent Percent Percent Percent In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts Figure 5.18: Labor market outcomes by expenditure quintiles Source: World Bank Staff based on SESRE 2023. a. Key labor market indicators b. Employment by sector c. Employment by skill (occupation-based) d. Work location for in-camp refugees Refugees’ Aspirations 53 Poverty relates to lack of access to markets for in-camp refugee households. Families with better access to essential resources such as education, healthcare, clean water, and stable employment are more likely to experience", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Employment by sector c. Employment by skill (occupation-based) d. Work location for in-camp refugees Refugees’ Aspirations 53 Poverty relates to lack of access to markets for in-camp refugee households. Families with better access to essential resources such as education, healthcare, clean water, and stable employment are more likely to experience improved economic stability and well-being. Access to these services provides a foundation for building a more secure financial future, enabling households to invest in their growth and development. Consequently, communities with better access to resources tend to have lower poverty incidences, as these critical assets empower individuals to break free from the cycle of economic hardship. For analysis, resource access is proxied by remoteness or proximity to resource hubs and market accessibility. Descriptive statistics show that poverty rates are higher in medium market-accessibility areas and lowest in high-market accessibility areas (Annex D, Table D.11). Poverty incidence also tends to be lower in areas closer to Woreda capitals. Results from regression analysis for in-camp refugees show that consumption expenditure per capita negatively correlates with distance to a Woreda capital; with a 1 percent increase in mean distance to the capital reducing consumption expenditure per capita by 0.09 percent, holding other factors", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "lower in areas closer to Woreda capitals. Results from regression analysis for in-camp refugees show that consumption expenditure per capita negatively correlates with distance to a Woreda capital; with a 1 percent increase in mean distance to the capital reducing consumption expenditure per capita by 0.09 percent, holding other factors constant (Annex D, Table D.13). Moreover, living in high market-accessibility areas is associated with a 0.24 percent increase in consumption expenditure per capita. Market and political shocks harm the welfare of refugees and host communities. As mentioned, refugee households in Ethiopia are vulnerable to various shocks, including market shocks that harm their well-being. Poor households are more likely to experience shocks; concurrently, they are less equipped to devise coping strategies. Market shocks, often manifested through escalated food prices, seem to predominantly harm host communities, while refugees are less affected (Annex D, Table D.12). This disparity may stem from refugees’ heavy dependence on food assistance and remittances, coupled with the international community’s concentrated efforts on enhancing refugee livelihoods. A significant observation is that political shocks, closely linked to displacement and insecurity issues, consistently result in adverse welfare outcomes for out-of-camp refugee and host community households (Annex D, Table D.12). 5.3 Cost", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "dependence on food assistance and remittances, coupled with the international community’s concentrated efforts on enhancing refugee livelihoods. A significant observation is that political shocks, closely linked to displacement and insecurity issues, consistently result in adverse welfare outcomes for out-of-camp refugee and host community households (Annex D, Table D.12). 5.3 Cost of basic needs for refugees This section estimates the cost of basic needs for in-camp refugees in Ethiopia and analyzes the determinants of these costs. This section identifies how much it costs to meet basic needs through aid. It also shows how the need for assistance depends on the degree of economic inclusion of refugees, following the work of Atamanov et al. (2023). Basic needs are defined using monetary poverty lines. The approach captures the cost of a minimum standard of living, grounded in a well- established methodology. (see Box 5.4 for in-depth explanation of the methodology used). On average, in-camp refugees receive about 56 percent of total consumption from aid or assistance (Figure 5.20). 1 1 .8 .8 .6 .6 .4 .4 .2 .2 0 0 .05 .1 .15 0 1 .8 .6 .4 .2 0 Share of employed HH members Share of employed HH members In Camp Refugees", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "in-camp refugees receive about 56 percent of total consumption from aid or assistance (Figure 5.20). 1 1 .8 .8 .6 .6 .4 .4 .2 .2 0 0 .05 .1 .15 0 1 .8 .6 .4 .2 0 Share of employed HH members Share of employed HH members In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts Figure 5.19: Poverty rates and employment for refugees and hosts Source: World Bank Staff based on SESRE 2023. Note: Predicted marginal probabilities of poverty based on the share of employed members after controlling for other factors. Refugees’ Aspirations 54 The share is substantially higher for refugees from the bottom quintile (67 percent) than those from the top quintile (24 percent). We estimate poverty levels for refugees and host communities using the standard consumption aggregate and the pre-assistance consumption aggregate. We present poverty rates separately for camp and out-of-camp refugees (Figure 5.21). Notably, poverty headcount for refugee camps is markedly higher when considering pre-assistance consumption; that is, consumption after deducting aid or assistance received (96 percent) as opposed to total consumption (84 percent). However, for refugees living outside of camps and for host communities, the changes in poverty rates are not large.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Notably, poverty headcount for refugee camps is markedly higher when considering pre-assistance consumption; that is, consumption after deducting aid or assistance received (96 percent) as opposed to total consumption (84 percent). However, for refugees living outside of camps and for host communities, the changes in poverty rates are not large. A similar trend is observed with the poverty gap. These findings underscore humanitarian aid’s vital importance for refugee camps. The lack of substantial change in poverty among out-of- camp refugees is due to their greater reliance on remittances rather than direct aid. Compared to the “no economic opportunities” scenario (see Box 5.4), Ethiopia’s “current” economic integration model reduces costs by 44 percent to an annual cost of US$210 per capita. Figure 5.22 shows the yearly costs of basic needs per refugee to cover, depending on economic inclusion across the three scenarios. Under a “no economic opportunities” scenario—in which refugees do not work and must rely solely on aid or assistance, the annual cost of basic needs per refugee is approximately US$378. Under the “current” scenario—where refugees can find opportunities to earn money or work—the amount of assistance needed to cover basic needs reduces annual costs by 44 percent to US$210", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "not work and must rely solely on aid or assistance, the annual cost of basic needs per refugee is approximately US$378. Under the “current” scenario—where refugees can find opportunities to earn money or work—the amount of assistance needed to cover basic needs reduces annual costs by 44 percent to US$210 per capita. The saving can be viewed as an economic-inclusion dividend made possible by Ethiopia’s prevailing refugee policies. Under a hypothetical “full inclusion” scenario— where in-camp refugees have equal opportunities as hosts—the cost of basic needs decreases further to only US$78 per refugee, per year. 0 10 20 30 40 50 60 70 80 Poorest 2 3 4 Richest Percent Figure 5.20: Share of consumption provided in-kind or for free by consumption per capita quintiles among in-camp refugees Source: World Bank Staff based on SESRE 2023. Note: Quintiles are constructed for in-camp refugees only. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Addis Ababa All camp Total Total consumption Pre-assistance income Percent Figure 5.21: Poverty incidence at consumption and pre-assistance consumption levels Source: World Bank Staff based on SESRE 2023. Note: Poverty rates are calculated based on $2.15 in the 2017", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Addis Ababa All camp Total Total consumption Pre-assistance income Percent Figure 5.21: Poverty incidence at consumption and pre-assistance consumption levels Source: World Bank Staff based on SESRE 2023. Note: Poverty rates are calculated based on $2.15 in the 2017 PPP line using total consumption and pre-assistance income Scenario 1: no economic opportunities Scenario 2: current scenario Scenario 3: full inclusion USD per capita per year, Dec 2022 prices Figure 5.22: Costs of basic needs per refugee per year under different scenarios Source: World Bank Staff based on SESRE 2023. Note: The costs are in December 2022 prices. Refugees’ Aspirations 55 In addition to their own resources, refugees rely on humanitarian aid to cover their expenditures on food, sanitation, hygienic products, and essential non-food items. “Successful” integration and economic inclusion—that is, earning sufficient income to be no longer poor and to consume more than the (international) poverty line—of refugees bring higher self-reliance and less reliance on humanitarian assistance. This opens two tracks for investigation: (i) First, how much aid would be needed if the policy objective were to bring refugee consumption up to the poverty line. The answer to this", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "to consume more than the (international) poverty line—of refugees bring higher self-reliance and less reliance on humanitarian assistance. This opens two tracks for investigation: (i) First, how much aid would be needed if the policy objective were to bring refugee consumption up to the poverty line. The answer to this question is found by identifying the poverty gap for refugees. (ii) Explore the factors that determine, or at least are associated with, the size of the poverty gap. In Ethiopia, the policy on living out-of-camp is somewhat unique in that refugees who live in camps are eligible for humanitarian assistance; all refugees receive the complete package. However, that package regularly changes when funding gaps arise. Refugees who live out-of-camp forego any assistance. Still, they can access education and health services. Regarding selection for the OCP, only those who are “better off”— that is, they can rely on remittances—qualify and are selected for OCP. This implies that the OCP refugees have a vastly different profile from those living in camps. The focus of this section is first to identify how much it would cost if basic needs were met through aid alone, and next, how the need for assistance depends on", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "selected for OCP. This implies that the OCP refugees have a vastly different profile from those living in camps. The focus of this section is first to identify how much it would cost if basic needs were met through aid alone, and next, how the need for assistance depends on the degree of economic inclusion of refugees. For this purpose, basic needs are defined using monetary poverty lines (Atamanov et al., 2023). Monetary poverty lines are used because they both capture the cost of a minimum standard of living and follow a well-established methodology combining: (i) a food allowance for adequate nutrition/minimum caloric intake using a national basket of goods, and (ii) a non-food allowance that captures the cost of essential non-food items such as clothing, shelter, and private expenses on health and education (Ravallion, 1998). The preferred poverty line was $2.15 per capita per day in 2017, and PPP converted to Ethiopian Birr in December 2022. The first step in estimating the cost of basic needs for refugees is the calculation of pre-assistance income or refugees—a proxy for income earned by refugees. The pre-assistance income for in-camp and out-of-camp refugees are calculated separately. For in-camp refugees, this involves deducting", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "converted to Ethiopian Birr in December 2022. The first step in estimating the cost of basic needs for refugees is the calculation of pre-assistance income or refugees—a proxy for income earned by refugees. The pre-assistance income for in-camp and out-of-camp refugees are calculated separately. For in-camp refugees, this involves deducting assistance from total consumption, including humanitarian assistance and housing—a proxy for gifts received from (international) donors. For those out-of-camp, only humanitarian assistance is deducted. The information about food and non-food consumption provided in-kind or free to households provides a measure of the role of existing humanitarian assistance. We assume that aid organization and the government provide these food and non-food products and services. The expenditure sources mapped to humanitarian aid to calculate pre-assistance income are consumption use of donation items from government or NGOs, sale of donation items from government or NGOs, donations in cash from government or NGOs, and imputed value of owned or subsidized dwelling units for in-camp refugees. The cost of basic needs for refugees is assessed using three scenarios: (i) “No economic opportunities”—the costliest scenario that assumes that refugees do not earn any income and need aid to cover all their basic needs. In this baseline", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "imputed value of owned or subsidized dwelling units for in-camp refugees. The cost of basic needs for refugees is assessed using three scenarios: (i) “No economic opportunities”—the costliest scenario that assumes that refugees do not earn any income and need aid to cover all their basic needs. In this baseline scenario, the poverty line’s full value is used as a proxy for costs. (ii) “Current”—based on the premise that, in practice, refugees find opportunities to earn money, even in the most restricted environments. By allowing refugees to work, the assistance needed to cover basic needs is lower. This could be a stringent assumption in light of Ethiopia’s refugee policy that does not facilitate swift access to work permits and refugee mobility within camps. The cost of basic needs under this current scenario is measured by removing humanitarian aid from total household consumption, then taking the difference between the poverty line and pre-assistance consumption. This difference indicates how much assistance is needed to bring the consumption of refugees to the poverty line. The value is lower than the costs under the “no economic opportunities” scenario, with the savings viewed as an economic inclusion dividend made possible by Ethiopia’s prevailing refugee policies.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "and pre-assistance consumption. This difference indicates how much assistance is needed to bring the consumption of refugees to the poverty line. The value is lower than the costs under the “no economic opportunities” scenario, with the savings viewed as an economic inclusion dividend made possible by Ethiopia’s prevailing refugee policies. (iii) “Full inclusion”—uses the current poverty gap of Ethiopian hosts as a proxy for basic needs costs, where an “average” refugee resembles an “average” Ethiopian in terms of human capital, access to productive assets, and economic opportunities. The “no economic opportunities” and “full economic inclusion” scenarios are hypothetical and only serve as upper and lower bounds for aid necessary to cover the costs of basic needs. Box 5.4: Estimation of the cost of basic needs for refugees Markets and Opportunities 56 R efugees must be able to engage in local markets to find better livelihoods and sustainable economic opportunities. Local labor markets shape the employment trajectories of refugees. Restrictions on land access for refugees restrict their access to rural labor markets, primarily shaped by agricultural activities. Livelihood activities in cities or work similar to that found in urban areas are most promising for refugees to utilize their labor and skills.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "labor markets shape the employment trajectories of refugees. Restrictions on land access for refugees restrict their access to rural labor markets, primarily shaped by agricultural activities. Livelihood activities in cities or work similar to that found in urban areas are most promising for refugees to utilize their labor and skills. Yet, many refugee camps are in more agrarian locations, and local labor market characteristics and connectivity drive refugees’ labor market outcomes (Hedberg and Tammaru, 2013; Kalter and Kogan, 2014; Kogan and Kalter, 2020; Schuettler and Caron, 2020; Dorian and Burmann, 2023). The GoE vision to create sustainable livelihood opportunities and build refugees’ self-reliance and resilience has yet to be fully implemented; roughly 88 percent of refugees in Ethiopia remain in camps based on SESRE data. Globally, approximately one- quarter of all refugees live in camps, a proportion that varies widely by country income status. Roughly half of refugees hosted in low-income countries live in camps (UNHCR, 2022b), but this share is much higher in Ethiopia (88 percent). Long-term encampment policies leave refugees isolated with limited or no economic rights, a situation that wastes their human capital and capacity for work (World Bank, 2017; Ibáñez et al., 2022). Although it may", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "low-income countries live in camps (UNHCR, 2022b), but this share is much higher in Ethiopia (88 percent). Long-term encampment policies leave refugees isolated with limited or no economic rights, a situation that wastes their human capital and capacity for work (World Bank, 2017; Ibáñez et al., 2022). Although it may appear practical to keep refugees in camps from the perspective cost, the speed of setting-up, delivering services, identifying individuals, and other reasons, refugees in camps (or specific hosting areas) live unproductive, unfulfilled lives that do not contribute to the local economy (World Bank, 2017). Usually, the only option for economic participation these refugees have is to work or in the informal sector in surrounding host communities. In Ethiopia, refugees live in 24 camps located across different regions45. Refugee camp locations are diverse. Some camps are part of Woreda cities, some are close to Zone capital cities, some are remote, 6. Markets and Opportunities 45 In Ethiopia, the refugee camps are located in Tigray, Afar, Amhara, Somali, Benishangul-Gumuz, and Gambella regions. Eritrean refugees who speak Tigrigna are basically located in Tigray region, though they moved to Amhara region following the North Ethiopia Conflict (IOM, 2023). Refugees who speak the Afar language", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Markets and Opportunities 45 In Ethiopia, the refugee camps are located in Tigray, Afar, Amhara, Somali, Benishangul-Gumuz, and Gambella regions. Eritrean refugees who speak Tigrigna are basically located in Tigray region, though they moved to Amhara region following the North Ethiopia Conflict (IOM, 2023). Refugees who speak the Afar language from Eritrea are settle in Afar region. Refugees from Somalia are located in different parts of the Somali region. Sudanese refugees live in Gambella region, whereas the South Sudanese settled in Benishangul-Gumuz. Markets and Opportunities 57 some are near a border to their home country, some are in the lowlands and some in the highlands. They are spatially dispersed, have different geographic, social, and economic contexts, and are in different ecological Zones. For example, about 38 percent of refugees live in drought-prone lowland and pastoralist areas, whereas 60 percent live in humid reliable lowland areas (Annex D, Figure D.31). In many refugee-hosting areas in Ethiopia, except for a few places such as Addis Ababa, refugees and host communities share cross-border cultural and economic connections; and common ties of kinship, language, and ethnicity (Vemuru et al., 2020). Location greatly affects socio-economic outcomes, economic activities, and livelihood opportunities, and poverty levels vary", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "many refugee-hosting areas in Ethiopia, except for a few places such as Addis Ababa, refugees and host communities share cross-border cultural and economic connections; and common ties of kinship, language, and ethnicity (Vemuru et al., 2020). Location greatly affects socio-economic outcomes, economic activities, and livelihood opportunities, and poverty levels vary profoundly by location in Ethiopia. Livelihood activities vary throughout the country and the refugee-hosting zones. Rural labor concentrates in the agricultural sector, with low non- and off-farm employment in rural areas and small towns (Pimhidzai et al., 2022), while work in the service and manufacturing sectors concentrates in urban centers46. Livestock production and sale represent the main livelihoods in lowland pastoral areas. Poverty rates are higher among households in the drought-prone lowlands, and the likelihood of escaping poverty is higher for households in lowland pastoral areas than those in moisture-reliable highlands (World Bank, 2020). Refugees in camps can neither choose nor participate in the local agricultural economy, so disparities in livelihood opportunities depending on location matter for refugees. For example, employment rates differ depending on the hosting zones, helping to explain the different labor market outcome of refugees’ experience across the country. This chapter aims to better understand how camp", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "nor participate in the local agricultural economy, so disparities in livelihood opportunities depending on location matter for refugees. For example, employment rates differ depending on the hosting zones, helping to explain the different labor market outcome of refugees’ experience across the country. This chapter aims to better understand how camp locations determine labor market outcomes and highlights the importance of refugees’ location as part of the development strategy for refugees in Ethiopia. First, we define refugees, resource hubs, connectivity, and local markets, and highlight differences in refugee communities depending on location. Second, we identify in-camp refugees’ performance in the labor market and investigate if there is a spatial disparity in such outcomes among refugees. We discuss refugees’ spatial disparity in labor market access and outcomes based on their proximity to Zone capital cities, Woreda cities, and the nearest international border. In addition, we investigate their level of accessibility to the given market where they are located. Third, using an econometric model, we assess to what extent refugees’ group differences in terms of labor market outcomes correlates with several variables: local factors, proximity to resource hubs, and connectivity. 6.1 Spatial disparities in refugees labor market access and outcomes To better understand", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "given market where they are located. Third, using an econometric model, we assess to what extent refugees’ group differences in terms of labor market outcomes correlates with several variables: local factors, proximity to resource hubs, and connectivity. 6.1 Spatial disparities in refugees labor market access and outcomes To better understand refugees’ spatial disparities, we look at their remoteness—measured as proximity to the nearest Zone capital cities, Woreda cities, and the international border—as well as their market accessibility. We selected the capital city of each Zone as it is a resource and market hub for surrounding Woredas and Kebeles (Box 6.1). Usually, these cities serve as a commerce center for agricultural goods and manufacturing products and provide better employment opportunities. Moreover, Zone Capital and Woreda cities offer better education and health services and improved transportation and communication infrastructure. In addition to cities and towns, people often use border areas to trade and purchase goods at better prices. In Ethiopia, refugees’ locations differ vastly in terms of proximity to resource and economic hubs. About 37 percent of refugees live within 10 kilometers of the nearest Woreda city and another 43 percent live within 10 to 20 kilometers. Zone capital cities are farther", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "and purchase goods at better prices. In Ethiopia, refugees’ locations differ vastly in terms of proximity to resource and economic hubs. About 37 percent of refugees live within 10 kilometers of the nearest Woreda city and another 43 percent live within 10 to 20 kilometers. Zone capital cities are farther away, but almost half (45 percent) of in-camp refugees live within 20 kilometers of the nearest Zone capital city (Figure 6.1a). Borders seem farther, with 18 percent of refugees living within 30 46 Labor Force and Migration Survey 2021. Markets and Opportunities 58 kilometers of the nearest border. When defining mutually exclusive location categories to measure proximity to resource hubs, we see that 44 percent of refugees live closest to the nearest Zone capital city. Another 28 percent live closest to a Woreda City, which is not a Zone capital city. About 11 percent live close to a border but not the Zone capital or Woreda city, and 17 percent of in-camp refugees live in remote areas far from a Zone capital city, Woreda city, or a border. When looking at accessibility, as defined by a market accessibility index, more than one-third of the refugees are located in areas with", "output": {"entities": {"named_data": ["Labor Force and Migration Survey 2021"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "not the Zone capital or Woreda city, and 17 percent of in-camp refugees live in remote areas far from a Zone capital city, Woreda city, or a border. When looking at accessibility, as defined by a market accessibility index, more than one-third of the refugees are located in areas with low market accessibility (Figure 6.1b). The analysis measures the nearest Zone and Woreda capital cities and the closest international border from refugee camps using straight-line distance in a projected coordinate system (Euclidean distance). The study indexed refugees’ proximity to resource hubs by classifying their presence to a combination of distance to cities and borders. Level one is the presence of refugees within a radius of 20km from a Zone capital city. Level two is 10km away from a Woreda city but not within a radius of 20km from the Zone capital city. Level three is for refugees located 30km from the nearest international border but not within a radius of 20km from the Zone capital city and 10km from Woreda city. We classify level four as “remote”; that is, not located 30km from the nearest international border, not within a radius of 20km from the Zone capital city, and not", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["market accessibility index"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "the nearest international border but not within a radius of 20km from the Zone capital city and 10km from Woreda city. We classify level four as “remote”; that is, not located 30km from the nearest international border, not within a radius of 20km from the Zone capital city, and not 10km from Woreda city. The market access indicator in Ethiopia is measured at the Woreda level. Accessibility for a Woreda is estimated as the sum of the travel time of the weighted population to the destination Woredas. With Woreda- to-Woreda origin-destination matrices, we calculate market accessibility by the following equation (Donaldson and Hornbeck, 2016; World Bank, 2019b): where is market access at Woreda “o”, is the trade cost between two Woredas “o” and “d”, is the population of Woreda “d”, and is the trade elasticity. Trade costs between two Woredas, is defined by =exp ( ) with = 0.02 and the optimal travel time between Woredas using the transport network of 2020. The trade elasticity, has a value of 8.28 (Eaton and Kortum, 2002). Box 6.1: Measurement of proximity and market access index in Ethiopia 37 43 19 To Woreda City 1-10km 10-20km >20km 1-20km 20-100km >100km 1-30km 30-50km >50km", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "the optimal travel time between Woredas using the transport network of 2020. The trade elasticity, has a value of 8.28 (Eaton and Kortum, 2002). Box 6.1: Measurement of proximity and market access index in Ethiopia 37 43 19 To Woreda City 1-10km 10-20km >20km 1-20km 20-100km >100km 1-30km 30-50km >50km 45 26 29 To Zone capital 18 59 23 To nearest border 44 28 11 17 Proximity to economic hub Nearest to Zone Nearest to Woreda but not Zone Nearest to border but not Zone & Woreda Remote 24 40 36 Market accessibility High accessibility Medium accessibility Low accessibility Figure 6.1: Refugee incidence Source: World Bank Staff based on SESRE 2023. Note: High, medium, and low accessibility refers to the level of market access, with >0, [-0.5, 0], and <-0.5 standard deviations from the average, respectively. a. Against distance to cities and borders b. By market accessibility, proximity to resource hub Markets and Opportunities 59 Labor market outcomes47 differ by proximity to resource hubs and connectivity. The labor force participation rate for refugees is highest in remote locations and areas with poor connectivity (Figure 6.2). Yet, most refugees in the labor force in remote and low-connected locations are unemployed, highlighting", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "hub Markets and Opportunities 59 Labor market outcomes47 differ by proximity to resource hubs and connectivity. The labor force participation rate for refugees is highest in remote locations and areas with poor connectivity (Figure 6.2). Yet, most refugees in the labor force in remote and low-connected locations are unemployed, highlighting challenges for those who want to work to find employment opportunities (Figure 6.3a and Figure 6.3b). In contrast, refugees near borders and Zone capitals have the highest employment rates. (Figure 6.3a and Annex D, Figure D.32). Refugees benefit from being close to Zone capital cities as the cities are resource hubs, creating many positive economic and social spillover effects on surrounding areas. For example, the employment rate increases by 14 percentage points for refugees closer to Zone capitals and with higher market accessibility (Figure 6.3b). The likelihood of refugees working in the agriculture sector increases with remoteness. The share of employed refugees in agriculture rises from 7 percent in camps nearest Zone capital cities to 40 percent in remote camps (Figure 6.4a). Similarly, refugees in areas with low market accessibility have a higher share of employment in the agriculture sector (Figure 6.4b). The labor market in remote and less connected", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "employed refugees in agriculture rises from 7 percent in camps nearest Zone capital cities to 40 percent in remote camps (Figure 6.4a). Similarly, refugees in areas with low market accessibility have a higher share of employment in the agriculture sector (Figure 6.4b). The labor market in remote and less connected areas is predominantly agrarian,48 providing worse employment opportunities for refugees other than the agriculture sector. Yet, they do not have easy access to land to work in agriculture. As a result, a higher share of the economically active working-age refugee population in these areas remains unemployed (54 percent). Proximity to resource hubs and better accessibility increase the likelihood for refugees to work in non-agriculture sectors. For example, in the camps nearest to Zone capital cities, 27 percent of the employed workers engage in trade activity and 62 percent work in other service sectors (Annex D, Figure D.33). Since refugees are not better positioned to work in the formal private or public sector, their participation in trade and service relates to economic 47 This analysis uses the relaxed definition to measure the current employment status of the host community and refugees. 48 As the previous chapter highlighted, most of refugees engaged", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "not better positioned to work in the formal private or public sector, their participation in trade and service relates to economic 47 This analysis uses the relaxed definition to measure the current employment status of the host community and refugees. 48 As the previous chapter highlighted, most of refugees engaged in agriculture activity are livestock holders (see Chapter 3). 44 37 42 55 LFPR Nearest to Zone Nearest to Woreda but not Zone Nearest to border but not Zone & Woreda Remote 40 40 49 LFPR High accessibility Medium accessibility Low accessibility Figure 6.2: Labor force participation rate by proximity to resource hub, market accessibility Source: World Bank Staff based on SESRE 2023. Note: High, medium, and low accessibility refers to the level of market access, with >0, [-0.5, 0], and <-0.5 standard deviations from the average, respectively. 60 40 56 44 68 32 46 54 Employment rate Unemployment rate Nearest to Zone Nearest to Woreda but not Zone Nearest to border but not Zone & Woreda Remote 64 36 60 40 50 50 Employment rate Unemployment rate High accessibility Medium accessibility Low accessibility Figure 6.3: Refugees’ labor market outcomes Source: World Bank Staff based on SESRE 2023. Note: High,", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Nearest to Zone Nearest to Woreda but not Zone Nearest to border but not Zone & Woreda Remote 64 36 60 40 50 50 Employment rate Unemployment rate High accessibility Medium accessibility Low accessibility Figure 6.3: Refugees’ labor market outcomes Source: World Bank Staff based on SESRE 2023. Note: High, medium, and low accessibility refers to the level of market access, with >0, [-0.5, 0], and <-0.5 standard deviations from the average, respectively. a. By proximity to resource hub b. By market accessibility Markets and Opportunities 60 11 15 17 34 26 34 40 58 Nearest to Zone Nearest to Woreda but not Zone Nearest to border but not Zone & Woreda Remote Boys Girls Boys Girls 13 9 27 32 26 47 High accessibility Medium accessibility Low accessibility Figure 6.5: The share of refugee youth who are NEET Source: World Bank Staff based on SESRE 2023. Note: High, medium, and low accessibility refers to the level of market access, with >0, [-0.5, 0], and <-0.5 standard deviations from the average, respectively activities inside camps or in the informal sectors in surrounding areas, such as construction, small shops, and street trades. The likelihood of engaging in the industry sector is", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "and low accessibility refers to the level of market access, with >0, [-0.5, 0], and <-0.5 standard deviations from the average, respectively activities inside camps or in the informal sectors in surrounding areas, such as construction, small shops, and street trades. The likelihood of engaging in the industry sector is higher (30 percent) for refugees in highly-accessible areas, highlighting that refugees can participate in different employment sectors as long as their location is well-connected to markets. The prevalence of youth refugees not in employment, education, or training (NEET) increases with remoteness and poor connectivity. Youth without employment, education, or training decreases their future labor market outcomes and lifetime earnings (Zanfrini and Giuliani, 2023). About 38 percent of the working-age refugee population is between ages 15 and 24, and one-fifth of these youth is NEET. Spatial inequalities in NEET are significant, with more girls being NEET in any area. About 58 percent of young women and 34 percent of young men in remote camps are NEET, but only 26 percent of young women and 11 percent of young men in camps nearest to Zone capital cities are NEET (Figure 6.5a). Similarly, a higher share of young women (47 percent) and young", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "percent of young women and 34 percent of young men in remote camps are NEET, but only 26 percent of young women and 11 percent of young men in camps nearest to Zone capital cities are NEET (Figure 6.5a). Similarly, a higher share of young women (47 percent) and young men (27 percent) are NEET in low-connected areas compared to others (Figure 6.5b). Figure 6.4: Sectoral employment Source: World Bank Staff based on SESRE 2023. Note: We classified the service sector as trade and another service, aiming to shed light on refugees’ engagement in trade activity. a. By proximity to resource hub b. By market accessibility a. By proximity b. By market accessibility Note: High, medium, and low accessibility refers to the level of market access, with >0, [-0.5, 0], and <-0.5 standard deviations from the average, respectively. 0 20 40 60 80 100 Remote Nearest to border Nearest to Woreda Nearest to Zone Share of individuals (%) Trade Industry Other service Agriculture Trade Industry Other service Agriculture Low accessibility Medium accessibility High accessibility Share of individuals (%) 0 20 40 60 80 100 Markets and Opportunities 61 6.2 Effects of local factors on refugees’ labor market outcomes This section", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Woreda Nearest to Zone Share of individuals (%) Trade Industry Other service Agriculture Trade Industry Other service Agriculture Low accessibility Medium accessibility High accessibility Share of individuals (%) 0 20 40 60 80 100 Markets and Opportunities 61 6.2 Effects of local factors on refugees’ labor market outcomes This section estimates the effect of local factors on refugees’ employment outcomes. It shows how local factors matter for employment opportunities by looking at refugees aged 18 to 64 not currently studying. More specifically, it sheds light on the importance of accessible locations and proximity to economic and resource hubs for refugees to perform better in local labor markets and to access sustainable economic opportunities. The analysis uses household and individual information from SESRE data and geospatial information. The estimation applies logistic regressions to predict the effects of the various indicators on the probability of being employed and working in different sectors of employment (see Annex D, Table D.14). Annex D, Table D.15 shows the average marginal effects of the explanatory variables. We discuss the results using predicted marginal probabilities of being employed based on various local factors. The local labor market structure affects49 the possibility of refugees finding jobs. Consistent with", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "of employment (see Annex D, Table D.14). Annex D, Table D.15 shows the average marginal effects of the explanatory variables. We discuss the results using predicted marginal probabilities of being employed based on various local factors. The local labor market structure affects49 the possibility of refugees finding jobs. Consistent with existing evidence (Andersen et al., 2023), the study reveals that in a local economy where most of the working-age host population is in the labor market, refugees have a higher prospect of employment. Across all model specifications, men are more likely to be employed than women. For example, in a local market where only 50 percent of the working-age population is active, employment prospects are 12 percent for female refugees but 22 percent for male refugees (Figure 6.6). However, this difference in the probability of employment between male and female refugees disappears in local labor markets where more of the working-age population is active. The finding implies that refugees will perform better in a labor market with better employment prospects. Moreover, local unemployment levels affect the odds of being employed for refugees, regardless of the gender of the refugee. The higher the unemployment rates in local area, the lower the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "working-age population is active. The finding implies that refugees will perform better in a labor market with better employment prospects. Moreover, local unemployment levels affect the odds of being employed for refugees, regardless of the gender of the refugee. The higher the unemployment rates in local area, the lower the refugees’ chance of obtaining jobs (Figure 6.7), consistent with existing evidence (Azlor et al., 2020). Proximity to resource hubs increases refugees’ chances or working, regardless of gender. In all of our proximity measurements, refugees nearest to the Zone capital cities have a higher chance of being employed (Annex D, Table D.16). Only about 34 percent of male and 23 percent of female refugees living 100 kilometers from a Zone capital city are employed. In contrast, the chance of obtaining 49 The analysis proxies the local labor market by the aggregate market of urban areas of each Zone where refugee camps are located. Seven Zones host refugees; this study calls them hosting Zones. North Gondar Zone hosts Eritreans in Alemwach camp. Awsi (Zone 1) hosts Eritreans in Asayita camp. Liben Zone hosts Somali refugees in Bokolmanyo, Buramino, Hilaweyn, Kobe, and Melkadida camp. Fafan Zone hosts Somali refugees in Aw-barre, Kebribeyah, and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "are located. Seven Zones host refugees; this study calls them hosting Zones. North Gondar Zone hosts Eritreans in Alemwach camp. Awsi (Zone 1) hosts Eritreans in Asayita camp. Liben Zone hosts Somali refugees in Bokolmanyo, Buramino, Hilaweyn, Kobe, and Melkadida camp. Fafan Zone hosts Somali refugees in Aw-barre, Kebribeyah, and Sheder camp. Agnuak Zone hosts refugees from South Sudan in Pinyudo 1 and 2, Jewi and Okugo camp. Itang Special Zone hosts refugees from South Sudan in Tierkidi, Kule, and Nguenyyiel. Assesa hosts refugees from South Sudan in Bambasi, Sherkole, and Tsore. Probability of being employed 1 .8 .6 .4 Female 50 60 70 80 90 100 Male Labor force participation rate .2 Figure 6.6: Local labor supply effect of refugee’s odds of employment Source: World Bank Staff based on SESRE 2023. Note: Predicted marginal probabilities of being employed based on the labor force participation rate of the local market, tabulated by employment experience. Probability of being employed .8 .6 .4 Female 2 6 10 14 18 4 8 12 16 20 Male Unemployment rate (5%) .2 Figure 6.7: Local unemployment level matters to obtain jobs Note: Predicted marginal probabilities of being employed based on the unemployment rate of the", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "tabulated by employment experience. Probability of being employed .8 .6 .4 Female 2 6 10 14 18 4 8 12 16 20 Male Unemployment rate (5%) .2 Figure 6.7: Local unemployment level matters to obtain jobs Note: Predicted marginal probabilities of being employed based on the unemployment rate of the local market, tabulated by gender. Markets and Opportunities 62 a job increases to 59 percent for male refugees and 47 percent for female refugees living within 10 kilometers of a Zone capital city (Figure 6.8). Overall, proximity to resource hubs leads to better employment outcomes for refugees. The chance of being employed is higher for male refugees proximate to resource hubs by 41 percentage points compared to those living in remote locations (Figure 6.9). Irrespective of distance to economic hubs, the gender employment gap persists, with female refugees having lower chances of being employed. However, the employment gap between male and female refugees narrows as the location gets more remote. Female refugees are 8 percentage points less likely to be employed then male refugees in remote areas, but 13 percentage points in locations near Zone capital cities (Figure 6.9). Refugees in Woredas well-connected to markets have better prospects of being", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "and female refugees narrows as the location gets more remote. Female refugees are 8 percentage points less likely to be employed then male refugees in remote areas, but 13 percentage points in locations near Zone capital cities (Figure 6.9). Refugees in Woredas well-connected to markets have better prospects of being employed. Compared to other locations, Woredas with above- average market accessibility increase employment probability for refugees (Annex D, Table D.15). Regardless of education level and gender, the chance of being employed is below 25 percent for refugees living in a Woreda with a level of market access >1.5 standard deviations below the average. Moreover, the gender gap in employment persists at any level of market access but becomes more pronounced with decreased market accessibility. Female Male 2 .6 .5 .4 .3 .2 .1 12 22 32 42 52 62 72 82 92 102 112 122 132 142 Distance to nearest capital city (Km) Probability of being employed Figure 6.8: Distance to the nearest city and the chance of obtaining a job for refugees Source: World Bank Staff based on SESRE 2023. Note: Predicted marginal probabilities of being employed based on the distance to the nearest Zone capital city, tabulated by", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "capital city (Km) Probability of being employed Figure 6.8: Distance to the nearest city and the chance of obtaining a job for refugees Source: World Bank Staff based on SESRE 2023. Note: Predicted marginal probabilities of being employed based on the distance to the nearest Zone capital city, tabulated by gender. 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Nearest to zone Nearest to Woreda Nearest to border Remote Probability of being employed Male Female Figure 6.9: Employment and proximity to resource hubs Note: Predicted marginal probabilities of being employed based on proximity to resource, tabulated by gender. Markets and Opportunities 63 S ocial cohesion is vital for refugees’ ability to integrate and contributes to social development. While social cohesion is often defined differently in different contexts, we define it here as “a sense of shared purpose, trust, and willingness to cooperate” (Barron et al., 2023). To be socially sustainable, communities must work together to overcome challenges, provide public goods, and allocate resources fairly, and social cohesion has long been seen as critical for solid institutions and economic growth (Easterly et al., 2006). This is often challenging in a refugee context, where refugees not only experienced traumatic shocks to their", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "communities must work together to overcome challenges, provide public goods, and allocate resources fairly, and social cohesion has long been seen as critical for solid institutions and economic growth (Easterly et al., 2006). This is often challenging in a refugee context, where refugees not only experienced traumatic shocks to their social, economic, and emotional wellbeing, but they also face host communities’ concerns regarding how refugees affect the local labor market, the availability of goods and services, and the environment (World Bank, 2023a). These challenges are even more significant when refugee camps are in underdeveloped and underserved regions of the country, where there is greater competition over scarce resources, livelihood opportunities, and services. Despite challenges, forced displacement does not always lead to poor social cohesion between refugees and hosts. Social cohesion can actually improve due to the benefits refugees bring to host communities, and with positive interactions between refugees and hosts. In remote areas, refugees often increase local economic development by increasing the availability of labor and demand for products and services. Aid inflow accompanying refugees can also promote economic development in the host community. Across the world, studies show that refugees are more likely to have positive, rather than adverse,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "In remote areas, refugees often increase local economic development by increasing the availability of labor and demand for products and services. Aid inflow accompanying refugees can also promote economic development in the host community. Across the world, studies show that refugees are more likely to have positive, rather than adverse, economic effects on the host community. Economic studies of refugee camps in East Africa tend to find benefits for local economic development (Verme et al., 2021; Alix-Garcia et al., 2018; Maystadt et al., 2014). In Ethiopia, Walelign et al. (2022) find that refugees increase income diversification and livestock product sales for hosts and increase local market activity. Similarly, in Uganda, Zhou et al. (2022) found that increased refugee inflows improved local access to health, education, and roads, and had no detectable effect 7. Social Cohesion Markets and Opportunities 64 on hosts’ attitudes toward refugees. Other evidence from Uganda finds that interactions between hosts and refugees may help improve hosts’ attitudes (Betts et al., 2023). In some contexts, refugee inflows have been found to harden in-group identification and increase support for ideological extremes. This was the case with refugee inflows in Denmark, for instance, but only in rural areas (Dustmann et", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "interactions between hosts and refugees may help improve hosts’ attitudes (Betts et al., 2023). In some contexts, refugee inflows have been found to harden in-group identification and increase support for ideological extremes. This was the case with refugee inflows in Denmark, for instance, but only in rural areas (Dustmann et al., 2019). On the other hand, refugees hosted in Austrian municipalities for extended periods, as opposed to those who passed through, were found to reduce support for anti-immigrant parties, pointing to the benefits of refugee-host interactions (Steinmayr, 2021). All-in-all, there is little evidence that refugee hosting tends to worsen attitudes toward refugees in the Global South (World Bank, 2023b). 7.1 Attitudes between refugees and hosts SESRE data show that, while some hosts have negative attitudes towards refugees, most attitudes are generally positive. Sixty-five percent of hosts agree that refugees are friendly and good people, and only 20 percent are uncomfortable with having a refugee neighbor. This is an important finding, highlighting the potential for integration policies. Host attitudes are generally most favorable in the Somali region and most negative around South Sudanese camps; the share not comfortable with having a refugee neighbor increases to 37 percent in the South Sudanese", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "with having a refugee neighbor. This is an important finding, highlighting the potential for integration policies. Host attitudes are generally most favorable in the Somali region and most negative around South Sudanese camps; the share not comfortable with having a refugee neighbor increases to 37 percent in the South Sudanese domain. The greater degree of cultural and linguistic overlap between Somali refugees and their Ethiopian hosts may explain why attitudes are generally better in the Somali domain and worse in the South Sudanese domain, but socio-political tensions over ethnic composition in the Gambella region is also a factor (see Box 7.1). Evidence from many settings show that host attitudes and propensity for positive host-migrant contact increase with cultural proximity between migrants and hosts (World Bank, 2023b; Hainmueller et al., 2014; Betts et al., 2023). As an historical example, political backlash during the U.S. age of mass migration (between roughly 1850 to 1910) was more significant against immigrant groups that were more culturally distant (Tabellini, 2020). In East Africa, the relationship between refugee- host interactions in Uganda and positive attitudes was higher when there was greater cultural overlap (Betts et al., 2023). In Ethiopia, Somali refugees and hosts benefit from speaking", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "to 1910) was more significant against immigrant groups that were more culturally distant (Tabellini, 2020). In East Africa, the relationship between refugee- host interactions in Uganda and positive attitudes was higher when there was greater cultural overlap (Betts et al., 2023). In Ethiopia, Somali refugees and hosts benefit from speaking a common language and having a common religion, which is not always the case in the South Sudanese domain. However, most South Sudanese refugees still think they are culturally similar to hosts (Figure 7.16). The worse attitudes towards refugees in the South Sudanese domain are also related to socio-political tensions over ethnic composition described in Box 7.1. Attitudes among male and female hosts are similar, but female hosts have slightly more positive attitudes, especially in the Somali domain. Female hosts are more likely to agree that refugees are good people (66 compared to 63 percent for males), and this gap is largest in the Somali domain (where it increases to 89 compared 76 percent for males). A similar pattern is observed regarding being comfortable with having a refugee neighbor. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Hosts Strongly agree", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "is largest in the Somali domain (where it increases to 89 compared 76 percent for males). A similar pattern is observed regarding being comfortable with having a refugee neighbor. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Hosts Strongly agree Agree Disagree Strongly disagree Percent Figure 7.1: Host response to “Refugees are good people” Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Hosts Comfortable Neutral Not comfortable Percent Figure 7.2: Host response to “Would you feel comfortable having a refugee as a neighbor?” Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 65 0 10 20 30 40 50 60 70 80 90 100 Male Female Male Female Male Female Male Female Eritrean Somali South Sudanese Addis Ababa Comfortable Neutral Not comfortable Percent Most Ethiopian hosts want refugees to have access to free primary education and healthcare and the right to work, and to live where they choose. Eighty-seven percent of hosts believe that refugees should have the right to free primary education and healthcare, increasing to 95 percent in", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Not comfortable Percent Most Ethiopian hosts want refugees to have access to free primary education and healthcare and the right to work, and to live where they choose. Eighty-seven percent of hosts believe that refugees should have the right to free primary education and healthcare, increasing to 95 percent in Somali areas, and falling to around 80 percent in the South Sudanese domain. Rates are similarly high regarding the right to work and to internal mobility. While there is more skepticism in the South Sudanese domain, still 63 percent of Ethiopian hosts agree that refugees should have the right to work and 51 percent agree that refugees should have the right to move and settle freely in Ethiopia. Social acceptability for integration of refugees in high; almost half of all hosts agree that refugees have add to economic opportunities in Ethiopia. Walelign et al. (2022) similarly find that refugees’ positively contribute to hosts’ income diversification. Fewer think that refugees increase insecurity or are taking their land, yet these concerns exist for many. Among hosts in the Somali, Eritrean, and South Sudanese domains, 86, 57, and 61 percent, respectively, think that refugees have increased overall economic opportunities, though this rate is", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "contribute to hosts’ income diversification. Fewer think that refugees increase insecurity or are taking their land, yet these concerns exist for many. Among hosts in the Somali, Eritrean, and South Sudanese domains, 86, 57, and 61 percent, respectively, think that refugees have increased overall economic opportunities, though this rate is lower (30 percent) in Addis Ababa. Fewer than half of hosts in all domains believe refugees are increasing insecurity or taking land, though security concerns are moderately higher (49 percent) in the South Sudanese domain. These questions refer to hosts’ perspectives on the effects of refugees in Ethiopia generally, not specifically towards them and their communities. The South Sudanese population is ethnically and culturally diverse, with more than sixty cultural and linguistic groups. The South Sudanese refugees in Ethiopia mainly speak five languages—Nuer, Juba-Arabic, Dinka, Murle, and Luo—and the majority are ethnic Nuer, who in South Sudan are pastoralists (UNHCR, 2023c; Peters and Golden, 2019). Over 90 percent of South Sudanese refugees in Ethiopia are in the Gambella region, a multi-ethnic region dominated by two ethnic groups: the agro-pastoralist Anywaa (Anyuak) and pastoralist Nuer (Hagos 2021). While these groups have a long history of peaceful coexistence, they also have a", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "2023c; Peters and Golden, 2019). Over 90 percent of South Sudanese refugees in Ethiopia are in the Gambella region, a multi-ethnic region dominated by two ethnic groups: the agro-pastoralist Anywaa (Anyuak) and pastoralist Nuer (Hagos 2021). While these groups have a long history of peaceful coexistence, they also have a history of conflicts over land and water resources and political representation (Vemuru et al., 2020; Hagos, 2021). The Anywaa were the majority of the population until the mid-1980s, when an influx of South Sudanese refugees shifted the demographic composition towards Nuer (Feyissa, 2015). This trend continued with the influx of more South Sudanese refugees in 2013, creating a sense of marginalization among the Anywaa in terms of changes in demographic composition, widening educational disparities, and increasing insecurity (Vemuru et al., 2020). The struggle between these two ethnic groups in the Gambella region has created socio-political tensions and influenced South Sudanese refugees’ social integration (ReDSS, 2018). Box 7.1: Socio-political tensions in the Gambella Region Figure 7.3: Host response to “Refugees are good people” by gender Source: World Bank Staff based on SESRE 2023. Figure 7.4: Host response to “Would you feel comfortable having a refugee as a neighbor?” by gender Source:", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "refugees’ social integration (ReDSS, 2018). Box 7.1: Socio-political tensions in the Gambella Region Figure 7.3: Host response to “Refugees are good people” by gender Source: World Bank Staff based on SESRE 2023. Figure 7.4: Host response to “Would you feel comfortable having a refugee as a neighbor?” by gender Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Male Female Male Female Male Female Male Female Eritrean Somali South Sudanese Addis Ababa Strongly agree Agree Disagree Strongly disagree Percent Markets and Opportunities 66 Most hosts do not think they have experienced adverse effects from refugees. However, a sizeable minority are concerned about the effects on employment, inflation, security, and deforestation in their communities, with significant differences across domains. The most consistent perceived effects are economic competition and price increases. Across domains, 29 to 39 percent of hosts think they have experienced either wage or employment competition due to refugees.50 Beliefs that refugees have increased prices are especially prevalent in the Eritrea and Addis Ababa domains (70 and 81 percent, respectively), possibly reflecting concerns over housing costs in Addis Ababa. Other studies have shed light on this phenomenon in more detail.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "experienced either wage or employment competition due to refugees.50 Beliefs that refugees have increased prices are especially prevalent in the Eritrea and Addis Ababa domains (70 and 81 percent, respectively), possibly reflecting concerns over housing costs in Addis Ababa. Other studies have shed light on this phenomenon in more detail. In rural areas, Ayenew (2021) finds that hosting refugees increased prices of food and agricultural inputs. Deforestation is a concern in the Somali and South Sudan domains (47 and 35 percent, respectively), where refugees and hosts rely on firewood for cooking fuel. Tesfaye (2021) also shows that hosts perceive negative environmental impact in terms of deforestation and loss of wildlife of South Sudanese refugees in Bambasi Woreda. Security concerns are highest at 34 percent in the South Sudanese and Addis Ababa domains. Fewer than 15 percent of hosts in each domain think refugees are deteriorating infrastructure (not presented). Some hosts think refugees have improved local infrastructure and access to health and education services. Thirty-eight percent of Somali hosts believe refugees have improved local infrastructure, and 36 percent think they have improved local services. In the South Sudan domain, these rates are 16 and 18 percent. 0 20 40 60 80", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "think refugees have improved local infrastructure and access to health and education services. Thirty-eight percent of Somali hosts believe refugees have improved local infrastructure, and 36 percent think they have improved local services. In the South Sudan domain, these rates are 16 and 18 percent. 0 20 40 60 80 100 120 Eritrean Somali South Sudanese Addis Ababa All Hosts Right to work Right to internal mobility Right to free primary education Right to free healthcare Figure 7.5: Share of hosts who agree refugees should have access to... Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 Eritrean Somali South Sudanese Addis Ababa All Hosts Price Increases Economic Competition Insecurity Increases Deforestation Figure 7.7: Negative experiences due to refugees Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 Eritrean Somali South Sudanese Addis Ababa All Hosts Increased economic opportunity Increased insecurity Are taking our land Figure 7.6: Host beliefs about refugee impact in Ethiopia Source: World Bank Staff based on SESRE 2023. 50 The numbers are combined here, but mainly measures employment competition since very few are concerned about wage competition. 0", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Addis Ababa All Hosts Increased economic opportunity Increased insecurity Are taking our land Figure 7.6: Host beliefs about refugee impact in Ethiopia Source: World Bank Staff based on SESRE 2023. 50 The numbers are combined here, but mainly measures employment competition since very few are concerned about wage competition. 0 5 10 15 20 25 30 35 40 Eritrean Somali South Sudanese Addis Ababa All Hosts Improved Infrastructure Improved Services Figure 7.8: Positive experience due to refugees Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 67 Overall, hosts and refugees show similar trust rates in each other; still, refugees are generally more trusting. Questions about the trustworthiness of hosts and refugees reveal that most people either trust both (hosts and refugees) or neither group. Only 17 percent of hosts trust other Ethiopians but not refugees, and only 10 percent of refugees trust other refugees but not Ethiopians. In comparison, 39 percent of hosts and 55 percent of refugees trust both groups. Once again, hosts’ trust towards refugees is highest in the Somali domain (67 percent) and lowest in the South Sudanese domain (29 percent). On the other hand, refugee trust towards hosts is lowest in the Eritrean", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "In comparison, 39 percent of hosts and 55 percent of refugees trust both groups. Once again, hosts’ trust towards refugees is highest in the Somali domain (67 percent) and lowest in the South Sudanese domain (29 percent). On the other hand, refugee trust towards hosts is lowest in the Eritrean camps (38 percent). Results of combining the survey answers into an index echo our prior findings; attitudes toward refugees are better in Somali areas, worse in South Sudan areas, and slightly better among women. The above questions on attitudes and trust towards refugees, the rights refugees should have, and the effects refugees have had on Ethiopia can be combined into an index. To construct the index, we average the response to the ten questions examined above, rescaled to range from 1-4. The index is highest in the Somali domain (.46 standard deviations above the mean) and lowest in the South Sudan domain (.48 standard deviations below the mean). It is higher for female than male hosts in both domains, by .14 standard deviations from average in the Somali domain and .19 standard deviations in the South Sudan domain. The difference in attitudes between male and female hosts is not statistically", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Sudan domain (.48 standard deviations below the mean). It is higher for female than male hosts in both domains, by .14 standard deviations from average in the Somali domain and .19 standard deviations in the South Sudan domain. The difference in attitudes between male and female hosts is not statistically significant, and inter-group attitudes vary little by age or education. We can study the characteristics associated with attitudes by putting this index into a regression 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Addis Ababa All Hosts All Refugees Neither are trustworthy Only my group Only the other group Both are trustworthy Percent Figure 7.9: Are most Ethiopians/refugees in Ethiopia trustworthy? Source: World Bank Staff based on SESRE 2023. Note: Combines two questions regarding trust in Ethiopians and refugees, with identical wording to both Ethiopians and refugees. 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 Eritrean Somali South Sudanese Addis Ababa All Hosts Figure 7.10: Host attitudes Index Source: World Bank Staff based on SESRE 2023. Note: This index is an average of ten questions regarding beliefs about refugees’ character, the rights they should", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "and refugees. 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 Eritrean Somali South Sudanese Addis Ababa All Hosts Figure 7.10: Host attitudes Index Source: World Bank Staff based on SESRE 2023. Note: This index is an average of ten questions regarding beliefs about refugees’ character, the rights they should receive, and their impact on the host community. The scale ranges from 1-4, where more positive indicates better attitudes. 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 Male Female Male Female Male Female Male Female Male Female Eritrean Somali South Sudanese Addis Ababa All Hosts Figure 7.11: Host attitudes index by gender Markets and Opportunities 68 framework. This is presented in Annex D, Table D.18. While attitudes are less positive for men, as we have seen, this difference is not statistically significant after controlling for age and education. There is no clear pattern in attitudes by age and education. Based on regression analysis, the most significant predictor of positive host attitudes and trust is whether they think the presence of refugees has improved local infrastructure or services. This hints at the importance of local service delivery in driving host attitudes. On average, controlling for other characteristics and regions, hosts", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Based on regression analysis, the most significant predictor of positive host attitudes and trust is whether they think the presence of refugees has improved local infrastructure or services. This hints at the importance of local service delivery in driving host attitudes. On average, controlling for other characteristics and regions, hosts who think refugees have improved local infrastructure and service delivery score .41 standard deviations higher on the Attitudes Index and are 12 percentage points more likely to feel hosts are trustworthy. This is consistent with extensive evidence that service delivery and aid inflows are crucial in improving social cohesion between hosts and refugees (World Bank, 2023a). From a policy perspective, this points to the benefits of ensuring that aid and programs to support refugees also benefit hosts (Baseler et al., 2021). Host trust for refugees does not significantly depend on gender, age, or education, and it does not increase with time in Ethiopia. While trust of refugees is higher for women and less-educated refugees, these differences are not statistically significant. There is little relationship between trust and time in Ethiopia. This may result from the lack of integration into Ethiopian society and social interaction with Ethiopians (discussed later in this", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "time in Ethiopia. While trust of refugees is higher for women and less-educated refugees, these differences are not statistically significant. There is little relationship between trust and time in Ethiopia. This may result from the lack of integration into Ethiopian society and social interaction with Ethiopians (discussed later in this section). However, refugees are more trusting if they believe they are more culturally similar to their hosts. Refugees responding positively to the question, “Do you agree that you are culturally similar to the host community?” are 20 percent more likely to believe Ethiopians are trustworthy. This is true after controlling for gender, age, education, and time in Ethiopia. Importantly, it also controls for domains, so this implies that variations in cultural proximity within the domain drive this result. This indicates that cultural similarity plays a role in facilitating social cohesion. On the other hand, we see that cultural similarity explains some but not all of the high trust in Somali camps relative to other domains. 7.2 Social interactions Social and community integration is fundamental to refugees’ ability to improve their livelihoods, support systems, and economic integration. Having friends in the host country is a valuable resource for refugees; Ethiopian friends", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "some but not all of the high trust in Somali camps relative to other domains. 7.2 Social interactions Social and community integration is fundamental to refugees’ ability to improve their livelihoods, support systems, and economic integration. Having friends in the host country is a valuable resource for refugees; Ethiopian friends can provide valuable information, employer connections, assistance with language, and countless other types of social and economic support. This can also promote more positive attitudes between groups. Social integration has improved well-being, health, and educational achievement for refugee adolescents across various settings (Boda et al., 2023). 0 5 10 15 20 25 30 35 Eritrean Somali South Sudanese Addis Ababa All Refugees Family Friends Figure 7.12: Share with family or friends in Ethiopia Source: World Bank Staff based on SESRE 2023. 0 5 10 15 20 25 30 35 40 Age Under 30 Age 30-44 Age 45-64 Age Over 64 Female Male Figure 7.13: Share with friends in Ethiopia by demographic group Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 69 Despite the generally positive attitudes described, social integration—measured by the friends and family refugees have in Ethiopia—is low. Only 7 percent of refugees report having family", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Female Male Figure 7.13: Share with friends in Ethiopia by demographic group Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 69 Despite the generally positive attitudes described, social integration—measured by the friends and family refugees have in Ethiopia—is low. Only 7 percent of refugees report having family in Ethiopia, and 25 percent report having an Ethiopian friend outside the refugee camp. This rate is slightly higher among OCP refugees in Addis Ababa but still relatively low at 11 for having family and 31 percent for having a friend. The share with Ethiopian friends is higher for men, but similar across age groups (though lower for refugees over age 64). This masks some variation across domains; refugees under age 30 are likely to have friends than older refugees in the Eritrean and South Sudan domains. In contrast, refugees under age 30 are less likely to have friends in Addis Ababa. Many refugees report that social interactions and sharing resources with hosts is “not easy,” especially in the South Sudanese domain. Overall, 30 percent of refugees say it is not easy to have social interactions with hosts, and 34 percent report that it is challenging to share resources such", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Addis Ababa. Many refugees report that social interactions and sharing resources with hosts is “not easy,” especially in the South Sudanese domain. Overall, 30 percent of refugees say it is not easy to have social interactions with hosts, and 34 percent report that it is challenging to share resources such as water and food. These rates fall to 21 for social interaction and 15 percent for challenge in sharing resources in the South Sudan domain. On the other hand, refuges do not report that it is difficult to conduct market interactions. Country-wide, the relationship between integration outcomes and demographic characteristics is complex. More-educated male refugees are more likely to have Ethiopian friends, but this does not appear to improve ease the creation of social interactions or sharing resources with hosts. The results in Column 2 of Annex D, Table D.19 show—controlling for other characteristics, including region and year of arrival—that refugee men are 6.7 percentage points more likely to have an Ethiopian friend than refugee women, and those who completed secondary education are 22 percentage points more likely to have an Ethiopian friend. However, no significant difference exists in the ease of which these groups find it to have social", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "refugee men are 6.7 percentage points more likely to have an Ethiopian friend than refugee women, and those who completed secondary education are 22 percentage points more likely to have an Ethiopian friend. However, no significant difference exists in the ease of which these groups find it to have social interactions with hosts. Men are notably worse in terms of their reported ease of sharing resources. As time passes, Ethiopian refugees become more likely to have Ethiopian family and friends and find market interactions more accessible; but this occurs slowly and only translates into greater ease of socializing or sharing resources. A refugee who has spent an additional ten years in Ethiopia is only six percentage points more likely to have an Ethiopian friend and 8 percentage points more likely to find market interactions easier. Still, there is no effect on ease of social interactions or sharing resources. Because of low social integration, refugees rely little on the local population in times of need. When refugees are asked, “Who is most reliable in a time of need?” almost none respond with the local population. Instead, refugees heavily rely on donations and their family and friends in Ethiopia, 0 10 20", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "low social integration, refugees rely little on the local population in times of need. When refugees are asked, “Who is most reliable in a time of need?” almost none respond with the local population. Instead, refugees heavily rely on donations and their family and friends in Ethiopia, 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Refugees Selling/buying items Social interactions Sharing resources Figure 7.14: Share of refugees who think interactions with hosts are “easy to do” Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Refugees Friend/family in Ethiopia Friend/family abroad Local population, Ethiopians NGOs/donations Myself Percent Figure 7.15: Who do refugees rely on in times of need Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 70 while 9 percent of refugees respond that they would depend on themselves. Only 0.2 percent of refugees in camps and 2.3 percent in Addis Ababa rely most on the local population in times of need. Refugees with Ethiopian friends are more likely to be employed and to work in high-skill occupations. Annex D,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "of refugees respond that they would depend on themselves. Only 0.2 percent of refugees in camps and 2.3 percent in Addis Ababa rely most on the local population in times of need. Refugees with Ethiopian friends are more likely to be employed and to work in high-skill occupations. Annex D, Table D.20 uses another regression framework to study the relationship between labor market outcomes and various social integration outcomes, controlling for domain, gender, age, education, and years in Ethiopia. For the regression, the social integration outcomes are: “having an Ethiopian friend and family”, whether they find social interactions with hosts “easy to do,” and their perceived cultural similarity to hosts. The results show that having an Ethiopian friend is associated with a 6 to 7 percentage point increase in the probability of refugee employment and, among those who are employed, a 4 to 5 percentage point increase in the likelihood of being in a high-skill occupation (manager, professional, or associate professional). The variables for having an Ethiopian family and finding social interactions easy are positive (not statistically significant). In contrast, the variable for cultural similarity is both large, at around 8 percentage points, and statistically significant, highlighting the continued importance", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "of being in a high-skill occupation (manager, professional, or associate professional). The variables for having an Ethiopian family and finding social interactions easy are positive (not statistically significant). In contrast, the variable for cultural similarity is both large, at around 8 percentage points, and statistically significant, highlighting the continued importance of cultural similarity for employment outcomes. Among refugees in camps, working outside of the camp appears to be relatively unrelated to all these characteristics. It is important to remember that none of these relationships are necessarily causal; they merely show that having an Ethiopian friend and sharing cultural similarity with hosts are the social integration measures most closely related to improved labor market outcomes. However, they do not necessarily explain who can, or does, work outside the camp. Low levels of refugee social integration is not due to lack of cultural similarity with Ethiopian hosts. While this is undoubtedly a challenge in some cases, notably among South Sudanese, refugees generally believe they are culturally similar to their hosts. This rate averages 78 percent for all refugees. It is lowest in South Sudanese camps at 68 percent and highest in Somali camps at 87 percent. Many of these refugees who say", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "challenge in some cases, notably among South Sudanese, refugees generally believe they are culturally similar to their hosts. This rate averages 78 percent for all refugees. It is lowest in South Sudanese camps at 68 percent and highest in Somali camps at 87 percent. Many of these refugees who say they are culturally similar to their hosts respond that they have no Ethiopian friends and that social interactions with Ethiopians are complex. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Refugees Figure 7.16: Share of refugees who agree they are “culturally similar to hosts” Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 Age Under 30 Age 30-44 Age 45-64 Age Over 64 Female Male Figure 7.18: Share or refugees engaged in a community representative body by demographic group Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 Eritrean Somali South Sudanese Addis Ababa All Refugees Figure 7.17: Share or refugees involved in a community representative body Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 71 Low refugee social integration is not", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "based on SESRE 2023. 0 10 20 30 40 50 60 70 80 Eritrean Somali South Sudanese Addis Ababa All Refugees Figure 7.17: Share or refugees involved in a community representative body Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 71 Low refugee social integration is not because they have a low willingness to engage with their communities. Refugees have an extremely high rate of involvement in refugee community representative bodies. This includes participation in refugee central committees, refugee outreach volunteers, refugee community leaders, and leaders in women and youth associations. Sixty percent of refugees participate in an organization like this, the lowest in Eritrean camps at 32 percent. On average, these rates are similar across age and gender groups. Main immediate refugee integration challenges are: (i) to expand involvement outside of refugee communities, and (ii) to better understand the social integration barriers refugees in Ethiopia face. In principle, the positive attitudes among many hosts towards refugees, the high degree of cultural similarity between groups, and the willingness of refugees to be engaged in their community are all promising signs for social integration. Yet, even in the Somali domain, where cultural similarity and host attitudes are greatest,", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "In principle, the positive attitudes among many hosts towards refugees, the high degree of cultural similarity between groups, and the willingness of refugees to be engaged in their community are all promising signs for social integration. Yet, even in the Somali domain, where cultural similarity and host attitudes are greatest, more than two-thirds of refugees do not have an Ethiopian friend, and more than half do not find social interactions with hosts easy. Better employment outcomes for refugees with Ethiopian friends indicate the benefits of facilitating social integration for refugee livelihoods and economic integration. 0 5 10 15 20 25 30 Age Under 30 Age 30-44 Age 45-64 Age Over 64 Female Male Discrimination/harassment in past year Victim of crime in past 2 weeks Figure 7.20: Discrimination and harassment by demographic group Source: World Bank Staff based on SESRE 2023. 0 5 10 15 20 25 30 35 Eritrean Somali South Sudanese Addis Ababa All Refugees Discrimination/harassment in past year Victim of crime in past 2 weeks Figure 7.19: Discrimination and harassment Source: World Bank Staff based on SESRE 2023. 72 A ddressing the challenges refugees face in Ethiopia requires a concerted effort to promote their self-reliance, economic integration, and", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "South Sudanese Addis Ababa All Refugees Discrimination/harassment in past year Victim of crime in past 2 weeks Figure 7.19: Discrimination and harassment Source: World Bank Staff based on SESRE 2023. 72 A ddressing the challenges refugees face in Ethiopia requires a concerted effort to promote their self-reliance, economic integration, and access to education. By leveraging data from initiatives like SESRE and adopting a comprehensive approach that considers the needs of both refugees and host communities, Ethiopia can maximize the benefits of hosting refugees while minimizing associated costs. The GoE has proven its strong commitment to protecting refugees, but the progressive policy framework has not yet translated into tangible socioeconomic outcomes for refugees. The encampment model previously followed in Ethiopia neglected how refugees affect socio-economic and environmental conditions of hosting communities, including the untapped potential for refugees to contribute to the local economy. Despite strong improvements in Ethiopia’s underlying legal framework to benefit refugee inclusion, and a strong international aid response, refugees still face various challenges accessing services and improving their socioeconomic outcomes. Refugees are unable to move to locations with better economic opportunities and require a work permit (which is difficult to get for work outside of refugee camps) to", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "to benefit refugee inclusion, and a strong international aid response, refugees still face various challenges accessing services and improving their socioeconomic outcomes. Refugees are unable to move to locations with better economic opportunities and require a work permit (which is difficult to get for work outside of refugee camps) to legally access the labor market. As a result, refugees in Ethiopia remain poor and depend heavily on humanitarian assistance. Concerted effort and policy interventions are necessary to better integrate refugees and improve the well-being of both refugees and host communities. The existing cultural and ethnic- based affiliation between refugees and their hosts is critical in facilitating and enhancing socio- economic integration. As highlighted in Chapter 7, the context for an integrated solution is favorable: social cohesion is high, creating a supportive context for policy rollout. Sixty-five percent of hosts agree that refugees are friendly and good people, and only 20 percent are uncomfortable with having a refugee neighbor. Moreover, the social acceptability for integrating refugees is high; almost half of all hosts agree that refugees have increased economic opportunities in Ethiopia. 8. Policy Recommendations 73 Improvements can be achieved by focusing policy attention on three areas: (i) Providing refugees with", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "percent are uncomfortable with having a refugee neighbor. Moreover, the social acceptability for integrating refugees is high; almost half of all hosts agree that refugees have increased economic opportunities in Ethiopia. 8. Policy Recommendations 73 Improvements can be achieved by focusing policy attention on three areas: (i) Providing refugees with a path to self-reliance. (ii) Implementing place-based interventions to alleviate the pressures for refugees and hosts. (iii) Continuing to implement the progressive policy framework for refugees. Path to self-reliance Pursue development approaches that enable and incentivize refugees’ self-reliance in Ethiopia to improve refugees’ outcomes and reduce burdens on host communities. Host communities can reap economic benefits from refugees’ presence. In Ethiopia, the path of self-reliance includes, at a minimum: (i) encouraging mobility to access areas with better economic opportunities, (ii) facilitating labor market access for refugees by easing restrictions and providing work permits, (iii) integrating refugee children into national education system to improve their long-term prospects, and (iv) strengthening inclusive healthcare systems to address the health needs of refugees. Encourage refugees to move where economic opportunities are highest, which can also benefit local economies. As outlined in Chapter 6, denying refugees mobility to settle where they would like comes at", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "system to improve their long-term prospects, and (iv) strengthening inclusive healthcare systems to address the health needs of refugees. Encourage refugees to move where economic opportunities are highest, which can also benefit local economies. As outlined in Chapter 6, denying refugees mobility to settle where they would like comes at a cost, as the choice of location within the host country matters for refugees’ labor market outcomes. Placing refugees in areas with lower economic opportunities without the ability to move makes it difficult for them to work (Azlor, Damm, and Schultz-Nielsen 2020; Eckert, Hejlesen, and Walsh 2020; Fasani, Frattini, and Minale 2022). Therefore, development approaches that allow refugees to move to areas with high economic potential can provide refugees with more job opportunities and boost demand in local economies. Increased economic demand can pull (host) people out of agriculture and contribute to rural transformation, a prerequisite for achieving structural transformation in Ethiopia. Promote improved refugee access to labor markets to provide sustainable economic opportunities, improved labor outcomes, and better prospects for long-term self-reliance (Muna, 2019). As highlighted in Chapter 3, not all refugees have favorable labor market outcomes and benefit from national economic opportunities. In-camp refugees mainly rely on assistance,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "in Ethiopia. Promote improved refugee access to labor markets to provide sustainable economic opportunities, improved labor outcomes, and better prospects for long-term self-reliance (Muna, 2019). As highlighted in Chapter 3, not all refugees have favorable labor market outcomes and benefit from national economic opportunities. In-camp refugees mainly rely on assistance, have low employment rates, and few opportunities to generate income. Some refugees work outside camps but without work authorization. This limits wages and job security. Easing restrictions on access to the labor market outside of camps and accelerating and automating issuance of work authorizations will have lasting effects in improving refugees’ livelihoods in camps. Given the importance of labor market participation for self-reliance, efforts to strengthen the human capital of refugees during displacement can have large payoffs. Strengthening their skills, knowledge, and experience could enable them to realize their potential and become productive members of society. Build inclusive education systems. Integrating refugees into functioning national education systems can improve future outcomes for refugee children and their hosts (UNHCR, 2020; Piper et al., 2020; Abu-Ghaida and Silva, 2020; Crawford et al., 2015; Bilgili et al., 2019). As Chapter 2 highlighted, more than half of all refugees are children under the age", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Integrating refugees into functioning national education systems can improve future outcomes for refugee children and their hosts (UNHCR, 2020; Piper et al., 2020; Abu-Ghaida and Silva, 2020; Crawford et al., 2015; Bilgili et al., 2019). As Chapter 2 highlighted, more than half of all refugees are children under the age of 15. Although over 70 percent of primary school age children attend primary education, they do not make it past primary education. Integrating refugee children into educational programs soon after their arrival in Ethiopia avoids the loss of valuable years of education and human capital accumulation, hindering prospects. It is critical to address the obstacles that hinder children from transitioning to secondary schooling, such as challenges in accessing school records, language barriers, or distance to schools. Supporting Regional Education Bureaus could increase the accessibility of secondary schools Policy Recommendations 74 Policy Recommendations to camp refugees.51 To improve the educational attainment of refugee children, the focus should be on increasing the number of qualified teachers in primary education, increasing the currently low compensation to incentive teachers with similar qualifications as nationals, improving primary-to- secondary transition rates, and reducing classroom overcrowding. Build an inclusive health system. Good health is an essential requirement", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "of refugee children, the focus should be on increasing the number of qualified teachers in primary education, increasing the currently low compensation to incentive teachers with similar qualifications as nationals, improving primary-to- secondary transition rates, and reducing classroom overcrowding. Build an inclusive health system. Good health is an essential requirement to rebuild refugees’ lives after displacement, but as highlighted in Chapter 2, refugee children are particularly prone to stunting and other nutritional challenges. Refugees have, as any other population, varied healthcare needs, including non-communicable diseases, infectious diseases, trauma from injuries, and violence. Research shows that conflict has extensive psychological impacts on refugees, particularly youth and children, which are often not addressed (Simpson 2018; Bosqui and Marshoud 2018; Dong 2018). Aligned with the GCR, refugees should be able to access healthcare and essential health services through the national health systems of the destination countries at affordable costs and sufficient quality. Services should consider the challenges refugees face, such as lack of familiarity with administrative procedures, uncertainty about the future, and psychological distress. This requires strengthening and expanding service delivery in the national health sector. This could, for example, be achieved by increasing the enrollment of refugees in the Community-Based Health Insurance", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "should consider the challenges refugees face, such as lack of familiarity with administrative procedures, uncertainty about the future, and psychological distress. This requires strengthening and expanding service delivery in the national health sector. This could, for example, be achieved by increasing the enrollment of refugees in the Community-Based Health Insurance (CBHI) scheme. Place-based intervention Pursue place-based development approaches complementing regional development policies to benefit both refugees and hosts. Place-based interventions are strategies or programs that address issues in a specific geographic location or community. Place-based interventions focus on the unique characteristics, needs, and resources of the particular area; they leverage local assets to address local challenges with the active participation of community members. In Ethiopia, investments in refugee hosting locations should benefit refugees and hosts. Development partners and the GoE should align their development plans to expand opportunities for refugees and host communities sustainably. Leveraging development resources to increase investment in refugee areas can support social cohesion by demonstrating to host communities that the presence of refugees can create new livelihood opportunities for all local people. Direct more educational resources to refugee- hosting school woredas. Despite the positive externalities of integrating refugees into the public- school systems, a large influx", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "in refugee areas can support social cohesion by demonstrating to host communities that the presence of refugees can create new livelihood opportunities for all local people. Direct more educational resources to refugee- hosting school woredas. Despite the positive externalities of integrating refugees into the public- school systems, a large influx of refugee children can exacerbate challenges in local schools where refugees are hosted. Where there are large inflows, the national education system might require additional resources to integrate newly-arrived children. Increasing the supply and improving the quality of schools in affected areas, supported by external assistance and financing, can avoid tension that may arise over competition for access to education services. Better coordination between humanitarian and development actors can support efforts to expand and strengthen national education systems to benefit all students. Support is particularly required in remote areas—where refugees are often hosted—where educational service is strained, even for local children (Abu-Ghaida and Silva, 2020). Expand refugee access to social safety nets. The most vulnerable refugees and hosts may not be able to reap benefits from better development approaches. Social protection (SP) systems can alleviate pressures and safeguard against risks 51 It may be noted that, through the General Education", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "children (Abu-Ghaida and Silva, 2020). Expand refugee access to social safety nets. The most vulnerable refugees and hosts may not be able to reap benefits from better development approaches. Social protection (SP) systems can alleviate pressures and safeguard against risks 51 It may be noted that, through the General Education Quality Improvement Program for Equity (GEQIP-E) mainly funded by the World Bank, the progressive transfer of refugee camps’ secondary schools from DICAC to Regional Education Bureaus has started. For instance, in Gambella region, secondary schools in Jewi and Pinyudo I refugee camps have been taken over by Gambella REB in September 2023 although Gambella REB still requires support to cover all existing needs in concerned schools (i.e., Gambella REB covers education and administrative personnel’s salaries but cannot afford additional construction/maintenance of these schools’ facilities, teachers’ transportation and accommodation, teaching and learning materials, etc.). 75 for vulnerable populations. Social protection encompasses a wide-ranging set of policies and programs to protect people against poverty and risks to their livelihoods and well-being. Implementing place-based SP approaches that allowing the most vulnerable hosts and refugees to participate in national programs—such as done under the Urban Safety Net and Jobs Project—can support social cohesion and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "a wide-ranging set of policies and programs to protect people against poverty and risks to their livelihoods and well-being. Implementing place-based SP approaches that allowing the most vulnerable hosts and refugees to participate in national programs—such as done under the Urban Safety Net and Jobs Project—can support social cohesion and integration. Continue implementing the progressive policy framework regarding refugees Implement concrete actions to realize pledges and proclamations. Action is needed to continue implementing Ethiopia’s progressive framework in refugee inclusion, service integration, and right to work. These relate to transforming camps into human settlements, which facilitate socio-economic opportunities for refugees to absorb the refugee camp into the local population, encouraging mobility to achieve self-reliance, accelerating and automating work authorization by virtue of status to engage refugees in three avenues of job opportunities (joint projects, wage-employment, and self-employment), expanding possibilities for access to land, and improving secondary legislation. Harmonize national and sub-national laws and policies to support the full implementation of the Refugee Proclamation. Although the Refugee Proclamation has provisions to protect refugees, some enabling regulations and directives to facilitate full implementation of the GoE pledges are still lacking. The absence of these regulations is delaying the implementation of most of the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "laws and policies to support the full implementation of the Refugee Proclamation. Although the Refugee Proclamation has provisions to protect refugees, some enabling regulations and directives to facilitate full implementation of the GoE pledges are still lacking. The absence of these regulations is delaying the implementation of most of the rights set out in the Refugee Proclamation. Secondary legislation is still required to provide additional guidance on the meaning and scope of the rights granted, to harmonize relevant national and sub- national laws and policies, and to clarify the roles and responsibilities of government agencies in their implementation. Better identify and document best practices and lessons to better coordinate and implement the Global Compact of Refugees and the CRRF. This includes establishing a system to track progress regularly in implementing the government pledges. To realize Government commitments, close coordination among many stakeholders is vital, including between RRS, line ministries, and humanitarian and development actors at all levels (federal, regional, Woreda, and Kebele). It may be necessary to leverage development resources to accomplish this. Redesigning the OCP to encourage mobility to realize greater socioeconomic opportunities for refugees while accelerating and automating the issuance of work authorizations can enable sustainable improvements in", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "humanitarian and development actors at all levels (federal, regional, Woreda, and Kebele). It may be necessary to leverage development resources to accomplish this. Redesigning the OCP to encourage mobility to realize greater socioeconomic opportunities for refugees while accelerating and automating the issuance of work authorizations can enable sustainable improvements in refugees’ lives. The current system of work authorizations is not implemented effectively. Though definitions for work permits have been improved, few work permits are issued, and restrictions to both wage-employment and self-employment around the areas of work exist. Accelerating and automating the issuance of work authorizations can achieve sustainable improvements in refugees’ lives. Reduce challenges refugees face in accessing business licenses for self-employment. Regarding self-employment, automate the existing procedural requirements that restrict refugees more than the most favorably treated foreign nationals, including a requirement for an investment permit subject to capital requirements. Moreover, the lack of access to finance and lack of credit—from financial service providers, including microfinance institutions, which are not yet able to give credit to refugees— are among the key challenges refugees who want to open businesses face. Improved cooperation and coordination In order to make commitments for sustained support to refugees, the GoE needs to have", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "lack of credit—from financial service providers, including microfinance institutions, which are not yet able to give credit to refugees— are among the key challenges refugees who want to open businesses face. Improved cooperation and coordination In order to make commitments for sustained support to refugees, the GoE needs to have predictable support and streams of resources. The GCR and the CRRF represent a significant step towards improving the current system by providing a renewed architecture for collective action. The Policy Recommendations 76 Policy Recommendations GCR is underpinned by the principles of greater international solidarity and responsibility-sharing. Yet, current development approaches to support refugees and their hosts in Ethiopia are still limited and lack specific mechanisms for sharing the responsibility of hosting refugees more equitably. Humanitarian and development actors need to swiftly invest in and accelerate inclusive approaches. Mobility to accelerate economic opportunities and self-reliance can support the GoE in implementing the Refugee Proclamation of 2019, while encampment undermines achieving the goals set out in the Proclamation. The GoE pledged to transform camps into settlements and facilitate mobility for refugees to take advantage of opportunities in the labor market and increase work authorization to allow for the formalization of working conditions.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "the Refugee Proclamation of 2019, while encampment undermines achieving the goals set out in the Proclamation. The GoE pledged to transform camps into settlements and facilitate mobility for refugees to take advantage of opportunities in the labor market and increase work authorization to allow for the formalization of working conditions. Humanitarian and development partners should strongly support this pledge by swiftly investing in and accelerating inclusive approaches through the engagement of line ministries. Yet, large gaps in financing remain to fill the needs of refugees and host communities. Better coordination and engaging line ministries can achieve better outcomes for refugees and their hosts. Implementing an overarching coordination mechanism across line ministries to track investments and progress on refugee inclusion could leverage the existing humanitarian resources to deliver the first mile investment into inclusive development approaches, led by development actors. Improved communication, collaboration, and connections between RRS and line ministries could support initiatives seeking to mainstream refugees into existing governance structures. Encouraging these collaborative efforts of departments and agencies of the GoE can achieve a successful implementation of development solutions. Efforts to improve the coverage, accuracy, reliability, quality, and comparability of data can provide the analytical underpinning for policy decisions. Better", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "support initiatives seeking to mainstream refugees into existing governance structures. Encouraging these collaborative efforts of departments and agencies of the GoE can achieve a successful implementation of development solutions. Efforts to improve the coverage, accuracy, reliability, quality, and comparability of data can provide the analytical underpinning for policy decisions. Better data enables better planning (and decisions). Integrating refugees as part of the national household survey system could provide high-quality data on a regular basis. This would include the ability to disaggregate data to a subset of the population surveyed and compare refugees with other population groups. This requires strengthening data collection and dissemination mechanisms at all levels. The GoE pledged to include refugee data in national statistics. This would ensure that systems are systematically built to serve all people in a particular “place” regardless of status. This includes the need for a full population count (including refugees) across Ethiopia’s territory to inform decisions such as the size of schools to ensure progress toward inclusive systems that support refugees and their hosts can be made. Strengthening the use of statistics includes facilitating access to data and disseminating results. The SESRE is an excellent start to this initiative. Yet, the need to", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["national household survey system"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "to inform decisions such as the size of schools to ensure progress toward inclusive systems that support refugees and their hosts can be made. Strengthening the use of statistics includes facilitating access to data and disseminating results. The SESRE is an excellent start to this initiative. Yet, the need to systematically integrate refugees in every round of the national household surveys and other data collection activities is key to allowing for evidence-based policy making. 77 Abdelhady D., and Al Ariss, A. (2023). How Capital Shapes Refugees’ Access to the Labour Market: The Case of Syrians in Sweden. The International Journal of Human Resource Management, 34(16), 3144-3168, DOI: 10.1080/09585192.2022.2110845 Abu-Ghaida, D., and Silva, K. (2020). Forced Displacement and Educational Outcomes: Evidence, Innovations, and Policy Indications. Second issue. Quarterly Digest on Forced Displacement. Washington, D.C.: World Bank Group, UNHCR and JDC. Adda, J., Dustmann, C., and Görlach, J. S. (2022). The Dynamics of Return Migration, Human Capital Accumulation, and Wage Assimilation. The Review of Economic Studies, 89(6), 2841-2871. Alix-Garcia, J., Walker, S., Bartlett, A., Onder, H., and Sanghi, A. (2018). Do Refugee Camps Help or Hurt Hosts? The Case of Kakuma, Kenya. Journal of Development Economics, 130, 66-83. Alkire, S., Kanagaratman, U.,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "of Return Migration, Human Capital Accumulation, and Wage Assimilation. The Review of Economic Studies, 89(6), 2841-2871. Alix-Garcia, J., Walker, S., Bartlett, A., Onder, H., and Sanghi, A. (2018). Do Refugee Camps Help or Hurt Hosts? The Case of Kakuma, Kenya. Journal of Development Economics, 130, 66-83. Alkire, S., Kanagaratman, U., and Suppa, N. (2021). The Global Multidimensional Poverty Index (MPI) 2021, OPHI MPI Methodological Note 51. Oxford Poverty and Human Development Initiative, University of Oxford. Altindag, O., Bakls, O., and Rozo, M. S. (2020). Blessing or Burden? Impacts of Refugees on Businesses and the Informal Economy. Journal of Development Economics, 146. https://doi.org/10.1016/j.jdeveco.2020.102490 Andersen, H.L., Osland, L. and Zhang, M.L. (2023). Labour Market Integration of Refugees and The Importance of the Neighborhood: Norwegian Quasi-experimental Evidence. Journal of Labour Market Research 57, 16. https://doi.org/10.1186/ s12651-023-00341-y Aracl, D., Demirci, M., and Klrdar, M. (2022). Development Level of Hosting Areas and the Impact of Refugees on Natives’ Labor Market Outcomes in Turkey. European Economic Review, 145. https://doi.org/10.1016/j.euroecorev.2022.104132 Atamanov, A., Hoogeveen, J., and Reese, B. (2023). The Costs Come Before the Benefits Follow. Why Should Donors Invest More in Refugee Autonomy in Uganda? 1–11. Azlor, L., Damm, A. P., and Schultz-Nielsen, M. L. (2020). Local", "output": {"entities": {"named_data": ["Global Multidimensional Poverty Index (MPI) 2021"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
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-{"input": "Hagos, S. Z. (2021). Refugees and Local Power Dynamics: The Case of The Gambella Region of Ethiopia. Hahn, E., Richter, D., Schupp, J., and Back, M. D. (2019). Predictors of Refugee Adjustment: The Importance of Cognitive Skills and Personality. Collabra: Psychology, 5(1), 23. Hainmueller, J., and Hopkins, D. J. (2014). Public Attitudes Towards Immigration. Annual Review of Political Science, 17, 225-249. Hainmueller, J., Hangartner, D., and Lawrence, D. (2016). When Lives Are Put on Hold: Lengthy Asylum Processes Decrease Employment among Refugees. Science Advances, 2. https://doi.org/10.1126/sciadv.1600432. Hanafi, L., Johns, M., Lacroix, R., Khadija Shaikh, Casaly, P., Aeschlimann, A., and Willy, A. (2021). Refugee Policy Review Framework. Washington, D.C.: World Bank. Heckman, J. J., Stixrud, J., and Urzua, S. (2006). Cognitive and Noncognitive Abilities Affect Labor Market Outcomes and Social Behavior. Journal of Labor Economics, 24(3), 411-482. Hedberg, C. and Tammaru, T. (2012). ‘Neighbourhood Effects’ and ‘City Effects’: The Entry of Newly Arrived Immigrants into the Labour Market. Urban Studies, 50(6). https://doi.org/10.1177/0042098012461674 References 79 Holzaepfel, E. A. (2015). Evaluating the Effectiveness of Livelihoods Programs for Refugees in Ethiopia. Washington, D.C: United States Department of State. Hvidtfeldt, C., Schultz-Nielsen, M. L., Tekin, E., and Fosgerau, M. (2018). An Estimate of the Effect", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
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-{"input": "of Forced Migration. Paper presented at the Annual Meeting of the Population Association of America, Dallas, TX. Maystadt, J. F., and Verwimp, P. (2014). Winners and Losers among a Refugee-Hosting Population. Economic Development and Cultural Change, 62(4), 769-809. MoE. (2022). Education Statistics Annual Abstract (ESAA). MoE MoLSA. (2019). Revised Directives on the Issuance of Work Permits for Expatriates in Ethiopia. MoTRI, MoLS , MoR and RRS. (2023). Memorandum of Understanding (MoU) to allow recognized asylum seekers and refugees to engaged in formal business. MoLS and RRS. (2023). MoU on technical and vocational training, work permit, job creation and livelihood improvement, and related issues for refugees. MoLS and RRS. (2023). Technical and vocational training for refugees, job creation and livelihood improvement, work permit and related issues service delivery manual (Standard Operating Procedure). Muna, S. (2019). In Pursuit of Self-reliance: Perspectives of Refugees in Jordan. Archnet-IJAR, 13(3), 612-626. https://doi. org/10.1108/ARCH-04-2019-0085 Nguyen, N. T. V., Savadogo, A., and Tanaka, T. (2021). Refugees in Chad: The Road Forward. The World Bank. Norman, T., Borjesson, M., and Anderstig, C. (2017). Labor Market Accessibility and Unemployment. Journal of Transport Economics and Policy. Pape, U. J., Petrini, B., and Iqbal, S. A. (2018). Informing Durable Solutions by", "output": {"entities": {"named_data": ["Education Statistics Annual Abstract"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "T. V., Savadogo, A., and Tanaka, T. (2021). Refugees in Chad: The Road Forward. The World Bank. Norman, T., Borjesson, M., and Anderstig, C. (2017). Labor Market Accessibility and Unemployment. Journal of Transport Economics and Policy. Pape, U. J., Petrini, B., and Iqbal, S. A. (2018). Informing Durable Solutions by Micro-Data: A Skills Survey for Refugees in Ethiopia. Peters, S. and Golden, S. (2019). Assessing Mental Health in Gambella, Ethiopia: A Representative Survey of South Sudanese Refugees in Nguenyyiel Camp. St. Paul, MN: The Center for Victims of Trauma. Pimhidzai, O., Chigumira, E., Tesfaye,W., and Yonis, M. (2022). Ethiopia - Rural Income Diagnostics Study: Leveraging the Transformation in the Agri-Food System and Global Trade to Expand Rural Incomes. Washington, D.C.: World Bank Group. https://documentsinternal.worldbank.org/search/33891468 Piper, B., Dryden-Peterson, S., Chopra, V., Reddick, C., and Oyanga, A. (2020). Are Refugee Children Learning? Early Grade Literacy in a Refugee Camp in Kenya. The Journal on Education in Emergencies, Inter-agency Network for Education in References 80 References Emergencies, 5(2). https://doi.org/10.33682/f1wr-yk6y. Ravallion, M. (1998). Poverty Lines in Theory and Practice: Living Standards Measurement Study. LSMS Working Paper, Issue 133. https://documents1.worldbank.org/curated/en/916871468766156239/pdf ReDSS. (2018). Local Integration Focus: Refugees in Ethiopia - Gaps and Opportunities for Refugees Who", "output": {"entities": {"named_data": ["Ethiopia - Rural Income Diagnostics Study"], "descriptive_data": ["Skills Survey for Refugees in Ethiopia"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
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-{"input": "with Disabilities in Addis Ababa. Briefing Note. UNICEF. (2019). Birth Registration for Every Child by 2030: Are WE on Track? New York. UNICEF. (2021). Education in South Sudan. Briefing Note. Vemuru, V., Sarkar, A., and Woodhouse, A.F. (2020). Impact of Refugees on Hosting Communities in Ethiopia: A Social Analysis. World Bank Group. Verme, P., and Schuettler, K. (2021). The Impact of Forced Displacement on Host Communities: A Review of the Empirical Literature in Economics. Journal of Development Economics, 150, 102606. Walelign, S. Z., Wang Sonne, S. E., and Seshan, G. (2022). Livelihood Impacts of Refugees on Host Communities. Wieser, C., Dampha,N.K., Ambel, A.A., Tsegay, A.H., Mugera, H.K., Tanner, J. (2020). Monitoring COVID-19 Impact on Refugees in Ethiopia : Results from a High-Frequency Phone Survey of Refugees (English). Monitoring COVID-19 Impact on Refugees in Ethiopia Washington, D.C.: World Bank Group. Whitaker B.E. (2023). Border Proximity and Attitudes Toward Free Movement in Africa. The Afro Barometer Working Papers No. 200. Woldehanna, T., Hoddinott, J., and Dercon, S. (2008). Poverty and Inequality in Ethiopia:1995/96 – 2004/05. May. World Bank (2017). Forcibly Displaced: Toward a Development Approach Supporting Refugees, the Internally Displaced, and Their Hosts. Washington, D.C: World Bank. https://doi.org/10.1596/978-1-4648-0938-5. World Bank. (2019). Informing", "output": {"entities": {"named_data": ["High-Frequency Phone Survey of Refugees"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Africa. The Afro Barometer Working Papers No. 200. Woldehanna, T., Hoddinott, J., and Dercon, S. (2008). Poverty and Inequality in Ethiopia:1995/96 – 2004/05. May. World Bank (2017). Forcibly Displaced: Toward a Development Approach Supporting Refugees, the Internally Displaced, and Their Hosts. Washington, D.C: World Bank. https://doi.org/10.1596/978-1-4648-0938-5. World Bank. (2019). Informing the Refugee Policy Response in Uganda. Results from the Uganda Refugee and Host Communities 2018 Household Survey. World Bank. (2019b). Better Opportunities for All: Vietnam Poverty and Shared Prosperity Update Report. Washington DC: World Bank Group. World Bank (2020). Ethiopia Regional Poverty Report: Promoting Equitable Growth for All Regions. Washington, D.C.: World Bank Group. World Bank (2020b). Ethiopia Poverty Assessment: Harnessing Continued Growth for Accelerated Poverty Reduction. Washington, D.C.: World Bank Group. World Bank (2023). World Development Report 2023: Migrants, Refugees, and Societies. Washington, D.C: World Bank Group. https://doi:10.1596/978-1-4648-1941-4 World Bank. (2023a). Social Cohesion and Forced Displacement: A Synthesis of New Research. World Bank. (2023b). Welfare in Forcibly Displaced Populations: From Measuring Outcomes to Building Capabilities. Leveraging Harmonized Data to Improve Welfare among Forcibly Displaced Populations and their Hosts: A Technical Brief Series. World Bank. (2023c). Do Legal Restrictions Affect Refugees’ Labor Market and Education Outcomes? Evidence from Harmonized Data", "output": {"entities": {"named_data": ["Uganda Refugee and Host Communities 2018 Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "New Research. World Bank. (2023b). Welfare in Forcibly Displaced Populations: From Measuring Outcomes to Building Capabilities. Leveraging Harmonized Data to Improve Welfare among Forcibly Displaced Populations and their Hosts: A Technical Brief Series. World Bank. (2023c). Do Legal Restrictions Affect Refugees’ Labor Market and Education Outcomes? Evidence from Harmonized Data World Bank. (2023d). Ethiopia Economic Opportunities Program: Aide Memoire. World Bank Group. 2021. “IDA19 Mid-Term Refugee Policy Review.” Washington, D.C.: World Bank Group. Zanfrini L., and Giuliani, C. (2023). Look at Me, but Better: The Experience of Young NEET Migrant Women between Vulnerability and Stifled Ambitions. Social Sciences 12, 110. https://doi.org/10.3390/socsci12020110. Zetter R., and Ruaudel, H. (2016). Refugees’ Right to Work and Access to Labor Markets – An Assessment, Part 1. World Bank Global Program on Forced Displacement (GPFD) and the Global Knowledge Partnership on Migration and Development (KNOMAD) Thematic Working Group on Forced Migration. http://bit.ly/KNOMAD-Zetter-Ruaudel-2016-1 Zhou, Y. Y., Grossman, G., and Ge, S. (2022). Inclusive Refugee Hosting in Uganda Improves Local Development and Prevents Public Backlash. References ANNEXES Annexes 83 E thiopia hosts refugees from some 24 countries. By far the largest groups are refugees from South Sudan, Somalia, and Eritrea. Each of these groups is described below, including", "output": {"entities": {"named_data": ["Harmonized Data World Bank"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "G., and Ge, S. (2022). Inclusive Refugee Hosting in Uganda Improves Local Development and Prevents Public Backlash. References ANNEXES Annexes 83 E thiopia hosts refugees from some 24 countries. By far the largest groups are refugees from South Sudan, Somalia, and Eritrea. Each of these groups is described below, including a short description of host communities around the camps where refugees are hosted. South Sudanese Refugees Following the outbreak of hostilities in parts of South Sudan in December 2013, a massive influx of refugees in Ethiopia led to the establishment of new refugee camps. The South Sudanese are the largest refugee population in Ethiopia. Currently, South Sudanese refugees are sheltered in nine camps located in the Gambella (seven camps) and Benishangul- Gumuz (two camps) regions of the country. Two refugee camps in the Benishangul-Gumuz region, namely Tongo and Gure-Shombola, were impacted by the clashes in the region, and the refugees were relocated to another camp called, Tsore, situated in the same region. In 2023, Ethiopia hosted 416,660 registered South Sudanese refugees, which is as high as the local population in the Gambella region and the largest refugee population in the country (UNHCR, 2023d). Despite ongoing international and regional peace efforts,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "were relocated to another camp called, Tsore, situated in the same region. In 2023, Ethiopia hosted 416,660 registered South Sudanese refugees, which is as high as the local population in the Gambella region and the largest refugee population in the country (UNHCR, 2023d). Despite ongoing international and regional peace efforts, including by the South Sudanese factions, Ethiopia continues to receive new arrivals from the country, mainly in dire need of food assistance, indicating limited opportunities for voluntary return and reintegration. Somali Refugees The Somali refugee inflow to Ethiopia started in early 1990s, with people seeking safety and protection. As of 2023, Ethiopia hosted 283,111 refugees from Somalia who were forced to flee their homes as a result of insecurity, political instability, conflict, and famine (UNHCR, 2023d). The Somali refugee population is currently supported in two Zones in the Somali Region: Fafan Zone (three camps) and Liben Zone (five camps). The population of Somali refugees in the Jigjiga area (Fafan Zone) is expected to increase modestly mainly because of natural population growth. In the case of the Dollo Ado area (Liben Zone), some new arrivals are anticipated due to the security situations and the prevalence of climate-change-induced drought in Somalia. Some", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "population of Somali refugees in the Jigjiga area (Fafan Zone) is expected to increase modestly mainly because of natural population growth. In the case of the Dollo Ado area (Liben Zone), some new arrivals are anticipated due to the security situations and the prevalence of climate-change-induced drought in Somalia. Some of those residing in the Jigjiga area have been assisted in Ethiopia for over three decades, while the majority of individuals in Dollo Ado have been in the region for eight years. In the Ethiopian Somali Region, the armed group of Al-Shabab based in Somalia perpetrated several attacks in Afder, Liben, and Shabelle zones in July 2022. The attacks prompted all aid partners to suspend movements and operations along the affected areas temporarily affecting the drought response in the region. Even with sluggish implementation, the Intergovernmental Authority on Development (IGAD) Special Summit on Durable Solutions for Somali Refugees and Reintegration of Returnees in Somalia, the related Nairobi Declaration, and the accompanying Plan of Action are still expected to provide impetus for delivering durable solutions. The Nairobi Declaration is a declaration by the Heads of State and Government of the IGAD Region on durable solutions for Somali refugees and reintegration of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "of Returnees in Somalia, the related Nairobi Declaration, and the accompanying Plan of Action are still expected to provide impetus for delivering durable solutions. The Nairobi Declaration is a declaration by the Heads of State and Government of the IGAD Region on durable solutions for Somali refugees and reintegration of returnees in Somalia adopted in Nairobi, Kenya on 25 March 2017 at the Special Summit on protection and durable solutions for Somali refugees and reintegration of returnees in Somalia. Eritrean Refugees Since 2000, Ethiopia has received and hosted thousands of Eritrean refugees fleeing persecution. As of 2023, Ethiopia hosted 165,793 registered Eritrean refugees (UNHCR, 2023d) in six camps, and under the out-of-camp policy in Addis Ababa. The five refugee camps are located in Tigray (two), Afar (three), and Amhara (one) regions of the country. Unlike other refugee groups, many Eritrean refugees leave their camps due to various pull and push factors to pursue onward movement to urban centers within Ethiopia, including Addis Ababa, and other countries, primarily Europe. Annex A: Description of Refugees by Country of Origin Annexes 84 Fighting initially broke out in the Tigray region of Ethiopia in November 2020 between Tigrayan forces and the Federal Government. Two", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "factors to pursue onward movement to urban centers within Ethiopia, including Addis Ababa, and other countries, primarily Europe. Annex A: Description of Refugees by Country of Origin Annexes 84 Fighting initially broke out in the Tigray region of Ethiopia in November 2020 between Tigrayan forces and the Federal Government. Two refugee camps in Tigray (Hitsats and Shimelba) were destroyed due to the conflict in November 2020. The refugees who previously resided in these camps were relocated to Mai-Ayni and Adi-Harush refugee camps in the region as well as to Addis Ababa. A new refugee site (Alemwach) was established in June 2021 in the Northern Gondar Zone, Dabat Woreda of the Amhara region to shelter Eritrean refugees relocated from Mai-Ayni and Adi-Harush refugee camps. The spreading of the conflict in Northern Ethiopia into the Afar region also caused the destruction of the Berhale refugee camp, previously hosting 20,639 Eritrean refugees in the Afar region (UNHCR, 2022d). The refugees who fled Berhale and surrounding areas were relocated to a new refugee site called Serdo, 40 kilometers from the regional capital Semera. Sudanese Refugees The arrival of Sudanese refugees in Ethiopia started in 1997, and their number has significantly increased in 2011. Since", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "the Afar region (UNHCR, 2022d). The refugees who fled Berhale and surrounding areas were relocated to a new refugee site called Serdo, 40 kilometers from the regional capital Semera. Sudanese Refugees The arrival of Sudanese refugees in Ethiopia started in 1997, and their number has significantly increased in 2011. Since 2011, the conflict in the Blue Nile State has forced many Sudanese to flee to Ethiopia. As of 2023, Ethiopia hosts 48,709 registered refugees from Sudan (UNHCR, 2023d), who are assisted in four camps in the Benishangul-Gumuz Region. While some refugees have resided in Sherkole camp for more than two decades, the majority of the Sudanese refugee population has recently arrived and sheltered in the other three camps. In addition to refugees with Sudanese nationality, refugees from the African Great Lakes region are also sheltered in Sherkole refugee camp in the Benishangul-Gumuz region. Refugees in the Benishangul-Gumuz region have also experienced the effects of internal conflict. Two refugee camps (Tongo and Gure-Shobola) became inaccessible to humanitarian actors as a result of attacks by armed groups, forcing refugees to self- relocate and to be relocated to Tsore refugee camp, also in Benishangul-Gumuz. Host Communities Except Harari and Sidama regions, the remaining", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "experienced the effects of internal conflict. Two refugee camps (Tongo and Gure-Shobola) became inaccessible to humanitarian actors as a result of attacks by armed groups, forcing refugees to self- relocate and to be relocated to Tsore refugee camp, also in Benishangul-Gumuz. Host Communities Except Harari and Sidama regions, the remaining regional states of Ethiopia host hundreds of thousands of refugees in camps and camp-like settlements. Most refugee-hosting areas are found in remote locations bordering major refugee- producing countries such as South Sudan, Somalia, Eritrea, and Sudan. The great majority of the refugee hosting communities not only share common socio- economic practices but also have similar cultures and ethnicities with refugees from neighboring countries. Despite cultural and ethnic-based commonalities, the overall level of socioeconomic integration between refugees and host communities varies across the refugee-hosting areas in the country due to several factors, including historical and resource- related tensions and perceptions towards refugees. In most of the refugee hosting regions, there is a huge competition over the meager natural resources between refugees and host communities, which not only depletes the resource base but sometimes results in local-level conflicts. For instance, Gambella's social and political context is exceptionally complicated due to a long", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "refugees. In most of the refugee hosting regions, there is a huge competition over the meager natural resources between refugees and host communities, which not only depletes the resource base but sometimes results in local-level conflicts. For instance, Gambella's social and political context is exceptionally complicated due to a long history of conflict among groups over land and political power. The presence of refugees is a significant component of these dynamics. Host communities access some services provided within the refugee camps. In some operational areas, refugees have better access to basic and social services than host communities. Relationships between refugees and hosts are generally largely amicable, except in Gambella. In Benishangul-Gumuz and in the Somali Region, incidences of community-wide violent conflict between refugees and hosts are rare. A long history of displacement, shared ethnic identity, and shared cultural ties, along with other structural factors have fostered some solidarity between the groups. Among the major refugee-hosting regions, four regions—Afar, Benishangul-Gumuz, Gambella, and Somali—are designated as “emerging regions,” and Tigray is considered post-conflict. These regions are the least developed regions in the country, characterized by harsh weather, poor infrastructure, low administrative capacity, high poverty, and poor development outcomes. The arid environment in", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "groups. Among the major refugee-hosting regions, four regions—Afar, Benishangul-Gumuz, Gambella, and Somali—are designated as “emerging regions,” and Tigray is considered post-conflict. These regions are the least developed regions in the country, characterized by harsh weather, poor infrastructure, low administrative capacity, high poverty, and poor development outcomes. The arid environment in the Afar and Somali regions and the small and scattered nomadic populations make it more challenging to provide services. Many parts of the four regions are inaccessible with poor or no roads. Annexes 85 Annex B: Refugee Policies in Ethiopia E thiopia ratified the first national Refugee Proclamation in 2004 (FDRE, 2004) to effectively implement international and regional conventions of the 1951 UN Convention relating to the Status of Refugees and its 1967 protocol and the 1967 OAU (Organization of African Unity) Convention Governing the Specific Aspects of the Refugee Problem in Africa. According to Article 21 of the proclamation, refugees have the right to stay in Ethiopia and are provided with identity cards and travel documents. Moreover, Article 21 (3) states refugees are entitled to the same rights and duties as foreigners concerning the right to education and work in wage-earning employment. Even though the proclamation states refugees are", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "refugees have the right to stay in Ethiopia and are provided with identity cards and travel documents. Moreover, Article 21 (3) states refugees are entitled to the same rights and duties as foreigners concerning the right to education and work in wage-earning employment. Even though the proclamation states refugees are entitled to other rights and duties contained in the Refugee Convention and the OAU Refugee Convention, it does not exhaustively address refugees’ rights to access basic services, right to work, mobility to access better economic opportunities, and grounds for local integration. Four factors mainly drove the need for a new refugee policy. First, the nature of the refugees’ situation in Ethiopia made it difficult to provide sustainable solutions to refugees only through humanitarian assistance and a camp-based approach. Second, the increase in refugees entering the country due to unresolved crises and emerging conflicts from neighboring countries was not accompanied by financial support from the international community. Third, the predominantly urban background of refugees made their accommodation in remote camps challenging, leading to illegal migration to cities. Finally, Ethiopia’s participated in the 2016 New York UN Summit on Addressing Large Scale Movement of Refugees and Migrants, followed by an agreement to", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "financial support from the international community. Third, the predominantly urban background of refugees made their accommodation in remote camps challenging, leading to illegal migration to cities. Finally, Ethiopia’s participated in the 2016 New York UN Summit on Addressing Large Scale Movement of Refugees and Migrants, followed by an agreement to implement nine pledges made at the Summit as part of the practical application of the Comprehensive Refugee Response Framework (CRRF) (Kassa et al., 2019). In 2016, the GoE made pledges to improve the rights and well-being of refugees following the adoption of the New York Declaration and initiation of the CRRF at the UN Summit. The GoE made pledges on nine thematic areas: out-of-camp, education, work and livelihoods, documentation, social and basic services, and local integration (Table B.1) (RRS, 2017). Moreover, in 2019, at the first Global Refugee Forum (GRF), Ethiopia made four additional pledges on Jobs and Livelihoods, Education, Protection, and Energy (Table B.2) (RRS & UNHCR, 2021). As a result, a new refugee proclamation was adopted in 2019, Refugee Proclamation No. 1110/2019 (FDRE, 2019), replacing the 2004 proclamation, which was not exhaustive and up to date with developments and progress made in refugee protection. The 2019 Refugee Proclamation", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Protection, and Energy (Table B.2) (RRS & UNHCR, 2021). As a result, a new refugee proclamation was adopted in 2019, Refugee Proclamation No. 1110/2019 (FDRE, 2019), replacing the 2004 proclamation, which was not exhaustive and up to date with developments and progress made in refugee protection. The 2019 Refugee Proclamation made major improvements concerning the rights and obligations of asylum-seekers, and recognized refugees under part four of the proclamation. These include the right to access basic services (education, health, banking, telecommunication, vital event registration), right to work, right to acquire and transfer property, special protection to vulnerable individuals (women, children, refugees with special needs), local integration and naturalization, and right to association, freedom of movement, and access to justice. Subsequently, three directives were implemented to implement refugees’ right to movement and residence outside of camp, right to work, and grievances and appeals handling. The first directive, Directive to Determine the Conditions for Movement and Residence of Refugees Outside of Camps, No.01/2019 (RRS, 2019), was issued to enable refugees to establish residence outside of camp to broaden employment opportunities and achieve self-reliance. The directive sets conditions for refugees to be eligible for out-of-camp regular residency and provides guidelines to obtain, renew,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Conditions for Movement and Residence of Refugees Outside of Camps, No.01/2019 (RRS, 2019), was issued to enable refugees to establish residence outside of camp to broaden employment opportunities and achieve self-reliance. The directive sets conditions for refugees to be eligible for out-of-camp regular residency and provides guidelines to obtain, renew, and terminate a residence permit. Regular out-of- camp residency permits allow refugees to freely move and establish residence in all areas of the country except Refugees and Returnees Service (RRS, formerly Agency for Refugees and Returnees Affairs) restricted areas, for the interest of refugees’ safety and to access basic protection and services. The directive also includes provisions that allow refugees to benefit from the urban assistance program. Temporary movement outside of refugee camps is Annexes 86 also granted to refugees through the issuance of pass permits at refugee camps, the Zonal Coordination Office, and the RRS Head Office. The second Directive to Determine the Procedure for Refugees' Right to Work, No. 02/2019 (RRS, 2019a) provides detailed working procedures to implement Article 26 of the 2019 Refugee Proclamation, the right to work for refugees to improve living condition and ensure economic benefits. Article 26 of the proclamation states recognized refugees and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Directive to Determine the Procedure for Refugees' Right to Work, No. 02/2019 (RRS, 2019a) provides detailed working procedures to implement Article 26 of the 2019 Refugee Proclamation, the right to work for refugees to improve living condition and ensure economic benefits. Article 26 of the proclamation states recognized refugees and asylum-seekers have the right to engage in wage- earning employment, agriculture, industry, small and micro-enterprises, handicrafts and commerce, and professional work (liberal professions) in the same circumstance as the most favorable treatment accorded to foreign nationals under relevant laws. Refugees married to Ethiopian nationals, or who have children in possession of Ethiopian nationality, are exempted from restrictions imposed on the employment of foreign nationals. In line with the proclamation, the directive covers detailed guidelines regarding refugees’ participation in joint projects, wage-earning employment, and self-employment. The joint project approach works through projects funded by the international community, either through the government or NGOs, based on the agreement made with the government. The projects, however, need to create economic opportunities for refugees and host communities. Refugees get equal treatment as Ethiopian nationals concerning participation in joint projects—rural and urban projects designed by the government and international community to benefit refugees and Ethiopian", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "or NGOs, based on the agreement made with the government. The projects, however, need to create economic opportunities for refugees and host communities. Refugees get equal treatment as Ethiopian nationals concerning participation in joint projects—rural and urban projects designed by the government and international community to benefit refugees and Ethiopian nationals. The second approach—wage- earning employment—is defined in the directive as “the performance of professional or manual work by a refugee who is employed permanently or temporarily in consideration for a wage.” Refugees are allowed to be employed in areas that Ethiopian nationals cannot cover. Under the third approach, self-employment, refugees are allowed to work, individually or in a group, in areas such as agriculture, industry, medium and small enterprise (MSME), handicraft, and commerce, and in sectors open for foreign nationals upon obtaining the appropriate license according to national laws. The requirements to engage in joint projects, wage employment, and self-employment are illustrated in Box B.1. Issuance of work permits or business licenses to refugees is put into practice with a limited scope due to a lack of clarity related to what “most favorable treatment accorded to foreign nationals” refers (World Bank, 2023d). For instance, the requirement for a work", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "self-employment are illustrated in Box B.1. Issuance of work permits or business licenses to refugees is put into practice with a limited scope due to a lack of clarity related to what “most favorable treatment accorded to foreign nationals” refers (World Bank, 2023d). For instance, the requirement for a work permit does not clearly state whether refugees should be exempted from certain requirements, such as the minimum business investment amount of US$150,000 and the need to present an employer letter that justifies that their skills cannot be found among Ethiopian nationals. According to current practice, the Ethiopian Diaspora, Djiboutians, and Rastafarians are exempted from these requirements. Thus, this created an implementation lag in issuing work permits or business licenses by the Ministry of Labor and Skills (MoLS, formerly Ministry of Labor and Social Affairs) or the Ministry of Trade and Regional Integration (MoTRI) (World Bank, 2023d). Moreover, the 2020 Investment Proclamation of Ethiopia provided a long list of areas52 exclusively reserved for Ethiopian nationals, including small businesses that would have been of interest to refugees (FDRE, 2020). Hence, it allows many business activities to go beyond the reach of refugees. A draft MoU signed between RRS and MoLS/MoTRI/ MoR proposed", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Proclamation of Ethiopia provided a long list of areas52 exclusively reserved for Ethiopian nationals, including small businesses that would have been of interest to refugees (FDRE, 2020). Hence, it allows many business activities to go beyond the reach of refugees. A draft MoU signed between RRS and MoLS/MoTRI/ MoR proposed exceptional treatment of refugees engaged in business due to their vulnerability. According to the MoU, refugees can own a business in agriculture, manufacturing, services, small and medium enterprises, handicrafts, and trade sectors through establishing private limited companies or cooperative societies. Hence, refugees can obtain a business license as a private business or association by providing proof of refugee ID, support letter from RRS on the type of business, source of capital, utilization of profits, qualification certification (depending on the sector), and tax identification number. A business license renewal requires a valid refugee ID, tax clearance, and audit report for a limited liability company. 52 Restaurants, tearooms, coffee shops, bars, nightclubs, catering services, producing bakery products and pastries, barbershop and beauty salon services, smithery, tailoring, sawmilling, timber manufacturing, brick and block manufacturing, quarrying, laundry services, translation secretarial services, security services, brokerage services, and attorney and legal consultancy. Annexes 87 Following the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "limited liability company. 52 Restaurants, tearooms, coffee shops, bars, nightclubs, catering services, producing bakery products and pastries, barbershop and beauty salon services, smithery, tailoring, sawmilling, timber manufacturing, brick and block manufacturing, quarrying, laundry services, translation secretarial services, security services, brokerage services, and attorney and legal consultancy. Annexes 87 Following the signing of the MoU between MoLS and RRS, a procedure on technical and vocational training, work permits, and job creation and livelihood improvement for refugees was ratified in September 2023. This authorizes RRS to issue work permits for refugees. The procedure tasks the RRS and MoLS to ensure that refugees get proper information and services to have a work permit and to create employment opportunities for refugees. To get a work permit, refugees must present a work permit application form, renewed refugee ID, and proof of employment from employers. A work permit is issued for three years for a specific field of work. The renewal of a work permit requires a renewal application request, prior work permit, proof of employment or business license, and tax payment verification. The procedure also includes provisions for replacing a lost work permit and canceling a work permit. Regarding wage employment, a directive by MoLS", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "field of work. The renewal of a work permit requires a renewal application request, prior work permit, proof of employment or business license, and tax payment verification. The procedure also includes provisions for replacing a lost work permit and canceling a work permit. Regarding wage employment, a directive by MoLS states that foreigners could only be employed in Ethiopia where there are no qualified nationals available for the job position in question, jobs in a Non- Governmental Organization (NGO), an organization headquartered abroad, and employment because of a bilateral or multilateral agreement concluded by the government (MoLSA, 2019). Hence, the law does not grant refugees the right to engage in gainful employment on par with nationals, and refugees are forced to compete with other foreigners for similar job opportunities. Thus, the law seems to have only eased the challenges of a few qualified refugees without addressing the needs of the broader and largely unskilled refugee community. The third directive, Refugees and Returnees Grievances and Appeals Handling Directive, No. 03/2019 (RRS, 2019b), provides procedures for refugees to submit grievances and appeals concerning any matters related to RRS services. RRS is responsible for organizing a grievance and appeal handling unit at all", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "broader and largely unskilled refugee community. The third directive, Refugees and Returnees Grievances and Appeals Handling Directive, No. 03/2019 (RRS, 2019b), provides procedures for refugees to submit grievances and appeals concerning any matters related to RRS services. RRS is responsible for organizing a grievance and appeal handling unit at all levels of operation with the mandate to receive and address refugees’ complaints. In the Global Refugee Forum 2023, Ethiopia made six pledges on climate action, human settlement, the inclusion of refugees into existing national systems (GBV prevention, National ID, secondary school, and TVET), private sector engagement, access to irrigable land, and access to documentation (UNHCR, 2024). A part of the National ID program, the government started a pilot to issue a digital ID in March 2024 with a plan to reach 77,000 refugees in Addis Ababa. The digital ID is expected to give refugees better access to services such as healthcare, school enrollment, financial services, and business registration (UNHCR, 2024a). • Refugees can work without a work permit just by obtaining a residence permit, and have obligations to use the residence permit only for the joint project and refrain from illegal activities. • For a residence permit, a refugee shall", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "healthcare, school enrollment, financial services, and business registration (UNHCR, 2024a). • Refugees can work without a work permit just by obtaining a residence permit, and have obligations to use the residence permit only for the joint project and refrain from illegal activities. • For a residence permit, a refugee shall live for three years in Ethiopia afer granting refugee status, must fulfill conditions to be employed in a joint project and be free of crime charges. The residence permit is valid for five years. • A refugee needs to have a renewed refugee identification paper, current residence permit, and employment contract or valid business license or evidence of membership in a cooperative union to renew a residence permit. • Refugees should obtain a work permit except refugees who obtained resident permit to participate in a joint project and who are legally married to Ethiopian nationals or have one or more child in possession of Ethiopian nationality. • An employer seeking to employ a refugee must obtain a work permit for a refugee up on providing documents including a support letter from RRS, an application form, a resume, authenticated educational and work experience qualifications, and a refugee ID. • A refugee", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "child in possession of Ethiopian nationality. • An employer seeking to employ a refugee must obtain a work permit for a refugee up on providing documents including a support letter from RRS, an application form, a resume, authenticated educational and work experience qualifications, and a refugee ID. • A refugee is not required to provide visa or residence permit to request a work permit. • A work permit is valid for three years. • Refugees are required to obtain residence permit, tax identification number, and one of the three license and certifications (cooperative membership, MSME registration, and business licenses and registration) to engage in self-employment. • Refugees with residence permits are allowed to be self-employed in joint projects without a work permit. • Refugees married to Ethiopian nationals or have one or more child in possession of Ethiopian nationality are allowed to engage in business limited to Ethiopian nationals without residence permit upon obtaining the required license. • Self-employment in agriculture and irrigation requires agreement on lease arrangements between RRS and regional governments. Joint projects Wage employment Self-employment Box B.1: Employment pathways of refugees Annexes 88 Table B.1: Pledges made at 2016 UN Leaders’ Summit and progress Pledges Progress as", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "residence permit upon obtaining the required license. • Self-employment in agriculture and irrigation requires agreement on lease arrangements between RRS and regional governments. Joint projects Wage employment Self-employment Box B.1: Employment pathways of refugees Annexes 88 Table B.1: Pledges made at 2016 UN Leaders’ Summit and progress Pledges Progress as of 2019 Out-of-Camp Policy (OCP) The GoE introduced OCP in 2010 for Eritrean refugees giving opportunities to live in Addis Ababa and other non-camp location of their choice. Expand OCP to all nationalities hosted by Ethiopia, which will benefit 10% of the refugee population. Refugees who live for more than one month in a camp can apply for a regular out-of-camp residency permit. To be eligible for out-of-camp residency, a refugee should be able to prove that he/she can cover the cost of living or provide a sponsor and receive a work permit. Out-of-camp residency permits can be issued with exceptions for refugees with special conditions (orphaned children, with medical issues, single mothers, elderly, and with urgent overseas travel). Refugees who are no longer beneficiaries of the urban assistance program can also get the permit if they meet the requirements of an out- of-camp residency permit. • 5 % of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "exceptions for refugees with special conditions (orphaned children, with medical issues, single mothers, elderly, and with urgent overseas travel). Refugees who are no longer beneficiaries of the urban assistance program can also get the permit if they meet the requirements of an out- of-camp residency permit. • 5 % of the total refugee population are registered to benefit from OCP, with regional high regional variation, 83% and 28% in Addis Ababa and Afar, respectively. • Enacted directives on out-of-camp residence and rights to work. Education Increase school enrollment: • Pre-primary, from 44% to 60% • Primary school, from 54% to 75% • Secondary school, from 9% to 25% • Tertiary education, 1600 to 2500 students Gross Enrollment Ratio (GER) improved at all school levels, pre-primary (51%), primary (67%) and secondary (13%, 19% for grades 9 to 10 and 6% for grades 11 to 12). • Male GER is higher than female GER, gap widens for primary and secondary levels • GER shows variation across regions and camp sites • Primary and secondary school GER is higher for hosts compared to refugees in most of refugee-hosting regions. Work and Livelihood Provide work permits for refugees with permanent residence ID and refugee", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "female GER, gap widens for primary and secondary levels • GER shows variation across regions and camp sites • Primary and secondary school GER is higher for hosts compared to refugees in most of refugee-hosting regions. Work and Livelihood Provide work permits for refugees with permanent residence ID and refugee graduates based on the relevant domestic laws and in areas permitted to foreign workers, both for in-camp and out-of-camp refugees. Revision of the Refugee Proclamation (No. 1110/2019) incorporating improvements on the right to work (Article 26), and endorsement of implementation directive (Directive to Determine the Procedure for Refugees Right to Work, 02/2019). Avail 10,000 hectares of irrigable land for crop production to refugees and local communities with a plan to benefit 20,000 households or 100, 000 individuals. 1,103 hectares of land have been made available, and 1,765 refugees and 1,463 hosts benefited, with the majority being from the Somali region. Create job opportunities through the development of the infrastructure for industrialization. 4,412 refugees benefited from other livelihood opportunities (income-generating activities startup support, technical and vocational skills, livestock support). Social and basic services Strengthen, expand, and enhance refugees’ access to basic social services such as health, nutrition, immunization, reproductive health, HIV,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Create job opportunities through the development of the infrastructure for industrialization. 4,412 refugees benefited from other livelihood opportunities (income-generating activities startup support, technical and vocational skills, livestock support). Social and basic services Strengthen, expand, and enhance refugees’ access to basic social services such as health, nutrition, immunization, reproductive health, HIV, and other medical services. • Refugees are included in national service provision programs related to TB, RH, HIV, mass immunization, and responses to disease outbreaks. • Health services provided in camps and health facilities through collaboration between RRS, Regional Health Bureaus, and NGOs. • Refugees have access to health posts and health centers within camps and hosting Woredas, and referral hospitals. Annexes 89 Local integration Permit local integration of refugees who have lived for prolonged period (over 20 years) in Ethiopia. • The 2019 refugee proclamation gave RRS the mandate to facilitate the local integration of refugees who lived in Ethiopia for a prolonged period upon their request. • Positive developments were observed regarding socio- economic integration (skills and entrepreneurial training, access to farming land and peaceful coexistence). Documentation Provide services such as issuance of birth certificates to refugees’ children born in Ethiopia, opening bank accounts and obtaining a driving", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Ethiopia for a prolonged period upon their request. • Positive developments were observed regarding socio- economic integration (skills and entrepreneurial training, access to farming land and peaceful coexistence). Documentation Provide services such as issuance of birth certificates to refugees’ children born in Ethiopia, opening bank accounts and obtaining a driving license. Vital event registration service has been made available to all refugees. • 8,080 events registered in 2019, with the majority being births (7,150), with variation across regions Opening a bank account is allowed by the 2019 Refugee Proclamation. • 13,960 bank accounts opened by refugees, where the majority (67%) in Tigray. Source: Ethiopia Pledge Progress Report 2019 (UNHCR, 2020) Pledges Progress as of 2019 Annexes 90 Table B.2: GRF pledges and implementation progress Pledges Progress as of 2023 Jobs and Livelihoods “Create up to 90,000 economic opportunities through agriculture and livestock value chains that benefit refugee and host communities in an equitable manner.” • A total of 129,449 individuals (38,621 refugees and 90,828 hosts) directly and indirectly benefited, respectively, from agriculture, livestock, market system development, and financial inclusion-related services and training. • Major activities include providing irrigable land in Dollo Ado and implementing projects involving agricultural and livestock activities", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "in an equitable manner.” • A total of 129,449 individuals (38,621 refugees and 90,828 hosts) directly and indirectly benefited, respectively, from agriculture, livestock, market system development, and financial inclusion-related services and training. • Major activities include providing irrigable land in Dollo Ado and implementing projects involving agricultural and livestock activities in Gambella, Assosa, and Jijiga. • Inclusion of refugees in urban social safety net program. Education “Expand government TVET system and facilities to provide quality and accredited skills training that is linked to labor market demand to 20,000 hosts and refugees by 2024.” • The Qualification and Employment Perspectives (QEP) initiative was implemented in the Addis Ababa, Tigray, Benishangul-Gumuz, and Gambella regions to integrate refugees in national TVET systems and strengthen the self-reliance of refugees and hosts. • Accredited skills training linked to the labor market is provided for 5,253 refugees and 6,696 hosts. Moreover, 13 TVET colleges are supported. • The government, UNHCR, and GIZ are working together to develop a national roadmap for including refugees in the national TVET system. Protection/Capacity “Strengthening Asylum System and Social Protection: (i) Refugee Status Determination (RSD), refugee registration, civil documentation, and permits; (ii) National social protection system in refuge hosting areas-particularly for", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "• The government, UNHCR, and GIZ are working together to develop a national roadmap for including refugees in the national TVET system. Protection/Capacity “Strengthening Asylum System and Social Protection: (i) Refugee Status Determination (RSD), refugee registration, civil documentation, and permits; (ii) National social protection system in refuge hosting areas-particularly for vulnerable individuals.” • The 2019 Refugee proclamation improved provisions related to registration, documentation, and protection of refugees and asylum seekers as well as refugee status determination, and three implementation directives adopted. • RSD procedures are simplified for asylum seekers from Syria and Sudan. • Refugees are included in a Civil Registration and Vital Statistics Systems (CRVS) and the National Social and Behavior Strategy (awareness raising about the need for vital events registration). • A backlog of birth registration of 120,000 refugee children is cleared, and 72,286 vital events (62,816 birth, 8,177 marriage, and 757 divorce ) have been registered since 2017. • 890,825 refugees enrolled in the Level 3 Registration and Biometric Identity Management System (BIMS). Refugee ID cards and proof of registration are issued to 55 and 98 percent of refugees, respectively. Out-of-camp Permit is issued to 48,346 refugees. • One-stop shops have been established in 13 refugee camps", "output": {"entities": {"named_data": ["Level 3 Registration and Biometric Identity Management System", "Civil Registration and Vital Statistics Systems"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "2017. • 890,825 refugees enrolled in the Level 3 Registration and Biometric Identity Management System (BIMS). Refugee ID cards and proof of registration are issued to 55 and 98 percent of refugees, respectively. Out-of-camp Permit is issued to 48,346 refugees. • One-stop shops have been established in 13 refugee camps (14 are under construction) to provide one-center registration, documentation, and protection services. Annexes 91 • The National Strategy on Violence Against Women and Children (2021 -2026) recognizes refugee women and children. Also, refugees are included in the national Gender Based Violence (GBV). • Digital Request and Compliant System (DRCS) is established and implemented as part of the digitization of refugee protection services. • Refugees are getting mobile courts and free legal aid services. Energy/Environment “Provide market-based sustainable, reliable, affordable, culturally acceptable, environmentally friendly clean/renewable energy solutions for 3 million people. • A National Cooking Fuel Strategy is developed by EEWG to guide and define camp-specific cooking energy options. • In Afar, Gambella, and Melkadida, more than 382,000 refugees and 85,000 hosts have access to alternative cooking fuels and market-based clean electricity from solar-mini grids. • More than 1,739,726 seedlings were planted, 160m3 check dams were built, and 8 km of", "output": {"entities": {"named_data": ["Level 3 Registration and Biometric Identity Management System"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "to guide and define camp-specific cooking energy options. • In Afar, Gambella, and Melkadida, more than 382,000 refugees and 85,000 hosts have access to alternative cooking fuels and market-based clean electricity from solar-mini grids. • More than 1,739,726 seedlings were planted, 160m3 check dams were built, and 8 km of soil stone band were built to rehabilitate degraded land. Additional GoE GRF Pledges 2023 Climate Action: Protect and restore the environment, manage natural resources, afforestation of degraded lands, and expand renewable energy solutions for the benefit of both refugees and host communities. Recognize vulnerability of women to climate change, and prevent violence against women in all environmental policies and programs, and empower women to have agency and influence in environmental stewardship and adaptation to climate. Human Settlement: Transform selected refugee camps into sustainable urban settlements by enhancing the quality and availability of shelter, infrastructure, and public services, such as roads, electricity, water, sanitation, health, and education by aligning them with adjacent towns’ masterplan, by 2027. Inclusion of refugees into existing national systems: Enhance the capacity of GoE to include 1,000,000 refugees into the national Central Statistics Service (CSS), the national Gender-Based Violence (GBV) prevention and response programs, 814,000 refugees into", "output": {"entities": {"named_data": ["national Central Statistics Service"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "electricity, water, sanitation, health, and education by aligning them with adjacent towns’ masterplan, by 2027. Inclusion of refugees into existing national systems: Enhance the capacity of GoE to include 1,000,000 refugees into the national Central Statistics Service (CSS), the national Gender-Based Violence (GBV) prevention and response programs, 814,000 refugees into the national ID program, refugee secondary schools into the national system and 30,000 refugees and host communities in the TVET systems with 70% job opportunities by 2027. Private Sector Engagement: Improve the enabling environment for private sector engagement and investment to foster socio- economic development and to boost productivity of refugee and hosting communities. Access to Land: Provide access to 10,000 hectares of irrigable land through lease arrangements and promote climate-smart agriculture and livestock value chain contributing to improved food security and socio-economic empowerment of refugees and host communities of which at least 50% being women and 30% refugees. Access to documentation: Enhance digital infrastructure in refugee hosting areas to facilitate refugee inclusion to the digital economy including digitally enabled livelihood opportunities and financial inclusion as well as to foster their access to socio- economic e-services, including standardized travel documents. Source: Ethiopia GRF Pledge Progress Report (RRS & UNHCR, 2021", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "to documentation: Enhance digital infrastructure in refugee hosting areas to facilitate refugee inclusion to the digital economy including digitally enabled livelihood opportunities and financial inclusion as well as to foster their access to socio- economic e-services, including standardized travel documents. Source: Ethiopia GRF Pledge Progress Report (RRS & UNHCR, 2021 and 2023) Pledges Progress as of 2023 Annexes 92 S ESRE is a separate but integrated survey alongside the Ethiopian Household Welfare Statistics Survey (HoWStat),54 the national household survey to measure poverty and other socio-economic outcomes. Like most national poverty surveys, HoWStat excludes displaced populations—Internally Displaced People (IDPs) or refugees—including in Ethiopia. To have up-to-date information on the socio-economic outcomes and poverty levels of refugees and to allow comparison to Ethiopian host communities, the SESRE applied the same questionnaire and data collection methods as the HoWStat, with some modifications. Training of the enumerator team and implementation arrangements of the survey followed the same standards and procedures as the HoWStat. SESRE data was not collected alongside HoWStat due to security concerns at the time of data collection for HoWStat, especially in the refugee areas. The SESRE aimed to solve two problems: (i) gaps in data on the socioeconomic dimensions of refugees,", "output": {"entities": {"named_data": ["Ethiopian Household Welfare Statistics Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "followed the same standards and procedures as the HoWStat. SESRE data was not collected alongside HoWStat due to security concerns at the time of data collection for HoWStat, especially in the refugee areas. The SESRE aimed to solve two problems: (i) gaps in data on the socioeconomic dimensions of refugees, and (ii) gaps in analytical studies presenting the socioeconomic outcomes of refugees and hosts. Lack of up-to-date evidence is a significant obstacle to designing effective policies and support for refugees and host communities. To this end, the availability of the data helps to analyze refugee hosting areas’ social dynamics and longer-term socioeconomic viability by focusing on the: (i) social impact of refugees on host communities, (ii) socioeconomic interaction, (iii) social inclusion, and (iv) social relations among refugees and between refugees and host communities. The data provides valuable information to development partners and governments to inform policies to facilitate refugees’ integration and improve their lives, along with refugee hosting communities. The SESRE covers all current major refugee camps: Eritreans, South Sudanese, and Somalis, as well as the out-of-camp refugees in Addis Ababa. In addition, the survey covers the respective host communities around the camps, including the host communities of Addis Ababa.", "output": {"entities": {"named_data": ["HoWStat", "SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "and improve their lives, along with refugee hosting communities. The SESRE covers all current major refugee camps: Eritreans, South Sudanese, and Somalis, as well as the out-of-camp refugees in Addis Ababa. In addition, the survey covers the respective host communities around the camps, including the host communities of Addis Ababa. Due to the conflict in the Tigray region of Ethiopia between 2020 and 2022, Eritrean refugees living in camps in Tigray could not be included in this survey. To avoid exclusion of Eritrean refugees in Ethiopia, we included Eritrean refugees living in camps in the Afar region and the newly established refugee hosting zone Alemwach. Eritrean refugees who were in the Tigray region prior to the conflict are included in this survey in two ways: we sampled (i) refugees from Alemwach, where most of the refugees previously located in Tigray moved after conflict broke out and (ii) from Addis Ababa, namely those refugees who arrived in Addis Ababa after November 2020. Data collection took place between November 2022 and January 2023. Sample population The SESRE covers three types of groups, all of which require a distinct sampling procedure:55 (i) refugees in camps; (ii) refugees out-of-camps; and (iii) host communities. This", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "namely those refugees who arrived in Addis Ababa after November 2020. Data collection took place between November 2022 and January 2023. Sample population The SESRE covers three types of groups, all of which require a distinct sampling procedure:55 (i) refugees in camps; (ii) refugees out-of-camps; and (iii) host communities. This section discusses the sampling frames of each group. (a) Refugees in Camps The sampling frame for refugee camps is based on UNHCR’s proGRES database. The refugee camps were grouped into three domains based on the concentration of refugees from the three major origin countries: South Sudan, Somalia, and Eritrea.56 The first sampling stage divided each camp into enumeration areas (EAs). Based on the proGRES database, we created pseudo EAs by taking 150-200 Annex C: Survey Design and Methodology 54 Formerly the Household Consumption and Expenditure Survey and Welfare Monitoring Survey. 55 Prior to the sampling process, the survey team conducted a pre-sampling assessment by visiting the camps to verify on-the-ground conditions. 56 Refugees from Sudan were not included in SESRE as, at the time of sampling, there were less than 50,000 Sudanese refugees in Ethiopia and inclusion was not deemed cost effective. Annexes 93 households in a row from the", "output": {"entities": {"named_data": ["proGRES database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "a pre-sampling assessment by visiting the camps to verify on-the-ground conditions. 56 Refugees from Sudan were not included in SESRE as, at the time of sampling, there were less than 50,000 Sudanese refugees in Ethiopia and inclusion was not deemed cost effective. Annexes 93 households in a row from the list; that is, 50-200 HHs grouped as EA1 and the next 150-200 households grouped as EA2, and so on. EAs and households from each sampled EAs were selected. (b) Refugees in Addis Ababa We used a slightly different approach for refugees in Addis Ababa because of the difficulty of obtaining a reliable, complete list of locations for refugees living there. The refugee sampling frame in Addis was based on UNHCR’s proGRES registration data, sorted by location. The UNHCR list has information about how many refugee households live in each Woreda in Addis Ababa, their contact details, location, and other information. We developed pseudo-EAs from the list by location (sub-city and Woreda); some EAs covered more than one Woreda, and multiple EAs were in a single Woreda. We selected a sample of EAs and households from each EAs in collaboration with UNHCR. Finding refugees in Addis Ababa was challenging, as they", "output": {"entities": {"named_data": ["proGRES registration data", "UNHCR’s proGRES registration data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "We developed pseudo-EAs from the list by location (sub-city and Woreda); some EAs covered more than one Woreda, and multiple EAs were in a single Woreda. We selected a sample of EAs and households from each EAs in collaboration with UNHCR. Finding refugees in Addis Ababa was challenging, as they change their location frequently. To minimize the burden of searching for selected refugees, representatives of the selected households were contacted before the survey to ask them to come to a UNHCR center to collect preliminary information, including their current residential address. Since many Eritrean refugees in Addis Ababa had fled from the conflict in Tigray, out- of-camp refugees in Addis Ababa were stratified into two domains: refugees who arrived before the start of the conflict in November 2020, and those who arrived after November 2020. (c) Host Communities Populations around the refugee camps under each domain were meticulously identified in consultation with UNHCR and RRS. We used the ESS EA maps to assess the settlement of communities around camps, ensuring a precise fit to the definition of a “host community”. The assessment highlighted that using the list of EAs obtained from the new cartographic frame57 meets the definition of host", "output": {"entities": {"named_data": ["ESS EA maps"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "consultation with UNHCR and RRS. We used the ESS EA maps to assess the settlement of communities around camps, ensuring a precise fit to the definition of a “host community”. The assessment highlighted that using the list of EAs obtained from the new cartographic frame57 meets the definition of host community In the SESRE, host community members are defined as those who live adjacent to a refugee camp but within a radius of 5km. We use the updated Ethiopian Statistics Service 2018 cartographic database of enumeration areas (EAs) to define them. An EA is a defined area where 100-150 households live in rural areas, while in urban areas, it is an area where 150- 200 households live. The first stage of sampling for the host community involved using simple random sampling to select EAs—the primary sampling unit— from the list of EAs that are adjacent but within a radius of 5km. Following EA selection, a fresh list of households was prepared at the beginning of this survey, which was used as a frame to choose sampled households from each sample EA. In Addis Ababa, a separate host domain was developed as refugees spatially concentrate in a few sub-cities and Woredas.", "output": {"entities": {"named_data": ["ESS EA maps"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "5km. Following EA selection, a fresh list of households was prepared at the beginning of this survey, which was used as a frame to choose sampled households from each sample EA. In Addis Ababa, a separate host domain was developed as refugees spatially concentrate in a few sub-cities and Woredas. We applied the ESS EA maps around the area where refugees in Addis Ababa are located. We selected EAs in the first stage and then conducted a complete listing. Sampling design The sample for this survey was 3,456 households from eight domains, with data was collected from 3,452 households (Table C.1).58 There are three domains for the three largest in-camp refugee groups—Eritreans, Somalis, and South Sudanese— three for host communities of these major refugee groups, and one for refugees and one for host communities in Addis Ababa. In all categories, a stratified, two-stage cluster sample design technique was used to select EAs and 12 households per EA, whereby the EAs were considered a Primary Sampling Unit and the households as the Secondary Sampling Unit. The SESRE is designed to estimate demographic, socioeconomic, welfare, and refugee- specific indicators of the eight domains. 57 The cartographic map (frame) was prepared in 2018", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "select EAs and 12 households per EA, whereby the EAs were considered a Primary Sampling Unit and the households as the Secondary Sampling Unit. The SESRE is designed to estimate demographic, socioeconomic, welfare, and refugee- specific indicators of the eight domains. 57 The cartographic map (frame) was prepared in 2018 for the upcoming Population and Housing Census. 58 See Annex C for sample size estimation. Annexes 94 Sample size estimation (a) First Stage Sampling In the first stage sampling, each domain is considered an explicit sampling domain. We used the list of all EAs as a sampling frame and their estimated population as a Measure of Size (MoS). A sample is selected with Probability Proportional to Size (PPS). The sample size is evaluated regarding the expected precision of the key indicator for the SESRE, the national household survey to measure poverty as the percentage is 0.235 (2016 Household Consumption and Expenditure). In the calculation, values for the measuring poverty rate (P) and design factor (deft) 1.5, the expected Relative Standard Error (RSE) of 4.63%, and finally, an adjusted Response Rate of 99% at a 95% Confidence level used to represent the expected precision is acceptable at the domain level. To", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["national household survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Expenditure). In the calculation, values for the measuring poverty rate (P) and design factor (deft) 1.5, the expected Relative Standard Error (RSE) of 4.63%, and finally, an adjusted Response Rate of 99% at a 95% Confidence level used to represent the expected precision is acceptable at the domain level. To select a representative sample from this population, first, the initial sample size was determined by using the following scientific formula: where the deft is the design factor defined as the ratio between the square root of standard error using the given sample design and the standard error resulting from a simple random sample used. Based on the above scenario, total sample size =3,456 Households, and EAs = 288 . An equal allocation method was used to ensure that the survey precision was comparable across domains, where 36 EAs were selected from each domain. Based on a fixed sample take of 12 households per cluster, Equal Allocation formula Where: = total number of sample households and = Number of sample households allocated to stratum Table C.1: The distribution of sampled and surveyed households by domains EA HH Sampled Covered Sampled Covered Eritrean refugee domain 36 36 432 432 Somalian refugee domain", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "12 households per cluster, Equal Allocation formula Where: = total number of sample households and = Number of sample households allocated to stratum Table C.1: The distribution of sampled and surveyed households by domains EA HH Sampled Covered Sampled Covered Eritrean refugee domain 36 36 432 432 Somalian refugee domain 36 36 432 432 South Sudanese refugee domain 36 36 432 432 Eritrean host domain 36 36 432 430 Somali host domain 36 36 432 431 South Sudanese host domain 36 36 432 432 Addis Ababa refugee domain 36 36 432 431 Addis Ababa host domain 36 36 432 432 Total 288 288 3,456 3,452 Note: We have three segmented EAs in Somali host domain. Annexes 95 (b) Second Stage Sampling In the second stage sampling, we selected 12 households per selected EA. The probability of of selecting a household in segment of EA of domain is given by , where ◆ is the number of EAs in the domain’s sample, ◆ is the estimated population of the EA, ◆ is the estimated population of the domain, ◆ is the number of segments listed in the EA (normatively always 2, or 1 if the EA is not segmented), ◆ is", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "where ◆ is the number of EAs in the domain’s sample, ◆ is the estimated population of the EA, ◆ is the estimated population of the domain, ◆ is the number of segments listed in the EA (normatively always 2, or 1 if the EA is not segmented), ◆ is the number of households visited in the EA (normatively always 12), and ◆ is the total number of households listed in the EA. There are 36 EAs per domain. With 8 survey domains, and 12 households per EA, a total of 3,456 households in the sample. (c) Replacement of Households We implemented a two-layer replacement strategy: First, in each EA, 12 additional households were sampled to serve as replacement households. We sampled the replacement households in the sample allocation and sample size determination stage. As part of the data collection protocol, each household needs to be visited at least three times before replacing a household from the list of replacements. The list of replacement households was only provided to the enumerators upon demonstrating that three visits were attempted. In the case of in- camp refugees, if the enumerators, together with the focal person from RRS and UNHCR, could not identify", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "times before replacing a household from the list of replacements. The list of replacement households was only provided to the enumerators upon demonstrating that three visits were attempted. In the case of in- camp refugees, if the enumerators, together with the focal person from RRS and UNHCR, could not identify the selected household within the camp, they were provided with a list of replacements. Second, in case of missing to identify even the replaced sampled households, the enumerators were requested to go back to the original sampled household and skip ten households using a counterclockwise rule to find a new replacement household. (d) Implementation Plan The survey implementation plan involved collaboration with the Ethiopian Statistical Service (ESS), World Bank, UNHCR, and RRS. The ESS was responsible for administering the pre- and post- fieldwork implementation and management, including fieldworkers’ recruitment, training, field tests, data collection, data quality assurance, and data management The UNHCR supported in engaging refugee communities and leaders. The RRS facilitated access to all camps for the survey teams; this is the first time that the RRS facilitated access to all camps for such an extensive survey. Notably, the UNHCR and RRS facilitated the collaboration of the field workers", "output": {"entities": {"named_data": ["Ethiopian Statistical Service (ESS)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "The UNHCR supported in engaging refugee communities and leaders. The RRS facilitated access to all camps for the survey teams; this is the first time that the RRS facilitated access to all camps for such an extensive survey. Notably, the UNHCR and RRS facilitated the collaboration of the field workers with refugee leaders in each camp and Addis Ababa to support the teams in identifying sampled households and maintaining the safety of the field workers, and the sampled households during the entire survey period. The World Bank team led the collaboration between ESS, UNHCR, and RRS and provided technical support to the ESS since the project’s inception. The SESRE used a logistics plan similar to HoWStat. Six ESS branches were responsible for administering the survey: the Asayita, Gondar, Jigjiga, Negele, Gambella, Assosa, and Addis Ababa branches. Twenty-four field teams carried out the fieldwork, each consisting of one statistician, one team supervisor, and four enumerators. All field staff involved in the SERSE participated in the HoWStat survey. Enumerators were knowledgeable about local cultures and languages and could detect inconsistencies and misunderstandings during interviews to ensure high-quality data. Supervisors were additionally trained on how to troubleshoot standard technical issues with tablets. The", "output": {"entities": {"named_data": ["HoWStat survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "supervisor, and four enumerators. All field staff involved in the SERSE participated in the HoWStat survey. Enumerators were knowledgeable about local cultures and languages and could detect inconsistencies and misunderstandings during interviews to ensure high-quality data. Supervisors were additionally trained on how to troubleshoot standard technical issues with tablets. The supervisors conducted reinterviews, consistency, spot-checking, and data syncing to the head office. Also, the statisticians from ESS branch offices were with the team all the time to support and monitor the fieldwork. The data collection system consisted of encrypted Android devices for prolonged usage in the field equipped with the chosen survey application and a GPS tracking application for EA delineations. Electronic data files were transferred daily to the ESS central office in Addis Ababa via the Annexes 96 secured link. The core team from ESS undertook field supervision and was responsible for the day-to- day field management. Also, the World Bank team undertook field supervision, providing on-time and on-the-spot guidance for the field teams whenever and wherever they encountered a challenge. (e) Challenges faced and lessons learned The SESRE served as a learning experience for including refugees in future rounds of the official household survey (HoWStat). Given the unique", "output": {"entities": {"named_data": ["HoWStat survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "World Bank team undertook field supervision, providing on-time and on-the-spot guidance for the field teams whenever and wherever they encountered a challenge. (e) Challenges faced and lessons learned The SESRE served as a learning experience for including refugees in future rounds of the official household survey (HoWStat). Given the unique feature of refugees compared to Ethiopians, the sampling methodology for the SESRE is unique for sampling refugees. Therefore, SESRE successfully tested the feasibility of sampling refugees and their hosts. To ensure the successful implementation of the sampling procedures, the ESS implemented a pilot sampling methodology before data collection started to ensure that all systems and processes were functioning. The ESS and World Bank teams conducted field visits for this pre-test in Afar and Addis Ababa. The field visits included discussions with camp community leaders, including the refugee community leaders, about the upcoming survey to understand better any sensitivities that may arise. The field visits helped to understand the camp administrative structure and environment of the teams facilitating the camp and to test the accessibility of sampled refugee households inside the camp, in Addis Ababa, and the identification of the host. ESS provided detailed feedback on the fieldwork procedures and adjustments", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["official household survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "arise. The field visits helped to understand the camp administrative structure and environment of the teams facilitating the camp and to test the accessibility of sampled refugee households inside the camp, in Addis Ababa, and the identification of the host. ESS provided detailed feedback on the fieldwork procedures and adjustments made before the fieldwork began. For instance, in Afar (Asayita), the visit helped to identify challenges in tracing sampled refugee households and to take the necessary corrective measures. Likewise, in Addis Ababa, the visit assisted in designing an appropriate strategy to select host communities. The survey created a good opportunity for a collaborative effort between different government institutions and development partners. This collaboration allowed the sharing of experiences across institutions and knowledge for ESS to implement such unique surveys in the future. The survey process, from preparation to implementation, focused on ensuring data quality for refugee data collection. During preparation, ESS translated the survey instrument into different main languages and undertook an in- depth training of supervisors and enumerators for enumerators to understand better the concepts of the questions related to the refugee context. Moreover, a close follow-up and coordination in the field helped to get better quality data and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "ESS translated the survey instrument into different main languages and undertook an in- depth training of supervisors and enumerators for enumerators to understand better the concepts of the questions related to the refugee context. Moreover, a close follow-up and coordination in the field helped to get better quality data and provided timely responses to challenges faced during data collection. During the survey implementation period, the main challenge was tracking sampled refugee households in all refugee domains. One of the Eritrean camps, Alemwach Camp, was newly established at time of data collection. Tracing the originally sampled and backup households initially took a lot of work. The issue of missing households in Asayita camp was severe during the second data collection phase. Moreover, some camps were very large; for example, there were more than 100,000 refugees in one camp, creating challenges for field workers in tracing the sampled households. Refugees in Addis Ababa live in rented houses; the team faced challenges in tracing some refugees due to changes in their residential locations. Challenges related to identifying the eligible sample households were also observed due to outdated names of the household heads in UNHCR lists, and UNHCR’s registration of names which is not", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Ababa live in rented houses; the team faced challenges in tracing some refugees due to changes in their residential locations. Challenges related to identifying the eligible sample households were also observed due to outdated names of the household heads in UNHCR lists, and UNHCR’s registration of names which is not consistent with the Ethiopian context58. Thus, these challenges required additional effort by the team to ensure that sampled households and replacements were traced, identified, and interviewed. Another challenge was that some refugees were not willing to provide their current location due to personal security reasons, but these situations were resolved by reaffirming the confidentiality of the survey. 58 UNHCR register names starting with last name, whereas, in Ethiopia names starts with first name. Annexes 97 Annex D: Descriptive Statistics and Regression Results Results on Sociodemographic Profile Table D.1: Demographic characteristics by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees Hosts Refugees Age group <15 39% 46% 54% 50% 47% 56% 15 to 24 19% 19% 17% 22% 21% 21% 25 to 44 26% 25% 21% 17% 21% 16% 45 to 64 13% 8% 7% 9% 8% 5% >=65 4% 2% 2% 2% 3% 1% Gender Male 48% 51%", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR lists"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Refugees Hosts Refugees Age group <15 39% 46% 54% 50% 47% 56% 15 to 24 19% 19% 17% 22% 21% 21% 25 to 44 26% 25% 21% 17% 21% 16% 45 to 64 13% 8% 7% 9% 8% 5% >=65 4% 2% 2% 2% 3% 1% Gender Male 48% 51% 50% 49% 48% 46% Female 52% 49% 50% 51% 52% 54% Marital Status Never Married 22% 27% 21% 30% 22% 25% Married 62% 56% 69% 52% 66% 57% Other 16% 17% 10% 18% 12% 18% Household characteristics Household size 4.3 4.7 5.7 6.0 5.4 6.5 Dependency ratio 0.8 1.1 1.5 1.4 1.1 1.7 Female-headed 38% 43% 43% 61% 51% 84% Head’s age 45.4 39.3 41.0 43.6 40.7 37.1 Source: World Bank Staff based on SESRE 2023. Annexes 98 Table D.2: Education outcomes by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees Hosts Refugees Education level No education 38% 49% 52% 56% 31% 43% Incomplete primary 20% 30% 13% 20% 24% 27% Complete primary 20% 17% 21% 18% 27% 25% Complete secondary 12% 4% 6% 4% 10% 3% Complete post-secondary 10% 0% 9% 2% 8% 2% Education level (youth -15 to 24 years) No education 4% 17% 20% 18%", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "49% 52% 56% 31% 43% Incomplete primary 20% 30% 13% 20% 24% 27% Complete primary 20% 17% 21% 18% 27% 25% Complete secondary 12% 4% 6% 4% 10% 3% Complete post-secondary 10% 0% 9% 2% 8% 2% Education level (youth -15 to 24 years) No education 4% 17% 20% 18% 4% 6% Incomplete primary 29% 59% 27% 41% 48% 62% Complete primary 50% 22% 48% 37% 41% 31% Complete secondary 11% 1% 4% 4% 6% 2% Complete post-secondary 5% 0% 2% 1% 1% 0% Children currently attending school (<=18 years) All 68% 51% 50% 51% 68% 70% Primary school 53% 43% 42% 44% 59% 59% Secondary school 11% 2% 7% 5% 5% 2% Primary school (7 to 14 years) 82% 64% 67% 61% 82% 81% Secondary school (15 to 18 years) 49% 15% 48% 30% 28% 18% Attending school above school age Primary school (15 to 18 years) 31% 49% 28% 49% 54% 73% Secondary school (19 to 24 years) 34% 11% 30% 32% 32% 38% Enrollment rates Primary NER 81% 65% 65% 62% 82% 80% Secondary NER 47% 14% 44% 32% 28% 17% Primary GER 101% 86% 79% 84% 114% 116% Secondary GER 54% 14% 47% 36% 31% 18%", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "28% 49% 54% 73% Secondary school (19 to 24 years) 34% 11% 30% 32% 32% 38% Enrollment rates Primary NER 81% 65% 65% 62% 82% 80% Secondary NER 47% 14% 44% 32% 28% 17% Primary GER 101% 86% 79% 84% 114% 116% Secondary GER 54% 14% 47% 36% 31% 18% Source: World Bank Staff based on SESRE 2023. Annexes 99 Table D.3: Health outcomes by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees Hosts Refugees Faced any health problem 21% 20% 6% 7% 29% 25% Received medical assistance 86% 85% 85% 85% 85% 93% Child Nutrition Stunted 43% 52% 37% 47% 26% 26% Underweight 28% 37% 31% 38% 26% 19% Wasted 10% 10% 14% 19% 17% 12% Disability Seeing 2% 3% 1% 2% 3% 3% Hearing 1% 3% 1% 1% 2% 2% Walking or climbing steps 2% 2% 1% 2% 3% 2% Remembering or concentrating 1% 2% 1% 1% 1% 2% Difficulty with self-care 1% 1% 1% 1% 1% 2% Communicating 0% 1% 1% 1% 1% 1% Any disability 5% 8% 3% 4% 6% 5% Source: World Bank Staff based on SESRE 2023. Table D.4: Living conditions by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "1% 1% 2% Difficulty with self-care 1% 1% 1% 1% 1% 2% Communicating 0% 1% 1% 1% 1% 1% Any disability 5% 8% 3% 4% 6% 5% Source: World Bank Staff based on SESRE 2023. Table D.4: Living conditions by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees Hosts Refugees Dwelling type Owned 57% 0% 77% 21% 72% 11% Rented 36% 2% 13% 0% 22% 2% UN/NGO temporary 0% 61% 0% 28% 1% 16% UN/NGO permanent 0% 36% 0% 50% 1% 71% Other 7% 0% 10% 1% 4% 0% Housing quality Overcrowded 22% 66% 44% 53% 42% 56% Improved wall 16% 7% 16% 5% 4% 0% Improved roof 72% 64% 75% 81% 58% 8% WASH Improved source of drinking water 80% 99% 97% 99% 64% 79% Improved bathing facilities 30% 28% 5% 9% 25% 14% Improved toilet facility 38% 39% 60% 59% 18% 34% Improved waste disposal method 24% 64% 30% 37% 10% 8% Source of lighting Electricity (meter) 74% 13% 25% 5% 29% 1% Electricity (meter, generator, solar) 89% 91% 38% 17% 34% 4% Source: World Bank Staff based on SESRE 2023. Annexes 100 0 10 20 30 40 50 60 70 80 90 100 < 15", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "24% 64% 30% 37% 10% 8% Source of lighting Electricity (meter) 74% 13% 25% 5% 29% 1% Electricity (meter, generator, solar) 89% 91% 38% 17% 34% 4% Source: World Bank Staff based on SESRE 2023. Annexes 100 0 10 20 30 40 50 60 70 80 90 100 < 15 15 to 24 25 to 44 45 to 64 >= 65 < 15 15 to 24 25 to 44 45 to 64 >= 65 < 15 15 to 24 25 to 44 45 to 64 >= 65 < 15 15 to 24 25 to 44 45 to 64 >= 65 Eritrean Somali South Sudanese Addis Ababa Male Female Percent Figure D.1: Age group by gender Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Average share of primary school age children in primary education Average share of secondary school age children in secondary education Percent Figure D.3: Share of school-age children in education per household Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 Eritrean Somali South", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Average share of primary school age children in primary education Average share of secondary school age children in secondary education Percent Figure D.3: Share of school-age children in education per household Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 Eritrean Somali South Sudanese Addis refugees Availability of education document Able to verify education document Percent Figure D.2: Refugees’ education document Source: World Bank Staff based on SESRE 2023. Annexes 101 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Primary school (7 to 14 years) Boys Primary school (7 to 14 years) Girls Secondary school (15 to 18 years) Boys Secondary school (15 to 18 years) Girls Percent Figure D.4: School-age children currently attending school by gender Source: World Bank Staff based on SESRE 2023. Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In Camp Addis Ababa Total Average household expenditure on education (children in school) Average household expenditure on education per child (school age (4 to 18 years)) - 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18.000 Figure D.6: Average annual household education expenditure", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Refugees Hosts Refugees Eritrean Somali South Sudanese In Camp Addis Ababa Total Average household expenditure on education (children in school) Average household expenditure on education per child (school age (4 to 18 years)) - 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18.000 Figure D.6: Average annual household education expenditure (in ETB) Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Boys Girls Boys Girls 8 to 14 years 15 to 18 years Need to work Unable to cover education expenses (fee and materials) School too far Too young Marriage or pregnancy Family not willing Sickness/injury or natural or human calamites Negative perception towards the benefit of education Other Percent Figure D.5: Reasons for not currently attending school by gender Source: World Bank Staff based on SESRE 2023. Annexes 102 0 5 10 15 20 25 30 35 40 45 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Refugees Hosts In camp Addis Ababa Total Sanitation problem Long waiting time Shortage of health professionals", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "10 15 20 25 30 35 40 45 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Refugees Hosts In camp Addis Ababa Total Sanitation problem Long waiting time Shortage of health professionals Too expensive Shortage/unavailability of medicines Unavailability of laboratory Shortage of medical equipment Lack of cooperativeness of the staff Percent Percent Figure D.8: Problems faced in health institutions Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Government/public Private Mission/Religious/NGO Other Percent Figure D.7: Type of health institutions Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Female Male Percent Figure D.9: Stunting by gender of children Source: World Bank Staff based on SESRE 2023. a. Faced any problem b. Types of problems faced Annexes 103 - 500 1,000 1,500 2,000 2,500 3,000 3,500 Hosts Refugees Hosts Refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Eritrean Somali South Sudanese In camp Addis Ababa Total Female Male Percent Figure D.9: Stunting by gender of children Source: World Bank Staff based on SESRE 2023. a. Faced any problem b. Types of problems faced Annexes 103 - 500 1,000 1,500 2,000 2,500 3,000 3,500 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Average household annual expenditure on health Average per capita annual expenditure on health Figure D.12: Average annual per capita health expenditure Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 Eritrean Somali South Sudanese In camp Addis Ababa Total Hosts Refugees Percent Figure D.11: No birth evidence available (children under five years) Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Health institutions (health center/hospital) Home Other Percent Figure D.10: Childbirth in health institutions (children under five years) Source: World Bank Staff based on SESRE 2023. Annexes 104 Hearing Walking or climbing steps Percent Refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Health institutions (health center/hospital) Home Other Percent Figure D.10: Childbirth in health institutions (children under five years) Source: World Bank Staff based on SESRE 2023. Annexes 104 Hearing Walking or climbing steps Percent Refugees Eritrean Somali South Sudanese In Camp Addis Ababa Total Hosts Seeing Difficulty with selfcare Communicating Remembering or concentrating Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts 50 40 30 20 90 100 80 70 60 10 0 Figure D.13: Types of disability Source: World Bank Staff based on SESRE 2023. - 10,000 20,000 30,000 40,000 50,000 60,000 Hosts Refugees Addis Ababa Total annual rent expenditure Per adult equivalent rent expenditure Figure D.14: Rent expenditure (Refugees and hosts in Addis Ababa) Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Eritrean Somali South Sudanese Addis Ababa Eritrean Somali South Sudanese Addis Ababa Eritrean Somali South Sudanese Addis Ababa Has hand washing place/item Water available Detergent available Hosts Refugees Percent Figure D.15: Hand washing facility Source: World Bank Staff based on SESRE 2023. Annexes 105 Table D.5: Labor force statistics by survey domains Eritrean Somali", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Addis Ababa Eritrean Somali South Sudanese Addis Ababa Eritrean Somali South Sudanese Addis Ababa Has hand washing place/item Water available Detergent available Hosts Refugees Percent Figure D.15: Hand washing facility Source: World Bank Staff based on SESRE 2023. Annexes 105 Table D.5: Labor force statistics by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees Hosts Refugees Participation (strict) 57% 38% 44% 35% 55% 28% Employment (strict) 89% 72% 94% 77% 96% 82% Unemployment (strict) 11% 28% 6% 23% 4% 18% Participation (relaxed) 62% 60% 51% 49% 57% 38% Employment (relaxed) 81% 45% 81% 55% 91% 60% Unemployment (relaxed) 19% 55% 19% 45% 9% 40% Source: World Bank Staff based on SESRE 2023. Table D.6: Determinants of refugee-host earnings gap (1) (2) (3) (4) Earnings Earnings Earnings Earnings Refugee (% difference from hosts) -69.8*** -63.7*** -62.1*** -40.7*** (0.077) (0.123) (0.122) (0.126) Control: Region Yes Yes Yes Yes Control: Demographics Yes Yes Yes Control: Occupation/Sector Yes Yes Sample: Working outside camp Yes Sample Size 743 742 742 572 Source: World Bank Staff based on SESRE 2023. Note: Monthly earnings are collected for employees only (including work for government, NGOs, and private households). Log earnings are winsorized at the 1st and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Yes Yes Yes Control: Occupation/Sector Yes Yes Sample: Working outside camp Yes Sample Size 743 742 742 572 Source: World Bank Staff based on SESRE 2023. Note: Monthly earnings are collected for employees only (including work for government, NGOs, and private households). Log earnings are winsorized at the 1st and 99th percentile within the domain. Regression coefficients are transformed to percent change interpretation using. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 106 Table D.7: Determinants of employment outcomes (1) (2) (3) (4) (5) (6) (7) (8) Hosts Refugees Working High-Skill Ln Income Working High-Skill Work Outside Ln Income Ln Income Male 0.235*** 0.030* 0.270*** 0.033 0.031* 0.172*** 0.506*** 0.876*** (0.021) (0.018) (0.061) (0.033) (0.017) (0.049) (0.101) (0.226) Age 0.067*** 0.008** 0.064*** 0.045*** 0.004 -0.005 0.007 -0.043 (0.004) (0.004) (0.017) (0.005) (0.003) (0.013) (0.028) (0.046) Age Sq. -0.001*** -0.000* -0.001*** -0.001*** -0.000 0.000 -0.000 0.001 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) Educ: < Primary - - - - - - - - Educ: Primary -0.028 0.058*** 0.127 -0.042* 0.077** -0.104 -0.172 0.121 (0.020) (0.021) (0.107) (0.022) (0.032) (0.069) (0.122) (0.361) Educ: Secondary 0.115*** 0.506*** 0.700*** -0.045", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "-0.000* -0.001*** -0.001*** -0.000 0.000 -0.000 0.001 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) Educ: < Primary - - - - - - - - Educ: Primary -0.028 0.058*** 0.127 -0.042* 0.077** -0.104 -0.172 0.121 (0.020) (0.021) (0.107) (0.022) (0.032) (0.069) (0.122) (0.361) Educ: Secondary 0.115*** 0.506*** 0.700*** -0.045 0.460*** -0.200** 0.098 0.849*** (0.031) (0.046) (0.100) (0.045) (0.107) (0.080) (0.212) (0.311) Educ: Post-sec 0.231*** 0.716*** 0.812*** 0.004 0.493*** -0.073 0.222 0.528** (0.024) (0.037) (0.078) (0.117) (0.121) (0.149) (0.367) (0.232) Years in Ethiopia 0.009*** 0.001 0.001 0.003 0.006 (0.003) (0.002) (0.004) (0.012) (0.015) Work outside camp 0.352*** (0.128) Region fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Restrict to workers outside camp Yes N 3321 1626 494 3,069 830 975 238 73 Source: World Bank Staff based on SESRE 2023. Note: Columns 2 and 5-6 are restricted to working respondents. Columns 3 and 7-8 are restricted to employees. High-skill occupations include managers, professionals, and associate professionals. Ln Earnings is the log of monthly earnings winsorized at the 1st and 99th percentiles. All other models are linear probability models. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "High-skill occupations include managers, professionals, and associate professionals. Ln Earnings is the log of monthly earnings winsorized at the 1st and 99th percentiles. All other models are linear probability models. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 107 Table D.8: Refugee Household Reliance on NGOs/Donations (1) (2) (3) (4) All Eritrea Somali South Sudan Years in Ethiopia -0.007** -0.017** -0.011** -0.001 (0.003) (0.006) (0.005) (0.005) Member works outside the camp -0.080** -0.335*** -0.180*** -0.001 (0.035) (0.099) (0.051) (0.041) Region Fixed Effects Yes No No No Demographic Controls Yes Yes Yes Yes N 1252 423 412 417 Source: World Bank Staff based on SESRE 2023. Note: Each column is a linear probability model where the outcome is a binary indicator of whether the household relies primarily on donations for income. Demographic controls include the household size and the share within each age and education group. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Table D.9: Determinants of refugee-host earnings gap (1) (2) (3) Earnings Earnings Earnings Refugee (% difference from hosts) -24.9** -22.4** -18.5* (0.137) (0.115) (0.114)", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "share within each age and education group. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Table D.9: Determinants of refugee-host earnings gap (1) (2) (3) Earnings Earnings Earnings Refugee (% difference from hosts) -24.9** -22.4** -18.5* (0.137) (0.115) (0.114) Control: Demographics Yes Yes Control: Occupation/Sector Yes N 503 503 503 Source: World Bank Staff based on SESRE 2023. Note: Monthly earnings are collected for employees only (including work for government, NGOs, and private households). Log earnings are winsorized at the 1st and 99th percentile within the domain. Regression coefficients are transformed to percent change interpretation using. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 108 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Eritrean Somali South Sudanese Lack of work/business opportunities High cost of living Poor services or institutional support Lack of freedom or mobility Lack of community/family networks Insecurity or discrimination Figure D.16: Top 3 difficulties with being a refugee by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "services or institutional support Lack of freedom or mobility Lack of community/family networks Insecurity or discrimination Figure D.16: Top 3 difficulties with being a refugee by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure D.18: Type of work by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent Figure D.17: Work status by survey domains Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Elementary Occupations Machine Operators/Assemblers Craf/Related Trade Workers Skilled Agricultural Workers Service/Sales Workers Clerical Support Workers Tech/Associate Professionals Managers/Professionals Percent Figure D.19: Occupation by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Inside the camp Outside the", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Operators/Assemblers Craf/Related Trade Workers Skilled Agricultural Workers Service/Sales Workers Clerical Support Workers Tech/Associate Professionals Managers/Professionals Percent Figure D.19: Occupation by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Inside the camp Outside the camp Percent Figure D.20: Work location by survey domains Source: World Bank Staff based on SESRE 2023. 0 5 10 15 20 25 30 35 40 45 50 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.21: Hours per week by survey domains Source: World Bank Staff based on SESRE 2023. Annexes 109 0 10 20 30 40 50 60 70 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.22: Hourly earnings by survey domains Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.24: Household owns livestock Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.23: Household owns crops Source: World Bank Staff based on SESRE 2023. 0", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Hosts Refugees Eritrean Somali South Sudanese Figure D.24: Household owns livestock Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.23: Household owns crops Source: World Bank Staff based on SESRE 2023. 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.25: Total value of livestock Source: World Bank Staff based on SESRE 2023. 0 5,000 10,000 15,000 20,000 25,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.26: Value per tropical livestock unit Source: World Bank Staff based on SESRE 2023. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.27: Household has non-farm business Source: World Bank Staff based on SESRE 2023. Annexes 110 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.28: Value of productive assets in households with business Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 Percent 70 80 90 100 Hosts Refugees Hosts Refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "10,000 15,000 20,000 25,000 30,000 35,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.28: Value of productive assets in households with business Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 Percent 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Figure D.30: Youth work status by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees-Current Refugees-COB Hosts Refugees-Current Refugees-COB Hosts Refugees-Current Refugees-COB Eritrean Somali South Sudanese Salary (employment/casual labor) Crops/livestock Donations(NGO/gov) Remittances (local/international) Other (rental income, PSNP, pension) Percent Figure D.29: Primary source of income pre-post migration by survey domains Source: World Bank Staff based on SESRE 2023. Annexes 111 Results on Refugees’ Aspirations Table D.10: Refugee intention to migrate abroad (1) (2) Camp-Based Refugees OCP Refugees Male 0.022 -0.001 (0.019) (0.008) Age Under 30 - - Age 30-44 -0.000 -0.007 (0.020) (0.008) Age 45-64 -0.082*** -0.088** (0.027) (0.036) Education: Primary incomplete - - Education: Completed primary -0.008 0.004 (0.036) (0.009) Education: Completed secondary 0.016 0.006 (0.064)", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "intention to migrate abroad (1) (2) Camp-Based Refugees OCP Refugees Male 0.022 -0.001 (0.019) (0.008) Age Under 30 - - Age 30-44 -0.000 -0.007 (0.020) (0.008) Age 45-64 -0.082*** -0.088** (0.027) (0.036) Education: Primary incomplete - - Education: Completed primary -0.008 0.004 (0.036) (0.009) Education: Completed secondary 0.016 0.006 (0.064) (0.009) Education: Completed post-secondary -0.016 -0.010 (0.067) (0.034) Years in Ethiopia -0.000 -0.000 (0.004) (0.001) Region Fixed Effects Yes No N 3,069 830 Source: World Bank Staff based on SESRE 2023. Note: The outcome positively responds to the question, ‘Do you intend to migrate abroad?’. The sample includes all refugees aged 15 or over who were born abroad. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 112 Results on Welfare and Equity Table D.11: Poverty headcount rate by subgroups Characteristics Subgroups In-camp Addis Ababa Hosts Refugees Hosts Refugees Location or domain Eritrean 16% 73% Somali 38% 76% South Sudanese 36% 89% Addis Ababa 18% 7% Sex of head Female 34% 87% 16% 10% Male 30% 74% 19% 3% Head education No education 38% 87% 35% 16% Primary incomplete 30% 81% 26% 13% Primary complete 29% 79% 14% 6%", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Location or domain Eritrean 16% 73% Somali 38% 76% South Sudanese 36% 89% Addis Ababa 18% 7% Sex of head Female 34% 87% 16% 10% Male 30% 74% 19% 3% Head education No education 38% 87% 35% 16% Primary incomplete 30% 81% 26% 13% Primary complete 29% 79% 14% 6% Secondary incomplete 24% 65% 14% 5% Secondary complete 16% 63% 14% 3% Post-secondary 19% 68% 12% 0% Sector of head’s employment Agriculture 44% 87% 76% Industry 24% 75% 17% 0% Service 23% 84% 17% 8% Unemployed 35% 84% 18% 8% Main livelihood source Salary 21% 62% 20% 6% Casual labor 45% 66% 37% 20% Crop/livestock farming 40% 87% Manufacturing 13% 65% 0% Trade and services 19% 67% 15% 0% Safety nets or aid 33% 87% 27% 0% Remittances 40% 53% 0% 7% Others 66% 85% 8% 0% Market accessibility Low accessibility 34% 78% Medium accessibility 45% 92% High accessibility 16% 77% Proximity to resource hubs Nearest to zone 34% 93% Nearest to woreda 24% 72% Nearest to border 37% 81% Remote 33% 78% Source: World Bank Staff based on SESRE 2023. Annexes 113 Table D.12: Determinants of welfare (total expenditure per capita) (1) (2) (3) (4) In-camp refugees In-camp hosts", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "77% Proximity to resource hubs Nearest to zone 34% 93% Nearest to woreda 24% 72% Nearest to border 37% 81% Remote 33% 78% Source: World Bank Staff based on SESRE 2023. Annexes 113 Table D.12: Determinants of welfare (total expenditure per capita) (1) (2) (3) (4) In-camp refugees In-camp hosts OCP refugees OCP hosts Female headed 0.00 0.03 -0.01 0.02 (0.03) (0.03) (0.04) (0.05) Age of head (year) 0.00** 0.00 -0.00 -0.00 (0.00) (0.00) (0.00) (0.00) Household size -0.12*** -0.09*** -0.20*** -0.16*** (0.01) (0.01) (0.01) (0.02) Head years of schooling 0.02*** 0.01*** 0.03*** 0.03*** (0.00) (0.00) (0.01) (0.00) Mobile phone 0.10*** 0.19*** 0.39*** 0.17 (0.03) (0.03) (0.11) (0.15) Household has electricity 0.15*** 0.22*** 0.14 0.19 (0.04) (0.03) (0.27) (0.13) HH owns any livestock 0.04 0.03 -0.42* (0.03) (0.04) (0.25) HH member has bank account 0.13*** 0.13*** 0.07 0.16 (0.03) (0.03) (0.10) (0.26) HH has agricultural holding -0.02 -0.08* -0.47* (0.03) (0.04) (0.24) HH operates a nonfarm enterprise 0.07** -0.00 -0.05 0.26*** (0.03) (0.03) (0.10) (0.07) Share of employed members 0.35*** 0.57*** 0.14** 0.06 (0.08) (0.06) (0.06) (0.08) Health shock 0.07 -0.02 -0.04 -0.20 (0.06) (0.06) (0.10) (0.18) Market shock -0.01 -0.09*** -0.04 -0.19*** (0.02) (0.03) (0.04) (0.05) Employment shock 0.01", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "(0.04) (0.24) HH operates a nonfarm enterprise 0.07** -0.00 -0.05 0.26*** (0.03) (0.03) (0.10) (0.07) Share of employed members 0.35*** 0.57*** 0.14** 0.06 (0.08) (0.06) (0.06) (0.08) Health shock 0.07 -0.02 -0.04 -0.20 (0.06) (0.06) (0.10) (0.18) Market shock -0.01 -0.09*** -0.04 -0.19*** (0.02) (0.03) (0.04) (0.05) Employment shock 0.01 -0.11 -0.06 -0.07 (0.07) (0.11) (0.31) (0.18) Drought shock -0.03 0.08 0.24 (0.05) (0.09) (0.32) Political shock -0.05 -0.23* -0.23** -0.06 (0.05) (0.12) (0.11) (0.38) Constant 10.05*** 10.22*** 10.58*** 10.49*** (0.07) (0.09) (0.32) (0.36) Survey domain Yes Yes Yes Yes Survey time Yes Yes Yes Yes Observations 1286 1287 432 430 Source: World Bank Staff based on SESRE 2023. Note: Dependent variable is the log of total per adult equivalent consumption expenditure. All regressions include fixed effects for the survey domain and time (year and month) to account for locational and temporal differences. Standard errors in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 114 The regression specification used is: where is the vector of control variables that include demographic characteristics (sex of household head, age of household head, family size, years of schooling completed by the head), assets and wealth (mobile phone ownership,", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "0.10, ** p < 0.05, *** p < 0.01 Annexes 114 The regression specification used is: where is the vector of control variables that include demographic characteristics (sex of household head, age of household head, family size, years of schooling completed by the head), assets and wealth (mobile phone ownership, livestock ownership in tropical livestock units, land ownership, bank account, non-farm business ownership, electricity access), employment (share of employed members), resource and market access (market accessibility and proximity to resource hubs), and shocks (health, market, employment, drought, political). The regression also controls for survey domain and survey time (month) fixed effects to account for the effects of location and time on welfare. Table D.13: Determinants of welfare for in-camp refugees (1) In-camp refugees Head years since refugee status (from 2022) 0.00 (0.00) Head wants to go back to own/parents 0.02 (0.03) Ration change -0.43*** (0.10) Head: has any relative in own/parents’ COB 0.05 (0.04) HH received humanitarian food aid in past 12 months -0.00 (0.04) Distance to woreda capital (log) -0.10*** (0.01) Distance to border (log) -0.03 (0.02) Medium market accessibility -0.02 (0.06) High market accessibility 0.29*** (0.05) Constant 11.10*** (0.34) Survey domain Yes Survey time Yes Observations 1,266 Source:", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "own/parents’ COB 0.05 (0.04) HH received humanitarian food aid in past 12 months -0.00 (0.04) Distance to woreda capital (log) -0.10*** (0.01) Distance to border (log) -0.03 (0.02) Medium market accessibility -0.02 (0.06) High market accessibility 0.29*** (0.05) Constant 11.10*** (0.34) Survey domain Yes Survey time Yes Observations 1,266 Source: World Bank Staff based on SESRE 2023. Note: Dependent variable is the log of total per capita consumption expenditure. All regressions include the controls in Table D.12. This table provides the results for the additional independent variables of interest. Standard errors in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 115 38% 60% 2% Drought prone, lowland, pastoralist Humid moisture reliable, lowland Moisture reliable, highland-Cereal Figure D.31: In-camp refugee locations by ecological Zone Source: World Bank Staff based on SESRE 2023 and Ethiopia Ecological Zone Classification from ESS. 0 10 20 30 40 50 60 70 80 90 100 Employment rate Unemployment rate LFP Share of individuals (%) 1-20km 20 -100km >100km 0 10 20 30 40 50 60 70 80 90 100 Share of individuals (%) 1-30km 30 -50km >50km Employment rate Unemployment rate LFP Figure D.32: Refugee’s labor market performance Source: World", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "60 70 80 90 100 Employment rate Unemployment rate LFP Share of individuals (%) 1-20km 20 -100km >100km 0 10 20 30 40 50 60 70 80 90 100 Share of individuals (%) 1-30km 30 -50km >50km Employment rate Unemployment rate LFP Figure D.32: Refugee’s labor market performance Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 >100km 20 -100km 1-20km Share of individuals (%) Trade Industry Other service Agriculture Trade Industry Other service Agriculture 0 20 40 60 80 100 >50km 30 - 50km 1- 30km Share of individuals (%) Figure D.33: Economic sector Source: World Bank Staff based on SESRE 2023. a. By distance to Zone city b. By distance to border a. By distance to Zone city b. By distance to border Results on Markets and Opportunities Annexes 116 Table D.14: Variables used to estimate employment outcomes Indicators type Variables Data source Individual characteristics Age, sex, education, language skill, years in exile SESRE Household characteristics Gender of household head, household size, head education level, access to electricity, productive asset ownership, food insecurity experience SESRE Community characteristics Community economic development status Predominant land cover in the community SESRE Local Factors Remoteness Distance to", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Individual characteristics Age, sex, education, language skill, years in exile SESRE Household characteristics Gender of household head, household size, head education level, access to electricity, productive asset ownership, food insecurity experience SESRE Community characteristics Community economic development status Predominant land cover in the community SESRE Local Factors Remoteness Distance to towns and cities, distance to the nearest international border Ethiopian shapefile and Refugee geospatial from ESS Local labor market LFPR, unemployment rate, the share of wage employment, the share of employment by economic sector LMS, 2021 Market access Market accessibility index Ethiopia transport network layer, 2020 (ERA) & gridded population (GPWv4) Notes: For logistic regression, we assume local factors are exogenous in the model as refugees do not select their location. Since refugees’ residential location is not self-selected, they do not choose their respective camps to maximize their utility. Instead, they come across the border and are either assigned to camps close to where they crossed or a new camp is established. Our model compares refugee employment status by local factors in the hosting Zones and community: where subscripts denote : individual and : camp. refers to local factors; represents personal and presents household characteristics; refers to the characteristics of", "output": {"entities": {"named_data": ["Ethiopian shapefile", "gridded population (GPWv4)"], "descriptive_data": ["Refugee geospatial from ESS"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "camps close to where they crossed or a new camp is established. Our model compares refugee employment status by local factors in the hosting Zones and community: where subscripts denote : individual and : camp. refers to local factors; represents personal and presents household characteristics; refers to the characteristics of the community. The model includes control for refugee camps. Annexes 117 Table D.15: Factors determining the odds of obtaining a job for refugees: logit model Variables Basic Model Local Market Proximity Market access Model I Model II Model I Model II Individual feature Male 0.1372*** 0.1372*** 0.1372*** 0.1372*** 0.1291*** 0.1372*** (0.0283) (0.0283) (0.0283) (0.0283) (0.0269) (0.0283) Age 0.0521*** 0.0521*** 0.0521*** 0.0521*** 0.05459*** 0.0521*** (0.0075) (0.0075) (0.0075) (0.0075) (0.0069) (0.0075) Age squared -0.0006*** -0.0006*** -0.0006*** -0.0006*** -0.0007*** -0.0006*** (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) Some primary 0.1172** 0.1172** 0.1172** 0.1172** 0.08619* 0.1172** (0.0384) (0.0384) (0.0384) (0.0384) (0.0364) (0.0384) Speaks additional language -0.0459 -0.0459 -0.0459 -0.0459 -0.04978 -0.0459 (0.0343) (0.0343) (0.0343) (0.0343) (0.0327) (0.0343) >15 years in exile 0.0857** 0.0857** 0.0857** 0.0857** 0.1048*** 0.0857** (0.0317) (0.0317) (0.0317) (0.0317) (0.0307) (0.0317) Household feature Male head -0.0458 -0.0458 -0.0458 -0.0458 -0.02736 -0.0458 (0.0308) (0.0308) (0.0308) (0.0308) (0.0290) (0.0308) Head: primary education -0.0375 -0.0375 -0.0375", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "-0.0459 -0.0459 -0.0459 -0.0459 -0.04978 -0.0459 (0.0343) (0.0343) (0.0343) (0.0343) (0.0327) (0.0343) >15 years in exile 0.0857** 0.0857** 0.0857** 0.0857** 0.1048*** 0.0857** (0.0317) (0.0317) (0.0317) (0.0317) (0.0307) (0.0317) Household feature Male head -0.0458 -0.0458 -0.0458 -0.0458 -0.02736 -0.0458 (0.0308) (0.0308) (0.0308) (0.0308) (0.0290) (0.0308) Head: primary education -0.0375 -0.0375 -0.0375 -0.0375 -0.001520 -0.0375 (0.0389) (0.0389) (0.0389) (0.0389) (0.0378) (0.0389) Head: secondary education -0.0919 -0.0919 -0.0919 -0.0919 -0.04109 -0.0919 (0.0628) (0.0628) (0.0628) (0.0628) (0.0595) (0.0628) Head: post-secondary education 0.2333+ 0.2333+ 0.2333+ 0.2333+ 0.2705* 0.2333+ (0.1318) (0.1318) (0.1318) (0.1318) (0.1115) (0.1318) HH size: member age [15,29] -0.0233* -0.0233* -0.0233* -0.0233* -0.01886* -0.0233* (0.0095) (0.0095) (0.0095) (0.0095) (0.0093) (0.0095) HH size: member age (30,44] -0.0792*** -0.0792*** -0.0792*** -0.0792*** -0.07340*** -0.0792*** (0.0220) (0.0220) (0.0220) (0.0220) (0.0220) (0.0220) HH size: member age (45,64] -0.0363 -0.0363 -0.0363 -0.0363 -0.02057 -0.0363 (0.0254) (0.0254) (0.0254) (0.0254) (0.0256) (0.0254) HH access electricity 0.0961+ 0.0961+ 0.0961+ 0.0961+ 0.1081* 0.0961+ (0.0550) (0.0550) (0.0550) (0.0550) (0.0551) (0.0550) HH own cart 0.0953* 0.0953* 0.0953* 0.0953* 0.09562* 0.0953* (0.0421) (0.0421) (0.0421) (0.0421) (0.0423) (0.0421) HH ran out of fooda -0.0859** -0.0859** -0.0859** -0.0859** -0.09167** -0.0859** (0.0304) (0.0304) (0.0304) (0.0304) (0.0300) (0.0304) Local demography and economy activity Most land cover: agricultureb -0.0798+ -0.0798+ -0.0798+ -0.0798+", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "(0.0550) (0.0550) (0.0550) (0.0551) (0.0550) HH own cart 0.0953* 0.0953* 0.0953* 0.0953* 0.09562* 0.0953* (0.0421) (0.0421) (0.0421) (0.0421) (0.0423) (0.0421) HH ran out of fooda -0.0859** -0.0859** -0.0859** -0.0859** -0.09167** -0.0859** (0.0304) (0.0304) (0.0304) (0.0304) (0.0300) (0.0304) Local demography and economy activity Most land cover: agricultureb -0.0798+ -0.0798+ -0.0798+ -0.0798+ -0.07447 -0.0798+ (0.0475) (0.0475) (0.0475) (0.0475) (0.0482) (0.0475) Distance to bank -0.0400 -0.0400 -0.0400 -0.0400 -0.0400 (0.0250) (0.0250) (0.0250) (0.0250) (0.0250) Internal migration: Rural-Urbanc -0.0876*** -0.0391* (0.0262) (0.0157) Local labor market Annexes 118 LFPR 0.0337* 0.0370*** (0.0139) (0.0091) Share of wage employment 0.0427+ (0.0239) Unemployment rate -0.0290** (0.0106) Share of employment in the service sector 0.0429** (0.0077) Share of employment in the trade sector 0.0665*** (0.0147) Share of employment in the manufacturing sector 0.0269 (0.0237) Share of employment in the agriculture sector 0.0206 (0.0131) Proximity and access to market Level two -0.3414** (0.1143) Level three -0.3308** (0.1127) Level four -0.2098** (0.0814) Distance to Zone city (Km) -0.0030* (0.0013) Market accessibility indicator 0.4056** (0.1381) Observations 2024 2024 2024 2024 2205 2024 Chi-square test 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 R2 0.1373 0.1373 0.1373 0.1373 0.1172 0.1373 Source: World Bank Staff based on SESRE 2023. Note: Average marginal effects are estimated. a", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "(0.0814) Distance to Zone city (Km) -0.0030* (0.0013) Market accessibility indicator 0.4056** (0.1381) Observations 2024 2024 2024 2024 2205 2024 Chi-square test 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 R2 0.1373 0.1373 0.1373 0.1373 0.1172 0.1373 Source: World Bank Staff based on SESRE 2023. Note: Average marginal effects are estimated. a refers whether a household ran out of food in the last 12 months. b refers if most of the land is covered in the community by agriculture activities i.e., less built-up and shops. c refers recent (5 years) internal migrants from rural to urban centers. Distance to the nearest bank variable is excluded from proximity model II, as it is captured by effect of distance to the nearest zone city. The left side for years in exile, Head education level, HH size, most land cover, and proximity level is less than 15 years, Head with no education, non-working age members, most land cover by built-up and shops, and nearest to Zone capital city, respectively. All estimates are controlled for refugee camps. Standard errors in parentheses. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001 Variables Basic Model Local Market Proximity Market access Model I Model II Model I Model II Annexes 119", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "most land cover by built-up and shops, and nearest to Zone capital city, respectively. All estimates are controlled for refugee camps. Standard errors in parentheses. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001 Variables Basic Model Local Market Proximity Market access Model I Model II Model I Model II Annexes 119 Table D.16: Proximity and market accessibility effects on engagement in agriculture activity: logit model Variables Proximity Market access Model I Model II Individual feature Male 0.1979*** 0.2016*** 0.1998*** (0.0289) (0.0286) (0.0297) Age -0.0307*** -0.0294*** -0.0296*** (0.0083) (0.0081) (0.0088) Age squared 0.0004*** 0.0004*** 0.0004** (0.0001) (0.0001) (0.0001) Some primary -0.1403*** -0.1436*** -0.1484*** (0.0305) (0.0298) (0.0307) Household feature HH size: member age [15,29] -0.0157 -0.0171 -0.0186 (0.0118) (0.0113) (0.0117) HH size: member age (30,44] 0.0422* 0.0414* 0.0320+ (0.0179) (0.0178) (0.0185) HH size: member age (45,64] 0.0187 0.0278 0.0314 (0.0221) (0.0226) (0.0237) HH access electricity -0.2980** -0.2655** -0.2658** (0.0929) (0.0884) (0.0974) Proximity and market access Level two 0.2065*** (0.0607) Level three 0.2358** (0.0788) Level four 0.3559*** (0.0769) Distance to Zone city (Km) 0.0022*** (0.0006) Market accessibility indicator -0.0792* (0.0330) Observations 742 737 742 Chi-square test 0.0000 0.0000 0.0000 R2 0.2517 0.2435 0.2128 Source: World Bank Staff based on SESRE 2023. Standard errors in", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "market access Level two 0.2065*** (0.0607) Level three 0.2358** (0.0788) Level four 0.3559*** (0.0769) Distance to Zone city (Km) 0.0022*** (0.0006) Market accessibility indicator -0.0792* (0.0330) Observations 742 737 742 Chi-square test 0.0000 0.0000 0.0000 R2 0.2517 0.2435 0.2128 Source: World Bank Staff based on SESRE 2023. Standard errors in parentheses. All estimates are controlled for regions. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001 Annexes 120 Table D.17: Proximity and market accessibility effects on engagement in service sector: logit model Variables Proximity Market access Model I Model II Individual feature Male -0.2151*** -0.2199*** -0.2153*** (0.0309) (0.0304) (0.0312) Age 0.0368*** 0.0331*** 0.0340*** (0.0089) (0.0089) (0.0096) Age squared -0.0005*** -0.0004*** -0.0004*** (0.0001) (0.0001) (0.0001) Some primary 0.1561*** 0.1636*** 0.1683*** (0.0333) (0.0334) (0.0344) Household feature HH size: member age [15,29] 0.0217+ 0.0226+ 0.0248* (0.0121) (0.0117) (0.0119) HH size: member age (30,44] -0.0664*** -0.0657*** -0.0553** (0.0197) (0.0195) (0.0205) HH size: member age (45,64] -0.0146 -0.0232 -0.0248 (0.0246) (0.0258) (0.0269) HH access electricity -0.0637 -0.0571 -0.1051+ (0.0489) (0.0499) (0.0559) Proximity and market access Level two -0.3000*** (0.0713) Level three -0.3988*** (0.0821) Level four -0.4862*** (0.0832) Distance to Zone city (Km) -0.002* (0.0008) Market accessibility indicator 0.0897** (0.0307) Observations 787 782 787 Chi-square test 0.0000 0.0000", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "-0.0232 -0.0248 (0.0246) (0.0258) (0.0269) HH access electricity -0.0637 -0.0571 -0.1051+ (0.0489) (0.0499) (0.0559) Proximity and market access Level two -0.3000*** (0.0713) Level three -0.3988*** (0.0821) Level four -0.4862*** (0.0832) Distance to Zone city (Km) -0.002* (0.0008) Market accessibility indicator 0.0897** (0.0307) Observations 787 782 787 Chi-square test 0.0000 0.0000 0.0000 R2 0.2871 0.2738 0.2538 Source: World Bank Staff based on SESRE 2023. Standard errors in parentheses. All estimates are controlled for regions. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001 Annexes 121 Results on Social Cohesion Table D.18: Regression analysis of host and refugee attitudes (1) (2) (3) Hosts: Attitudes Index Hosts: Trusts Refugees Refugees: Trusts Hosts Male -0.021 -0.025 -0.029 (0.060) (0.036) (0.039) Age 0.004 0.003 -0.003 (0.009) (0.005) (0.006) Age Sq. -0.000 -0.000 0.000 (0.000) (0.000) (0.000) Educ: Primary incomplete - - - Educ: Completed primary -0.094 -0.040 -0.009 (0.066) (0.050) (0.052) Educ: Completed secondary -0.003 0.033 -0.083 (0.098) (0.055) (0.066) Educ: Completed post-sec. -0.074 -0.073 -0.055 (0.103) (0.050) (0.106) Agrees on improved local services 0.408*** 0.116** (0.103) (0.055) Years in Ethiopia 0.002 (0.005) Agrees hosts culturally similar 0.215*** (0.055) Region Fixed Effects Yes Yes Yes N 1724 1666 1613 Source: World Bank Staff based on SESRE 2023.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "(0.098) (0.055) (0.066) Educ: Completed post-sec. -0.074 -0.073 -0.055 (0.103) (0.050) (0.106) Agrees on improved local services 0.408*** 0.116** (0.103) (0.055) Years in Ethiopia 0.002 (0.005) Agrees hosts culturally similar 0.215*** (0.055) Region Fixed Effects Yes Yes Yes N 1724 1666 1613 Source: World Bank Staff based on SESRE 2023. The Attitudes Index is the average of 10 questions regarding beliefs about refugees’ character, the rights they should receive, and their impact on the host community, standardized to a mean of 0 and SD of 1, where positive indicates better attitudes. Trusts Refugees is the binary response of hosts to “Do you think most refugees in Ethiopia are trustworthy?” and Trusts Hosts is the binary response of refugees to “Do you think most Ethiopians are trustworthy?”. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 122 Table D.19: Regression analysis of social integration outcomes (1) (2) (3) (4) (5) Has in Ethiopia: Easy to do: Family Friend Market Interactions Social Interactions Sharing Resources Male 0.007 0.067** 0.033 0.010 -0.076** (0.017) (0.033) (0.027) (0.036) (0.035) Age Under 30 - - - - - Age 30-44 -0.030 0.033 0.007 0.004 -0.048*", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "analysis of social integration outcomes (1) (2) (3) (4) (5) Has in Ethiopia: Easy to do: Family Friend Market Interactions Social Interactions Sharing Resources Male 0.007 0.067** 0.033 0.010 -0.076** (0.017) (0.033) (0.027) (0.036) (0.035) Age Under 30 - - - - - Age 30-44 -0.030 0.033 0.007 0.004 -0.048* (0.021) (0.025) (0.025) (0.029) (0.028) Age 45-64 -0.041 0.024 0.002 0.011 0.012 (0.025) (0.038) (0.038) (0.050) (0.041) Age Over 64 -0.009 -0.062 0.100** 0.041 0.046 (0.046) (0.058) (0.043) (0.089) (0.073) Educ: Primary incomplete - - - - - Educ: Completed primary -0.004 0.121*** 0.008 0.028 0.063** (0.027) (0.033) (0.035) (0.041) (0.032) Educ: Completed secondary -0.026 0.218*** -0.020 0.061 0.060 (0.028) (0.054) (0.051) (0.060) (0.057) Educ: Completed post-sec. 0.003 0.238* 0.100** -0.020 0.238 (0.045) (0.131) (0.040) (0.095) (0.155) Years in Ethiopia 0.003** 0.006* 0.008*** -0.001 0.006 (0.002) (0.004) (0.003) (0.004) (0.004) Agrees hosts culturally similar 0.021 0.047 0.020 -0.010 -0.030 (0.016) (0.039) (0.037) (0.051) (0.036) Region Fixed Effects Yes Yes Yes Yes Yes N 1667 1667 1667 1667 1667 Source: World Bank Staff based on SESRE 2023. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 123 Table D.20: Regression", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "(0.039) (0.037) (0.051) (0.036) Region Fixed Effects Yes Yes Yes Yes Yes N 1667 1667 1667 1667 1667 Source: World Bank Staff based on SESRE 2023. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 123 Table D.20: Regression analysis of social integration and labor market outcomes (1) (2) (3) (4) (5) (6) Working High-Skill Work Outside Has Ethiopian friend 0.069* 0.062* 0.046* 0.044* -0.085 -0.095 (0.037) (0.037) (0.024) (0.024) (0.066) (0.068) Has Ethiopian family 0.051 0.041 0.026 0.024 0.149 0.139 (0.057) (0.057) (0.035) (0.036) (0.120) (0.116) Social interactions easy 0.053 0.010 0.050 (0.033) (0.019) (0.073) Agrees hosts culturally similar 0.078* 0.079* 0.034** 0.034** 0.122 0.125 (0.046) (0.046) (0.017) (0.017) (0.079) (0.079) Region Fixed Effects Yes Yes Yes Yes Yes Yes Individual Controls Yes Yes Yes Yes Yes Yes N 1625 1625 524 524 445 445 Source: World Bank Staff based on SESRE 2023. Note: Columns 3-4 are restricted to workers, and Columns 5-6 are restricted to workers in camps. High-skill occupations include managers, professionals, and associate professionals (around 7% of refugees). Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "Staff based on SESRE 2023. Note: Columns 3-4 are restricted to workers, and Columns 5-6 are restricted to workers in camps. High-skill occupations include managers, professionals, and associate professionals (around 7% of refugees). Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 124 Table E.1: Food aid data/information received from UNHCR Item Remark Assumptions Cereal Not clear Other cereals Wheat Matched Maize Matched Rice Matched Sorghum Matched CSB/famex (CSB+) Not in SESRE Average of other cereals/pulses Pulse Not clear Peas Biscuit Matched Date biscuit Not in SESRE Merged with biscuits Dates Matched Oil Merged with edible oil Vegetable oil Merged with edible oil Salt Matched Cash - - Source: UNHCR Annex E: Robustness Checks of Refugees’ Consumption This Annex discusses assessing the disparity between refugee ration aid and reported consumption quantities. This is reported as a robustness check. As discussed earlier, the expenditure of in-camp refugees is almost half that of hosts despite sizeable food aid and significant investments made by the WFP and UNHCR in cash transfers (in selected camps). The significantly lower expenditures (food and non-food) among refugees compared to the host population led to higher poverty", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "discussed earlier, the expenditure of in-camp refugees is almost half that of hosts despite sizeable food aid and significant investments made by the WFP and UNHCR in cash transfers (in selected camps). The significantly lower expenditures (food and non-food) among refugees compared to the host population led to higher poverty rates. The team cross-checked the food aid received by in-camp refugees based on administrative data from the UNHCR and WFP with food consumption data from SESRE. The analysis looks at both separately for information provided by UNHCR and WFP. While WFP is not responsible for distributing non-food items such as mattresses, cooking, feeding utensils, etc., UNHCR provides non-food items; maybe mattresses were distributed in Alemwach since it is a relatively new camp. WFP’s food and cash assistance targets all individuals in refugee households. The information received from UNHCR on food aid provided to refugees in each camp includes quantities per food item per month and cash transfers per person per month for each camp and period. The food items include cereal, wheat, maize, rice, sorghum, CSB/famex (CSB+), pulse, biscuit, date biscuit, dates, oil, vegetable oil, salt, and cash (Table E.4). We have computed the per person per month in-kind aid", "output": {"entities": {"named_data": [], "descriptive_data": ["administrative data from the UNHCR", "food consumption data from SESRE"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "food item per month and cash transfers per person per month for each camp and period. The food items include cereal, wheat, maize, rice, sorghum, CSB/famex (CSB+), pulse, biscuit, date biscuit, dates, oil, vegetable oil, salt, and cash (Table E.4). We have computed the per person per month in-kind aid quantities into annual values using the household size and prices from SESRE and mapped them to the closest food item in SESRE (this was not straightforward as the items are different) considering food ration change periods. The food ration scaling factor is 50 percent vs. 84 percent. Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food aid admin data for every item except Biscuits. Refugee households still report lower quantities, even correcting for shares indicated as sold. Annexes 125 Table E.2: Food aid and consumption comparisons Items Quantity (per capita/year) Expenditure (per capita/year) SESRE UNHCR SESRE UNHCR Nonzero All Net of sold ration* Cereals/other cereals 23.2 186.1 175.5 493 12404 Wheat 60.5 133.2 116.6 1427 4710 Maize 39.6 125.5 118.3 312 4267 Rice 21.3 48.5", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": ["UNHCR food aid admin data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "as sold. Annexes 125 Table E.2: Food aid and consumption comparisons Items Quantity (per capita/year) Expenditure (per capita/year) SESRE UNHCR SESRE UNHCR Nonzero All Net of sold ration* Cereals/other cereals 23.2 186.1 175.5 493 12404 Wheat 60.5 133.2 116.6 1427 4710 Maize 39.6 125.5 118.3 312 4267 Rice 21.3 48.5 39.8 560 3392 Sorghum 12.7 132.8 125.7 4 5652 Pulses 7.1 18.9 17.5 248 1514 Vegetable oil/oil 3.7 9.7 8.9 627 1774 Salt 1.6 7.9 7.7 57 232 Biscuits 5.4 4.5 4.4 16 112 Dates 0.0 4.2 3.7 0 . CSB+ 19.3 15.0 14.0 36 532 Other food 565.9 - - 2558 Peas 5.7 - - 137 All cereals 78.2 - - 2994 All pulses 8.2 - - 385 Aggregate ration/month 46.7 - - Source: UNHCR and World Bank Staff based on SESRE 2023. Valuing food aid quantities with prices from SESRE suggests that if UNHCR food aid quantities were received/ reported by refugees, refugees’ food expenditures would be much more comparable to those of hosts (Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "received/ reported by refugees, refugees’ food expenditures would be much more comparable to those of hosts (Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food aid admin data for every item except Biscuits. Refugee households still report lower quantities, even correcting for shares indicated as sold (Table E.2). Table E.3: Aggregate food expenditures Value/year (per capita) Value/year (per adult) Food expenditure (all) [A] 11,412 13,898 Food expenditure (UNHCR items only) [B] 3,528 4,335 Food expenditure (UNHCR in-kind) [C] 11,313 13,933 Food expenditure (UNHCR in-kind + cash) [D] 16,179 19,965 Source: UNHCR and World Bank Staff based on SESRE 2023. Note: Food expenditure (all): aggregate food expenditure. Food expenditure (UNHCR items only): aggregate food expenditure from UNHCR items only Food expenditure (UNHCR in-kind): aggregate food expenditure from UNHCR items valued using SESRE prices Food expenditure (UNHCR in-kind + cash): aggregate food expenditure from UNHCR items valued using SESRE prices plus the cash equivalent transfers Annexes 126 Food aid data received from WFP Food aid information received information from the WFP includes five food items and their", "output": {"entities": {"named_data": [], "descriptive_data": ["WFP Food aid information", "UNHCR food aid admin data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "from UNHCR items valued using SESRE prices Food expenditure (UNHCR in-kind + cash): aggregate food expenditure from UNHCR items valued using SESRE prices plus the cash equivalent transfers Annexes 126 Food aid data received from WFP Food aid information received information from the WFP includes five food items and their quantities distributed to refugees: cereal (mainly wheat but in some camps rice), pulses (mostly yellow split peas), CSB+, vegetable oil, and salt (Table E.5). The per person per month aid (in-kind) are converted into annual values and mapped them to closest food item in SESRE (this was not straightforward as the items are different). We assumed a 50 percent ration until November 2022 (for our Oct/Nov sample) and an 84 percent after Dec 2022 (Dec, Jan, and Feb sample). The WFP data are converted to annual values using household size and prices from SESRE. The data source is the “Revised cash transfer value from Oct 2022_refugee camps” file received from the WFP document that helps to get information regarding the changes in cereal cash equivalent – data on cereal cash equivalent for cash camps which is used to calculate cereals provided in those camps and cash transfer value per year.", "output": {"entities": {"named_data": ["Revised cash transfer value from Oct 2022_refugee camps"], "descriptive_data": ["cereal cash equivalent for cash camps"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "cash transfer value from Oct 2022_refugee camps” file received from the WFP document that helps to get information regarding the changes in cereal cash equivalent – data on cereal cash equivalent for cash camps which is used to calculate cereals provided in those camps and cash transfer value per year. Based on this information, we compare how the list shared with us on what refugees should have received to what they reported regarding food consumption. Table E.4: Food aid data/information received from WFP Item Remark Assumptions Cereal Not clear Mapped to wheat* Pulse Not clear Mapped to peas* Vegetable oil Mapped to edible oil CSB/famex (CSB+) Not in SESRE Average of other cereals/pulses Salt Matched Cash - - Source: UNHCR Note: Rice was distributed for some months in Afar and Somali Dollo area camps, though wheat remained the main cereal distributed. YSP (Yellow Split Pea) was the main pulse distributed among refugees and their best-preferred pulse. CSB is a corn soya blend with added essential micronutrients and vitamins called super cereal. Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported", "output": {"entities": {"named_data": [], "descriptive_data": ["cereal cash equivalent for cash camps"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "among refugees and their best-preferred pulse. CSB is a corn soya blend with added essential micronutrients and vitamins called super cereal. Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported quantities lower than WFP food aid admin data for every item except for CSB+ and salt (Table E.6). Even when correcting for shares indicated as sold, refugee households still reported lower quantities. Using the WFP food aid information, we found a picture similar to UNHCR’s. Annexes 127 Table E.5: Food quantity and expenditure comparisons Items Quantity (per capita/year) Expenditure (per capita/year) SESRE WFP SESRE WFP Nonzero All Net of sold ration* Cereals 82.0 77.9 2,179 Wheat 60.5 1,427 Pulse 17.1 15.6 1,366 Peas 5.7 137 Vegetable oil 3.7 5.4 5.0 627 967 CSB+ 19.3 11.1 10.1 36 394 Salt 1.6 1.8 1.7 57 63 All cereals 76.9 - - 2,994 All pulses 8.3 - - 385 Aggregate ration/month 46.7 - Source: WFP and World Bank Staff based on SESRE 2023. Note: *Net of sold ration = quantity*share of ration sold (we asked the share of ration sold in SESRE) Table", "output": {"entities": {"named_data": [], "descriptive_data": ["WFP food aid information", "WFP food aid admin data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "1.8 1.7 57 63 All cereals 76.9 - - 2,994 All pulses 8.3 - - 385 Aggregate ration/month 46.7 - Source: WFP and World Bank Staff based on SESRE 2023. Note: *Net of sold ration = quantity*share of ration sold (we asked the share of ration sold in SESRE) Table E.6: Aggregate food expenditures Value/year (per capita) Value/year (per adult) Food expenditure (all) [A] 11,414 13,898 Food expenditure (WFP items only) [B] 2,226 2,741 Food expenditure (WFP in-kind) [C] 4,968 6,165 Food expenditure (WFP in-kind + cash) [D] 7,653 9,440 Source: WFP and World Bank Staff based on SESRE 2023. Average food expenditures from SESRE (13,898 birr) are higher than valued in-kind food aid reported by WFP (6,165 birr). Valuing quantities of WFP food aid with prices from SESRE suggests that if food aid quantities were received/reported, refugees’ food expenditure would be 9,440 birr slightly above the values we get in SESRE of 13,898 birr, but still low compared to hosts at 28,324 birr. The level of disaggregation of food items matters. The more disaggregated, the higher the food aggregates. To summarize, food rations received are lower than admin data suggests, regardless of the data source. Possible explanations for lower", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "in SESRE of 13,898 birr, but still low compared to hosts at 28,324 birr. The level of disaggregation of food items matters. The more disaggregated, the higher the food aggregates. To summarize, food rations received are lower than admin data suggests, regardless of the data source. Possible explanations for lower food quantities include. First, SESRE only asks one aggregate question: “How much on average of your food ration do you sell in the market”? Second, food rations are only received once a month, which may not coincide with the interview date. Yet, SESRE asks about what food they consumed (not even a list of food items), not about food received as aid. Third, refugees might carry over food aid in the future. Annexes 128 Annex F: Comparison of Results from Skills Profile Survey and SESRE Table F.1: Results on common indicators from SPS 2017 and SESRE 2023 Skills Profile Survey 2017 SESRE 2023 (In camp) Hosts Refugees Hosts Refugees Country of origin South Sudanese 23% 53% Somali 24% 30% Eritrean 25% 5% Sudan 28% Demographics Female headed 35% 66% 44% 73% Children (0 to 14) 55% 61% 47% 53% Youth (15 to 24) 13% 17% 19% 21% Adults (25 to", "output": {"entities": {"named_data": ["Skills Profile Survey 2017", "SPS 2017"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "2017 SESRE 2023 (In camp) Hosts Refugees Hosts Refugees Country of origin South Sudanese 23% 53% Somali 24% 30% Eritrean 25% 5% Sudan 28% Demographics Female headed 35% 66% 44% 73% Children (0 to 14) 55% 61% 47% 53% Youth (15 to 24) 13% 17% 19% 21% Adults (25 to 64) 29% 22% 31% 24% Elderly (>=65) 2% 1% 3% 2% Education Net primary enrollment 74% 79% 75% 69% Net secondary enrollment 35% 13% 39% 22% Living conditions Own a house 72% 5% 69% 14% Overcrowding 32% 59% 36% 56% Improved sources of water 96% 98% 91% 100% Access to electricity (grid) 46% 8% 41% 3% Improved toilet facility (shared/not shared) 51% 69% 38% 43% Employment Employed 61% 22% 48% 25% Unemployed 2% 6% 9% 19% Inactive, not in school 23% 44% 20% 23% Inactive, in school 14% 27% 24% 33% Poverty incidence US$1.9 per capita per day 27% 65% National Poverty line 32% 84% Food security High food insecurity 26% 67% Food insecurity scale 4.0 8.1 Social cohesion Economic competition 33% 49% Increased insecurity 37% 39% Source: Pape et al. (2018) and World Bank Staff based on SESRE 2023.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "National Poverty line 32% 84% Food security High food insecurity 26% 67% Food insecurity scale 4.0 8.1 Social cohesion Economic competition 33% 49% Increased insecurity 37% 39% Source: Pape et al. (2018) and World Bank Staff based on SESRE 2023.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "seis"}
-{"input": "[This report was prepared by 3E,](http://www.3e.eu/) under contract to the [World Bank.](https://www.worldbank.org/) It is one of several outputs Wind Power Resource Mapping Papua New Guinea [Project ID: P145864]. This activity is funded and supported by the Energy Sector Management Assistance Program (ESMAP), a multi-donor trust fund administered by the World Bank, under a global initiative on Renewable Energy Resource Mapping. Further details on the initiative can be obtained from the [ESMAP website.](https://www.esmap.org/) The content of this document is the sole responsibility of the consultant authors. Any improved or validated\n[wind resource data will be incorporated into the Global Wind Atlas.](https://globalwindatlas.info/) Copyright © 2018 THE WORLD BANK Washington DC 20433 Telephone: +1-202-473-1000\n[Internet: www.worldbank.org](https://www.worldbank.org/) The World Bank does not guarantee the accuracy of the data included in this work and accept no responsibility for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of the World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.\n\nRights and Permissions", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "The material in this work is subject to copyright. Because the World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for non-commercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, World Bank Group, 1818 H Street NW, Washington, DC 20433, USA;\n[fax: +1-202-522-2625; e-mail: pubrights@worldbank.org. Furthermore, the ESMAP Program Manager would](mailto:pubrights@worldbank.org) appreciate receiving a copy of the publication that uses this publication for its source sent in care of the address above, or to [esmap@worldbank.org.](mailto:esmap@worldbank.org) All images remain the sole property of their source and may not be used for any purpose without written permission from the source.\n\nAttribution Please cite the work as follows: World Bank. 2019. Wind Resource Mapping in Papua New Guinea: 12 Month site resource report. Washington, DC: World Bank.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "#### World Bank/AEDB Wind Mapping Project – Papua New Guinea\n\n##### 1-year Site Resource Report of Meteorological Masts 3 Sites\n\n1. Launakalana, Central Province\n2. UMI Station, Morebe Province 3. LNG Plant, Central Province Document date: May 8 [th] 2019 Prepared by: Olgu Yildirimlar info@3E.eu www.3E.eu 3E nv/sa Kalkkaai 6 Quai à la Chaux B-1000 Brussels T +32 2 217 58 68 F +32 2 219 79 89", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "### **TABLE OF CONTENTS**\n\n1. Introduction .......................................................................................................................................... 1\n\n2. Site Overview ........................................................................................................................................ 2 3. Mast Characteristics .............................................................................................................................. 3 4. Configuration of mast ........................................................................................................................... 4 5. Site 1 – Launakalana, Central Province ................................................................................................. 6 6. Site 2 – UMI Station, Morebe Province............................................................................................... 13 7. Site 3 – LNG Plant, Central Province ................................................................................................... 19 References .................................................................................................................................................. 26 Annex A Coordinates of the sites .......................................................................................................... 27 Annex B Mast instrument serial number and calibration information ................................................. 28 Annex C Data recovery rates over the short-term ................................................................................ 31 Annex D The wind regime observed over the short-term..................................................................... 33 Annex E Weibull parameters of the short-term wind regime .............................................................. 35 Annex F Comparison of predicted wind regime and estimated long-term wind regime ..................... 36 Annex G Short-term seasonal and diurnal variations in wind characteristics ....................................... 37 Annex H Long-term correlation coefficients ......................................................................................... 41 Annex I Estimates of equivalent mean and directional Weibull wind speed distributions ................. 44 Annex J Uncertainties associated with AEP results – single turbine at mast location ......................... 50 Annex K Mean air density ..................................................................................................................... 51 Annex L Potential Layout ...................................................................................................................... 52 Annex M Calibration certificates ............................................................................................................ 55", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "###### **1. INTRODUCTION**\n\nThe implementation of the ESMAP initiative from the World Bank Group, is aiming at Renewable Energy Resource Mapping and Geospatial Planning for Papua New Guinea. In total 3 wind masts are installed at the sites identified in different areas in the province of Central Province, Morebe Province and Western Highlands. All 3 Masts installed have a height of 80 m all under the specification heights authorized by Civila Avaition Safety Authority (CASA).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "The wind masts were designed according to TIA/EIA 222-G and their installation was carried out according to the latest version of the IEC Standard 61400-12-1. The design was verified with a detailed FEA analysis for required design parameters. After design verification, detailed manufacturing drawings were issued for fabrication of the mast and quality was ensured at each stage of fabrication including material selection, sizing, fixture, cutting, bending, welding, assembly, galvanizing and painting of the mast. Civil works at the site was commenced under the supervision of an experienced civil engineer and strict quality check of material at different stages of the work was maintained. The foundations were allowed to sit for recommended time of 1 week and the installation of the wind mast was started after the completion of the civil works at the site. The proper functioning of the instruments was verified by remote connection before going on site as well as during the commissioning while on site.\n\nMast characteristics are given in Section 3 but further detailed in the installation report [1].\n\nThe aim of this report is; to provide an overview of the wind regime of different parts of Papua New Guinea.\n\n1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "###### **2. SITE OVERVIEW**\n\nThree wind masts are installed in the locations of Central Province, Morebe Province and Western Highlands Province. The sites are located in either private or government land having flat terrain. All wind mast sites are easily accessible by any type of vehicle. The following table presents the overview of the 3 sites.\n\n|Site # 1 2 3|Col2|Col3|Col4|\n|---|---|---|---|\n|**Site name**|Launakalana|UMI Station|LNG Plant|\n|**Province**|Central Province|Morebe Province|Central Province|\n|**Host**<br>**institution**|-|-|-|\n|**Land**|Private land|Government land|Private land|\n|**Site access**|85 km (approx. 2-hour<br>drive) from Port Moresby;<br>easily accessible by any<br>type of vehicle|75 km (2-hour drive) from Lae;<br>easily accessible by any type<br>of vehicle|25 km (1-hour drive) from<br>Port Moresby;<br>easily accessible by any type<br>of vehicle|\n|**Site**<br>**description**|Land with relatively flat<br>terrain. The soil conditions<br>are adequate for Mast<br>foundations and<br>respectiviely good network<br>coverage.|Land with flat terrain with no<br>obstruction or roughness in<br>the surroundings of the wind<br>mast .|Land with flat terrain with no<br>major obstruction or<br>roughness in the surroundings<br>of the wind mast|\n\n2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "|Site 1 Launakalana 2 UMI Station 3 LNG Plant|Col2|Col3|Col4|\n|---|---|---|---|\n|**Area**|1000 m2|6000 m2|3000 m2|\n|**Mast height**|80 m|80 m|80 m|\n|**Fencing**|100mx100m<br>Around mast|100mx100m<br>Around mast|100mx100m<br>Around mast|\n|**Coordinates**|**Coordinates**|**Coordinates**|**Coordinates**|\n|**Longitude1 **|147°46'50.05\"E|146°11'44.2\"E|147°1’96\"E|\n|**Latitude**|9°55'33.06\"S|6°13'59.77\"S|9°19'3.37\"S|\n|**UTM X**|585,569|411,014|502,988|\n|**UTM Y**|8,902,685|9,310,941|8,970,036|\n|**UTM zone**|55L|55M|55L|\n|**Elevation**|17 m|305 m|14 m|", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "###### **4. CONFIGURATION OF MAST**\n\nThe following figure presents the overview of the meteo mast, the placement of the sensors and the equipment on the mast which is common to all the sites.\n\n**Figure 1: Configuration of mast**\n\n4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "6 5. SITE 1 – LAUNAKALANA, CENTRAL PROVINCE 5.1 WIND DATA PROCESSING Short-term wind regime The anemometer calibration parameters stated in the calibration reports are applied to the raw data. The data are then cleaned.\nThe period covering 1 complete year (16/02/2018 to 15/02/2019) has been selected for use in the following steps of the study.\nThe mast shading effect is corrected by alternatively using the measurements of both anemometers at each height depending on the wind direction.\nHeight Primary anemometer Secondary anemometer Wind directions where secondary anemometer is used 80 m C2 C1 171° - 222 60 m C3 C4 13° - 70° 40 m C6 C5 180° - 234 20 m C8 C7 178° - 237", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "The Weibull parameters of the short-term wind regime over this period per sector are presented in Annex D.\nAnnex G presents the seasonal and diurnal variations observed over the short-term.\nLong-term wind regime The long-term extrapolation is performed in three steps: first, the most reliable reference datasets are identified, then the best combination of reference data and extrapolation method is selected.\nEventually, the combination of dataset and method resulting in the lowest uncertainty (cf. section 5.4) is selected.\n3E selects reference dataset from the following sources: • MERRA-2 and post-processed ERA-Interim and ERA-5 reanalysis data from WindPRO (4 closest grid points), • Meteorological station data from WindPRO, The following criteria are used to select reference datasets from these sources: • Agreement: the reference dataset should agree with the measurements in terms of wind speed variations over time. This agreement is quantified by the Pearson correlation coefficient “r”. 3E considers a Pearson coefficient of 0.7 (all data or monthly averages) as a minimum prerequisite for a reference dataset to be considered for long-term extrapolation.\n• Time resolution: the time resolution of the reference dataset should be constant over time. In case time resolution varies, 3E resamples data to a constant time resolution.", "output": {"entities": {"named_data": [], "descriptive_data": ["Meteorological station data from WindPRO"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "7 • Data availability: missing periods should be limited and evenly distributed over time. 3E considers data availability above 80 % as a minimum prerequisite for a reference dataset to be used for long-term extrapolation.\n• Consistency: the reference dataset should not reveal any abrupt change or unrealistic trend. 3E applies a SNHT test [2] order to identify discontinuities. If this happens, then the available period is limited to ensure homogeneity. 3E then also applies a Mann-Kendall test [3][4] (90% confidence interval) in order to identify possible trends. Again, the available period is limited to ensure the absence of a trend.\nWhen several reference datasets from the same reanalysis project are considered, 3E only selects the one providing the best r (all data) and the one providing the best r (monthly averages).\nThe correlation between the on-site measurements and the reference datasets is presented in Annex H.\nThe least uncertainty is obtained from ERA5 S09.69 E147.65 data using the Wind Index method, which is therefore the selected combination of reference data and extrapolation method. Due to the trend detection in datasets of MERRA-2 and ERA-Interim, the data from earlier 2000s are discarded for the long-term extrapolation.\nThe result of the Wind Index method is a long-term correction factor, as presented in the following table. This correction factor is applied to the energy production calculated from the short-term wind regime. It should be noted that an additional uncertainty contribution is introduced in the uncertainty assessment to account for the fact that this method relies on the assumption that the short-term wind rose is representative of the long-term.\nHeight AGL\n[m] 80 Long-term period\n[-] 19 years Long-term correction factor\n[-] 0.89", "output": {"entities": {"named_data": ["ERA5 S09.69 E147.65 data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**5.2** **WIND FLOW MODELLING**\n\nTerrain features influence the wind flow and thus play a significant role in the spatial extrapolation of the wind regime. The software package WindPRO and the WAsP wind flow model are used in the present study. WAsP requires a terrain model describing elevation, roughness and other relevant obstacles to the wind flow that are not modelled as roughness.\n\nThe terrain model used in this study represents the current conditions, which are assumed to remain the same over the wind farm lifetime.\n\n**Terrain model**\n\n_**a.**_ _**Elevation**_ The wind regime can be highly influenced by elevation differences across the site. For this study, terrain elevation is modelled within a radius of 15 km (in line with WAsP recommendations [5]) based on SRTM data. Height contour lines are then generated with an elevation difference of 10 m between two successive lines.\n\nIt should be noted that SRTM is a digital surface model (DSM), which includes features such as forests and buildings. This is accounted for in the uncertainty assessment (cf. section 5.4).", "output": {"entities": {"named_data": ["SRTM data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "_**b.**_ _**Roughness length**_ Roughness length is a key parameter of the equation that governs wind shear. Changes in roughness length cause variations of wind shear, which propagate vertically as the air flows over the site. The impact at measurement or hub height therefore varies with distance to roughness changes, but is also related to atmospheric conditions.\n\nGiven that roughness length is closely related to land use, terrain roughness is typically modelled using a land-use database. However, no such suitable database with the required quality and resolution is available for the project sites considered in this study. Therefore, the roughness maps have been created manually based on aerial photos and covering an area with a radius of 20km around each site.\nThe roughness length values considered are presented in the caption of the following figures.\n\n**Figure 3: Elevation map 15x15km (with mast in center) with**\n\n**10m elevation difference between lines. Altitudes in map**\n\n**range from 0m to 207m (warmer colors indicate higher**\n\n**Figure 4: Ground roughness map 20x20km (with mast in**\n\n**center). Background roughness length is 0.1m, corresponding**\n\n**to open field with distributed rows of trees and shrubs and a**\n\n**river bypassing on the left. Roughness length for specific areas**\n\n8", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["land-use database"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "9 altitudes). RIX2 value at mast is 0.0% using radius of 3,500m, steepness threshold of 30% (17°) and frequency distributed directional weight are 0.0m for the river(yellow) and 0.75m for areas covered with forrest trees and shrubs in the wetland region (purple color) Wind flow model WAsP is used to extrapolate the wind regime to the mast location. It involves two steps: a vertical extrapolation of the wind regime and a horizontal extrapolation of the wind regime.\na. Horizontal extrapolation of the wind regime In this study, wind measurements are only available at a single location, which does not allow any validation of the horizontal extrapolation of the wind regime.\nb. Vertical extrapolation of the wind regime By default, WAsP is configured for atmospheric conditions typical of North-Western Europe. Therefore, parameters sometimes need to be adapted. In particular, some parameters strongly affect the vertical extrapolation of the wind regime and can be validated and calibrated if necessary by comparison of the measured and calculated wind shears.\nIn this study, WAsP parameters are adapted so that the calculated wind shear agrees with the shortterm measured wind shear.\nThe mean wind speeds measured at the various heights over the short-term period limited to 1 complete year (cf. Section 5.1) and the vertical wind speed profile calculated by WAsP from the measurements at 80 m AGL and from the adapted model parameters are given in the following graph.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Figure 5: Mean wind speeds measured over the short-term period limited to 1 complete year and vertical wind speed profile calculated by WAsP using measurements at 80 m AGL 2 The ruggedness index (RIX) at a specific location is the percentage of the ground surface that has a slope above a given threshold (here 30%) within a certain distance (here 3.5 km).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "10 5.3 ENERGY PRODUCTION CALCULATION In agreement with the World Bank, the energy production is calculated for a single turbine at the mast location. A generic 2.5MW turbine with a hub height of 100 m is selected for the purpose of this study.\nGross energy production A gross energy production refers to the theoretical energy production that would be achieved if there was no operational loss. It is calculated by combining the wind regime at a wind turbine location and hub height to the power curve specific to the considered wind turbine type and corrected for local hub height air density. This is done using the software WindPRO.\nSince the energy content of the wind varies proportionally to air density, power curves are adapted accordingly before being used in calculations. The adaptation is done using the new recommended WindPRO method (adjusted IEC 61400-12 method, improved to match turbine control) [6].\nFor this project, air density at hub height is estimated to be 1.154 kg/ m3. Air density is calculated by WindPRO based on the temperature and pressure measurements from the Launakalana mast, located at the site.\nEnergy production losses In addition to energy conversion losses taken into account in the power curve, other losses affect the electrical power expected to be delivered to the grid. The following losses are taken into account in this study and are summarised below.\n• Wake losses: Wake losses are due to the mutual influence of the wind turbines and are calculated using the N.O. Jensen (EMD) : 2005 wake model implemented in WindPRO.\n• Unavailability losses: Unavailability losses are due to downtime of the wind turbines or balance of plant (maintenance or technical incidents) as well as downtime of the power grid.\n• Performance losses: Turbine performance losses are typically due to high wind hysteresis, yaw misalignment, wind flow inclination, turbulence, wind shear and other differences between turbine power curve test conditions.\n• Electrical losses: Electrical losses occur in cables and transformers ensuring electrical transmission to the wind farm substation.\n• Environmental losses: Environmental losses account for the performance degradation of the wind turbines due to environmental conditions.\nThe energy production losses defined in the preceding section are summarized below.\nWake losses\n[%] 0.0 Unavailability losses\n[%] 4.0 Performance losses\n[%] 0.3 Electrical losses\n[%]\n1.5\nEnvironmental losses\n[%] 0.5 Total losses\n[%] 6.1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "11 Net energy production The expected wind farm energy production figures for a single generic 2.5MW wind turbine at the mast location are as follows: Gross energy production\n[MWh/y] 3,076 Total energy production losses\n[%] 6.1 Net energy production (AEP)\n[MWh/y] 2,887 Net full load equivalent hours\n[h/y] 1,155 Net capacity factor\n[%] 13.2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "5.4 UNCERTAINTY ANALYSIS Some uncertainty components are directly quantified in terms of energy production, whereas some other uncertainty components are first quantified in terms of wind speed, then translated into uncertainties in terms of energy production, by applying a sensitivity factor. The sensitivity factor relates energy production change to wind speed change.\nThe global uncertainty is then calculated from the individual uncertainty components by assuming that they are independent and that the resulting uncertainty follows a normal distribution (central-limit theorem). They can therefore be combined by calculating the square root of the sum of the squares of each uncertainty.\nIn this study, the following sources of uncertainty are considered.\n• Wind measurements (wind speed) • Long-term extrapolation (wind speed) • Vertical extrapolation (wind speed) • Future wind variability (wind speed) • Spatial variation (wind speed) • Power curve (production) • Energy production losses (production) The table below presents the breakdown of uncertainty analysis in terms of annual energy production.\nWind measurements\n[% AEP] 5.4 Long-term extrapolation\n[% AEP] 9.8 Vertical extrapolation\n[% AEP] 3.3 Future wind variability [20 years]\n[% AEP] 4.8 Spatial variation\n[% AEP]\n1.4\nPower curve\n[% AEP] 8.8 Production losses\n[% AEP]\n1.2\nCombined uncertainty [20 years]\n[% AEP] 15.5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "AEPs over 20 year period exceeded with various probabilities of 50% to 95% are are as follows: AEP (50)\n[MWh/y] 2,887 AEP (75)\n[MWh/y] 2,585 AEP (90)\n[MWh/y] 2,313 AEP (95)\n[MWh/y] 2,151", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Some uncertainty components are directly quantified in terms of energy production, whereas some other uncertainty components are first quantified in terms of wind speed, then translated into uncertainties in terms of energy production, by applying a sensitivity factor. The sensitivity factor relates energy production change to wind speed change. For this specific project, the sensitivity factor is calculated to be 2.7.\n\n**5.5** **TURBULENCE ANALYSIS**\n\nThe measured turbulence at 80 m AGL compared to the IEC curves is given below.\n\nThe effective turbulence intensity measured at 80 m AGL is above the characteristic turbulence intensity of Class A wind turbines (IEC 61400-1, ed. 3) for wind speeds above 15.5-16.5 m/s.\n\n**Figure 6: Turbulence at 80 m AGL compared to the IEC curves**\n\n12", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "13 6. SITE 2 – UMI STATION, MOREBE PROVINCE 6.1 WIND DATA PROCESSING Short-term wind regime The anemometer calibration parameters stated in the calibration reports are applied to the raw data. The data are then cleaned.\nThe period covering 1 complete year (09/02/2018 to 08/02/2019) has been selected for use in the following steps of the study.\nThe mast shading effect is corrected by alternatively using the measurements of both anemometers at each height depending on the wind direction.\nHeight Primary anemometer Secondary anemometer Wind directions where secondary anemometer is used 80 m C1 C2 42° - 82° 60 m C3 C4 29° - 122° 40 m C5 C6 39° - 125° 20 m C7 C8 45° - 120°", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "The Weibull parameters of the short-term wind regime over this period per sector are presented in Annex D.\nAnnex G presents the seasonal and diurnal variations observed over the short-term.\nLong-term wind regime The long-term extrapolation is performed in three steps: first, the most reliable reference datasets are identified, then the best combination of reference data and extrapolation method is selected.\nEventually, the combination of dataset and method resulting in the lowest uncertainty (cf. section 6.4) is selected.\n3E selects reference dataset from the following sources: • MERRA-2 and post-processed ERA-Interim reanalysis data from WindPRO (4 closest grid points), • Meteorological station data from WindPRO, The following criteria are used to select reference datasets from these sources: • Agreement: the reference dataset should agree with the measurements in terms of wind speed variations over time. This agreement is quantified by the Pearson correlation coefficient “r”. 3E considers a Pearson coefficient of 0.7 (all data or monthly averages) as a minimum prerequisite for a reference dataset to be considered for long-term extrapolation.\n• Time resolution: the time resolution of the reference dataset should be constant over time. In case time resolution varies, 3E resamples data to a constant time resolution.", "output": {"entities": {"named_data": [], "descriptive_data": ["post-processed ERA-Interim reanalysis data from WindPRO", "Meteorological station data from WindPRO"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "- **Data availability** : missing periods should be limited and evenly distributed over time. 3E\nconsiders data availability above 80 % as a minimum prerequisite for a reference dataset to be used for long-term extrapolation.\n\n- **Consistency** : the reference dataset should not reveal any abrupt change or unrealistic trend. 3E\napplies a SNHT test [2] in order to identify discontinuities. If this happens, then the available period is limited to ensure homogeneity. 3E then also applies a Mann-Kendall test [3][4] (90% confidence interval) in order to identify possible trends. Again, the available period is limited to ensure the absence of a trend.\n\nWhen several reference datasets from the same reanalysis project are considered, 3E only selects the one providing the best r (all data) and the one providing the best r (monthly averages).\n\nThe correlation between the on-site measurements and the reference datasets is presented in Annex H.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["reference dataset"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Although the data availability is high for some other datasets, for none of them, a high enough correlation has been obtained with the measurements (much lower than 0.7 both for r (all data) and for r (monthly averages)). Therefore, no long-term extrapolation has been applied. The short-term data has been used for the further purpose of the study. This has been taken into account in the uncertainty analysis (Section 6.4).\n\n**6.2** **WIND FLOW MODELLING**\n\nTerrain features influence the wind flow and thus play a significant role in the spatial extrapolation of the wind regime. The software package WindPRO and the WAsP wind flow model are used in the present study. WAsP requires a terrain model describing elevation, roughness and other relevant obstacles to the wind flow that are not modelled as roughness.\n\nThe terrain model used in this study represents the current conditions, which are assumed to remain the same over the wind farm lifetime.\n\n**Terrain model**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "_**a.**_ _**Elevation**_ The wind regime can be highly influenced by elevation differences across the site. For this study, terrain elevation is modelled within a radius of 15 km (in line with WAsP recommendations [5] based on SRTM data. Height contour lines are then generated with an elevation difference of 10 m between two successive lines.\n\nIt should be noted that SRTM is a digital surface model (DSM), which includes features such as forests and buildings. This is accounted for in the uncertainty assessment (cf. section 6.4).\n\n_**b.**_ _**Roughness length**_ Roughness length is a key parameter of the equation that governs wind shear. Changes in roughness length cause variations of wind shear, which propagate vertically as the air flows over the site. The impact at measurement or hub height therefore varies with distance to roughness changes, but is also related to atmospheric conditions.\n\nGiven that roughness length is closely related to land use, terrain roughness is typically modelled using a land-use database. However, no such suitable database with the required quality and resolution is available for the project sites considered in this study. Therefore, the roughness maps have been 14", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["land-use database"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "created manually based on aerial photos and covering an area with a radius of 20km around each site.\nThe roughness length values considered are presented in the caption of the following figures.\n\n**Figure 7: Elevation map 15x15km (with mast in center) with**\n\n**10m elevation difference between lines. Altitudes in map**\n\n**range from 225m to 1217m (warmer colors indicate higher**\n\n**altitudes). RIX** **[4]** **value at mast is 0.2% using radius of 3,500m,**\n\n**steepness threshold of 30% (17°) and frequency distributed**\n\n**directional weight**\n\n**Figure 8: Ground roughness map 20x20km (with mast in**\n\n**center). Background roughness length is 0.0790m,**\n\n**corresponding to open field with distributed rows of trees and**\n\n**Forrest area. Roughness length for specific areas are 0.0790m**\n\n**for Dry river (purple color), 0.750m for forrest area (rose/pink**\n\n**color)**\n\n**Wind flow model**\nWAsP is used to extrapolate the wind regime to the mast location. It involves two steps: a vertical extrapolation of the wind regime and a horizontal extrapolation of the wind regime.\n\n_**a.**_ _**Horizontal extrapolation of the wind regime**_ In this study, wind measurements are only available at a single location, which does not allow any validation of the horizontal extrapolation of the wind regime.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "_**b.**_ _**Vertical extrapolation of the wind regime**_ By default, WAsP is configured for atmospheric conditions typical of North-Western Europe. Therefore, parameters sometimes need to be adapted. In particular, some parameters strongly affect the vertical extrapolation of the wind regime and can be validated and calibrated if necessary by comparison of the measured and calculated wind shears.\n\nIn this study, WAsP parameters are adapted so that the calculated wind shear agrees with the shortterm measured wind shear.\n\nThe mean wind speeds measured at the various heights over the short-term period limited to 2 complete years (cf. Section 6.1) and the vertical wind speed profile calculated by WAsP from the measurements at 80 m AGL and from the adapted model parameters are given in the following graph.\n\n15", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Figure 9: Mean wind speeds measured over the short-term period limited to 1 complete year and vertical wind**\n\n**speed profile calculated by WAsP using measurements at 80 m AGL**\n\n**6.3** **ENERGY PRODUCTION CALCULATION**\n\nIn agreement with the World Bank, the energy production is calculated with a single turbine at the mast location. A generic 2.5MW turbine with a hub height of 100 m is selected for the purpose of this study.\n\n**Gross energy production**\nA gross energy production refers to the theoretical energy production that would be achieved if there was no operational loss. It is calculated by combining the wind regime at a wind turbine location and hub height to the power curve specific to the considered wind turbine type and corrected for local hub height air density. This is done using the software WindPRO.\n\nSince the energy content of the wind varies proportionally to air density, power curves are adapted accordingly before being used in calculations. The adaptation is done using the new recommended WindPRO method (adjusted IEC 61400-12 method, improved to match turbine control) [6].", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "For this project, air density at hub height is estimated to be 1.114 kg/ m3. Air density is calculated by WindPRO based on the temperature and pressure measurements from the UMI Station mast, located at the site.\n\n**Energy production losses**\nIn addition to energy conversion losses taken into account in the power curve, other losses affect the electrical power expected to be delivered to the grid. The following losses are taken into account in this study and are summarised below.\n\n- **Wake losses:** Wake losses are due to the mutual influence of the wind turbines and are\ncalculated using the N.O. Jensen (EMD) : 2005 wake model implemented in WindPRO.\n\n- **Unavailability losses:** Unavailability losses are due to downtime of the wind turbines or balance\nof plant (maintenance or technical incidents) as well as downtime of the power grid.\n\n16", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "17 • Performance losses: Turbine performance losses are typically due to high wind hysteresis, yaw misalignment, wind flow inclination, turbulence, wind shear and other differences between turbine power curve test conditions.\n• Electrical losses: Electrical losses occur in cables and transformers ensuring electrical transmission to the wind farm substation.\n• Environmental losses: Environmental losses account for the performance degradation of the wind turbines due to environmental conditions.\nThe energy production losses defined in the preceding section are summarized below.\nWake losses\n[%] 0.0 Unavailability losses\n[%] 4.0 Performance losses\n[%] 0.3 Electrical losses\n[%]\n1.5\nEnvironmental losses\n[%] 0.5 Total losses\n[%] 6.2 Net energy production The expected wind farm energy production figures for a single generic 2.5MW wind turbine at the mast location are as follows: Gross energy production\n[MWh/y] 3,180 Total energy production losses\n[%] 6.2 Net energy production (AEP)\n[MWh/y] 2,984 Net full load equivalent hours\n[h/y] 1,194 Net capacity factor\n[%] 13.6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "6.4 UNCERTAINTY ANALYSIS Some uncertainty components are directly quantified in terms of energy production, whereas some other uncertainty components are first quantified in terms of wind speed, then translated into uncertainties in terms of energy production, by applying a sensitivity factor. The sensitivity factor relates energy production change to wind speed change.\nThe global uncertainty is then calculated from the individual uncertainty components by assuming that they are independent and that the resulting uncertainty follows a normal distribution (central-limit theorem). They can therefore be combined by calculating the square root of the sum of the squares of each uncertainty.\nIn this study, the following sources of uncertainty are considered.\n• Wind measurements (wind speed) • Long-term extrapolation (wind speed) • Vertical extrapolation (wind speed) • Future wind variability (wind speed) • Spatial variation (wind speed) • Power curve (production) • Energy production losses (production)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "18 The table below presents the breakdown of uncertainty analysis in terms of annual energy production.\nWind measurements\n[% AEP] 5.0 Long-term extrapolation\n[% AEP] 0.0 Vertical extrapolation\n[% AEP]\n1.7\nFuture wind variability [20 years]\n[% AEP] 15.0 Spatial variation\n[% AEP]\n1.1\nPower curve\n[% AEP] 8.5 Production losses\n[% AEP]\n1.2\nCombined uncertainty [20 years]\n[% AEP] 18.1 AEPs over 20 year period exceeded with various probabilities of 50% to 95% are are as follows: AEP (50)\n[MWh/y] 2,984 AEP (75)\n[MWh/y] 2,620 AEP (90)\n[MWh/y] 2,293 AEP (95)\n[MWh/y] 2,097 Some uncertainty components are directly quantified in terms of energy production, whereas some other uncertainty components are first quantified in terms of wind speed, then translated into uncertainties in terms of energy production, by applying a sensitivity factor. The sensitivity factor relates energy production change to wind speed change. For this specific project, the sensitivity factor is calculated to be 2.5.\n6.5 TURBULENCE ANALYSIS The measured turbulence at 80 m AGL compared to the IEC curves is given below.\nThe effective turbulence intensity measured at 80 m AGL is below the characteristic turbulence intensity of Class A wind turbines (IEC 61400-1, ed. 3).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "19 7. SITE 3 – LNG PLANT, CENTRAL PROVINCE 7.1 WIND DATA PROCESSING Short-term wind regime The anemometer calibration parameters stated in the calibration reports are applied to the raw data. The data are then cleaned.\nThe measurement campaign is interrupted due to vandalism on mast. As a result, it is decided to stop the measurement campaign by covering 10 months (17/02/2018 to 10/12/2018) and this period has been selected for use in the following steps of the study.\nThe mast shading effect is corrected by alternatively using the measurements of both anemometers at each height depending on the wind direction.\nHeight Primary anemometer Secondary anemometer Wind directions where secondary anemometer is used 80 m C1 C2 42° - 81° 60 m C4 C3 234° - 273° 40 m C5 C6 43° - 90° 20 m C7 C8 33° - 108°", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "The Weibull parameters of the short-term wind regime over this period per sector are presented in Annex D.\nAnnex G presents the seasonal and diurnal variations observed over the short-term.\nLong-term wind regime The long-term extrapolation is performed in three steps: first, the most reliable reference datasets are identified, then the best combination of reference data and extrapolation method is selected.\nEventually, the combination of dataset and method resulting in the lowest uncertainty (cf. section 5.4) is selected.\n3E selects reference dataset from the following sources: • MERRA-2 and post-processed ERA-Interim and ERA-5 reanalysis data from WindPRO (4 closest grid points), • Meteorological station data from WindPRO, The following criteria are used to select reference datasets from these sources: • Agreement: the reference dataset should agree with the measurements in terms of wind speed variations over time. This agreement is quantified by the Pearson correlation coefficient “r”. 3E considers a Pearson coefficient of 0.7 (all data or monthly averages) as a minimum prerequisite for a reference dataset to be considered for long-term extrapolation.", "output": {"entities": {"named_data": [], "descriptive_data": ["Meteorological station data from WindPRO"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "20 • Time resolution: the time resolution of the reference dataset should be constant over time. In case time resolution varies, 3E resamples data to a constant time resolution.\n• Data availability: missing periods should be limited and evenly distributed over time. 3E considers data availability above 80 % as a minimum prerequisite for a reference dataset to be used for long-term extrapolation.\n• Consistency: the reference dataset should not reveal any abrupt change or unrealistic trend. 3E applies a SNHT test [2] order to identify discontinuities. If this happens, then the available period is limited to ensure homogeneity. 3E then also applies a Mann-Kendall test [3][4] (90% confidence interval) in order to identify possible trends. Again, the available period is limited to ensure the absence of a trend.\nWhen several reference datasets from the same reanalysis project are considered, 3E only selects the one providing the best r (all data) and the one providing the best r (monthly averages).\nThe correlation between the on-site measurements and the reference datasets is presented in Annex H.\nThe least uncertainty is obtained from ERA5 S09.13 E146.81 data using the Wind Index method, which is therefore the selected combination of reference data and extrapolation method. Due to the trend detection in datasets of MERRA-2 and ERA-Interim, the data from earlier 2000s are discarded for the long-term extrapolation.\nThe result of the Wind Index method is a long-term correction factor, as presented in the following table. This correction factor is applied to the energy production calculated from the short-term wind regime. It should be noted that an additional uncertainty contribution is introduced in the uncertainty assessment to account for the fact that this method relies on the assumption that the short-term wind rose is representative of the long-term.\nHeight AGL\n[m] 80 Long-term period\n[-] 19 years Long-term correction factor\n[-] 0.80", "output": {"entities": {"named_data": [], "descriptive_data": ["ERA5 S09.13 E146.81 data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**7.2** **WIND FLOW MODELLING**\n\nTerrain features influence the wind flow and thus play a significant role in the spatial extrapolation of the wind regime. The software package WindPRO and the WAsP wind flow model are used in the present study. WAsP requires a terrain model describing elevation, roughness and other relevant obstacles to the wind flow that are not modelled as roughness.\n\nThe terrain model used in this study represents the current conditions, which are assumed to remain the same over the wind farm lifetime.\n\n**Terrain model**\n\n_**c.**_ _**Elevation**_ The wind regime can be highly influenced by elevation differences across the site. For this study, terrain elevation is modelled within a radius of 15 km (in line with WAsP recommendations [5]) based on SRTM data. Height contour lines are then generated with an elevation difference of 10 m between two successive lines.\n\nIt should be noted that SRTM is a digital surface model (DSM), which includes features such as forests and buildings. This is accounted for in the uncertainty assessment (cf. section 5.4).", "output": {"entities": {"named_data": ["SRTM data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "_**d.**_ _**Roughness length**_ Roughness length is a key parameter of the equation that governs wind shear. Changes in roughness length cause variations of wind shear, which propagate vertically as the air flows over the site. The impact at measurement or hub height therefore varies with distance to roughness changes, but is also related to atmospheric conditions.\n\nGiven that roughness length is closely related to land use, terrain roughness is typically modelled using a land-use database. However, no such suitable database with the required quality and resolution is available for the project sites considered in this study. Therefore, the roughness maps have been created manually based on aerial photos and covering an area with a radius of 20km around each site.\nThe roughness length values considered are presented in the caption of the following figures.\n\n**Figure 12: Elevation map 15x15km (with mast in center) with**\n\n**10m elevation difference between lines. Altitudes in map**\n\n**Figure 13: Ground roughness map 20x20km (with mast in**\n\n**center). Background roughness length is 0.0790m,**\n\n21", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["land-use database"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "22 range from 0m to 169m (warmer colors indicate higher altitudes). RIX3 value at mast is 0.1% using radius of 3,500m, steepness threshold of 30% (17°) and frequency distributed directional weight corresponding to distributed rows of trees and shrubs.\nRoughness length for specific areas are 0.790m for the forest area (rose color) and 0m for rivers and sea (yellow color) Wind flow model WAsP is used to extrapolate the wind regime to the mast location. It involves two steps: a vertical extrapolation of the wind regime and a horizontal extrapolation of the wind regime.\ne. Horizontal extrapolation of the wind regime In this study, wind measurements are only available at a single location, which does not allow any validation of the horizontal extrapolation of the wind regime.\nf. Vertical extrapolation of the wind regime By default, WAsP is configured for atmospheric conditions typical of North-Western Europe. Therefore, parameters sometimes need to be adapted. In particular, some parameters strongly affect the vertical extrapolation of the wind regime and can be validated and calibrated if necessary by comparison of the measured and calculated wind shears.\nIn this study, WAsP parameters are adapted so that the calculated wind shear agrees with the shortterm measured wind shear.\nThe mean wind speeds measured at the various heights over the short-term period (10 months) (cf.\nSection 5.1) and the vertical wind speed profile calculated by WAsP from the measurements at 80 m AGL and from the adapted model parameters are given in the following graph.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Figure 14: Mean wind speeds measured over the short-term period limited to 10 months and vertical wind speed profile calculated by WAsP using measurements at 80 m AGL 3 The ruggedness index (RIX) at a specific location is the percentage of the ground surface that has a slope above a given threshold (here 30%) within a certain distance (here 3.5 km).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "23 7.3 ENERGY PRODUCTION CALCULATION In agreement with the World Bank, the energy production is calculated for a single turbine at the mast location. A generic 2.5MW turbine with a hub height of 100 m is selected for the purpose of this study.\nGross energy production A gross energy production refers to the theoretical energy production that would be achieved if there was no operational loss. It is calculated by combining the wind regime at a wind turbine location and hub height to the power curve specific to the considered wind turbine type and corrected for local hub height air density. This is done using the software WindPRO.\nSince the energy content of the wind varies proportionally to air density, power curves are adapted accordingly before being used in calculations. The adaptation is done using the new recommended WindPRO method (adjusted IEC 61400-12 method, improved to match turbine control) [6].\nFor this project, air density at hub height is estimated to be 1.159 kg/ m3. Air density is calculated by WindPRO based on the temperature and pressure measurements from the LNG Plant mast, located at the site.\nEnergy production losses In addition to energy conversion losses taken into account in the power curve, other losses affect the electrical power expected to be delivered to the grid. The following losses are taken into account in this study and are summarised below.\n• Wake losses: Wake losses are due to the mutual influence of the wind turbines and are calculated using the N.O. Jensen (EMD) : 2005 wake model implemented in WindPRO.\n• Unavailability losses: Unavailability losses are due to downtime of the wind turbines or balance of plant (maintenance or technical incidents) as well as downtime of the power grid.\n• Performance losses: Turbine performance losses are typically due to high wind hysteresis, yaw misalignment, wind flow inclination, turbulence, wind shear and other differences between turbine power curve test conditions.\n• Electrical losses: Electrical losses occur in cables and transformers ensuring electrical transmission to the wind farm substation.\n• Environmental losses: Environmental losses account for the performance degradation of the wind turbines due to environmental conditions.\nThe energy production losses defined in the preceding section are summarized below.\nWake losses\n[%] 0.0 Unavailability losses\n[%] 4.0 Performance losses\n[%] 0.3 Electrical losses\n[%]\n1.5\nEnvironmental losses\n[%] 0.5 Total losses\n[%] 6.1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "24 Net energy production The expected wind farm energy production figures at the mast location are as follows: Gross energy production\n[MWh/y] 3,849 Total energy production losses\n[%] 6.1 Net energy production (AEP)\n[MWh/y] 3,612 Net full load equivalent hours\n[h/y] 1,445 Net capacity factor\n[%] 16.5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "7.4 UNCERTAINTY ANALYSIS Some uncertainty components are directly quantified in terms of energy production, whereas some other uncertainty components are first quantified in terms of wind speed, then translated into uncertainties in terms of energy production, by applying a sensitivity factor. The sensitivity factor relates energy production change to wind speed change.\nThe global uncertainty is then calculated from the individual uncertainty components by assuming that they are independent and that the resulting uncertainty follows a normal distribution (central-limit theorem). They can therefore be combined by calculating the square root of the sum of the squares of each uncertainty.\nIn this study, the following sources of uncertainty are considered.\n• Wind measurements (wind speed) • Long-term extrapolation (wind speed) • Vertical extrapolation (wind speed) • Future wind variability (wind speed) • Spatial variation (wind speed) • Power curve (production) • Energy production losses (production) The table below presents the breakdown of uncertainty analysis in terms of annual energy production.\nWind measurements\n[% AEP] 5.9 Long-term extrapolation\n[% AEP] 9.0 Vertical extrapolation\n[% AEP] 2.7 Future wind variability [20 years]\n[% AEP] 4.4 Spatial variation\n[% AEP]\n1.2\nPower curve\n[% AEP] 7.6 Production losses\n[% AEP]\n1.2\nCombined uncertainty [20 years]\n[% AEP] 14.2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "AEPs over 20 year period exceeded with various probabilities of 50% to 95% are are as follows: AEP (50)\n[MWh/y] 3,612 AEP (75)\n[MWh/y] 3,265 AEP (90)\n[MWh/y] 2,953 AEP (95)\n[MWh/y] 2,766", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Some uncertainty components are directly quantified in terms of energy production, whereas some other uncertainty components are first quantified in terms of wind speed, then translated into uncertainties in terms of energy production, by applying a sensitivity factor. The sensitivity factor relates energy production change to wind speed change. For this specific project, the sensitivity factor is calculated to be 2.5.\n\n**7.5** **TURBULENCE ANALYSIS**\n\nThe measured turbulence at 80 m AGL compared to the IEC curves is given below.\n\nThe effective turbulence intensity measured at 80 m AGL is below the characteristic turbulence intensity of Class A wind turbines (IEC 61400-1, ed. 3).\n\n**Figure 15: Turbulence at 80 m AGL compared to the IEC curves**\n\n25", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "###### **ANNEX A COORDINATES OF THE SITES**\n\n|Site 1 Launakalana 2 UMI Station 3 LNG Plant|Col2|Col3|Col4|\n|---|---|---|---|\n|**Area**|1000 m2|6000 m2|3000 m2|\n|**Mast height**|80 m|80 m|80 m|\n|**Coordinates**|**Coordinates**|**Coordinates**|**Coordinates**|\n|**Latitude (y)**<br>**Longtitude (x)**|9°55'33.06\"S<br>147°46’50.05”E|6°13'59.77\"S<br>146°11’44.2”E|9°19'3.37\"S<br>147°1’96”E|\n|**Elevation**|17 m|305 m|14 m|\n\n27", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "###### **ANNEX B MAST INSTRUMENT SERIAL NUMBER AND CALIBRATION INFORMATION**\n\n**Site 1: Launakalana, Central Province**\n\n|Instrument|MAKE|MODEL|SERIAL NO.|CALIBRATION<br>CERTIFICATE|CHANNEL|SLOPE|OFFSET|HEIGHT<br>[m]|BOOM<br>ORIENTATION|\n|---|---|---|---|---|---|---|---|---|---|\n|Anemometer|Thies FCA|T33511|11159427|1536189|C1|0.04604|0.22130|80|25|\n|Anemometer|Thies FCA|T33511|11159428|1536187|C2|0.04607|0.21280|80|205|\n|Anemometer|Thies FCA|T33511|11159429|1536186|C3|0.04605|0.21950|60|25|\n|Anemometer|Thies FCA|T33511|4140808|1522880|C4|0.04615|0.22190|60|205|\n|Anemometer|Thies FCA|T33511|11159430|1536185|C5|0.04604|0.22500|40|25|\n|Anemometer|Thies FCA|T33511|4140809|1522883|C6|0.04618|0.21000|40|205|\n|Anemometer|Thies FCA|T33511|11159431|1536184|C7|0.04599|0.24060|20|25|\n|Anemometer|Thies FCA|T33511|4140810|1522876|C8|0.04614|0.21300|20|205|\n|Wind Vane|Thies FC<br>TMR|T315110BS|9150125|1620401|D1|0.35194|0.24480|76.5|25|\n|Wind Vane|Thies FC<br>TMR|T315110BS|9150126|1620402|D2|0.35204|0.14570|56.5|25|\n|Temperature<br>Sensor|TP|TPC1.S/6-ME|155867|-|A4|100|-30|76|-|\n|Temperature &<br>Humidity Sensor|KP|KPC1.S/6-ME|159362|-|A2|100|0|10|-|\n|Barometer|Ammonit<br>AB 100|AB 100|B16-0001|-|A1|100|600|4|-|\n\n28", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Site 2: UMI Station, Morebe Province**\n\n|Instrument|MAKE|MODEL|SERIAL NO.|CALIBRATION<br>CERTIFICATE|CHANNEL|SLOPE|OFFSET|HEIGHT<br>[m]|BOOM<br>ORIENTATION|\n|---|---|---|---|---|---|---|---|---|---|\n|Anemometer|Thies FCA|T33511|11159432|1536183|C1|0.04604|0.2098|80|65|\n|Anemometer|Thies FCA|T33511|11159433|1536182|C2|0.04604|0.2234|80|245|\n|Anemometer|Thies FCA|T33511|11159434|1536181|C3|0.04598|0.2262|60|65|\n|Anemometer|Thies FCA|T33511|11159041|1522886|C4|0.04610|0.2225|60|245|\n|Anemometer|Thies FCA|T33511|11159435|1536180|C5|0.04599|0.2277|40|65|\n|Anemometer|Thies FCA|T33511|11131456|1522884|C6|0.04618|0.2083|40|245|\n|Anemometer|Thies FCA|T33511|11159436|1536179|C7|0.04603|0.2300|20|65|\n|Anemometer|Thies FCA|T33511|8120177|1522875|C8|0.04599|0.2274|20|245|\n|Wind Vane|Thies FC<br>TMR|T315110BS|9150127|1620403|D1|0.35197|-0.1573|76.5|65|\n|Wind Vane|Thies FC<br>TMR|T315110BS|9150128|1620404|D2|0.35122|0.7734|56.5|65|\n|Temperature<br>Sensor|TP|TPC1.S/6-ME|155868|-|A4|100|-30|76|-|\n|Temperature &<br>Humidity Sensor|KP|KPC1.S/6-ME|1593623|-|A2|100|0|10|-|\n|Barometer|Ammonit<br>AB 100|AB 100|B16-0002|-|A1|100|600|4|-|\n\n29", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Site 3: LNG Plant, Central Province**\n\n|Instrument|MAKE|MODEL|SERIAL NO.|CALIBRATION<br>CERTIFICATE|CHANNEL|SLOPE|OFFSET|HEIGHT<br>[m]|BOOM<br>ORIENTATION|\n|---|---|---|---|---|---|---|---|---|---|\n|Anemometer|Thies FCA|T33511|11159437|1536178|C1|0.04603|0.2431|80|65|\n|Anemometer|Thies FCA|T33511|11159438|1536177|C2|0.04595|0.2543|80|245|\n|Anemometer|Thies FCA|T33511|11159439|1536176|C3|0.04598|0.2265|60|65|\n|Anemometer|Thies FCA|T33511|11159455|1520352|C4|0.04597|0.2643|60|245|\n|Anemometer|Thies FCA|T33511|11159440|1536175|C5|0.04591|0.2475|40|65|\n|Anemometer|Thies FCA|T33511|11136257|1520354|C6|0.04617|0.2125|40|245|\n|Anemometer|Thies FCA|T33511|11159441|1536174|C7|0.04598|0.2288|20|65|\n|Anemometer|Thies FCA|T33511|11136256|1520353|C8|0.04611|0.2240|20|245|\n|Wind Vane|Thies FC<br>TMR|T315110BS|9150129|1620405|D1|0.35211|0.0388|76.5|65|\n|Wind Vane|Thies FC<br>TMR|T315110BS|9150130|1620407|D2|0.35197|0.3134|56.5|65|\n|Temperature<br>Sensor|TP|TPC1.S/6-ME|155869|-|A4|100|-30|76|-|\n|Temperature &<br>Humidity Sensor|KP|KPC1.S/6-ME|159364|-|A2|100|0|10|-|\n|Barometer|Ammonit<br>AB 100|AB 100|B16-0003|-|A1|100|600|4|-|\n\n30", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "31 ANNEX C DATA RECOVERY RATES OVER THE SHORT-TERM Site 1 Launakalana (16/02/2018 – 15/02/2019) Months Availability [%] @80m @60m @40m @20m 02/2018 100* 100* 100* 100* 03/2018 99.9 100 100 99.9 04/2018 100 100 100 99.9 05/2018 100 100 100 100 06/2018 100 100 100 100 07/2018 100 100 100 100 08/2018 100 100 100 100 09/2018 100 100 100 100 10/2018 100 99.9 99.9 100 11/2018 100 99.9 100 99.9 12/2018 100 100 100 100 01/2019 96.8 96.7 96.3 96.7 02/2019 100* 100* 100* 100* Mean 99.7 99.7 99.7 99.7\n*Not covering the full month\nSite 2 UMI Station (09/02/2018 – 08/02/2019) Months Availability [%] @80m @60m @40m @20m 02/2018 100* 99.9* 100* 100* 03/2018 99.9 99.8 99.9 99.9 04/2018 100 99.9 100 99.9 05/2018 100 99.9 100 100 06/2018 100 100 100 100 07/2018 100 100 100 100 08/2018 100 100 100 99.9 09/2018 100 99.8 100 99.9 10/2018 100 99.7 100 100 11/2018 100 99.7 99.9 99.9 12/2018 83.8 83.3 83.6 83.7 01/2019 90.3 90.2 90.2 90.3 02/2019 94.4* 94.4* 94.4* 94.4* Mean 97.6 97.4 97.6 97.5\n*Not covering the full month", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "32 Site 3 LNG Plant (17/02/2018 – 10/12/2018) Months Availability [%] @80m @60m @40m @20m 02/2018 100* 100* 100* 100* 03/2018 100 100 100 100 04/2018 100 100 100 100 05/2018 100 100 100 100 06/2018 100 100 100 100 07/2018 100 100 100 100 08/2018 100 100 100 100 09/2018 100 100 100 100 10/2018 100 100 100 100 11/2018 100 100 99.9 100 12/2018 100* 100* 100* 100* Mean 100 100 100 100\n*Not covering the full month", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "###### **ANNEX D THE WIND REGIME OBSERVED OVER THE SHORT-TERM**\n\n**Site 1: Launakalana, Central Province (16/02/2018 – 15/02/2019)**\n\n_Energy Rose_ _Mean Wind Speed Rose_ _Wind Frequency Rose_\n\n**Site 2: UMI Station, Morebe Province (09/02/2018 – 08/02/2019)**\n\n_Energy Rose_ _Wind Frequency Rose_ 33", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "_Mean Wind Speed Rose_\n\n**Site 3: LNG Plant, Central Province (17/02/2018 – 10/12/2018)**\n\n_Energy Rose_ _Mean Wind Speed Rose_ _Wind Frequency Rose_ 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "35 ANNEX E WEIBULL PARAMETERS OF THE SHORT-TERM WIND REGIME Site 1 Launakalana 2 UMI Station 3 LNG Plant Height AGL\n[m] 80 80 80 Selected period\n[-] 16/02/2018 15/02/2019 09/02/2018 08/02/2019 17/02/2018 – 10/12/2018 Arithmetic mean wind speed\n[m/s] 4.75 4.55 5.38 Weibull mean wind speed\n[m/s] 4.73 4.56 5.45 Weibull A\n[m/s] 5.34 5.11 6.15 Weibull k\n[m/s] 2.040\n1.716\n2.063 Prevailing wind directions\n[-] ESE, SSE, NNW, WNW SSE, NNW SSE Wind directions with most energy content\n[-] ESE, SSE, WNW NNW, SSE SSE", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "36 ANNEX F COMPARISON OF PREDICTED WIND REGIME AND ESTIMATED LONG-TERM WIND REGIME During the site selection phase, the “preliminary wind regime” was predicted at each site via the use of mesoscale datasets provided by DTU.\nAfter the measurement campaign, long-term wind regime is indicated as “modelled wind regime” at each site.\nBoth preliminary and modelled wind regimes as well as the expected power density at each site are given in the tables below.\nSite 1 Launkalana 2 UMI Station* 3 LNG Plant Preliminary wind regime Height AGL\n[m] 80 80 80 Expected mean wind speed\n[m/s] 6.3 4.0 5.3 Expected power density\n[kWh/m2/year] 3,321 1,428 2,199 Modelled wind regime Expected mean wind speed\n[m/s] 4.9 4.6 5.4 Expected power density\n[kWh/m2/year] 1,168 1,139 1,459\n*Short-term wind regime is provided for Site 2: UMI Station as no long-term extrapolation has been applied", "output": {"entities": {"named_data": [], "descriptive_data": ["mesoscale datasets provided by DTU"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "37 ANNEX G SHORT-TERM SEASONAL AND DIURNAL VARIATIONS IN WIND CHARACTERISTICS Hourly wind regime is calculated for each specific month based on the short-term data for each site.\nThus, seasonal and diurnal variations in wind characteristics are given for each site as frequency in the table below.\nSite 1: Launakalana, Central Province (16/02/2018-15/02/2019) HOUR/MONTH JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC YEAR 00:00 4.6% 18.6% 3.4% 4.2% 4.4% 5.6% 5.4% 5.9% 5.7% 4.2% 3.0% 3.8% 5.7% 01:00 6.3% 18.6% 4.3% 4.7% 4.6% 5.8% 6.2% 5.8% 6.8% 4.5% 3.6% 4.5% 6.2% 02:00 7.1% 17.3% 5.5% 5.8% 4.3% 5.7% 6.1% 6.5% 7.3% 5.4% 5.5% 5.8% 6.8% 03:00 8.1% 17.0% 4.6% 5.6% 5.0% 5.8% 7.2% 7.1% 8.0% 5.7% 6.6% 7.1% 7.2% 04:00 7.2% 16.9% 4.1% 5.7% 6.7% 6.0% 7.1% 7.4% 8.5% 5.1% 7.3% 6.7% 7.3% 05:00 6.1% 17.2% 3.8% 5.8% 5.9% 5.7% 6.4% 7.3% 8.2% 5.1% 6.6% 5.7% 6.9% 06:00 4.2% 13.9% 3.5% 4.7% 5.6% 5.2% 5.5% 5.7% 6.3% 4.1% 5.5% 3.8% 5.6% 07:00 2.7% 9.1% 2.4% 2.9% 4.9% 4.7% 4.9% 4.6% 4.9% 3.2% 3.7% 2.7% 4.2% 08:00\n1.9%\n6.1%\n1.7%\n2.0% 4.1% 4.0% 4.4% 3.2% 3.7% 2.9% 2.3%\n1.8%\n3.2% 09:00\n1.2%\n4.1%\n1.7%\n1.8%\n3.2% 4.2% 4.2% 2.7% 3.2% 2.3%\n1.8%\n1.2%\n2.6% 10:00\n1.0%\n3.1%\n1.9%\n2.0% 3.5% 7.1% 3.9% 2.9% 3.1% 2.0%\n1.7%\n1.1%\n2.8% 11:00 0.9% 2.3%\n1.8%\n2.2% 3.2% 4.7% 4.4% 2.8% 2.8%\n1.8%\n1.3%\n1.1%\n2.4% 12:00 0.8% 2.3%\n1.5%\n2.0% 3.8% 4.4% 3.2% 2.2% 3.0%\n1.4%\n1.1%\n1.1%\n2.2% 13:00 0.8% 2.4%\n1.3%\n1.6%\n4.5% 6.4% 3.8%\n1.9%\n3.4%\n1.4%\n1.3%\n2.1% 2.6% 14:00\n1.9%\n2.8%\n1.4%\n1.7%\n2.7% 5.7% 3.5%\n1.9%\n2.9% 2.9% 2.2% 2.2% 2.6% 15:00 2.3% 3.4% 2.6% 3.4% 3.6% 3.8% 3.0%\n1.9%\n3.4% 3.6% 2.8% 2.2% 3.0% 16:00 2.8% 4.2% 2.9% 3.1% 4.4% 4.2% 3.0% 2.4% 3.9% 3.9% 2.8% 2.6% 3.3% 17:00 3.0% 4.7% 3.2% 3.6% 4.1% 3.8% 3.2% 2.4% 4.0% 3.9% 2.3% 2.8% 3.4% 18:00 3.3% 5.3% 3.6% 3.9% 3.8% 3.9% 2.8% 3.4% 3.7% 4.3% 2.5% 3.2% 3.6% 19:00 3.5% 4.8% 3.3% 3.8% 3.6% 3.8% 2.6% 2.4% 3.7% 3.8% 2.1% 2.3% 3.3% 20:00 4.4% 5.4% 3.6% 2.9% 3.2% 3.5% 3.0% 2.2% 3.7% 3.8% 2.2% 2.7% 3.4% 21:00 3.2% 5.8% 3.2% 3.0% 4.7% 3.4% 2.9% 2.2% 3.4% 3.5%\n1.8%\n2.1% 3.3% 22:00 3.1% 7.0% 2.7% 3.4% 3.9% 4.0% 3.2% 2.8% 3.7% 4.0% 2.3% 3.2% 3.6% 23:00 4.2% 12.4% 3.4% 3.6% 4.7% 5.3% 4.4% 4.9% 5.1% 4.0% 2.8% 3.5% 4.8% TOTAL 7.0% 17.1% 5.9% 6.9% 8.5% 9.7% 8.7% 7.7% 9.4% 7.2% 6.2% 6.3%", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "39 Site 2: UMI Station, Morebe Province (09/02/2018-08/02/2019) HOUR/MONTH JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC YEAR 00:00 4.5% 12.0% 2.4% 5.2% 3.0% 3.5% 3.3% 2.9% 3.1% 3.5%\n1.9%\n4.2% 4.2% 01:00 5.0% 12.5% 3.0% 5.5% 2.6% 3.5% 3.5% 3.2% 3.3% 3.5%\n1.9%\n4.7% 4.4% 02:00 4.9% 12.7% 3.4% 6.5%\n1.7%\n3.2% 3.5% 3.1% 3.7% 3.5% 2.0% 4.9% 4.4% 03:00 5.0% 11.6% 2.6% 7.5%\n1.9%\n3.4% 3.6% 3.7% 3.8% 2.8%\n1.8%\n4.9% 4.4% 04:00 4.9% 11.6% 2.1% 8.5% 3.0% 4.1% 3.7% 4.0% 4.6% 2.4%\n1.9%\n5.3% 4.6% 05:00 4.8% 14.0% 2.2% 7.9% 3.0% 4.3% 4.0% 4.6% 5.5% 3.4% 2.7% 5.0% 5.1% 06:00 4.5% 14.5% 3.0% 8.5% 4.2% 4.2% 4.5% 5.8% 6.0% 4.4% 5.2% 6.8% 6.0% 07:00 5.3% 13.0% 4.2% 8.2% 4.4% 4.7% 4.8% 6.3% 5.5% 4.5% 5.0% 6.7% 6.0% 08:00 7.1% 12.7% 4.0% 6.5% 5.1% 4.7% 5.0% 5.2% 4.7% 4.8% 3.3% 5.4% 5.7% 09:00 7.3% 10.8% 4.4% 5.7% 4.2% 4.4% 5.5% 4.0% 4.2% 3.5% 2.6% 4.2% 5.1% 10:00 6.0% 8.5% 3.9% 5.0% 4.4% 6.3% 4.6% 3.7% 3.0% 2.9% 2.5% 3.9% 4.6% 11:00 4.8% 7.1% 3.2% 3.0% 3.4% 4.2% 4.4% 3.3% 2.5% 2.5% 2.0% 3.9% 3.7% 12:00 4.3% 6.9% 3.1% 2.7% 3.1% 3.3% 3.1% 2.5% 2.4%\n1.8%\n1.7%\n3.3% 3.2% 13:00 3.0% 5.9% 2.2%\n1.9%\n3.9% 4.6% 3.3%\n1.9%\n2.1%\n1.4%\n1.9%\n3.1% 3.0% 14:00 3.9% 6.5% 2.8% 2.0%\n1.9%\n3.9% 2.6%\n1.1%\n2.0% 2.5% 2.4% 4.9% 3.1% 15:00 4.2% 7.6% 3.6% 3.5% 2.6% 2.8% 2.1%\n1.1%\n2.1% 3.0% 2.7% 4.0% 3.3% 16:00 4.6% 9.2% 4.7% 3.5% 3.3% 2.9% 2.0%\n1.6%\n2.6% 3.6% 2.8% 4.2% 3.8% 17:00 4.5% 8.7% 4.7% 4.6% 3.1% 2.4%\n1.9%\n1.4%\n2.7% 3.5% 2.5% 4.5% 3.7% 18:00 5.0% 8.8% 4.2% 5.3% 3.0% 2.8%\n1.6%\n2.3% 2.7% 3.8% 2.8% 3.7% 3.8% 19:00 4.7% 8.2% 3.6% 5.0% 3.1% 2.9%\n1.7%\n1.5%\n2.7% 3.3% 2.6% 4.4% 3.6% 20:00 5.2% 7.8% 4.1% 4.6% 2.9% 2.3% 2.3%\n1.4%\n2.8% 3.3% 2.4% 5.6% 3.7% 21:00 5.7% 7.1% 3.9% 4.4% 3.9% 2.4% 2.0%\n1.4%\n2.3% 3.2%\n1.9%\n4.5% 3.6% 22:00 5.3% 6.7% 3.0% 4.2% 2.9% 2.5%\n1.7%\n1.7%\n2.2% 3.4%\n1.8%\n4.2% 3.3% 23:00 5.2% 8.8% 2.7% 4.4% 3.3% 3.2% 2.0% 2.4% 3.0% 3.3% 2.0% 4.8% 3.8% TOTAL 10.0% 19.4% 6.7% 10.3% 6.5% 7.2% 6.4% 5.8% 6.6% 6.5% 5.0% 9.2%", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "40 Site 3: LNG Plant, Central Province (17/02/2018-10/12/2018) HOUR/MONTH FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC YEAR 00:00\n1.4%\n2.3%\n1.7%\n1.4%\n3.0% 2.4% 2.8% 5.2%\n1.9%\n1.7%\n1.9%\n2.4% 01:00 2.6% 3.1% 2.3% 2.4% 3.9% 4.0% 4.9% 7.1% 3.5% 3.4% 2.7% 3.7% 02:00 3.1% 4.5% 3.1% 3.9% 6.2% 5.2% 6.9% 8.9% 5.4% 4.4% 3.4% 5.2% 03:00 4.0% 5.4% 4.0% 4.8% 7.8% 7.2% 8.9% 11.3% 6.5% 5.6% 4.1% 6.6% 04:00 4.4% 5.2% 4.7% 6.8% 9.6% 9.5% 11.8% 14.0% 8.1% 7.0% 4.6% 8.2% 05:00 4.1% 4.7% 5.7% 8.5% 11.7% 12.4% 14.8% 15.8% 9.9% 8.1% 4.3% 9.7% 06:00 2.6% 3.7% 6.5% 10.8% 12.9% 15.5% 16.3% 16.8% 10.0% 8.4% 3.8% 10.6% 07:00 2.3% 2.9% 5.5% 10.5% 12.3% 14.9% 15.2% 15.6% 7.4% 7.8% 2.6% 9.7% 08:00\n1.7%\n1.9%\n4.7% 9.0% 11.4% 14.4% 12.8% 13.9% 5.4% 6.1%\n1.2%\n8.3% 09:00\n1.2%\n1.1%\n4.3% 7.5% 9.9% 12.8% 11.0% 12.3% 4.2% 4.5% 0.7% 7.0% 10:00\n1.1%\n0.9% 2.7% 5.9% 8.1% 10.0% 8.5% 9.9% 3.2% 2.0% 0.6% 5.3% 11:00\n1.3%\n1.0%\n2.2% 4.1% 6.0% 7.0% 6.1% 6.9% 2.6%\n1.3%\n0.7% 3.9% 12:00\n1.1%\n0.9%\n1.4%\n2.7% 3.9% 4.8% 3.6% 5.0%\n1.7%\n1.0%\n0.8% 2.6% 13:00\n1.5%\n1.3%\n0.9%\n1.8%\n2.7% 4.1% 2.9% 4.3%\n1.5%\n0.9% 0.9% 2.2% 14:00\n1.4%\n1.2%\n1.0%\n1.5%\n1.7%\n2.8% 2.3% 3.9%\n1.1%\n0.8%\n1.2%\n1.8%\n15:00\n1.4%\n1.5%\n0.8%\n1.4%\n2.0% 2.9%\n1.9%\n3.6%\n1.1%\n0.8% 0.8%\n1.7%\n16:00\n1.7%\n2.0%\n1.1%\n1.2%\n2.0% 2.2% 2.0% 3.1% 0.8% 0.8%\n1.1%\n1.7%\n17:00\n1.8%\n2.2%\n1.8%\n0.8%\n1.6%\n2.1%\n1.7%\n2.5% 0.7% 0.6%\n1.6%\n1.6%\n18:00\n1.8%\n1.7%\n1.4%\n0.6%\n1.4%\n1.9%\n1.4%\n2.1% 0.8% 0.6%\n1.6%\n1.4%\n19:00\n1.9%\n1.8%\n1.4%\n0.6%\n1.6%\n1.4%\n1.2%\n2.0% 0.8% 0.7%\n1.4%\n1.3%\n20:00\n1.8%\n1.7%\n1.4%\n0.7%\n1.6%\n1.1%\n1.3%\n1.8%\n0.6% 0.6%\n1.2%\n1.2%\n21:00\n1.6%\n1.4%\n1.1%\n0.4%\n1.5%\n1.1%\n1.4%\n1.9%\n0.5% 0.3% 0.7%\n1.1%\n22:00 0.8%\n1.3%\n0.8% 0.5%\n1.8%\n1.0%\n1.7%\n3.0% 0.5% 0.3% 0.4%\n1.2%\n23:00 0.9%\n1.5%\n1.1%\n0.9% 2.2%\n1.4%\n2.2% 3.5% 0.8% 0.8% 0.8%\n1.5%\nTOTAL 4.0% 4.6% 5.1% 7.4% 10.6% 11.8% 12.0% 14.5% 6.6% 5.7% 3.6%", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "41 ANNEX H LONG-TERM CORRELATION COEFFICIENTS Site 1: Launakalana, Central Province Type Name Timeshift r (all data) r (monthly averages) Selected LT period Concurrent period Time resolution Data availability ERA5 ERA5_S09.69_E147.65 +1h 0.646 0.966 01/01/200031/12/2018 0.88\n1.00\n100.00 ERA5 ERA5_S09.69_E147.93 0h 0.670 0.963 01/01/200031/12/2018 0.88\n1.00\n100.00 ERA5 ERA5_S09.97_E147.65 +1h 0.600 0.917 01/01/200031/12/2018 0.88\n1.00\n100.00 ERA5 ERA5_S09.97_E147.93 +1h 0.640 0.916 01/01/200031/12/2018 0.88\n1.00\n100.00 ERA-INTERIM EmdERA_S09.47_E147.63 0h 0.341 0.881 01/01/200331/12/2018 0.88 6.00 100.00 ERA-INTERIM EmdERA_S09.47_E148.33 +3h 0.160 0.796 01/01/200531/12/2018 0.88 6.00 100.00 ERA-INTERIM EmdERA_S10.17_E147.63 0h 0.473 0.846 01/01/200531/12/2018 0.88 6.00 100.00 ERA-INTERIM EmdERA_S10.17_E148.33 0h 0.441 0.852 01/01/200631/12/2018 0.88 6.00 100.00 MERRA2 MERRA2_S10.00_E148.12 0h 0.586 0.901 01/02/200031/01/2019 0.96\n1.00\n100.00 MERRA2 MERRA2_S10.00_E147.50 +1h 0.542 0.885 01/02/200331/01/2019 0.96\n1.00\n100.00 MERRA2 MERRA2_S09.50_E147.50 0h 0.560 0.908 01/02/200431/01/2019 0.96\n1.00\n100.00 MERRA2 MERRA2_S09.50_E148.12\n-2h\n0.347 0.398 01/02/200031/01/2019 0.96\n1.00\n100.00\n*SYNOP\nSYNOP_94-174_S10.58_E142.28\n-1h\n0.499 0.759 01/02/200131/01/2019 0.96 3.00 98.07 SYNOP SYNOP_COCONUT_IL_AWS(AUT) _94-182_S10.05_E143.05\n-2h\n0.172\n-0.491\n01/02/200931/01/2019 0.96 3.00 96.92 SYNOP SYNOP_LOCKHART_RIVER_94186_S12.78_E143.30\n-1h\n0.490 0.629 01/02/200931/01/2019 0.96 3.00 91.27\n* Discarded from further analysis despite good correlation due to their inconsistent behavior over time", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "42 Site 2: UMI Station, Morebe Province Type Name Timeshift r (all data) r (monthly averages) Selected LT period Concurrent period Time resolution Data availability ERA5 ERA5_S06.042_E145.96 0h 0.251 0.228 01/01/200031/12/2018 0.90\n1.00\n100 ERA5 ERA5_S06.32_E145.96\n-1h\n0.376 0.154 01/01/200031/12/2018 0.90\n1.00\n100 ERA5 ERA5_S06.32_E146.25\n-1h\n0.347\n-0.075\n01/01/200031/12/2018 0.90\n1.00\n100 ERA5 ERA5_S06.04_E146.25\n-1h\n0.437 0.554 01/01/200331/12/2018 0.90\n1.00\n100 MERRA2 MERRA2_S06.00_E146.25\n-2h\n0.287 0.432 01/02/200031/01/2019 0.98\n1.00\n100 MERRA2 MERRA2_S06.00_E145.62\n-3h\n0.154 0.260 01/02/200431/01/2019 0.98\n1.00\n100 MERRA2 MERRA2_S06.50_E145.62\n-3h\n0.134 0.301 01/02/200631/01/2019 0.98\n1.00\n100 MERRA2 MERRA2_S06.50_E146.25\n-2h\n0.265 0.204 01/02/200631/01/2019 0.98\n1.00\n100 ERA-INTERIM EmdERA_S05.96_E146.92 +1h 0.261 0.422 01/01/200031/12/2018 0.90 6.00 100 ERA-INTERIM EmdERA_S06.66_E146.22 +3h 0.120 0.069 01/01/200031/12/2018 0.90 6.00 100 ERA-INTERIM EmdERA_S05.96_E146.22 +3h 0.217 0.424 01/01/200231/12/2018 0.90 6.00 100 ERA-INTERIM IEmdERA_S05.96_E145.52 +2h 0.117 0.343 01/01/200431/12/2018 0.90 6.00 100 SYNOP SYNOP_COCONUT_IL_AWS(AUT) _94-182_S10.05_E143.05 +2h 0.109 0.525 01/02/201031/01/2019 0.98 3.00 96.92", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "43 Site 3: LNG Plant, Central Province Type Name Timeshift r (all data) r (monthly averages) Selected LT period Concurrent period Time resolution Data availability ERA5 ERA5_S09.13_E146.81 2h 0.644 0.976 01/01/200031/12/2018 0.81\n1.00\n100.00 ERA5 ERA5_S09.13_E147.09 0h 0.619 0.929 01/01/200031/12/2018 0.81\n1.00\n100.00 ERA5 ERA5_S09.41_E146.81 +3h 0.566 0.912 01/01/200031/12/2018 0.81\n1.00\n100.00 ERA5 ERA_5S09.41_E147.09 +2h 0.609 0.949 01/01/200031/12/2018 0.81\n1.00\n100.00 MERRA2 MERRA2_S09.000E146.87\n-1h\n0.663 0.937 01/02/200031/01/2019 0.81\n1.00\n100.00 MERRA2 MERRA2_S09.50_E146.87 +1h 0.473 0.860 01/02/200531/01/2019 0.81\n1.00\n100.00 MERRA2 MERRA2_S09.50_E147.50\n-1h\n0.548 0.918 01/02/200531/01/2019 0.81\n1.00\n100.00 ERA-Interim EmdERA_S09.47_E146.92 2h 0.510 0.888 01/01/200131/12/2018 0.81 6.00 100.00 ERA-Interim EmdERA_S08.77_E146.92 +3h 0.315 0.815 01/01/200131/12/2018 0.81 6.00 100.00 ERA-Interim EmdERA_S09.47_E147.63 +3h 0.420 0.860 01/01/200331/12/2018 0.81 6.00 100.00 ERA-Interim EmdERA_S08.77_E147.63 +3h 0.193 0.787 01/01/200531/12/2018 0.81 6.00 100.00 SYNOP SYNOP_94-174_S10.58_E142.28\n-3h\n0.477 0.722 01/02/201431/01/2019 0.81 3.00 97.28", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "44 ANNEX I ESTIMATES OF EQUIVALENT MEAN AND DIRECTIONAL WEIBULL WIND SPEED DISTRIBUTIONS Weibull parameters (A, k) at different heights A.G.L. with a roughness value of 0.03m and directional wind frequency for the selected short-term periods are given in the tables below for each site.\nThen wind speed and power density at each height are calculated and given as a graph below.\nSite 1: Launakalana, Central Province (16/02/2018-15/02/2019) Roughness length: 0.03m Sectors 0° 30° 60° 90° 120° 150° 180° 210° 240° 270° 300° 330° frequency [%] 3.5\n1.5\n1.9\n9.8 32.8 13.1 2.5\n1.9\n2.6 4.9 10.9 14.5 50m A.G.L.\nA [m/s) 3.0 2.4 3.6 5.5 7.1 7.6 5.4 4.8 6.7 8.4 6.0 4.4 k\n1.71\n1.73\n1.72\n2.26 2.86 3.56 2.69 2.65 2.39 2.15\n1.31\n1.94\n100m A.G.L.\nA [m/s) 3.3 2.7 4.0 6.1 7.9 8.4 5.9 5.3 7.4 9.2 6.6 4.8 k\n1.70\n1.72\n1.72\n2.26 2.85 3.55 2.68 2.64 2.38 2.17\n1.32\n1.94\n150m A.G.L.\nA [m/s) 3.5 2.8 4.2 6.4 8.3 8.9 6.2 5.6 7.8 9.7 6.9 5.1 k\n1.63\n1.65\n1.65\n2.16 2.73 3.40 2.57 2.53 2.28 2.09\n1.28\n1.85\n200m A.G.L.\nA [m/s) 3.6 2.9 4.3 6.7 8.7 9.3 6.5 5.8 8.1 10.1 7.2 5.3 k\n1.57\n1.59\n1.59\n2.08 2.63 3.28 2.48 2.44 2.20 2.02\n1.25\n1.79", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "_Roughness length: 0.03m / z: 50m A.G.L._ _Roughness length: 0.03m / z: 150m A.G.L._ _Roughness length: 0.03m / z: 100m A.G.L._ _Roughness length: 0.03m / z: 200m A.G.L._ 45", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "46 Site 2: UMI Station, Morebe Province (09/02/2018-08/02/2019) Roughness length: 0.03m Sectors 0° 30° 60° 90° 120° 150° 180° 210° 240° 270° 300° 330° frequency [%] 5.1\n1.1\n0.9\n1.4\n10.6 38.6 15.1 2.4\n1.1\n0.9 3.5 19.3 50m A.G.L.\nA [m/s) 4.8 3.5 2.9 4.7 5.3 5.4 4.4 3.6\n1.9\n4.2 6.6 7.0 k\n1.49\n1.34\n1.22\n1.93\n2.35 2.45\n1.80\n1.34\n0.89\n1.07\n1.56\n1.68\n100m A.G.L.\nA [m/s) 5.2 3.8 3.2 5.2 5.8 6.0 4.8 4.0 2.0 4.5 7.2 7.7 k\n1.47\n1.31\n1.20\n1.90\n2.31 2.41\n1.77\n1.32\n0.88\n1.06\n1.54\n1.66\n150m A.G.L.\nA [m/s) 5.4 4.0 3.3 5.4 6.1 6.3 5.0 4.1 2.0 4.6 7.5 8.0 k\n1.34\n1.21\n1.10\n1.73\n2.10 2.20\n1.62\n1.21\n0.81 0.97\n1.42\n1.53\n200m A.G.L.\nA [m/s) 5.5 4.0 3.3 5.6 6.3 6.5 5.2 4.2 2.0 4.7 7.7 8.2 k\n1.25\n1.12\n1.03\n1.60\n1.95\n2.04\n1.51\n1.13\n0.76 0.91\n1.32\n1.42\n\nRoughness length: 0.03m / z: 50m A.G.L.\n\nRoughness length: 0.03m / z: 100m A.G.L.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "48 Site 3: LNG Plant, Central Province (17/02/2018-10/12/2018) Roughness length: 0.03m Sectors 0° 30° 60° 90° 120° 150° 180° 210° 240° 270° 300° 330° frequency [%] 4.5 10.3 7.6 4.1 13.6 35.4 6.4 3.1 4.4 4.7 3.0 3.0 50m A.G.L.\nA [m/s) 3.6 4.1 3.8 3.3 5.3 8.5 7.1 5.1 4.5 5.1 5.0 4.3 k 2.42 2.62 2.12\n1.65\n2.13 3.51 3.41 3.17 3.90 2.95 2.06\n1.71\n100m A.G.L.\nA [m/s) 3.8 4.3 4.0 3.5 5.6 9.1 7.6 5.4 4.8 5.5 5.3 4.6 k 2.35 2.53 2.06\n1.60\n2.06 3.40 3.31 3.08 3.79 2.86 2.00\n1.65\n150m A.G.L.\nA [m/s) 4.0 4.5 4.1 3.6 5.8 9.4 7.8 5.6 5.0 5.6 5.5 4.7 k 2.23 2.41\n1.96\n1.53\n1.96\n3.24 3.15 2.94 3.62 2.73\n1.91\n1.58\n200m A.G.L.\nA [m/s) 4.0 4.5 4.2 3.6 5.9 9.5 7.9 5.7 5.1 5.7 5.6 4.7 k 2.13 2.31\n1.87\n1.46\n1.88\n3.10 3.03 2.82 3.49 2.63\n1.83\n1.51\n\nRoughness length: 0.03m / z: 50m A.G.L.\n\nRoughness length: 0.03m / z: 100m A.G.L.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "50 ANNEX J UNCERTAINTIES ASSOCIATED WITH AEP RESULTS – SINGLE TURBINE AT MAST LOCATION Site 1 Launakalana 2 UMI Station 3 LNG Plant Turbine sensitivity\n[%AEP / %WS] 2.7 2.5 2.5\n[WS]\n[% AEP]\n[WS]\n[% AEP]\n[WS]\n[% AEP] Wind measurements 2.0 5.4 2.0 5.0 2.0 5.9 Long-term extrapolation 3.6 9.8 0.0 0.0 3.6 9.0 Vertical extrapolation\n1.2\n3.3 0.7\n1.7\n1.1\n2.7 Future wind variability (20 years)\n1.8\n4.8 6.1 15.0\n1.8\n4.4 Spatial variation 0.5\n1.4\n0.5\n1.1\n0.1\n1.2\nPower curve /// 8.8 /// 8.5 /// 7.6 Production losses ///\n1.2\n///\n1.2\n///\n1.2\nCombined Uncertainty (20 years) /// 15.5 /// 18.1 /// 14.2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "51 ANNEX K MEAN AIR DENSITY Site 1 Launakalana 2 UMI Station 3 LNG Plant Height AGL\n[m] 80 80 80 Air density at hub height\n[kg/m3]\n1.154\n1.114\n1.159", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "52 ANNEX L POTENTIAL LAYOUT A potential wind farm layout with a capacity of 30 MW has been studied for Site 1: Launakalana, Central Province considering 12 2.5MW generic turbines at 100m hub height. It should be noted that this layout is findicative and for educational purposes and to give an overview of the energy potential of the site.\nFigure 19 illustrates the best practice with a wind turbine spacing of at least five rotor diameters along the prevailing wind directions and three rotor diameters across.\n\nFigure 19: Wind farm layout, ellipses indicating the recommended spacing The energy production losses are summarized below.\nWake losses\n[%] 3.6 Unavailability losses\n[%] 4.0 Performance losses\n[%] 0.3 Electrical losses\n[%] 2.0 Environmental losses\n[%] 0.5 Total losses\n[%] 9.9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "53 Uncertainties associated with energy production results were then evaluated. They equal 20.1 % in total for a period of 20 years, for this specific wind farm configuration and break down as follows: Configuration 30MW wind farm @80m Wind measurements\n[% AEP] 4.3 Long-term extrapolation\n[% AEP] 7.7 Vertical extrapolation\n[% AEP] 2.6 Future wind variability (20 years)\n[% AEP] 3.8 Spatial variation\n[% AEP] 16.2 Power curve\n[% AEP] 6.4 Production losses\n[% AEP]\n1.9\nCombined uncertainty (20 years)\n[% AEP] 20.1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "The expected AEP and other energy production figures are presented below. The following results are provided: • Mean wind speed: corresponds to the lowest and highest mean wind speeds expected at the location and hub height of wind turbines.\n• Gross energy production: corresponds to the theoretically recoverable annual energy production at the outlet side of the generator, without production losses.\n• Energy production losses: corresponds to wake losses, unavailability losses, performance losses, electrical losses, environmental losses and curtailment losses (if any).\n• Net energy production (AEP): corresponds to the annual energy production expected to be delivered to the grid (taking into account all energy production losses).\n• Net full load equivalent hours: is the amount of time it would take for the wind farm to yield its annual production if it was able to constantly produce at full load.\n• Net capacity factor: is the net full load equivalent hours divided by the total number of hours in a year. It represents the usage of the installed capacity.\nConfiguration", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "30MW wind farm @80m Gross energy production\n[MWh/y] 63,403 Wake losses\n[%] 3.6 Other losses\n[%] 6.6 Total energy production losses\n[%] 9.9 Net energy production (AEP)\n[MWh/y] 57,098 Net full load equivalent hours\n[h/y] 1,903 Net capacity factor\n[%] 21.7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "54 AEPs over 1, 10, 15 and 20 year periods exceeded with probabilities of 50% (P50) to 95% (P95) are provided below.\nConfiguration 30MW wind farm @100m 1 year AEP (P50)\n[MWh/y] 57,098 AEP (P75)\n[MWh/y] 47,973 AEP (P90)\n[MWh/y] 39,760 AEP (P95)\n[MWh/y] 34,845 10 years AEP (P50)\n[MWh/y] 57,098 AEP (P75)\n[MWh/y] 49,253 AEP (P90)\n[MWh/y] 42,192 AEP (P95)\n[MWh/y] 37,967 15 years AEP (P50)\n[MWh/y] 57,098 AEP (P75)\n[MWh/y] 49,343 AEP (P90)\n[MWh/y] 42,363 AEP (P95)\n[MWh/y] 38,185 20 years AEP (P50)\n[MWh/y] 57,098 AEP (P75)\n[MWh/y] 49,365 AEP (P90)\n[MWh/y] 42,404 AEP (P95)\n[MWh/y] 38,239", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "55 ANNEX M CALIBRATION CERTIFICATES Individual calibration certificates for the sensors for all masts could be found in a separate document (annex file to the installation report), available for access on the World Bank/ESMAP online data repository energydata.info.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**ABOUT ESMAP**\nESMAP is a partnership between the World Bank and 21 development partners and private non-profits to help low- and middle-income countries reduce poverty and boost prosperity through environmentally sustainable energy solutions. ESMAP’s analytical and advisory services are fully integrated into the World Bank Group’s strategies, country financing and policy dialogue in the energy sector, through which it works to accelerate the energy transition required to achieve Sustainable Development Goal 7 and the Paris Agreement targets.\n\n**ACKNOWLEDGMENTS**\nThis report was prepared by staff and consultants at The World Bank and the International Finance Corporation (IFC) and was funded by ESMAP. The lead author was Alastair Dutton (Consultant, ESMAP/The World Bank), supported by Charlene Sullivan (Research Officer, IFC), Elizabeth Minchew (Consultant, IFC), Oliver Knight (Senior Energy Specialist, ESMAP/The World Bank), and Sean Whittaker (Senior Industry Specialist, IFC). The GIS analysis and mapping were carried out by Clara Ivanescu (Geographer, The World Bank) and Rachel Fox (Consultant, ESMAP/The World Bank). The report was edited and designed by Shepherd, Inc., under the supervision of Marjorie K. Araya (Production Editor, ESMAP/The World Bank).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Copyright © October 2019 The International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street, NW | Washington DC 20433 | USA\n\nThis work is a product of the staff of the World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of the World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.\n\n**RIGHTS AND PERMISSIONS**\n\nThe material in this work is subject to copyright. Because the World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; pubrights@worldbank.org. ESMAP would appreciate a copy of or link to the publication that uses this publication for its source, addressed to ESMAP Manager, World Bank, 1818 H Street NW, Washington, DC,\n[20433 USA; esmap@worldbank.org.](mailto:esmap@worldbank.org) All images remain the sole property of their source and may not be used for any purpose without written permission from the source.\n\nAttribution—Please cite the work as follows: ESMAP. 2019. Going Global: Expanding Offshore Wind to Emerging Markets. Washington, DC: World Bank.\n\nFull page versions of the maps provided in this report are available for download on the ESMAP website: https://esmap.org/offshore-wind Front cover: Dudgeon Offshore Wind Farm 22 in August of 2017. Photographer: Jan Arne Wold/Woldcam, 2017 © Interior: © Danish Wind Industry Association Back Cover: © MHI Vestas—Blyth Aerial", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "### **CONTENTS**\n\n**Abbreviations and Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v**\n\n**Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi**\n\n**The Development of Offshore Wind Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Lessons learned . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6\n\n**Estimating Offshore Wind Potential in Emerging Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . .** **9**\n\nMethodology and early findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Case studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Philippines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 South Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20 Sri Lanka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Turkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Vietnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Next steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28\n\n**Appendix: Geospatial Data Sources and Detailed Methodology . . . . . . . . . . . . . . . . . . . . . . . . 29**\n\nData Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Thresholds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Geospatial analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Footnotes/References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32**\n\n**iii**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Tables**\n\n**Table 1:** Summary Technical Potential for Offshore Wind in Select Emerging Markets\nwithin 200 km of coast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii\n\n**Table 2:** Technical viability requirements of offshore wind by technology . . . . . . . . . . . . . 10\n\n**Table 3:** Country RISE score and fixed and floating potential within 200 km of coast . . . . . . . . 11\n\n**Figures**\n\n**Figure 1:** Annual offshore wind installations by country and cumulative capacity,\n2008–2018 (MW) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3\n\n**Figure 2:** Levelized offshore wind tariffs, 2005–2030 (2018 $/MWh) . . . . . . . . . . . . . . . 3", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Figure 3:** Expected growth in offshore wind turbine size, 1980–2030 (m) . . . . . . . . . . . . . . 5\n\n**Figure 4:** Example floating offshore wind designs . . . . . . . . . . . . . . . . . . . . . . . . 5\n\n**Figure 5:** Map of global offshore wind speeds (100 m) . . . . . . . . . . . . . . . . . . . . . . 9\n\n**Figure 6:** Progression toward actual deployment . . . . . . . . . . . . . . . . . . . . . . . . 10\n\n**Maps**\n\n**Map 1:** Brazil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Map 2:** India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15\n\n**Map 3:** Morocco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17\n\n**Map 4:** Philippines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19\n\n**Map 5:** South Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Map 6:** Sri Lanka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23\n\n**Map 7:** Turkey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25\n\n**Map 8:** Vietnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27\n\n**Exchange Rate**\n\nAt the time of publication, 1 United States Dollar ($) = 0.8 Pound Sterling (£)\n\n**iv** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "### **ABBREVIATIONS AND ACRONYMS**\n\nGEBCO General Bathymetric Chart of the Oceans GW gigawatt IEA International Energy Agency IFC International Finance Corporation IPP independent power producer IRENA International Renewable Energy Agency km kilometer kV kilovolts LCOE levelized cost of energy m/s meters per second MNRE Indian Ministry of New and Renewable Energy MW megawatt MWh megawatt hour NGOs Nongovernmental organizations nm nautical mile OECD Organisation for Economic Co-operation and Development PPA power purchase agreement WBG World Bank Group\n\n**v**", "output": {"entities": {"named_data": ["GEBCO General Bathymetric Chart of the Oceans"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "### **EXECUTIVE SUMMARY**\n\nFor many years, offshore wind was the expensive cousin of onshore wind with generation costs in the range of $150 to $200 per megawatt hour (MWh). This changed dramatically between 2016 and 2017 when a series of competitive tenders in Europe witnessed strike prices fall below $100/MWh, culminating in projects that bid into merchant markets with no subsidy at all. As of September 2019, the lowest bid price is just below $50/MWh in the United Kingdom (UK) Contract for Differences Allocation Round 3 including the cost of transmission. [1] Prices have continued to drop thanks to technological improvements, economies of scale, maturation of supply chains, better procurement strategies, and the efforts of large and sophisticated project developers, including several from the utility and oil and gas sectors. However, to date the offshore wind industry has remained largely confined to Europe and China.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "As prices continue to drop, offshore wind is increasingly gaining traction in emerging markets. Projections suggest that offshore wind will add between 7 to 11 gigawatts (GW) per year from 2019 to 2024, reaching between 15 to 21 GW/year from 2025 to 2030. [2] While much of the growth is expected in Europe, China, and new Organisation for Economic Co-operation and Development (OECD) markets including Japan, South Korea, and the United States, there is ample potential for developing countries to ride on this momentum and ramp up their local offshore markets.\n\nThis report presents eight case studies on the technical potential for offshore wind in Brazil, India, Morocco, the Philippines, South Africa, Sri Lanka, Turkey, and Vietnam (here, technical potential is calculated on the basis of wind speed and water depth). Considering offshore areas within 200 kilometers (km) of the coast, [3] these eight countries have a total technical potential of approximately 3.1 terawatts, including 1,016 GW of fixed capacity and 2,066 GW of floating capacity (See Table 1). [4]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "For most of these countries, offshore wind estimates represent a multiple of their currently installed total generation capacity. This suggests that offshore wind can play a transformational role in meeting national goals ranging from expanding electricity access to increasing the mix of renewable resources in the energy mix, all while contributing to the Sustainable Development Goals and commitments made under the Paris Agreement.\n\nConverting this potential into actual deployment must take into account country-specific technical, economic, social, and environmental considerations. To effectively harness their offshore wind opportunity, countries must take a “big picture” approach to their grid and port infrastructure development, adopt innovative approaches to financing, establish stable policy frameworks, and cooperate on sensible supply chain development. Further analysis is necessary to develop a complete understanding of potential at the country level, looking at challenges with regards to grid capacity and integration issues, shipping lanes, migratory patterns, impacts on fisheries, and various logistical considerations.\n\n**vi** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**TABLE 1: SUMMARY TECHNICAL POTENTIAL FOR OFFSHORE WIND IN SELECT EMERGING**\n\n**MARKETS WITHIN 200 KM OF COAST**\n\n**Country** **Fixed (GW)** **Floating (GW)** **Total (GW)**\n\nBrazil 480 748 1,228 India 112 83 195 Morocco 22 178 200 Philippines 18 160 178 South Africa 57 589 646 Sri Lanka 55 37 92 Turkey 12 57 70 Vietnam 261 214 475\n\n**Total** **1,016** **2,066** **3,082**\n\nSource: Authors, 2019.\n\nTo assist in this effort, the Energy Sector Management Assistance Program (ESMAP), in partnership with the International Finance Corporation (IFC), launched a new World Bank Group (WBG) initiative in March 2019 to support the inclusion of offshore wind into the energy sector policies and strategies of WBG client countries and the technical work needed to build a pipeline of bankable projects. This report is the first in a series of planned knowledge products to be published by ESMAP, and we hope that it will help to increase awareness of the huge technical potential that exists in key emerging markets. [5] Executive Summary **vii**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "### **THE DEVELOPMENT OF OFFSHORE** **WIND MARKETS**\n\n##### **INTRODUCTION**\n\nThanks to rapid advances in technology and dramatic reductions in cost, offshore wind has come into the mainstream. Since 2016, the cost of generation from offshore wind in its European birthplace has rapidly declined to the point where projects are now bidding subsidy-free into competitive tenders. In China, the only non-­European market with significant offshore wind development, the sector is seen as a key element of the country’s plans to reduce the carbon intensity of its grid at a competitive cost. Just like its predecessors—onshore wind and solar PV—it is only a matter of time before offshore wind truly ‘goes global’.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "As offshore wind technology spreads across new geopolitical landscapes and into promising new markets, it will evolve from what is seen in Europe to accommodate local conditions. Fortunately, there is a deep well of lessons learned that can serve to accelerate this transition, allowing emerging markets to achieve cost-competitive, international standard offshore wind projects in a shorter time frame. As a first step toward this goal, the present report looks at the challenges and opportunities faced by offshore wind with a focus on case studies of eight promising markets: Brazil, India, Morocco, the Philippines, South Africa, Sri Lanka, Turkey, and Vietnam. Each case study includes an assessment of the country’s technical potential, proximity to demand centers, relevant policies and targets, and how it fits within current and future regional offshore wind markets.\n\nUnderstanding these specific circumstances can help contribute to broader knowledge, influencing the sustainable and functional expansion of offshore wind to emerging markets. It is important to note that further research and active stakeholder engagement are required to achieve a comprehensive understanding of the true opportunities within each market. As such, this report serves as a starting point for policy makers and stakeholders at the local level.\n\n##### **HISTORY**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "When considering offshore wind’s future, it is important to understand where, how, and why the technology first became viable. Europe has been at the forefront of the offshore wind industry since the first offshore wind farm was installed in 1991 at Vindeby in Eastern Denmark. Vindeby consisted of 11 ‘marinized’ onshore turbines, each generating 450 kilowatts (kW) for a total capacity of 5 megawatts (MW).\nDismantled in 2017, Vindeby paved the way for offshore wind farms across Europe.\n\nThroughout the 2000s, offshore wind continued to expand in the southern North Sea, Irish Sea, and Baltic Sea. [6 ] These areas feature ideal conditions for offshore wind, with strong winds, average wind speeds over 8 meters per second (m/s) and relatively shallow water depths, less than 50 meters (m). Sixteen years later, the UK is now home to the world’s largest offshore wind industry, with a cumulative capacity of 8.5 GW [7] and the lowest costs in the world. As shown in Figure 1, Belgium, Denmark, Germany, and the Netherlands, have also contributed to the considerable growth of the European offshore wind\n\n**1**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**FIRST COMMERCIAL WIND FARM LOCATED IN VINDEBY, DENMARK. DECOMMISSIONED**\n\n**IN 2017.**\n\n_Photo credit:_ [Danish Wind Industry Association, https://www.thegwpf.com/content/uploads/2017/10/Screenshot-](https://www.thegwpf.com/content/uploads/2017/10/Screenshot-2017-10-18-16.49.20.png)\n[2017-10-18-16.49.20.png](https://www.thegwpf.com/content/uploads/2017/10/Screenshot-2017-10-18-16.49.20.png) market, which remains home to the lion’s share of global installed capacity. [8] Outside of Europe, China has steadily been building its offshore wind industry, adding 1.6 GW in 2018 (the most of any country) to bring its total up to 4.4 GW. As of September 2019, global offshore wind capacity stands at around", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**FIGURE 1: ANNUAL OFFSHORE WIND INSTALLATIONS BY COUNTRY AND CUMULATIVE**\n\n**CAPACITY, 2008–2018 (MW)**\n\n5,000 4,000 3,000 2,000 1,000 20,000 15,000 10,000 5,000 United States China Mainland Japan Korea (Republic) Belgium Denmark Finland France Germany Netherlands Portugal Spain Sweden United Kingdom 2010 2011 2012 2013 2014 2015 2016 2017 2018 _Source:_ Data from BNEF. 2018. _2H 2018 Offshore Wind Market Outlook._ [Available at: https://www.bnef.com/core/insights/19859](https://www.bnef.com/core/insights/19859)\n\n**FIGURE 2: LEVELIZED OFFSHORE WIND TARIFFS, 2005–2030 (2018 $/MWh)**\n\n2018 $/MWh United States 80 60 40 20 0 2000 2005 2010 2015 2020 2025 2030 Belgium China Denmark Finland Germany Italy Japan United Kingdom Netherlands France _Source:_ BNEF. 2019. _Offshore Wind Roundtable Tokyo: Global trends & local opportunities._ [Available at: https://www.bnef](https://www.bnef.com/core/insights/20553)\n[.com/core/insights/20553](https://www.bnef.com/core/insights/20553) _Note:_ Figures refer to an estimated levelized price, taking into account tariff price and length, inflation, a merchant tail assumption and a 25-year lifetime. Prices above $150/MWh were omitted. The full cost of transmission to shore is included in some but not others.\n\nThe Development of Offshore Wind Markets **3**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "trend in cost reduction for new offshore projects with commissioning dates up to 2025. With these declines in LCOE, offshore wind is within the competitive range of new coal ($60–$143/MWh), nuclear ($112–$183/MWh), and even combined cycle gas ($42–$78/MWh) projects. [14] The speed and magnitude of offshore wind cost declines have led some observers to conclude that in certain European markets offshore wind will become the cheapest form of new generation as early as 2022. Falling prices are driven by a series of factors, including larger and more efficient turbines, bigger wind farms, access to better offshore wind resources, lower cost financing, more developed supply chains, price pressure from competitive tenders, and the emergence of larger, more sophisticated project developers.\n\n##### **TECHNOLOGY**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "The offshore wind sector was built on expertise collected from related industries. In the early 2000s, the UK adopted the then-largest onshore turbines (2 MW) and marinized them for the offshore environment.\nInstallation vessels, specifically jack-up barges, were brought in from the offshore oil industry but had limited capability both in terms of lift capacity and reach. Early projects tended to be very expensive and took longer to build than expected, as developers were unfamiliar with the relatively complex logistical requirements of planning and executing projects in the ocean.\n\nOver time, offshore wind evolved into a distinct, specialized technology. Offshore turbines are now specifically designed to reduce maintenance requirements given the relatively high cost of marine access.\nAt the same time, offshore wind farms are subject to fewer limitations faced by onshore wind, including land use pressures, concerns about views, and transportation/infrastructure constraints. As such, they have evolved to become the largest pieces of rotating machinery on the planet (See Figure 3), capable of generating much higher capacity factors than their onshore cousins. [15]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "As of September 2019, the MHI Vestas 9.5 MW turbine was the largest installed turbine in the world, with blades longer than an Airbus A380 airplane is wide. In October 2019, General Electric completed installation of its ­Haliade-X 12 MW prototype in the Port of Rotterdam as part of a three to five year $400 million testing program. [16] Designs in the 13 to 15 MW range are expected to be commercial by the mid-2020s. Equipment of this size and scale would be extremely difficult to site onshore.\n\nBut innovation has not stopped there. New ‘floating wind’ technologies (see Figure 4) are considered by many in the industry as the next leap forward. While still in its infancy, floating wind has excellent potential, namely because it can be installed in waters between 50 m and 1,000 m in depth, unlocking deep-water sites unsuitable for the fixed foundation technology that has dominated so far.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "While floating turbine capital costs are more than double that of fixed foundations, they are expected to drop substantially over the next decade. Since 2009, the cost of floating turbines has fallen 86 percent as projects move from single to multiple turbine demonstrations. [17] While more than 30 floating foundation designs exist, none have yet been deployed at full commercial scale. The world’s first multi-turbine floating wind farm, the 30 MW Hywind Scotland project, was installed nearly 30 km off Scotland’s northwest coast in 2017 and recorded an impressive 57 percent capacity factor in its first full year of operation.\n\n**4** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**FIGURE 3: EXPECTED GROWTH IN OFFSHORE WIND TURBINE SIZE, 1980–2030 (M)**\n\n_Source:_ Berkeley Lab. 2016. _Reducing Wind Energy Costs through Increased Turbine Size: Is the Sky the Limit?_ [Available at: https://](https://emp.lbl.gov/sites/all/files/scaling_turbines.pdf)\n[emp.lbl.gov/sites/all/files/scaling_turbines.pdf](https://emp.lbl.gov/sites/all/files/scaling_turbines.pdf)\n\n**FIGURE 4: EXAMPLE FLOATING OFFSHORE WIND DESIGNS**\n\n###### **Spar Semi-sub TLP**\n\n**Buoy** **Hanging**\n\n**counterweight**\n\n**V-column** **Barge** **TLP**\n\n_Source:_ [BNEF. 2019. “Offshore Wind Roundtable Tokyo: Global trends & local opportunities.” Available at: https://www.bnef](https://www.bnef.com/core/insights/20553/view)\n[.com/core/insights/20553/view](https://www.bnef.com/core/insights/20553/view) _Note:_ TLP = tension leg platform, m = meters. Based on current design announcements.\n\nThe Development of Offshore Wind Markets **5**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "##### **PROJECTIONS**\n\n2018 saw the addition of 4.8 GW of new offshore wind capacity worldwide. While the majority (60%) was in Europe, the continent’s grip on the market is loosening, with high capacity additions in China.\nProjections suggest that offshore wind will add between 7 to 11 GW per year from 2019 to 2024 at which time it will accelerate, adding between 15 GW and 21 GW per year between 2025 and 2030. Altogether, annual growth from 2019 to 2027 will average 11 GW per year, a fivefold increase over annual installations from the preceding eight-year period.\n\nBy 2030, cumulative installations will reach 190 GW and an estimated $700 billion in investment. [18] Estimates indicate that European markets will continue steady growth, but Asia will accelerate and see the majority of installations over the next decade, while the United States will account for around 10 percent of the global market.\n\nFloating wind will become important for many developing countries, particularly states with deeper waters, seismic activity, and/or significant extreme weather risk. [19] By 2030, Bloomberg estimates a cumulative installed capacity of 1.2 GW of floating wind across seven countries and 19 different sites. [20]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "While global emerging markets have ample wind resources and the need for clean, cost-competitive power, significant barriers to offshore wind investment remain. According to International Energy Agency (IEA), if offshore wind is to become a mainstay of the global clean energy transition, more work is required by governments and industry, including the development of long-term visions and work to promote investment and spur innovation. [21] Fortunately, emerging markets have an opportunity to harness lessons learned from more developed markets to aid them in ‘jumping the queue’ and more rapidly scaling offshore wind.\n\n##### **LESSONS LEARNED**\n\nDevelopment of the European offshore wind sector yields several useful lessons learned for emerging markets:", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "1. **There is a steep learning curve for offshore wind.** Initially, offshore wind projects in emerging markets will require concessional financing or other forms of public support, as they will be more expensive than both conventional generation and onshore renewables (both wind and solar PV). It is likely\nthat countries with existing strong port infrastructure and experience in oil and gas will be early movers. By starting on the right foot with top-quality projects, subsequent offshore wind projects will see rapid cost declines, becoming a cost-competitive part of the energy mix within a shorter time frame than was the case in European markets.\n2. **Offshore wind construction is much more complex and time-consuming than onshore construc-**\n\n**tion.** Onshore wind development typically takes two to three years from inception to commissioning\nwith development costs ranging from $1 to 2 million per project. In contrast, an offshore wind farm typically takes five to ten years to develop, requiring $10 to $50 million in development costs. Grid connection, grid reinforcement, transformers, and export cabling are critical long lead items.\n3. **Financing offshore wind is very complex and requires innovative structuring to reduce risks**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**and ensure bankability.** Offshore wind projects have high capital expenditures (often more than\n$2 billion) and risks due to the complexity of offshore construction. As a result, innovation will be required for both financing (involving many different structures and financing consortia) and project\n\n**6** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "management (handling multiple contracts and associated interface risks), including the potential use of guarantees and concessional financing.\n4. **Offshore wind development is driven by policy, requiring stable frameworks and phased procure-**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**ment to draw competition and spur industrial development.** The sector is politically attractive,\nbeing a scalable technology with high public acceptance and high potential for job creation, direct investment, and local economic development. To be successful, a stable policy environment is critical, with a logical progression of procurement approaches (e.g., moving from set tariff demonstration projects to competitively auctioned commercial scale projects once the market has matured).\n5. **Offshore wind development must be adaptable to new market contexts.** Offshore wind in emerging markets is not going to look exactly as it has in Europe. From a technical perspective, the industry must adapt to more challenging water depths, less robust grids, extreme weather events (typhoons, high waves) and increased seismic activity. From an environmental perspective, new approaches may be required to mitigate impacts on marine and avian wildlife as well as income generating activities like fishing and aquaculture. From a financing perspective, bankability will be a challenge in the context of utility off-takers who do not have a long history of independent power producers (IPPs) development and have some difficulty in putting forward bankable power purchase agreements (PPAs).\n6. **Regional cooperation is key to achieving economies of scale.** To achieve competitive pricing and drive supply chain development, a regional approach is required to generate sufficient scale (over 5 GW yearly). Without regional cooperation, individual governments might be more inclined to attempt to create markets independently, building supply and value chains where they do not necessarily make sense.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "### **ESTIMATING OFFSHORE WIND** **POTENTIAL IN EMERGING MARKETS**\n\nFigure 5 represents a global overview of offshore wind speeds up to 200 km from shore, at 100 m hub height. Offshore wind projects are potentially viable with wind speeds over 7 m/s (although most European development has occurred in areas with wind speeds over 8 to 9 m/s); these areas are marked in orange and red. The map clearly illustrates significant proximate opportunities to many developing countries, particularly in Asia and the Americas. However, wind speed is only one consideration in determining where offshore wind may be viable.\n\n**FIGURE 5: MAP OF GLOBAL OFFSHORE WIND SPEEDS (100 M)**\n\n_Source:_ Data obtained from the Global Wind Atlas (version 3.0), a free, web-based application developed, owned, and operated by the Technical University of Denmark (DTU) in partnership with the World Bank Group, utilizing data provided by Vortex, with funding provided by ESMAP. Available at: https://globalwindatlas.info\n\n##### **METHODOLOGY AND EARLY FINDINGS**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "To date, relatively little research has been undertaken on the potential for offshore wind in emerging markets. Any assessment of this kind must start with an estimate of _technical potential_, that is, the maximum possible installed capacity as determined by wind speed and water depth. This is the focus of the present report. As illustrated in Figure 6, the _locational potential_, _economic potential_ and finally, _actual_ _deployment_ represent increasingly restrictive amounts:\n\n1. **Technical potential:** [22] As indicated in Table 2, fixed foundation is considered technically viable in\nareas with water depth less than 50 m and average wind speeds over 7 m/s. Floating wind is considered technically viable with water depths from 50 m up to 1,000 m. The input data is sourced from the Global Wind Atlas, [23] which provides wind speeds up to 200 km offshore, and bathymetry surveys from the General Bathymetric Chart of the Oceans (GEBCO). [24, 25]\n\n**9**", "output": {"entities": {"named_data": ["General Bathymetric Chart of the Oceans"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**FIGURE 6: PROGRESSION TOWARD ACTUAL DEPLOYMENT**\n\n_Source:_ Authors, 2019.\n\n**TABLE 2: TECHNICAL VIABILITY REQUIREMENTS OF OFFSHORE WIND BY**\n\n**TECHNOLOGY**\n\n**Types of Offshore Wind** **Wind Speed (meters/second)** **Water Depth (meters)**\n\nFixed foundation >7 <50 _Source:_ Authors, 2019.\n\n2. **Locational potential:** Only a portion of the technical potential will translate into locational potential, represented by the physical area where developers can obtain consent to build. This is restricted to areas of the seabed that are available and suitable for offshore wind development.\n3. **Economic potential:** This represents areas of the locational potential where offshore wind can be developed at a competitive tariff.\n4. **Actual deployment** is the final subset, restricted to areas where a country’s energy policies can support offshore wind development, including constraints and restrictions pertaining to shipping corridors, migratory pathways, and other logistical concerns. Altogether, actual deployment potential is a small fraction of the overall technical potential.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "To assess locational and economic potential, detailed country-specific work is required involving stakeholder engagements, assessment of the regulatory environment, analysis of supply chain capabilities, and so on. While this level of analysis goes beyond the scope of this report, the World Bank Group is prepared to assist emerging markets as they progress through subsequent stages, leading ultimately to bankable, international-standard projects.\n\n**10** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "##### **CASE STUDIES**\n\nThe following analysis focuses on the technical potential for offshore wind in eight emerging markets: Brazil, India, Morocco, Philippines, South Africa, Sri Lanka, Turkey, and Vietnam. Table 3 provides a summary of the results, split between fixed foundation and floating wind. The table also includes the country’s RISE (Regulatory Indicators for Sustainable Energy) score [26] which accounts for seven metrics (legal, planning, incentives, financial, network connection, counterparty risk, and carbon pricing) to indicate attractiveness and maturity of the renewable energy market.\n\nLooking at offshore areas within 200 km of the coast, there is a total technical potential of 3.1 terawatts in these eight countries, including 1,016 GW of fixed and 2,066 GW of floating.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "For most countries, the amounts represent a multiple of their total installed generation capacity. Fixed foundations represent the bulk of the opportunity in India, Sri Lanka, and Turkey. Other countries such as the Philippines and South Africa will require floating foundations, for which commercial scale is currently a barrier to deployment. Brazil, Morocco, and Vietnam can benefit from both technologies. Additionally, the magnitude of technical potential in these three countries is significantly higher than in all the other markets considered, with the exception of South Africa.\n\nThe Appendix provides a breakdown of these figures by water depth and wind speed for each country.\n\n**TABLE 3: COUNTRY RISE SCORE AND FIXED AND FLOATING POTENTIAL WITHIN 200 KM**\n\n**OF COAST**\n\n**RISE**\n\n**Country** **Score**\n\n**Potential GW**\n\n**Fixed** **Floating** **Observations**\n\nBrazil 71 480 748 Brazil has excellent potential with shallow waters close to demand centers and a strong supply chain potential.\n\nIndia 87 112 83 Set an offshore wind target of 5 GW by 2022 and 30 GW by 2030. Best opportunities are in Tamil Nadu and Gujarat.\n\nMorocco 67 22 178 Excellent wind speeds and suitable depths along the Atlantic Coast; regional synergy possible with Spain and Portugal.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Philippines 62 18 160 Best potential in the north and central areas; potentially synergies with regional development (Taiwan, Vietnam).\n\nSouth Africa 76 57 589 High wind speeds but deep waters will favor floating foundations. Few regional development synergies likely.\n\nSri Lanka 55 55 37 Strong winds and shallow waters suggest potential for fixed foundation. But limited power demand affects scalability.\n\nTurkey 75 12 57 Good winds but deeper waters may favor floating; key issues around shipping and proximity to demand.\n\nVietnam 67 261 214 Excellent resource off southwest coast; existing nearshore development; strong potential for floating and fixed.\n\n**Totals** **1,016** **2,066** **Grand Total = 3,082 GW**\n\n_Source:_ Authors, 2019.\n\nEstimating Offshore Wind Potential in Emerging Markets **11**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "##### **BRAZIL**\n\n**Technical potential for offshore wind**\n\n- **■** Offshore wind development may occur after or alongside\nfurther development of the sizeable unexploited onshore resource capacity, which can be scaled at a lower cost in the short term.\n\n|Technical potential for offshore<br>wind within 200 km|Col2|Col3|\n|---|---|---|\n|**_RISE Score_**|**_Fixed_**|**_Floating_**|\n|71|480 GW|748 GW|\n\n- **■** When offshore wind enters the market, it will likely be fixed foundation.\n\n- **■** Brazil’s coast has three broad shelves <50 m deep which have average wind speeds >7 m/s:\n\n1. The northeast coast (São Luis to Natal) has wind speeds up to 9 m/s, with a technical potential of 237 GW for fixed foundations. The shelf falls away rapidly, leaving little scope for floating foundations.\n\n2. The southeast coast (south of Vitória) has wind speeds up to 8.5 m/s with technical potential for 67 GW of fixed and 227 GW of floating foundations.\n\n3. The southern coast (from Florianopolis to the Uruguayan border) has the best wind resources of over 9 m/s, with a potential of 173 GW for fixed foundations and 430 GW for floating.\n\n**Transmission to demand centers** **[27]**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "- **■** Power demand is concentrated in the southern and eastern regions, and major electricity generation assets (primarily hydropower) are in the northern and western regions. As a result, the main\ntransmission lines have developed as a central spine running from north to south.\n\n- **■** Extensions from the central grid run to the coasts and could service all three offshore wind zones,\nbut they are of lower voltage and would require substantial upgrades to evacuate substantial levels of offshore wind capacity.\n\n- **■** The southern and southeast wind areas are located closer to demand centers.\n\n**Relevant policies and targets:** In 2018, Brazil announced it aims to source 45 percent of its energy from\nrenewable by 2030, with 23 percent derived from wind, solar, and biomass. [28]\n\n**Fit with regional offshore wind market:** There are potential synergies with Southern Cone countries\n(Argentina, Chile, and Uruguay).\n\n**12** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "##### **INDIA**\n\n**Technical potential for offshore wind**\n\n- **■** The best offshore wind resources are at the southern tip of\nIndia in Tamil Nadu. There is a sizeable shallow area which has a technical potential of 54 GW.\n\n|Technical potential for offshore<br>wind within 200 km|Col2|Col3|\n|---|---|---|\n|**_RISE Score_**|**_Fixed_**|**_Floating_**|\n|87|112 GW|83 GW|\n\n- **■** In the northwest, off Gujarat in the Gulf of Khambhat, there is an area with weak winds between 7\nand 7.25 m/s in waters less than 50 m deep. The technical potential is estimated to be 36 GW.\n\n- **■** India’s first 1 GW of offshore wind development is planned for the Gujarat zone, with 35 developers\nhaving registered for an upcoming auction. [29]\n\n- **■** A third wind area is north in the Gulf of Kutch with wind speeds of 7 to 7.25 m/s and shallow water.\nThe technical potential for this area is 5 GW.\n\n**Transmission to demand centers** **[30]**\n\n- **■** The grid near the southern tip of Tamil Nadu is 400 kilovolts (kV), suitable for large-scale offshore\nwind. There are plans to extend the nearby 765 kV line to Bangalore, creating a connection with a substantial demand center.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "- **■** Gujarat’s grid is less robust near the coast, though it is understood that the Gujarat Energy Transmission Corporation has planned reinforcement to 400 kV to facilitate the initial 1 GW offshore\nwind project.\n\n**Relevant policies and targets:**\n\n- **■** In June 2018, the Ministry of New and Renewable Energy (MNRE) set an offshore wind energy target of 5 GW by 2022 and 30 GW by 2030. It will be challenging to meet the 2022 target given the\nlong development cycle for offshore wind. [31]\n\n- **■** The FOWIND studies look at project feasibility in the areas off Tamil Nadu and Gujarat. [32, 33]\n\n**Fit with regional offshore wind market:** Opportunity in Tamil Nadu has potential synergies with regional\ndevelopment in the northwest of Sri Lanka.\n\n**14** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "##### **MOROCCO**\n\n**Technical potential for offshore wind**\n\n- **■** Morocco’s western coast along the Atlantic Ocean has\nexcellent wind speeds in shallow and deeper waters suitable for offshore wind.\n\n|Technical potential for offshore<br>wind within 200 km|Col2|Col3|\n|---|---|---|\n|**_RISE Score_**|**_Fixed_**|**_Floating_**|\n|67|22 GW|178 GW|\n\n- **■** There are two areas well-suited for fixed foundations in the southern and central regions of the\ncountry, with technical potentials of 11 GW for the southernmost area, and 10 GW for the middle.\n\n- **■** Further from shore, there is a band of waters up to 1,000 m deep with wind speeds reaching over\n9 m/s which would be suitable for floating offshore wind with a total technical potential of 135 GW.\n\n- **■** On the northern coast there is another band of waters with 43 GW of floating wind potential.\n\n**Transmission to demand centers**\n\n- **■** Grid access points exist near the potential development areas though substantial reinforcement\nwill be needed to transmit power to the main demand centers of Rabat and Casablanca.\n\n- **■** Solar and onshore wind generation in Morocco have recently expanded but still have some way to\ngo. As such, offshore wind must join the queue for renewable energy deployment.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Relevant policies and targets**\n\n- **■** As of 2014, Morocco imported approximately 90% of its energy needs. [34] Morocco’s national strategic objective is to improve security of supply by reducing dependence on energy imports, including\nincreasing use of renewable sources for electricity production.\n\n- **■** By 2030, renewable energy sources are planned to provide 52% (10 GW) of the total installed\ncapacity. [35]\n\n**Fit with regional offshore wind market:** Morocco’s market could potentially sell energy into Europe’s\nmarket, specifically Spain and Portugal, as they have yet to deploy offshore wind themselves. That said, these two countries have plans to do so in the future (particularly in floating foundations).\n\n**16** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "##### **PHILIPPINES**\n\n**Technical potential for offshore wind**\n\n- **■** There are reasonable wind resource areas in the northern\nand central areas of the Philippine archipelago, some of which are in waters shallow enough for fixed foundation offshore wind.\n\n|Technical potential for offshore<br>wind within 200 km|Col2|Col3|\n|---|---|---|\n|**_RISE Score_**|**_Fixed_**|**_Floating_**|\n|62|18 GW|160 GW|\n\n- **■** There is one area suitable for fixed foundations in the Guimaras Strait with 7 GW of technical\npotential.\n\n- **■** Three main areas suitable for floating offshore wind are north of Luzon and to the north and south\nof the Mindoro Island. The largest of these areas is off the south coast of Mindoro with a technical potential of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "##### **SOUTH AFRICA**\n\n**Technical potential for offshore wind**\n\n- **■** South Africa’s entire 2,500 km coastline has wind speeds\nover 7 m/s; however the waters are deep (>50 m) and the currents on the southern and eastern coasts are\n\n|Technical potential for offshore<br>wind within 200 km|Col2|Col3|\n|---|---|---|\n|**_RISE Score_**|**_Fixed_**|**_Floating_**|\n|76|57 GW|589 GW|\n\nsome of the strongest in the world which will present a significant challenge, particularly for floating wind.\n\n- **■** On the eastern coast around Durban, there is a small area of less than 50 m water depth which\nhas a technical potential for 18 GW of fixed foundation offshore wind.\n\n- **■** Moving clockwise along the coast west from Durban up to the Namibian border, there is a strip of\nwater between 50 m and 1,000 m off the coastline that has a technical potential of 567 GW for floating wind.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "- **■** If the government applies restrictions on the minimum required distance from shore, the area’s\neconomic potential would decrease. However, there is a 200 m deep plateau directly below the southernmost tip of Africa which stretches 200 km from shore, which might be suitable for early stage floating wind, and alone has a technical potential of 204 GW.\n\n**Transmission to demand centers** **[37]**\n\n- **■** Power demand is concentrated in the northeast of South Africa, with major demand being in the\nsouthwest around Cape Town.\n\n- **■** Floating wind resources around Cape Town are particularly good. There is a 765 kV grid connection\nwith plans for reinforcement which could transfer power to the north. Similarly, Durban has a 765 kV grid connection.\n\n- **■** Growth in renewables will shift the balance of generation to the southeast requiring substantial\ninvestment in grid infrastructure, which would assist offshore wind power evacuation.\n\n**Relevant policies and targets:** A draft of the Integrated Resource Plan was updated in March 2019 and\nshows a substantial increase in renewables with wind power rising to 15.1 percent of generation by 2030.\nHowever, there is no mention of offshore wind. [38]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Fit with regional offshore wind market:** The offshore wind potential is of such a size that it would not\nneed to depend on regional demand. While Namibia has some potential, power demand is relatively low such that it is unlikely to yield synergies.\n\n**20** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "##### **SRI LANKA**\n\n**Technical potential for offshore wind**\n\n- **■** Windspeeds northwest of Sri Lanka range from 7.5 to over\n9 m/s with water depths below 50 m deep. The total area has an offshore wind technical potential of 45 GW. The\n\n|Technical potential for offshore<br>wind within 200 km|Col2|Col3|\n|---|---|---|\n|**_RISE Score_**|**_Fixed_**|**_Floating_**|\n|55|55 GW|37 GW|\n\nseabed falls away rapidly to 2,000 m leaving little potential for near-term floating turbines but to 1,000 m there is a resource with 27 GW technical potential.\n\n- **■** However, given the shallow water in the straits, there may be substantial fishing activity and bird\npopulations, which could substantially reduce the area’s consentable potential.\n\n- **■** Southeast of the island, windspeeds are 7 to 9.5 m/s in water depths up to 50 m. This area is close\nto the shore and approximately 200 km long with an offshore wind technical potential of 6 GW.\nFurther from shore, the seabed deepens to 1,000 m, viable for floating wind and adding 10 GW of potential.\n\n- **■** If the government applies restrictions on the minimum required distance from shore, the area’s\neconomic potential would dramatically decrease.\n\n**Transmission to demand centers** **[39]**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "- **■** Power demand is concentrated in the south and west of Sri Lanka, with Colombo (the capital)\nabout 150 km from a potential landing of offshore wind export cables in the northwest of the island.\n\n- **■** The government is proposing the addition of a high-voltage direct current (HVDC) link with India,\nwhich could substantially reduce the cost of necessary grid reinforcement.\n\n- **■** The southeast potential wind area is further from the grid and the planned HVDC link.\n\n**Relevant policies and targets:** The Sri Lankan government has a target to generate electricity with\n100 percent renewable energy by 2050. [40]\n\n**Fit with regional offshore wind market** : The northwest area overlaps with Tamil Nadu’s potential\noffshore wind area, representing a possibility for collaboration between the Indian and Sri Lankan governments.\n\n**22** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "##### **TURKEY**\n\n**Technical potential for offshore wind**\n\n- **■** The most attractive areas for offshore wind lie in the northwest in the Aegean Sea where wind speeds rise to 9 m/s;\nthe largest area has a technical offshore wind potential of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "##### **VIETNAM**\n\n**Technical potential for offshore wind**\n\n- **■** World class wind resources lie off the southwest coast\nof Vietnam in the South China Sea, where average wind speeds exceed 10 m/s off the coast of Binh Thuan and\n\n|Technical potential for offshore<br>wind within 200 km|Col2|Col3|\n|---|---|---|\n|**_RISE Score_**|**_Fixed_**|**_Floating_**|\n|67|261 GW|214 GW|\n\nNinh Thuan provinces, in water depths <50 m. This area then extends south and, with average wind speeds falling to 7 m/s the area extends up to 125 km from shore. This area alone has technical potential for 165 GW of fixed offshore wind.\n\n- **■** Outside this is a very large area below 1,000 m water depth which runs from the south of the\ncountry all the way to the Central region south of Hue, with a technical potential of 175 GW for floating wind.\n\n- **■** In the Gulf of Tonkin (northern region), there is a sizeable area with windspeeds of 7 to 8.5 m/s at\n<50 m, with a technical potential for fixed foundation wind of 88 GW. To the south of the gulf there is also an area <1,000 m with a technical potential of 39 GW for floating wind.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Transmission to demand centers** **[42]**\n\n- **■** The southwest area is approximately 200 km from Ho Chi Minh City. The government has plans\nfor 500 kV reinforcement of the grid to the city by 2025 in the proximity of landfall for offshore wind, but additional grid reinforcement may still be needed.\n\n- **■** The Gulf of Tonkin could supply demand in the Hanoi area, where grid reinforcement is planned as\nwell.\n\n**Relevant policies and targets:** Vietnam’s Power Development Plan (Revised No. 7) aims to derive 10 percent of its electricity from renewable energy by 2030, with 6 GW from wind. Power Development Plan\nNo. 8 is expected to increase this target but currently offshore wind potential is not incorporated. [43]\n\n**Fit with regional offshore wind market:** There is potential synergy with regional development in China\nand elsewhere in Asia Pacific.\n\n**26** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "##### **NEXT STEPS**\n\nOffshore wind is ready to go mainstream. Since the 1990s, nearly all northwest European countries have deployed fixed foundation projects at scale. The recent dramatic fall in offshore wind power prices is actively driving other countries around the world to embrace the technology. This report demonstrates that considerable opportunity also exists in developing countries to capitalize on their natural resources in offshore wind.\n\nWhile complex, there are common worldwide themes and trends to guide offshore wind deployment.\nEmerging economies can learn from more established markets, while simultaneously adapting those lessons to their local context to avoid a one-size-fits-all approach. Further analysis is required, particularly related to geopolitical contexts. Regional cooperation and program management to attract supply chain investment and competition will further drive cost reduction. Floating wind will be required to fully realize market prospects in many developing countries. Commercial-scale projects are needed in the next few years, followed by industrial deployment to bring this technology to maturity.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "In March 2019, the WBG announced a new initiative, to be led by ESMAP in partnership with IFC, to assist client country governments assess their offshore wind potential and develop a pipeline of bankable, IPP-led projects. To ensure a close connection to industry, ESMAP and IFC will be working with the Global Wind Energy Council (GWEC) and its Offshore Wind Task Force, consisting of world-leading Original Equipment Manufacturers (OEMs), developers, consultants, and supply chain providers. ESMAP and IFC will be drawing on the expertise of national partners, think tanks, financial institutions, and nongovernmental organizations (NGOs) in generating and disseminating global knowledge on offshore wind to support development in emerging markets. ESMAP will also provide funding to WBG country teams for in-country technical assistance activities and technical studies in response to requests from client governments.\n\n**28** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "### **APPENDIX: GEOSPATIAL DATA** **SOURCES AND DETAILED** **METHODOLOGY**\n\n##### **DATA SOURCES**\n\nFor the presented analysis, the following open source datasets have been used: Annual mean wind speed: The wind speed at 100 m height, obtained from the Global Wind Atlas has a coverage of up to 200 km offshore Water depth: The water depth levels were obtained from the GEBCO gridded bathymetric dataset Economic exclusive zones: The EEZ boundaries were used for the delineation of the focus area for the geospatial analysis\n\n##### **THRESHOLDS**\n\n**Dataset** **Value Threshold** **Details**\n\nWind speed - 7 m/s Wind speeds above 7 m/s are assumed as optimal resources for offshore wind deployment Bathymetry < 50 m Water depths of up to 50 m are considered suitable for fixed foundations 50 m–1,000 m Water depths between 50 and 1,000 m are considered suitable for floating foundations Resulting areas - 10 km [2] The resulting areas were further filtered by size, with areas covering a minimum of 10 km [2] considered suitable for the installation of an offshore wind farm\n\n##### **GEOSPATIAL ANALYSIS**\n\nThe geospatial analysis is comprised of five main operations:", "output": {"entities": {"named_data": ["GEBCO gridded bathymetric dataset"], "descriptive_data": [], "vague_data": ["EEZ boundaries"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "1. **Clipping of all layers to the Economic Exclusive Zones:** The analysis is relevant only within the EEZ\nborders, so the first step is clipping the Wind Speed and the Water Depth datasets to the maritime territory of each country.\n2. **Subset the main datasets per threshold values:** For the potential calculation, it was important to divide the water depth into 2 categories: above 50 meters depth, considered optimal for fixed foundations and 50–1,000 m, where floating wind can be considered. In parallel, the wind speed dataset was filtered for values greater than or equal to 7m/s.\n3. **Subtract the wind speed for each water depth category:** Once the water depth categories that correspond to fixed and floating technical potential have been created, these are used as a mask for extracting the wind speed values within two classes of wind speeds (1) wind speeds between\n\n**29**", "output": {"entities": {"named_data": [], "descriptive_data": ["wind speed dataset"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "7–8 m/s and (2) wind speeds > 8 m/s. These two classes are important for planning the turbine type as well as power generation potential.\n4. **Remove resulting areas smaller than 10 km** **[2]** **:** It has been assumed that each opportunity zone must have a minimum area of 10 km [2] to be considered suitable for offshore wind deployment; therefore areas smaller than 10 km [2] have been removed from the analysis.\n5. **Compute the technical potential for the suitable zones:** The technical potential for each opportunity zone has been computed by assuming a density of 3 MW per km [2] for wind speeds between 7–8 m/s and 4 MW per km [2] for wind speeds greater than 8 m/s.\n\nThe following tables provide supplemental calculations illustrating technical potential at varying water depths.\n\n**BRAZIL**\n\n**Water** **Wind Speed**\n\n**Type** **Depth (m)** **(m/s)** **GW**\n\n**Fixed** ≤50 7–8 87.3\n\n≤50 8 392.5 Total **480**\n\n**Floating** ≤250 7–8 118.9\n\n≤250 8 430.2 ≤500 7–8 23.7 ≤500 8 52.7 ≤1,000 7–8 41.3 ≤1,000 8 81.2 Total **748**\n\n**MOROCCO (excluding disputed territory)**\n\n**Water** **Wind Speed**\n\n**Type** **Depth (m)** **(m/s)** **GW**\n\n**Fixed** ≤50 7–8 10.9\n\n≤50 8 11.3 Total **22**\n\n**Floating** ≤250 7–8 41.9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "≤250 8 54.7 ≤500 7–8 12.9 ≤500 8 19.4 ≤1,000 7–8 15.2 ≤1,000 8 33.5 Total **178**\n\n**INDIA**\n\n**Water** **Wind Speed**\n\n**Type** **Depth (m)** **(m/s)** **GW**\n\n**Fixed** ≤50 7–8 89.8\n\n≤50 8 22.2 Total **112**\n\n**Floating** ≤250 7–8 15.6\n\n≤250 8 36.7 ≤500 7–8 6.4 ≤500 8 3.6 ≤1,000 7–8 8.9 ≤1,000 8 11.5 Total **83**\n\n**PHILIPPINES**\n\n**Water** **Wind Speed**\n\n**Type** **Depth (m)** **(m/s)** **GW**\n\n**Fixed** ≤50 7–8 13.1\n\n≤50 8 4.7 Total **18**\n\n**Floating** ≤250 7–8 42.8\n\n≤250 8 16.3 ≤500 7–8 19.6 ≤500 8 24.3 ≤1,000 7–8 23.3 ≤1,000 8 33.8 Total **160**\n\n**30** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**SOUTH AFRICA**\n\n**Water** **Wind Speed**\n\n**Type** **Depth (m)** **(m/s)** **GW**\n\n**Fixed** ≤50 7–8 14.36\n\n≤50 8 42.1 Total **57**\n\n**Floating** ≤250 7–8 53.4\n\n≤250 8 536 Total **589** _*No further results: WS above 7 beyond 250 m_ _water depths_\n\n**TURKEY**\n\n**Water** **Wind Speed**\n\n**Type** **Depth (m)** **(m/s)** **GW**\n\n**Fixed** ≤50 7–8 6.6\n\n≤50 8 5.6 Total **12**\n\n**Floating** ≤250 7–8 21.3\n\n≤250 8 20.6 ≤500 7–8 5.4 ≤500 8 5.6 ≤1,000 7–8 7.4 ≤1,000 8 2.7 Total **57**\n\n**SRI LANKA**\n\n**Water** **Wind Speed**\n\n**Type** **Depth (m)** **(m/s)** **GW**\n\n**Fixed** ≤50 7–8 35.3\n\n≤50 8 19.4 Total **55**\n\n**Floating** ≤250 7–8 10\n\n≤250 8 5.9 ≤500 7–8 3.1 ≤500 8 2.8 ≤1,000 7–8 4.3 ≤1,000 8 10.9 Total **37**\n\n**VIETNAM**\n\n**Water** **Wind Speed**\n\n**Type** **Depth (m)** **(m/s)** **GW**\n\n**Fixed** ≤50 7–8 176.8\n\n≤50 8 83.7 Total **261**\n\n**Floating** ≤250 7–8 99.1\n\n≤250 8 17.5 ≤500 7–8 20.3 ≤500 8 22.9 ≤1,000 7–8 35 ≤1,000 8 19.2 Total **214** Appendix: Geospatial Data Sources and Detailed Methodology **31**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "### **FOOTNOTES/REFERENCES**\n\n1BEIS. Contracts for Difference (CfD) Allocation Round 3: results Available at: https://www.gov.uk/ government/publications/contracts-for-difference-cfd-allocation-round-3-results 2GWEC Global Offshore Wind Report 2019.\n\n3This is the extent of the Global Wind Atlas version 3.0, which provided the data on resource potential used in this report. https://globalwindatlas.info 4Fixed foundation offshore wind is considered technically viable where there are wind speeds more than 7 m/s and water depths less than 50 m. Floating wind is considered viable where there are wind speeds over 7 m/s and water depths are less than 1,000 m.\n\n5WBG. 2019. Press release: “New Program to Accelerate Expansion of Offshore Wind Power in Developing Countries.” Available at: https://www.worldbank.org/en/news/press-release/2019/03/06/new-programto-accelerate-expansion-of-offshore-wind-power-in-developing-countries 6WindEurope. 2019. “History of Europe’s Wind Industry.” Available at: https://windeurope.org/ about-wind/history/ 7RenewableUK. UK Wind Energy Database. Available at: https://www.renewableuk.com/page/ UKWEDhome 8BNEF. 2018. _2H 2018 Offshore Wind Market Outlook._ Available at: https://www.bnef.com/core/ insights/19859/view 9GWEC. https://gwec.net/ 10IRENA. 2019. Future of Wind. Available at: https://www.irena.org/publications/2019/Oct/ Future-of-wind", "output": {"entities": {"named_data": ["Global Wind Atlas version 3.0", "UK Wind Energy Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "11The contract-for-difference (CfD) is a two-way contract: developers sign up to a price floor and ceiling to their revenue. For example, if a CfD is signed at 40 £/MWh, and the power price is 30 £/MWh, then the government’s Low Carbon Contracts Company (LCCC) pays the developer 10 £/MWh. In reverse, if the power price is 50 £/MWh, then the developer pays 10 £/MWh to the LCCC. _Source:_ BNEF, 2019 “U.K.\nAuction: Offshore Giants, Off-Target Sums?” Available at: https://www.bnef.com/core/insights/21399/ view 12BEIS. Contracts for Difference (CfD) Allocation Round 3: results Available at: https://www.gov.uk/ government/publications/contracts-for-difference-cfd-allocation-round-3-results 13Durakovic, Adnan. Offshorewind.biz. 20 September 2019. “Breaking: UK Offshore Wind Strike Prices Slide Down to GBP 39.65/MWh.” Available at: https://www.offshorewind.biz/2019/09/20/ukoffshore-wind-strike-prices-slide-down-to-gbp-39-65-mwh/ 14Lazard. 2018. Lazard’s Levelized Cost of Energy Analysis—Version 12.0. Available at: https://www .lazard.com/media/450784/lazards-levelized-cost-of-energy-version-120-vfinal.pdf\n\n**32**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "15Roberts, David. Vox. 2019. “These huge new wind turbines are a marvel. They’re also the future.” Available at: https://www.vox.com/energy-and-environment/2018/3/8/17084158/wind-turbine-powerenergy-blades 16GE. 1 March 2018. “GE announces Haliade-X, the world’s most powerful offshore wind turbine.” Available at: https://www.genewsroom.com/press-releases/ge-announces-haliade-x-worldsmost-powerful-offshore-wind-turbine 17BNEF. 2019. Floating Wind Drifts Towards Viability. Available at: https://www.bnef.com/core/ insights/20531/view 18GWEC. 2019. Global Offshore Wind Report 2019. [Available to GWEC members] 19BNEF. 2019. Floating Wind Drifts Towards Viability. Available at: https://www.bnef.com/core/ insights/20531/view 20BNEF. 2019. Floating Wind Drifts Towards Viability. Available at: https://www.bnef.com/core/ insights/20531/view 21IEA. 2019. Offshore Wind Outlook 2019: World Energy Outlook Special Report. Available at: https:// www.iea.org/offshorewind2019/ 22Technical potential = the maximum capacity of feasible offshore wind that may be realized in a given country.\n\n23Technical University of Denmark (DTU). 2019. “Global Wind Atlas (version 3.0).” Available at: https:// globalwindatlas.info 24GEBCO. 2019. Available at: https://www.gebco.net/ 25The technical potential has then calculated for average wind speeds of 7–8 m/s using a density of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "32FOWIND. 2018. _Offshore Tamil Nadu Feasibility Report._ Available at: https://gwec.net/wp-content/ uploads/2018/03/FEASIBILITY-STUDY-FOR-OFFSHORE-WIND-FARM-DEVELOPMENT-IN-TAMILNADU.pdf 33FOWIND. 2018. _Offshore Gujarat Feasibility Report._ Available at: https://gwec.net/wp-content/uploads/ 2018/03/FEASIBILITY-STUDY-FOR-OFFSHORE-WIND-FARM-DEVELOPMENT-IN-GUJARAT.pdf 34World Bank. 2019. “Energy imports, net (% of energy use).” Available at: https://data.worldbank.org/ indicator/eg.imp.cons.zs 35Oxford Business Group. 2019. “Morocco plans to add 10 GW of power from renewable energy sources by 2030.” Available at: https://oxfordbusinessgroup.com/analysis/viablealternative-plans-add-10-gw-power-renewable-sources-2030 36Philippines Department of Energy. 2019. “National Renewable Energy Program.” Available at: https:// www.doe.gov.ph/national-renewable-energy-program 37“The Eskom Transmission Development Plan 2019–2028,” Eskom, 25 Oct 2018 http://www.eskom .co.za/Whatweredoing/TransmissionDevelopmentPlan/Documents/TDP2018PublicForumPres OctRev6.pdf 38Draft Integrated Resource Plan 2018. Updated Mar 2019, https://www.egsa.org.za/wp-content/ uploads/2019/03/Updated-Draft-IRP2019-6-March-2019.pdf 39Ceylon Electricity Board Knowledge Hub. Retrieved November 2018. “Transmission Network.” Available at: https://www.ceb.lk/transmission/en 40ADB and UNDP. 2017. 100% Electricity Generation through Renewable Energy by 2050, Assessment of Sri Lanka’s Power Sector. Available at: http://www.undp.org/content/dam/LECB/docs/pubs-reports/ UNDP-LECB-Assessment-Sri-Lanka-Power-Sector.pdf 41The Daily Sabah. 7 October 2019. “Latest development plan lays out Turkey’s 2023 ambitions for energy, digital transformation.” Available at: https://www.dailysabah.com/energy/2019/07/10/ latest-development-plan-lays-out-turkeys-2023-ambitions-for-energy-digital-transformation 42JETRO. 2010. “Vietnam Energy Map.” Available at: https://www.geni.org/globalenergy/library/national_ energy_grid/vietman/graphics/Vietnam-Energy-Map-2010.pdf 43Vietnam Energy Online. 19 Mar 2016. “Adjusting Vietnam Power Development Planning.” Available at: http://nangluongvietnam.vn/news/en/policy-planning/adjusting-vietnam-national-powerdevelopment-planning.html\n\n**34** GOING GLOBAL Expanding Offshore Wind to Emerging Markets", "output": {"entities": {"named_data": ["Vietnam Energy Map"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Energy Sector Management Assistance Program The World Bank 1818 H Street, N.W.\nWashington, DC 20433 USA esmap.org | esmap@worldbank.org\n\nInternational Finance Corporation (IFC) 2121 Pennsylvania Ave NW Washington, DC 20433 www.ifc.org", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "ARTICLE IN PRESS\n\n### **Nature Communications**\n\n##### **Article in Press**\n\n**https://doi.org/10.1038/s41467-026-71625-3**\n\n## **Geospatial green ammonia co-fring in China’s coal** **power fleets to avoid CO emissions lock-in**\n\n#### **2**\n\n**Received: 15 August 2025**\n\n**Accepted: 24 March 2026**\n\n**Cite this article as: Wu, H., Hu, X.,**\n\n**Wang, X. et al. Geospatial green**\n\n**ammonia co-firing in China’s coal**\n\n**power fleets to avoid CO2 emissions**\n\n**lock-in. Nat Commun (2026). https://**\n\n**doi.org/10.1038/s41467-026-71625-3**\n\n**Huihuang Wu, Xiurong Hu, Xian Wang, Yuhan Zhou, Junfeng Liu, Yang Ren, Boyang Wu,**\n\n**Wendong Ge, Ying Liu & Shu Tao**\n\nWe are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.\n\nIf this paper is publishing under a Transparent Peer Review model then Peer Review reports will publish with the final article.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "© The Author(s) 2026. **Open Access** This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "ARTICLE IN PRESS\n\n###### **Geospatial green ammonia co-firing in China's coal power fleets to** **avoid CO2 emissions lock-in**\n\nHuihuang **Wu** [1], Xiurong **Hu** [2], Xian **Wang** [1], Yuhan **Zhou** [1], Junfeng **Liu** [1] *, Yang **Ren** [1], Boyang **Wu** [1], Wendong **Ge** [3], Ying **Liu** [4], Shu **Tao** [1 ]\n\n1. Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China.\n\n2. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.\n\n3. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing, 100012, China.\n\n4. School of Statistics, University of International Business and Economics, Beijing, 100029, China.\n\n*** Correspondence:** Junfeng Liu (jfliu@pku.edu.cn);\n\n**Abstract:**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "China’s 600 gigawatts (GW) of post-2010 coal power capacity locks in over 2 gigatons (Gt) of annual CO2 emissions. Green ammonia co-firing offers a viable decarbonization pathway, yet spatial mismatch between ammonia production hubs and coal power plants remains a barrier. Here, we develop a spatially explicit framework covering 3,958 islanded green ammonia production hubs to evaluate coal power units’ spatial retrofit outcomes under two distinct co-firing strategies: local sourcing and geospatially optimized allocation.\nResults show that despite green ammonia subsidies, high fuel utilization costs persist for the local sourcing strategy, thereby limiting cumulative energy-related CO2 reductions to 1.9–4.2 Gt (2020–2060) depending on climate policy stringency. In contrast, a geospatially optimized allocation strategy leveraging key transport corridors (e.g., Inner Mongolia to Hebei, Shandong, and Shanxi) delivers cumulative CO2 abatement of 7.8– 15.2 Gt, cuts cumulative CO2 capture demand by 1.6–6.8 Gt, and lowers cumulative system costs between 2020 and 2060, relative to the local sourcing strategy.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "ARTICLE IN PRESS\n\n**Introduction**\n\nChina operates the world's largest fleet of coal-fired power plants, with an installed capacity reaching 1,170 gigawatts (GW) in 2024, constituting over 50% of the total capacity worldwide [1] . Concurrently, over 80% of these coal power plants in China have been operational for less than two decades ( **Supplementary Fig. 1** ) [2], sustaining employment for over one million workers across both direct and ancillary industries [3] . As a result, the premature retirement of these coal power units presents significant challenges due to concerns regarding stranded assets [4] and the potential displacement of coal power workers [5] . In addition, wind–solar systems lacking dispatchable and flexible coal support are particularly vulnerable to climate-induced variability, thereby exacerbating systemic reliability risks [6] .", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "In response, strategies for retrofitting existing coal power plants to reduce coal emission intensity have been widely discussed. China's 2024 policy guidance flagged three principal retrofit strategies: biomass co-firing, carbon capture and storage (CCS), and green ammonia co-firing [7] . Major research efforts have been pursued toward switching to biomass as an alternative fuel or implementing post-combustion CCS technology [8] [,] [9] . In practice, biomass co-firing, while important, is constrained by limited feedstock availability and combustion compatibility, typically capping substitution ratios below 20% [10] . In contrast, green ammonia synthesized from renewable electricity via electrolysis offers higher co-firing potential, with demonstration-scale tests achieving substitution ratios of up to 35% and beyond [11] [,] [12] .", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "At the same time, ammonia is increasingly recognized as a prospective zero-carbon fuel alternative [13] for diverse applications, including transportation [14] and electricity generation [15] . As compared to hydrogen, ammonia has higher energy density, enables more convenient storage and transportation, and leverages a mature, well-established production infrastructure [16] . Its co-firing in conventional coal-fired boilers has been technically validated, with multiple pilot projects demonstrating its feasibility under operational conditions (see **Supplementary Table 1** ) [17] . According to the International Renewable Energy Agency, declining costs of renewables and electrolyzers could drive green hydrogen below 1 USD·kg [-1] by 2050 [18], bolstering the economic viability of green ammonia for large-scale adoption.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Existing literature on green ammonia co-firing has largely evolved along two main pathways. The first focuses on experimental investigations of combustion dynamics, flame stability, NOX formation, and ammonia slip under varying co-firing conditions [11] [,] [19] . The second strand, perhaps more relevant to this study, employs techno-economic modeling, focusing on life-cycle assessments [20] and cost optimization under varying assumptions for electrolyzer capital costs, electricity prices, and carbon pricing [20-22] . However, few studies have systematically assessed the decarbonization potential of ammonia retrofits across China's coal fleet [23] . In particular, green ammonia production costs exhibit strong spatial and seasonal variability [24] [,] [25], yet existing literature has not examined spatiotemporal mismatch between production hubs and retrofit-suitable coal power plants. Nor does it quantify economic viability in line with a specific low-carbon emission trajectory.\nMore importantly, these retrofitted coal power plants are largely absent from integrated energy system models, rendering their mitigation potential much less known.\n\nWe thereby seek to develop a spatially explicit modeling framework to evaluate the retrofit potential of green ammonia co-firing across China's 3,983 coal-fired power units ( **Supplementary Fig. 2 and Supplementary Note**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**1** ). This framework integrates unit-level retrofit choices with a power system optimization model to analyze\nvarious scenarios involving combined climate policies and co-firing strategies from 2020 to 2060. Moreover, by linking ammonia production hubs, transport networks, and the economic feasibility of unit-level retrofits, our approach captures how spatially differentiated green ammonia production costs, heterogeneous coal-fired", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "ARTICLE IN PRESS power unit characteristics, and policy support shape deployment outcomes. The results yield a high-resolution roadmap of where ammonia-based coal power decarbonization is both technically feasible and economically viable while remaining consistent with broader system transitions.\n\n**Results**\n\n**Spatiotemporal disparities in green ammonia production costs**\n\nWe first divide China into 3,958 spatially explicit production zones, each characterized by local terrain, land use, and renewable resource availability. We then estimate the maximum technical potential of wind and solar energy in each zone, and develop a zone-level cost-optimization model for green ammonia production. The model integrates forward-looking cost trajectories for capital and operational expenditures (CAPEX and OPEX) associated with solar and wind generation, energy storage, nitrogen capture, ammonia synthesis, and electrolysis, projecting the evolution of the levelized cost of green ammonia (LCOA, see **Eq. (3)** ) in each zone from 2020 to 2060.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Results reveal significant temporal dynamics in green ammonia costs. As compared to the 2020s, should the future cost trajectories of green ammonia production components align with current literature projections ( **Supplementary Table 2** ), the average LCOA would generally decline by over 30% in the 2050s. The largest reductions exceeding 50% expected in provinces such as Qinghai, Shaanxi, Sichuan ( **Fig. 1a** ). In 58% of provinces, over half of the LCOA stems from the CAPEX of variable renewable energy (VRE) infrastructure, cementing the dominant influence of renewable infrastructure investment on green ammonia's cost competitiveness ( **Fig. 1b** and **Supplementary Fig. 3** ). The observed cost declines from 2020 to 2060 are primarily driven by ongoing reductions in the capital costs of solar and wind installations.\n\nBeyond temporal variation, green ammonia production costs display marked spatial heterogeneity. By 2060, major production zones in provinces such as Inner Mongolia, Xinjiang, Liaoning, and Jilin, would have a green ammonia production potential exceeding 2.5 megatons (Mt), with levelized costs below 2,000 CNY per ton ( **Fig.**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**1a** ). These economically favorable and technologically feasible zones closely align with China's designated\ngreen ammonia pilot hubs (orange markers in **Fig. 1a** ; e.g., Ordos, Inner Mongolia). Based on province-level averages, Inner Mongolia, Ningxia, and Gansu are expected to achieve the lowest LCOA nationwide in the 2050s, at 2,011, 2,076, and 2,181 CNY per ton, respectively ( **Fig. 1b** ). By contrast, Chongqing, Guizhou, and Hunan, exhibit 15–40% higher LCOA levels than the national average in the 2050s, primarily due to lower VRE capacity factors (see **Supplementary Fig. 4** ) and limited land suitability. These spatiotemporal trends partially aligned with provincial-level estimates from prior studies indicate a growing cost advantage for inland energy bases in North China [26] . This trend will likely influence future ammonia-oriented industrial siting and export strategies.\n\n**Dynamic evolution of coal power retrofits**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Large-scale coal power units (≥300 MW, see **Supplementary Fig. 2** ) drawn from China's fleet of 3,983 facilities, which are either operational or officially slated for near-term commissioning, are included in the scope of cofiring retrofit evaluations. Three co-firing strategies are considered: (1) no green ammonia use (NOC), with only carbon capture retrofitting allowed; (2) co-firing based on locally available green ammonia (ONS); (3) geospatially optimized co-firing (GEO), which allocates ammonia from 3,958 production zones to minimize integrated production and transportation costs (see **Eq. (6)** ).\n\nThe primary distinction between ONS and GEO strategies is in supply allocation. Whereas ONS strategy employs", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "ARTICLE IN PRESS locally available green ammonia in line with current policy [7], GEO strategy enables green ammonia delivery to each plant via spatial reallocation. Nationwide, the GEO strategy results in significant cost savings compared to ONS—averaging 1,383, 876, 606, and 408 CNY per ton of ammonia in the 2020s through the 2050s, respectively ( **Fig. 2a** ). These cost savings are accompanied by a decline in the average distance between coal power plants and their matched ammonia supply zones, from 527 km in the 2020s to 410 km in the 2050s. Two dynamics drive this trend: the retirement of plants distant from low-cost supply hubs, and the convergence in production costs across provinces (see **Supplementary Fig. 5** ), which reduces the marginal benefits of long-distance matching.\n\n**Fig. 2b** illustrates the evolution of four coal-based technologies under distinct policy scenarios and co-firing\nstrategies, as simulated by minimizing coal power generation costs for each unit (only units commissioned by 2025 are displayed herein). Despite declining green ammonia utilization costs, 37% (~478 GW) of coal power capacity is projected to remain operational in the 2050s in the absence of policy intervention ( **Supplementary**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Fig. 6a** )—excluding the candidate coal power plants approved for construction after 2025. Such capacity could\npotentially lock in up to ~1,669 Mt of CO2 emissions annually based on the current emission factor [27] . Ammonia subsidies and carbon pricing serve as key levers for near-term and long-term decarbonization ( **Supplementary**\n\n**Figs. 7-8** ). We further embed three co-firing strategies—NOC, ONS, and GEO—within a policy scenario matrix\nspanning Conservative, Moderate, and Aggressive policy trajectories.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Results show that without green ammonia co-firing (NOC strategy), coal power capacity without carbon abatement technologies will decline to 100 GW, while CCS-equipped coal power capacity will reach 378 GW in the 2050s under the Conservative policy scenario ( **Fig. 2b** ). The integration of green ammonia co-firing strategies further displaces coal-fired capacity—replacing pure coal-fired power in the near term and coal-fired power with CCS in the long term. Specifically, the ONS strategy reduces the pure coal-fired power capacity from the NOC strategy of 1,084, 1,084, and 83 GW (2040s) to 929, 824, and 60 GW under Conservative, Moderate, and Aggressive policy, respectively, while the GEO strategy achieves a deeper reduction to 409, 190, 60 GW; these displaced coal power units are repurposed for green ammonia co-firing. In the 2050s, as CCS technology matures, a substantial portion of co-fired units could be further upgraded into hybrid ammonia co-firing + CCS configurations, reaching 168-233 GW under the ONS strategy and 341-424 GW under the GEO strategy, thereby enabling deeper emissions reductions.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "The generation costs of coal-fired power plants are governed by coal prices, carbon prices, and heat rates. As reported in **Fig. 2c**, provincial generation costs exhibit heterogeneous sensitivity to coal price fluctuations: for the 2020s, a rise in coal prices from the historical median (MeC_HiT_HiS scenario) to the historical high (HiC_HiT_HiS scenario) would lead to a 0.11 Chinese Yuan per kilowatt-hour (CNY·kWh−1) increase in Inner Mongolia’s generation cost. In contrast, Hainan’s generation cost would increase by 0.30 CNY·kWh−1—a nearly three-fold increment relative to Inner Mongolia. In the absence of a green ammonia co-firing option— corresponding to the NOC strategy under the HiC_HiT_HiS scenario—sustained high carbon price growth would drive a 0.14–0.28 CNY·kWh−1 increase in coal power generation costs across most provinces in the 2050s, relative to 2020s levels.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Green ammonia co-firing expands the decarbonization options for coal power units. Cost reductions under the ONS strategy are modest, spanning 0–0.1 CNY·kWh−1, in contrast to the more substantial declines of 0.01– 0.16 CNY·kWh−1 achieved by the GEO strategy. Cost declines exceed 0.1 CNY·kWh [-1] in provinces such as Fujian (-0.16 CNY·kWh−1), Hainan (-0.16 CNY·kWh−1), Guangxi (-0.14 CNY·kWh−1), Jiangxi (-0.14 CNY·kWh−1) under the GEO strategy. In contrast, Xinjiang and Inner Mongolia see limited cost-saving benefits of green ammonia cofiring, as their inherently low coal power costs partially offset the economic advantage of nearby ammonia resources. These cost dynamics are further underpinned by differences in the technical performance of coal", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "ARTICLE IN PRESS combustion technologies. **Supplementary Fig. 9** further elaborates on the technical underpinnings of these cost dynamics: ultra-supercritical units within China exhibit a unit-averaged heat rate of 8.5 kilo British thermal units per kilowatt-hour (kBtu·kWh−1), lower than that of supercritical (9.4 kBtu·kWh−1), subcritical (11 kBtu·kWh−1), and circulating fluidized bed (CFB) units (10.5 kBtu·kWh−1) **,** enabling ultra-supercritical units to achieve the lowest unit-averaged generation cost of 0.57 CNY·kWh−1 under the Aggressive scenario in the 2050s by green ammonia co-firing.\n\n**CO2 emissions and system costs**\n\nComparing generation cost in isolation, while intuitive, offers limited insight into broader system-wide implications. To quantify the system-wide impact of green ammonia co-firing, we integrate nine retrofit-policy combinations—encompassing three co-firing strategies and three carbon pricing levels—into a coal power retrofit-retirement model. In contrast to simply comparing generation costs in isolation, this coal power retrofit-retirement model allows for the early retirement of coal-fired power plants when competing with other power generation technologies (e.g., solar, wind). In parallel, a baseline scenario (Base) is constructed to reflect current policy inertia, assuming modest carbon pricing and no ammonia co-firing.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Results show that, under the Base scenario, annual CO2 emissions from the power sector will remain at approximately 1,918 Mt in the 2050s (the grey bar and line in **Fig. 3a** )—far exceeding the thresholds consistent with net-zero. In contrast, implementing elevated carbon pricing and coal prices without green ammonia cofiring (the NOC strategy) would reduce energy-related emissions to roughly 1,075-1,093 Mt by 2060 (colored solid lines in **Fig. 3a** ). Concurrently, the power system will generate an annual carbon capture demand of 918– 1,092 Mt in the 2050s ( **Fig. 3b** ), underscoring the critical reliance on CCS in high-ambition decarbonization pathways.\n\nGreen ammonia co-firing, despite exerting limited influence on the long-run emissions endpoint, contributes substantially to near- and mid-term abatement. Specifically, relative to the NOC strategy, the ONS and GEO strategies can deliver cumulative energy-related emission reductions of 1.9–4.2 Gt and 7.8–15.2 Gt, respectively, over the 2020–2060 period, with the level depending on policy stringency (the right panel in **Fig.**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**3a** ). For context, under the Aggressive policy scenario, cumulative energy-related emission reductions over\n2020–2060 period under the GEO strategy are roughly three times the total 2024 emissions of China’s power sector. More importantly, relative to the NOC strategy, the GEO strategy alleviates the burden on CCS: under the Aggressive policy scenario, capture requirements are reduced by 246 Mt·yr−1 in the 2050s, with cumulative capture demand lowered by 6.8 Gt over the 2020–2060 period (the right panel in **Fig. 3b** ). This temporal rebalancing of mitigation efforts mitigates the emission lock-in risks associated with the delayed scalability of CCS infrastructure.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "We further assess the system-level cost impacts of ammonia co-firing from 2020 to 2060 ( **Fig. 3c** ). Although cumulative fuel expenditures increase under the GEO strategy (even with green ammonia subsidy) by up to 1,988 billion CNY from 2020 to 2060 in the Aggressive scenario compared to the NOC strategy, these are offset by avoided carbon costs and reduced investment in capital-intensive power capacity. The retention of dispatchable coal units retrofitted for ammonia co-firing preserves system flexibility while decarbonizing carbon-intensive units, lowering total system costs. However, the system-level benefits of green ammonia cofiring are influenced by green ammonia subsidy policies and potential future competition for green ammonia among power plants; we thus focus primarily on the cost savings potential of the GEO strategy relative to the ONS strategy. Overall, the system-level value of green ammonia co-firing retrofits becomes most pronounced when spatial deployment is optimized. Under the Conservative, Moderate, and Aggressive policy scenarios, the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "ARTICLE IN PRESS GEO strategy yields additional cumulative cost savings of 206, 325, and 669 billion CNY, respectively, over the 2020-2060 period, compared to the ONS strategy.\n\n**Geographic deployment and transport networks**\n\nWe further investigate the spatial patterns of green ammonia co-firing under the GEO strategy and aggressive policy scenarios. The geographic distribution of green ammonia co-firing retrofits exhibits an evident dualcluster pattern. The first cluster emerges in renewable-abundant northern provinces, such as Inner Mongolia, Xinjiang, and Shanxi, where the co-location of ammonia production and utilization is enabled by abundant solar and wind resources. By 2060, the combined installed capacity of CNE and CNECCS will reach approximately 124 GW in Inner Mongolia, 45 GW in Xinjiang, and 37 GW in Shaanxi. The second cluster is located in load-intensive eastern and central provinces, including Jiangsu, Guangdong, Anhui, and Fujian ( **Fig.**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**4a** ). Yet, somewhat differentiated from the northern cluster, retrofit deployment in these regions peaks in the\n2030s and 2040s, but declines sharply thereafter as many of these units retire from the power system. For example, CNE capacity reaches up to 66 GW in Jiangsu, 40 GW in Anhui, and 71 GW in Guangdong during the 2030s. Most of these CNE capacity, however, will be decommissioned in the 2050s rather than further transitioning to CNECCS as observed in the first cluster.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "This divergence is partly explained by regional disparities in proximity to geological carbon storage sites ( **Supplementary Fig. 2** ). Much of North China is located near large sedimentary basins, enabling coal-fired power units there to be retrofitted for ammonia co-firing integrated with CCS in later decades and thus avoiding high carbon prices projected under aggressive climate policy scenarios. In contrast, most of South China lacks accessible nearby storage formations, rendering CCS retrofits more costly due to the need for long-distance CO2 transport. As a result, provinces in the South China are more likely to shift toward new wind and solar capacity rather than continue operating ammonia-based retrofitted coal power units. Offshore CO2 storage, while not investigated here, may offer a promising pathway for CNECCS development in southeastern coastal areas.\n\nAt the same time, this geographically dispersed deployment of co-firing retrofits also creates a growing need for inter-regional ammonia transport, particularly from inland production hubs to coastal demand centers ( **Fig.**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**4b** ). Inner Mongolia emerges as the dominant exporter, projected to deliver more than 100 Mt of ammonia by\n2040—yet its current green ammonia production capacity remains vastly insufficient to meet this coal power co-firing fuel demand—with average outbound transport distances of approximately 570 km. Inner Mongolia’s green ammonia turnover is expected to account for approximately 50% of the national total in the 2030s, rising to 76% in the 2050s ( **Supplementary Fig. 10** ). Major outbound corridors originate from Inner Mongolia and flow to Hebei, Shanxi, Shandong, Liaoning, and Shaanxi, with each having annual transport volumes exceeding 10 Mt. On the receiving end, Hebei (30 Mt), Shandong (30 Mt), and Jiangsu (22 Mt) are the primary importers, with average inbound distances exceeding 400 km. Line color coding indicates that in specific high-flow ammonia pairs, interprovincial electricity flows are reduced (red lines), suggesting that ammonia transport partially substitutes for electricity transmission.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "As existing power plants are progressively phased out between 2020 and 2060, ammonia transport demand will decline across all interprovincial routes except the corridor linking Inner Mongolia to Shanxi, Shaanxi, and Ningxia, and transport corridors will consequently be concentrated in North China ( **Figs. 4c-e** ). Those ammonia transport routes, no longer utilized by coal-fired power plants, offer substantial repurposing potential: 68% of cement and concrete capacity and 83% of steel and iron capacity lie within 30 km of the network ( **Supplementary Note 3** ). This potential, however, exhibits pronounced provincial disparities. Economically", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "ARTICLE IN PRESS advantaged coastal regions show stronger spatial alignment with the corridors than inland areas, underscoring the need for targeted policies to address regional inequities in access to decarbonization infrastructure in the future.\n\n**Discussion**\n\nChina's blueprint for coal-fired power decarbonization flagged three primary retrofit options—CCS, biomass co-firing, and green ammonia blending [7] —yet their practical deployment is profoundly shaped by spatial resource availability. For instance, Fan et al.(2023) [8] identify provinces such as Inner Mongolia and Gansu as unsuitable for biomass blending and CCS retrofits, citing constraints in feedstock availability and infrastructure readiness. Our analysis, however, reveals that these regions are among the most promising sites for large-scale solar-wind-based green ammonia production and co-firing retrofitting, uncovering a spatial complementarity between ammonia and biomass pathways that has been largely overlooked. Notably, as core renewable energy hubs, Inner Mongolia and Gansu face an urgent need for flexible, dispatchable power [28] . This is precisely the role that ammonia-blended coal units can fulfill. Looking ahead, greater attention should be given to how the three retrofit options can complement each other spatially and temporally [29] .", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Green ammonia co-firing, however, should not be regarded as a one-size-fits-all solution, but as a geo-targeted option. Our findings reveal that most coal power units in provinces such as Guizhou and Chongqing face inherent constraints. Limited solar and wind potential, coupled with challenging terrain collectively hinder the deployment of green ammonia infrastructure. Such region-specific bottlenecks align with those of Fan et al. [8], which similarly rule out biomass co-firing and CCS retrofits in these regions. In the absence of viable retrofit options, coal power plants in these provinces are unlikely to contribute meaningfully to power sector decarbonization. As such, they should be prioritized for early retirement or reclassified as reserve capacity, rather than being considered for further investment. Beyond wind- and solar-based pathways, hydropowerderived green ammonia production, although not evaluated herein, may offer a regionally tailored alternative [30] [,] [31], particularly in hydro-abundant provinces in Southwest China.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Regarding co-firing strategy, spatially optimized allocation strategy warrants consideration over purely local sourcing. This approach lowers average fuel utilization costs, particularly in regions facing high near-term onsite green ammonia production costs yet situated close to green ammonia-rich areas, such as Hebei and Shandong. A second key rationale for prioritizing GEO lies in its markedly stronger resilience to cost volatility compared to the localized (ONS) strategy. Substantial uncertainty in future component costs will introduce potential deviations in projected cost pathways. Our sensitivity analysis (see **Supplementary Note 4** ) reveals divergent uncertainty profiles across technologies: mature options (e.g., onshore wind) show stable projections, while less mature ones (e.g., batteries) exhibit wide variability; LCOA sensitivity also displays marked spatial heterogeneity. These dual cost properties (variability and spatial heterogeneity) critically shape retrofit feasibility and capacity scales. The GEO strategy mitigates such uncertainties via cross-regional optimization of production, transportation, and costs, which reduces capacity fluctuations, especially for retrofitting initiatives requiring concurrent CCS deployment or implementation in regions with unevenly distributed solar and wind resources.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Yet, green ammonia co-firing remains hindered by economic and technical barriers that limit its near-term scalability. The first pertains to the cost of green ammonia production, which is still significantly higher than that of fossil-based fuels, thereby rendering retrofitting economically unviable in the absence of targeted policy support (e.g., green ammonia subsidies in this study). The second, perhaps more technical barrier, is that ammonia blending enables partial substitution of emission-intensive coal, but the remaining coal combustion", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "ARTICLE IN PRESS still requires CCS to achieve deep emission reductions, thereby increasing retrofit complexity and capital intensity. Moreover, green ammonia production initiatives have made sluggish progress globally, with current installed capacity falling drastically short of the fuel demand for large-scale coal power co-firing. This represents another critical bottleneck to the widespread deployment of this decarbonization pathway.\n\nTaken together, despite these barriers and limitations, green ammonia co-firing explored in this study broadens decarbonization options for these emission-intensive yet relatively young coal power units, thereby providing a entry point for shaping their long-term decarbonization trajectories.\n\n**Methods**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "We develop a spatially explicit modeling framework to quantify the spatial heterogeneity of green ammonia co-firing retrofit for China’s coal power fleets. This framework covers 3,958 green ammonia production zones ( _z_ ) and 3,983 coal-fired power units across China. To ensure system reliability and environmental integrity, we adopt a fully islanded configuration powered solely by renewables within each production zone ( _z_ ). While semiislanded approaches, such as grid connection leveraging curtailed grid power, may reduce costs by almost 11% [32] and enhance flexibility, they introduce critical environmental trade-offs. Specifically, in regions with carbon-intensive or unreliable grids, grid-electricity-based production can negate net emissions reductions [33] and, in some cases, result in higher lifecycle CO2 intensity than conventional SMR-based ammonia production [24] .\nThese risks reinforce the rationale for prioritizing fully islanded systems as a more climate-consistent pathway.\n\n**Photovoltaic and wind supply potential**\nThe technical potential of solar photovoltaics and wind energy ( CapMaxz The technical potential of solar photovoltaics and wind energy ( CapMaxzREP ) is estimated based on theoretical installation densities ( ρzREP ) and a suitable area within the specific zone ( Sz ), as shown in **Eq. (1)** . Land suitability", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "installation densities ( ρzREP ) and a suitable area within the specific zone ( Sz ), as shown in **Eq. (1)** . Land suitability for photovoltaic and wind energy deployment is determined by filtering pixels based on land cover types and environmental thresholds. Following Wang et al. (2023) [34], areas such as forests, wetlands, urban zones, and protected regions are excluded, while shrublands, grasslands, croplands, and barren lands are selectively retained depending on technology-specific criteria ( **Supplementary Table 3** ). For photovoltaics, only locations with annual solar irradiance above 1,200 kWh·m [-2] and surface temperatures above 0°C are considered suitable, while wind development additionally requires mean capacity factors above 20% and elevations below 3,000 meters. Only pixels meeting all suitability criteria are retained for potential estimation.\n\nzREP = ρz CapMaxz zREP - Sz **(1)** Building on the 2024 China wind and solar power potential assessment report [35] and the national standard Land Use Control Indicators for Photovoltaic Power Station Projects (TD/T 1075–2023) [36], the photovoltaic installation density ( ρzSolar ) is estimated under the assumptions of a 10 MW fixed-tilt configuration, 110 kV grid", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "connection, and 20% conversion efficiency. Installation densities are differentiated across terrain types and latitudinal bands. Terrain is categorized into three classes ( **Supplementary Table 4** ): Type I (flat plains, slope ≤ 3°), Type II (undulating lowlands, 3° < slope ≤ 20°, elevation difference ≤ 200 m), and Type III (hilly and mountainous areas, slope > 20°, elevation difference > 200 m). Specific installation density values for each category and latitude are provided in **Supplementary Table 5** .\n\nFollowing Wang et al. (2023), the maximum onshore wind power density ( ρzWind ) is set at 3.682 MW·km-2, based on a representative wind turbine specification with a rated power ( PWWind ) of 2.5 MW and a rotor diameter ( DWind ) of 103 meters ( **see Eq. (2)** ) [34] . A slope-adjusted density approach is applied to account for terrain variability, with the slope-adjusted density detailed in **Supplementary Table 6** [35] .", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "ρzWind = ARTICLE IN PRESS PWWind **(2)** 8DWind ∙ 8DWind\n\n**Levelized cost of green ammonia**\nFor each zone, we evaluate a fully islanded renewable-powered system [24] in which ammonia is synthesized via the Haber–Bosch (HB) process using hydrogen from water electrolysis and nitrogen from cryogenic air separation. Among the available electrolyzer technologies, proton exchange membrane (PEM) electrolyzers, despite greater operational flexibility and turndown capability, remain more costly than alkaline electrolyzers (AE). The AE system is therefore selected as the electrolyzer technology modeled in this study. Both the electrolyzer and HB synthesis loops possess inherent minimum operational load constraints. Following the previous study [37], the system turndown ratio for electrolyzers is set to 80% of the installed capacity (i.e., a minimum stable operational load of 20% must be maintained), while the HB synthesis loop conventionally operates with a minimum load requirement of 60% (i.e., a maximum turndown of 40%).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "Considering the intermittency and complementarity of solar and wind resources, we minimize the levelized cost of ammonia (LCOA) by integrating solar PV, onshore wind and energy storage systems. Compared to standalone solar or wind configurations, such integrated systems can reduce LCOA by 6.51–48.03% through temporal complementarity [26], thereby supporting cost-efficiency required for large-scale retrofitting.\nSpecifically, LCOAt,z includes capital ( capext,zCTP ) and operating expenditure ( opext,zCTP ) for solar, wind, Specifically, LCOAt,z includes capital ( capext,zCTP ) and operating expenditure ( opext,zCTP ) for solar, wind, electrolyzer, electricity storage system, hydrogen storage system, nitrogen separation units and ammonia synthesis plants [37] . The LCOAt,z is calculated as **Eqs. (3)-(5)** .\n\nt,zCTP ) and operating expenditure ( opext,z GA **(3)** CTP opext,zCTP LCOAt,z = ∑CTP capext,z CTP capext,zCTP + ∑CTP opext,z qt,z t,zCTP = capt,z CTP r∙(1+r) [−1]\n\n**(4)**\n1−(1+r) [−LifespanCTP] capext,z t,zCTP ∙UCt t,zCTP = capt,z tCTP + varopext,z CTP **(5)** opext,z t,zCTP ∙UFt The installed capacity of different equipment types capt,zCTP is multiplied by their corresponding unit capital cost ( UCtCTP ) and unit fixed operation and maintenance cost ( UFtCTP ) to calculate capital cost and fixed The installed capacity of different equipment types capt,z", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "cost ( UCtCTP ) and unit fixed operation and maintenance cost ( UFtCTP ) to calculate capital cost and fixed operating expenditure. All capital costs are amortized over their respective equipment lifetimes ( LifespanCTP ) using a fixed interest rate r, which is set to 5% in this study. The variable operation and maintenance costs ( varopext,zCTP ) determined separately based on the type of equipment. The optimal green ammonia production CTP ) and unit fixed operation and maintenance cost ( UFt ( varopext,zCTP ) determined separately based on the type of equipment. The optimal green ammonia production potential for each site _z_ ( qt,zGA ) is endogenously determined within the model and is highly dependent on the potential for each site _z_ ( qt,zGA ) is endogenously determined within the model and is highly dependent on the spatial variation of solar and wind capacity factors. Leveraging high-resolution solar and wind capacity factor data ( https://globalsolaratlas.info/map and https://globalwindatlas.info/en/ ), we downscale provincial-level generation profiles [38] to individual production zones.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["high-resolution solar and wind capacity factor data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Policy scenario design**\nWe consider three policy-related dimensions: coal price (C), carbon price (T), and green ammonia subsidy (S).\nEach policy dimension is specified at three intensity levels. Based on historical coal prices ( https://www.cctd.com.cn/ ), we define low (LoC), medium (MeC), and high (HiC) coal price scenarios. For carbon pricing, we adopt the medium (MeT) and high (HiT) trajectories from previous studies representing gradual and strengthened policy pathways, respectively [39] . We further derive a low carbon price pathway (LoT) from MeT by assuming weaker policy ambition ( **Supplementary Table 7** ). Regarding green ammonia subsidies,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["historical coal prices"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "ARTICLE IN PRESS informed by emerging practices in China [40], we specify a high subsidy level of 2,000 CNY·ton [-1] (HiS), a medium level of 1,000 CNY·ton [-1 ] (MeS), and a zero-subsidy case (LoS). By combining these three dimensions, we obtain 27 policy scenarios (3×3×3), summarized in **Supplementary Table 8** . In addition, we define a baseline scenario (Base) to represent the business-as-usual policy inertia, assuming the current high coal price, a carbon price of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "ARTICLE IN PRESS generation. CPTt, STOt, and TCtCO2 represent the unit costs for CO2 capture, storage, and transport, as summarized in **Supplementary Table 9** [39] . Dcpccs refers to the minimum distance between coal power plant and generation. CPTt, STOt, and TCt summarized in **Supplementary Table 9** . Dcpccs refers to the minimum distance between coal power plant and the nearest large-scale carbon storage site [9] . PCt,cp is the price of coal.\n\nThe cost-optimal strategy is identified under different policy scenarios and periods. For each retrofit strategy, we define flag parameters ( Flagt,cpCoalccs, Flagt,cpCne, Flagt,cpCneccs, and Flagt,cpCoal ) that indicate whether a given coal we define flag parameters ( Flagt,cpCoalccs, Flagt,cpCne, Flagt,cpCneccs, and Flagt,cpCoal ) that indicate whether a given coal power plant adopts the corresponding retrofit options. The flag variable takes a value of 1 if the retrofit option yields the minimum cost, and zero otherwise. Based on these flag variables, we calculate the aggregated installed capacity associated with each retrofit option under different policy scenarios and periods.\n\nt,cpCoalccs, Flagt,cpCne t,cpCne, Flagt,cp t,cpCneccs, and Flagt,cp", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Coal power retrofit-retirement modeling**\nWe refine the coal-fired power plant retirement model [5] by incorporating a retrofit decision module to simulate retrofit options alongside decommissioning sequences (see **Supplementary Note 1** ). Coal power units information is also updated and sourced from the Global Energy Monitor database (July 2025 release) [41] . The updated model captures the choices between retrofitting, continued operation, and early retirement for 3,983 coal power plants/units across China, including 2,857 existing units and 1,126 candidate units ( **Supplementary**\n\n**Fig. 2** ).\n\nGiven the model’s planning horizon commencing in 2020, existing coal power plants are defined as units with a commissioning year of 2020 or earlier. Candidate coal power plants, in contrast, include two subsets: projects under construction but not yet completed, and units commissioned and operational between 2020 and 2025 (i.e., with a post-2020 commissioning year). The latter subset is required to be in operation during the model’s initial planning phase (2020–2030), reflecting their actual operational status in reality, while the commissioning of the former (under-construction, uncompleted projects) is determined endogenously within the model.", "output": {"entities": {"named_data": ["Global Energy Monitor database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "The updated retrofitting module includes two key assumptions regarding ammonia co-firing in existing coalfired power plants. First, the capital expenditure for boiler retrofitting is assumed to be negligible, in accordance with Ref. [21] [,] [42] . This is because ammonia co-firing requires only limited modifications to existing systems [17], mainly involving the addition of ammonia storage and injection modules and fine-tuning combustion controls, without the substantially altering core boiler components [42-44] . Second, boiler efficiency is assumed to remain constant. Although its response to ammonia blending depends on factors such as furnace configuration, ventilation, and coal type, experimental and simulation evidence indicate that efficiency variations are typically within 3% (see **Supplementary Table 10** ). After accounting for turbine and generator efficiencies, the overall impact on plant-level performance falls below 1% and is therefore negligible.\nAccordingly, the model applies a constant boiler efficiency assumption consistent with Ref. [23] .", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**Green ammonia transport modeling**\nWe model green ammonia transport ( qgat,cp,hub ) to meet the demand arising from retrofitted coal power plants. Specifically, coal power plants adopting either the green ammonia co-firing retrofit or the combined CCS and green ammonia co-firing retrofit generate a green ammonia demand ( gadt,cp ). Each coal power plant can be supplied by multiple green ammonia production hubs, and each production hub can also serve multiple coal power plants, subject to its production capacity constraint ( qt,zGA ). The model jointly considers green coal power plants, subject to its production capacity constraint ( qt,zGA ). The model jointly considers green ammonia production costs ( LCOAt,hub ) and transport costs ( TCtGA ), and aims to minimize the total system cost ammonia production costs ( LCOAt,hub ) and transport costs ( TCtGA ), and aims to minimize the total system cost while satisfying all plant demands and production hub capacity limits. The optimization problem is formulated as **Eqs. (12)–(14)** .\n\nMinimize ∑cp ∑hub∈z(LCOAt,hub + Dcp,z ∙TCtGA) ∙qgat,cp,hub **(12)** ∑hub qgat,cp,hub = gadt,cp **(13)**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "ARTICLE IN PRESS ∑cp qgat,cp,hub ≤qt,zGA **(14)** We retrieve transport routes via the Amap (Gaode) API, based on hub-plant matching results generated by **Eqs.**\n\n**(12)–(14)** . This approach assumes that green ammonia transport corridors, including potential pipeline\nalignments, can be developed along existing national highway rights-of-way, consistent with Fan et al.’s CO2pipeline planning framework [8] . Such co-location substantially reduces land acquisition and negotiation costs, thus rendering the modeled routes a reasonable proxy for future transport pathways.\n\nTo facilitate interpretation, **Supplementary Table 11** lists all provinces mentioned in the study (excluding Hong Kong, Macao, Taiwan, and Tibet due to data constraints), along with their abbreviations and corresponding regional classifications. Moreover, the primary sets, parameters, and variables involved in the model formulation are summarized in **Supplementary Tables 12 and 13** .\n\n**Data Availability**\n\nThe model scenario results generated and analyzed in this study are available on Figshare at", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "[https://doi.org/10.6084/m9.figshare.30928874]. Coal-fired power plant data are retrieved from the Global Energy Monitor (https://globalenergymonitor.org/). The carbon storage site data can be accessed from Xing et al. [9] (https://doi.org/10.1038/s41467-021-23282-x). The generation hour curve data for renewable energy are available from Wang et al. [38] (https://doi.org/10.1038/s41467-023-40670-7). High-resolution capacity factor data for photovoltaic and wind power are retrieved from the Global Solar Atlas (https://globalsolaratlas.info/map) and Global Wind Atlas (https://globalwindatlas.info/en/), respectively.\nLand use data are available via the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/datasets/). Topographic data (elevation and slope) are obtained from EarthEnv (http://www.earthenv.org/topography). Solar duration data are available at http://gis5g.com/data/qxsj?id=185. Source data for the figures in the main text are provided with this paper.\n\n**Code Availability**\n\nThe code for analyzing and creating figures in the main text is open-source and available on Figshare at\n\n[https://doi.org/10.6084/m9.figshare.30928874].", "output": {"entities": {"named_data": ["Copernicus Climate Data Store"], "descriptive_data": ["Coal-fired power plant data"], "vague_data": ["carbon storage site data", "generation hour curve data for renewable energy", "Solar duration data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "esmap"}
-{"input": "**FOR OFFICIAL USE ONLY**\n\nReport No: PAD00194 INTERNATIONAL BANK FOR RECONSTRUCTION AND DEVELOPMENT AND INTERNATIONAL DEVELOPMENT ASSOCIATION PROJECT APPRAISAL DOCUMENT", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "CURRENCY EQUIVALENTS (Exchange Rate Effective December 31, 2023) Currency Unit = [KENYA SHILLING ] (KES) KES 157.00 = US$1 EURO 0.90= US$1 SDR 0.75 US$1 FISCAL YEAR July 1 - June 30 Regional Vice President: Victoria Kwakwa Regional Director: Daniel Dulitzky Country Director: Keith E. Hansen Practice Manager: Francisca Ayodeji Akala Task Team Leader: Jane Chuma", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "ABBREVIATIONS AND ACRONYMS AM Accountability Mechanism ANC Antenatal Care AWPB Annual Work Plan and Budget AWP Annual Work Plan BREHS Building Resilient and Responsive Health Systems CBK Central Bank of Kenya CHERP COVID-19 Health Emergency Response Project CHU Community Health Unit CO2e Carbon dioxide equivalent COVID-19 Coronavirus Disease 2019 CPF Country Partnership Framework CRA County Revenue Allocation CRF County Revenue Fund DA Designated Account DP Development Partner DPHK Development Partners Health Kenya DRS Department of Refugees Services EDGE Excellence in Design for Building Efficiencies ERP Enterprise Resource Planning E&S Environmental and Social ESCP Environmental and Social Commitment Plan ESIA Environmental and Social Impact Assessment ESMF Environmental and Social Management Framework ESMP Environmental and Social Management Plan ESRS Environmental and Social Review Summary ESS Environmental and Social Standards ESSD Environmental and Social Due Diligence FM Financial Management GDP Gross Domestic Product GFF Global Financing Facility GISEDP Garissa Integrated Socioeconomic Development Plan GRM Grievance Redress Mechanism GRS Grievance Redress Service HFMC Health Facility Management Committee HIV/AIDS Human Immunodeficiency Virus / Acquired Immunodeficiency Syndrome HMIS Health Management Information System HPT Health Products and Technologies IBRD International Bank for Reconstruction and Development IDA International Development Association IEC International Electrotechnical Commission IFMIS Integrated Financial Management Information System IPC Integrated Food Security Phase Classification", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "IPF Investment Project Financing KEMSA Kenya Medical Supplies Authority KISEDP Kalobeyei Integrated Socio-Economic Development Plan 2 KHIS Kenya Health Information System KHSSP Kenya Health Sector Strategic Plan KISEDP Kalobeyei Integrated Socio-Economic Development Plan LMP Labor Management Plan M&E Monitoring and Evaluation MoH Ministry of Health MPDSR Maternal and Perinatal Death Surveillance and Response MWMP Medical and Waste Management Plan NCCF National Climate Change Framework Policy NCCRS National Climate Change Response Strategy NCD Non-Communicable Disease NDC Nationally Determined Contribution NHIF National Health Insurance Fund NT National Treasury OAG Office of the Auditor General O&M Operations and Maintenance OP Operational Policy ORS Oral Rehydration Salts PA Project Account PAD Project Appraisal Document PFM Public Financial Management PHC Primary Health Care PDO Project Development Objective PMT Project Management Team PNC Postnatal Care POM Project Operations Manual PP Procurement Plan PPH Postpartum Hemorrhage PPSD Project Procurement Strategy for Development PS Principal Secretary QoC Quality of Care RMNCAH Reproductive, Maternal, Newborn, Child, and Adolescent Health SEA / SH Sexual Exploitation and Abuse / Sexual Harassment SEP Stakeholder Engagement Plan SDPHS State Department for Public Health and Professional Standards SHA Social Health Authority SHIF Social Health Insurance Fund SPA Special Purpose Account STEP Systematic Tracking of Exchanges in Procurement STEPS STEPwise Approach to NCD Risk Factor Surveillance THS-UCP Transforming Health Systems for Universal Care Project", "output": {"entities": {"named_data": ["KHIS Kenya Health Information System"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "UHC Universal Health Coverage UN United Nations UNHCR United Nations High Commissioner for Refugees VGPF Vulnerable Groups Planning Framework WBG World Bank Group WHO World Health Organization WHR Window for Host Communities and Refugees", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) TABLE OF CONTENTS\n\n**DATASHEET .......................................................................................................................... 1**\n\n**I.** **STRATEGIC CONTEXT ..................................................................................................... 9**\n\nA. Country Context ............................................................................................................................... 9 B. Sectoral and Institutional Context* ................................................................................................. 9 C. Relevance to Higher Level Objectives ............................................................................................11\n\n**II.** **PROJECT DESCRIPTION ................................................................................................ 12**\n\nA. Project Development Objective (PDO) ..........................................................................................12 B. Project Components ......................................................................................................................13 C. Project Beneficiaries ......................................................................................................................15 D. Results Chain ..................................................................................................................................16 E. Rationale for Bank Involvement and Role of Partners ...................................................................16 F. Lessons Learned and Reflected in the Project Design ....................................................................17\n\n**III.** **IMPLEMENTATION ARRANGEMENTS ........................................................................... 17**\n\nA. Institutional and Implementation Arrangements ..........................................................................18 B. Results Monitoring and Evaluation Arrangements ........................................................................18 C. Sustainability ..................................................................................................................................18\n\n**IV.** **PROJECT APPRAISAL SUMMARY .................................................................................. 19**\n\nA. Technical, Economic and Financial Analysis (if applicable) ............................................................19 B. Fiduciary .........................................................................................................................................20 C. Legal Operational Policies ..............................................................................................................22 D. Environmental and Social ..............................................................................................................23\n\n**V.** **GRIEVANCE REDRESS SERVICES .................................................................................... 24**\n\n**VI.** **KEY RISKS .................................................................................................................... 24**\n\n**VII.** **RESULTS FRAMEWORK AND MONITORING .................................................................. 26**\n\n**ANNEX 1: Implementation Arrangements and Support Plan ................................................ 36**\n\n**ANNEX 2: Climate Change ................................................................................................... 40**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) @#&OPS~Doctype~OPS^dynamics@padbasicinformation#doctemplate\n**DATASHEET**\n\n**BASIC INFORMATION**\n\nProject Operation Name Beneficiary(ies) Kenya Building Resilient and Responsive Health Systems Environmental and Social Risk Operation ID Financing Instrument Classification Investment Project P179698 Moderate Financing (IPF) @#&OPS~Doctype~OPS^dynamics@padprocessing#doctemplate\n\n**Financing & Implementation Modalities**\n\n[ ] Multiphase Programmatic Approach (MPA) [ ] Contingent Emergency Response Component (CERC)\n\n[ ] Series of Projects (SOP) [ ] Fragile State(s)\n\n[ ] Performance-Based Conditions (PBCs) [ ] Small State(s)\n\n[ ] Financial Intermediaries (FI) [ ] Fragile within a non-fragile Country ✓\n\n[ ] Project-Based Guarantee [ ] Conflict\n\n[ ] Deferred Drawdown [ ] Responding to Natural or Man-made Disaster\n\n[ ] Alternative Procurement Arrangements (APA) [ ] Hands-on Expanded Implementation Support (HEIS) Expected Approval Date Expected Closing Date 13-Mar-2024 30-Jun-2029 Bank/IFC Collaboration No\n\n**Proposed Development Objective(s)**\n\nTo improve utilization and quality of primary healthcare services and strengthen institutional capacity for service delivery.\n\nPage 1 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698)\n\n**Components**\n\n**Component Name** **Cost (US$)**\n\nStrengthening institutional capacity for health service delivery towards 55,000,000.00 achieving UHC Improving utilization of quality health services at primary care level 150,000,000.00 Project management and evaluation 10,000,000.00 @#&OPS~Doctype~OPS^dynamics@padborrower#doctemplate\n\n**Organizations**\n\nBorrower: Republic of Kenya Implementing Agency: Ministry of Health @#&OPS~Doctype~OPS^dynamics@padfinancingsummary#doctemplate\n\n**PROJECT FINANCING DATA (US$, Millions)**\n\n**Maximizing Finance for Development**\n\n**Is this an MFD-Enabling Project (MFD-EP)?** No\n\n**Is this project Private Capital Enabling (PCE)?** No\n\n**SUMMARY**\n\n**Total Operation Cost** **215.00**\n\n**Total Financing** **215.00**\n\n**of which IBRD/IDA** **200.00**\n\n**Financing Gap** **0.00**\n\n**DETAILS**\n\n**World Bank Group Financing**\n\nInternational Development Association (IDA) 200.00 IDA Credit 160.00 IDA Grant 40.00\n\n**Non-World Bank Group Financing**\n\nTrust Funds 15.00 Page 2 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) Global Financing Facility 15.00\n\n**IDA Resources (US$, Millions)**\n\nWindow for Host Communities and Refugees (WHR) National Performance-Based Allocations (PBA)\n\n**Guarantee**\n**Credit Amount** **Grant Amount** **SML Amount** **Total Amount**\n\n**Amount**\n\n0.00 40.00 0.00 0.00 40.00 160.00 0.00 0.00 0.00 160.00\n\n**Total** **160.00** **40.00** **0.00** **0.00** **200.00**\n\n@#&OPS~Doctype~OPS^dynamics@paddisbursementprojection#doctemplate\n\n**Expected Disbursements (US$, Millions)**\n\n**WB Fiscal**\n**Year** 2024 2025 2026 2027 2028 2029\n\n**Annual**\n5.00 40.00 55.00 55.00 55.00 5.00\n\n**Cumulative**\n5.00 45.00 100.00 155.00 210.00 215.00 @#&OPS~Doctype~OPS^dynamics@padclimatechange#doctemplate\n\n**PRACTICE AREA(S)**\n\n**Practice Area (Lead)** **Contributing Practice Areas**\n\nHealth, Nutrition & Population\n\n**CLIMATE**\n\n**Climate Change and Disaster Screening**\n\nYes, it has been screened and the results are discussed in the Operation Document @#&OPS~Doctype~OPS^dynamics@padrisk#doctemplate Page 3 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698)\n\n**SYSTEMATIC OPERATIONS RISK- RATING TOOL (SORT)**\n\n**Risk Category** **Rating**\n\n1. Political and Governance   Moderate\n\n2. Macroeconomic  Substantial 3. Sector Strategies and Policies  Moderate 4. Technical Design of Project or Program  Moderate 5. Institutional Capacity for Implementation and Sustainability  Moderate 6. Fiduciary  Substantial 7. Environment and Social  Moderate 8. Stakeholders  Moderate 9. Other  Substantial 10. Overall  Moderate @#&OPS~Doctype~OPS^dynamics@padcompliance#doctemplate\n\n**POLICY COMPLIANCE**\n\n**Policy**\nDoes the project depart from the CPF in content or in other significant respects?\n\n[ ] Yes [ ] No ✓ Does the project require any waivers of Bank policies?\n\n[ ] Yes [ ] No ✓\n\n**ENVIRONMENTAL AND SOCIAL**\n\n**Environmental and Social Standards Relevance Given its Context at the Time of Appraisal**\n\n**E & S Standards** **Relevance**\n\nESS 1: Assessment and Management of Environmental and Social Risks and Relevant Impacts ESS 10: Stakeholder Engagement and Information Disclosure Relevant ESS 2: Labor and Working Conditions Relevant ESS 3: Resource Efficiency and Pollution Prevention and Management Relevant Page 4 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) ESS 4: Community Health and Safety Relevant ESS 5: Land Acquisition, Restrictions on Land Use and Involuntary Resettlement Not Currently Relevant ESS 6: Biodiversity Conservation and Sustainable Management of Living Natural Relevant Resources ESS 7: Indigenous Peoples/Sub-Saharan African Historically Underserved Relevant Traditional Local Communities ESS 8: Cultural Heritage Not Currently Relevant ESS 9: Financial Intermediaries Not Currently Relevant NOTE: For further information regarding the World Bank’s due diligence assessment of the Project’s potential environmental and social risks and impacts, please refer to the Project’s Appraisal Environmental and Social Review Summary (ESRS).\n\n@#&OPS~Doctype~OPS^dynamics@padlegalcovenants#doctemplate\n\n**LEGAL**\n\n**Legal Covenants**\n\n**Sections and Description**\n\nSection I.A.2 of Schedule 2. Not later than three (3) months after the Effective Date, the Recipient shall establish and thereafter maintain throughout the implementation of the Project, a Project steering committee - to be chaired by the Principal Secretary for SDPHS and comprising, amongst others, of Principal Secretary for The National Treasury, Principal Secretary for Immigration and Citizen Services, CEO of Council of County Governors, and the Solicitor-General with mandate, powers and resources satisfactory to the Association (“Project Steering Committee’ or “PSC”), as detailed in the Project Operations Manual. The PSC shall be responsible for providing general advisory in the implementation of the Project.\nSection I.E.2 of Schedule 2. The Recipient shall prepare and furnish to the Association not later than September 30 of each Fiscal Year during the implementation of the Project (beginning in calendar year 2024), a consolidated work plan and budget containing inter alia: (i) all activities proposed to be implemented under the Project during the following Fiscal Year; (ii) a proposed financing plan for expenditures required for such activities, setting forth the proposed amounts and sources of financing therefor and disbursement schedule; and (iii) the training plan for such period.\nSection I.E.5 of Schedule 2. Without limitation on the provisions of this Section, the Recipient shall prepare and furnish to the Association the first proposed annual work plan required under the Project not later than one month after the Effective Date.\nSection II.2 of Schedule 2. Not later than thirty (30) months after the Effective Date, the Recipient shall, in conjunction with the Association, carry out a mid-term review of the Project (the “Mid-term Review”), covering the progress achieved in the implementation of the Project. To this end, the Recipient shall prepare - under terms of reference satisfactory to the Association - and furnish to the Association not less than three (3) months prior to the beginning of the Mid-term Review, a report integrating the results of the Project’s monitoring and evaluation activities, on the progress achieved in the carrying out of the Project during the period preceding the date of such report, and setting out the measures recommended to ensure the efficient carrying out of the Project and the achievement of the objective of the Project during the period following such date. Following the Mid-term Review, the Recipient shall act promptly and diligently in order to take, or cause to be taken, measures recommended to ensure the efficient completion of the Page 5 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) Project and the achievement of the objective as well as any corrective action deemed necessary by the Association to remedy any shortcoming noted in the carrying out of the Project in furtherance of the objective of the Project.\n\n@#&OPS~Doctype~OPS^dynamics@padconditions#doctemplate\n**Conditions**\n\n**Type** **Citation** **Description** **Financing Source**\n\nThe Association is satisfied Effectiveness Article V. 5.01(a) Effectiveness Article V. 5.01(b) Effectiveness Article V. 5.01(b) Effectiveness Article V. 5.01(c) Effectiveness Article V. 5.01(d) that the Recipient has an adequate refugee protection framework.\n\nestablished a dedicated Project Management Team (“PMT”), with adequate resources and facilitation, The Recipient and the Project Implementing Entity have executed a Subsidiary Agreement in accordance with Section I.B of Schedule 2 to this Agreement.\n\nThe Co-financing Agreement has been executed and delivered and all conditions precedent to its effectiveness or to the right of the Recipient to make withdrawals under it (other than the effectiveness of this Agreement) have been fulfilled.\n\nThe Recipient has prepared and adopted the Project Operations Manual (“POM”) in form and substance satisfactory to the Association, in accordance with the provisions of Section I.C.1(b)ii of Schedule 2 to this Agreement.\n\nThe Recipient has IBRD/IDA IBRD/IDA IBRD/IDA IBRD/IDA IBRD/IDA Page 6 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) key staff holding such qualifications and under terms of reference acceptable to the Association, such staff to include a project manager, Project components coordinators, assistant coordinators, a monitoring and evaluation specialist, at least one dedicated specialist for each of social risks and environmental risks management and any other technical and fiduciary specialists as may have been agreed with the Association and as further detailed in the POM.\n\nThe Recipient has Effectiveness Article V. 5.01(e) Effectiveness Article V. 5.01(f) Effectiveness Article V. 5.01(g) prepared, consulted upon, adopted and publicly disclosed the Environmental Social and Management Framework, the Vulnerable Groups Planning Framework, the Medical Waste Management Plan, the Stakeholder Engagement Plan and the Labor Management Procedures.\n\nThe Recipient has assessed environmental and social risks management (E&S) focal person to maintain coordination and support E&S implementation of the Project.\n\nthe security risks of the Project and included measures to mitigate such security risks in the POM.\n\nKEMSA has assigned an IBRD/IDA IBRD/IDA IBRD/IDA Page 7 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) Effectiveness Article IV. 4.01 This Agreement shall not become effective until evidence satisfactory to the Bank has been furnished to the Bank that the Financing Agreement has been executed and delivered and all conditions precedent to its effectiveness (other than effectiveness of this Agreement) have been fulfilled.\n\nTrust Funds Page 8 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698)\n\n**I.** **STRATEGIC CONTEXT**\n\n**A. Country Context**\n\n1. **The Kenya economy has continued to recover from the impacts of the COVID-19 pandemic, but the growth has**\n**slowed down in the last year.** Real gross domestic product (GDP) increased by 4.8 percent in 2022, a decrease from 7.5\npercent annual growth in 2021, largely due to the weather shocks experienced in the last two years, domestic macroeconomic policies, and challenging global conditions. [1] The economy is expected to grow by 5.0 percent in 2023 and 5.2 percent on average in 2024-2025, higher than the pre-pandemic average of 5.0 percent in 2010-2019. While growth prospects remain optimistic, the economy remains vulnerable to shocks such as drought, rising inflation and food insecurity.\n\n2. **Kenya is highly vulnerable to climate change, particularly extreme floods, and droughts, which has affected**\n**food security for millions of people particularly in the arid and semi-arid north and north-east of the country** . [2] Floods\nare the most significant and frequent climate-related hazard in Kenya making up 40.0 percent of all-natural hazards from 1980-2020. [3 ] Flooding is particularly impactful to communities around Kenya’s lakes and waterways. For example, a 2021 United Nations (UN) report [4] projected climate change induced flooding may cause the expansion of Lake Turkana, displacing communities while impinging on agriculture and livelihoods. Simultaneously, drought is likely to adversely affect the country’s agriculture sector, which is predominantly dependent on seasonal rains and is expected to negatively impact the economy and food security, reversing the gains in health and nutrition outcomes. Since 2015, Kenya has experienced a steady increase in the prevalence of severe food insecurity from 15.0 percent to 26.1 percent. [5] Between July and September 2022, an estimated 3.5 million people (24.0 percent of the Kenya population in arid-and semi-arid counties) experienced acute food insecurity. [6] 3. **For more than three decades, Kenya has been home to a significant population of refugees and asylum seekers.** There are 691,868 refugees and asylum seekers in the country, including in cities such as Nairobi, Mombasa, Nakuru, and Eldoret, but the majority live in two designated refugee camps [7] - with 320,572 in Dadaab Camp in Garissa County and 271,995 in Kakuma Camp and Kalobeyei Settlement in Turkana County. [8] The two camps are under the management of the Government of Kenya’s Department of Refugee Services (DRS), with support from the United Nations High Commissioner for Refugees (UNHCR) and humanitarian partners who provide operational support and humanitarian assistance, including primary and secondary health and nutrition services. The Government has demonstrated its commitment to the Global Compact on Refugees by enacting the Refugees Act of 2021, which grants refugees more rights and protections, and by supporting the Shirika Plan, [ 9] which seeks to create integrated settlements where refugees can live, access social services, and work alongside Kenyans.\n\n**B. Sectoral and Institutional Context***\n\n1 Kenya Economic Update, June 2023 2 World Bank, Climate Change Knowledge Portal 3 World Bank Climate Change Knowledge Portal - Kenya.https://climateknowledgeportal.worldbank.org/country/kenya/vulnerability 4 United Nations Environment Program: “Climate change could spark floods in world’s largest desert lake: new study”, 2021.\n\n5 World Bank data. Prevalence of food insecurity in the population – Kenya **Error! Hyperlink reference not valid.** 6 Kenya: IPC Acute Food Insecurity and Acute Malnutrition Analysis (July - December 2022) 7 For a detailed map see: https://data2.unhcr.org/en/country/ken 8 UNHCR Statistics package. Kenya registered refugees and asylum seekers (31 July 2023) 9 The Shirika Plan is a Government of Kenya socioeconomic development plan outlining the transition from refugee encampment to integrated settlements.\n\nPage 9 of 43", "output": {"entities": {"named_data": ["UNHCR Statistics package", "World Bank Climate Change Knowledge Portal"], "descriptive_data": [], "vague_data": ["World Bank data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) 4. **The health status of Kenyans has improved in the last five years, but significant geographic and socioeconomic**\n**inequities remain, affecting women and children.** The life expectancy of Kenyans has improved from 63 years in 2013 to\n67 years in 2020 but fell to 61 years in 2021 owing to the COVID-19 pandemic. [10] Under-five and infant mortalities dropped from 52 and 39 deaths per 1,000 live births in 2014 to 41 and 32 in 2022, respectively, partly due to improved coverage of primary healthcare services and significant progress in malaria and HIV/AIDS response. [11] Despite the number of women delivering under the care of a skilled health worker increasing significantly, challenges remain around neonatal mortality, which is high at 21 deaths per 1,000 live births in 2022: a marginal decline from 22 per 1,000 live births in 2014. Similarly maternal mortality remains high at 342 maternal deaths per 100,000 live births, [12] suggesting challenges related to quality of care (QoC). Teenage pregnancy declined only slightly from 18.0 percent in 2014 to 15.0 percent in 2022. While the country has recorded improvements in childhood nutrition, 18.0 percent of children aged below 5 years are stunted, a decline from 26.0 percent in 2014. Despite improvements in coverage and utilization of health care services, especially for maternal and child health, geographic and socioeconomic inequities remain. Although 89.3 percent of pregnant women deliver under the care of a skilled health worker, five counties reported skilled delivery below 66.0 percent (Turkana-53.0 percent; Mandera-55.0 percent; Wajir-57.0 percent; Samburu-57.0 percent; Tana River-59.0 percent). Wider gaps are reported for utilization of antenatal care (ANC), with only 32.1 percent of women in Garissa County attending at least 4 ANC visits, compared to 82.2 percent in Nyeri County. Only 53.9 percent of women from the lowest wealth quintile attended at least 4 ANC visits compared to 82.0 percent of women from the highest wealth quintile.\n\n5. **In Garissa and Turkana counties, refugees and their host communities face barriers to healthcare services,**\n**mostly affecting women and children.** Garissa and Turkana counties record the lowest percentages of women receiving\nat least 4 ANC visits (31.2 percent) in 2022 and deliveries by a skilled provider (52.6 percent) respectively. [13 ] In the refugee camps, most health services are provided by UNHCR and non-governmental organizations in collaboration with the Government. The overcrowded conditions, clean water supply shortages and hygiene challenges present heightened risks of communicable disease outbreaks such as cholera. Other recent outbreaks in the refugee camps include polio, dengue fever, and chikungunya. Refugees and host communities have also been affected by prolonged drought in the region and the food security of refugees has been further affected by cuts in the general food assistance. From 2020 to July 2022, there has been a steady and significant increase in malnutrition cases across all refugee camps, with children under 5 years being particularly affected by malnutrition and micronutrient deficiencies. [14] 6. **The devolution of health service delivery in 2013 has presented mixed results** . Decentralization of responsibility for public sector health service delivery to the 47 county Governments has been accompanied by a 34.0 percent increase in the number of facilities, a 46.0 percent improvement in public health worker density between 2014 and 2020, and many counties have equipped their health facilities to respond to the evolving health needs. County Governments are also exploring approaches to strengthen primary care service delivery through governance and financial management reforms, such as the Facility Improvement Fund. However, county Governments have faced significant challenges in management of human resources for health, ensuring availability of Health Products and Technologies (HPTs), improving quality of care,\n\n[10 World Bank Estimates: https://data.worldbank.org/indicator/SP.DYN.LE00.IN?locations=KE](https://data.worldbank.org/indicator/SP.DYN.LE00.IN?locations=KE) 11 Kenya Demographic Health Survey, 2022. Key Indicators Report 12 Ministry of Health Kenya (2020) Kenya Progress Report on Health and Health-Related SDGs.\n13 Kenya Demographic Health Survey, 2022 14 UNHCR & WFP, Joint Assessment Mission Kenya-Refugee Operations (2022) Page 10 of 43", "output": {"entities": {"named_data": ["Kenya Demographic Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) and facilitating effective governance of health facilities to deliver comprehensive networked primary care. [15,16,17,18] Stockouts of essential medicines persist and access to essential diagnostics remains low with only 17.0 percent of facilities assessed as having a full set of basic diagnostic items. [19] 7. **The Government initiated various reforms aimed at strengthening capacity of the National Health Insurance**\n**Fund (NHIF) and the Kenya Medical Supplies Authority (KEMSA).** Both institutions play a critical role in Kenya’s universal\nhealth coverage (UHC) agenda. NHIF reforms include changes to governance arrangements, re-engineering business processes, modernization, and realignment of the information and communications technology, modification of provider payment mechanisms and payment systems among others. To fast-track progress towards UHC, the Government introduced four laws in October 2023 (Social Health Insurance Act, Primary Health Care Act, Facility Improvement Financing Act, and the Digital Health Act). Under the new health financing arrangement, all Kenyan citizens and residents will be required to be members of the Social Health Insurance Fund (SHIF). Primary health care will be purchased through the primary care fund and financed through tax allocation by the national Government. Additionally, management of chronic illnesses and health emergencies will be through the emergency, chronic and critical illness fund, which will be drawn upon once a member has exhausted their SHIF benefits. The Government is in the process of developing the regulations and implementation arrangements to operationalize these Acts. Key issues in KEMSA relate to: (a) inadequate funding to stock commodities leading to long turnaround times; (b) suboptimal use of information systems in procurement processes; (c) weak business processes; (d) weak governance; (e) weak human resource realignment; (f) outdated Enterprise Resource Planning (ERP) system; and (g) weak credit control systems among others. Significant progress has been made in implementing reforms to address these challenges, however major gaps remain. The World Bank continues to provide technical support, but operational support is required for KEMSA to function efficiently.\n\n**C. Relevance to Higher Level Objectives**\n\n8. **The project is fully aligned with the World Bank’s mission and the Regional priorities** by contributing to increasing the number of people receiving essential health services in Kenya. **The project is strongly aligned with the**\n**World Bank Group’s (WBG) FY23-28 Country Partnership Framework (CPF) for Kenya, discussed by the Board of**\n**Executive Directors on November 22, 2022 (Report No. 172255) and Government health sector priorities.** Objective 2 of\nthe CPF aims to “improve public expenditure transparency and efficiency”, a focus of the project. The CPF also aims for “greater equity in service delivery outcomes”, which will be achieved partly through reducing disparities in health outcomes (objective 4). The project is also aligned to objective 6, “increase household resilience to, and national preparedness for shocks”, highlighted in the CPF through the need to increase access to social health insurance, improving access to quality health care services and expanding UHC reforms. In addition, the project will directly contribute to the priorities identified in the Kenya Health Policy 2014-2030, the Kenya Health Sector Strategic Plan (KHSSP, 2018-2023), the Kenya UHC Policy (2020-2030), and the Kenya Health Financing Strategy (2020-2030).\n\n15 Waithaka, D., Kagwanja, N., Nzinga, J. et al. Prolonged health worker strikes in Kenya- perspectives and experiences of frontline health managers and local communities in Kilifi County. Int J Equity Health 19, 23 (2020).\n16 Nyawira, L., Tsofa, B., Musiega, A. et al. Management of human resources for health: implications for health systems efficiency in Kenya. BMC Health Serv Res 22, 1046 (2022).\n17 McCollum R, Limato R, Otiso L, et al. Health system governance following devolution: comparing experiences of decentralisation in Kenya and IndonesiaBMJ Global Health 2018;3:e000939 18 Kairu, A., Orangi, S., Mbuthia, B. et al. Examining health facility financing in Kenya in the context of devolution. BMC Health Serv Res 21, 1086 (2021).\n19 Ministry of Health Kenya Harmonized Health Facility Assessment 2018-19. The diagnostic tests were: HIV, malaria, and syphilis rapid test; urine test for pregnancy; blood glucose; urine dipstick for glucose and protein; and hemoglobin levels Page 11 of 43", "output": {"entities": {"named_data": ["Kenya Harmonized Health Facility Assessment"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) 9. **The project is consistent with Kenya’s Nationally Determined Contribution (NDC, updated 2020)** **[20]** **[21]** **, the**\n**National Climate Change Framework Policy (NCCF, 2016) and National Climate Change Response Strategy (NCCRS,**\n**2010).** The project is anticipated to have a considerable contribution to improving climate adaptation and resilience in the\ncountry and is not anticipated to contribute to greenhouse gases emissions. Kenya’s climate policies emphasize key actions which the project supports including: integration of climate change adaptation into county level development planning; improving resilience of vulnerable populations to climate-related shocks; addressing vector-borne disease transmission (malaria, dengue), water-borne disease outbreaks, food insecurity and malnutrition; reducing direct injuries and mortality; and minimizing damage to infrastructure. Further, the Kenya Health Policy (2014) and KHSSP [22] acknowledge the role of climate change in increasing the burden of disease in the country. [23] 10. **The World Bank, following consultation with UNHCR, confirms that the protection framework for refugees** **[24]**\n**continues to be adequate in Kenya for accessing financing from the IDA20 Window for Host Communities and Refugees**\n**(WHR).** Kenya’s treatment of refugees is governed by the Refugees Act of 2021 and is largely in line with the international\nand regional refugee protection standards and Kenya’s commitments under the Global Compact on Refugees. The Act recognizes the rights of refugees to participate in economic and social development and supports refugee inclusion in national and county development planning. In 2023, the second phase of the Kalobeyei Integrated Socio-Economic Development Plan 2 (KISEDP) and the Garissa Integrated Socioeconomic Development Plan (GISEDP) were launched, which highlight the key role being played at the County level to support local solutions that benefit refugees and host communities, including in the health sector. In its IDA20 WHR Strategy Note, the Government identified as a priority supporting social services needs in health and addressing the health priorities outlined in the KISEDP and GISEDP. The Refugee Regulations, to operationalize the Refugees Act of 2021 are in the final stages of development and are due for publication. The regulations will provide clarity in the Act’s operationalization, which will strengthen the protection framework. In September 2023, Kenya gazetted Legal Notice No. 143 of 2023, recognizing refugee identification documents for the purposes of acquiring services provided by the Government, including health. The Shirika Plan, which is currently under development, is a multi-sectoral plan that delineates development solutions that benefit both refugees and host communities through an integrated service delivery approach, building on the KISEDP and GISEDP. The Ministry of Health (MoH) is involved in the Plan’s drafting and this operation will ensure close alignment of refugee health policy priorities and county level implementation.\n\n**II.** **PROJECT DESCRIPTION**\n\n**A. Project Development Objective (PDO)**\n\n**PDO Statement**\n11. To improve utilization and quality of primary healthcare services and strengthen institutional capacity for service delivery.\n\n20 Government of Kenya. (April, 2010) National Climate Change Response Strategy.\n21 Government of Kenya (2016). National Climate Change Framework Policy. Accessed.\n22 Government of Kenya (2018) Kenya Health Sector Strategic Plan.\n23 Ministry of Health (July, 2014) Kenya Heath Policy 2014-2030.\n24 Based on UNHCR’s Kenya Refugee Protection Assessment Update No. 5 from January to June 2023.\n\nPage 12 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698)\n\n**PDO Level Indicators**\n12. The PDO level indicators are: (a) Percentage of women receiving postnatal care within 48 hours, including in refugee hosting counties; (b) Percentage of children immunized with three doses of Pentavalent vaccine; (c) Percentage of pregnant women attending 4 or more ANC visits, including in refugee hosting counties; (d) Proportion of Children Under 5 with diarrhea treated with Zinc/ORS Co-Pack; (e) Order fill rate for priority health products and technologies (HPTs); (f) order turnaround time.\n\n**B. Project Components**\n13. The project will comprise three Components focusing on both the national and county level, with clear linkages between the two levels of Government.\n\n14. **COMPONENT 1: STRENGTHENING INSTITUTIONAL CAPACITY FOR HEALTH SERVICE DELIVERY TOWARDS**\n**ACHIEVING UHC (US$55 million)** : Component 1 will focus on (a) strengthening the institutional capacity of KEMSA and\navailability of HPTs; (b) supporting health financing reforms; and (c) improving availability and use of quality data for decision making.\n\n15. **Sub-component 1.1: Institutional and operational reforms to enhance efficiency and transparency of KEMSA**\n**(US$30 million):** This sub-component will support: (a) building up buffer stocks in KEMSA to ensure timely availability of\nHPTs at primary care level, thus increasing the order-fill rate, reducing the order turn-around time, and promoting efficiency. Funds will be earmarked for the procurement and distribution of HPTs for primary care services (levels 1-3) in all 47 counties during the life of the project. Counties will draw down HPTs from an agreed list, based on their resource allocation as described in sub-component 2.1. To ensure transparency and accountability in the procurement process, an HPT governance committee incorporating key stakeholders will be established. Climate sensitive planning for HPTs distribution will be included; (b) automation of the procurement processes, through rolling out a new ERP system with extended supply chain modules to ensure end-to-end visibility; and (c) strengthening governance and accountability, including development and implementation of an accountability dashboard that provides visibility of the procurement process and distribution of HPTs to various stakeholders. The project will use seasonal data to inform pharmaceutical planning for climate sensitive conditions (e.g., malaria, cholera, anti-diarrheal medicines, etc.). The Directorate of HPTs, MoH will work closely with KEMSA to ensure maximum efficiency in implementation of this sub-component.\n\n16. **Sub-component 1.2: Health financing and quality of care reforms (US$15 million):** This sub-component will support the recently introduced Government UHC reforms, including but not limited to the transition from the NHIF to the Social Health Authority (SHA). Areas of support include development of regulations and implementation roadmaps, design and rationalization of a benefit package, developing a framework for review of the benefit package including strengthening capacity for the health technology assessment, design of business processes and claims processing, stakeholder engagement among others. Additionally, the project will support the MoH to establish/strengthen regulatory bodies and operationalize quality of care reforms for improved strategic purchasing.\n\n17. **Sub-component 1.3: Improve availability and use of quality data for decision making (US$10 million):** This subcomponent will support the Government to improve generation and use of strategic information for decision making, specifically through conducting relevant cross-sectional surveys including, but not limited to, the WHO STEPwise approach to non-communicable diseases (NCD) risk factor surveillance (STEPS) survey, and the Household Health Expenditure and Utilization Survey. Climate sensitive planning for surveys will be used and questions on climate and health impacts will be included in the survey to generate relevant data to inform decision making. Support will also be provided towards dissemination of findings to the lowest level.\n\nPage 13 of 43", "output": {"entities": {"named_data": ["Household Health Expenditure and Utilization Survey", "risk factor surveillance (STEPS) survey"], "descriptive_data": [], "vague_data": ["seasonal data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) 18. **COMPONENT 2: IMPROVING UTILIZATION OF QUALITY HEALTH SERVICES AT PRIMARY CARE LEVEL (US$150**\n**million consisting of a US$95 million IDA credit, a US$40 million IDA grant, and a US$15 million Global Financing Facility**\n**(GFF) grant):** This Component will support delivery of quality services at the primary care level (levels 1-3: community,\ndispensary, health center) in all 47 counties, ensuring availability of selected HPTs. Additional support will include implementation of: (a) key primary care level interventions in all 47 counties; (b) a package of evidence-based, high impact interventions for selected counties lagging on key Reproductive, Maternal, Newborn, Child, and Adolescent health (RMNCAH) indicators; and (c) key interventions aimed at improving access to and quality of health services in refugee camps and host communities of Garissa and Turkana counties. Details on Component 2 operationalization will be described in the Project Operations Manual (POM).\n\n19. **Sub-component 2.1: Improving availability of essential HPTs and delivery of key quality services at the primary**\n**care level (US$90 million):** This Sub-component will support (a) procurement and distribution of selected HPTs to primary\ncare facilities; and (b) implementation of key quality of care related interventions delivered at the primary care level.\n\n20. **Counties will receive an annual allocation that is based on the Government’s Equitable Share ratio.** The allocation will consist of two parts: (a) drawing rights for selected HPTs; and (b) funds to support implementation of key interventions in their annual work plans (AWPs). All counties will be required to meet the agreed upon eligibility criteria.\n\n- **Availability of essential HPTs at the primary care level.** Counties will be issued with drawing rights earmarked\nfor levels 1-3. A reliable and steady supply of HPTs will be established through providing KEMSA with resources to purchase essential HPTs for the primary care level, as well as other supply chain reforms described under Sub-component 1.1. Support will focus on selected HPTs which have been identified jointly with county Governments. HPTs to support NCD screening and treatment will also be included to address the changing burden of disease in Kenya.\n\n- **Implementation of selected interventions in county AWPs.** Funds will be disbursed to each county to\nimplement key interventions, from a positive list of activities, agreed upon with county Governments and prioritized into four thematic areas: (a) strengthening community health services; (b) supporting levels 2 and 3 facility operations and maintenance and functionality of Health Facility Management Committees (HFMC); (c) supporting drivers of quality improvement described in the positive list; and (d) strengthening intercounty coordination and learning. Each year, counties will select and implement interventions from the positive list as part of their AWPs. This approach gives flexibility to counties to choose relevant activities to implement based on their specific needs.\n\n21. **Sub-component 2.2:** **Improving delivery of quality health services in selected counties (US$20 million, consisting**\n**of a US$5 million IDA credit and a US$15 million GFF grant):** This Sub-component will provide additional targeted support\nto 10 under-performing counties lagging on key RMNCAH indicators. All level 2 and 3 health facilities, and selected level 4 health facilities, will receive support to implement high impact and evidenced based RMNCAH interventions within the following 5 priority areas: (a) emergency obstetric care with emphasis on management of post-partum hemorrhage (b) essential newborn care; (c) nutrition services to address stunting among children and anemia in pregnant women; (d) enhance coverage and quality of ANC and PNC, including post-partum family planning; and (e) institutionalization of measures to improve quality of clinical practice by frontline providers. Specifically, the Sub-component will support: (a) dissemination of standards, guidelines, standard operating procedures, and job aides; (b) strengthening capacity of frontline healthcare workers; (c) procurement of relevant essential medicines and commodities; (d) support for relevant Page 14 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) quality of care committees (e.g., the Maternal and Perinatal Death Surveillance and Response (MPDSR) committees); (e) support for quality monitoring activities; and (f) demand generation activities.\n\n22. **Sub-component 2.3: Improving access to and utilization of quality health services in refugee camps and host**\n**communities in Garissa and Turkana Counties (US$40 million):** The Sub-component aims to address the barriers to\naccessing and utilizing quality services in refugee camps and their host communities in Garissa and Turkana counties. More specifically, the Sub-component will support: (a) strengthening community health services; (b) improving availability of essential HPTs for services at levels 1-4 (level 4 is the sub-county hospital); (c) improving the availability of energy efficient diagnostic and medical equipment; (d) training of community enrolled health nurses through the Kenya Medical Training College; (e) recruitment of health workers; (f) strengthening referral systems; (g) climate resilient and energy efficient renovation of health facilities; and (h) support towards management of the transition process of health facilities and health workers to County Governments. Analytical work and capacity support will be provided to the refugee hosting counties and DRS to support county planning and coordination with humanitarian stakeholders in the management of the transition of health services. Both counties will develop AWPs, under the County Integrated Development Plans focusing on the identified areas of support. This will build on counties’ refugee and host communities plans that include health priorities, the KISEDP and GISEDP.\n\n23. **COMPONENT 3: PROJECT MANAGEMENT AND EVALUATION (US$10 million):** This Component will support project management activities at national and county level. Key areas of support will include (a) providing operational costs and logistical services for day-to-day management of the project; (b) project monitoring and evaluation activities; (c) environmental and social risk management; (d) stakeholder engagement; (e) fiduciary management; (f) contracting of staff on a need basis; (g) technical assistance and county peer-to-peer learning among others; and (h) development of a climate emergency plan at the national level, which will inform county level actions to reduce the risk of climate change on health service delivery activities. Counties will be encouraged to include relevant climate mitigation actions in their AWPs.\n\n**C. Project Beneficiaries**\n\n24. **The project will benefit all Kenyans;** however, the main beneficiaries are women and children from the poorest population who tend to utilize primary care services more, including refugees and host communities in Garissa and Turkana. The project will provide support to all 47 counties to address key priority areas that impact on primary health care (PHC) and focus on addressing inequities in counties that have poor RMNCAH service coverage and outcomes.\nStrengthened health services will support the needs of about 1.8 million host community members in Garissa and Turkana (100 percent) and more than 590,000 refugees in these counties (100 percent).\n\nPage 15 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698)\n\n**D. Results Chain**\n\n**CHALLENGES** **ACTIVITIES** **OUTPUTS** **SHORT-TERM**\n**OUTCOMES**\n\n**CHALLENGES** **ACTIVITIES** **OUTPUTS** **SHORT-TERM** **PROJECT** **LONG-TERM**\n**OUTCOMES** **OUTCOMES** **OUTCOMES**\n\n**Component 1**\nSuboptimal availability Finance key health surveys Improved availability of Improved and use of data for quality data evidence -based decision making decision making\n\n**PROJECT**\n**OUTCOMES**\n\nFinance key health surveys Improved availability of quality data Improved evidence -based decision making A2 Improved (i) transparency, efficiency of KEMSA and (ii) availability of essential HPTs at the lowest levels A1 Improved delivery of quality PHC services, including in refugees hosting areas Improved utilization and quality of primary healthcare services and strengthened institutional capacity for service delivery, including for refugees and host communities Reduced mortality and morbidity, and greater human capital attainment Suboptimal funding, inefficiency, and lack of transparency at KEMSA Upgrade of ERP system at KEMSA Build up buffer HPTs stock in KEMSA\n\n**Component 2**\nFrequent stockouts of Procure and distribute HPTs, essential commodities including HPTs for NCDs at PHC level Low quality of maternal and child health services at PHC level Inequitable geographic health outcomes particularly for RMNCAH Shortages of skilled human resources for health (HRH) Parallel health services for refugees with limited county engagement Implement priority interventions at PHC facilities in all 47 counties Support processes to strengthen clinical quality of care related to RMNCAH services in selected counties Implement priority interventions in Garissa and Turkana, including refugee hosting areas (including support to HRH, renovations of health facilities) Improved functionality of the ERP system modules Increased number of orders received within required lead time Increased number of HPTs procured and distributed to health facilities Improved operations of health facilities, systems, and community health services Improved processes for quality clinical practices Improved availability and functionality of health facilities, community health units, including for refugees and host communities Assumptions: A1- Beneficiaries have access to system performance information; A2- Policy makers utilize information to improve project implementation.\n\n25. **The above diagram presents the results chain.** The project aims to \"improve utilization and quality of primary healthcare services and strengthen institutional capacity for service delivery”. This will be achieved by addressing key health challenges including inefficiencies at KEMSA leading to frequent stockouts of essential commodities at PHC level, inequitable geographic health outcomes, shortages of HRH, parallel health services for refugees with limited county engagement, and suboptimal availability and use of data. By investing in high impact activities the project will contribute towards improving delivery of quality PHC services and evidence-based decision making. The project is anticipated to contribute to increasing utilization of PHC services, quality of PHC services, and strengthening institutional capacity.\nSpecific PDO level indicators are detailed under paragraph 12.\n\n**E. Rationale for Bank Involvement and Role of Partners**\n\n26. **The World Bank has a solid track record in supporting Kenya’s health sector.** Over the years, the World Bank has supported several projects, including two regional operations: the East Africa Public Health Laboratory Networking Page 16 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["key health surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) Project (P171915) and the African Medicine Regulatory Harmonization Project (P155163) to strengthen core public health functions following a harmonized regional approach. The recently completed Transforming Health Systems for Universal Care Project (THS-UCP, P152394) supported the Government to improve access to RMNCAH services, through interventions at the national and county level. The project builds on the lessons learnt from these projects.\n\n27. **The Development Partners for Health Kenya (DPHK) plays a coordination role among development partners**\n**(DPs) and works closely with the MoH to ensure that investments are aligned to Government priorities.** Several DPs are\nsupporting delivery of quality PHC services, as outlined in the Government’s Community Health Strategy (2020-2025). A recent mapping exercise identified 14 DPs supporting various aspects of PHC. However, this support is targeted to only a few counties. To reduce fragmentation of development support, the MoH is working towards ensuring that all partners support implementation of a one health plan, with a joint monitoring and evaluation (M&E) framework. Implementation of this project will be aligned to the MoH plan. The World Bank works closely with other DPs to promote coordination and alignment and will continue to do so during project implementation. For example, the World Bank has worked closely with the Government of Denmark, through the Danish International Development Agency (Danida) and the Government of Japan to support the Government’s health financing and UHC agenda. Additionally, the World Bank is collaborating with the Bill and Melinda Gates Foundation to strengthen county capacity to deliver quality PHC services.\n\n**F. Lessons Learned and Reflected in the Project Design**\n\n28. **The project design incorporated lessons learned from previous World Bank engagements in the sector and at**\n**the devolved level in Kenya.**\n\n- **Focus on equity.** Amongst the 47 counties, there is variation in the coverage of key primary care services and\nRMNCAH outcomes. For the counties that are lagging, it is critical to provide sufficient and tailored support to achieve meaningful change.\n\n- **Balance standardization of interventions for cross-cutting issues while allowing for flexibility to address county**\n**specific gaps.** There are cross cutting issues that affect most, if not all, counties which can be addressed with a\nstandard package of interventions; however, there are differences amongst the 47 counties which call for a flexible approach to support appropriate, context-specific, and efficient implementation of interventions.\n\n- **Support reforms at national level to facilitate improved implementation of key interventions at county level.**\nWeak information, logistics management and quality assurance systems at KEMSA are a barrier to timely, responsive, and transparent distribution of HPTs to the counties. To improve the availability of selected HPTs, the project will support building-up a buffer stock at KEMSA as well as strengthening of the management information and quality assurance systems.\n\n- **Implementation efficiency.** The design of the project will draw from implementation experience from the THSUCP and other World Bank-financed projects implemented at county level that revealed weaknesses in fiduciary\ncapacity at the county level. Therefore, the project will maintain a project team at the Council of Governors to support and monitor county implementation and limit procurement to the national level.\n\n- **Building on refugee reforms.** The design is informed by and builds on the reforms supported by DRS and UNHCR\nwhich have progressed the integration of refugee health services into county management, including human resource management and the coding of humanitarian managed health services.\n\n**III.** **IMPLEMENTATION ARRANGEMENTS**\n\nPage 17 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698)\n\n**A. Institutional and Implementation Arrangements**\n\n29. **The project will be implemented by multiple entities at both national and county level.** The MoH, through the State Department of Public Health and Professional Standards (SDPHS), will have the overall responsibility of overseeing implementation of the project. A project steering committee chaired by the Principal Secretary (PS) for the SDPHS, and including the PS National Treasury, PS State Department of Immigration and Citizen Services, Chief Executive Officer of the Council of County Governors, and the Solicitor General will advise the project. County Governments will be responsible for implementation of county-level activities under Component 2, with support from KEMSA for procurement and delivery of HPTs to primary care facilities. Counties may conduct procurement on a need-by-need basis for activities in the positive list (for Sub-component 2.2) and Sub-component 2.3. Both KEMSA and county Governments will put in place a management team to oversee project implementation. In refugee camps, implementation will be undertaken by relevant county Governments in close coordination with DRS and UNHCR.\n\n30. **The Project Management Team (PMT) under the MoH, will have overall responsibility for the coordination and**\n**implementation of the project.** The PMT will be headed by the Project Manager who will be responsible for the effective\nfunctioning of the project. The MoH will be required to fully designate and maintain PMT members with appropriate skills, including Component coordinators, safeguards and fiduciary staff, and a M&E officer. The PMT will (a) coordinate the project activities; (b) ensure the financial management of all project activities in all Components; and (c) prepare consolidated AWPs, budgets, monitoring and evaluation, and quarterly and annual financial and technical implementation reports. The PMT will compile reports from each of the 47 counties and all national implementing entities and share them with the World Bank.\n\n31. **Each county will designate and maintain for the project period:** (a) a Project focal point; (b) a head of the unit responsible for procuring HPTs; (c) an environmental specialist; (d) a social development specialist; (e) a monitoring and evaluation specialist; (f) a procurement officer (if required as indicated in the Project Operations Manual); and (g) a project accountant and internal auditor to support project’s financial management functions.\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n32. **The M&E approach for the project is aligned with the Government’s procedures and data sources and will**\n**contribute to improved data quality.** All project indicators (a) are a subset of the health sector’s performance indicators\navailable in various data sources including the Kenya Health Information System (KHIS); and (b) will be collected routinely through project reports. The project will support county health sector annual performance data review meetings as well as availability of key surveys under Component 1. Where relevant, at project closure, data from household and facility surveys will be used to complement routine data to measure project achievement of the PDO.\n\n**C. Sustainability**\n\n33. **The project will support priority interventions outlined in the national health strategies to ensure sustainability.** The project will build on existing national systems and structures for implementation and fiduciary arrangements. The Government remains committed to improving delivery of primary healthcare services to advance progress towards UHC, and key project activities are aligned with these objectives. The project implementation entities will be drawn from existing Government structures which will ensure continuity of the expected results beyond the project period. In addition, Page 18 of 43", "output": {"entities": {"named_data": ["Kenya Health Information System"], "descriptive_data": ["household and facility surveys"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) the project includes a wide range of capacity building activities that will further enhance institutional capacities of the implementing entities.\n\n34. **The project funds will complement domestic resources and not replace them.** Under THS-UCP, counties were required to allocate at least 20 percent of their budget to health and increase the allocation from the previous year. This project will build on this experience to ensure that project funds do not crowd out Government investments.\n\n**IV.** **PROJECT APPRAISAL SUMMARY**\n\n**A. Technical, Economic and Financial Analysis (if applicable)**\n\n35. **The project will support delivery of core health interventions at primary care level, with a focus on RMNCAH in**\n**selected counties.** RMNCAH remains a major contributor to the high disease burden in Kenya. The country is investing\nheavily in the primary care level, as a foundation for UHC. The Government recently launched a community health program that promotes preventive and promotive health services at the community level. This project is therefore relevant as it is designed to strengthen primary care services, improve quality of care, and reduce inequities in access to health care services. Additionally, these primary care facilities mainly benefit the poor and rural populations; strengthening their capacity will ensure that these facilities are able to provide services to most of the population in the country. It is recognized that without improving the health of the population, Kenya will not realize its aspirations enshrined in the constitution.\n\n36. **There is a strong justification for Government intervention and public financing of PHC services.** By investing at the PHC level, the project will contribute towards laying a strong foundation for the delivery of health services in Kenya.\nFinally, the project is technically sound, and the design considers the respective mandates of the key entities involved in implementation. The project is adequately structured with clear linkages between activities and the results. It focuses on the key challenges facing delivery of essential health services at the primary level and is geared to addressing inequities in access to quality health services.\n\n37. **Investments in PHC show that every US$1 invested in PHC interventions saves up to US$16 in spending on**\n**conditions such as stunting, NCDs, anemia, tuberculosis, malaria, and maternal and child morbidity.** [25] The project aims\nto support investments in improving performance of PHC services at county level to enhance access to high impact interventions. Global evidence shows that investing in RMNCAH has a significant economic impact. Every additional US$1 invested in women’s and children's health translates to US$9 of economic and social benefit. [26] In Kenya, guided by the RMNCAH Investment Framework, priority areas for investment and action have been identified at national and county level. The project will support effective delivery of high impact evidence-based interventions to improve RMNCAH outcomes particularly in low performing counties. Analyses in Uganda [27 ] show the significant savings in integrative approaches to hosting refugees; integrating refugees into national systems, including health and jobs, which have reduced the total costs of supporting refugees by forty-five percent.\n\n25 Mwai D, Hussein S, Olago A, Kimani M, Njuguna D, Njiraini R, et al. (2023) Investment case for primary health care in low- and middle-income countries: A case study of Kenya. PLoS ONE 18(3): e0283156. 6 26 Global Strategy for Women’s Children’s and Adolescent’s Health 2016-2030, UN 27 Aziz Atamanov, Johannes Hoogeveen and Benjamin Reese. The Costs Come before the Benefits. Why Donors Should Invest More in Refugee Autonomy in Uganda.2023. World Bank.\n\nPage 19 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) 38. **The project is consistent with the adaptation and mitigation goals of the Paris Agreement, Kenya’s NDC**\n**(updated 2020) and is consistent with Kenya’s country climate policies.** Details on the project activities are in Annex 2.\n\n39. **Adaptation goal and risk reduction measures.** The main identified climate-related hazards which are anticipated to pose risk to project activities include floods, high heat, landslides, and coastal storms. To reduce the risk of climaterelated shocks to the project activities, adaptation measures are integrated within each activity. Under Sub-component\n1.1, the project will support KEMSA to procure buffer stocks of HPTs to ensure timely availability of HPTs and improved\nuptake of health services at the primary care level. To reduce the risk of floods and extreme heat to the buffer stocks, climate-sensitive planning for distribution of HPTs will be supported under the project. Under Sub-component 1.3, climate sensitive planning for surveys will be used and questions on climate and health impacts will be included in the survey to generate relevant data to inform decision making. To reduce the risk of climate shocks to health service delivery in Subcomponent 2.1, 2.2, and 2.3 the MoH will prepare a national climate emergency preparedness and response plan which will inform county level actions depending on their locations and anticipated disaster risks. Building rehabilitation under Sub-component 2.3 will include climate resiliency measures to prevent damage from climate shocks.\n\n40. **Mitigation goal and risk reduction measures.** Most activities in the project are on the universally aligned list for climate change mitigation. Minor rehabilitation activities under Sub-component 2.3 will ensure at least 20.0 percent more energy efficiency than standard practice, aligning with the Excellence in Design for Building Efficiencies (EDGE) level 1 building criteria. Sub-component 2.3, will also finance the purchase and installation of solar panels for sustainable health facility electrification, where applicable.\n\n**B. Fiduciary**\n\n**(i)** **Financial Management**\n41. **A Financial Management (FM) assessment has been carried out for the project in accordance with the World**\n**Bank Policy and directives on Investment Project Financing (IPF).** The assessment was carried out on MoH, KEMSA and\nselected counties during project preparation. Based on the assessment conducted, the FM risk of the project is rated Substantial. The implementing entities have adequate experience in managing World Bank financed operations and have complied with key FM deliverables. However, the following key risks are identified: (a) irregularities noted in procurement process for health commodities leading to ineligible expenditures; (b) issues on long-outstanding imprests; (c) inaccuracies on financial reporting due to use of manual system for financial reporting at the MoH; (d) inadequate budget allocations which has been hindering implementation of activities; and (e) weak financial management in some counties - among them delays in transfers of funds from the county revenue fund account to a dedicated special purpose account for health, commingling of funds at some counties, frequent transfers of staff without adequate handover leading to disruptions of financial reporting for the project, long-outstanding imprests, and errors in financial reports.\n\n42. **Mitigation measures have been incorporated in the project.** The project will inherit various strengths of the country’s public financial management system and the experience gained in the implementation of the other World Bank financed projects. The following measures have been incorporated: (a) the Office of the Auditor General will provide oversight of project resources and conduct the role of an independent integrated fiduciary review agent; (b) the Chief Finance Officer, MoH will liaise with the PMT to ensure adequate budget allocation; (c) the World Bank will organize a training to sensitize PMT staff on World Bank FM requirements. The internal audit at MoH will provide regular reviews and recommendations for continued strengthening of internal controls at national and county levels; (d) all implementing entities under MoH will be required to sign memorandums of understanding clearly highlighting the requirements for a Page 20 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) separate bank account, financial reporting, and other financial management requirements; and (e) preparation of a POM with a detailed FM section to address FM aspects of the proposed project. These mitigation measures will provide reasonable assurance that the project resources will be used for the intended purposes.\n\n**(ii)** **Procurement**\n43. **Procurement Regulations:** Procurement activities under the project will be carried out in accordance with “The World Bank’s Procurement Regulations for IPF Borrowers, First published July 2016 and revised Fifth Edition September 2023”, hereafter referred to as “Procurement Regulations”; the World Bank’s “Guidelines on Preventing and Combating Fraud and Corruption in Projects Financed by IBRD Loans and IDA Credits and Grants (revised as of July 1, 2016) and other provisions to be stipulated in the project’s Legal Agreement with the Borrower.” 44. **A Project Procurement Strategy for Development (PPSD) has been prepared by the Borrower** to support the implementation and achievement of development objectives of the project and deliver Value for Money. The PPSD will form the basis for preparation of the Procurement Plan (PP), providing justification for procurement decisions including the selection methods and market approaches. The PP has been prepared for the first 18 months of project implementation and will be updated at least annually, or as required, to reflect the actual project implementation needs.\n\n45. **Systematic Tracking of Exchanges in Procurement (STEP):** The project will use STEP, the World Bank’s online procurement planning and tracking tool to record all procurement actions including planning, updating, and clearing PP and seeking and receiving World Bank’s review and No Objection to procurement actions as needed and establish benchmarks, monitor delays, and measure procurement performance.\n\n46. **Profile of Procurement Activities:** The key procurements of the project comprise of (a) Goods: purchase of buffer HPTs stock in KEMSA; Procurement and distribution of HPTs; (b) Non-consulting services: Upgrade of ERP system at KEMSA; (c) Works: Renovation of works under Component 2.3; and (d) Consulting Services: institutional reforms, key health surveys; project monitoring and evaluation activities; environmental and social safeguards related activities; fiduciary management, contracting of staff on a need basis; technical assistance etc.\n\n47. **National Procurement Procedures:** The country’s own procurement procedures may be used when approaching the national market as agreed in the procurement plan. When the Borrower uses its own national open competitive procurement procedures as set forth in the Public Procurement and Asset Disposal Act 2015 (revised Edition 2022) and the attendant Regulations 2020 such arrangements shall be subject to the provisions of paragraph 5.3-5.6 of the Procurement Regulations. The other national procurement arrangements (other than national open competitive procurement), that may be applied by the Borrower shall be required to be consistent with the requirements set out in paragraphs 5.3 and 5.4 d as appropriate of the Procurement Regulations.\n\n48. **Institutional Arrangements for Procurement:** The MoH will be the implementing agency responsible for procurement activities at the national level, while KEMSA will be responsible for procurement and distribution of HPTs to primary care facilities. Counties will also conduct procurement on a need-by-need basis under Sub-components 2.2 and 2.3, based on their AWPs. All entities have gained significant experience in implementing World Bank-financed projects.\nThe MoH and the 47 counties implemented a Bank-financed THS-UCP using Procurement Guidelines in the period 20162023. Furthermore, the MoH and KEMSA have been implementing the World Bank-financed COVID-19 Health Emergency Response Project (CHERP, P173820) since 2020 using Procurement Regulations. Implementation of these Bank-financed operations have enhanced and continue to strengthen the agencies’ institutional procurement capacities in implementing Page 21 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["key health surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) World Bank-financed projects. The procurement decision making process will utilize the internal institutional structures of the MoH, KEMSA and the counties.\n\n49. **Procurement Capacity Assessment:** The MoH’s, KEMSA’s and counties’ procurement capacities to implement the project have been assessed and confirmed at project appraisal stage to be reasonably adequate to implement the envisaged project procurements. The MoH, KEMSA and counties have previous institutional experiences in implementing World Bank-financed projects. The MoH and KEMSA have qualified procurement staff who have experience in the application of the Bank’s Procurement Regulations. Subject to addressing the key risks identified in the next paragraph, the MoH, KEMSA and counties are assessed to have reasonably adequate capacity to execute the envisaged profile of procurements under the project.\n\n50. **Procurement Risk Assessment and Management:** Based on the current assessment conducted for this Project the MoH, KEMSA and counties as the implementing agencies, the overall residual procurement risk rating is “Substantial”.\nThe procurement risk rating for THS-UCP and CHERP is “Substantial”. The risk ratings have been reviewed and confirmed at project appraisal stage. The assessment applied the five risk factors of the Procurement Risk Assessment and Management System Risk Framework that may affect the overall project implementation. The key risks identified include the frequent transfers of procurement staff, inadequate number of experienced procurement staff assigned to the project procurement activities, inadequate capacity and delays in preparation of project procurement requirements and readiness activities, lengthy administrative processes in evaluation of proposals/bids and contract awards, insufficient preparation of procurement records and delayed upload of records in STEP, inadequate inspection, testing and acceptance procedures and limited contract management capacity. To mitigate the identified risks, the MoH, KEMSA, and counties will deploy additional qualified and experienced procurement and relevant technical staff to enhance procurement capacity and retain them for the project period, provide focused training on procurement and contract management and streamline their internal procurement processes to minimize procurement delays. The World Bank will provide regular and targeted capacity building on procurement and contract management to MoH and other implementing agencies and monitor the performance of fiduciary systems for smooth project implementation.\n\n51. **Record Keeping and Asset Management:** All records pertaining to the procurement and contract management activities including contract monitoring and payment records and contract completion will be retained by the implementing agencies in procurement files for each procurable activity in accordance with requirements of the financing agreement and uploaded in the STEP system on a timely basis. The MoH will also ensure that all inventory, stores, and assets procured are received by respective beneficiary counties, taken on charge, and used by the counties for intended purposes.\n\n**C. Legal Operational Policies**\n\n@#&OPS~Doctype~OPS^dynamics@padlegalpolicy#doctemplate Legal Operational Policies **Triggered?** Projects on International Waterways OP 7.50 No Projects in Disputed Area OP 7.60 No Page 22 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698)\n\n**D. Environmental and Social**\n\n52. **The project’s Environmental and Social (E&S) Risk has been classified as Moderate.** The project has a national scope where Component 1 and Sub-component 2.1 are being implemented across all 47 counties in Kenya while Subcomponent 2.2 (improving delivery of quality primary care services in selected counties) will be executed in selected counties. The project also targets refugees and refugee host communities in Turkana and Garissa counties (subcomponent 2.3).\n\n53. The project’s key environmental and social risks and impacts based on the World Bank’s Environmental and Social Due Diligence (ESSD) which is detailed in the Appraisal Environmental and Social Review Summary (A-ESRS) include: (a) occupational health and safety risks; (b) air, soil and water pollution from solid, liquid and hazardous waste; (c) increased generation of medical waste; (d) exclusion of other vulnerable groups such as indigenous people meeting the criteria in Environmental and Social Safeguards (ESS7), persons with disability, and women from project benefits under Component 1 and 2; (e) sexual exploitation and abuse/sexual harassments (SEA/SH) which is rated Moderate; (f) community health and safety risks such as the transmission of communicable diseases, including HIV/AIDS and COVID-19, due to interactions among project workers and between the project workforce and local communities despite low risk due to minimal civil works in the project; (g) security related risks associated with counties in the north and northeastern part of the country that may affect project personnel associated with implementation of project; and (h) improvement in access to healthcare to the project host population’s, vulnerable and marginalized groups including refugees and host communities.\n\n54. **The ESS relevant to the Project are ESS 1,2,3,4,6,7 and 10.** Based on the E&S risks and the relevant standards, the project has prepared and consulted on the following framework documents between October 26 to 30, 2023: (a) Environmental and Social Management Framework (ESMF); (b) Medical Waste Management Plan (MWMP); (c) Stakeholder Engagement Plan (SEP); (d) Labor Management Procedures (LMP); (e) Vulnerable Groups Planning Framework (VGPF); and (f) Environmental and Social Commitment Plan (ESCP). The project has also prepared a Grievance Redress Mechanism (GRM) as part of the SEP to be adopted and implemented under each of the sub-project activities. The ESS standard documents have been disclosed in-country on December 5, 2023, and the World Bank’s website on December 6, 2023.\n\n55. **Citizen Engagement.** The project will ensure citizen engagement [28 ] in the design and implementation of the project. A SEP was prepared as part of project preparation. Under the Project, stakeholders including healthcare providers for level 1-3 facilities, the refugees, and refugee host communities will be engaged through the guidelines prescribed in the project’s Stakeholder Engagement Plan (SEP). The project will leverage the GRM developed under the CHERP and THSUCP to collect community complaints about all project-related services, following guidelines in the SEP and involve DRS in host community and refugee areas to ensure the applicable GRM is practical and can effectively mitigate any conflicts among refugees and between refugees and host communities. Community health units (CHUs) will also be encouraged to sensitize their catchment area on the different avenues to channel complaints and engagement with refugee community health workers in Dadaab and Kakuma/Kalobeyei will also be supported. In addition, under Component 1, the project will support the development of an accountability dashboard as part KEMSA support. This will give the community more visibility on the HPTs procurement and distribution processes.\n\n28 Citizen engagement (CE) here means a two way-interaction between the state and stakeholders that gives stakeholders a stake in decision making with the objective of improving development outcomes.\n\nPage 23 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) 56. **Gender.** A gender gap analysis has identified disparity in access to ANC services in a subset of counties, which can lead to disproportionate health outcomes for mothers and newborns. This is largely due to demand side barriers, including limited awareness of the availability and benefits of attending ANC visits, as well as supply side barriers including stockouts of essential health products and technologies and non-adherence to maternal health standards of care. The project will address this gender gap by: (a) encouraging counties to include ANC as a topic for all CHUs dialogue days with the community; and (b) supporting ANC service delivery described under Sub-component 2.2, and support for procurement and distribution of ANC related HPTs under Sub-component 2.1. A sub-indicator measuring the percentage of pregnant women attending four or more ANC visits in the selected counties where coverage has been lower is included in the results framework as a PDO level sub-indicator, to monitor how this gender gap will be closed.\n\n**V.** **GRIEVANCE REDRESS SERVICES**\n\n57. **Grievance Redress.** Communities and individuals who believe that they are adversely affected by a project supported by the World Bank may submit complaints to existing project-level grievance mechanisms or the Bank’s Grievance Redress Service (GRS). The GRS ensures that complaints received are promptly reviewed in order to address project-related concerns. Project affected communities and individuals may submit their complaint to the Bank’s independent Accountability Mechanism (AM). The AM houses the Inspection Panel, which determines whether harm occurred, or could occur, as a result of Bank non-compliance with its policies and procedures, and the Dispute Resolution Service, which provides communities and borrowers with the opportunity to address complaints through dispute resolution. Complaints may be submitted to the AM at any time after concerns have been brought directly to the attention of Bank Management and after Management has been given an opportunity to respond. For information\n[on how to submit complaints to the Bank’s Grievance Redress Service (GRS), visit http://www.worldbank.org/GRS. For](https://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service) information on how to submit complaints to the Bank’s Accountability Mechanism, visit\n[https://accountability.worldbank.org.](https://www.worldbank.org/en/programs/accountability)\n\n**VI.** **KEY RISKS**\n\n58. **The overall risk rating for the project is Moderate.** The risk areas rated Substantial include macroeconomics, fiduciary, and others (refugee protection). While the project involves many implementing entities, the potential risks will be mitigated by strengthening coordination through the Council of Governors, rationalizing the distribution of HPTs, limiting project activities to a “positive list of interventions” in the majority of the counties, and providing technical assistance and fiduciary support to the 10 selected counties, including the two counties that host refugees. Furthermore, the project will work in close collaboration with UNHCR and other implementing agencies providing services to the refugee population.\n\n59. **Macroeconomics risk is rated Substantial.** Although Kenya’s medium-term economic prospects remain optimistic, the ongoing shocks, including prolonged drought, continued turbulence in the global economy and rising inflation and associated increases in the prices of commodities are creating challenges for Kenya to sustain economic recovery. So far, the macroeconomic risks have not had an adverse impact on the financing of the sector; however, the possible impacts on project implementation will be regularly monitored and discussed with the Government. Recently, the Government passed new health financing laws with the potential to impact the project. The Primary Health Care, Facility Improvement Financing and Social Health Insurance Acts of 2023 will establish several funds to be managed by the SHA, while giving public health facilities greater financial autonomy. The transition will include making health insurance mandatory, increasing contribution rates to 2.75 percent for those in the formal sector, introducing income rated contributions for Page 24 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) the informal sector, changes in the benefit package, claims management processes and provider payment systems, among others. The transition process from the NHIF to SHIF, if not carefully implemented, poses a risk that may affect overall health system performance. The project is designed to support the Government to implement some of the new reforms, which will help mitigate the potential risks. To mitigate risks associated with flooding, KEMSA will develop and use a risk calendar in the distribution of HPTs.\n\n60. **Fiduciary risk is rated Substantial.** Based on an assessment carried out on MoH, KEMSA and selected counties, the FM risk of the project is rated “Substantial”. The implementing entities have adequate experience in managing World Bank financed operations and have complied with key FM deliverables. However, the following key risks are identified: (a) irregularities noted in procurement process for health commodities leading to ineligible expenditures; (b) issues on longoutstanding imprests; (c) inaccuracies on financial reporting due to use of manual system for financial reporting at the MoH; (d) inadequate budget allocations which has been hindering implementation of activities; and (e) weak financial management in some counties. FM risk mitigation measures are described in paragraph 42. The overall residual procurement risk rating is “Substantial”. The key risks identified include the frequent transfers of procurement staff, inadequate number of experienced procurement staff assigned to the project procurement activities, inadequate capacity and delays in preparation of project procurement requirements and readiness activities, lengthy administrative processes in evaluation of proposals/bids and contract awards, insufficient preparation of procurement records and delayed upload of records in STEP, inadequate inspection, testing and acceptance procedures and limited contract management capacity.\nProcurement risk mitigation measures are described in paragraph 50.\n\n61. **Other risks, defined here as refugee protection, are rated Substantial.** While offering a range of benefits to refugee protection in Kenya, there are a range of resourcing, operational, policy and context specific challenges in the implementation of the Refugee Act 2021 that could be addressed to ensure full compliance with the relevant provisions of the 1951 Refugee Convention. There are protection risks regarding: delays and a backlog in the processing of refugee status determinations, the freedom of movement and residence, as well as a lack of simplified access to work permits and documentation. Security challenges and social cohesion risks linked to equitably accessing health services present a substantial risk across refugees and host community members. These risks will be managed through policy dialogue with Government led by the DRS, and joint engagement with relevant agencies such as UNHCR and other stakeholders supporting the development of the Shirika Plan and the development of the Refugee Regulations. In addition, the World Bank, in collaboration with the DRS and UNHCR, will undertake periodic reviews of Kenya’s refugee protection, policy and institutional environment under its Refugee Policy Review Framework.\n\nPage 25 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems(P179698)\n\n**VII.** **RESULTS FRAMEWORK AND MONITORING**\n\n@#&OPS~Doctype~OPS^dynamics@padannexresultframework#doctemplate\n\n**PDO Indicators by PDO Outcomes**\n\nBaseline Period 1 Closing Period\n**Improve utilization of quality primary health care services**\n**Percentage of women receiving postnatal care within 48 hours (Percentage)**\nOct/2023 Jun/2027 Jun/2029 57.3 61 65 Percentage of women receiving postnatal care within 48 hours in the 10 selected counties (Percentage) 56.6 60 65 Percentage of host community women receiving postnatal care within 48 hours in Garissa and Turkana (Percentage) 57.3 61 65 Percentage of refugee women receiving postnatal care within 48 hours in Garissa and Turkana (Percentage) 83.5 84.5 86\n**Percentage of children immunized with three doses of Pentavalent vaccine (Percentage)**\nOct/2023 Jun/2027 Jun/2029 77.2 81 85 Percentage of children immunized with three doses of the Pentavalent vaccine in the 10 selected counties (Percentage) 76 79 82\n**To improve quality of primary health care services**\n**Percentage of pregnant women attending 4 or more ANC visits (Percentage)**\nOct/2023 Jun/2027 Jun/2029 50.1 53 56 Percentage of pregnant women attending 4 or more ANC visits in the 10 selected counties (Percentage) 42.7 46 50 Percentage of host community pregnant women attending 4 or more ANC visits in Garissa and Turkana (Percentage) 51.6 53.5 56 Percentage of refugee pregnant women attending 4 or more ANC visits in Garissa and Turkana (Percentage) 80 83 86 Feb 21, 2024 Page 26 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems(P179698)\n\n**Proportion of Children Under 5 with diarrhea treated with Zinc/ORS Co-Pack (Percentage)**\nOct/2023 Jun/2027 Jun/2029 57.7 64 70 Proportion of Children Under 5 with diarrhea treated with Zinc/ORS Co-Pack in the 10 selected counties (Percentage) 48.8 54 60\n**To strengthen institutional capacity for service delivery**\n**Order fill rate for priority health products and technologies (HPTs) (Percentage)**\nOct/2023 Jun/2027 Jun/2029 43 66 90\n**Order turnaround time (Days)**\nOct/2023 Jun/2027 Jun/2029 13 12 10\n\n**Intermediate Indicators by Components**\n\nBaseline Period 1 Closing Period\n**Strengthening institutional capacity for health service delivery towards achieving UHC**\n**Number of functional ERP modules (Number)**\nOct/2023 Jun/2027 Jun/2029 0 35 71\n**Number of surveys completed (Number)**\nOct/2023 Jun/2027 Jun/2029 0 1 2\n**Health insurance benefits package developed (Yes/No)**\nOct/2023 Jun/2027 Jun/2029 No No Yes\n**Improving utilization of quality health services at primary care level**\n**Proportion of functional community health units (CHUs) (Percentage)**\nOct/2023 Jun/2027 Jun/2029 75 82 90\n**Percentage of quarterly priority HPTs orders placed within the required timeframe (Percentage)**\nOct/2023 Jun/2027 Jun/2029 0 50 100\n**People who have received essential health, nutrition, and population (HNP) services (Number)** **[CRI ]**\n\nFeb 21, 2024 Page 27 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems(P179698) Oct/2023 Jun/2027 Jun/2029 0 4500000 9000000 Number of children immunized (Number) [CRI ] Oct/2023 Jun/2027 Jun/2029 0 2000000 4000000 Number of deliveries attended by skilled health personnel (Number) [CRI ] Oct/2023 Jun/2027 Jun/2029 0 2500000 5000000\n**Number of community enrolled health nurses trained in Garissa and Turkana (Number)**\nOct/2023 Jun/2027 Jun/2029 0 50 100\n**Number of refugee health facilities supported under the project, as part of the transition to county management, in Garissa and Turkana (Number)**\nOct/2023 Jun/2027 Jun/2029 0 10 20\n**People in Garissa and Turkana who have received essential health, nutrition, and population (HNP) services (Number)**\nOct/2023 Jun/2027 Jun/2029 0 158000 316000 Number of children immunized among the host community in Garissa and Turkana (Number) Oct/2023 Jun/2027 Jun/2029 0 42000 84000 Number of children immunized among refugees in Garissa and Turkana (Number) Oct/2023 Jun/2027 Jun/2029 0 18000 36000 Number of deliveries attended by skilled health personnel among the host community in Garissa and Turkana (Number) Oct/2023 Jun/2027 Jun/2029 0 68600 137200 Number of deliveries attended by skilled health personnel among refugees in Garissa and Turkana (Number) Oct/2023 Jun/2027 Jun/2029 0 29400 58800\n**Project management and evaluation**\n**Percentage of complaints in the GRM satisfactorily addressed within 4 weeks of initial complaint being recorded (Percentage)**\nOct/2023 Jun/2027 Jun/2029 0 40 80\n**A vulnerable and marginalized group plan is developed and disseminated (Yes/No)**\n\nFeb 21, 2024 Page 28 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems(P179698) Oct/2023 Jun/2027 Jun/2029 No No Yes\n**Percentage of transfers of project funds to the CRF that occur within 7 business days after the project funds are transferred into the MoH Development Account (Percentage)**\nMar/2024 Jun/2027 Jun/2029 0 50 100 Feb 21, 2024 Page 29 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698)\n\n**Monitoring & Evaluation Plan: PDO Indicators by PDO Outcomes**\n\n**Outcome 1: Improve utilization of quality primary health care services**\n**Percentage of women receiving postnatal care within 48 hours (Percentage)**\n\nNumerator: Number of women receiving postnatal care after delivery within 48 hours.\nDescription Denominator: Total number of expected live births during the reporting period Frequency Every six months Data source KHIS Methodology for Data Routine Health Management Information System (HMIS) data collection Collection Responsibility for Data MoH Collection\n\n**Percentage of women receiving postnatal care within 48 hours in the 10 selected counties (Percentage)**\n\nNumerator: Number of women, in the 10 selected counties, receiving postnatal care after delivery within 48 hours.\nDescription Denominator: Total number of expected live births, in the 10 selected counties, during the reporting period Frequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Responsibility for Data MoH Collection\n\n**Percentage of host community women receiving postnatal care within 48 hours in Garissa and Turkana (Percentage)**\n\nNumerator: Number of host community women in Garissa and Turkana, receiving postnatal care after delivery within 48 Description hours. Denominator: Total number of expected live births within the host commnunity of Garissa and Turkana, during the reporting period Frequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Description Responsibility for Data MoH Collection\n\n**Percentage of refugee women receiving postnatal care within 48 hours in Garissa and Turkana (Percentage)**\n\nNumerator: Number of refugee women in Garissa and Turkana, receiving postnatal care after delivery within 48 hours.\n\nDescription Denominator: Total number of expected live births within the refugee community in Garissa and Turkana, during the reporting period Frequency Every six months Data source UNHCR reports Methodology for Data Routine UNHCR data collection Collection Description Responsibility for Data MoH Collection\n\n**Percentage of children immunized with three doses of Pentavalent vaccine (Percentage)**\n\nNumerator: Number of children under 1 year who have received three doses of the Pentavalent vaccine Description Denominator: Total number of surviving children under 1 year Frequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Responsibility for Data MoH Feb 21, 2024 Page 30 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) Collection\n**Percentage of children immunized with three doses of Pentavalent vaccine in the 10 selected counties (Percentage)**\n\nNumerator: Number of children under 1 year who have received three doses of the Pentavalent vaccine in the 10 Description selected counties. Denominator: Total number of surviving children under 1 year in the 10 selected counties.\nFrequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Responsibility for Data MoH Collection\n\n**Outcome 2: To improve quality of primary health care services**\n**Percentage of pregnant women attending 4 or more ANC visits (Percentage)**\n\nNumerator: Number of women attending 4 or more ANC visits.\nDescription Denominator: Total number of expected live births during the reporting period.\nFrequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Responsibility for Data MoH Collection\n\n**Percentage of pregnant women attending 4 or more ANC visits in the 10 selected counties (Percentage)**\n\nNumerator: Number of women attending 4 or more ANC visits in the 10 selected counties.\nDescription Denominator: Total number of expected live births during the reporting period in the 10 selected counties.\nFrequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Responsibility for Data MoH Collection\n\n**Percentage of host community pregnant women attending 4 or more ANC visits in Garissa and Turkana (Percentage)**\n\nNumerator: Number of pregnant women within the host cummunity of Garissa and Turkana attending 4 or more ANC Description visits. Denominator: Total number of expected live births during the reporting period within the host community of Garissa and Turkana Frequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Description Responsibility for Data MoH Collection\n\n**Percentage of refugee pregnant women attending 4 or more ANC visits in Garissa and Turkana (Percentage)**\n\nNumerator: Number of refugee pregnant women attending 4 or more ANC visits.\n\nDescription Denominator: Total number of expected live births during the reporting period within the refugee community of Garissa and Turkana Frequency Every six months Data source UNHCR reports Methodology for Data Routine UNHCR data collection Collection Responsibility for Data MoH Collection\n\n**Proportion of Children Under 5 with diarrhea treated with Zinc/ORS Co-Pack (Percentage)**\n\nFeb 21, 2024 Page 31 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) Numerator: Total number of children receiving Zinc/ORS co-pack Description Denominator: Total number of children under 5 years with diarrhea Frequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Responsibility for Data MoH Collection\n\n**Proportion of Children Under 5 with diarrhea treated with Zinc/ORS Co-Pack in the 10 selected counties (Percentage)**\n\nNumerator: Total number of children receiving Zinc/ORS co-pack in the 10 selected counties.\nDescription Denominator: Total number of children under 5 years with diarrhea in the 10 selected counties.\nFrequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Responsibility for Data MoH Collection\n\n**Outcome 3: To strengthen institutional capacity for service delivery**\n**Order fill rate for priority health products and technologies (HPTs) (Percentage)**\nDescription Percentage of orders fulfilled as per agreed requirements for priority health products and technologies (HPTs).\nFrequency Every six months Data source ERP Methodology for Data Routine ERP data Collection Responsibility for Data KEMSA, MoH Collection\n\n**Order turnaround time (Days)**\n\nAverage number of days taken to process and deliver to the last mile, priority health products and technologies (HPTs) Description orders from the date the order is placed.\nFrequency Every six months Data source ERP Methodology for Data Routine ERP data Collection Responsibility for Data KEMSA, MoH Collection\n\n**Monitoring & Evaluation Plan: Intermediate Results Indicators by Components**\n\n**Strengthening institutional capacity for health service delivery towards achieving UHC**\n**Number of functional ERP modules (Number)**\nDescription Total number of functional modules in the new KEMSA ERP system.\nFrequency Every six months Data source Project report Methodology for Data Project monitoring Collection Responsibility for Data KEMSA, MoH Collection\n\n**Number of surveys completed (Number)**\nDescription Total number of surveys completed with support from the project.\nFrequency Every six months Feb 21, 2024 Page 32 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) Data source Project report Methodology for Data Project monitoring Collection Responsibility for Data MoH Collection\n\n**Health insurance benefits package developed (Yes/No)**\nDescription A health insurance benefits package is developed, rationalized, and disseminated.\nFrequency Every six months Data source Routine ERP data Methodology for Data ERP Collection Responsibility for Data KEMSA, MoH Collection\n\n**Improving utilization of quality health services at primary care level**\n**Proportion of functional community health units (CHUs) (Percentage)**\n\nNumerator: Number of CHUs scoring at least 80% on the CHU assessment. Denominator: Total number of existing CHUs.\n\nDescription Comment: At appraisal, the total number of functional CHUs was 7,780 and the total number of existing CHUs (functional, non-functional, and semi-functional) was 10,382.\nFrequency Every six months Data source Project report Methodology for Data Project monitoring Collection Description Responsibility for Data MoH Collection\n\n**Percentage of quarterly priority HPTs orders placed within the required timeframe (Percentage)**\n\nPercentage of quarterly priority HPTs orders placed by Counties within the required timeframe. The required timeframe Description is by the 15th of the ordering month.\nFrequency Every six months Data source Project report Methodology for Data Project monitoring Collection Responsibility for Data KEMSA, MoH Collection\n\n**People who have received essential health, nutrition, and population (HNP) services (Number)** **[CRI  ]**\nDescription Total number of deliveries attended by skilled health personnel and total number of children immunized.\nFrequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Responsibility for Data MoH Collection\n\n**Number of children immunized (Number)** **[CRI  ]**\nDescription Total number of children immunized.\nFrequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Responsibility for Data MoH Collection\n\n**Number of deliveries attended by skilled health personnel (Number)** **[CRI  ]**\n\nFeb 21, 2024 Page 33 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) Description Total number of deliveries attended by skilled health personnel Frequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Responsibility for Data MoH Collection\n\n**Number of community enrolled health nurses trained in Garissa and Turkana (Number)**\nDescription Total number of community enrolled health nurses trained in Garissa and Turkana Frequency Every six months Data source Project report Methodology for Data Project monitoring Collection Responsibility for Data MoH Collection\n\n**Number of refugee health facilities supported under the project, as part of the transition to county management, in Garissa and Turkana**\n**(Number)**\n\nTotal number of refugee health facilities supported under the project, as part of the transition to county management, in Description Garissa and Turkana (registration in the master facility list, provision of non-program HPTs from KEMSA, staffing).\nFrequency Every six months Data source Project report Methodology for Data Project monitoring Collection Responsibility for Data MoH Collection\n\n**People in Garissa and Turkana who have received essential health, nutrition, and population (HNP) services (Number)**\n\nTotal number of deliveries attended by skilled health personnel and total number of children immunized among the host Description community and refugees in Garissa and Turkana.\nFrequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Responsibility for Data MoH Collection\n\n**Number of children immunized among the host community in Garissa and Turkana (Number)**\nDescription Total number of children immunized among the host community in Garissa and Turkana.\nFrequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Responsibility for Data MoH Collection\n\n**Number of children immunized among refugees in Garissa and Turkana (Number)**\nDescription Total number of children immunized among refugees in Garissa and Turkana.\nFrequency Every six months Data source UNHCR reports Methodology for Data Routine UNHCR data collection Collection Responsibility for Data MoH Collection Feb 21, 2024 Page 34 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698)\n\n**Number of deliveries attended by skilled health personnel among the host community in Garissa and Turkana (Number)**\nDescription Total number of deliveries attended by skilled health personnel among the host community in Garissa and Turkana.\nFrequency Every six months Data source KHIS Methodology for Data Routine HMIS data collection Collection Responsibility for Data MoH Collection\n\n**Number of deliveries attended by skilled health personnel among refugees in Garissa and Turkana (Number)**\nDescription Total number of deliveries attended by skilled health personnel among the refugees in Garissa and Turkana Frequency Every six months Data source UNHCR reports Methodology for Data Routine UNHCR data collection Collection Responsibility for Data PMT Collection\n\n**Project management and evaluation**\n**Percentage of complaints in the GRM satisfactorily addressed within 4 weeks of initial complaint being recorded (Percentage)**\n\nNumerator: Number of complaints to the GRM satisfactorily addressed within 4 weeks of initial complaint being Description recorded. Denominator: Total number of recorded complaints to the GRM.\nFrequency Every 6 months Data source Project report Methodology for Data Project monitoring Collection Responsibility for Data MoH Collection\n\n**A vulnerable and marginalized group plan is developed and disseminated (Yes/No)**\nDescription A vulnerable and marginalized group plan is developed and disseminated Frequency Every six months Data source Project report Methodology for Data Project monitoring Collection Responsibility for Data MoH Collection\n\n**Percentage of transfers of project funds to the CRF that occur within 7 business days after the project funds are transferred into the MoH**\n**Development Account**\n\nNumerator: Number of transfers of project funds to the CRF that occur within 7 business days after the project funds are transferred into the MoH Development Account Description Denominator: Total number of transfers of project funds to the CRF that occur after the project funds are transferred into the MoH Development Account in a given year Frequency Every six months Data source Project report Methodology for Data Project monitoring Collection Description Responsibility for Data MoH Collection Feb 21, 2024 Page 35 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698)\n\n**ANNEX 1: Implementation Arrangements and Support Plan**\n\n**COUNTRY: Republic of Kenya**\n**Building Resilient and Responsive Health Systems**\n\n**Project institutional and implementation arrangements**\n\n1. **The implementation support plan for the project is based on the following considerations:** (a) for Component\n2, the annual planning, budgeting, implementation, and monitoring will follow county public financial management (PFM) process and county Governments will be responsible for implementation with support from KEMSA for procurement and delivery of HPTs to primary care facilities; (b) all activities under Component 1 will be implemented by the MoH and KEMSA as part of their work program; (c) all implementing entities will put in place a management team to oversee project implementation; (d) in refugee camps, implementation will be undertaken in close coordination with DRS and UNHCR; and (e) the PMT will coordinate both the county and national level activities.\n\n**Financial Management**\n\n2. **Planning and Budgeting** . The project budgeting will be done in accordance with existing Government procedures.\nThe budget shall be based on the Annual Work Plan and Budget (AWPB) developed by the MoH including activities to be carried out by Counties and KEMSA. The project planning and budgetary process shall be implemented in accordance with the standard Government fiscal year which begins on 1st July of each financial year as provided for by the Public Financial Management Act 2012 and the Government Financial Regulations and Procedures. These will form the basis for defining the project activities and ensuring that sufficient funds are allocated to achieve the agreed results. There are possible challenges on inadequate budget allocations in the printed estimates at MoH which may delay implementation of project activities. Coordination timing of the Government budgeting activities/calendar across the two project levels (National and County) may be a challenge. The Chief Finance Officer at MoH will be tasked to liaise with the PMT to ensure there is adequate budget provided **.** 3. **Accounting and Staffing.** All implementing entities including the participating counties will be required to designate project finance staff with adequate qualifications and experience to ensure project financial processes are well coordinated, including preparations of financial reports for the project. The proposed staff will be reviewed and cleared by the World Bank as an effectiveness condition. The MoH maintains projects’ books of account using the integrated financial management information system (IFMIS) and manual ledgers. There will be comprehensive start-up workshop where finance staff of the implementing entities will be sensitized on FM requirements for the project to build capacity on managing the project. Additional training will be provided on a need basis during project implementation.\n\n4. **Internal Control and Internal Audit.** The project expenditure initiation, authorization and payments will be in line with PFM Act 2012 and PFM regulations (2015) of Kenya as elaborated and customized in the FM manual. Project work plans will be integrated into the internal audit work plans. Internal Audit reports covering project activities will be shared with the World Bank on a semi-annual basis (i.e. 30 June and 31 December). Regular internal audit, implementation support, monitoring and reporting will be undertaken. There have been material weaknesses on imprest management at MoH regarding long outstanding imprest and multiple imprest contravening PFM regulations. The internal audit at MoH Feb 21, 2024 Page 36 of 43", "output": {"entities": {"named_data": ["integrated financial management information system"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) will continue to provide this assurance and recommendations for continued strengthening of internal controls. The Internal Audit Department reports to the Audit Committee of the MoH that is required to meet on a quarterly basis. The audit committees are independent of the MoH and are appointed in accordance with the PFM Act 2012.\n\n5. **Financial Reporting.** The PMT Project Accountant will be responsible for preparation of financial reports for the project. An interim unaudited Financial Report shall be prepared by the counties and submitted to the PMT by the 15th day after the end of the quarter. The PMT shall consolidate and submit to the World Bank 45 days after the end of the quarter. The annual project financial statements shall be consolidated and submitted to the Office of the Auditor General (OAG) for external audit on or before September 30 of each financial year. There have been regular trainings provided to Counties teams for THS-UCP which has improved financial reporting. The dedicated Project Accountants at national and counties levels will be provided with capacity building trainings at the commencement of the project which will include refresher financial reporting requirements among other financial management procedures. PMT finance team will regularly review financial reports by counties and identify any further tailored support that may be provided.\n\n6. **External Audit.** On an annual basis, the financial statements for the project will be audited by OAG and audited financial statements submitted to the World Bank within six months after the financial year end in accordance with World Bank’s FM guidelines. The terms of reference for the external audit shall be cleared by the World Bank.\n\n**Disbursement**\n\n7. **Disbursements and Funds Flow.** The disbursements from IDA will be report based. The initial advance will be disbursed based on initial cash flow requirement for at least the first six months. Subsequent disbursement will be based on the advance amount requested as per the approved Interim Financial Report submitted in every quarter by the MoH for both national county level activities. The banking arrangements for purposes of funds flow will consist of (a) three DAs denominated in US dollars or Euros as shall be agreed with the NT (DA-A for national level activities disbursement category 1 part 1 and 3 DA-B for county-level activities category 1 and 3 part 2.1 and 2.3, DA-C for county level activities category 2 part 2.2 financed by both IDA Grant and GFF. These accounts shall be opened at the CBK and managed by the National Treasury (NT). One project account shall be opened by the Ministry for receipt of funds from DA-A. The funds received through DA-B, and DA-C shall be transferred through the exchequer to the MoH Development Account for transfer to the County Revenue Fund and to the County Special Purpose Account. The project account shall be opened in Kenyan shillings at the CBK or financial institution acceptable to IDA and shall be to pay for eligible project activities. (b) Each participating county shall open a Special Purpose Account (SPA), at CBK. Triggers for the initial transfer from DA-B and DA-C to SPA will include the signing of the intergovernmental participation agreement, and approved county AWP&B. Subsequent transfer of funds from DA-B, and DA-C shall be based on consolidated advance request by the MoH through the approved IFR. To resolve the funds flow delays observed in devolved projects, the funds transferred from the Ministry Development Account to the CRF shall be within 7 working days. The funds transfer from CRF to project’s SPA shall be within 15 working days failure to which an adjustment shall be applied as described in the POM. There will be continuous engagement with the MoH, Council of Governors and the NT to unlock challenges impacting on the fund flows.\n\nFeb 21, 2024 Page 37 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) Diagram 1: Funds Flow IDA DA-A- National-Category 1 part 1,3 DA-B County category 1,3 part 2.1,2.3 DA-C County category 2 part 2.2 GoK Exchequer Min Dev Account Project Account in Kshs County Revenue Fund County Special Purpose Account Payments for goods, services, and other eligible expenditures\n\n**Procurement**\n\n8. **Institutional Arrangements for Procurement:** The MoH will be the implementing agency responsible for procurement activities at the national level, while KEMSA will be responsible for procurement and distribution of HPTs to primary care facilities. Counties will also conduct procurement on a need-by-need basis under Sub-components 2.2 and 2.3, based on their AWPs. All entities have gained significant experience in implementing World Bank-financed projects.\nThe MoH and the 47 counties implemented a Bank-financed THS-UCP using Procurement Guidelines in the period 20162023. Furthermore, the MoH and KEMSA have been implementing the World Bank-financed COVID-19 Health Emergency Response Project (CHERP, P173820) since 2020 using Procurement Regulations. Implementation of these Bank-financed operations have enhanced and continue to strengthen the agencies’ institutional procurement capacities in implementing World Bank-financed projects. The procurement decision making process will utilize the internal institutional structures of the MoH, KEMSA and the counties.\n\nFeb 21, 2024 Page 38 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) 9. **Record Keeping and Asset Management:** All records pertaining to the procurement and contract management activities including contract monitoring and payment records and contract completion will be retained by the implementing agencies in procurement files for each procurable activity in accordance with requirements of the financing agreement and uploaded in the STEP system on a timely basis. The MoH will also ensure that all inventory, stores, and assets procured are received by respective beneficiary counties, taken on charge, and used by the counties for intended purposes.\n\n**Strategy and Approach to Implementation Support**\n\n10. **Implementation Support.** A core World Bank technical team, including a task team leader (TTL) and Forced Displacement Officers will provide implementation support to implementing entities. The World Bank will provide hands on operational support to PMT members and county Government teams with no prior direct experience with implementation of World Bank-financed projects. This will be in addition to the support provided during bi-annual missions, especially at the beginning of project implementation. The task team will use a risk-based approach to operational support given the large number of implementing entities. Cross-county knowledge sharing and learning will be encouraged throughout project implementation.\n\n**Implementation Support Plan and Resource Requirements**\n\n11. **Bi-annual Review and Midterm Review.** Biannual World Bank visits will be organized to review progress and mitigate risks. A midterm review will be organized in due time to assess project implementation progress and make the necessary changes to accelerate implementation.\n\n12. **Fiduciary and Safeguards.** World Bank fiduciary and E&S staff are all based in Nairobi which will allow timely support to the PMT and implementing entities. During the bi-annual implementation support missions, the fiduciary and safeguards team will join the field trips and provide hands-on support to the county counterparts.\n\n13. **M&E.** The World Bank will work closely with the PMT to plan and implement the required project M&E and ensure disaggregation of results for refugees and host community members.\n\nFeb 21, 2024 Page 39 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698)\n\n**ANNEX 2: Climate Change**\n1. **The project has been screened for climate disasters and risks and been found to be highly exposed, while the**\n**risk to project activities is low.** Kenya is highly vulnerable to the impacts of climate change and is ranked 152 out of 181\ncountries in the 2019 Notre Dame Global Adaptation Index (ND-GAIN). Kenya’s topography is highly diverse including varied formations of plains, escarpments, and hills, as well as low and high mountains which has an influence on varying climatology and related climate vulnerability across the country. Kenya’s mean annual temperature is 24.3°C, with average monthly temperatures between 22°C in the coolest months (July/August) and 25.6°C in the warmest months (February/March). The mean annual temperature across Kenya is projected to increase by 1.0°C in the 2030s, 1.7°C in the mid-century and 3.5°C by the end of the century, with rapid warming in the northern and coastal parts of the country, intensifying droughts, heat waves with impacts on heat health risks, and food insecurity and malnutrition. The mean annual precipitation (1901-2020) is 668.6mm and is projected to remain highly variable and uncertain, with an increase in average rainfall in the 2050s with increased extreme rainfall events (frequency, intensity, duration) triggering floods and landslides with implications for vector-borne disease transmission, water-borne disease outbreaks, direct injuries, and mortalities; and impacts on health infrastructure. Floods and droughts are the most significant and frequent climaterelated hazards accounting for 40.0 percent and 10.0 percent respectively of all-natural hazard occurrences in Kenya between 1980-2020.\n\n2. **Climate change has significant impacts on health in Kenya** . Food insecurity and malnutrition remain high particularly in the Arid and Semi-Arid Lands of Kenya. Increased precipitation variability and related prolonged droughts and erratic rainfall induced floods are posing significant impacts on food security, driving hunger and malnutrition in the country and projected increases in temperature and decrease in precipitation will worsen food insecurity and associated malnutrition in the country by the 2030s and 2050s. Three consecutive years of below than average rains, prolonged drought and erratic rains have significantly reduced crop yields and livestock productivity leaving 17.0 percent (2.8 million people) facing acute food insecurity, and 940,000 children 6-59 months and 145,000 pregnant or lactating women acutely malnourished. [29] Furthermore, warming temperatures and precipitation variability are already increasing vector-borne disease transmission risk (especially for malaria, dengue, schistosomiasis, chikungunya, yellow fever, rift valley fever) in the country. For instance, in Kenya, malaria is a major cause of death and disease with an estimated 3.5 million cases and 10,700 deaths each year; with 31 million people at risk in the endemic Lake Western, Nyanza and Coastal regions. [30] [31] Projected increasing temperatures and rainfall variability are predicted to increase habitat suitability and seasonal transmission in traditionally low malaria risk areas such as the Central highlands and Nairobi. Women, children, and pregnant mothers, particularly those from poor households and in rural areas are the most vulnerable to malaria. Climate change is also exacerbating water-borne diseases especially diarrhea and cholera outbreaks in Kenya. Flash floods are overwhelming sewage systems contaminating drinking water sources and prolonged droughts are reducing the quantity and quality of water and compromising the functioning of Water, Sanitation, and Hygiene infrastructure even in health facilities. In the aftermath of heavy rains during March-May 2018, flooding across the country led to a major increase in cholera outbreaks of 5,470 cases and 78 deaths in 19 counties with over 700 cases in Turkana and Garissa Counties. [32] [33] Floods, storms, landslides, and extreme heat threaten the functioning of health infrastructure and hinder health service delivery and access especially in flood prone Coastal regions, Tana River region, the Lake Victoria Basin, and rural remote areas of the country.\n\n29 Kenya: IPC Acute Food Insecurity and Acute Malnutrition Analysis (July 2023 - January 2024) 30 https://www.cdc.gov/malaria/malaria_worldwide/cdc_activities/kenya.html 31 Kenya Malaria Indicator Survey, 2020.\n32 Kenya Humanitarian Situation Report. 31 December 2018.\n33 MSF responds to cholera outbreak amid heavy rains and flooding. Project update. 18 May 2018.\n\nFeb 21, 2024 Page 40 of 43", "output": {"entities": {"named_data": ["Kenya Malaria Indicator Survey", "Notre Dame Global Adaptation Index", "IPC Acute Food Insecurity and Acute Malnutrition Analysis"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) 3. The project intends to implement measures to adapt to climate change while mitigating greenhouse gas emissions, as outlined in the Table below.\n\n**Table 1: Climate Adaptation Measures Financed by the Project**\n\n**Components/Sub-component** **Climate Action**\n\nSub-component 1.1: Institutional and operational reforms to enhance efficiency and transparency of KEMSA (US$30 million) Sub-component 1.3: Improve availability of quality data for decision making (US$10 million) Sub-component 2.1: Improving availability of essential HPTs and delivery of key RMNCAH and NCDs interventions at the primary care level (US$90 million): Sub-component 2.3: Improving access to and utilization of quality health services in refugee and host communities (US$40 million)\n\n**HPTs for Climate Sensitive diseases:** The list of selected HPTs will include\nthose related to climate sensitive diseases and conditions (malaria, diarrheal diseases, etc.). Availability of HPTs for climate related diseases will lead to enhanced and continued quality health service delivery for poor and rural populations even during extreme events (floods, heat waves). **(adaptation)**\n\n**Climate measures in surveys:** To improve decision making and planning for\nclimate sensitive diseases and conditions, and climate impacts on health infrastructure and health service delivery, there is need for availability of reliable health data. Therefore, questions on climate and health impacts will be included in the survey. **(adaptation)**\n\n**HPTs for Climate Sensitive diseases** : Pharmaceuticals for climate sensitive\nconditions such as malaria, diarrheal diseases, and other climate sensitive conditions will be included in the pharmaceutical list. This will improve the capacity of primary care level facilities to provide better health services in the face of the increasing burden of disease due to climate change. Seasonal case and pharmaceutical consumption data will be used to inform procurement of the pharmaceuticals to ensure adequate supplies based on seasonal patterns to address climate sensitive conditions. **(adaptation)**\n\n**Climate sensitive community health service planning:** Climate sensitive\nplanning including the use of climate vulnerability and meteorologic data will be used to guide the distribution of essential HTPs, diagnostic and medical equipment avoiding stormy, heavy rains and heavy flooding days.\nSpecifically, data on climate vulnerable locations and data on previous use of HPTs for climate sensitive diseases as well as patterns of pharmaceutical use following climate shocks will be used to ensure adequate quantities of pharmaceuticals are being used. To ensure pharmaceuticals are available ahead of shocks and that these do not impact distribution, funding will be made available, and planning will be done to ensure distributions ahead of shocks. This will reduce the risk of damage to HPTs and medical equipment by floods and storms but will also improve the acquisition and availability of adequate amounts of essential HPTs (for climate sensitive diseases such as malaria, diarrhea) for improved health service delivery. **(adaptation)**\n\n**Availability of climate sensitive essential HPTs, diagnostic and medical**\n**equipment:** This Sub-component will finance the procurement of HPTs for\nclimate sensitive diseases, particularly malaria and diarrhea, which are Feb 21, 2024 Page 41 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Seasonal case and pharmaceutical consumption data", "data on climate vulnerable locations", "climate vulnerability and meteorologic data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) climate sensitive in Kenya and form a large share of the disease burden in the country. **(adaptation)**\n\n**Energy efficient rehabilitation of health facilities:** This Sub-component\nwill support climate energy efficient health facility rehabilitation. Guided by the building standards developed under this Sub-component for the rehabilitation of energy-efficient health facilities. Aligned with Criteria 9.1 of the ‘Buildings, public installations and end-use energy efficiency’ section of the Multilateral Development Bank Mitigation Finance Methodology, the Project will adopt measures that substantially reduce net energy consumption, resource consumption, and CO2e emissions of health facilities to be rehabilitated. The project will finance: i) technical assistance for the implementation of energy efficient rehabilitation, and ii) energy efficient rehabilitation of health facilities ensuring at least 20 percent greater energy efficiency in comparison to standard practice, aligning with EDGE level 1 building criteria. **(mitigation)**\n\n**Climate resilient health facility rehabilitation:** climate resilient health\nfacility rehabilitation will be conducted based on the climate resilient health facility rehabilitation guidelines developed under this Sub-component.\nClimate adaptation measures will go beyond standard practice to ensure resilience to floods, high heat, and storms. **(adaptation)**\n\n**Solar electrification of health facilities:** This Sub-component will finance\nthe purchase and installation of solar panels as well as operations and maintenance for existing and newly purchased equipment for sustainable health facility electrification to contribute to greenhouse gas emission reductions. **(mitigation)**\n\n**Energy efficient medical equipment** : Aligned with Criteria 9.5 of the\nMultilateral Development Bank Mitigation Finance Methodology medical equipment purchases through this Component will utilize energy efficiency standards to ensure a substantial reduction of energy consumption, resource consumption, or CO2e emissions. Energy Star efficiency standards, International Electrotechnical Commission (IEC) energy efficiency standards, and similar viable standards for medical equipment will be used [34], with particular reference to IEC 60601-1-9, ‘Medical Equipment – General requirements for basic safety and essential performance – Collateral Standard: Requirements for environmentally conscious design’ [35] .\nThe highest energy efficiency rating or labelling that allows to perform quality medical and laboratory services adequately will be pursued. While 34 Noting that there are currently no national energy efficiency standards for medical equipment in Kenya, this would be promoting best-available technology.\n35 International Electrotechnical Commission. 2020 report. Medical electrical equipment - Part 1-9: General requirements for basic safety and essential performance - Collateral Standard: Requirements for environmentally conscious design. IEC. 2020. IEC 60601-19:2007+AMD1:2013+AMD2:2020 CSV - Consolidated version.\n\nFeb 21, 2024 Page 42 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nBuilding Resilient and Responsive Health Systems (P179698) exact cost estimates of equipment purchases are not possible given cost fluctuations and county autonomy in choosing activities, a conservative minimum estimate of ten percent of the Sub-components’ financing is anticipated to go to energy efficient equipment purchases. **(mitigation)** Sub-component 2.2: Improving delivery of **Climate change and undernutrition:** This Sub-component will finance quality health services in selected counties nutrition services to address stunting in children and anemia in pregnant (US$5 million, IDA) women including provision of micronutrient supplements and growth monitoring. Undernutrition in Kenya is climate related, particularly in arid and semi-arid parts of the country which will be covered under this Subcomponent. These nutrition services will help the population adapt to the health impacts of climate change. (adaptation) Component 3: Project management and evaluation (US$10 million): Under this Component, the MoH will develop national climate emergency preparedness and response plan which will inform county-level actions to reduce the risk of climate change on health service delivery activities.\nCounties will be encouraged to include relevant climate mitigation actions in their AWPs. **(adaptation)** Feb 21, 2024 Page 43 of 43", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "Document of\n# **The World Bank**\n\n**FOR OFFICIAL USE ONLY**\n\nReport No: PP5087 INTERNATIONAL BANK FOR RECONSTRUCTION AND DEVELOPMENT PROJECT PAPER ON A PROPOSED GRANT IN THE AMOUNT OF US$ 2 MILLION TO THE STATE COMMITTEE FOR AFFAIRS OF REFUGESS AND INTERNALLY DISPLACED PERSONS OF THE REPUBLIC OF AZERBAIJAN FOR A SPF: IMPROVED LIVELIHOODS FOR INTERNALLY DISPLACED PERSONS IN AZERBAIJAN December 20, 2022 Social Sustainability And Inclusion Global Practice Europe And Central Asia Region", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "CURRENCY EQUIVALENTS (Exchange Rate Effective December 1, 2022) Currency Unit = [New Azerbaijanian ] Manat AZN 1.70 = US$1 FISCAL YEAR January 1 - December 31 Regional Vice President: Anna M. Bjerde Country Director: Sebastian-A Molineus Global Director: Louise J. Cord Practice Manager: Varalakshmi Vemuru Task Team Leader(s): Erik Caldwell Johnson", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "ABBREVIATIONS AND ACRONYMS CPF Country Partnership Framework ESF Environmental and Social Framework E&S Environment and Social FCS Fragile and Conflict-Affected Situations FCV Fragility, Conflict, and Violence FY Fiscal Year GoA Government of Azerbaijan GRS Grievance Redress Service IA Implementing Agency IDP Internally Displaced Person LSLP Living Standards and Livelihood Project MLSPP Ministry of Labor and Social Protection of Population M&E Monitoring and Evaluation NGO Non-Governmental Organization OM Operations Manual PDO Project Development Objective SCRI State Committee for Refugees and IDPs SFDI Social Fund for Development of Internally Displaced Persons SOFAZ State Oil Fund of Azerbaijan SPF State and Peacebuilding Fund SEA/SH Sexual Exploitation and Abuse / Sexual Harassment USD United States Dollar", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**BASIC INFORMATION**\n\nIs this a regionally tagged project? Country (ies) No Classification Financing Instrument Investment Project Financing Small Grants Approval Date Closing Date Environmental and Social Risk Classification 15-Sep-2022 31-Dec-2025 Low Approval Authority Bank/IFC Collaboration CDA No Please Explain\n\n**Proposed Development Objective(s)**\n\nEnhance civic engagement, technical skills and opportunities for income generation for vulnerable IDP households in Azerbaijan.\n\n**Components**\n\n**Component Name** **Cost (USD Million)**\n\nComponent 1: Skills development 640,000.00 Component 2: Job placement and business development support 845,200.00 Component 3: Civic engagement, social cohesion, monitoring and operational support 514,800.00\n\n**Organizations**\n\nBorrower : State Committee for Affairs of Refugees and Internally Displaced Persons Page 1 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) Implementing Agency : State Committee for Affairs of Refugees and Internally Displaced Persons\n\n**PROJECT FINANCING DATA (US$, Millions)**\n\n**SUMMARY-NewFin1**\n\n**Total Project Cost** 2.00\n\n**Total Financing** 2.00\n\n**Financing Gap** 0.00\n\n**DETAILS-NewFinEnh1**\n\n**Non-World Bank Group Financing**\n\nTrust Funds 2.00 State and Peace Building Fund 2.00\n\n**Expected Disbursements (in USD Million)**\n\n**Fiscal**\n2023 2024 2025 2026\n**Year**\n\n**Annu**\n0.50 1.00 0.40 0.10\n**al**\n\n**Cumu**\n0.50 1.50 1.90 2.00\n**lative**\n\n**INSTITUTIONAL DATA**\n\n**Financing & Implementation Modalities**\n\n**Situations of Urgent Need of Assistance or Capacity Constraints**\n\n[ ] Fragile State(s) [✔] Fragile within a non-fragile Country\n\n**Other Situations**\n\n[ ] Small State(s) [ ] Conflict [ ] Responding to Natural or Man-made Disaster\n\n[ ] Financial Intermediaries (FI) [ ] Series of Projects (SOP)\n\n[ ] Performance-Based Conditions (PBCs) [ ] Contingent Emergency Response Component (CERC) Page 2 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n[ ] Alternative Procurement Arrangements (APA) [ ] Hands-on Expanded Implementation Support (HEIS)\n\n**Practice Area (Lead)**\n\nSocial Sustainability and Inclusion\n\n**Contributing Practice Areas**\n\nFragile, Conflict & Violence\n\n**OVERALL RISK RATING**\n\n**Risk Category** **Rating**\n\nOverall ⚫ Moderate\n\n**COMPLIANCE**\n\n**Policy**\nDoes the project depart from the CPF in content or in other significant respects?\n\n[ ] Yes [✔] No Does the project require any waivers of Bank policies?\n\n[ ] Yes [✔] No Page 3 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**Environmental and Social Standards Relevance Given its Context at the Time of Appraisal**\n\n**E & S Standards** **Relevance**\n\nAssessment and Management of Environmental and Social Risks and Impacts Relevant Stakeholder Engagement and Information Disclosure Relevant Labor and Working Conditions Relevant Resource Efficiency and Pollution Prevention and Management Not Currently Relevant Community Health and Safety Not Currently Relevant Land Acquisition, Restrictions on Land Use and Involuntary Resettlement Not Currently Relevant Biodiversity Conservation and Sustainable Management of Living Natural Resources Indigenous Peoples/Sub-Saharan African Historically Underserved Traditional Local Communities Not Currently Relevant Not Currently Relevant Cultural Heritage Not Currently Relevant Financial Intermediaries Not Currently Relevant\n\n**Legal Covenants**\n\n**Conditions**\n\n**PROJECT TEAM**\n\n**Bank Staff**\n\n**Name** **Role** **Specialization** **Unit**\n\nTeam Leader(ADM Erik Caldwell Johnson SCASO Responsible) Murat Fatin Onur Team Leader SCASO Sepehr Fotovat Ahmadi Procurement Specialist(ADM EECRU Page 4 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) Responsible) Financial Management Tural Jamalov EECG1 Specialist(ADM Responsible) Social Specialist(ADM David Jijelava Social Risk SCASO Responsible) Environmental Specialist(ADM Gulana Enar Hajiyeva SCAEN Responsible) Alkadevi Morarji Patel Team Member SCASO Liliia Zhukovska Team Member SCASO Sabina Vagif Majidova Team Member ECCAZ Sophia V. Georgieva Team Member SEAS1 Turgut Mustafayev Team Member SCASO Vusala Asadova Procurement Team ECCAZ Yu Ri Park Team Member SSIGL\n\n**Extended Team**\n\n**Name** **Title** **Organization** **Location**\n\nPage 5 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) AZERBAIJAN SPF: IMPROVED LIVELIHOODS FOR INTERNALLY DISPLACED PERSONS IN AZERBAIJAN\n\n**TABLE OF CONTENTS**\n\n**I.** **STRATEGIC CONTEXT ...................................................................................................... 7**\n\n**A. Country Context** ................................................................................................................. 7\n\n**B. Sectoral and Institutional Context** ..................................................................................... 8\n\n**C. Higher Level Objectives to which the Project Contributes** ............................................. 10\n\n**II.** **PROJECT DEVELOPMENT OBJECTIVES ............................................................................ 11**\n\n**A. PDO** ................................................................................................................................... 11\n\n**B. Project Beneficiaries** ......................................................................................................... 11\n\n**C. PDO-Level Results Indicators** ........................................................................................... 12\n\n**III.** **PROJECT DESCRIPTION .................................................................................................. 12**\n\n**A. Project Components** ......................................................................................................... 12\n\n**B. Project Cost and Financing** ............................................................................................... 15\n\n**IV.** **IMPLEMENTATION ........................................................................................................ 16**\n\n**A. Institutional and Implementation Arrangements** ........................................................... 16\n\n**B. Results Monitoring and Evaluation** ................................................................................. 17\n\n**C. Sustainability** .................................................................................................................... 17\n\n**V.** **KEY RISKS ..................................................................................................................... 18**\n\n**A. Overall Risk Rating and Explanation of Key Risks** ........................................................... 18\n\n**VI.** **APPRAISAL SUMMARY .................................................................................................. 20**\n\n**A. Legal Operational Policies** ................................................................................................ 22\n\n**B. Environmental and Social** ................................................................................................. 22\n\n**VII.** **WORLD BANK GRIEVANCE REDRESS .............................................................................. 22**\n\n**VII. RESULTS FRAMEWORK AND MONITORING .................................................................... 24**\n\nPage 6 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**I.** **STRATEGIC CONTEXT**\n\n**A. Country Context**\n\n1. **Driven by double-digit growth in the 2000s, Azerbaijan became an upper middle-income**\n**country in 2009. This growth led to a dramatic decline in poverty from 49 percent of the**\n**population in 2001 to 4.8 percent in 2019, before rising slightly to 6.2 percent in 2020** . Rapid\ngrowth helped finance social programs and public investments in infrastructure and improve the well-being of the population. In recent years, dependency on natural resources has been associated with slower economic growth, suggesting that more stable growth could be achieved by focusing on nonhydrocarbon sectors and by building a more diversified asset base of human, physical, and natural capital.\n\n2. **Triple shocks in 2020 - the COVID-19 pandemic, a collapse in energy demand and prices,**\n**and resumption of the armed conflict with Armenia – hit Azerbaijan’s economy, with GDP**\n**contracting by an estimated 4.3 percent in 2020.** Growing concern for climate change and\nenvironmental sustainability also raises questions about the resilience of Azerbaijan’s economy.\nIt has become more evident that the growth model that enabled Azerbaijan to achieve its status of upper middle-income country cannot sustain similar growth trends into the future.\n\n3. **As Azerbaijan positions itself for the next phase of its development process, many of**\n**the issues related to the transformation are being recognized and addressed in the** _**Azerbaijan**_\n_**2030: National Priorities for Socio-Economic Development**_ **.** Government of Azerbaijan (GoA) is pursuing economic, social and structural transformation through a series of reforms that address some of its longstanding challenges.\n\n4. **In September 2020, conflict reignited between Azerbaijan and Armenia, leaving**\n**thousands of casualties and tens of thousands of people displaced across both countries.** After\nsix weeks of fighting, the President of Azerbaijan, the Prime Minister of Armenia and the President of Russia signed a joint Statement on November 10, 2020. While this Statement brought an end to the spike of confrontation, the situation on the ground remains far from resolved. Fighting flared up again between the two countries in September 2022 yet there continue to be advances in negotiations towards a peace agreement.\n\n5. **As of July 1, 2021, Azerbaijan is included in the WBG list of Fragile and Conflict-Affected**\n**Situations (FCS), as a country affected by high-intensity international conflict.** The 2020 World\nBank Group Strategy on Fragility and Conflict highlights the importance of supporting FCS countries and the complex challenges they face with tailored approaches, policies and instruments. In the case of Azerbaijan, this includes tailored engagement in support of postconflict recovery and reconstruction.\n\nPage 7 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**B. Sectoral and Institutional Context**\n\n6. **The breakup of the Soviet Union and conflict over the Karabakh region in the early 1990s**\n**severely affected the lives of people in Azerbaijan.** As a result of this conflict approximately oneseventh of Azerbaijan’s population was displaced – approximately 600,000 persons from\nKarabakh and surrounding regions of Azerbaijan became internally displaced persons (IDPs) and settled across other regions of Azerbaijan. The situation of displacement has lasted almost 30 years. Since then, the GoA has made significant investments in housing, social benefits, and livelihood programs for these IDPs in the different parts of the country where they settled, including through the World Bank-financed _IDP Living Standards and Livelihood Project (“LSLP_ _Project”)_, over 2011-2019. IDPs remain among the most vulnerable social groups in Azerbaijan: over 40 percent reside in either ‘collective centers’ or other temporary housing with substandard living conditions, many of them far from social and economic centers, and with lower access to basic services, skills, land, and assets.\n\n7. **Over the past twenty years, the State Committee for Affairs of Refugees and IDPs (SCRI),**\n**via the State Fund for Development of IDPs (SFDI) including through World Bank support, has**\n**implemented community-based activities for IDPs and host communities** . These have included:\n(i) community mobilization, (ii) implementation of small infrastructure projects such as construction and renovation of housing, roads, schools, hospitals, community centers, and water supply, as well as (iii) a series of livelihood activities focused on youth beneficiaries such as business and vocational training, establishment of income generation groups, provision of assets and equipment for starting a business, and micro-credit with the support of the State Oil Fund of Azerbaijan (SOFAZ). Through evaluation of its support, the Bank has identified a range of lessons that can be applied to the design of future interventions in support of IDPs in Azerbaijan.\nMoreover, through these programs and via the local government representatives (Ex Coms) of IDPs, the SCRI has remained in close contact with IDP communities and developed operational expertise in implementing programs for IDPs. Since the closure of the latest World Bank project in December 2019, however neither SFDI nor the SCRI have a steady stream of funding for livelihood support activities.\n\n8. **With the clarification of land control in some areas specified in the 2020 joint statement,**\n**there are emerging opportunities for people to return to their places of origin.** The Government\nof Azerbaijan has established a policy called “The Great Return” and has already relocated 66 households to a newly-constructed “smart village” in the village of Aghali in Zangilan district as of July 2022 with plans for resettling a total of 200 households in the village. However, it is clear that further movement of displaced people will take time for various reasons, including ongoing border clarifications and safety concerns due to the presence of land mines in the conflictaffected areas. As such, there is a need to address the needs of IDPs who remain poor and Page 8 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) vulnerable due to their long-standing situation of displacement and who may not have an opportunity to return to their places of origin for some time.\n\n9. Since November 2021, the World Bank has been providing analytical and technical assistance to the Government through a State and Peacebuilding Fund-financed, Bank-executed grant called _Support for Peacebuilding and Recovery in Azerbaijan_ . This grant has financed analytical work that aims to inform a policy dialogue between the Bank and SCRI, including a survey of IDPs which focused on understanding the current livelihoods, service delivery, and social inclusion, and future aspirations; and a lessons learned paper on livelihood activities for IDPs. As IDPs are just beginning to return to liberated areas, it is a critical time for the Bank to engage with GoA on a policy dialogue as the steps that are taken now will have long-lasting effects. The Bank will mobilize its growing knowledge and experience in addressing the challenges of forced displacement to support the Government. In addition to the analytical work undertaken, activity under the SPF grant also includes support for the Joint Recovery Needs Assessment (JRNA) for Fizuli, Agdam and Jabrayil, and just-in-time advice to the Office of Special Representative (OSR) of the President to the Karabakh Economic Region on issues including management information systems, local governance, and smart city development. Preparation of the JRNA will benefit from the knowledge gained through the proposed Improved Livelihoods for Internally Displaced Persons (ILIDP) Project and the OSR will benefit from the Project as well given their role in facilitating the sustainable return of IDPs.\n\n10. **The IDP survey and lessons learned paper on livelihoods described above have informed**\n**the design of the proposed project by summarizing the current living conditions of IDPs as well**\n**as lessons from implementation of previous livelihood programs** . Though both studies are in\nthe process of being finalized, they have provided valuable inputs to the design of this project.\nThe study of lessons from other livelihoods and job training programs revealed the need for close support for training participants to ensure the sustainability of their achievements. This has resulted in the incorporation of mentors into the project design from the time of project launch through completion and an emphasis on community-based support to address the unique context of each IDP settlement. The household survey found that 22 percent of household members are unemployed and 30 percent of respondents are looking for work. There remains a reliance on state support with 90 percent of respondents receiving an IDP allowance. To address their income generation needs, respondents identified various skills they would like to acquire with males wanting to have skills in the agriculture/fishery, automotive and land transport, and construction sectors while women preferred garments/sewing, health care and community development. While the data collected through the survey on job and skills provides useful benchmarking information, more localized labor market surveys will need to be undertaken to identify targeted opportunities in the communities where IDPs are living to support livelihoods that provide greater incomes over sustained periods.\n\nPage 9 of 34", "output": {"entities": {"named_data": ["Joint Recovery Needs Assessment"], "descriptive_data": ["IDP survey", "survey on job and skills", "survey of IDPs"], "vague_data": ["household survey", "localized labor market surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) 11. **Findings from the IDP survey reveal limitations in IDP civic engagement and social**\n**cohesion.** There is a very low level of IDP participation in social activities in their communities\nsuch as youth and women’s groups, cultural activities, agricultural or entrepreneurship activities.\nOnly eight percent of respondents participate in such activities. Forty two percent of respondents said that they either do not know where to, or would not want to, lodge a complaint or make a request regarding the delivery of services. In terms of social cohesion and community integration, 61 percent of respondents said that they felt well integrated into their village/city and only 40 percent felt that if someone in their family was in an emergency, they could count on support of their community. While the survey did not collect comparative data for non-IDPs, these findings indicate that the unique living conditions of IDPs may limit their participation in community-based activities and lead them to feel less well-supported by their communities. This may be a significant challenge for people who are returning to their places of origin as these places are less likely to have established local governance arrangements. Given that these are essentially newly created villages and cities, they will take time to operate effectively, posing challenges for the returnees. In order for these new settlements to succeed, there will need to be active engagement of residents to identify and to help resolve challenges that arise. By participating in local decision-making processes and social organizations, IDPs can contribute to improving living conditions in their places of origin upon return and facilitating a smoother transition through more actively engaging.\n\n**C. Higher Level Objectives to which the Project Contributes**\n\n12. **The proposed project activities will contribute to the World Bank Group’s Azerbaijan**\n**Country Partnership Framework (CPF) FY16 – FY20, Focus Area 1, Public Sector Management**\n**and Service Delivery, Objective 1.2: Support access to, and satisfaction with, public services** .\nWhile the CPF period concluded in FY20, its focus and priorities remain relevant as the process of preparing a new CPF moves forward. The CPF specifically states that it will build on successful engagement in selected areas of public services and applying global knowledge and expertise, by contributing to the agenda of improved service delivery through support to increased coverage of the most vulnerable, specifically IDPs to sustain their livelihoods.\n\n13. **The proposed project will also contribute to World Bank FCV Strategy (2020 – 2025)**\n**Pillar 3. Helping Countries to Transition out of Fragility** . The FCV Strategy highlights the\nimportance of WBG focus on strengthening the capacity and legitimacy of core institutions, renewing the social contract, and supporting livelihoods and economic development including through private sector development and entrepreneurship. Financing from the World Bankadministered State and Peacebuilding Fund (SPF) for the proposed project would be a reflection of World Bank support to Azerbaijan as part of the FCV Strategy.\n\nPage 10 of 34", "output": {"entities": {"named_data": [], "descriptive_data": ["IDP survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**II.** **PROJECT DEVELOPMENT OBJECTIVES**\n\n**A. PDO**\n\nEnhance civic engagement, technical skills and opportunities for income generation for vulnerable IDP households in Azerbaijan.\n\n**B. Project Beneficiaries**\n\n14. **The main beneficiaries will be members of IDP households that meet the selection**\n**criteria set under the proposed project** . While there are no age limitations to be eligible for\nparticipation, beneficiaries are expected to be mostly youth and middle-aged persons as they may have more flexibility to participate in and benefit from the short-term training offered by the project and the kinds of employment and business opportunities this would lead to.\n\n15. **The geographic coverage of participation will be largely open to eligible participants**\n**wherever they may be residing in the country** . There will be an effort to ensure distribution of\nparticipants across the four main regions where they are residing, with a majority residing in the Southern Region and Baku, Sumgayit, Absheron Peninsula Region, but also to concentrate on specific locations in each Region where demand is high. Distribution of support across fewer settlements will be more cost-efficient and allow for more intensive support to beneficiaries. If demand is high across and range of locations, efforts can be made to mobilize additional financial resources to meet this demand. Just as these coverage areas reflect the current residence of target beneficiaries, project activities will take place in areas under the control of the government prior to the 2020 conflict with the exception of consultations to be conducted in the newly reestablished villages of Aghali in Zangilan District, Talish in Terter District, and potentially other villages where the lessons of resettlement would help to inform livelihood support to IDPs. See a breakdown of districts within each region in Table 1.\n\n**Table 1: Regional Breakdown of districts**\n**Baku, Sumgayit,** **Northern region**\n\n**Northern region**\n\n**Southern region**\n\n**Western region**\n\nSamukh Dashkesan Goygol Goranboy Ganja city Yevlakh Page 11 of 34\n\n**Absheron**\n**Peninsula**\n\nBaku Sumgayit Absheron Peninsula Oghuz Gabala Mingechevir city Agdam Agdjabedi Barda Beylagan Fizuli Tartar", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) 16. **The project is targeting 520 vulnerable IDP participants with the aim of benefiting their**\n**households, so a broader group of 2,100 total beneficiaries** . The average household size in\nAzerbaijan is 4.1 persons.\n\n**C. PDO-Level Results Indicators**\n\n17. **Achievement of the proposed Project Development Objective, will be measured**\n**through the following indicators:**\n\n- Percentage of participants self-employed or employed by firms\n\n- Increase in income of households with individuals participating in the project\n\n- Percentage of registered participants completing training and receiving certificates\n\n- Beneficiaries of job-focused interventions, of which female (core World Bank\nindicator)\n\n- Percentage of beneficiaries taking a more active role in their communities\ndisaggregated by gender and persons with disability 18. **Baseline data on indicators will be collected to facilitate the measurement of project**\n**impact.** Upon registration of participants for project support, data will be gathered to establish\nbaseline conditions for each beneficiary and their household. Follow-up surveys will be conducted to compare baseline conditions to those after the completion of project activities.\n\n**III.** **PROJECT DESCRIPTION**\n\n**A. Project Components**\n\n19. **Component 1: Skills development.** This component will support IDPs who are interested in pursuing business/employment opportunities to obtain specialized skills to improve their incomegenerating potential through enrollment in vocational training and apprenticeships to be followed by job acquisition and business skills support under Component 2. There will be two, staggered rounds of participant intakes, each with at least 260 students. Implementation of the component will begin with SCRI utilizing its networks in IDP communities to organize meetings with representatives of ExCom, municipalities, CSOs, communities and other relevant bodies in target areas, to advertise the project and to solicit applications. Local governments will be trained so that they can advise potential applicants on the eligibility criteria and how to apply for project support. SCRI will also recruit mentors who will provide ongoing support throughout the duration of the project, beginning with hands-on support to applicants in filling out applications and advising on career options. Special efforts will be made to reach out to persons with disabilities to enable them to apply for participation in the project and to identify suitable business and/or job opportunities. Each mentor will be responsible for Page 12 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Baseline data on indicators"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) approximately 45 students per round of participants.\n\n20. Criteria for selection of participants will include IDPs who are considered vulnerable as measured by: a) housing status, b) income level, c) level of education, d) access to services, e) employment status, and f) disability. Selection of participants will also aim to include a balance of men and women, with at least 50% of beneficiaries being women. Applicants who are seeking training in skills that have been determined in the needs survey as being in high demand will also be preferred over those proposing training in areas where there is more limited labor market demand. Skills areas may include, among others: business/trade, agri-business, vocational activities (i.e., carpenter, cook, hairdresser), use of digital technology, among others, with a focus on employment potential and to the extent possible avoiding gender stereotypes. A Selection Committee will be formed to review and endorse the list of approved applicants and the most appropriate training institute or apprenticeship for each applicant. As it is expected that the majority but not all participants will successfully complete the training courses, a modest oversubscription will be planned with the aim of reaching or exceeding the target of 520 total beneficiaries.\n\n21. To understand better the real opportunities for IDPs, particularly about the types of professions that are most in demand in the project areas, the project will ask IDPs (as part of the application process) to fill out a basic labor market assessment form. The process for this will be a day where the mentors work with potential project participants and explain to them how they can find out more about the kinds of opportunities they have in their communities and how to find out about potential income from different activities. In addition to the information collected by IDPs from local employers, the State Employment Agency and the mentors will share additional information that is available about the local labor market and broader national labor market trends that could be used to strategically direct project participants into promising areas of career development and form partnerships with employers and/or training providers to facilitate this. An effort will be made to identify employment pathways where labor market demand is growing and which move away from conservative gender norms which often limit women to professions such as sewing, cooking and beauty salons where income may be limited due to market saturation and low pay.\n\n22. Public and private training providers will be identified by SCRI together with the State Employment Agency, Ministry of Education and Science, and the State Agency for Vocational Education under the Ministry of Education and Science. The use of training providers under the LSLP allowed for flexible arrangements that are responsive to participant needs as well as partnership arrangements that were efficient to manage. Training institutes will be chosen based on the effectiveness of their performance under the LSLP, their accessibility to the project areas, quality of facilities, range of skills taught, demonstrated success with quality training, experience in training of IDPs, student retention, and track record of placing students into jobs and/or self-employment. After reviewing applicant training, checking these against labor market needs and aggregating these needs into courses offered by identified training providers, service agreements will be signed with each institution for an agreed curriculum of training lasting up to 6 months, with some flexibility to extend Page 13 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["needs survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) beyond 6 months on an exceptional basis. The agreements will include all details such as how many students will be trained, in which subjects, what the exact duration will be, and payment schedule.\n\n23. As an alternative to pursuing training with an established training institution, some participants may be matched with a “master” trainer in an apprenticeship that will last approximately the same duration as the training provider-based courses. The Project Implementation Unit (PIU) in SCRI will seek the advice of local government offices regarding the identification of professional apprenticeship “masters”. The selection of the masters will be decided based on their knowledge and relevant experience in their respective fields and their availability.\n\n24. All participants will undertake an examination of the basic knowledge and skills obtained through their training. Only those passing their exams will be eligible for business development and equipment support under Component 2.\n\n25. **Component 2: Job placement and business development support.** Upon successful completion of the vocational training and apprenticeships, the participants in skills development activities will be either supported with placement into relevant, good quality jobs or provided with 10 days of business development training to help them start their own businesses, focusing on financial literacy, financial management and good business practices. This training will be provided by a firm contracted by SCRI.\n\n26. All participants completing business development training will prepare business plans which will be the basis of requests for project support in the provision of tools/equipment for business startup. Business plans will be collated by the PIU and submitted to the Selection Committee for review, revision (as needed) and endorsement. Participants who have obtained jobs working for employers may also apply for equipment support. They will be asked to provide an endorsement from their employer of the need for the equipment.\n\n27. Tools/equipment are intended to help participants to establish new businesses or to obtain employment. These may be assets such as construction tools (i.e., saws, drills, etc.), hairdressing equipment (i.e., hairdryers, scissors, etc.), agricultural assets such as greenhouse materials, beehives, small machinery, etc., or other eligible equipment. Once project participants begin to undertake new business activities, technical support will be provided by the mentors to help participants to manage their finances, invest to grow their businesses, market their services and to develop other skills that will help them to succeed and continue to increase their incomes. The project will also facilitate meetings between participants, commercial banks and micro-credit agencies, for the purpose of extending support for business growth and ongoing assistance to those who decide to take out loans.\nParticipants in training will also be provided with information on other state programs for selfemployment and business support and assisted with applying to such programs.\n\n28. **Component 3: Civic engagement, social cohesion, monitoring and operational support.** This Page 14 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) component includes both support for enhancing the capacity of project beneficiaries as well as a wider group of IDPs to engage actively in their communities, as well as overall project implementation capacity which is largely in the form of the Project Implementation Unit (PIU). Capacity building on citizen engagement and social cohesion will help to address a lack of participation among IDPs in community organizations and local decision-making, a gap which limits their ability to influence the quality and responsiveness of government services to their unique needs.\n\n29. A civil society organization (CSO) will be contracted as a Civic Engagement Service Provider (CESP) to support the civic engagement and social cohesion activities under the project. The CESP will undertake activities including: Civic Engagement Needs Assessment of IDPs in target areas, including areas where IDPs have newly resettled or planning to resettle; Civic Engagement Action Plan for IDPs to support the active engagement of IDPs in the communities where they are living, including areas where IDPs have newly resettled; 2-day training in Civic Engagement and Social Cohesion for all beneficiaries of Components 1 and 2; regional capacity-building seminars in five target regions for locally-oriented CSOs, to enable them to offer context-based, quality, hands-on trainings for the IDP community members on civic participation and social cohesion; design and implement a digital civic engagement platform/mechanism for IDPs; and facilitate beneficiary feedback on the implementation of the project to enhance the effectiveness of implementation through to project closure. The digital civic engagement platform will be designed as a tool which SCRI can scale up to extend to interactions with IDPs beyond the project beneficiaries and beyond the project duration. The design of this system can benefit from the World Bank’s experience in implementing the Geo-enabling Initiative for Monitoring and Supervision (GEMS) which has helped implementing agencies in the collection of monitoring and evaluation information, including feedback from project beneficiaries.\n\n30. This component would also finance the contracting of consultants for the PIU, operational costs, and technical assistance needed by SCRI for implementation of the project. Activities the PIU would be responsible for include: manage all procurement matters in accordance with procedures agreed with the World Bank; carry out all financial management issues in compliance with the World Bank fiduciary requirements; ensure adherence to applicable Environmental and Social Standards; prepare overall project progress reports for the World Bank and the Government; oversee and coordinate the activities of the Selection Committee; and coordinate the project with other government and donor-financed and implemented interventions aimed at IDP communities.\n\n**B. Project Cost and Financing**\n\n**Project Components** **Project cost** **Trust Funds** **Counterpart Funding**\n\nComponent 1: Skills Development 640,000 640,000 0 Component 2: Job placement and 845,200 845,200 0 business development support Page 15 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) Component 3: Civic engagement, social cohesion, monitoring and operational support 514,800 514,800 0\n\n**Total Costs** 2,000,000 2,000,000 0\n\n**IV.** **IMPLEMENTATION**\n\n**A. Institutional and Implementation Arrangements**\n\n31. **The State Committee for Affairs of Refugees and IDPs (SCRI) will implement the**\n**proposed grant.** SCRI was originally established in 1992 along with the passing of the law \"On the\nStatus of Refugees and Internally Displaced Persons.\" SCRI is responsible for working with the government and international agencies to reduce poverty, create conditions for IDPs to return to their places of origin, supervise the targeted delivery of monthly benefits and assistance to IDPs, construct and maintain housing and social facilities for IDPs. It engages in the formulation of government policy for the support of IDPs. The implementation agency for the former LSLP World Bank-financed project, Social Fund for the Development of IDP (SFDI), reports directly to SCRI.\nFor the purpose of the present project, SCRI will establish a Project Implementation Unit (PIU), which will include contracted experts from SFDI with experience in the LSLP particularly in financial management, procurement, monitoring and evaluation, and environmental and social risk management. Thus, while the PIU for the present project will not be the same as for the prior project (LSLP), and it will be housed within the SCRI and not within SFDI, key project staff from SFDI will still be involved in the new PIU (e.g., FM, Procurement, Project Manager) and will bring continuity, institutional knowledge and implementation capacity relevant to managing World Bank-funded operations.\n\n32. The Selection Committee (SC) will be composed of three representatives of the SCRI, the State Employment Agency, and of the international donor community, represented by the United Nations High Commissioner for Refugees (UNCHR). The SC will review and approve proposals from the PIU for beneficiaries of Components 1 and 2.\n\n33. **The SCRI does not have prior experience with the World Bank's Environmental and**\n**Social Framework (ESF) as LSLP was financed under the Bank’s Safeguard Policies.** Its experience\nwith the LSLP has built a strong track record in certain areas of ESF Screening for Environmental and Social (E&S) risk and impacts and risk management of community infrastructure and housing; strong community mobilization, information disclosure and stakeholder engagement; and a functional grievance mechanism. Other areas of the ESF will be new to the SCRI team, specifically, labor and working conditions, occupational health and safety (OHS), and provisions on the prevention and mitigation of sexual exploitation and abuse and sexual harassment (SEA/SH). SCRI Page 16 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) will recruit an environmental and social specialist for the purposes of the project and all PIU and field staff will undergo training on relevant aspects of the ESF at project initiation.\n\n34. **The SCRI team has not yet implemented projects under the World Bank’s new**\n**procurement framework.** The team will be supported with training and assistance from WB\nprocurement staff in the implementation of the project procurement strategy for development (PPSD) which was developed during project preparation.\n\n**B. Results Monitoring and Evaluation**\n\n35. **The PIU within SCRI will be responsible for monitoring and evaluating the outcomes of**\n**the project** **against agreed indicators as set out in the Results Framework.** A consultant will be\nhired as an M&E Specialist to undertake and coordinate this work and to report on results indicators. The M&E Specialist will collect baseline data, which will enable the Committee to compare the before and after situation for project participants. Data after training program completion will be collected by the M&E Specialist and if additional data collection support is needed, SCRI will engage the staff of its Monitoring Department and the M&E Specialist will provide staff with the needed training and quality assurance supervision. Participants in civic engagement and social cohesion training will be tested on their knowledge and tracked to assess whether they are more active in their communities as a result of the training. Special emphasis will be placed on assessing the difference in project benefits between male and female participants and for persons with disabilities. The PIU will prepare semi-annual reports to provide a summary of implementation progress on project activities and cross-cutting functions (FM, Procurement, and Environmental and Social Risk Management) of the project.\n\n36. **Baseline data that is collected on income and other household characteristics will be**\n**structured in such a way to allow for comparison to national poverty lines and levels of income.**\nWhile such data comparisons will not be incorporated into the results framework, they will be used to assess the extent to which the project is helping to move beneficiaries out of poverty and into situations of greater economic self-reliance.\n\n**C. Sustainability**\n\n37. **As the Government is in the process of adjusting its approach to support IDPs as some**\n**of them prepare to relocate, or consider future relocation back to homeland territories, the**\n**proposed activities will assist the government in developing and implementing a revised**\n**approach to IDPs that can be scaled up in the future.** As the response to the recent conflict is\nstill unfolding, IDP support is likely to evolve and be the focus of increased support from the Government and international development partners, including the Bank. The grant is expected to contribute directly to improving the wellbeing and livelihood prospects of IDPs, as well as to strengthening government capacity to plan for effective livelihood support programs for IDPs.\n\nPage 17 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["baseline data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) The activities piloted under the grant, and the lessons learned, in the areas of employment and livelihoods, civic engagement and social cohesion have the potential to be scaled with support of further state, international, and private funds.\n\n38. **SCRI and the State Employment Service (SES) are starting to work together closely to**\n**facilitate the process of preparing IDPs to return to their places of origin.** This collaboration\nprovides a channel by which the lessons from the ILIDP Project can be incorporated into ongoing government employment services provided to IDPs, including through the Bank-financed _Employment Support Project_ . The ILIDP Project’s focus on using community-based support, apprenticeships and job placement will complement new initiatives planned by SES to enable Government to better respond to the needs of IDPs and other vulnerable groups. As the return of IDPs to their places of origin is a new experience for both SCRI and SES, the consultations with returnees undertaken through the ILIDP Project will also be an important source of information on how best to prepare IDPs for this transition. And as the pace of return begins to rapidly accelerate, there will be a need for increased capacity beyond the levels available within SES and SCRI to support this process. While Government has initially allocated funds through its State Program on the Great Return to the Liberated Territories of the Republic of Azerbaijan in 2023 and likely beyond, scaled-up financing from the World Bank could be a source of resources to help finance this significantly increasing need.\n\n**V.** **KEY RISKS**\n\n**A. Overall Risk Rating and Explanation of Key Risks**\n\n39. **The Overall risk rating for the project is** **Moderate** . Most of the technical design of the proposed activities has been implemented under the LSLP project and determined to be effective according to Bank and client assessments. While the main implementing agency was not the agency responsible for implementation of the proposed activities under the LSLP, many of the staff from that agency are now working for the SCRI so there is institutional knowledge and capacity to implement. There are political, governance and policy risks that accompany the potential evolving status of IDPs, but the Bank’s dialogue with the SCRI can help to anticipate any such changes and ensure that any impacts on project implementation are limited, if any.\n\n40. **Macroeconomic risks are Low** as the financing requirements are modest and there is no co-financing of activities by government.\n\n41. **Political and Governance risks are considered to be Substantial** as the status of IDPs may evolve over the course of the project as territories that have come under government control as a result of the 2020 conflict are integrated into national government systems. This may change the eligibility of some individuals to participate in the project or the types of support provided to IDPs. As the implementing agency for the project is the main government body for developing Page 18 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) and carrying out government policy with respect to IDPs the Bank will engage in dialogue with the SCRI to adjust beneficiary selection if needed and to ensure continuity in support to beneficiaries that enter the program so that they benefit from the full scope of project support even if their status changes during the project duration. There is also a risk that the civic engagement activities planned under the project could be limited due to Government concerns that they could be challenging the role of the state. As the Project is being implemented by a central government agency, there should be a level of comfort within Government that civic engagement of IDPs is in the interest of helping SCRI and other Government agencies to provide the best possible support to their intended beneficiaries.\n\n42. **Risks related to Sector Strategies and Policies are Substantial** . Similar to the assessment of political and governance risks, there could be specific policy changes with respect to support for IDPs during project duration. Government has launched its State Program for Great Return which is an ambitious strategy for reconstruction and return of 34,500 families by 2026. There is unlikely to be a change in overall government support for resolving the situation of IDPs as the government’s position has been consistent over many years and has only been reinforced by the outcomes of the recent conflict, but there could be adjustments to the approach to reflect the challenges faced in implementing the Great Return program. Several important issues have yet to be clarified such as whether IDPs can return to the exact location of their former residence, whether ownership of land will be transferred to them upon return and if not, when, and under what circumstances can this happen.\n\n43. **Technical Design risks are Low** as the methodology for the main project components has been applied previously and proven effective. Improvements would be introduced to further enhance the effectiveness of the design.\n\n44. **Institutional Capacity and Sustainability risks are Moderate** . While institutional capacity risk is low since the SCRI has prior experience with the proposed activities, the sustainability of activities is largely dependent upon the capacity of training participants to carry on their employment activities after the project closes. The component on business support and the use of mentors is intended to increase the likelihood of sustainability as it allows time for an assessment of participant capacity and targeted support to address challenges they are facing before project support ceases.\n\n45. **The** **Environmental and Social risks of the project are considered Low** . There are no physical infrastructure investments proposed, so there is very little in the way of environmental risks. As the core of the project is to reduce the vulnerability of IDPs it should reduce the social risks faced by this group rather than exacerbating their challenging circumstances. The focus on building awareness and skills for civic engagement and social cohesion will also help to mitigate social risks. Lack of familiarity of PIU staff with the ESF will be mitigated with training to PIU staff and consequently to all field staff on relevant aspects with a focus on adherence to the grievance Page 19 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) mechanism, stakeholder engagement, labor terms and conditions and occupational health and safety, among other applicable requirements.\n\n46. **Fiduciary risks are considered Moderate.** While SCRI did not directly manage funds from the LSLP, FM and Procurement experts who worked in SDFI on the LSLP are now working in SCRI and would be engaged in the PIU. These experts have long-term experience with World Bank fiduciary requirements and will receive training on any new aspects of FM and Procurement introduced since the closing of the LSLP. There are also no large-scale procurement activities included in the design, but rather smaller payments to finance training and small goods packages for participants. Strong fiduciary mechanisms will be put in place for the procurement of equipment and assets under the project.\n\n47. **Stakeholder risks are Moderate.** There is only one implementing agency responsible for implementation, so intragovernmental coordination should not be complicated. There are different development partners and civil society organizations supporting IDPs, so coordination with these partners would be important. Existing Government-convened coordination bodies would be used to facilitate information sharing and collaboration with other organizations that are engaged in supporting IDPs. In addition, a Selection Committee will be formed to make decisions regarding the selection of project beneficiaries and the provision of supplies and equipment under Component 2.\n\n**VI.** **APPRAISAL SUMMARY**\n\n**Technical, Economic and Financial Analysis**\n\n48. Most of the technical design of the project has been implemented under the Youth Support Program (YSP) which was a subcomponent of the LSLP. These activities were implemented between 2018 and 2020 and evaluated using baseline and endline data collected from 827 persons out of the total number of 833 individuals that had participated in the YSP. This survey found that 86.7% of respondents (717 persons) were employed or self-employed. This figure was 0.4% (only 3 persons) at the baseline. 52% of beneficiaries were men and 48% women.\nMore than 91% of working beneficiaries were self-employed with the remainder working for other employers. Almost all the employed/self-employed respondents were found to be working in the field of their vocational study, except for 3 persons. Average monthly nominal income of the households surveyed, from all working individuals, increased by more than 2.5 times between the baseline and endline survey. As such, the design of the YSP has proven to be effective, so it has served as the basis of the design of this project.\n\n**Financial Management**\n\n49. Financial Management (FM) functions under the proposed grant, including flow of funds, Page 20 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["baseline and endline data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) staffing, accounting, reporting, and auditing, will be under the responsibility of the PIU at the SCRI. The current staff responsible for FM in SCRI have prior experience in implementation of WB projects. The project will follow the financial management covenants: semi-annual Interim Unaudited Financial Reports (IFRs) and the annual project audit will be part of the arrangements.\nThe overall FM risk for the Project remains moderate.\n\n50. The audit will be conducted by an independent private auditor in accordance with terms of reference acceptable to the World Bank and procured by the PIU. The annual audited project financial statements will be submitted to the World Bank within six months after the end of each reporting period. The audited financial statements will be posted on the internet within one month of the receipt of audited reports from the auditor. In addition, following the World Bank’s formal receipt of the financial statements from the recipient, the World Bank will make them available to the public in accordance with Bank Policy on Access to Information for Bank-financed operations. The cost of the audit would be financed from the proceeds of the grant. Project management oriented IFRs will be used for the grant. The IFR formats will be agreed with the PIU covering half a year throughout the life of the Project and will submit them to the World Bank no later than 45 days after the semester end.\n\n51. The proposed grant would follow standard flow of funds and disbursement arrangements, i.e., reimbursement, direct payment, advances, and special commitments including the use of Statement of Expenditure procedures. A separate Designated Account (DA) for grant funds would be opened in a commercial bank on terms and conditions acceptable to the Bank. The Ceiling of Advances to the DA will be elaborated in the Disbursement and Financial Information Letter (DFIL).\n\n**Procurement**\n\n52. Procurement will be carried out in accordance with the World Bank’s Procurement Regulations for Investment Project Financing (IPF) Borrowers for Goods, Works, Non-Consulting and Consulting Services, dated November 2020, Fourth Edition (Procurement Regulations) and is subject to the World Bank’s Anti-Corruption Guidelines, dated October 15, 2006 (revised in January 2011 and as of July 1, 2016). The Project will use the Systematic Tracking of Exchanges in Procurement (STEP) to plan, record and track procurement transactions.\n\n53. Procurement under the Project includes small scale, post review goods, consultancy & non-consultancy services including selection of NGOs and CSOs, where only Procurement Plans and Terms of References (TORs) are subject to prior review.\n\n54. Procurement risk is Moderate. The project will be managed by the PIU at SCRI. Even though the implementation team does not have prior experience with Bank’s new procurement Regulations, it has in-house procurement capacity for the size and type of procurement under Page 21 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) the project, and the Bank would provide relevant training and hands on support as needed. The Operational Manual that has been prepared for the project includes the relevant procurement process. The Bank’s oversight of the procurement will be done through implementation support and annual post reviews.\n\n. **A. Legal Operational Policies**\n\n**Triggered?**\n\nProjects on International Waterways OP 7.50 No Projects in Disputed Areas OP 7.60 No .\n\n**B. Environmental and Social**\n\n55. **The environmental and social risks of the project are considered Low.** There are no physical infrastructure investments proposed, so there is very little in the way of environmental risks. As the core of the project is to reduce the vulnerability of IDPs it should reduce the social risks faced by this group rather than exacerbating their challenging circumstances. The focus on building awareness and skills for social cohesion and conflict sensitivity will also help to mitigate social risks. SCRI has prepared a Stakeholder Engagement Plan and an Environmental and Social Commitment Plan (ESCP) for the project.\nThe ESCP will form part of the Grant Agreement package. The Operations Manual prepared by SCRI covers key environmental and social commitments, roles and responsibilities. As SCRI does not have prior experience implementing projects under ESF, the PIU staff and consequently all field staff involved in project implementation will receive training on applicable ES requirements with a focus on adherence to stakeholder engagement, grievance mechanism, labor terms and conditions, and occupational health and safety.\n\n**VII.** **World Bank Grievance Redress**\n\n38. **Grievance Redress** _**.**_ Communities and individuals who believe that they are adversely affected by a project supported by the World Bank may submit complaints to existing project-level grievance mechanisms or the Bank’s Grievance Redress Service (GRS). The GRS ensures that complaints received are promptly reviewed in order to address project-related concerns. Project affected communities and individuals may submit their complaint to the Bank’s independent Accountability Mechanism (AM). The AM houses the Inspection Panel, which determines whether harm occurred, or could occur, as a result of Bank non-compliance with its policies and procedures, and the Dispute Resolution Service, which provides communities and borrowers with the opportunity to address complaints through dispute resolution. Complaints may be submitted to the AM at any time after concerns have been brought directly to the attention of Bank Management and after Management Page 22 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) has been given an opportunity to respond. For information on how to submit complaints to the\n[Bank’s Grievance Redress Service (GRS), please visit http://www.worldbank.org/GRS. For](http://www.worldbank.org/grs) information on how to submit complaints to the Bank’s Accountability Mechanism, please visit\n[https://accountability.worldbank.org.](https://accountability.worldbank.org/) .\n\nPage 23 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**[The World Bank]**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**VII. RESULTS FRAMEWORK AND MONITORING**\n\n**Results Framework**\n**COUNTRY : Azerbaijan**\n**SPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan**\n\n**Project Development Objectives**\n\nEnhance civic engagement, technical skills and opportunities for income generation for vulnerable IDP households in Azerbaijan.\n\n**Project Development Objective Indicators**\n\n**Unit of** **Data Source /**\n**Indicator Name** **Corporate** **End Target** **Frequency**\n**Measure** **[Baseline ]** **Methodology**\n\n**Name:** Percentage of\nparticipants selfemployed or employed by firms Percentag e 0.00 80.00 Once, starting three months after trainees complete their courses.\n\nPost-Training Completion Survey conducted at least three months after training completion.\n\n**Responsibility for**\n**Data Collection**\n\nM&E Specialist with support from supplementary data collectors, as needed.\n\nDescription: The total number of individual participants completing their training programs divided by the number of individual participants completing their training programs that have either registered a business or where an employer has verified employment.\n\n**Name:** Increase in income\nof households with individuals participating Percentag e 0.00 30.00 Twice, once at baseline and once after training completion.\n\nBaseline survey conducted after applicants are registered in the project, and a Post-Training M&E Specialist and supplementary data collectors, as Page 24 of 34", "output": {"entities": {"named_data": [], "descriptive_data": ["Post-Training Completion Survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**Unit of** **Data Source /**\n**Indicator Name** **Corporate** **End Target** **Frequency**\n**Measure** **[Baseline ]** **Methodology**\n\nin the project Completion Survey conducted at least three months after training completion.\n\n**Responsibility for**\n**Data Collection**\n\nneeded.\n\nDescription: Total participant household income reported at the time of applying for project enrolment divided by total income reported before project completion.\n\n**Name:** Percentage of\nregistered participants completing training and receiving certificates Percentag e 0.00 85.00 Twice, once upon registration and once upon training certification.\n\nReports provided by training providers.\n\nDescription: Total number of participants receiving certificates divided by the total number of registered participants.\n\nTotal number of household members of applicants accepted and registered for training support.\n\nTotal number of household members in female participants accepted and registered for training support.\n\n**Name:** Beneficiaries of\njob-focused interventions Beneficiaries of jobfocused interventions Female ✔ Number 0.00 520.00 Once ✔ Number 0.00 260.00 Once M&E Specialist, training providers.\n\nM&E Specialist M&E Specialist Page 25 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**Unit of** **Data Source /**\n**Indicator Name** **Corporate** **End Target** **Frequency**\n**Measure** **[Baseline ]** **Methodology**\n\nDescription:\n\n**Name:** Percentage of\nbeneficiaries taking a more active role in their communities Percentage of beneficiaries taking a more active role in their communities - female Percentag e Percentag e 0.00 50.00 Twice, once before civic engagement training and again at least three months after civic engagement training completion.\n\n0.00 50.00 Twice, once before civic engagement training and again at least three months after civic engagement training completion.\n\nFor participants in Component 1 and 2 of the project, the Baseline Survey and Post-Training Completion Survey will be used for data collection.\nFor individuals trained only as part of Component 3, a separate pre-training survey will be conducted as well as a follow-up survey conducted at least three months after civic engagement training.\n\nFor participants in Component 1 and 2 of the project, the Baseline Survey and Post-Training Completion Survey will be used for data collection.\nFor individuals trained only as part of Component 3, a separate pre-training survey will be conducted as well as a follow-up survey conducted at least\n\n**Responsibility for**\n**Data Collection**\n\nM&E Specialist M&E Specialist Page 26 of 34", "output": {"entities": {"named_data": ["Baseline Survey and Post-Training Completion Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**Unit of** **Data Source /**\n**Indicator Name** **Corporate** **End Target** **Frequency**\n**Measure** **[Baseline ]** **Methodology**\n\nthree months after civic engagement training.\n\nPercentage of beneficiaries taking a more active role in their communities disabled Percentag e 0.00 50.00 Twice, once before civic engagement training and again at least three months after civic engagement training completion.\n\nFor participants in Component 1 and 2 of the project, the Baseline Survey and Post-Training Completion Survey will be used for data collection.\nFor individuals trained only as part of Component 3, a separate pre-training survey will be conducted as well as a follow-up survey conducted at least three months after civic engagement training.\n\n**Responsibility for**\n**Data Collection**\n\nM&E Specialist Description: Total number of individuals trained in civic engagement topics who report a higher level of community activity compared to the level reported before receiving training and support.\n\n**Intermediate Results Indicators**\n\n**Responsibility for**\n**Data Collection**\n\nPage 27 of 34\n\n**Indicator Name** **Corporate**\n\n**Unit of**\n**Measur**\n**e**\n\n**Data Source /**\n**Baseline** **End Target** **Frequency**\n**Methodology**", "output": {"entities": {"named_data": ["Baseline Survey and Post-Training Completion Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**Indicator Name** **Corporate**\n\n**Name:** Number of local\ngovernments participating in training on civic engagement\n\n**Unit of**\n**Measur**\n**e**\n\n**Data Source /**\n**Baseline** **End Target** **Frequency**\n**Methodology**\n\nNumber 0.00 15.00 Semi-annually\n\n**Responsibility for**\n**Data Collection**\n\nCivic Engagement Service Provider, M&E specialist Civic Engagement Service Provider Civic Engagement Service Provider M&E Specialist and supplementary data collectors, as needed.\n\nPage 28 of 34 Description: Total number of local government units who attended trainings\n\n**Name:** Percentage\nincrease in participant knowledge of civic engagement concepts and methodologies Percentage increase in knowledge of civic engagement concepts and methodologies among persons with disabilities and representatives of organizations of persons with disabilities Percentag e Percentag e 0.00 50.00 Before and after each civic engagement training event 0.00 50.00 Before and after each civic engagement training event Reports provided by Civic Engagement Service Provider Pre and post training tests Pre and post training tests Description: Percentage increase in pre and post training test scores for individuals who participated in civic engagement training\n\n**Name:** Percentage of\nnew businesses and employment still active after three months Percentag e 0.00 75.00 Once, at least three months after training completion, with the possibility of Post-Training Completion Survey", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**Data Source /**\n**Baseline** **End Target** **Frequency**\n**Methodology**\n\nsupplementary surveys, as needed\n\n**Indicator Name** **Corporate**\n\n**Unit of**\n**Measur**\n**e**\n\nDescription: Total number of businesses established or jobs obtained by project participants which are still active after three months\n\n**Name:** Digital civic\nengagement platform created and operational Yes/No N Y Once, with follow-up to ensure ongoing operations Announcement by Civic Engagement Service Provider\n\n**Responsibility for**\n**Data Collection**\n\nCivic Engagement Service Provider Description: Launch of a digital platform for project beneficiaries and other IDPs in target areas to provide share views on the project and related livelihood issues Page 29 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125)\n\n**Target Values**\n\n**Project Development Objective Indicators FY**\n\n**RESULT_FRAME_TBL_PDO**\n\n**Indicator Name** **End Target**\n\nPercentage of participants self-employed or employed by firms 80.00 Increase in income of households with individuals participating in the project 30.00 Percentage of registered participants completing training and receiving certificates 85.00 Beneficiaries of job-focused interventions 520.00 Beneficiaries of job-focused interventions - Female 260.00 Percentage of beneficiaries taking a more active role in their communities 50.00 Percentage of beneficiaries taking a more active role in their communities - female 50.00 Percentage of beneficiaries taking a more active role in their communities - disabled 50.00\n\n**Intermediate Results Indicators FY**\n\n**RESULT_FRAME_TBL_IO**\n\n**Indicator Name** **Baseline** **End Target**\n\nNumber of local governments participating in training on civic engagement 0.00 15.00 Percentage increase in participant knowledge of civic engagement concepts and 0.00 75.00 methodologies Percentage increase in knowledge of civic engagement concepts and methodologies among persons with disabilities and representatives of organizations of persons with disabilities 0.00 75.00 Percentage of new businesses and employment still active after three months 0.00 75.00 Digital civic engagement platform created and operational N Yes Page 30 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSPF: Improved Livelihoods for Internally Displaced Persons in Azerbaijan (P178125) Page 31 of 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**FOR OFFICIAL USE ONLY**\n\n\nINTERNATIONAL DEVELOPMENT ASSOCIATION\n\n\nPROJECT APPRAISAL DOCUMENT\n\n\n\nReport No: PAD4647", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "CURRENCY EQUIVALENTS\n\n\n(Exchange Rate Effective April 30, 2022)\n\n\nCurrency Unit =  Uganda Schillings (UGX)\n\nUGX 3,569.05 = US$1\n\nUS$1 = SDR 0.74388157\n\n\nFISCAL YEAR\nJuly 1 - June 30\n\n\nRegional Vice President: Hafez M. H. Ghanem\n\nCountry Director: Keith E. Hansen\n\nActing Regional Director: Catherine Signe Tovey\n\nPractice Manager: Helene Monika Carlsson Rex\n\nTask Team Leaders: [Margarita Puerto Gomez, Fatima Naqvi, Samuel Thomas ]\n\nClark", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "ABBREVIATIONS AND ACRONYMS\n\n\nBMGF Bill and Melinda Gates Foundation\nBTVET Business Technical Vocational Education and Training\nCDOs Community Development Officers\nCOVID-19 Corona Virus Disease of 2019\nCPF Country Partnership Framework\nCRI Corporate Results Indicator\nCRRF Comprehensive Refugee Response Framework\nCSO Civil Society Organization\nDRDIP Development Response to Displacement Impacts Project\nEIRR Economic internal rate of return\nESF Environment and Social Framework\nESMF Environmental and Social Management Framework\nESS Environmental and Social Standards\nFI Financial Institution\nGBV Gender Based Violence\nGDP Gross Domestic Product\nGoU Government of Uganda\nGRID Green, Inclusive, Resilient Development\nGRM Grievance Redress Mechanism\nGRS Grievance Redress Services\nGROW Generating Growth Opportunities and Productivity for Women Enterprises\nICT Information and Communication Technology\nIE Impact Evaluation\nIDA International Development Association\nINVITE Investment For Industrial Transformation and Employment Project\nJLIRP Jobs and Livelihoods Integrated Response Plan for Refugees and Host Communities\nKCCA Kampala Capital City Authority\nLED Local Economic Development\nM&E Monitoring and Evaluation\nMFI Microfinance Institution\nMFPED Ministry of Finance, Planning, and Economic Development\nMGLSD Ministry of Gender, Labor, and Social Development\nMIS Management Information System\nMLG Ministry of Local Government\nMSMEs Micro, Small, and Medium Enterprises\nMUBS Makerere University Business School\nNDP National Development Plan\nNEMA National Environmental Management Authority\nNGO Non-government Organizations\nNPV Net present value\nNUSAF Northern Uganda Social Action Fund\nOPM Office of the Prime Minister\nPACR Project Advisory Committee for Refugees\nPBCs Performance-based Conditions\nPDM Parish Development Model\nPFI Participating Financial Institution", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "PIT Project Implementation Team\nPO Production officers\nPOM Program Operations Manual\nPPDA Public Procurement and Disposal of Public Assets\nPPP Private-Public Partnership\nPPSD Project Procurement Strategy for Development\nPSC Project Steering Committee\nPSFU Private Sector Foundation Uganda\nPTC Project Technical Committee\nRHD Refugee-Hosting District\nSOPs Standard Operating Procedures\nSORT Systematic Operations Risk-rating Tool\nSTEP Systematic Tracking of Exchanges in Procurement\nUBOS Uganda Bureau of Statistics\nUGGDS Uganda Green Growth Development Strategy\nUIA Uganda Investment Authority\nUIRI Uganda Industrial Research Institute\nUNHCR United Nations High Commissioner for Refugees\nUNHS Uganda National Household Survey\nUEW Unsafe Environment for Women\nUWEP Uganda Women Entrepreneurship Program\nVSLAs Village Savings and Loans Associations\nWEE Women’s Economic Empowerment\nWHR Window for Host Communities and Refugees", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda (P176747)\n\n\n**TABLE OF CONTENTS**\n**DATASHEET** _**.......................................................................................................................................... 1**_\n\n**I.** **STRATEGIC CONTEXT ........................................................................................................ 7**\n\nA. Country Context ................................................................................................................................ 7\n\nB. Sectoral and Institutional Context .................................................................................................... 8\n\nC. Relevance to Higher Level Objectives ............................................................................................. 12\n\n**II.** **PROJECT DESCRIPTION ................................................................................................... 14**\n\nA. Project Development Objective ..................................................................................................... 14\n\nB. Project Components ....................................................................................................................... 15\n\nC. Project Costs and Financing ............................................................................................................ 27\n\nD. Project Beneficiaries ....................................................................................................................... 27\n\nE. Results Chain ................................................................................................................................... 28\n\nF. Rationale for World Bank Involvement and Role of Partners ......................................................... 29\n\nG. Lessons Learned and Reflected in the Project Design .................................................................... 29\n\n**III.** **IMPLEMENTATION ARRANGEMENTS .............................................................................. 30**\n\nA. Institutional and Implementation Arrangements .......................................................................... 30\n\nB. Results Monitoring and Evaluation Arrangements ......................................................................... 32\n\nC. Sustainability ................................................................................................................................... 32\n\n**IV.** **PROJECT APPRAISAL SUMMARY ..................................................................................... 33**\n\nA. Economic and Technical Analysis ................................................................................................... 33\n\nB. Fiduciary .......................................................................................................................................... 35\n\nC. Legal Operational Policies ............................................................................................................... 36\n\nD. Environmental and Social ............................................................................................................... 36\n\n**V.** **GRIEVANCE REDRESS SERVICES ....................................................................................... 38**\n\n**VI.** **KEY RISKS ....................................................................................................................... 38**\n\n**VII.** **RESULTS FRAMEWORK AND MONITORING ..................................................................... 40**\n\n**ANNEX 1: Implementation Arrangements and Support Plan ........................................... 51**\n\n**ANNEX 2: Alignment of World Bank supported Projects in Refugee and Host Districts**\n**(RHD) .............................................................................................................................. 70**\n\n**ANNEX 3: Eligibility Criteria for Selection of Participating Financial Institutions (PFIs) .... 72**\n\n**ANNEX 4: Climate Change Co-Benefits ............................................................................ 75**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda (P176747)\n\n\nDATASHEET\n\n\n**BASIC INFORMATION**\n~~BASIC~~ ~~INFO~~ ~~TABLE~~\n\nCountry(ies) Project Name\n\n\nUganda Generating Growth Opportunities and Productivity for Women Enterprises Project\n\n\nProject ID Financing Instrument Environmental and Social Risk Classification\n\n\nInvestment Project\nP176747 Substantial\nFinancing\n\n\n**Financing & Implementation Modalities**\n\n[ ] Multiphase Programmatic Approach (MPA) [ ] Contingent Emergency Response Component (CERC)\n\n\n[ ] Series of Projects (SOP) [ ] Fragile State(s)\n\n\n[ ] Performance-Based Conditions (PBCs) [ ] Small State(s)\n\n[ ] Financial Intermediaries (FI) [ ] Fragile within a non-fragile Country\n✓\n\n[ ] Project-Based Guarantee [ ] Conflict\n\n[ ] Deferred Drawdown [ ] Responding to Natural or Man-made Disaster\n\n\n[ ] Alternate Procurement Arrangements (APA) [ ] Hands-on Enhanced Implementation Support (HEIS)\n\n\nExpected Approval Date Expected Closing Date\n\n\n17-Jun-2022 31-Dec-2027\n\n\nBank/IFC Collaboration\n\n\nNo\n\n\n**Proposed Development Objective(s)**\n\n\nTo increase access to entrepreneurial services that enable female entrepreneurs to grow their enterprises in targeted\nlocations, including host and refugee communities\n\n\n**Components**\n\n\n**Component Name** **Cost (US$, millions)**\n\n\nPage 1 of 78", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\nSupport for Women Empowerment and Enterprise Development Services, including in\n42.00\nhost and refugee communities\n\nAccess to Finance for Women Entrepreneurs 90.00\n\n\nEnabling Infrastructure and Facilities for Women Enterprise Growth and Transition 70.00\n\n\nProgram Management Support, Policy Innovation, and Evidence Generation 15.00\n\n\n**Organizations**\n\n\nBorrower: Republic of Uganda\n\nImplementing Agency: Ministry of Gender, Labor and Social Development\nPrivate Sector Foundation Uganda\n\n\n**PROJECT FINANCING DATA (US$, Millions)**\n\n\n**-NewFin1**\n**SUMMARY**\n\n\n**Total Project Cost** 217.00\n\n**Total Financing** 217.00\n\n**of which IBRD/IDA** 217.00\n\n**Financing Gap** 0.00\n\n\n**-NewFinEnh1**\n**DETAILS**\n\n\n**World Bank Group Financing**\n\n\nInternational Development Association (IDA) 217.00\n\n\nIDA Grant 217.00\n\n\n**IDA Resources (in US$, Millions)**\n\n\n**Credit Amount** **Grant Amount** **Guarantee Amount** **Total Amount**\n\n**Uganda** 0.00 217.00 0.00 217.00\n\nNational PBA 0.00 181.00 0.00 181.00\n\n\nRefugee 0.00 36.00 0.00 36.00\n\n**Total** **0.00** **217.00** **0.00** **217.00**\n\n\nPage 2 of 77", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\n**Expected Disbursements (in US$, Millions)**\n\n\n**WB Fiscal Year** 2022 2023 2024 2025 2026 2027\n\n\n**Annual** 0.00 21.29 25.77 32.10 40.01 55.07\n\n**Cumulative** 0.00 21.29 47.06 79.16 119.17 174.24\n\n\n**INSTITUTIONAL DATA**\n\n\n**Practice Area (Lead)** **Contributing Practice Areas**\n\nEducation, Finance, Competitiveness and Innovation, Gender,\nSocial Sustainability and Inclusion\nSocial Protection & Jobs\n\n\n**Climate Change and Disaster Screening**\n\nThis operation has been screened for short and long-term climate change and disaster risks\n\n\n**SYSTEMATIC OPERATIONS RISK-RATING TOOL (SORT)**\n\n\n**Risk Category** **Rating**\n\n\n1. Political and Governance  Moderate\n\n\n2. Macroeconomic  Moderate\n\n\n3. Sector Strategies and Policies  Moderate\n\n\n4. Technical Design of Project or Program  Substantial\n\n\n5. Institutional Capacity for Implementation and Sustainability  Substantial\n\n\n6. Fiduciary  Substantial\n\n\n7. Environment and Social  Substantial\n\n\n8. Stakeholders  Low\n\n\n9. Other  Moderate\n\n\n10. Overall  Substantial\n\n\nPage 3 of 77", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\n**COMPLIANCE**\n\n\n**Policy**\nDoes the project depart from the CPF in content or in other significant respects?\n\n[ ] Yes   [ ] No\n✓\n\n\nDoes the project require any waivers of Bank policies?\n\n[ ] Yes   [ ] No\n✓\n\n\n**Environmental and Social Standards Relevance Given its Context at the Time of Appraisal**\n\n\n**E & S Standards** **Relevance**\n\n\nAssessment and Management of Environmental and Social Risks and Impacts Relevant\n\n\nStakeholder Engagement and Information Disclosure Relevant\n\n\nLabor and Working Conditions Relevant\n\n\nResource Efficiency and Pollution Prevention and Management Relevant\n\n\nCommunity Health and Safety Relevant\n\n\nLand Acquisition, Restrictions on Land Use and Involuntary Resettlement Relevant\n\n\n\nBiodiversity Conservation and Sustainable Management of Living Natural\nResources\n\nIndigenous Peoples/Sub-Saharan African Historically Underserved Traditional\nLocal Communities\n\n\n\nRelevant\n\n\nRelevant\n\n\n\nCultural Heritage Relevant\n\n\nFinancial Intermediaries Relevant\n\n\n**NOTE** : For further information regarding the World Bank’s due diligence assessment of the Project’s potential\nenvironmental and social risks and impacts, please refer to the Project’s Appraisal Environmental and Social Review\nSummary (ESRS).\n\n\n**Legal Covenants**\n\n\nPage 4 of 77", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\n**Conditions**\n\nType Financing source Description\nEffectiveness IBRD/IDA The Association is satisfied that the Recipient has an adequate\nrefugee protection framework.\n\nType Financing source Description\nEffectiveness IBRD/IDA The Recipient has prepared and adopted the Project Operations\nManual (POM), in form and substance satisfactory to the\nAssociation.\n\nType Financing source Description\nEffectiveness IBRD/IDA A Subsidiary Agreement, acceptable to the Association, has been\nduly executed and delivered on behalf of the Recipient and the\nProject Implementing Entity, and such Subsidiary Agreement has\nbecome effective and binding upon the parties in accordance with\nits terms;\n\nType Financing source Description\nEffectiveness IBRD/IDA The Recipient has, consistent with ESS 10, prepared, consulted on,\nadopted and implemented, the Stakeholder Engagement Plan (SEP)\nin accordance with the Stakeholder Engagement Framework (SEF),\nin form and substance satisfactory to the Association\n\nType Financing source Description\nEffectiveness IBRD/IDA The Recipient has: (i) established the Project Implementation\nTeams (PITs) with functions, and terms of reference satisfactory to\nthe association; and (ii) designated to said PITs a Project\ncoordinator, a procurement specialist, a financial management\nspecialist, an environmental specialist, a social development\nspecialist, and a gender specialist, all in accordance with the\nprovisions of the Procurement Regulations.\n\nType Financing source Description\nDisbursement IBRD/IDA No withdrawal shall be made, under Categories (3), (4) and (5),\nunless, the Recipient has prepared and adopted the Grants Manual,\nin the form and substance satisfactory to the Association\n\nType Financing source Description\nDisbursement IBRD/IDA No withdrawal shall be made, under category (4), unless, consistent\nwith ESS9, the Recipient and the Project Implementing Entity have\nensured that: (A) the Recipient has conducted the readiness\nassessment of the Project Implementing Entity’s environmental and\nsocial management systems, in form and substance satisfactory to\nthe Association; (B) each PFI has adopted and operationalized the\n\n\nPage 5 of 77", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\nenvironmental and social management systems, acceptable to the\nAssociation, for screening subprojects; (C) assessed, in form and\nsubstance satisfactory to the Association, the organizational\ncapacity and competency of each PFI for implementing\nenvironmental social management systems; and (D) hired or\ndesignated a senior representative with experience, qualifications,\nand terms of reference satisfactory to the Association, with the\nresponsibility for overall accountability of environmental and social\nperformance of approved activities\n\nType Financing source Description\nDisbursement IBRD/IDA No withdrawal shall be made under Category (5), unless, the\nRecipient has prepared, consulted on, adopted, and disclosed the\nLabor Management Procedures (LMP) for the Project, in form and\nsubstance satisfactory to the Association.\n\n\nPage 6 of 77", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\n**I.** **STRATEGIC CONTEXT**\n\n\n**A. Country Context**\n\n\n1. **Uganda has made significant progress towards socioeconomic transformation over the last three decades. The**\n**country registered an average annual Gross Domestic Product (GDP) growth above 5 percent during the 2002 -2012**\n**period.** However, the country’s growth trajectory remains volatile, following a leveling off of growth and the continuing\nimpacts of the Corona Virus Disease of 2019 (COVID-19). GDP growth decelerated to around 5 percent during 2013–2019\nand the economy contracted by 1.1 percent in 2020 because of the Government of Uganda (GoU) strict COVID-19\ncontainment measures and disruptions in global supply chains, which prompted a tightening of public investment and a\ndrop in consumption. With the loosening of containment measures, real GDP growth rebounded by over 13 percent in\nthe fourth quarter of fiscal 2021, as restrictions to contain COVID-19 were relaxed. Consumption, public investment, and\nservices all recovered strongly. However, new waves of infection in late 2021 and early 2022 led to the imposition of new\nlockdown measures, disrupting the nascent economic recovery. The medium-term outlook remains uncertain. Under a\nbaseline scenario, real GDP is expected to grow by around 3.5-4.0 percent in fiscal 2022 and about 5.5 percent in fiscal\n2023. Considering large global and domestic uncertainties, such as oil and food price shocks exacerbated by the war in\nUkraine, the recovery could be slower.\n\n\n2. **The COVID-19 shock has been accompanied by increases in poverty and unemployment** . According to the latest\nUganda National Household Survey (UNHS), although overall poverty in 2019/20 (20.3 percent) was slightly lower than\nin 2016/17 (21.4 percent), poverty in the COVID-19 period was significantly higher than in the pre-COVID-19 period. [1] It\nincreased to 21.9 percent during the first COVID-19 wave. Rising unemployment and work stoppages have pushed many\nUgandans, especially women, back into subsistence agriculture, setting back achievement of the country’s development\ngoal of reducing the share of the population dependent on subsistence agriculture as a main source of livelihood from\n69 to 55 percent between 2020/21 and 2024/25. [2]\n\n\n3. **Uganda is experiencing accelerating impacts from climate change that affect livelihoods in key sectors.** Rising\ntemperatures and variability in rainfall is producing more intense and longer lasting droughts as well as more frequent,\nheavy precipitation events leading to flooding and landslides. These vulnerabilities are heightened given the economy’s\nstrong reliance on climate-sensitive sectors like agriculture and tourism. While women are particularly vulnerable to the\nimpacts of climate change, given their reliance on subsistence agriculture, they are also active in safeguarding resources\nsuch as water, giving them important roles in protecting the environment.\n\n\n4. **Supporting women’s entrepreneurship holds a critical place in Uganda’s efforts to revive its economy.** Currently,\nwomen are less likely than men to be paid employees (13 percent of women compared with 23 percent of men), and\nmore likely to be self-employed (80 percent compared to men’s 70 percent). [3] In this context, promoting ways for women\nto grow and expand their businesses is a good option to promote economic recovery. Micro, small, and medium\nenterprises (MSMEs) created within the past five years now generate over 50 percent of formal jobs, and household\nenterprises provide employment for another 3.1 million households. [4] Furthermore, women are particularly vulnerable\n\n\n1 The Uganda Bureau of Statistics (UBOS) has recently announced poverty rates based on the UNHS 2019/2020. The data for this survey was\ncollected in two periods with a break during the strictest lockdown period between March–June 2020. The first data collection period started in\nSeptember 2019 and ended in February 2020, then it resumed in July 2020 and ended in November 2020.\n2 Government of Uganda (2020), Third National Development Plan (NDP III).\n3 GoU 2018. National Labour Force Survey.\n4 World Bank. 2019. “Uganda Jobs Strategy for Inclusive Growth.”\n\n\nPage 7 of 77", "output": {"entities": {"named_data": ["Uganda National Household Survey", "National Labour Force Survey", "UNHS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\nto climate change impacts but are also stewards of environmental management, have rich traditional knowledge and\nexperience adapting to climate change, and have great potential to contribute green, resilient recovery of the Ugandan\neconomy.\n\n_**Refugees and Host Communities**_\n\n\n5. **Uganda hosts the largest number of refugees in Africa, of which 52 percent are female.** Their number has more\nthan doubled since 2015 to almost 1.6 million. [5] About 94 percent of the refugees live in settlements across 12 Refugee\nHosting Districts (RHDs) with a population of 4,437,500 people (excluding Kampala), while the remainder live in urban\nareas. GoU has invested consistently in improving access to services, infrastructure and livelihoods opportunities for\nrefugees and hosting communities both in rural and urban settings. There is a working age population (between 18 and\n59) in refugee hosting districts of more than 315,000 refugee women and 918,000 host community women. [6] Half of all\nrefugee households are headed by women. [7 ]\n\n6. **Uganda’s refugee population is overwhelmingly young and female, highly vulnerable to climate and other**\n**shocks, and heavily dependent on government aid** . For refugee women, reduced humanitarian assistance and fewer\nfood rations coupled with the lockdowns and economic recession has further reduced their incomes and exacerbated\ntheir vulnerability. They have been more adversely affected by climate-related and COVID-19 shocks than their Ugandan\ncounterparts and have been slower to recover. [8] Female refugees were more likely to stop working following COVID-19\nlockdowns than Ugandan nationals or male refugees. While there was no difference based on gender, refugee businesses\nwere less likely to continue operating after COVID-19 shocks than those of Ugandan nationals. Compared to less than a\nquarter of Ugandan households, at least half of refugee households borrowed money to cope with the impacts of the\nCOVID-19 emergency. Preliminary evidence suggests that many refugees in urban areas moved to rural areas during\nlockdown and the subsequent months, due to difficulties of paying rent with reduced income. Under these intense\npressures, refugees are ten times more likely to suffer from depression. [9]\n\n\n**B. Sectoral and Institutional Context**\n\n\n7. **Uganda has the highest proportion of women’s business ownership in the Africa region.** The 2020 Mastercard\nGlobal Index of Women Entrepreneurs estimated that women own nearly 40 percent of all businesses. [10] Earlier surveys\nhave presented more varied estimates, suggesting female-owned enterprises make up between 23–44 percent of all\nbusinesses. [11] MSMEs are critical to the economic growth. They employ nearly 2.5 million people, 90 percent of all private\nsector employees, produce 80 percent of manufactured products, and generate 20 percent of GDP. [12]\n\n8. **Yet most women-led firms never grow past the micro level, while male-owned firms are twice as likely to move**\n**from micro to small size** . Estimates from various surveys suggest that 80–94 percent of all women-owned firms in Uganda\n\n\n5 United Nations High Commissioner for Refugees (UNHCR) and the Office of the Prime Minister (OPM). 2022. Uganda Comprehensive Refugee\nResponse Poral.\n\n6 Host community numbers are UNHCR and OPM figures based on projected UBOS 2020 census data for women aged 20-59.\n7 World Bank. 2019 Informing the Refugee Policy Response in Uganda: Results from the Uganda Refugee and Host Communities 2018 Household\nSurvey (English). Washington, DC: World Bank.\n8 World Bank. 2021. Monitoring Social and Economic Impacts of COVID-19 on Refugees in Uganda: Results from the High-Frequency Phone - Third\nRound. World Bank, Washington, DC. World Bank.\n9 High-Frequency Phone Survey- Third Round. 2021.\n10 Understood as firms in which at least 51 percent of shares are owned by women.\n11 2021. Rapid Profiling of the Socioeconomic Dimensions of Female Entrepreneurs in Uganda. GROW Preparation, October 2021.\n12 Financial inclusion and the growth of small medium enterprises in Uganda: empirical evidence from selected districts in Lango subregion. _J_\nInnov Entrep 10, 23 (2021).\n\n\nPage 8 of 77", "output": {"entities": {"named_data": ["Uganda Refugee and Host Communities 2018 Household Survey", "2020 Mastercard Global Index of Women Entrepreneurs"], "descriptive_data": ["UBOS 2020 census data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\nare microenterprises, those with fewer than five employees and annual turnover of less than 10 million Uganda shillings\n(less than US$2,810). About 60 percent of female-owned microenterprises have only one employee, with nearly 77\npercent having four or fewer employees [13] . Notably, male-owned firms also cluster at the micro level, with 76 percent\nhaving four or fewer employees. [13] Very few firms transition to employing 10 people or more (7.5 percent), but more than\ntwice as many male-owned firms make this jump (9.4 percent) compared to women-owned firms (2.6 percent).\n\n\n9. **Women’s businesses also tend to be located in more vulnerable sectors, and earn 30 percent less in profits than**\n**firms owned by men** . [14] Fewer than 10 percent of women entrepreneurs run businesses in sectors traditionally dominated\nby men, such as transport, or agribusiness **.** However, women who managed to enter male-dominated sectors attained\nrevenues equal to those of male-owned firms. [15] This points to significant potential for benefits in terms of growth and\njob creation if women’s micro-enterprises are able to expand to new sectors and grow.\n\n\n10. **Lockdowns in 2020 and 2021 hit MSMEs hard, especially those owned by women** . A study by the National\nFederation of Small and Medium-Sized Enterprises reported that 49 percent of MSMEs struggled to pay their bills, and\n45 percent of businesses in Kampala had to close as a direct consequence of the pandemic. [16] While women-led\nbusinesses were about as likely to close as male-led firms during 2020–21, women entrepreneurs were more than twice\nas likely to require financial assistance to reopen (85 percent) than male entrepreneurs (31 percent). [17] Younger, female\nentrepreneurs (ages 15–30) were the most affected, with business closure rates about twice as high as those for males\nof the same age. [18]\n\n\n_**Constraints to Women’s Entrepreneurship and Enterprise Growth**_\n\n\n11. **Several factors limit women’s ability to take advantage of economic opportunities.** These accrue throughout\nchildhood, adolescence, and adulthood, culminating in women’s much greater exclusion from growth-oriented private\nenterprise. Limiting factors include weak implementation of Uganda’s progressive legal and policy framework to promote\ngender equality, social norms that promote women’s economic dependence on men, caretaking demands on women,\nhigh exposure to violence and harassment in the workplace, and climate related and other stressors. Other critical\nconstraints include: (a) gender discriminatory barriers in accessing finance; (b) skills mismatch and occupational\nsegregation; (c) limited access to technology, information, and opportunities to build skills and networks; and (d) lack of\naccess to infrastructure and facilities such as childcare facilities and safe and accessible transport.\n\n12. **Chief among the factors that limit female entrepreneurship are household roles and responsibilities** . Rigid\ntraditional norms dictate that women and girls are responsible for family care and housekeeping duties, whereas men\nare traditionally perceived as the primary income earners of the household. Before the COVID-19 lockdowns, 92 percent\nof women and 31 percent of men ages 31–64 were engaged in care work. Women care for children, sick, and elderly\nhousehold members at much higher rates than men. The competing demands on women’s time affect their businesses’\nprofits: 37 percent of women entrepreneurs bring their children to work, compared to no men, and profits of enterprises\nwhere small children are present are 48 percent lower than those without small children present, including female\n\n13 2021. Rapid Profiling of the Socioeconomic Dimensions of Female Entrepreneurs in Uganda. GROW Preparation, October 2021.\n14 World Bank Group. 2019. Profiting from Parity: Unlocking the Potential of Women's Business in Africa. World Bank, Washington, DC.\n15 2015. Breaking the Metal Ceiling. Female Entrepreneurs Who Succeed in Male-Dominated Sectors. Washington, D.C.: World Bank.\n16 Federation of Small and Medium Sized Enterprises in Uganda 2021, August.\n17 2022. Gendered Impacts of the COVID-19: Crisis in Uganda and Opportunities for an Inclusive and Sustainable Recovery (English). Washington,\nD.C.: World Bank Group.\n18 2022. Gendered Impacts of the COVID-19: Crisis in Uganda and Opportunities for an Inclusive and Sustainable Recovery (English). Washington,\nD.C.: World Bank Group.\n\n\nPage 9 of 77", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nGenerating Growth Opportunities and Productivity for Women Enterprises Uganda Project (P176747)\n\n\nowned enterprises without children. [19] With total fertility rates in Uganda still very high at 4.7 children per woman, care\nburdens are compounded for women.\n\n13. **Social norms and risks of violence against women also influence the choices of Ugandan women for businesses**\n**sectors and sizes.** Women can feel discouraged from entering or expanding in more profitable (male-dominated) sectors,\nas doing so may signal their transgression of gender norms about men being the main income providers in households.\nRisk of violence also constitutes a significant barrier to women’s entrepreneurship in Uganda. A 2020 national survey of\nviolence against women reports that almost all (95 percent) of Ugandan women between 15–49 years old have\nexperienced physical or sexual violence from either an intimate partner or a non-partner during their lifetime. [20] This is\nmore than three times the global average (27 percent lifetime,) and the averages for Sub-Saharan Africa (33 percent\nlifetime). _[21 ]_ More than half reported that their partners insisted on knowing where they were at all times (54 percent) and\ncontrol how they spend their money (29 percent).\n\n14. **Women entrepreneurs are likely to be excluded from the channels of information, networks, and mentors**\n**associated with the more profitable, male-dominated sectors and businesses within them** . Throughout a firm’s life, the\ndiversity of networks can impact whether an entrepreneur has access to credit, learns about new market opportunities,\nand acquires the skills needed to successfully operate their businesses. [22] Studies of women entrepreneurs in Uganda find\nthat women who work closely with a mentor—often male, and usually a family member—are more likely to transition\ninto higher-profit sectors. [ 23] [24]\n\n15. **Additional factors that block women from developing growth-oriented enterprises in profitable sectors are**\n**related to the failure of existing business development services to address the needs of women-owned firms** .\nAccording to an enterprise survey conducted in 2014, MSMEs lacked key skills needed for business growth. Only 28\npercent of firms surveyed said they do book-keeping to track revenues and expenses; a mere 10 percent had invested in\ntraining for employees; and just 36 percent had access to the internet. Female-owned firms appear to be particularly\nlacking when it comes to the use of standard business practices. A recent microenterprise survey showed a gender gap\nof 24 percentage points on an index of adoption of good business practices. Few training courses address the specific\nchallenges of formalizing a business, including meeting tax obligations, preparing proper records, fulfilling reporting\nrequirements, and obtaining licenses. Training tends to focus on limited topics, such as financial or computer literacy,\nbut leaves out training in life skills and support for network. Yet, global evidence demonstrates that developing socioemotional skills, through psychology-based trainings, are as important to enterprise success as strengthening business\nskills. [25] Finally, many business development services continue to train women for sectors where women-owned firms are\nover-represented, such as small trade or food service, rather than where they could diversify their business and earn\nhigher profits.\n\n\n19 Delecourt, S. and Fitzpatrick, A. 2021. “Childcare Matters: Female Business Owners and the Baby-Profit Gap.” Management Science, Vol, 67, No.\n7. May 13.\n20 Uganda Bureau of Statistics (2021). Uganda Violence Against Women and Girls Survey 2020. Uganda Bureau of Statics. Kampala, Uganda. This\nsurvey was designed as part of the UNHS and drew from UNHS samples which are nationally representative.\n21 World Health Organization (2021). Violence against women prevalence estimates, 2018: global, regional and national prevalence estimates for\nintimate partner violence against women and global and regional prevalence estimates for non-partner sexual violence against women. Geneva:\nWorld Health Organization.\n22 World Bank (2019). Profiting from Parity: Unlocking the Potential of Women’s Business in Africa. Washington, D.C.: World Bank.\n23 Campos et al. 2015.\n24 World Bank, 2022. Breaking Barriers: Female Entrepreneurs Who Cross Over to Male-Dominated Sectors. Washington, D.C.: World Bank.\n25 Campos, F., Frese, M., Goldstein, M., Iacovone, L., Johnson, H. C., McKenzie, D., and Mensmann, M. 2017. “Teaching personal initiative beats\ntraditional training in boosting small business in West Africa.” Science, 357(6357), 1287-1290.\n\n\nPage 10 of 77", "output": {"entities": {"named_data": ["Uganda Violence Against Women and Girls Survey 2020"], "descriptive_data": ["national survey of violence against women"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**FOR OFFICIAL USE ONLY**\n\nReport No: PAD4591 INTERNATIONAL BANK FOR RECONSTRUCTION AND DEVELOPMENT PROJECT APPRAISAL DOCUMENT ON", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "CURRENCY EQUIVALENTS (Exchange Rate Effective August 20, 2021) Currency Unit = Iraqi Dinar (IQD) IQD 1,459.0 =US$1 FISCAL YEAR January 1 - December 31 Regional Vice President: Ferid Belhaj Country Director: Saroj Kumar Jha Regional Director: Keiko Miwa Practice Manager: Rekha Menon Task Team Leader(s): Iryna Postolovska, Amr Elshalakani", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) ABBREVIATIONS AND ACRONYMS AEFI Adverse Events Following Immunization AF Additional Financing ACG Anti-Corruption Guidelines BFP Bank Facilitated Procurement CERC Contingent Emergency Response Component COVAX Facility COVID-19 Vaccines Global Access Facility COVID-19 Coronavirus Disease 2019 CT Computed Tomography DA Designated Account DO Development Objective EHS Environment, Health and Safety EOC Emergency Operations Center EODP Emergency Operation for Development Project EPI Expanded Program for Immunization EPRP Emergency Preparedness and Response Plan ESCP Environmental and Social Commitment Plan ESF Environmental and Social Framework ESMF Environmental and Social Management Framework EUA Emergency Use Authorization EUL Emergency Use Listing FM Financial Management FTCF Fast Track COVID-19 Facility GAVI Global Alliance for Vaccines and Immunization GDP Gross Domestic Product GOI Government of Iraq GHG Greenhouse Gas GRM Grievance Redress Mechanism GRS Grievance Redress Service HEIS Hands-on Enhanced Implementation Support HNP Health, Nutrition, and Population I3RF Iraq Reform, Recovery and Reconstruction Fund IBM Iterative Beneficiary Monitoring IBRD International Bank for Reconstruction and Development ICU Intensive Care Unit IDA International Development Association IDP Internally Displaced Persons IFC International Finance Corporation IHR International Health Regulation IPF Investment Project Financing ISR Implementation Status and Results Report LMIS Logistics Management Information System", "output": {"entities": {"named_data": ["LMIS Logistics Management Information System"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) M&E Monitoring and Evaluation MIS Management Information System MOHE Ministry of Health and Environment MPA Multiphase Programmatic Approach MWMP Medical Waste Management Plan NCC National Coordinating Committee NCD Non-communicable Disease NDVP National Deployment and Vaccination Plan NGO Non-Government Organization NTWG National Technical Working Group OHS Occupational Health and Safety OOP Out-of-Pocket PAD Project Appraisal Document PDO Project Development Objective PHC Primary Health Care PMU Project Management Unit POM Project Operational Manual PQ Prequalification PPE Personal Protective Equipment R&D Research and Development RFQ Request for Quotation RT-PCR Reverse Transcription Polymerase Chain Reaction SAGE Strategic Advisory Group of Experts on Immunization SEA/SH Sexual Exploitation and Abuse/Sexual Harassment SEP Stakeholder Engagement Plan SMS Short Message Service SRA Stringent Regulatory Authorities STEP Systematic Tracking of Exchanges in Procurement TA Technical Assistance UHC Universal Health Coverage ULT Ultra Low Temperature UNICEF United Nations Children’s Fund UNOPS United Nations Office for Project Services VAC Vaccine Approval Criteria VIRAT Vaccine Introduction Readiness Assessment Tool VRAF Vaccine Readiness Assessment Framework WBG World Bank Group WHO World Health Organization", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) TABLE OF CONTENTS\n\n**DATASHEET ........................................................................................................................... 1**\n\n**I.** **PROGRAM CONTEXT ....................................................................................................... 6**\n\nA. Introduction .................................................................................................................................. 6 B. MPA Program Context .................................................................................................................. 7 C. Updated MPA Program Framework .............................................................................................. 8 D. Learning Agenda ........................................................................................................................... 9\n\n**II.** **CONTEXT AND RELEVANCE ............................................................................................. 9**\n\nA. Country Context ............................................................................................................................ 9 B. Sectoral and Institutional Context ................................................................................................ 9 C. Relevance to Higher Level Objectives ......................................................................................... 18\n\n**III.** **PROJECT DESCRIPTION .................................................................................................. 19**\n\nA. Development Objectives ............................................................................................................. 19 B. Project Components ................................................................................................................... 19 C. Project Beneficiaries ................................................................................................................... 23\n\n**IV.** **IMPLEMENTATION ARRANGEMENTS ............................................................................ 24**\n\nA. Institutional and Implementation Arrangements ....................................................................... 24 B. Results Monitoring and Evaluation Arrangements ..................................................................... 25 C. Sustainability ............................................................................................................................... 25\n\n**V.** **PROJECT APPRAISAL SUMMARY ................................................................................... 25**\n\nA. Technical, Economic and Financial Analysis................................................................................ 25 B. Financial Management................................................................................................................ 26 C. Procurement ............................................................................................................................... 27 D. Legal Operational Policies ........................................................................................................... 29 E. Environmental and Social Standards .......................................................................................... 29 F. Climate Co-Benefits..................................................................................................................... 31 G. Citizen Engagement .................................................................................................................... 32 H. Gender ........................................................................................................................................ 33 I. Grievance redress mechanisms .................................................................................................. 35\n\n**VI.** **GRIEVANCE REDRESS SERVICES ..................................................................................... 35**\n\n**VII.** **KEY RISKS ..................................................................................................................... 35**\n\n**VIII.** **RESULTS FRAMEWORK AND MONITORING ................................................................... 39**\n\n**ANNEX 1: Status of Vaccines as of 09/15/2021 .......................................................................... 47**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**ANNEX 2: Financial Management Assessment Report ................................................................ 48**\n\n**ANNEX 3: Implementation Arrangements and Support Plan ....................................................... 53**\n\n.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) DATASHEET\n\n**BASIC INFORMATION**\n~~BASIC~~ ~~INFO~~ ~~TABLE~~ Country(ies) Project Name Iraq Iraq COVID-19 Vaccination Project Project ID Financing Instrument Environmental and Social Risk Classification Investment Project P177038 Substantial Financing\n\n**Financing & Implementation Modalities**\n\n[✓] Multiphase Programmatic Approach (MPA) [ ] Contingent Emergency Response Component (CERC)\n\n[ ] Series of Projects (SOP) [✓] Fragile State(s)\n\n[ ] Performance-Based Conditions (PBCs) [ ] Small State(s)\n\n[ ] Financial Intermediaries (FI) [ ] Fragile within a non-fragile Country\n\n[ ] Project-Based Guarantee [✓] Conflict\n\n[ ] Deferred Drawdown [ ] Responding to Natural or Man-made Disaster\n\n[ ] Alternate Procurement Arrangements (APA) [ ] Hands-on Enhanced Implementation Support (HEIS) Expected Project Approval Date Expected Project Closing Date Expected Program Closing Date 24-Sep-2021 30-Jun-2023 31-Mar-2025 Bank/IFC Collaboration No\n\n**MPA Program Development Objective**\n\nThe Program Development Objective is to prevent, detect and respond to the threat posed by COVID-19 and strengthen national systems for public health preparedness\n\n**MPA Financing Data (US$, Millions)** **Financing**\n\nPage 1 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) MPA Program Financing Envelope 18,000.00\n\n**Proposed Project Development Objective(s)**\nThe development objective is to support the Government of Iraq in the acquisition and deployment of COVID-19 vaccines.\n\n**Components**\n\n**Component Name** **Cost (US$, millions)**\n\nCOVID-19 Vaccines and Deployment 97.00 Project Management and Monitoring and Evaluation 3.00\n\n**Organizations**\n\nBorrower: Republic of Iraq Implementing Agency: Ministry of Health and Environment\n\n**MPA FINANCING DETAILS (US$, Millions)**\n\n**MPA FINANCING DETAILS (US$, Millions)** Approved\n\n**Board Approved MPA Financing Envelope:** 18,000.00\n\n**MPA Program Financing Envelope:** 18,000.00\n\n**of which Bank Financing (IBRD):** 9,900.00\n\n**of which Bank Financing (IDA):** 8,100.00\n\n**of which other financing sources:** 0.00\n\n**PROJECT FINANCING DATA (US$, Millions)**\n\nFIN_SUMM_NEW\n**SUMMARY-NewFin1**\n\n**Total Project Cost** 100.00\n\n**Total Financing** 100.00\n\n**of which IBRD/IDA** 98.00\n\n**Financing Gap** 0.00\n\nPage 2 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**DETAILS-NewFinEnh1**\n\n**World Bank Group Financing**\n\nInternational Bank for Reconstruction and Development (IBRD) 98.00\n\n**Non-World Bank Group Financing**\n\nTrust Funds 2.00 Reform and Reconstruction in Iraq MDTF 2.00\n\n**Expected Disbursements (in US$, Millions)**\n\n**WB Fiscal**\n2022 2023 2024\n**Year**\n\n**Annual** 75.00 22.00 1.00\n\n**Cumulative** 75.00 97.00 98.00\n\n**INSTITUTIONAL DATA**\n\n**Practice Area (Lead)** **Contributing Practice Areas**\n\nHealth, Nutrition & Population\n\n**Climate Change and Disaster Screening**\n\nThis operation has been screened for short and long-term climate change and disaster risks\n\n**SYSTEMATIC OPERATIONS RISK-RATING TOOL (SORT)**\n\n**Risk Category** **Rating**\n\n1. Political and Governance ⚫ High\n\n2. Macroeconomic ⚫ High 3. Sector Strategies and Policies ⚫ Substantial 4. Technical Design of Project or Program ⚫ Substantial 5. Institutional Capacity for Implementation and Sustainability ⚫ High Page 3 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) 6. Fiduciary ⚫ High 7. Environment and Social ⚫ Substantial 8. Stakeholders ⚫ Substantial 9. Other ⚫ Substantial 10. Overall ⚫ High\n\n**Overall MPA Program Risk** ⚫ High\n\n**COMPLIANCE**\n\n**Policy**\nDoes the project depart from the CPF in content or in other significant respects?\n\n[ ] Yes [✓] No Does the project require any waivers of Bank policies?\n\n[ ] Yes [✓] No Page 4 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**Environmental and Social Standards Relevance Given its Context at the Time of Appraisal**\n\n**E & S Standards** **Relevance**\n\nAssessment and Management of Environmental and Social Risks and Impacts Relevant Stakeholder Engagement and Information Disclosure Relevant Labor and Working Conditions Relevant Resource Efficiency and Pollution Prevention and Management Relevant Community Health and Safety Relevant Land Acquisition, Restrictions on Land Use and Involuntary Resettlement Not Currently Relevant Biodiversity Conservation and Sustainable Management of Living Natural Resources Not Currently Relevant Indigenous Peoples/Sub-Saharan African Historically Underserved Traditional Local Communities Not Currently Relevant Cultural Heritage Not Currently Relevant Financial Intermediaries Not Currently Relevant\n\n**NOTE** : For further information regarding the World Bank’s due diligence assessment of the Project’s potential\nenvironmental and social risks and impacts, please refer to the Project’s Appraisal Environmental and Social Review Summary (ESRS).\n\n**Legal Covenants**\n\n**Conditions**\n\nType Financing source Description Effectiveness Trust Funds The Borrower, through MOHE, appoints TPMA for purposes of carrying out a technical audit.\n\nPage 5 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) I. **PROGRAM CONTEXT**\n\n**A.** **Introduction**\n\n**1.** **This Project Appraisal Document (PAD) seeks the approval to provide financing for the proposed Iraq COVID-19**\n**Vaccination Project comprised of a loan in the amount of US$98 million from the International Bank of Reconstruction**\n**and Development (IBRD) and a grant in the amount of US$2 million from the Multi-Donor Trust Fund for Iraq Reform,**\n**Recovery and Reconstruction Fund (I3RF) to the Republic of Iraq.** The project financing will be extended under the\nCOVID-19 Strategic Preparedness and Response Program (SPRP) using the Multiphase Programmatic Approach (MPA) approved by the World Bank’s Board of Executive Directors on April 2, 2020 and the additional financing (AF) to the SPRP approved on October 13, 2020. [1] The Government of Iraq (GOI) has formally requested the World Bank’s support for Iraq’s COVID-19 vaccination efforts on May 26, 2021. The project builds on the ongoing support to the Government of Iraq’s (GOI) COVID-19 response and health system strengthening by the World Bank as well as other development partners.\n\n**2.** **The project will provide upfront financing to help the government purchase and deploy COVID-19 vaccines from**\n**a range of sources that meet the World Bank’s Vaccine Approval Criteria (VAC).** The financing will enable affordable\nand equitable access to COVID-19 vaccines for approximately 7 percent of the country’s population and help ensure effective vaccine deployment in Iraq through vaccination system strengthening. In particular, it will support the country in procuring additional doses through direct supply agreements with vaccine manufacturers in order to build a portfolio of options to expand Iraq’s access to vaccines under the right conditions (e.g., of value-for-money, regulatory approvals, and delivery time among other key features). As of April 16, 2021, the World Bank will accept as threshold for eligibility of IBRD/IDA resources in COVID-19 vaccine acquisition and/or deployment under all World Bank-financed projects: (i) the vaccine has received regular or emergency licensure or authorization from at least one of the Stringent Regulatory Authorities (SRAs) identified by the World Health Organization (WHO) for vaccines procured and/or supplied under the COVID-19 Vaccines Global Access (COVAX) Facility, as may be amended from time to time by WHO; or (ii) the vaccine has received WHO Prequalification (PQ) or WHO Emergency Use Listing (EUL). As vaccine development is rapidly evolving, the World Bank's VAC may be revised. All vaccines financed by the World Bank will be provided free of charge, and no user fees will be levied. The project financing enables a portfolio approach that will be adjusted during implementation in response to developments in the country’s pandemic situation and the global market for vaccines.\n\n**3.** **Iraq is one of the countries hardest hit by COVID-19 in the Middle East and North Africa (MENA) region** . As of\nSeptember 19, 2021, Iraq has recorded a total of 1,975,220 confirmed cases and 21,822 deaths. A total of 7,161,754 COVID-19 vaccine doses have been administered. Of the total number of vaccinated people, 4,444,542 received one dose (approximately 11 percent of the total population), while 2,717,212 have been fully immunized with two doses (approximately 7 percent of the total population).\n\n1 The World Bank approved a US$12 billion WBG Fast Track COVID-19 Facility (FTCF or “the Facility”) to assist IBRD and IDA countries in addressing the global pandemic and its impacts. Of this amount, US$6 billion came from IBRD/IDA (“the World Bank”) and US$6 billion from the International Finance Corporation (IFC). The IFC subsequently increased its contribution to US$8 billion, bringing the FTCF total to US$14 billion. The AF of US$12 billion (IBRD/IDA) was approved on October 13, 2020 to support the purchase and deployment of vaccines as well as strengthening the related immunization and health care delivery system.\n\nPage 6 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**B.** **MPA Program Context**\n\n4. **The Additional Financing (AF) to the SPRP approved by the World Bank’s Board of Executive Directors on**\n**October 13, 2020 to the existing COVID-19 SPRP utilizing the MPA (“Global COVID-19 MPA”) will significantly expand**\n**the World Bank support to client countries for COVID-19 vaccination, with the aim to support vaccination of one**\n**billion people globally.** An effective and safe COVID-19 vaccine is the most promising path forward for the world to\nreopen safely, building on global efforts to develop treatments and to expand testing capacity. The timing of potential vaccine development was not known when the Global COVID-19 MPA was approved, but global vaccine development efforts have progressed rapidly. Production is underway of several vaccines that have been approved for use since the end of 2020. Many high-income countries have made large-scale advance purchases to reserve supply for their populations and have the systems in place to get people vaccinated efficiently. The approval of an envelope of US$12 billion (US$6 billion from IDA and US$6 billion from IBRD) in financing was critical to expand affordable and equitable financing for vaccine purchase and deployment. It also sent a signal to potential suppliers that World Bank financing is available for the demand of vaccines from low- and middle-income countries (LMICs), providing an incentive for production capacity at levels that can also supply developing economies at affordable prices, not only high-income countries. The World Bank’s Global COVID-19 MPA AF is expected to enable vaccination for up to 750 million people, with potential surge capacity for an additional 250 million people in the poorest countries (depending on the delivered price of approved vaccines) while scaling up support to strengthen immunization delivery, with design flexibility at the country level. The Iraq COVID-19 Vaccination Project will enable support to the GOI’s COVID-19 vaccination efforts and will be a key contribution to the World Bank Group’s (WBG) overall COVID-19 response.\n\n5. **The COVID-19 pandemic has had massive global impact and continues to spread** . Since December 2019, following the diagnosis of the initial cases in Wuhan, Hubei Province, China, the number of COVID-19 cases has increased rapidly. On March 11, 2020, the WHO declared a global pandemic. As of September 20, 2021, more than 228 million people have been infected with COVID-19 and 4.7 million have died. The pandemic has caused the largest global economic contraction since the Great Depression in 1929, driving millions of people into poverty. The economic recovery is expected to be slow. Furthermore, many countries are seeing a ‘third wave’ with the spread of the Delta variant.\n\n6. **The World Bank’s response to the pandemic was quick.** On March 3, 2020, the World Bank’s Board of Executive Directors endorsed urgent actions supporting client countries’ response to the COVID-19 pandemic. Subsequently, the Board approved the establishment of a US$12 billion WBG Fast Track COVID-19 Facility (FTCF or “the Facility”) to assist IBRD and International Development Association (IDA) countries in addressing the global pandemic and its impacts. Of this amount, US$6 billion came from IBRD/IDA (“the Bank”) and US$6 billion from the International Finance Corporation (IFC). The IFC subsequently increased its contribution to US$8 billion, bringing the FTCF total to US$14 billion. On March 17, 2020, the World Bank’s Board granted approval of specific waivers and exceptions required to enable the rapid preparation and implementation of country operations under the FTCF. On April 2, 2020, the World Bank’s Board approved the SPRP with a US$6 billion financing envelope of which up to US$4 billion for health financing (up to US$1.3 billion IDA and up to US$2.7 billion under IBRD). The SPRP utilizes MPA, to be supported by the FTCF. On April 2, 2020, the World Bank’s Board also approved the first 25 country projects.\n\n7. **Since the initial FTCF response, the WBG has significantly expanded its support for countries as they respond to**\n**the COVID-19 pandemic and its overall impacts** . In March 2020, the WBG announced that the institution has the\ncapacity to provide up to US$160 billion in total financial support through June 2021 to help countries address the social Page 7 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) and economic impacts of the pandemic. On June 16, 2020, the World Bank’s Board endorsed the COVID-19 Crisis Response Approach Paper, outlining priorities for supporting countries in the longer term, including: a continued focus on saving lives; protecting the poor and vulnerable; ensuring sustainable business growth and job creation; and strengthening policies, institutions, and investments for rebuilding better. By September 30, 2020, the World Bank had committed nearly US$22 billion in new financing for the overall COVID-19 response, of which more than 50 percent has disbursed. In addition to new financing, the World Bank restructured funds in existing projects in at least 68 countries to focus on COVID-19 response, many of these using the contingent emergency response component (CERC). By September 30, 2020, the IFC had committed nearly US$6 billion in new financing, reflecting new investments in more than 300 companies as well as extending trade finance and working capital lines to clients. As of September 17, 2021, 87 MPA operations have been approved with a total commitment of US$4.2 billion, and the Bank has approved 56 operations to support vaccine procurement and rollout in 54 countries amounting to $4.6 billion.\n\n8. **The Global COVID-19 MPA provides a critical and highly effective operational programmatic framework for the**\n**World Bank’s emergency health response to COVID-19 with FTCF resources** . The Program development objective of\nthe Global MPA is “to prevent, detect and respond to the threat posed by COVID-19 and strengthen national systems for public health preparedness.” At the time of the approval of the Global MPA, and in the absence of a safe and effective COVID-19 vaccine, immediate needs were focused on early detection, diagnosis, confirmation, and treatment of patients (including those afflicted with other chronic conditions that increase the risk of COVID-19 severity and mortality). The Global MPA provided a common operational framework to support individual countries’ specific needs in preventing the spread of the disease and limiting immediate socioeconomic losses, as well as strengthening public health and essential medical care structures and operations to build resilience and reduce the risk from emerging and re-emerging pathogens.\n\n**C.** **Updated MPA Program Framework**\n\n**Table 1. MPA Program Framework**\n\n**Estimated**\n**IDA**\n**Amount**\n**($ million)**\n\n**Estimated**\n**Other**\n**Amount**\n**($**\n**million)**\n\n**Estimated**\n**Environme**\n**ntal &**\n**Social Risk**\n**Rating**\n\n**Sequential or**\n**Phase #** **Project ID**\n**Simultaneous**\n\n2 P177038 Simultaneous\n\n**Phase’s**\n**Proposed DO***\n\nTo prevent, detect and respond to the threat posed by COVID-19 and strengthen national systems for public health preparedness.\n\n**IPF, DPF**\n**or PforR**\n\nSeptember\n**IPF** $98 million -- $2 million Substantial\n\n24, 2021\n\n**Estimated**\n**IBRD**\n**Amount**\n**($ million)**\n\n**Estimated**\n**Approval**\n**Date**\n\n9. All projects under SPRP are assessed for Environmental and Social Framework (ESF) risk classification following the World Bank procedures and the flexibility provided for COVID-19 operations.\n\nPage 8 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**D.** **Learning Agenda**\n\n10. The country project under the MPA Program will support adaptive learning throughout implementation, as well as from international organizations including the WHO, International Monetary Fund (IMF), Centers for Disease Control (CDC), United Nations Children's Fund (UNICEF), and others. It will adjust to emerging technical, social, and economic evidence, as applicable, and incorporate lessons learned from the ongoing global vaccine rollout and COVID-19-related service delivery. In Iraq, this learning agenda involves a continuation of on-going work supported through the World Bank technical assistance (TA) under the I3RF and the ongoing COVID-19 response supported through interventions outside of the MPA, including:\n\n- **Technical:** Vaccine deployment readiness assessments; rapid assessment of infection prevention and control\nmeasures and patient flow; and application of digital tools for contact tracing.\n\n- **Social behaviors** : Studies to assess vaccine hesitancy and A/B message testing to improve uptake of COVID19 vaccines.\n\n**II.** **CONTEXT AND RELEVANCE**\n\n**A.** **Country Context**\n\n11. **Iraq is a large upper-middle income country with a gross national income (GNI) per capita of US$4,660 and a**\n**population of 40.15 million in 2020.** Almost two decades after the Iraq war began, the country remains caught in a\nfragility trap and faces increasing political instability and fragmentation, geopolitical risks, growing social unrest, and a deepening divide between the state and its citizens. Oil price volatility and COVID-19 have amplified Iraq’s economic woes, reversing two years of steady recovery. These twin shocks have deepened existing economic and social fragilities, adding further to public grievances that existed pre-COVID-19. The absence of fiscal space has limited the ability of the GOI to provide a stimulus to an economy highly dependent on oil exports for growth and fiscal revenues. As a result, the country experienced the largest contraction of its economy since 2003, with gross domestic product (GDP) contracting by 15 percent in 2020. Budget rigidities have constrained the GOI’s ability to respond to COVID-19 and offer a stimulus package to restart the economy.\n\n12. **The economic downturn has worsened the welfare of Iraqis, especially informal workers and the self-employed.** Unemployment is more than 10 percentage points higher than the pre-pandemic level. Unemployment and underemployment are likely to rise, particularly among youth and internally displaced persons (IDPs). The loss of household income and social assistance has increased vulnerability to food insecurity. COVID-19 has also severely limited child learning as evidenced by the small proportion of students engaged in learning activities during school closure. These impacts coupled with reduced access to market and health care services undermined human capital accumulation and economic mobility.\n\n13. **An additional 2.7 to 5.5 million Iraqis could become poor due to the COVID-19 crisis.** This is in addition to the 6.9 million Iraqis already living in poverty. A large vaccination campaign is a key element for future recovery from the health and economic impact of the COVID-19 pandemic.\n\n**B.** **Sectoral and Institutional Context**\n\n**14.** **While Iraq has made progress with some health outcomes, the significant negative impact of conflicts and**\n\nPage 9 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**political instability is apparent in the poor performance of the health system** . Iraq’s life expectancy has increased by\n2.3 years over the past decade, rising from 68.3 years in 2009 to 70.6 in 2019. There has been progress in child health and nutrition indicators; with under-5 mortality decreasing by more than 40 percent over the past two decades, falling from 44.9 deaths per 1,000 live births in 1999 to 25.9 deaths in 2019. Despite such progress, Iraq still has some of the worst health outcomes among its peers, mostly driven by the continued conflict. The under-5 mortality rate is 1.4 times higher than the average for upper-middle income countries of 18.5 deaths per 1,000 live births. While Iraq is undergoing a demographic transition, with an increase in the working age share of the population, it still has one of the highest total fertility rates in the MENA region at 3.7. Maternal mortality ratio has been persistently high over the past two decades (75 in 2009, 92 in 2014, and 79 in 2019, deaths per 10,000 live births respectively). There are also regional and socioeconomic inequities in terms of fertility, early childbirth, and family planning outcomes. Utilization of health services has decreased over the years, particularly at the primary care level, and inequities remain. For example, women in the poorest quintile are only 66 percent as likely to receive four or more antenatal care visits during their last pregnancy compared to those in the richest quintile. Child immunization rates in Iraq have not improved over the past decade and have remained significantly lower than those of peer countries. Due to conflict and instability, Iraq faces a significant challenge of delivering care to a large number of refugees and IDPs. In summary, Iraq performs poorly across most universal health coverage (UHC) index indicators, and the UHC effective coverage index stands at only 57.7. [2] Iraq’s Human Capital Index is one of the lowest in the MENA region at 0.41 in 2020. [3] 15. **Non-communicable diseases (NCDs) have become the most significant cause of mortality and morbidity**, representing 77.1 percent of deaths and 65.8 percent of disability-adjusted life years (DALYs) in 2019. In 2019, ischemic heart disease, stroke, diabetes, and chronic kidney disease were the top four causes of mortality in the country. The prevalence of the main NCD risks remains high: 21 percent of the population uses tobacco, 14 percent of the population has diabetes, 35.6 percent of the population has high blood pressure, 65 percent are overweight or obese, and 39.6 percent have elevated cholesterol.\n\n16. **Health infrastructure was severely damaged by conflicts.** With the exception of Tal Afar, Al-Muqdadya (Ibid) and Al-Ramadi, all cities for which data were available have at least half of their facilities either partially or fully damaged.\nService delivery is also affected by the functionality of facilities. Data show high rates of nonfunctional health facilities in cities with high levels of damages. According to the Damage and Needs Assessment conducted by the World Bank in 2018, [4] the estimated damages to Iraq’s health system in the seven governorates directly affected by the prolonged conflict total US$2.3 billion. Out of this amount, damages to hospitals in the 16 assessed cities are approximately US$1 billion, while damages to health centers and health offices are around US$12.6 million.\n\n17. **The Iraqi health system is primarily financed by general government revenues and direct payments by**\n**households, and fiscal space has remained constrained.** Over the past decade, per capita health spending in Iraq has\nfluctuated from a low of US$150.5 in 2010 to a high of US$239.4 in 2018. Despite recent increases, Iraq spends considerably less than peer countries per capita. Compared to its peers, Iraq also spends the lowest share of total 2 Collaborators, G. 2. (2020, October 17). Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1250-1284. Retrieved from https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30750-9/fulltext#%20 3 World Bank (2020).. Iraq Human Capital Index 2020 Brief. The World Bank Group: Washington, D.C.\nhttps://databank.worldbank.org/data/download/hci/HCI_2pager_IRQ.pdf?cid=GGH_e_hcpexternal_en_ext 4 World Bank (2018). Iraq Reconstruction and Investment: Damage and Needs Assessment of Affected Governorates. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/29438 Page 10 of 54", "output": {"entities": {"named_data": ["Iraq Human Capital Index", "UHC effective coverage index", "universal health coverage (UHC) index"], "descriptive_data": ["index of effective coverage of health services"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) government budget on health (about 4.3 percent in 2019), a figure that has not changed considerably over the past decade. This results in an overreliance on out-of-pocket (OOP) expenditures (representing 51.4 percent of current health spending in 2018) and limited financial protection for the population. In 2017, about a third of the population incurred catastrophic health expenditures (i.e., spending more than 10 percent of their household expenditure on health), and 15 percent of households were pushed into poverty due to OOP health spending. [5] 18. **Against this backdrop, Iraq is one of the most significantly impacted countries by COVID-19 in the MENA region.** Iraq remains susceptible to a high risk of morbidity and mortality due to COVID-19, not only through its direct effects but also through the indirect effects on the burden to be imposed on the health system. This risk is attributable to a high and growing burden of NCDs, a diverse range of vulnerable and at-risk populations due to poverty, inequality and displacement, as well as a weak health system with low and inequitable levels of financing, fragmented and inflexible service delivery, limited human and physical resources, and weak surveillance and health information systems.\n\n19. **The GOI launched the national COVID-19 response in March 2020.** This included non-pharmaceutical interventions such as bans on gatherings, closure of businesses, and use of work shifts in public administration buildings.\nThe measures were successful in limiting the first wave of the pandemic, but their subsequent premature easing contributed to a large second wave of COVID-19 cases. As a result, movement restrictions and a curfew were reinstated from January 14 until March 7, 2021. The GOI reduced spending in non-essential areas and safeguarded budgetary allocations to the Ministry of Health and Environment (MOHE). The GOI also invested in expanding testing and case management capacity. From May 1, 2020 through March 31, 2021, the daily number of reverse transcription and polymerase chain reaction (RT-PCR) tests increased 13-fold (from 3,338 to 44,649 RT-PCR tests per day). Although the number of RT-PCR tests has increased, the number of tests performed per one new confirmed case has declined (below 10 tests per confirmed case).\n\n20. **The World Bank has been providing both financial and technical support for the GOI’s COVID-19 response.** In April 2020, US$7.8 million in uncommitted funds under the Health Component of the ongoing Emergency Operation for Development Project (EODP) was mobilized to purchase essential medical equipment for the COVID-19 response, including 136 intensive care beds, 100 ventilators, 17 mobile x-ray machines, and 17 defibrillators. The project was restructured in May 2020 to reallocate US$25.825 million for the procurement of fixed and mobile computed tomography scanners for COVID-19 case management. The World Bank has also been providing TA for COVID-19 response to the MOHE under the I3RF. This includes support to the MOHE in the: (i) assessment of COVID-19 testing, contact tracing, surveillance, infection prevention and control and patient flow; (ii) development of the National Deployment and Vaccination Plan (NDVP) for COVID-19; (iii) design and implementation of a Facebook survey on COVID19 vaccine hesitancy - a first study of its kind in Iraq; and (iv) development of a COVID-19 Vaccination Communication Action Plan, incorporating the results from the survey. Per MOHE’s request, I3RF is also funding additional TA for a social media-based communication campaign to address vaccine hesitancy.\n\n21. **The project will play a critical role in enabling affordable and equitable access to COVID-19 vaccines in Iraq.** Improved access to vaccination is needed to limit the spread of the disease and lessen the burden on the already weak health system. COVID-19 vaccination, along with improved diagnostics and therapeutics, is essential to protect lives and enable the country to reopen safely. The global economy will not recover fully until people feel they can live, socialize, 5 World Bank (2021). Addressing the Human Capital Crisis: A Public Expenditure Review for Human Development Sectors in Iraq. The World Bank: Washington, D.C. http://documents.worldbank.org/curated/en/568141622306648034/Iraq-HD-PER-Final Page 11 of 54", "output": {"entities": {"named_data": [], "descriptive_data": ["Facebook survey on COVID19 vaccine hesitancy"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) work, and travel with confidence. Given the importance of limiting the spread of COVID-19 for both health and economic recovery, providing access to COVID-19 vaccines will be critical to accelerate economic and social recovery. The project activities will build on the ongoing World Bank’s COVID-19 response and health sector support, as well as the support of other development partners (Box 1). Under I3RF, the World Bank has coordinated closely with the WHO and UNICEF on supporting the GOI’s COVID-19 vaccination efforts. WHO and UNICEF have provided technical support for vaccine introduction and deployment, including support for training of health workers for vaccination, communication activities, and monitoring and evaluation (e.g., establishing dashboards to track COVID-19 transmission and vaccination). UNICEF is also the procurement agent for COVID-19 vaccines through the COVAX Facility and has provided support for the procurement of some cold chain equipment. The World Bank’s financing will build on the activities conducted by other development partners, filling the critical gaps to ensure successful deployment of the vaccines. Most importantly, it will provide the needed financing to expand COVID-19 vaccination coverage.\n\n**Box 1: Roles of Partner Agencies in COVID-19 Vaccination in Iraq**\n\n**WHO** **Financing amount (if known)**\n\n- Providing technical support for vaccine introduction and deployment, including\nstrategies, vaccine safety issues, development guidelines, conducting of training on Adverse Events Following Immunization (AEFI) surveillance for COVID-19 vaccine related issues and other issues of vaccine pharmacovigilance N/A\n\n**UNICEF** **Financing amount (US$)**\n\n- Supporting the development of a roadmap for integration of COVID-19 vaccine\ndeployment with Expanded Program on Immunization (EPI) and other primary health care (PHC) services; quantification and forecasting of supply needs; cold chain assessment, procurement and maintenance\n\n- Acting as the procurement agent for the COVID 19 vaccine through the COVAX\nfacility and facilitating the procurement and delivery of vaccines\n\n- Supporting the communication strategy and community engagement\n\n- Supporting the establishment of a robust information system for data\nmanagement, monitoring and reporting, etc.\n\nUS$1,000,000 (for procurement and to fund various PHC services and supplies; cold chain; training of personnel etc.) US$150,000 (communications) US$100,000 (Management Information System (MIS)) 22. **The project is being introduced at a crucial juncture in the GOI’s response to COVID-19.** A critically important change in the state of science since the early stages of the pandemic has been the emergence of new therapies, as well as the successful development and expanding production of COVID-19 vaccines (see Annex 1). A key rationale for the project is to provide upfront financing for safe and effective vaccine acquisition and deployment in Iraq thus enabling the country to acquire the vaccine at the earliest, recognizing that there are currently supply constraints and excess demand for vaccines from both high-income and lower-income countries.\n\n23. **The GOI, with the support of the World Bank, WHO, and UNICEF, has conducted the COVID-19 vaccine readiness**\n**assessment using the integrated Vaccine Introduction Readiness Assessment Tool (VIRAT)/Vaccine Readiness**\n**Assessment Framework (VRAF 2.0) instrument and prepared a comprehensive NDVP (dated February 2021 and**\n**amended on August 8, 2021)** . Key findings of the VIRAT/VRAF 2.0 Assessment are summarized in Table 2.\n\nPage 12 of 54", "output": {"entities": {"named_data": ["integrated Vaccine Introduction Readiness Assessment Tool"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**Table 2: Summary of Vaccination Readiness Findings of the VIRAT/VRAF 2.0 Assessment** **[6]**\n\n**Readiness** **Readiness of government** **Key gaps to address during**\n**domain** **deployment**\n\n- Establishment of a\nmechanism for subnational level coordination.\n\n- The costing is expected to\nbe updated periodically to reflect subsequent developments in additional vaccine purchase(s) and deployment.\n\n**Planning,**\n**coordination,**\n**and regulation**\n\n**Costing,**\n**budgeting, and**\n**financial**\n**sustainability**\n\n- A National Coordinating Committee (NCC) and a\nNational Technical Working Group (NTWG) have been established. The NCC is chaired by the Deputy Minister of Health for Technical Affairs.\n\n- The National Authority for Drug Selection issued\nEmergency Use Authorization (EUA) for the Pfizer vaccine on December 27, 2020, for AstraZeneca and Sinopharm vaccines on January 19, 2021, and for Sputnik vaccine on March 8, 2021.\n\n- The Council of Ministers issued a decree on\nFebruary 20, 2021 authorizing the MOHE to sign contracts with vaccine manufacturers waiving liability.\n\n- The Parliament adopted the Law on the Response\nto the COVID-19 Pandemic on March 8, 2021. The Law includes provisions for indemnity.\n\n- High level costing of NDVP has been completed for\nfour different scenarios based on population coverage targets.\n\n- The GOI is exploring other funding sources to secure\nrequired doses to increase coverage (including World Bank support).\n\n6 A multi-partner effort led by WHO and UNICEF developed the Vaccine Introduction Readiness Assessment Tool (VIRAT) to support countries in developing a roadmap to prepare for vaccine introduction and identify gaps to inform areas for potential support. Building upon the VIRAT, the World Bank developed the Vaccine Readiness Assessment Framework (VRAF) to help countries obtain granular information on gaps and associated costs and program financial resources for deployment of vaccines. To minimize burden and duplication, in November 2020, the VIRAT and VRAF tools were consolidated into one comprehensive framework, called VIRAT-VRAF 2.0.\n\nPage 13 of 54", "output": {"entities": {"named_data": ["Vaccine Introduction Readiness Assessment Tool"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**Prioritization,**\n**targeting, and**\n**surveillance**\n\n- Priority groups have been identified to ensure just,\nefficient, and timely vaccination of all eligible populations willing to be immunized based on the following principles adapted for the Iraq situation: `o` The WHO Strategic Advisory Group of Experts on Immunization (SAGE) values framework; `o` The WHO SAGE prioritization roadmap; `o` The fair allocation mechanism for COVID-19 vaccines through the COVAX Facility.\n\n- Displaced individuals residing in camps are included.\n\n- The MOHE has developed a digital registry for\nvaccination.\n\n- Vaccine access was expanded to the entire adult\npopulation due to the short shelf-life of some received vaccines and vaccine hesitancy.\n\n**Service delivery** - Fifty vaccination sites in hospitals (often teaching\nhospitals), which meet ultra-cold chain requirements, were initially identified for the Pfizer vaccine.\n\n- As of September 9, 2021, there were 1,301\nvaccination sites. With the revised guidance for safe storage temperatures for Pfizer vaccine (up to 30 days from +2 to +8 degrees C), the Pfizer vaccine is now delivered at most vaccination sites. The number of sites can be expanded.\n\n- Plans for site readiness assessments are outlined.\n\n- Prioritization of\nbeneficiaries will be a dynamic process based on multiple factors specific to context in Iraq (feasibility, expiration of available vaccine doses, groups at risk, and vaccine hesitancy among eligible groups).\n\n- Microplanning for\nvaccination rollout is underway.\n\n- The GOI is considering the\nuse of large public venues (e.g., sports halls) as vaccination sites.\n\n- The preliminary estimates\nof vaccinators required may need to be updated.\n\n- A well-defined plan to\nmobilize human resources to ensure adequate numbers of health care workers at vaccination sites, including through task shifting, adoption of multiple shifts, volunteers, needs to be developed.\n\nPage 14 of 54\n\n**Training and**\n**supervision**\n\n- Descriptions, roles, and broad estimates of staff and\nhealth workers for the campaign are outlined (including vaccination, supervision, communications and community engagement, supply chain, logistics, monitoring, pharmacovigilance, and disease surveillance).\n\n- A training manual has been developed. The MOHE\nhas conducted training for managers in all health departments, cold chain officials, and health workers at the designated vaccination sites with support from the WHO and UNICEF.\n\n- Mix of different staff and health workers that is\nrequired by each site for deployment specified.", "output": {"entities": {"named_data": [], "descriptive_data": ["digital registry for vaccination"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n- As part of the M&E\nframework, an information system including a vaccine registry and dashboard will be enhanced to monitor vaccine coverage, facilitate follow-up, issue vaccine certificates, and ensure data privacy.\n\n- The existing hotline for\nreceiving grievances and addressing queries of beneficiaries will be enhanced further for the vaccination campaign.\n\n- Detailed distribution plans\nare currently being developed.\n\n- AEFI plan is currently being\nfinalized with preparations for training and implementation activities underway.\n\n- Adoption of the\ncommunication by the high-level government bodies is instrumental in ensuring its successful implementation.\n\nPage 15 of 54\n\n**Monitoring and**\n**evaluation** **and**\n**grievance**\n**redressal**\n\n**Vaccine, cold**\n**chain, logistics,**\n**infrastructure**\n\n**Safety**\n**surveillance**\n\n**Demand**\n**generation and**\n**communication**\n\n- Data collection systems and tools to collect COVID19 immunization data are outlined.\n\n- A vaccination record card has been developed and\nwill include a hotline number for reporting adverse events.\n\n- The GOI has secured and distributed the required\nUltra Low Temperature (ULT) freezers to the initial 50 designated sites for the Pfizer vaccine.\n\n- The estimated needs for ancillary supplies and\nPersonal Protective Equipment (PPEs) are outlined and covered by KIMADIA. KIMADIA has contracted for 18 million syringes for the Pfizer vaccine (of which 6 million syringes have already been delivered).\n\n- High level vaccine distribution and transportation\nnetworks are outlined in the NDVP.\n\n- Vaccine safety surveillance approach is aligned with\nWHO recommendations to detect serious AEFIs to provide timely data that can be shared with relevant stakeholders for rapid action.\n\n- A demand generation and community engagement\nplan for optimizing the uptake of the COVID-19 vaccine has been developed in collaboration with the World Bank, UNICEF, and WHO and is included as an annex in the NDVP.\n\n- The communication and demand generation plan\nincorporates social and behavioral data from a national Facebook survey, which gathered data on vaccine hesitancy in the population, and is aimed at", "output": {"entities": {"named_data": [], "descriptive_data": ["national Facebook survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) generating demand and improving acceptance of COVID-19 vaccines.\n\n24. **The NDVP has all the key elements recommended by the World Health Organization and represents the**\n**blueprint for Iraq’s vaccination efforts.** According to the NDVP, Iraq seeks to vaccinate 40 percent of the total\npopulation by the end of 2021 and 70 percent coverage by the end of 2022. The NDVP identifies seven categories of prioritized population groups, regardless of their citizenship status. The WHO SAGE Allocation Framework was used for the prioritization process, with modifications based on Iraq’s context (i.e., significantly younger population and FCV status).\n\n25. **The GOI signed a Committed Purchase Agreement with the COVAX Facility to procure 16 million doses of COVID-**\n**19 vaccines for 8 million individuals (with a two-dose regimen), covering almost 20 percent of the total population.**\nThe total amount due to the COVAX Facility was paid in full by the MOHE. As of August 16, 2021, the MOHE received 1,085,000 doses of the AstraZeneca vaccine, with the next shipment expected by the end of September 2021. Amid the global shortage, Iraq has received the vaccines from the COVAX Facility with delays. The first shipment of the AstraZeneca vaccine also arrived with an impending expiration date, causing the government to expand vaccination eligibility to the entire adult population to ensure that the vaccines would not be wasted.\n\n26. **The GOI also signed a Manufacturing and Supply Agreement with Pfizer on March 21, 2021 to purchase**\n**1,500,525 doses for 750,000 individuals.** The agreement was subsequently amended to include additional doses and\nmodify the delivery schedule. In total, as of June 17, 2021, the GOI has contracted 12 million doses from Pfizer to be delivered by the end of 2021, of which 6 million doses are expected to be financed by the project under retroactive financing. [7] The Manufacturing and Supply Agreement was reviewed and cleared, retroactively, by the World Bank on June 21, 2021.\n\n27. The GOI will finance vaccines for: (i) 20 percent of the population through the COVAX self-financing arm (Committed Purchase Agreement); and (ii) 10 percent through direct procurement. The World Bank financing will cover an additional 7 percent of the population with 6 million doses of the Pfizer-BioNTech vaccine. Table 3 describes the number of secured and received doses of COVID-19 vaccines by financing source.\n\n7 The retroactive financing of up to US$72 million will be available for the eligible expenditures paid by the GOI during a period of 12 months before the signing of the Loan Agreement for COVID-19 vaccines meeting the World Bank’s VAC that have been purchased but have not been deployed prior to the disclosure of the Environmental and Social Management Framework.\n\nPage 16 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nThe Iraq COVID-19 Vaccination Project (P177038)\n\n**Table 3: Overview of Iraq’s Vaccine Coverage and Purchase Plan**\n_Based on the current available estimates as of September 20, 2021_\n\n**Contract status** **Vaccines already arrived in the**\n\n**Source of**\n**financing**\n\n**Population**\n\n**Targeted** **VAC Status of** **country**\n\n**%** **Number** **Source** **Name(s)** **the Vaccine** **Name**\n\n**Targeted**\n\n**Vaccines** **[a]** **Number of doses World Bank’s**\n\n**VAC Status of**\n\n**Name** **Doses**\n\n**Source** **Name(s)**\n\n**the Vaccine**\n\nGOI 20.0% 8 million Other 0.1% 0.05 million GOI 2.4% 1 million Other 0.8% 0.325 million GOI 7.0% 3 million Direct procurement Direct procurement COVAX AstraZeneca 16 million Eligible Signed AstraZeneca 1,085,000 Donation AstraZeneca 0.1 million Eligible Received in full AstraZeneca 100,800 Direct procurement Sinopharm 2.0 million Eligible Received in full Sinopharm 2,000,000 Donation Sinopharm 0.75 million Eligible Received in full Sinopharm 750,000 GOI 7.0% 3 Direct Pfizer 6 million Eligible Signed Pfizer million procurement 5,625,360 IBRD 7.0% 3 Direct Pfizer 6 million Eligible Signed Pfizer 0 million procurement Pfizer 6 million Eligible Signed Pfizer 0 Other 0.6% 0.25 million\n\n**National**\n**Total**\n\n**37.9%** **15.625**\n**million**\n\nDonation Pfizer 0.5 million Eligible Received in full Pfizer 503,100\n\n**31.35 million** **10,064,260**\n\nPage 17 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**C.** **Relevance to Higher Level Objectives**\n\n28. **The project is aligned with WBG’s strategic priorities, particularly the WBG’s twin goals to end extreme**\n**poverty and boost shared prosperity in a sustainable manner.** The project is focused on pandemic preparedness\nwhich is also critical to achieving Universal Health Coverage. It is also aligned with the World Bank’s support for national plans and global commitments to strengthen pandemic preparedness through three key actions under preparedness: (i) improving national preparedness plans including organizational structure of the government; (ii) promoting adherence to the International Health Regulations (IHR); and (iii) utilizing international framework for monitoring and evaluation of IHR. The economic rationale for investing in the MPA interventions is strong, given that success can reduce the economic burden suffered both by individuals and countries. The project contributes to the implementation of the WBG MENA Strategy by providing support to building human capital and strengthening resilience. The project complements both WBG and development partner investments in health systems strengthening, disease control and surveillance, attention to changing individual and institutional behavior, and citizen engagement. The project contributes to the implementation of IHR (2005), Integrated Disease Surveillance and Response (IDSR), and the Office International des Epizooties (OIE) international standards, the Global Health Security Agenda, the Paris Climate Agreement, the attainment of Universal Health Coverage and of the Sustainable Development Goals (SDGs), and the promotion of a One Health approach.\n\n29. **The WBG remains committed to providing a fast and flexible response to the COVID-19 epidemic,**\n**utilizing all WBG operational and policy instruments and working in close partnership with government and other**\n**agencies.** Grounded in One-Health, which provides for an integrated approach across sectors and disciplines, the\nproposed WBG response to COVID-19 will include emergency financing, policy advice, and technical assistance, building on existing instruments to support IDA/IBRD-eligible countries in addressing the health sector and broader development impacts of COVID-19. The WBG COVID-19 response will be anchored in the WHO’s COVID-19 global SPRP outlining the public health measures for all countries to prepare for and respond to COVID-19 and sustain their efforts to prevent future outbreaks of emerging infectious diseases.\n\n30. **The project is consistent with the Iraq Country Partnership Framework (CPF) for FY22-26, discussed on August**\n**3, 2021 (Report #153633-IQ) and contributes directly to CPF Objective 2.1. (Effective and Efficient Deployment of**\n**COVID-19 Vaccines and Health Systems Strengthening).** Similarly, the Strategic Country Diagnostic dated February 8,\n2017 (Report #112333-IQ) identified “Rebuilding the Social Contract and State Legitimacy” as a priority through, _inter_ _alia_, improving the delivery of public services to fortify trust and legitimacy between citizens and the state. The need to invest in health systems to ensure the productive capabilities of the population is recognized, as is the challenge of overcoming a legacy of limited investment in human capital and social resilience systems. The project will be an important step towards building the strength of the health system and its resilience to shocks. The project is also aligned with both global health priorities and World Bank priorities in pandemic preparedness. In addition, the project complements activities being implemented through other existing World Bank-financed projects in the country.\n\nPage 18 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**III.** **PROJECT DESCRIPTION**\n\n**A.** **Development Objectives**\n\n31. The project development objective (PDO) is to support the GOI in the acquisition and deployment of COVID-19 vaccines.\n\n32. _**PDO level indicators**_ :\n\n**i.** Percentage of specific priority [8] populations fully vaccinated (total and disaggregated by sex).\n**ii.** Number of project-supported COVID-19 vaccinations sites with adequate healthcare waste management\n\nfor vaccination.\n**iii.** Number of COVID-19 vaccine doses acquired through project financing.\n\n33. _**Intermediate results indicators**_ :\n**i.** Percentage of administered COVID-19 vaccine doses captured in the national vaccination digital registry;\n**ii.** Percentage of vaccination sites which publicized detailed performance data on a regular basis in the last\n\nquarter;\n**iii.** Percentage of vaccination sites with functional cold chain;\n**iv.** Percentage of reported serious AEFI cases for which investigations were initiated within 48 hours;\n**v.** Number of health workers who received training in vaccination with gender-based violence (GBV) related\n\ncontent;\n**vi.** Percentage of feedback cases registered in the project's grievance redress mechanism (GRM) in the last\n\nquarter addressed within a timeframe specified by the project;\n**vii.** Number of communication initiatives supported by the project to address vaccine hesitancy;\n**viii.** Percentage of vaccination sites visited by the project third-party monitoring agency (TPMA) in the last\n\nquarter;\n**ix.** Number of public discussion meetings conducted on the results of the TPMA.\n\n**B.** **Project Components**\n\n**34.** The project comprises two components.\n\n35. **Component 1: COVID-19 Vaccines and Deployment (US$97 million IBRD)** . The component will support the purchase of COVID-19 vaccines and related deployment activities.\n\n36. _**Sub-component 1.1: COVID-19 Vaccine Support**_ **(US$72 million IBRD).** This sub-component will support COVID19 vaccine acquisition. Specifically, this will include the purchase of approximately 6 million doses of the COVID-19 vaccines that meet the World Bank’s VAC. This is expected to cover 3 million individuals or approximately 7 percent of the population in Iraq. The vaccines financed under the project will be prioritized for groups most at risk as defined in the NDVP. This will be supported by awareness raising campaigns targeting the priority groups and encouraging 8 As listed in Table 4, priority groups include health care workers, elderly, social care workers, people with chronic disease and displaced populations.\n\nPage 19 of 54", "output": {"entities": {"named_data": ["national vaccination digital registry"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) them to register for vaccination.\n\n37. _**Sub-component 1.2: Support for Deployment of COVID-19 Vaccines**_ **(US$25 million IBRD).** This subcomponent will support institutional system strengthening to enable safe and effective deployment of COVID-19 vaccines at scale.\nThis will include, inter alia: (i) procurement of equipment for health care waste management, (ii) development and support for refining the electronic registration system for vaccination, (iii) vaccine logistics and supply chain management; (iv) communication initiatives to address vaccine hesitancy, (v) monitoring and management of adverse effects following immunization (AEFI), and (vi) technical assistance associated with vaccine rollout. The project will prioritize supporting Iraq to address the key gaps identified by the readiness assessment, in close coordination with WHO, UNICEF, and other development partners. Given the uncertainties surrounding COVID-19 vaccination, the activities will be updated throughout project implementation through time-bound work plans agreed with the MOHE.\nTo the extent possible, sustainable, and high efficiency energy solutions will be defined to improve the deployment of vaccines. Technical assistance can be provided to ensure that energy efficiency standards for upgraded cold chain are applied for COVID-19 vaccines and beyond, including through the development of micro-plans to integrate climaterelated considerations (e.g., energy efficiency or promotion of hybrid energy source consumption for cold chain). The project will also support the procurement of effective and low-emissions health care waste management equipment that will contribute to improving the resilience of health care waste management systems to extreme precipitation.\nIn addition, the financing will support the implementation of the COVID-19 communication action plan by the MOHE and hired firms. Communication campaigns will be tailored where necessary to specific groups (e.g., women in rural groups, IDPs) and include information on procedures/plans in case of extreme weather or other climate-changeinduced events.\n\n**38.** **Component 2: Project Management and Monitoring and Evaluation (M&E) (US$3 million, including IBRD and**\n**I3RF).** This component will support the coordination, implementation, and management of project activities, including\nthird party monitoring.\n\n**39.** _**Sub-component 2.1. Project Management and M&E (US$1 million IBRD)**_ will support the coordination,\nimplementation, monitoring and evaluation, and management of project activities, including through: (i) development of a system for project monitoring and evaluation; and (ii) provision of relevant technical assistance to support the MOHE in the implementation, management, monitoring and evaluation of the project, including through operating costs and ensuring compliance with the Environmental and Social Commitment Plan. Specifically, this may include support for: (i) the supervision by MOHE teams of the deployment of COVID-19 vaccines and installation, functionality, and use of equipment and supplies acquired under the project; (ii) development of a system for project monitoring and evaluation by the PMU team; (iii) hiring of an external auditor for the project; (iv) hiring of a media production company to assist with the production of relevant materials for dissemination to project beneficiaries. This component will monitor COVID-19 vaccines deployment and therefore improve data collection, analysis, reporting and use of data for action and decision-making. Climate and gender-specific activities supported by the project will also be monitored.\n\n40. _**Sub-component 2.2. Third Party Monitoring (US$2 million I3RF).**_ A third-party monitoring agency (TPMA) will be contracted by the MOHE using grant financing from I3RF. The TPMA will be responsible for monitoring compliance of the vaccination efforts with Iraq’s NDVP and WHO standards, as well as World Bank technical, environmental, and social requirements. A draft terms of reference (TOR) has already been prepared. The final TOR will be subject to World Bank technical approval, defining the specific roles and responsibilities of the TPMA. The TPMA role can be fulfilled by a United Nations (UN) agency (or agencies), international or local non-governmental organizations (NGO), or consulting firms that meet the criteria agreed upon between the World Bank and MOHE.\n\nPage 20 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) 41. **The WHO SAGE Allocation Framework was used to determine the priority groups for COVID-19 vaccination of**\n**populations in the early phases, and the prioritization was modified based on Iraq’s context (i.e., significantly**\n**younger population and fragility, conflict, and violence).** Table 4 presents the priority groups outlined in the NDVP.\nTargeting criteria and implementation plans are described below. Efforts are being made by the government to ensure equitable access to vaccines for people with disabilities and other vulnerable groups. Vaccinations take place at fixed health points and through mobile units. Displaced individuals and refugees living in camps are explicitly prioritized for vaccination under phase 2. According to the NDVP, they will be vaccinated by vaccination teams within the nearest health district or the nearest health center after providing proof that they are displaced or have refugee status. The central committees in the health district can also use fixed or mobile MOHE medical clinics located within the camps, or health institutions or the sites of supporting organizations and non-governmental organizations (NGOs) located inside the camps provided that all the logistical requirements for vaccination are available according to the type of vaccine. Vaccination for this population group will be conducted under direct supervision of the health district or the health directorate and in coordination with camp directors.\n\n**Table 4. Priority Groups for Vaccination in Iraq**\n\n**Population** **Percentage** **of**\n**Phase** **Category/population group** **Risk category**\n**number** **population**\n\n**Phase 1 A**\n\n**Phase 1 B**\n\n**Phase 2**\n\n**Phase 3**\n\n**Phase 4**\n\nHealth workers [1] 100,000 High risk 0.2% Elderly 450,000 ≥70 years old 1.1% People with chronic disease 750,000 >2 chronic diseases 1.7% Cancer and immune-deficiency 30,000 0.07% patients Health workers 300,000 Moderate risk 0.7% Elderly 1,350,000 ≥60 and < 70 years old 3.2% People with chronic disease 1,250,000 2 chronic diseases 3.0% Patients with hereditary blood 20,000 0.03% diseases Health workers 100,000 Low risk 0.2% Elderly 2,700,000 ≥50 and < 60 years old 6.5% People with chronic disease 2,000,000 1 chronic disease 4.8% Security personnel [2] at high risk of 100,000 High risk 0.2% exposure to cases Displaced populations/refugees 500,000 Moderate risk 1.2% living in camps Social care staff and residents, 200,000 0.5% prisons staff and prisoners Security personnel at moderate risk 900,000 Moderate risk 2.2% of exposure to cases People working in professions at risk 300,000 ≥40 and <50 years old 0.7% of exposure Security personnel at low risk of 500,000 Low risk 1.2% exposure People working in professions at low 1,000,000 < 40 years old 2.4% risk of exposure [3] 12,550,000 **TOTAL** **30%** Page 21 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) 1 MOHE will categorize its staff into these three categories and that high risk will come in phase 1A, moderate in phase 1B, and low in phase 2.\n2 Security personnel are classified into three categories: i) high risk are those in direct contact with people (e.g., security of governmental facilities, at check points, traffic police); ii) intermediate risk (e.g., in barracks, working in groups); and iii) low risk, including administrative staff.\n3 Including employees of border points and train stations, educational staff, butchers, barbers, restaurants and bakeries workers, prisoners and State bodies staff).\nNote: Total population in 2021 is estimated at 41,190,700 (Iraq Central Statistics Agency).\n\n42. **The government initially faced low uptake of COVID-19 vaccines.** In addition, the first shipment of the AstraZeneca vaccine through COVAX arrived with a short shelf life. To overcome vaccine hesitancy and avoid wastage of vaccines, the GOI expanded the vaccination eligibility to the entire adult population, while continuing to prioritize health workers and those above the age of 60 years. Having started this policy, the GOI is not able to reverse it, and, as such, the vaccination is currently open to all individuals ages 18 or older residing in Iraq. While eligibility is open to all individuals in Iraq 18 years or older, the MOHE continues to prioritize groups pre-defined in the NDVP, including through the use of mobile vaccination sites.\n\n43. **The MOHE developed a digital registry for COVID-19 vaccination**, which includes four components: (i) preregistration; (ii) appointment scheduling; (iii) vaccination; and (iv) tracking AEFI. Online preregistration is encouraged for vaccination. All residents of Iraq are eligible to pre-register. [9] To ensure universal access, staff at vaccination centers will also be able to register on behalf of the recipient, and individuals can also register directly at the vaccination sites.\n\n**Box 2: Liability and Indemnification Issues in Vaccine Acquisition**\n\nThe rapid development of vaccines increases manufacturers’ potential liability for AEFI. Manufacturers want to protect themselves from this risk by including immunity from suit and liability clauses, indemnification provisions, and other limitation of liability clauses in their supply contracts. Contractual provisions and domestic legal frameworks can all operate to allocate that risk among market participants, but no mechanism will eliminate this risk entirely. Iraq has signed indemnity agreements that were satisfactory to providers (e.g., Pfizer) and has introduced corresponding legislation. On February 20, 2021 the Council of Ministers issued a decree authorizing the MOHE to sign contracts with vaccine manufacturers waiving liability. The Parliament subsequently adopted the Law on the Response to the COVID-19 Pandemic (Law No. 9) on March 8, 2021. The Law includes provisions to provide statutory immunity for manufacturers and calls for an establishment of a national no fault compensation scheme. The GOI, however, has not taken any actions yet to establish the no fault compensation scheme system. Possible World Bank support to Iraq, depending on needs, may include information sharing on lessons from other countries in implementing a national no fault compensation scheme and Hand-on Expanded Implementation Support. The project operation documents (for example, POM) will clarify that the country’s regulatory authority is responsible for its own assessment of the project COVID-19 vaccines’ safety and efficacy and is solely responsible for the authorization and deployment of vaccines in the country.\n\n9 https://cov19reg.phd.iq/ Page 22 of 54", "output": {"entities": {"named_data": [], "descriptive_data": ["digital registry for COVID-19 vaccination"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**B. Project Cost and Financing**\n\n**44.** The overall project cost will be US$100 million. Component 1 for vaccine acquisition, planning and distribution\nhas an allocation of US$97 million from IBRD (97 percent of the project financing) (see Table 5 below). Component 2, which will support project management and monitoring of activities, will be allotted US$3 million (US$1 million from IBRD and US$2 million from I3RF) (3 percent of the project financing). Table 6 provides a summary of vaccine sourcing and World Bank financing.\n\n**Table 5: Project Cost and Financing**\n\n**IBRD Financing**\n\n**(US$ million)**\n\n**Trust Fund**\n**(US$ million)**\n\n**Project Components**\n\n**Component 1: COVID-19 Vaccines and Deployment** **97.0** 0\n_Subcomponent 1.1: COVID-19 Vaccine Support_ 72.0 0 _Subcomponent 1.2: Support for Deployment of COVID-19 Vaccines_ 25.0 0\n**Component 2: Project Management and Monitoring and Evaluation (M&E)** **1.0** **2.0**\n\n_Subcomponent 2.1 Project Management and M&E_ 1.0 0 _Subcomponent 2.2 Third party monitoring agency_ 0 2.0\n**Total Costs** **98.0** **2.0**\n\n**Table 6: Summary of Vaccine Sourcing and Bank Financing**\n\n**Estimated allocation of**\n\n**Specific vaccines**\n**and sourcing plans**\n\n**National plan**\n**target (population**\n\n**%)**\n\n**Source of vaccine financing and population coverage**\n\n**Bank-financed**\n\n**Doses**\n**purchased**\n\n**with Bank**\n\n**finance**\n**(2 doses**\n**assumed)**\n\n**and sourcing plans** **finance** **Bank financing (US$)**\n\n**COVAX** **Through**\n\n**grant**\n\n**Through** **Other***\n\n**COVAX**\n\n**Through**\n\n**direct**\n**purchase**\n\n**Purchase** : US$72 million\n\nCOVAX, direct **Deployment** : US$25 million 29.4% government purchase (Pfizer 6 million **Other:** US$3 million* Stage 1: 40% 7% financed and 1.5% and Sinopharm), Pfizer doses from donations and donations - _Project management and_ _monitoring & evaluation._\n\n**45.** **Retroactive Financing.** At the Borrower’s request, the project will include retroactive financing of up to 74\npercent (i.e., US$72 million) of the IBRD loan related to COVID-19 vaccine purchase. The retroactive financing will be available for the eligible expenditures paid by the GOI using their own resources during the period of 12 months before the signing of the Loan Agreement for COVID-19 vaccines meeting the World Bank’s VAC that have been purchased but have not been deployed prior to the disclosure of the ESMF. The objective of including retroactive financing is to ensure that the GOI can lock in the price and secure enough doses to expand coverage to achieve the vaccination target of 70 percent of the adult population by the end of 2022.\n\nCOVAX, direct purchase (Pfizer and Sinopharm), and donations 6 million Pfizer doses Stage 1: 40% 7% 29.4% government financed and 1.5% from donations\n\n**C.** **Project Beneficiaries**\n**46.** The expected project beneficiaries will be at least 7 percent of Iraq’s population. It is expected that the entire\npopulation of Iraq will also benefit from project activities given the nature of the disease. Benefits from COVID-19 vaccination are direct for those included in the priority groups of population that will receive COVID-19 vaccines, Page 23 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) including staff of health care facilities (medical and non-medical), social workers, age groups deemed to be at high risk as per the NDVP prioritization, teachers and education workers, and adults with comorbidities. As the project will invest in systems strengthening for deployment of the COVID-19 vaccines, other population groups eligible for COVID19 vaccines will also directly benefit from project investments. The population at large would also benefit through the potential slowdown in transmission due to a reduction in cases among the vaccinated.\n\n**IV.** **IMPLEMENTATION ARRANGEMENTS**\n\n**A.** **Institutional and Implementation Arrangements**\n\n47. **The MOHE will be the implementing agency for the project.** The GOI has established a new Project Management Unit (PMU) headed by the Deputy Minister of Health to oversee project implementation. The PMU will be responsible for the day‐to‐day project management, including fiduciary management (procurement and financial management (FM)), and will: (i) coordinate implementation of project activities; (ii) ensure the technical, environmental and social, procurement and financial management of the project activities in both components; (iii) prepare consolidated annual work plans and budgets; (iv) conduct monitoring and evaluation of project activities; and (iv) prepare the implementation reports of the project to be submitted to the World Bank on a quarterly basis. The PMU has been established and is fully staffed with environmental/social, financial management, procurement, and monitoring and evaluation staff (from the MOHE and other government agencies). Additional personnel will be recruited if needed to ensure sufficient capacity to implement the project.\n\n48. **A Project Operational Manual (POM), which will guide project implementation, will be developed no later**\n**than 30 days after loan effectiveness, in a manner satisfactory to the Bank** . The POM will describe detailed\narrangements and procedures for the implementation of the project, such as responsibilities of the PMU operational systems and procedures, project organization structure, office operations and procedures, financial and accounting procedures (including funds flow and disbursement arrangements), procurement procedures, and implementation arrangements. The POM will include: (i) description of COVID-19 vaccine deployment activities to ensure inclusive, safe, efficient and effective deployment following a ‘whole of Iraq’ approach; (ii) environmental and social requirements; (iii) personal data protection measures; and (iv) fiduciary (procurement and financial management) requirements. The project will be carried out in accordance with the arrangements and procedures set out in the POM, which can be amended from time to time, provided all modifications are agreed upon with the World Bank in writing prior to any changes taking effect. The POM will also include a Vaccine Delivery and Distribution Manual (VDDM) to define the operational aspects of vaccine deployment, including the details related to the distribution of vaccines eligible for retroactive financing that have already been deployed to enable third-party verification.\n\n49. Large volumes of personal data, personally identifiable information and sensitive data are likely to be collected and used in connection with the management of the COVID-19 outbreak under circumstances where measures to ensure the legitimate, appropriate and proportionate use and processing of that data may not feature in national law or data governance regulations or be routinely collected and managed in health information systems. In order to guard against abuse, the project will incorporate best international practices for data privacy in such circumstances. The PMU will ensure that these principles apply through assessments of existing or development of new data governance mechanisms and data standards for emergency and routine health care, data sharing protocols, rules or regulations, revision of relevant regulations, training, sharing of global experience, unique identifiers for health system clients, strengthening of health information systems, etc.\n\nPage 24 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) 50. **A TPMA will be contracted by the MOHE using grant financing from I3RF.** The TPMA will be responsible for monitoring compliance of the vaccination efforts with Iraq’s NDVP and WHO standards, as well as World Bank requirements on technical, environmental, and social issues. The work of the TPMA will therefore contribute to ensuring safe, effective, efficient, and equitable vaccine rollout and maximizing its population benefits. This will also contribute to the GOI’s efforts to increase the demand for and build trust in COVID-19 vaccination among the population. The TPMA will prepare regular reports covering a period of three months and will share the draft report simultaneously with the Bank upon its delivery to the MOHE. A draft TOR has already been prepared. The final TOR will be subject to World Bank technical approval, defining the specific roles and responsibilities of the TPMA.\nAppointment of the TPMA is a condition of loan effectiveness. The TPMA role can be fulfilled by a United Nations (UN) agency (or agencies), international or local non-governmental organizations (NGO), or consulting firms that meet the criteria agreed upon between the World Bank and MOHE.\n\n**B.** **Results Monitoring and Evaluation Arrangements**\n\n51. **Progress towards project objectives and results indicators will be monitored by the PMU.** The PMU will include monitoring and evaluation specialists who will be responsible for collecting and processing relevant data, working closely with the Directorate of Public Health. The MOHE has established a system for monitoring the implementation of the vaccination campaign in line with the NDVP according to which the Inspection Directorate at the MOHE and its branches in all health directorates will be responsible for monitoring vaccination activities.\n\n**C.** **Sustainability**\n\n52. **There is strong political commitment in Iraq to mobilize financial resources for COVID-19 response, including**\n**for vaccine purchase and deployment.** By supporting vaccine purchase and deployment, the project will establish an\nenabling environment for other development partners, including multilateral development banks and UN agencies, to contribute to supporting vaccination efforts in the country. Investments under the project are expected to strengthen the health system in the country, ensuring institutional sustainability to deal with infectious diseases.\n\n**V.** **PROJECT APPRAISAL SUMMARY**\n**A.** **Technical, Economic and Financial Analysis**\n\n53. **The World Bank conducted a technical review of Iraq’s NDVP.** While readiness gaps exist, it is critical to note that such gaps need to be put into perspective. First, globally, no country can claim or should aim for full readiness before COVID-19 vaccine rollout. Second, based on the findings of the Effective Vaccine Management (EVM) 2.0, which was implemented in in July 2019, the overall rating for Iraq was 82 percent, indicating that Iraq’s vaccine supply chains and supply chain performance are slightly above WHO recommended minimum score of 80 percent. Third, one of the biggest concerns with messenger ribonucleic acid (mRNA) vaccines like Pfizer’s is the ultracold chain requirement.\nSuch ultracold chain already exists in Iraq for the currently planned Pfizer doses with the government’s procurement of 75 ultra-low temperature freezers. Based on the lessons learned with the initial vaccine delivery, the GOI will continuously adapt the NDVP for subsequent rollout of vaccines.\n\n54. **The economic rationale for investment in a COVID-19 vaccine is strong, considering the massive and**\n**continuing health and economic losses due to the pandemic** . As of September 20, 2021, over 228 million COVID-19\ncases and 4.7 million deaths have been confirmed worldwide. Global output is projected to have declined by 4.9 percent in 2020, with cumulative losses across 2020 and 2021 exceeding US$12 trillion. The primary benefit of Page 25 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) successful vaccination will be avoiding further human health costs from death and sickness. Global deployment of a COVID-19 vaccine will also generate economic benefits by enabling economic recovery and an increase in productive activity. Ensuring vaccine purchase and delivery in developing economies will also achieve significant distributive benefits and contribute to poverty reduction.\n\n55. **The successful development, production, and delivery of a vaccine has the best potential to reverse these**\n**trends, generating benefits that will far exceed vaccine-related costs.** Indeed, a rapid and well-targeted deployment\nof a COVID-19 vaccine would help reduce the increases in poverty and accelerate economic recovery. Even at levels of imperfect effectiveness, a COVID-19 vaccine that is introduced and deployed effectively to priority populations would assist in significantly reducing mortality and the spread of the coronavirus and accelerating a safe reopening of key sectors that are impacted. It would also reverse human capital losses by ensuring schools are reopened. The effective administration of a COVID-19 vaccine will also help avoid the associated health care costs for potentially millions of additional cases of infection and associated health-related impoverishment. Global experience with immunization against diseases shows that by avoiding these and other health costs, vaccines are one of the best buys in public health. For the most vulnerable population groups, especially in countries without effective universal health coverage, the potential health-related costs of millions of additional cases of COVID-19 infection in the absence of a vaccine represent a significant or even catastrophic financial impact and risk of impoverishment. The pandemic is also having dire effects on other non-COVID health outcomes. Increased morbidity and mortality due to interruption of essential services associated with COVID-19 containment measures hinder access to care for other health needs of the population, including maternal and childcare services, routine immunization services have been affected, threatening polio eradication and potentially leading to new outbreaks of preventable diseases, with associated deaths, illnesses, and long-term costs. Simultaneous epidemics are overwhelming public health systems in different countries that had few resources to begin with, and services needed to address the needs of people with chronic health conditions, and mental and substance use disorders have also been disrupted.\n\n56. **While the uncertainty around the costs and effectiveness of a COVID-19 vaccine makes it difficult to calculate**\n**its cost-effectiveness, the effective launch of a COVID-19 vaccine will have direct benefits in terms of averted costs**\n**of treatment and disability, as well as strengthened health systems.** Estimated COVID-19 treatment costs from lowand middle-income countries is at US$50 for a non-severe case and US$300 for a severe case. This excludes costs of\ntesting of negative cases, as well as the medical costs associated with delayed or forgone care-seeking, which usually results in higher costs. Even if the vaccine averts 2 million non-severe cases and no other benefits are considered, the investment will break even. Further, investments in vaccine delivery systems generate health and economic benefits beyond just delivering the COVID-19 vaccine. First, investments in last-mile delivery systems to administer the COVID19 vaccine to remote communities will require strengthening community health systems, which would have spillover effects to effective delivery of other services, helping close the significant urban-rural gap. Second, as the COVID-19 vaccine is introduced and lockdowns and movement restrictions are eased, patients would continue to access care for other conditions. Third, the economic benefits of slowing down the economic downturn are likely to significantly exceed the US$100 million needed to vaccinate 7 percent of the population, leaving aside the immediate health benefits. Given both the economic and health system benefits, an effectively deployed COVID-19 vaccine presents significant benefits.\n\n**B.** **Financial Management**\n\n57. The World Bank undertook an assessment of the financial management (FM) system within the MOHE, during the preparation of the ongoing Iraq EODP (P155732). The FM assessment was updated for the purpose of the project Page 26 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) in accordance with the World Bank Policy on the Investment Project Financing (IPF) and in line with paragraph 12 of section III of the IPF policy, as the project is in situation of urgent need of assistance or capacity constraints. The assessment was updated remotely considering the nationwide movement restriction due to the COVID-19 pandemic.\nThe FM assessment of MOHE concluded that with the implementation of agreed actions, the FM arrangements will satisfy the Bank requirements.\n\n58. Due to the nature of this project and the urgent need of assistance, the FM approach was streamlined and based on more simplified ex-ante requirements, while relying more heavily on ex-post requirements as additional fiduciary controls and review.\n\n59. Annex 3 provides additional information on the FM assessment and the agreed mitigation measures.\n\n60. The schedule of loan disbursements will be significantly influenced by the availability of vaccines. The World Bank will provide financial and risk assurances to manufacturers under advance purchase mechanisms.\n\n61. The deployment of vaccines funded by the project (including retroactive financing) will be subject to tight controls to mitigate risks. The supply chain will be closely monitored by a TPMA and a robust internal control framework (including for the supply chain) will be designed and established prior to the receipt and deployment of any project-funded vaccines.\n\n**C.** **Procurement**\n\n62. **Applicable procurement regulations** . Procurement will be carried out in accordance with the World Bank’s Procurement Regulations for IPF Borrowers, dated November 2020, and is subject to the World Bank’s “Guidelines on Preventing and Combating Fraud and Corruption in Projects Financed by IBRD Loans and IDA Credits and Grants”, dated October 15, 2006, and revised in January 2011, and as of July 1, 2016. Procurement under this project is also being processed under paragraph 20 of OP 11.00 “ _Procurement under Situations of Urgent Need of Assistance or_ _Capacity Constraints_ ”, where “ _Simplified Procurement Procedures_ ” may apply in accordance with paragraph 12 of the IPF policy. This will enable the delivery of early visible results in a context of extreme needs and high expectations in the targeted project areas. The project will use the Systematic Tracking of Exchanges in Procurement (STEP) to plan, record, and track procurement transactions.\n\n63. **The major planned procurement include:** (i) purchase of approximately 6 million doses of the COVID-19 vaccines, (ii) procurement of equipment for health care waste management, (iii) refining the electronic registration system for vaccination, (iv) procurement of vaccine logistics and supply chain management; (v) communication initiatives to address vaccine hesitancy, (vi) hiring a TPMA, and (vii) other technical assistance needed for vaccine rollout. The GOI has contracted 12 million doses from Pfizer to be delivered by the end of 2021, of which 6 million doses are expected to be financed under retroactive financing. The Manufacturing and Supply Agreement was reviewed and cleared retroactively by the World Bank. In addition, advance procurement will be used for the selection of the TPMA to ensure the project becomes effective in a timely manner and third-party monitoring can be initiated at the start of project implementation. The draft request for proposal (RFP) including the TOR has been prepared and the selection process will be prior reviewed by the Bank under advance procurement.\n\n64. The proposed procurement approach for other non-vaccine purchases prioritizes fast-track emergency Page 27 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) procurement for required emergency goods and services. Key measures to fast track procurement include among others: (i) direct contracting of United Nations (UN) Agencies to supply goods and services as specified in Section VI (Para 6.47 and 6.48) and Section VII (Para 7.27 and 7.28) of the applicable Procurement Regulations, respectively; (ii) limited bidding where justified; (iii) Request for Quotations (RFQ) as appropriate; (iv) streamlined competitive procedures with shorter bidding time; and (v) use of framework agreements including existing ones procured in a satisfactory manner to the Bank.\n\n**65.** **Assessment of MOHE‘s procurement capacity.** The assessment of the procurement system within the MOHE\nwas carried out during the preparation of the ongoing Iraq EODP (P155732) and recorded in the Procurement Risk Assessment and Management System (PRAM). It was noted that MOHE through the PMU has limited experience in World Bank Procurement Regulations and limited experience in procurement planning, monitoring, and contract management. In addition, procurement of COVID-19 vaccines is subject to high level of uncertainties in terms of prices and quantities. The major issue facing Iraq public procurement is the current uncertainty of public procurement law and regulations and their enforcement, including outdated practices. Additionally, Iraq’s ability to manage public resources is undermined by poor security. Iraq ranks among the lowest in the region on Transparency International’s Corruption Perception Index. This is further compounded by limited human capital for procurement and contract management, as commonly evidenced by delays in decision making. In addition, there is a general lack of emphasis on procurement, including principles in areas such as transparency, conflict of interest, independent complaint mechanism, value for money, fit for purpose, among others. Based on the above, **the residual procurement risk for**\n**this project is High.**\n\n66. **Hands-on Expanded Implementation Support (HEIS)** . At the Borrower’s request, the Bank may provide Hands on Expanded Implementation Support (HEIS) to support and build the capacity of staff directly involved in the project in drafting procurement documents and agreements with UN agencies/NGOs to speed up implementation. However, the MOHE will remain fully responsible for signing and entering into contracts and implementation, including assuring relevant logistics with suppliers (e.g., arranging the necessary freight/shipment of the goods to their destination, receiving and inspecting goods).\n\n67. **Project Procurement Strategy for Development (PPSD)** defines how the identified procurement arrangements will enable delivery of value-for-money in achieving the PDO by supporting a fit-for-purpose reconstruction and enhancement of services, where bidding will be done with no substantial delays and no rebidding, and cost and time overruns will be prevented. The preparation of the PPSD is deferred to the project implementation phase.\n\n68. **Systematic Tracking of Exchanges in Procurement (STEP).** The PMU at MOHE will use the World Bank online procurement planning and tracking tool to prepare, clear and update its procurement plans and conduct procurement transactions as referred to in the Procurement Regulations Section V, article 5.9. Any contract not uploaded in STEP, with award notification not being uploaded prior to signing of contracts, will not be eligible for financing **.** The World Bank will organize training on STEP before project effectiveness to register the PMU users and familiarize them with the STEP system. An initial procurement plan for the first six months has been agreed on with the MOHE and will be updated during implementation using STEP.\n\n69. **Complaint mechanism** . The complaint handling mechanism specified in the World Bank’s Procurement Regulations and included in the World Bank’s Standard Procurement Documents (SPDs) will be followed when Bank SPDs are used irrespective of the situation within the country. For the national procedures, Regulation No. 2 of 2014 of “Executing Public Contracting” in Iraq, establishes the right of bidder to raise a complaint to a centralized committee Page 28 of 54", "output": {"entities": {"named_data": ["Procurement Risk Assessment and Management System", "Transparency International’s Corruption Perception Index", "World Bank online procurement planning and tracking tool"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) at each procuring entity. However, bidders do not have adequate access to independent administrative review and appeal processes and access to civil courts is perceived as inadequate. To enhance the administrative bidders’ complaints review system, the POM will include a section on how to handle complaints that includes the initial submission of complaint by the bidders to the contracting authority, formal requirements for a complaint, establishment of a complaint committee, decision on the complaints, standard response time to decision on complaints, etc.\n\n70. **Dispute resolution systems** . When the Bank’s SPDs are used, the conditions of the contracts in the Bank’s SPDs apply. When national procedures are used, the condition of contracts in the national documents would apply through amicable resolution, dispute resolution board, and arbitration.\n\n71. **Frequency of supervision.** World Bank implementation support missions and post-procurement reviews will be undertaken at least twice and once a year, respectively. The post-procurement reviews will cover all project-related contracts eligible for post review.\n\n**D.** **Legal Operational Policies**\n\n**.** .\n\n**Triggered?**\n\nProjects on International Waterways OP 7.50 No Projects in Disputed Areas OP 7.60 No .\n\n**E.** **Environmental and Social Standards**\n\n71. **Environmental risk rating** . The environmental risk associated with the project is substantial. The main environmental risks identified at this stage are: (i) the Occupational Health and Safety (OHS) issues related to testing and handling of supplies during vaccination; (ii) the logistical challenges in transporting vaccines across the country in a timely manner, adhering to the recommended temperature and transportation requirements; (iii) generation and management of medical health care waste; (iv) community health and safety issues related to unforeseen effects of vaccination, traffic/road safety risks associated with transporting vaccines as well as with handling, transportation, disposal of hazardous and infectious health care waste and further spread of COVID-19 during the vaccination process due to gatherings and close proximity; and (v) increase of water and energy use. Infectious waste from vaccination campaigns such as used sharps (needles), specimen cultures and biological waste pose a high risk to human health and the environment, if inadequately managed.\n\n72. **Social risk rating** . It is anticipated that the project will have positive social impacts both at the individual and community levels. However, the social risk associated with activities under this component is substantial. The anticipated risks include: (i) inequitable access for marginalized and vulnerable social groups including disabled, elderly, internally displaced populations and refugees to access vaccines, (ii) social conflict, and risks to human security resulting from limited availability of vaccines and social tensions related to the challenges of a pandemic situation; (iii) gender inequalities and social norms to access critical health services such as vaccinations; (iv) Sexual Exploitation and Abuse/ Sexual Harassment (SEA/SH) risks among patients and health care providers, especially in relation to distribution of lifesaving vaccines; (v) inappropriate data protection measures and insufficient/not effective stakeholder communication on the vaccine rollout strategy; (vi) risks associated with adverse events following Page 29 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) immunization, (vii) the risk of elite capture and/or corruption as the COVID-19 vaccine will be in short supply relative to the demand; and (viii) the potential social risks due to the security risks including the engagement of security personnel for the transportation of vaccines and the protection of vaccination sites .\n\n73. **To manage these risks, the MOHE has prepared an Environmental and Social Management Framework (ESMF),**\n**a Stakeholder Engagement Plan (SEP), and an Environmental and Social Commitment Plan (ESCP).** The ESMF covers\nthe procedures for screening and identification of the environmental and social risks as well as the mitigation measures to be implemented for the project activities. It includes an Infection Control and Waste Management Plan, which describes in detail appropriate waste management practices to be utilized under the project. The ESMF also includes an elaboration of roles and responsibilities within the PMU and the MOHE, training requirements, timing of implementation and budgets. The ESMF includes a chapter on Labor Management Procedures (LMP) to manage risks associated with labor and working conditions of project workers, including proper working conditions and management of worker relationships; occupational health and safety (OHS) and mitigations to COVID-19 specific risks; grievance redress mechanism; and capacity strengthening for social, environment, health, and safety management. The ESMF also includes security risk management. It includes mitigation measures to address GBV risks. The Code of Conduct attached to the ESMF will establish a framework of ethical standards and rules, which is binding for relevant project workers. The Code of Conduct lays down provisions that help to deal with GBV-related risks. The ESMF has been disclosed on the MOHE website and on the World Bank website on September 21, 2021.\n\n74. On June 24, 2021, the MOHE conducted a virtual consultation session with different stakeholders (e.g., environmental directorates in governorates, health directorates in governorates, NGOs, CSOs, academics, research centers, etc.). Based on the outcome of the consultations, the SEP has been prepared and the MOHE will implement inclusive stakeholder engagement activities throughout the project life. The SEP has been disclosed on the MOHE website and the World Bank website on September 7, 2021.\n\n75. The MOHE has developed the ESCP to ensure project compliance with the Environmental and Social Standards, the World Bank Environmental, Health and Safety (EHS) Guidelines and the environmental and social instruments of the project. The ESCP has been disclosed on September 7, 2021. The MOHE has assigned six environmental and social specialists (including two environmental specialists, two social specialists, one communications specialist, and one GRM officer) to support management of environmental, social, health and safety (ESHS) risks and impacts of the project in accordance with the ESF requirements.\n\n76. **Role of the military.** Due to ongoing conflict and instability in the country, the project will require appropriate security arrangements for the safe deployment of vaccines. Upon request by Pfizer, the National Coordination Committee issued a decision for the security forces to accompany the distribution of Pfizer’s shipments. The Iraq national army, reporting to the Joint Operations Command under the Ministry of Defense, accompanies the cold trucks to secure transportation of the vaccine shipments from the airport to the place of destination. In addition, security forces under the Facilities Protection Directorate of the Ministry of Interior are present outside vaccination sites to provide protection. The security forces are represented at the high-level multi-sectoral committee for COVID-19 (established by Decree no. 217 issued by the General Secretariat of the Council of Ministers), chaired by the Minister of Health. Based on the nature of roles of security forces in this project, the security personnel will have very limited direct interaction with communities and project workers. The potential social risks of engaging security forces are therefore assessed as insignificant. The ESMF includes appropriate security risk mitigation measures, including the proportionate use of force, code of conduct for security personnel, and grievance redress.\n\nPage 30 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) 77. **Rapid Environmental and Social Assessment.** A baseline assessment is currently being undertaken by the Bank to measure the environmental and social impacts of the vaccination program at the national and local levels to: (i) assess the environmental and social (E&S) systems, guidelines and institutional capacity put in place by GOI for COVID-19 vaccine procurement and deployment against the requirements of the E&S Standards, World Bank Group (WBG) EHS General Guidelines, WBG EHS Health Guidelines for Health Care Facilities and relevant WHO guidelines; and (ii) assess the E&S aspects of ongoing vaccine deployment activities implemented by the GOI. The findings of the assessment will be used to further enhance the project’s environmental and social risk management. The project’s environmental and social instruments will be updated following completion of the assessment, as appropriate.\n\n**F.** **Climate Co-Benefits**\n\n**78.** **The effects of climate change are large and exacerbate the already significant environmental, security, political**\n**and economic challenges in Iraq.** It is thus critical to increase Iraq’s resilience capacity for current and future crises.\nOver 40 percent of the country is desert and is sparsely populated due to harsh weather conditions. In many parts of the country, good quality water is sparse due to salinity. Desertification and water scarcity due to river flow fluctuations render Iraq vulnerable to the adverse effects of climate change. Forecasts suggest that the average annual temperature will increase by 2 degrees Celsius by 2050, with a higher frequency of heat waves. Among others, this will negatively impact agricultural productivity, increase water scarcity, and intensify epidemics. Increase in temperatures is also known to be a direct cause of death, especially among the elderly who may suffer from strokes or heart attacks in extreme heat environments and who are also highly vulnerable to COVID-19.\n\n**79.** **The project has been screened for climate change and disaster risks and is highly exposed to extreme**\n**temperature and drought** . The most at-risk groups from climate-related exposures coincide with those vulnerable to\nCOVID-19. These include women, who form a large proportion of frontline workers in Iraq including health workers, caregivers, and teachers; the elderly; individuals who are ill, including with chronic diseases; the poor, displaced and marginalized who mostly reside in crowded locations with poor access to water and sanitation. However, the risk to project activities and outcomes is categorized as moderate due to several adaptation measures. Relevant mitigation measures have also been integrated in project design and will reduce the impact of the project’s activities on the environment and reduce greenhouse gases (GHG) emissions. Dust storms which are common in Iraq and correlate with dry/arid climatic conditions contribute to vulnerability of population to climate change and to COVID-19 vulnerability.\nThe project will help improve survival rates in patients who are vulnerable to climate change and dust storms.\nTherefore, vaccination could be viewed as a climate change adaptation measure.\n\n**80.** **The project intends to address climate change, address vulnerabilities, enhance climate resilience and**\n**adaptation, and mitigate GHG emissions through several activities** . The nationwide vaccination efforts supported by\nthis project considerably reduce the need for energy-intensive treatment in hospital critical care units, that would have otherwise been required, thus preventing adverse climate change consequences. Deployment of COVID-19 vaccines is key to climate resilience for several reasons. Firstly, the population group to first receive the vaccines are those vital to supporting the health care system (medical professions and support staff) and ensuring the continuity of essential public services, including to populations affected by climate change. Secondly, the project ensures that those most at risk from both the virus as well as from the health impacts of climate change are effectively targeted for COVID-19 vaccination. The GOI aims to cover almost two thirds of its population to achieve herd immunity thereby reducing the health risks of these climate vulnerable groups. Further, the communications strategy will inform the general population on the GOI vaccine deployment strategy during extraordinary events including natural disasters. This will Page 31 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) contribute to increasing the preparedness of the population in the event of a climate-related disaster. Finally, the widespread loss of power may seriously threaten the COVID-19 vaccine cold chain as vaccine conservation standards will be impacted. Therefore, as an adaptation measure, some of the cold chain equipment purchased will be off-grid solar equipment/supplies such as solar powered fridges and freezers that will provide reliable 24/7 power and efficient cooling. The NDVP includes measures to deal with any unexpected disruptions to the vaccine supply chain, distribution and storage from climate change impacts and other unexpected disasters (i.e., power outages from flooding and extreme heat).\n\n**81.** **The project includes activities from which adaptation co-benefits are expected.** These activities include technical\nassistance to update the national deployment and vaccination plans; support to integration of vaccination database with other health information systems; and a communication campaign to provide information to climate-vulnerable populations on vaccine delivery and contingency plans in case of extreme weather.\n\n**82.** **Climate change mitigation co-benefits will be generated under the project through** : (i) support for effective health\ncare waste management, such as use of non-burn technologies; and (ii) support for the development of micro-plans that promote the use of high energy efficiency or hybrid energy consumption.\n\n**83.** The project will support the procurement and deployment of approximately 170 integrated sterilization-shredding\nmachines in the amount of US$24 million to provide for instant and safe disposal of medical waste arising from the COVID-19 vaccine deployment. The machines can shred and disinfect medicine bottles, tubes, blister packs, catheters, syringes, glucose bottles, blood bags, ampoule bottles, and used needles. Utilizing only water and electricity, the machines using newer technology do not require any chemicals to operate and produce shredded disinfected waste that can be disposed of through regular municipal waste channels. The machines do not produce any emissions and provide a safer alternative for operators and the surrounding environment through minimizing human interference and/or contact with raw medical waste or the final by product. Further, owing to the slow and minimal nature of its internal mechanical parts, the machines provide anywhere from 20 to 40 percent less consumption in electricity. In addition, the average life span of the machines is 12-15 full years of operational functionality. This comes in contrast to the regular separate autoclave and shredder machines, which have 8-10 years of productive service, on average.\nFinally, the introduction of such machines will minimize the environmental, medical, and social hazards associated with the process of collection, storage, transportation, and disposal through regular incineration process of medical waste management.\n\n**G.** **Citizen Engagement**\n\n**84.** A key part of the COVID-19 vaccination campaign is the community engagement and outreach element of the\noverall framework and implementation plan put in place by the GOI to tackle the pandemic during its various stages.\nThe engagement of communities is critical to build community knowledge and confidence, establish trust, ensure governments respond to community needs (including vulnerable groups), and is thus a critical component of the COVID-19 response. The GOI recognizes the importance of citizen engagement in the COVID-19 vaccination campaign.\nThis is clearly demonstrated in the Demand Generation and Community Engagement Plan for COVID-19 Vaccines in Iraq developed under I3RF, which seeks to: (i) effectively communicate with the Iraqi population about COVID-19 vaccination to ensure vaccine acceptance and encourage uptake; and (ii) empower communities in feedback and accountability measures to improve decision making and service delivery related to vaccination.\n\nPage 32 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["vaccination database"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**85.** To support the equitable implementation of subcomponent 1.2 on Support for Deployment of COVID-19 Vaccines\nand Component 2 on Project Management and M&E, the project will support the implementation of the Demand General and Community Engagement Plan through the development of:\n\n- communication strategies, mass campaigns and information, education and awareness building to disseminate\nofficial information to communities and sensitize citizens of the risks related to COVID-19, supported by tailored awareness raising on preventative actions and the GOI’s COVID-19 response;\n\n- participatory platforms to engage communities in assessing needs and prioritizing solutions and ensure\ncommunity members, vulnerable groups (elderly, disabled, large households), and community-based organizations are able to articulate local needs (for immediate emergency needs or for reestablishing livelihoods).\n\n- a participatory monitoring and reporting mechanism to enable communities to help monitor the COVID-19\nresponse at the local level. At the outset this might include identification of gaps at the point of service delivery (information availability, access to testing, access to relevant care, equal treatment etc.) to ensure inclusivity and identify improvements for GRM.\n\n**86.** While these processes ensure that communities can provide informed feedback and play a role in local monitoring,\nthe challenge of implementation lies in social distancing policies. To ensure that communities can engage nevertheless, the project will actively engage with citizens to collect feedback on project performance, including through the use of the Iterative Beneficiary Monitoring (IBM) survey and social media surveys. Findings from such surveys will be used to improve the communication campaign and citizen engagement. Through the IBM, as well as social media surveys, engagement with community and religious leaders, especially in remote areas, will ensure the inclusion of their ongoing feedback in the rollout and implementation of the COVID-19 vaccination campaign to strengthen targeting accuracy and increase uptake. To ensure citizen engagement, the project will: (a) ensure community engagement teams are gender-balanced; (b) target messages to areas where vulnerable groups, including refugees and IDPs, reside to inform them about safety measures and benefits; (c) tailor messages to the elderly and those with medical risks including their target family members and health care providers; and (d) provide information for disabled people in accessible formats, like Braille, large print; text captioning; videos etc. The project will also explore the possibility of including NGO representation in oversight bodies established to oversee transparent and inclusive administration of vaccines.\n\n**H.** **Gender**\n\n**87.** Gender inequities and norms influence access to critical health services, as well as risk of exposure to disease,\nparticularly in emergency situations and pandemics. Factors that constrain access to and use of health services by women in Iraq include limited mobility and financial capacity, competing demands of paid and unpaid work, and limited access to information. [10] The reported incidence of COVID-19 is higher among men than women – 59 percent of registered COVID-19 cases in Iraq to date were among men. Moreover, women have also been impacted by the discontinuity of essential RMNCAH-N services, including for maternal and sexual and reproductive health, and GBV. [11] The GBV Information Management System (GBVIMS) has recorded a marked rise in the number of reported incidents of violence in 2020. [12] 10 UN Women (2018), Gender Profile- Iraq, A situation analysis on gender equality and women empowerment in Iraq.\n11 UN Women (2020). Report on the Impact of COVID-19 on Women.\n12 Gender Based Violence Information Management System Annual Narrative Report. January – December 2020.\nhttps://iraq.unfpa.org/sites/default/files/resource-pdf/gbvims_narrative_report_of_2020.pdf Page 33 of 54", "output": {"entities": {"named_data": ["Iterative Beneficiary Monitoring (IBM) survey", "GBV Information Management System"], "descriptive_data": [], "vague_data": ["social media surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**88.** COVID-19 vaccine uptake is lower among women in Iraq. According to the findings of the Facebook survey\nconducted under I3RF, only 25 percent of female respondents indicated they would get vaccinated when the COVID19 vaccine is made available compared to 40 percent of male respondents. Actual vaccination coverage shows more stark gender differences in uptake, with men receiving approximately 65 percent of vaccines delivered to date. [13] Until recently, nursing mothers and pregnant women were not eligible to receive COVID-19 vaccines. This can also partly explain the lower vaccination rates among women.\n\n**89.** Lack of understanding of the benefits and importance of the vaccine could have serious repercussions in the uptake\namong priority population groups, especially women who have more limited options to access information than men.\nFor example, 67 percent of women in Iraq use the Internet compared to 84 percent of men. These gender dimensions intersect with other inequities, particularly for populations that are poor, with limited access to formal education, living in hard-to-reach areas, temporary or informal settlements, or living with disabilities.\n\n**90.** Specific considerations in terms of media tools and messaging will be made when targeting women, men, and\nvulnerable populations in rural areas who are much more likely to have limited access to information. The project aims to do this by training female workers in community-based organizations and women-led NGOs to help with the dissemination of vaccine information and ensure that the targeted messaging will resonate and lead to vaccination awareness and uptake among women and men. The communication plan will ensure registration/vaccination sites be made accessible to women by taking into account timing and locations convenient for them and that female workers will be available at sites to answer questions. Additional details will be included in the POM. All data collection, monitoring, and analysis will be done in a sex-disaggregated way to the extent possible.\n\n**91.** The project components also address gender dimensions with targeted interventions including: (i) positive\ndiscrimination in vaccine registration and targeting activities to increase the proportion of women receiving the vaccine to discontinue the trend of male preferencing; (ii) integration of gender-responsive approaches in communication strategies with the public, including use of multiple accessible mediums in local languages; (iii) use of targeted messaging, and the creation of responsive platforms for registry of inquiries and grievances through a variety of mediums to target women and different vulnerable groups; and (iv) support for promotion of awareness and use of gender-based violence services, including the expanded network of integrated services at health facilities that offer medical, legal, psychosocial support and referrals. These services are offered by health facility staff that received GBV counselling and messaging as part of their regular on-the-job training to support and direct vulnerable women to specific support channels and resources. MOHE, supported by UN agencies, developed a remote and face-to-face GBV counseling flowchart targeting primary health care workers to clarify management methods and referral pathways.\nThese service adaptations were informed by a rapid assessment of available health care options for survivors of GBV during the COVID-19 outbreak. The survey included health care workers from primary health care centers, hospitals, and mobile medical clinics from 16 districts in Iraq. Of those surveyed, 69 percent of health facilities reported that their staff have already been trained on GBV. Following the COVID-19 outbreak, 81 percent of health facilities surveyed have already updated their referral pathways. Among those health facilities, 95 percent included GBV services in their updates. These interventions will be monitored and measured through the project’s results framework, TPMA reports, and through ESF instruments.\n\n13 Sex-disaggregated data by priority group on vaccination uptake is not available, however, the gender gap in uptake among these groups is likely to be similar to the overall trend. This project will contribute to collection of sex-disaggregated data across priority groups whenever possible.\n\nPage 34 of 54", "output": {"entities": {"named_data": [], "descriptive_data": ["Facebook survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**I.** **Grievance redress mechanisms**\n\n**92.** The MOHE has established a GRM system with a dedicated hotline (07901939809 and 07726180982) and an email\naddress for grievances and feedback. The project will further strengthen the GRM system to enable stakeholders to raise their concerns, comments, and suggestions. The hotline number will be publicized throughout the country using broadcast, print, and social media. The GRM also includes an appeal process for unresolved grievances. The MOHE PMU will assign a communication specialist and a dedicated GRM officer to closely monitor the implementation of the environmental and social mitigation measures as per the relevant ESF instruments to ensure adequate implementation of the GRM dedicated to the vaccination deployment. The GRM will be equipped to handle cases of SEA/SH following a survivor-centered approach and guidance on how to respond to these cases will be developed and shared with operators.\nIndividuals will be able to use the GRM to submit anonymous and non-anonymous feedback (e.g., complaints, suggestions, and queries) regarding the project.\n\n**VI.    GRIEVANCE REDRESS SERVICES**\n\n92. Communities and individuals who believe that they are adversely affected by a World Bank supported project may submit complaints to existing project-level grievance redress mechanisms or the Bank’s Grievance Redress Service (GRS).\nThe GRS ensures that complaints received are promptly reviewed in order to address project-related concerns. Project affected communities and individuals may submit their complaint to the Bank’s independent Inspection Panel which determines whether harm occurred, or could occur, as a result of Bank non-compliance with its policies and procedures.\nComplaints may be submitted at any time after concerns have been brought directly to the World Bank's attention, and Bank Management has been given an opportunity to respond. For information on how to submit complaints to the Bank’s corporate Grievance Redress Service (GRS), please visit: [http://www.worldbank.org/en/projects-operations/products-](http://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service)\n[and-services/grievance-redress-service. For information on how to submit complaints to the World Bank Inspection](http://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service) Panel, please visit _[www.inspectionpanel.org](http://www.inspectionpanel.org/)_ .\n\n**VII.** **KEY RISKS**\n\n93. **The overall risk to achieving the PDO is high**, with political and governance, macroeconomic, institutional capacity for implementation, and fiduciary residual risks rated high. Specific risk assessments and associated mitigation measures related to planned vaccination are informed by the findings of relevant assessments in Iraq, including the VIRAT/VRAF 2.0, and are described below.\n\n94. **Risk associated with the technical design of the project is substantial.** The large-scale acquisition and deployment of COVID-19 vaccines entails significant risks. First, the GOI has only secured access to 31.35 million vaccines to date, covering 38 percent of the population. Second, the vaccines may not be purchased in a timely manner. Third, a mass vaccination effort stretches capacity, particularly in low-capacity environments such as Iraq, entailing risks. The Bank support for Iraq to develop vaccination acquisition strategies and investment in deployment system capacity specifically aim to mitigate these risks. Fourth, there remains a possibility that health facilities will be simultaneously using vaccines that do not meet Bank’s VAC and are not supported by the project. This risk will be mitigated through vaccine traceability efforts to monitor vaccine deployment and associated side effects. These risks must be considered against the risk of the Page 35 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) country having less timely and effective deployment of vaccines, potentially exacerbating development gaps and eroding past development gains.\n\n95. **Political and governance risks are high.** The political and governance risks are related to the commitment and ability of the authorities to ensure appropriate targeting of the project-supported vaccines to reach the priority populations, based on objective public health criteria, and ability to manage public sentiment should there be a gap between vaccine targets and vaccine delivery. These risks will be mitigated through the assurance mechanisms that the project will support, such as the enhancement of the digital registry platform for targeting and monitoring of vaccine rollout. There are also risks related to governance of vaccine purchase and deployment, such as potential fraudulent attempts to gain access to vaccines without following the prioritization criteria for vaccination. This includes the risk of elite capture and corruption in the implementation of the vaccination program. This will be further mitigated through the application of anti-corruption guidelines for vaccine purchase and deployment and robust financial management oversight of the use of funds, as elaborated in the fiduciary section.\n\n96. **Macroeconomic risk for Iraq is rated high.** Overall macroeconomic risk is high given the spread of the pandemic, high oil dependence as well as budget rigidities linked to public wage bill and pension. These have led to severe external and fiscal pressures that limits the availability of the government to secure financing. As such it faces the risk of not having sufficient additional fiscal space for the purchase of vaccines at scale and other COVID-19 related response interventions. Lower oil receipts could severely impact service delivery and wage bill payments and have knock-on effects on social and political stability. Fiscal policy could remain expansionary and not address rigidities in recurrent expenditures due to parliamentary election dynamics (scheduled in October 2021). The continuation of reforms will also depend on the policy stance of the elected government. On the upside, a faster pickup in non-oil activity together with implementation of fiscal reforms could improve Iraq’s fiscal outlook. This risk is mitigated through the prioritization of vaccine acquisition and deployment activities by the GOI, as national priority. Donor-funded resources will complement nationally budged funds that have been allocated for COVID-19 vaccination activities.\n\n97. **Institutional capacity for implementation and sustainability risks are high** . The project is designed to address key institutional capacity risks related to vaccine deployment and distribution, but residual risks remain high. The key institutional risk remains the MOHE’s capacity to carry out the activities and is heightened by the complexity of vaccine acquisition and deployment. Vaccine deployment cold-chain and distribution capacity have increased rapidly but required additional support to meet the anticipated scale and population group coverage for COVID-19 vaccination. This risk will be mitigated through the project’s financial and technical support for immunization system strengthening needs, including coordination with partners, such as WHO and UNICEF, in their provision of systems strengthening support. The continuous monitoring by the TPMA will provide regular reports focusing on areas of improvement and course corrections, where applicable.\n\n98. **Sector strategies and policies risks are rated substantial.** Iraq is in the process of introducing social health insurance. The reform implementation should not undermine the ability of the government health system to provide the needed services during the COVID-19 pandemic. Improving access to and quality of health services, including by strengthening primary care, remain key priorities for the health system. Further, the health sector’s activities for COVID19 response require further strengthening (e.g., case detection and reporting, social distancing measures, health system strengthening, communications, multi-sector policy for prevention and preparedness, infrastructure, etc.). The GOI has prioritized COVID-19 vaccination efforts as the best pathway for recovery from the pandemic. This commitment will enable supplementary or emergency measures to support COVID-19 vaccination related activities by all national entities, including financing, negotiations with vaccine suppliers, deployment efforts and leveraging multi-lateral institutions Page 36 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) towards supporting the nationally consolidated plan for vaccination. The NDVP provides the roadmap for the GOI’s vaccination efforts.\n\n99. **Fiduciary risks are high** . The procurement and FM risks associated with the procurement and distribution of vaccines include fraud and corruption risks.\n\n100. The residual FM risk for the project is substantial. The key FM risks associated with the project include: (i) limited capacity at the implementing agencies to meet the project’s FM requirements; (ii) potential misuse of vaccine doses and inefficiencies in supply chain management and administration including acquisition, storage and distribution due to low capacity and limited accountability; (iii) security conditions and COVID-19 pandemic do not allow visits by the Bank staff to perform physical verification; and (iv) potential delays in developing the POM including the FM chapter that will show in detail the deployment and distribution plan processes, and procedures, thus delaying the vaccination process. These risk will be mitigated by: (i) establishing a centralized FM function within the PMU’s authority with FM team consisting of a qualified financial officer, accountant(s), and internal controller(s) seconded from the MOHE own staff who would receive periodic trainings inside and outside the authority to improve and enforce their knowledge; (ii) PMU hiring a parttime FM consultant; (iii) engaging with UN agencies for support in the vaccine supply chain management; and (iv) hiring the TPMA to ensure that the vaccines have been provided to the targeted beneficiaries as per the approved phased selection criteria. The TPMA will ensure transparency in the distribution of vaccines and distribution and consistency with the vaccine deployment plan.\n\n101. Given the significant disruption in the supply chain of health supplies, the overall procurement risk for the project is assessed as high. The key procurement risks associated with COVID-19 vaccines relate to: (i) the complexity of the vaccines market given the significant market power enjoyed by vaccine manufactures and weak bargaining power by low and middle income countries; (ii) limited market access due to advance orders by developed countries; (iii) inability of the market to supply adequate quantities of vaccines to meet the demand; (iv) delays by countries in triggering emergency procurement procedures which could delay procurement and contract implementation including payments, the risk associated with vaccines is failed procurement; (v) limited capacity and lack of knowledge of World Bank Procurement Regulations by the implementing agency, especially under emergency conditions; (vi) lack of proper coordination of and interaction with various stakeholders, which may cause procurement and project implementation delays; (vii) inadequate capacity in supervision of vaccine acquisition contracts and mobile cold chain equipment and supplies; and (ix) lack of responsiveness and anticipation and limited experience in supervising the execution of similar contracts. There are also risks related to governance of vaccine purchase and deployment, such as potential fraud and substandard quality. These risks will be mitigated by: (i) providing options to fast-track procurement through direct or advance purchase; (ii) Bank’s prior review of the vaccine contracts to advise on their acceptance; and (iii) information technology systems and smart systems for the traceability of vaccines. Other mitigation measures include: (i) direct contracting of UN agencies by the MOHE to supply major medical equipment and supplies due to the emergency nature of the project and the supply chain constraints as a result of the global pandemic; (ii) further flexibilities, such as Direct Selection, limited bidding where justified, and increased threshold for request for quotations (RFQs), to reduce procurement processing times; and (iii) increased implementation support and provision of HEIS when requested by the Borrower.\n\n102. **Environment and social risks are substantial.** The environmental risk associated with this project is substantial due to the direct impacts related to OHS for health workers in health facilities. In addition, the quantity of health care waste is likely to increase due to the project activities and may affect the capacity of local authorities to manage this waste, resulting in indirect and long-term environmental and public health impacts.\n\nPage 37 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) 103. The social risk associated with activities under the project is substantial due to the potential unequal access to vaccines, perception of unfair distribution and exclusion of certain vulnerable groups including the disabled, the elderly, and IDPs, potential rising social tensions, and gender inequities. Gender inequities and norms can play an important role for access to critical health services such as vaccinations. Moreover, inappropriate data protection measures and insufficient/ineffective stakeholder communication on the vaccine rollout strategy; risks associated with adverse events following immunization, the risk of elite capture and/or corruption as the Covid-19 vaccine will be in short supply relative to the demand. Section V.E provides more details of the environment and social risks and mitigation measures. The E&S risks will be mitigated through adhering to the relevant mitigation measures under the ESCP, SEP, ESMF and LMP.\n\n104. **Stakeholder risk is rated as substantial** . The project implementation success depends on strong coordination with different stakeholders. Denial of and misinformation associated with COVID-19 vaccination, in addition to mistrust of some government actions, have been documented in the media, social media, and political spheres. This may contribute to individuals rejecting public health interventions and contribute to sustained misinformation in some areas of the country, resulting in difficulties in drawing vaccine beneficiaries to the vaccination centers and vaccine skepticism among the population. To mitigate this risk, the project will support the GOI’s efforts in advocacy and coalition building to sensitize key groups including policy makers, media, religious leaders, and community interest groups. This will be complemented by carefully designed mass communication campaigns to build support for response and mitigation measures among the wider population. During project implementation, the MOHE will also coordinate with partners on different technical assistance activities, including communication efforts.\n\n**105.** **Other.** There are several substantial residual risks associated with data management and privacy. These include\nrisk of inadequate management and storage or inappropriate sharing of personal data from the vaccination digital platform and other databases. Mitigation measures may include legal, institutional, and technical measures, as well as investments in data security and training of staff. To guard against abuse of such data, the project will incorporate best international practices for dealing with such data in such circumstances. Such measures may include, by way of example, data minimization (collecting only data that is necessary for the purpose); data accuracy (correct or erase data that are not necessary or are inaccurate), use limitations (data are only used for legitimate and related purposes), data retention (retain data only for as long as they are necessary), informing data subjects of use and processing of data, and allowing data subjects the opportunity to correct information about them, etc.\n.\n\nPage 38 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**VIII.** **RESULTS FRAMEWORK AND MONITORING**\n\n**Results Framework**\n\n**COUNTRY: Iraq**\n**Iraq COVID-19 Vaccination Project**\n\n**Project Development Objective(s)**\n\nThe development objective is to support the Government of Iraq in the acquisition and deployment of COVID-19 vaccines.\n\n**Project Development Objective Indicators**\n\n**RESULT_FRAME_TBL_PDO**\n\n**Indicator Name** **PBC Baseline** **Intermediate Targets** **End Target**\n\n**1** **2** **3** **4** **5** **6**\n\n**To support the Government of Iraq in the acquisition and deployment of COVID-19 vaccines**\n\nPercentage of specific priority populations fully vaccinated (Percentage) Percentage of fully vaccinated priority groups who are female (Percentage) Number of projectsupported COVID-19 vaccinations sites with adequate health care waste management for vaccination (Number) 5.00 30.00 50.00 60.00 70.00 70.00 70.00 70.00 35.00 40.00 50.00 50.00 50.00 50.00 50.00 50.00 0.00 0.00 50.00 90.00 130.00 156.00 156.00 156.00 Number of COVID-19 0.00 2,000,000.00 5,000,000.00 6,000,000.00 6,000,000.00 6,000,000.00 6,000,000.00 6,000,000.00 vaccine doses acquired Page 39 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**RESULT_FRAME_TBL_PDO**\n\n**Indicator Name** **PBC Baseline** **Intermediate Targets** **End Target**\n\n**1** **2** **3** **4** **5** **6**\n\nthrough project financing (Number)\n\n**PDO Table SPACE**\n\n**Intermediate Results Indicators by Components**\n\n**RESULT_FRAME_TBL_IO**\n\n**Indicator Name** **PBC Baseline** **Intermediate Targets** **End Target**\n\n**1** **2** **3** **4** **5** **6**\n\n**COVID-19 Vaccines and Deployment**\n\nPercentage of administered doses which are captured in the national vaccination digital registry (Percentage) Percentage of vaccination sites which publicized detailed performance data on a regular basis in the last quarter (Percentage) Percentage of vaccination sites with functional cold chain (Percentage) Percentage of reported serious AEFI cases for which investigations were initiated within 48 hours (Percentage) 0.00 25.00 40.00 55.00 79.00 85.00 95.00 95.00 0.00 50.00 70.00 80.00 90.00 90.00 90.00 90.00 0.00 50.00 90.00 100.00 100.00 100.00 100.00 100.00 0.00 20.00 50.00 80.00 80.00 80.00 80.00 80.00 Number of health 0.00 200.00 400.00 600.00 800.00 1,000.00 1,000.00 1,000.00 Page 40 of 54", "output": {"entities": {"named_data": [], "descriptive_data": ["national vaccination digital registry"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**RESULT_FRAME_TBL_IO**\n\n**Indicator Name** **PBC Baseline** **Intermediate Targets** **End Target**\n\n**1** **2** **3** **4** **5** **6**\n\nworkers who received training in vaccination with GBV-related content (Number) Number of communication initiatives supported by the project to address vaccine hesitancy (Number) 0.00 1.00 2.00 3.00 4.00 5.00 5.00 5.00\n\n**Project Management and Monitoring and Evaluation**\n\nPercentage of feedback cases registered in the project's grievance redress mechanism (GRM) in the last quarter addressed within a timeframe specified and publicly communicated by the project (Percentage) Percentage of vaccination sites visited by the project TPMA in the last quarter (Percentage) Number of public discussion meetings conducted on the results of the TPMA (Number)\n\n**IO Table SPACE**\n\n0.00 10.00 40.00 60.00 80.00 90.00 95.00 95.00 0.00 50.00 50.00 50.00 50.00 50.00 50.00 50.00 0.00 1.00 2.00 3.00 4.00 4.00 4.00 4.00 Page 41 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**UL Table SPACE**\n\n**Monitoring & Evaluation Plan: PDO Indicators**\n\n**Methodology for Data**\n**Indicator Name** **Definition/Description** **Frequency** **Datasource**\n**Collection**\n\n**Responsibility for Data**\n**Collection**\n\nPMU/MOHE PMU/MOHE MOHE/TPMA Administrative data Administrative data Survey by TPMA Percentage of specific priority populations fully vaccinated Percentage of fully vaccinated priority groups who are female Number of project-supported COVID-19 vaccinations sites with adequate health care waste management for vaccination The indicator will track the number of the eligible people as defined being among a specific set of priority groups in the National Deployment and Vaccination Plan (NVDP)/government prioritization list who are fully vaccinated from COVID-19 using vaccines that meet Bank's vaccine approval criteria.\n\nThe denominator is the number of people who were in the target groups and were fully vaccinated with 2 doses, and the numerator will be the number of women vaccinated with 2 doses in the target groups.\n\nThe project will invest in providing adequate waste management equipment at the facility level.\nMonitoring of the 3 months 3 months 3 months NDVP, digital vaccination registry, national paper-based vaccination registry NDVP, digital vaccination registry, national paper-based vaccination registry TPMA reports Page 42 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) continuous availability and adequate functionality of adequate wastemanagement processes as per established standards will be maintained throughout project implementation.\n\nNumber of COVID-19 vaccine doses acquired through project financing\n\n**ME PDO Table SPACE**\n\nThis indicator will measure the number of COVID-19 vaccines that have been procured by the GOI through World Bank financing support.\n\n3 months MOHE records Administrative data\n\n**Monitoring & Evaluation Plan: Intermediate Results Indicators**\n\n**Methodology for Data**\n**Indicator Name** **Definition/Description** **Frequency** **Datasource**\n**Collection**\n\nPMU/MOHE\n\n**Responsibility for Data**\n**Collection**\n\nPMU/MOHE PMU/MOHE Percentage of administered doses which are captured in the national vaccination digital registry Percentage of vaccination sites which publicized detailed performance data on a regular basis in the last quarter The indicator will track the percentage of administered COVID-19 vaccines which are captured in the national vaccination digital registry.\n\nPercentage of vaccination sites which publicize detailed performance data 3 months 3 months Digital vaccination registry, vaccine logistics management information system National vaccination dashboard Administrative data Administrative data Page 43 of 54", "output": {"entities": {"named_data": ["national vaccination digital registry", "Digital vaccination registry", "vaccine logistics management information system", "National vaccination dashboard"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) on a regular basis in the last quarter 3 months 3 months Every 3 months Percentage of vaccination sites with functional cold chain Percentage of reported serious AEFI cases for which investigations were initiated within 48 hours Number of health workers who received training in vaccination with GBV-related The project will track the continuous functionality of the cold supply chain to ensure that vaccines are at all times - maintained at optimal condition until being administered to beneficiaries This indicator will measure the percentage of reported serious Adverse Events Following Immunization (AEFI) post COVID-19 vaccinations that have been reported to the Iraqi MOHE surveillance system, GRM and other channels that have been addressed and investigated within 48 hours of reporting to the total number of reported AEFIs. The aim is to measure the adequate and timely response and investigation to the reported AEFIs reported post COVID-19 vaccinations.\n\nThis indicator will measure the number of Healthcare MOHE and TPMA reports Iraq MOHE surveillance system, GRM data, MOHE incident reporting and media sources.\n\nMOHE and TPM reports TPM Administrative and public data TPM MOHE/TPMA PMU/MOHE and TPMA MOHE/TPMA Page 44 of 54", "output": {"entities": {"named_data": ["Iraq MOHE surveillance system"], "descriptive_data": ["Iraqi MOHE surveillance system"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) content workers who have received training on Gender Based Violence related content.\nThe training will address the dimensions of : identification of GBV victims, simple counselling and support mechanisms and possible referral pathways for further assessment and management of possible such cases.\n\nNumber of communication initiatives supported by the project to address vaccine hesitancy Percentage of feedback cases registered in the project's grievance redress mechanism (GRM) in the last quarter addressed within a timeframe specified and publicly communicated by the project This indicator will track the number of communication initiatives that are either conducted or/and substantially supported by MOHE for the public to address the issue of vaccine hesitancy.\n\nThe project will maintain a functioning grievance redress mechanism (GRM).\nGrievances will be tracked and analyzed, and feedback will be provided to MOHE management for corrective actions, as needed. The project operations manual will include the specific process Every 3 months 3 months MOHE/TPMA MOHE GRM records Administrative data Administrative data MOHE/TPMA MOHE/TPMA Page 45 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Administrative data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) to be followed.\n\nPercentage of vaccination sites visited by the project TPMA in the last quarter Number of public discussion meetings conducted on the results of the TPMA\n\n**ME IO Table SPACE**\n\n~~.~~ This indicator will monitor the percentage of project supported facilities visited by the Third Party Monitoring Agency in each quarter of the project lifetime. This will be calculated by dividing the number of the visited facilities divided by the number of the total number of supported facilities.\n\nThe indicator will track the number of public meetings/consultations conducted by MOHE on the results of the project's TPMA reports to elicit citizen and public participation on the needed course correction measures.\n\n3 months Every 3 months TPMA reports, PMU records meeting minutes, PMU documentatio n Administrative data Administrative data TPMA, PMU/MOHE PMU/MOHE Page 46 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Administrative data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**ANNEX 1: Status of Vaccines as of 09/15/2021**\n\n**Vaccine** **Stringent Regulatory Authority**\n\n**Emergency Use Approval**\n\n**WHO PQ/EUL**\n\nBNT162b2/COMIRNATY Tozinameran (INN) Pfizer BioNTech United Kingdom: December 2, 2020 Canada: December 9, 2020 United States of America: December 11, 2020 European Union: December 21, 2020 Switzerland: December 19, 2020 Australia: January 25, 2021 mRNA-1273 - Moderna USA: December 18, 2020 Canada: December 23, 2020 EU: January 6, 2021 Switzerland: January 12, 2021 UK: January 8, 2021 WHO Emergency Use Listing (EUL): December 31, 2020 WHO EUL: April 20, 2021 WHO EUL: February 15, 2021, for vaccines manufactured by SK Bio and Serum Institute of India WHO EUL: March 12, 2021 AZD1222 (also known as ChAdOx1_nCoV19/ commercialized as COVISHIELD in India) AstraZeneca/Oxford Ad26.COV2.S - Johnson & Johnson UK: December 30, 2020 EU: January 29, 2021 Australia: February 16, 2021 (overseas manufacturing); March 21 [, ] 2021 (for local manufacturing by CSL – Seqirus) Canada: February 26, 2021 USA: February 27, 2021 Canada: March 5, 2021 EU: March 11, 2021 Switzerland: March 22, 2021 UK: May 28, 2021 Australia: June 25, 2021 BBIBP-CorV - Sinopharm WHO EUL: May 7 [, ] 2021 for vaccines manufactured by Beijing Institute of Biological Products Co Ltd E-Town Vaccine Industry Base No. 6 &9 Bo’xing 2nd Road Economic-Technological Development Area Beijing, P.R. China CoronaVac - Sinovac WHO EUL: June 1 [, ] 2021 for vaccines manufactured by Sinovac Life Sciences Co., Ltd. No. 21, Tianfu Street, Daxing Biomedicine Industrial Base of Zhongguancun Science Park, Daxing District, Beijing, P.R. China Page 47 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**ANNEX 2: Financial Management Assessment Report**\n\n1. **Project objectives and activities** : The project objectives are aligned with the results chain of the COVID19 Strategic Preparedness and Response Program (SPRP). Critical interventions are needed to reduce morbidity\nand mortality rates from COVID-19 in Iraq. The implementation of Iraq’s National Deployment and Vaccination Plan (NDVP) will strengthen the capacity of the Government of Iraq (GOI), and more specifically, the Ministry of Health Environment (MOHE) to ensure access to affordable COVID-19 vaccines for the population.\n\n2. **Staffing and financial management (FM) implementation arrangements** . The project will be implemented by a Project Management Unit (PMU) established at the MOHE to oversee the project implementation with full day-to-day responsibilities while ensuring that all activities are fully coordinated.\nQualified financial officer, accountant(s), and internal controller(s) will be provided from the MOHE own staff and will be dedicated to the project. The World Bank undertook an assessment of the financial management system within the MOHE, during the preparation of the ongoing Iraq Emergency Operation Development Project (P155732). The FM assessment was updated for the purpose of the project in accordance with the World Bank Policy on the Investment Project Financing (IPF) and in line with paragraph 12 of section III of the IPF policy, as the project is in situation of urgent need of assistance or capacity constraints. The assessment was updated remotely considering the nationwide movement restriction due to the COVID-19 pandemic. The FM assessment of MOHE concludes that with the implementation of agreed actions, the proposed FM arrangements will satisfy the Bank policy requirements.\n\n3. Although the MOHE has been implementing the ongoing EODP for almost 4 years, the FM performance rating is Moderately Unsatisfactory. The PMU will be responsible for planning and coordinating specific activities, including FM (payment authorization, disbursement, accounting, and reporting), procurement of goods, consulting services (and related contract management), and monitoring and evaluation (M&E). Due to the limited experience of the MOHE FM team with the World Bank FM policies and guidelines, the World Bank will provide close support to the FM project staff in addition to the MOHE hiring an FM consultant.\n\n4. **Project FM risk** . Based on the results of the assessment, the overall FM risk is High. With mitigation measures in place, the project will have acceptable project FM arrangements and the residual FM risk will be Substantial. The pre-mitigation FM risk is assessed as High mainly due to: i) Limited capacity at the implementing agencies to meet the project’s financial management requirements; ii) Potential misuse of vaccine doses and inefficiencies in supply chain management and administration including acquisition, storage and distribution due to low capacity and limited accountability; iii) Security conditions and COVID-19 pandemic do not allow visits by the Bank to perform physical verification; iv) Potential delays in developing the POM, including the FM chapter that will show in detail the deployment and distribution plan processes, and procedures, thus delaying the vaccination process.\nv) Overall weaknesses and shortcomings in the control environment; vi) Limited accounting and reporting systems in providing timely and comprehensive information; and vii) Delays in making payments due to the shortfalls in the Iraqi banking sector; The following agreed measures will mitigate FM-related risks to Substantial: Page 48 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) i) Establishing a centralized FM function within the PMU’s authority with FM team consisting of a qualified Financial Officer, Accountant(s), and internal controller(s) seconded from the MOHE own staff who would receive periodic trainings inside and outside the authority to improve and enforce their knowledge; ii) Hiring a part-time FM consultant; iii) Engaging with United Nations agencies for support in the vaccine supply chain management; iv) Engaging with Third Party Monitoring Agent (TPMA) to ensure that the vaccines have been provided to the targeted beneficiaries as per the approved phased selection criteria; v) Accounting and reporting arrangements to give timely information on the project financial performance and status; off the shelf accounting software will be used to record project financial transactions and generate simplified Interim Unaudited Financial Reports (IFRs); vi) Opening A Designated Account (DA) in US dollars with sufficient advance, to ensure that funds are readily available for project implementation; vii) Hiring an independent external auditor acceptable to the Bank to provide an independent opinion of the project financial statements.\nviii) An FM manual for this project documenting the procedures, inter alia, on internal controls, budgeting, financial reporting and auditing, responsibilities and duties, flow of information, and others.\n\n**TYPE OF RISKS** **CURRENT RISK RATING**\n\n**INHERENT RISKS (IR)**\n\nCountry Level High Entity Level High Project Level Substantial\n\n**Overall IR** **High**\n\n**CONTROL RISKS (CR)**\n\nBudgeting Substantial Accounting Substantial Internal Controls High Funds Flow Substantial Financial Reporting Substantial Auditing Substantial\n\n**Overall CR** **Substantial**\n\n**COMPLIANCE RISKS (COR)**\n\n**Overall CoR** **Substantial**\n\n**Overall FM Risk** **Substantial**\n\nPage 49 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) 5. **Budgeting and flow of funds** . The PMU will maintain a detailed disbursement plan per quarter. This plan will be developed based on the initial six-month procurement plan or will be based on the schedule of outputs, as defined in the implementation schedule and estimated payments cycles, and revised upon need. It will be used as a monitoring tool to analyze budget variances and manage cash and will feed into the quarterly IFRs.\n\n6. To ensure that funds are readily available for project implementation, a Designated Account (DA) will be opened. The PMU will be responsible of managing its DA, preparing the reconciliations, and submitting monthly replenishment applications with appropriate supporting documentation.\n\n7. The flowchart below depicts the flow of documentation and flow of funds at the PMU:\n\n**Cash and documents flow – Consultancy Services and Goods supplied**\n\nDocument flow Cash flow Interim Un- Accounti Audited ng books Financial Reports (IFRs) Project DA PMT Finance Team Bank replenishes Send WA to WB Consultants and/or Goods supplied Submit invoices & supporting documents d PMT internal Controller PMT Finance team Payment DA Companies/UN Direct/LC/ UN payment World Bank IBRD PMT Technical PMT Manager team _Note: Cash and document flow of consultancy services will take the same stream of the above chart except there_ _is no role to the “Technical team”._ 8. **Accounting and financial reporting.** All government agencies in Iraq follow the accounting cash basis whereas the mixed and private sector are using the unified (accrual) accounting basis Bylaw issued in 2011 by the Iraq Federal Supreme Audit Institute. Since the PMU will have a centralized FM structure, the project will follow its own financial management procedures as demonstrated in the FM manual. The project will follow the cash basis of accounting and key accounting policies and procedures will be documented in the financial procedure manual which will be finalized no later than 30 days after loan effectiveness. MOHE uses very basic accounting software to capture its daily financial transactions. This software, which is developed by the MOHE, is not capable of generating the project’s quarterly Interim Unaudited Financial Reports (IFRs) in accordance with the World Bank FM guidance and record commitments. Adequate accounting and reporting arrangements will be used to give timely information on the project’s financial performance and status. Off the shelf accounting software will be used to record project financial transactions whereas the excel sheet will be used to generate the quarterly IFRs.\n\nPage 50 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) 9. **The PMU will be responsible for preparing the following:** i) **Quarterly IFRs** and submitting them to the Bank and through the Bank’s digital platform (client Connection) within 45 days from the quarter then ended. These reports will consist of: (i) Statement of Cash Receipts and Payments by each category, (ii) Statement of Comparison between Actual and Budgeted Cash Payments by Component/Category, (iii) Reconciliation Statement for the balance of the Designated Account, (iv) the list of all signed Contracts per category” showing Contract amounts committed, paid, and unpaid under each contract, and physical progress against financial progress of each contract, and (v) list of assets (goods and equipment).\n\nii) **Annual Project Financial Statements (PFS)** which will be audited by an independent external auditor. The audit report will be submitted to the Bank and through the Bank’s digital platform not later than six months after the end of each fiscal year. The PFS include: (i) “Statement of Cash Receipts and Payments by category” and accounting policies and explanatory notes, including a footnote disclosure on schedules; (ii) “the list of all signed Contracts per category” showing Contract amounts committed, paid, and unpaid under each contract; (iii) Reconciliation Statement for the balance of the DA; and (iv) list of assets (good and equipment).\n\n10. **Internal controls** : The project will be implemented through centralized management and disbursement functions within the PMU authority with specific controls and procedures that will be documented in the FM manual. The PMU will follow the FM instructions in the FM manual which will be finalized no later than 30 days after effectiveness. The manual will document the project’s implementation of internal control functions and process and describe the responsibilities of the PMU staff, which are summarized in terms of authorization and execution processes. The expenditure cycle will specify the following steps: (a) technical approval for deliverable vaccines, (b) approval by relevant PMU manager, (c) issuance of payments will be made upon receipt of supportive documentation and written requests by authorized officials, and (d) verification by the financial officer of the accuracy and compliance of the payment requests with the Loan Agreement. On a monthly basis, the Financial Officer will reconcile the project account bank statement with the account book balance. Reconciliations will be prepared by the Financial Officer and checked by an independent person. All reconciling items (if any) will be listed, explained, and followed up on. Copies of the reconciliation together with the account bank statement will be kept in the project files and attached to the IFRs.\n\n11. The bulk of the project’s expenditures will finance vaccines with some consultancy service contracts and incremental operating costs. Goods contracts will be financed mainly through Letter of Credit (LC) and direct payments. TPMA, financed by the I3RF, will be engaged to ensure that vaccines are deployed as it is agreed in the plan.\n\n12. **Safeguard the purchased vaccines.** The vaccines will be purchased from outside Iraq and will be supplied to special MOHE warehouses located near distribution points. Supply contracts are financed through either LCs or direct payments or special UN payments. All goods shipped are insured. All advances to suppliers (non-UN agencies) are provided against bank guarantees.\n\n13. As agreed with the implementing agency, the following measures are implemented to safeguard project’s purchased vaccines/goods/equipment: Page 51 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) i) The PMU uses a register “spreadsheet” to record the details of purchased vaccines under the project, including among others, description, reference to contract, quantity, location, and the Governorate in which the vaccines were deployed; ii) The PMU will prepare a detailed distribution plan, which will include among others the description of all vaccines and beneficiary governorate; iii) All items will be traceable; iv) Special conditioned warehouse register will be used for the received vaccines; v) Special committees will be established to receive the purchased vaccines. The committee will be responsible for vaccine inception upon delivery at the location to confirm quantity and quality as per the signed contract; vi) Items will be stored in a designated area that would be easy to differentiate from all other inventory (vaccines) items; vii) Warehouses will be maintained to provide the necessary conditions to protect the vaccines from weather, heat, theft, damaged, etc.); and viii) Annual stocktaking will be performed by Directorates of Health, and the PMU will compare to its own register of assets.\n\n14. **Financial audit** : The project’s financial statements will be audited annually by an independent auditor acceptable to the World Bank, in accordance with internationally accepted auditing standards and terms of reference cleared by the World Bank. The PMU will be responsible for preparing the TORs for the auditor and will submit them to the World Bank for clearance. The audit scope will cover the activities of the project implemented by the PMU. The audit report will be sent to the Bank no later than 6 months following the end of the project’s fiscal year. The report will include an opinion on the project’s financial statement. The auditor will also be requested to provide an opinion on the project’s effectiveness of internal control system including the vaccines safeguard measures used. Finally, a management letter will accompany the audit report, identifying any deficiencies in the control system the auditor finds pertinent, including recommendations for their improvement.\nIn accordance with the World Bank’s Policy on Access to Information, the World Bank requires that the borrower discloses the audited financial statements in a manner acceptable to the World Bank.\n\n15. **Implementation support** . The project will require close implementation support during the start-up phase to ensure that the PMU’s fiduciary requirements are completed in a timely manner, minimizing project fiduciary risk. During the implementation phase, implementation support will be conducted on semester basis to ensure compliance with Bank’s requirements and to develop internally generated project risk assessment.\n\nPage 52 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038)\n\n**ANNEX 3: Implementation Arrangements and Support Plan**\n\n1. The World Bank’s implementation support for the MOHE will include providing advice and undertaking\nanalytics to strengthen the technical quality of implementation and assure timely implementation of the project.\nThe extent of implementation support that will be provided depends on recognized needs and opportunities.\n\n2. In terms of strengthening compliance, technical assistance may be needed as described in the relevant sections of the Project Appraisal Summary. With fiduciary risk rated high, technical assistance to procurement and FM will be prioritized, also with the UN agencies supporting procurement process. The project will use the existing PMU, appropriately staffed, with relevant qualifications. The project would support additional training in the use of the STEP and the World Bank Procurement Framework. Implementation support for FM will be undertaken mainly during, and in response to the findings of, the semiannual FM supervision reviews. For environmental and social aspects, the World Bank will monitor compliance through the reports submitted by the PMU and take remedial and supportive action as needed.\n\n3. Within the technical domain, the focus for the World Bank’s implementation support will be related to the timely coordination of the pandemic response and COVID-19 vaccination. This will include technical assistance to: (i) COVID-19 vaccination and testing messages prepared; (ii) coordination mechanisms in place; and (iii) curriculum and training approaches; and (iv) use of the relevant IT systems.\n\n4. Development partners are expected to provide technical assistance and procurement operational support to strengthen the implementation of select project activities, in line with their respective mandates. The WHO, with its in-country expertise and overall coordination role for COVID-19 response activities, will continue to be an important technical partner. UNICEF will have both a technical and an operational role with respect to the procurement. The World Bank will coordinate its implementation support with these partners to get the most value-for-money, avoid duplication, and exploit synergies.\n\n5. While implementation support will be provided throughout project implementation, it is anticipated that more intense support will be needed in the first 12 months after project approval. World Bank staff based in the country will provide in-depth support for the project set-up, during the first 12 months – from approval to effectiveness, and through early implementation – and after the main activities are completed. Implementation support in the first 12 months will focus on coordinating with development partners and capacity building of the MOHE to support effective preparation and deployment of COVID-19 vaccination plans.\n\n**Summary of activities in the implementation arrangements and support plan**\n**Timeline** **Focus** **Skills Needed** **Resource Estimate**\n0–12 Setting up project Project management, At minimum, 3 months implementation activities operational, technical (including implementation support Setting up project implementation activities through institutional capacity strengthening, preparation for first procurement packages and technical assistance for implementation design.\n\nProject management, operational, technical (including M&E), fiduciary, environment, and social.\n\nAt minimum, 3 implementation support missions. Just-in-time technical assistance.\n\n12–24 Continued institutional capacity Project management, Two implementation Page 53 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nIraq COVID-19 Vaccination Project (P177038) months enhancement, implementation monitoring, operational and technical assistance to support implementation.\n\noperational, technical (including M&E), fiduciary, environment, and social.\n\nProject management, technical, fiduciary.\n\nsupport missions; justin-time technical assistance.\n\nImplementation completion report mission Page 54 of 54 Completion phase Implementation completion report and final payments", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**FOR OFFICIAL USE ONLY**\n\nINTERNATIONAL DEVELOPMENT ASSOCIATION PROJECT APPRAISAL DOCUMENT Report No: PAD3611", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "CURRENCY EQUIVALENTS (Exchange Rate Effective March 31, 2020) Currency Unit = SDG 58 = US$1 FISCAL YEAR January 1 - December 31 Regional Vice President: Hafez M. H. Ghanem Country Director: Carolyn Turk Regional Director: Dena Ringold Practice Manager: Safaa El Tayeb El-Kogali Task Team Leaders: Omer Nasir Elseed, Thanh Thi Mai", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "ABBREVIATIONS AND ACRONYMS AFS Annual Financial Statement BERP Basic Education Recovery Project BESP Basic Education Support Project DA Designated Account DPs Development Partners ESA Education Sector Analysis ESCP Environmental and Social Commitment Plan ( ESSP Education Sector Strategic Plan ESPIG Education Sector Program Implementation Grant FM Financial Management GDP Gross Domestic Product GER Gross Enrollment Rate GOS Government of Sudan GPE Global Partnership for Education IDP Internally Displaced Person IFT Interim unaudited Financial Reports ISN Interim Strategy Note ISP Intermediary Support Provider MOE Ministry of Education MOFEP Ministry of Finance and Economic Planning NAC National Audit Chamber NER Net Enrollment Rate NHBPS National Household Budget and Poverty Survey NLA National Learning Assessment OOSC Out-of-School-Children PCU Project Coordination Unit PFS Project Financial Statements PDO Project Development Objective PPSD Project Procurement Strategy for Development PSC Project Steering Committee PTA Parents and Teachers Association PTR Pupil-teacher Ratio SDG Sudanese Pounds SOE Statement of Expenditures SRR Social Risk Rating SSA Sub-Saharan Africa UNICEF United Nations Children’s Fund USD United States Dollar WDR World Development Report", "output": {"entities": {"named_data": ["National Household Budget and Poverty Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) TABLE OF CONTENTS\n\n**DATASHEET ........................................................................................................................... 1**\n\n**I.** **STRATEGIC CONTEXT ...................................................................................................... 6**\n\nA. Country Context................................................................................................................................ 6 B. Sectoral and Institutional Context .................................................................................................... 7 C. Relevance to Higher Level Objectives ............................................................................................. 13\n\n**II.** **PROJECT DESCRIPTION .................................................................................................. 13**\n\nA. Project Development Objective ..................................................................................................... 13 B. Project Components ....................................................................................................................... 14 C. Project Beneficiaries ....................................................................................................................... 16 D. Results Chain .................................................................................................................................. 17 E. Rationale for Bank Involvement and Role of Partners ................................................................... 17 F. Lessons Learned and Reflected in the Project Design .................................................................... 18\n\n**III.** **IMPLEMENTATION ARRANGEMENTS ............................................................................ 18**\n\nA. Institutional and Implementation Arrangements .......................................................................... 18 B. Results Monitoring and Evaluation Arrangements......................................................................... 19 C. Sustainability ................................................................................................................................... 19\n\n**IV.** **PROJECT APPRAISAL SUMMARY ................................................................................... 19**\n\nA. Technical, Economic and Financial Analysis ................................................................................... 19 B. Fiduciary .......................................................................................................................................... 20 C. Legal Operational Policies ............................................................................................................... 24 D. Environmental and Social ............................................................................................................... 24\n\n**V.** **GRIEVANCE REDRESS SERVICES ..................................................................................... 26**\n\n**VI.** **KEY RISKS ..................................................................................................................... 26**\n\n**VII.** **RESULTS FRAMEWORK AND MONITORING ................................................................... 28**\n\n**ANNEX 1: Costing for the Sudan Basic Education Emergency Support Project ................. 31**\n\n**ANNEX 2: Implementation Support Plan ....................................................................... 32**\n\n**ANNEX 3: Economic and Financing Analysis ................................................................... 35**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) DATASHEET\n\n**BASIC INFORMATION**\n~~BASIC~~ ~~INFO~~ ~~TABLE~~ Country(ies) Project Name Sudan Sudan Basic Education Emergency Support Environmental and Social Risk Project ID Financing Instrument Process Classification Investment Project P172812 Moderate Financing\n\n**Financing & Implementation Modalities**\n\nUrgent Need or Capacity Constraints (FCC)\n\n[ ] Multiphase Programmatic Approach (MPA) [ ] Contingent Emergency Response Component (CERC)\n\n[ ] Series of Projects (SOP) [✓] Fragile State(s)\n\n[ ] Disbursement-linked Indicators (DLIs) [ ] Small State(s)\n\n[ ] Financial Intermediaries (FI) [ ] Fragile within a non-fragile Country\n\n[ ] Project-Based Guarantee [ ] Conflict\n\n[ ] Deferred Drawdown [ ] Responding to Natural or Man-made Disaster\n\n[ ] Alternate Procurement Arrangements (APA) Expected Approval Date Expected Closing Date 05-May-2020 28-Feb-2021 Bank/IFC Collaboration No\n\n**Proposed Development Objective(s)**\n\nTo sustain enrollment in public basic education in Sudan during the transition school year.\n\nPage 1 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**Components**\n\n**Component Name** **Cost (US$, millions)**\n\nSchool Grants Program 11,280,000.00 Program coordination and management 300,000.00\n\n**Organizations**\n\nBorrower: Federal Ministry of Finance Implementing Agency: Federal Ministry of Education\n\n**PROJECT FINANCING DATA (US$, Millions)**\n\n**SUMMARY-NewFin1**\n\n**Total Project Cost** 11.58\n\n**Total Financing** 11.58\n\n**of which IBRD/IDA** 0.00\n\n**Financing Gap** 0.00\n\n**DETAILS-NewFinEnh1**\n\n**Non-World Bank Group Financing**\n\nTrust Funds 11.58 EFA-FTI Education Program Development Fund 11.58\n\n**Expected Disbursements (in US$, Millions)**\n\n**WB Fiscal Year** 2020 2021\n\n**Annual** 1.58 10.00\n\n**Cumulative** 1.58 11.58\n\n**INSTITUTIONAL DATA**\n\nPage 2 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**Practice Area (Lead)** **Contributing Practice Areas**\n\nEducation\n\n**SYSTEMATIC OPERATIONS RISK-RATING TOOL (SORT)**\n\n**Risk Category** **Rating**\n\n1. Political and Governance ⚫ High\n\n2. Macroeconomic ⚫ High 3. Sector Strategies and Policies ⚫ Moderate 4. Technical Design of Project or Program ⚫ Moderate 5. Institutional Capacity for Implementation and Sustainability ⚫ Moderate 6. Fiduciary ⚫ Substantial 7. Environment and Social ⚫ Moderate 8. Stakeholders ⚫ Low 9. Other ⚫ Low 10. Overall ⚫ Substantial\n\n**COMPLIANCE**\n\n**Policy**\nDoes the project depart from the CPF in content or in other significant respects?\n\n[ ] Yes [ ] No Does the project require any waivers of Bank policies?\n\n[ ] Yes [ ] No Page 3 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**Environmental and Social Standards Relevance Given its Context at the Time of Appraisal**\n\n**E & S Standards** **Relevance**\n\nAssessment and Management of Environmental and Social Risks and Impacts Relevant Stakeholder Engagement and Information Disclosure Relevant Labor and Working Conditions Relevant Resource Efficiency and Pollution Prevention and Management Relevant Community Health and Safety Relevant Land Acquisition, Restrictions on Land Use and Involuntary Resettlement Not Currently Relevant Biodiversity Conservation and Sustainable Management of Living Natural Resources Indigenous Peoples/Sub-Saharan African Historically Underserved Traditional Local Communities Not Currently Relevant Relevant Cultural Heritage Not Currently Relevant Financial Intermediaries Not Currently Relevant\n\n**NOTE** : For further information regarding the World Bank’s due diligence assessment of the Project’s potential\nenvironmental and social risks and impacts, please refer to the Project’s Appraisal Environmental and Social Review Summary (ESRS).\n\n**Legal Covenants**\n\nSections and Description By no later than six (6) months after the Effective Date, the Recipient shall prepare and submit to the Bank a GBV Action Plan for its approval. After obtaining the Bank’s approval, the Recipient shall carry out the Project in accordance with said Gender-Based Violence Action Plan.\n\n**Conditions**\n\nType Description Effectiveness The Project Coordination Unit has been established and key staff namely the Project manager, financial officer and procurement officer, all with terms of reference and qualifications have been recruited; all in a manner satisfactory to the Bank Page 4 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) Page 5 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**I.** **STRATEGIC CONTEXT**\n\n**A. Country Context**\n\n1. **Sudan is a lower-middle-income country with a fast-growing population, close to half of which is living in**\n**poverty.** Despite economic sanctions and secession of the oil-rich Southern states, Sudan’s gross domestic product\n(GDP) grew at an annual average rate of 2 percent between 2008 and 2017. In nominal terms, GDP per capita increased four-fold from Sudanese Pounds (SDG) 3,617 to SDG 14,485. However, in constant 2016 prices, there was a 5 percent decrease in per capita GDP owing to a slower growth relative to population increase and high inflation.\nThe total population is estimated to have reached 40 million in 2017 and growing at an annual average of 2.5 percent in the last 10 years (World Bank, 2016). The school-aged population (4-to-16-year-olds) accounts for one third of the population and continues to grow, contributing to the rising demand for basic services such as education and healthcare. The country has made considerable progress in human development: child mortality reduced from 105 per 1,000 (2000) to 65 per 1,000 (2016); and maternal mortality dropped from 544 per 1,000 (2000) to 311 per 1,000 (2015) (World Bank WDI). The youth literacy rate, defined as the proportion of youth between the age of 15 and 24 that can read and write a simple sentence in any language, increased from 78 percent in 2000 to 86 percent in 2014 (World Bank WDI).\n\n2. **Sudan is currently at a very important crossroads in the country’s history.** A popular uprising brought to power a civilian government with the attention to carry out necessary reforms to stabilize the economy, reallocate resources away from commodity subsidies and toward social spending, liberalize the exchange rate, and reintegrate Sudan into the world economy. In the short run, however, large arrears on foreign debt and its inclusion on the US State Sponsors of Terrorism List (SSTL) restrict the country’s access to needed finance from international financial institutions and markets.\n\n3. **The new transitional Government is facing one of the most challenging environments in the world** . The country faces a macroeconomic crisis: rampant inflation, massive currency devaluation, rapidly increasing arrears on international debt, and ostracism from the dollar-based international financial system. Modest economic growth persists, and the country is marked by deep poverty and inequality. Social indicators remain low and vary markedly across states, gender and poverty level. Social indicators are aggravated by the country’s service delivery function, which is still compromised by low levels of public expenditure, shortage of relevant personnel and dilapidated infrastructure.\n\n4. **As for other African countries, the direct impact of COVID-19 on Sudan will depend greatly on if current**\n**measures prove adequate to contain the spread of the virus in the country** . Sudan has little capacity at present to\nmanage a major COVID-19 epidemic, which would add to an already exceedingly difficult economic and political situation. Tourism, air transport, and the oil sector are visibly impacted. However, invisible impacts of COVID-19 are expected in 2020 regardless of the duration of the pandemic.\n\n5. **The economic impact of COVID-19 includes the increased price of basic foods, rising unemployment, and**\n**falling exports.** Restrictions on movement are making the economic situation worse, with commodity prices soaring\nacross the country. According to the IMF projections, consumer prices are expected to increase by 81.3 percent in Page 6 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) 2020. The IMF has already forecasted an overall economic stagnation in 2020 in Sudan. GDP is expected to decrease between 4-10 percent in 2020 due to the combined impact of the economic crisis exacerbated by the social distancing measures to curb the spread of COVID-19. Slowing growth and COVID-19 policy responses will have a significant negative impact on government revenue. Slowing activity will automatically translate into lower levels of tax and other government revenue collection. The combined effect on government revenues is projected to be significant.\n\n6. **Poverty reduction stagnated in 2018 mainly due to weak economic growth, political and macroeconomic**\n**instability and the shortage of essential food items such as bread.** According to the most recent official estimates\nof poverty based on the 2014/15 National Household Budget and Poverty Survey (NHBPS), 36.1 percent of Sudanese population (or 13.4 million people) are poor. However, the overall/national poverty rate masks wide disparities across Sudan’s 18 states. For example, Central Darfur State in western Sudan recorded the highest rate of poverty (67.2 percent). Generally, the states of South Kordofan, West and Central Darfur, in which two in three people are poor, are the states with the highest poverty rate followed by Red Sea, East and South Darfur. However, when poverty was measured against the World Bank’s international poverty line for lower middle-income countries (US$3.2 per capita per day), 46.1 percent was deemed poor. The poor are particularly affected by rising inflation given their high food share in consumption, and limited means to preserve the erosion of the value of their savings.\n\n**B. Sectoral and Institutional Context**\n\n7. **Education provision in Sudan is a shared responsibility among various administrative layers**, **managed at**\n**the Federal, State and locality levels.** The Federal level has the policy mandate for strategic planning, coordination,\nand definition of standards. The 18 states are responsible for secondary education provision, human resource management, coordination of work of the Directorate of Education at the locality level, and basic education certification. Localities are the frontline service providers, responsible for basic education day-to-day management.\nThe new Education Sector Strategic Plan 2018-2022 was endorsed by the Government in December 2018 and defines the overall direction of the sector in the medium term.\n\n8. **According to the current structure, basic education comprises two years of preschool, five years of lower**\n**primary, and three years of upper primary education (2:5:3 structure)** . In 2015, Sudan began reform of the basic\neducation curriculum and is currently moving to a new education structure comprising nine-year education cycle (2:6:3 structure). The first cohort of students is expected to reach Grade 9 of basic education in 2023. The increase in the number of years of free education requires a reassessment of the system’s ability to provide complete basic education to all children in Sudan, including internally displaced persons (IDPs).\n\n9. **Sudan has seen significant improvements in basic education over the last decade.** Between 2008/09 and 2017/18, the total number of schools (public and private) increased by 2,800, allowing one million more children to access education. The number of students completing primary education and proceeding to secondary school increased from 251 to 336 thousand during the same time. Provision of preschool education, an important step to build school readiness, is relatively high with Gross Enrollment Ratio (GER) reaching 43 percent in 2017, ten percentage points above average for Sub-Saharan Africa. Around 26 percent of basic schools have preschool facilities. In 2018, 65 percent of learners enrolled in Grade 1 reported having some preschool education, an improvement of about 16 percentage points from 49 percent recorded in mid-2000 (ESA, 2018).\n\nPage 7 of 40", "output": {"entities": {"named_data": ["National Household Budget and Poverty Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) 10. **Despite recent progress in student enrollments, the education sector suffers from multiple challenges:** (i) low and unequal levels of access and completion; (ii) low and stagnant student learning outcomes; (iii) poor education system management; (iv) inadequate learning environments; and (v) low level of public spending on education and significant contributions from households. In Sudan, the school system is also currently severely affected by the COVID-19 outbreak, with all education institutes closed since March 14, 2020. An estimated 6.2 million students are out of school due to the lockdown (Annual School Census, 2018). If this situation is permitted to continue unabated, it could have profound, long-term negative impacts on the country's development.\n\n_Low and unequal levels of access and completion_ 11. **Sudan has not managed to cope with the increased demand for education imposed by the high population**\n**growth hence the overall level of access to basic education in Sudan has been stagnant over the past decade.** GER\nhas been stagnant and low compared to other comparator countries: 72 percent (2008/09) and 73 percent (2016/17). According to the data from 2014/15 Multiple Indicator Cluster Survey (MICS), Net Enrollment Rate (NER) is 69 percent with NER for boys 2 percentage points higher compared to girls (70 and 68 percent, respectively).\nWhile girls’ and boys’ Grade 1 enrollment rates in urban areas are similar, male Grade 1 enrollment rates in rural areas are six percentage points higher than those for girls. Grade 8 enrollment rates are in favor of boys, and the gap is especially evident in rural areas.\n\n**Figure 1: Primary education enrollment rates**\nAccess to basic education in Sudan at the beginning and end of Primary education GER in 2016 or the the cycle in Sudan by gender, location, and wealth quintile latest available, selected countries (2014) 98 96 Boys Girls 92 86 81 82 77 100 94 34 55 46 Grade 1 Grade 8 Grade 1 Grade 8 Urban Rural Grade 1 Grade 8 Bottom 20% Top 20% _Source:_ Authors’ estimates based on MICS2014/15. _Source:_ Authors on UNESCO UIS data.\n\n**12.** **Socioeconomic disparities in basic education are large.** While Grade 1 enrollment rates for the wealthiest\nfifth of households were universal, only 81 percent of children in the poorest fifth of households were enrolled. This socioeconomic gap in primary access widens by the end of the education cycle. Only 34 percent of children from the poorest quintile reach the last grade of primary education compared to 94 percent of children from the wealthiest quintile. The socioeconomic disparities further translate into access to secondary education: only 9 percent of Page 8 of 40", "output": {"entities": {"named_data": ["Multiple Indicator Cluster Survey", "UNESCO UIS data", "Annual School Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) children from the bottom income quintile of households proceed to Form 1 of secondary education, while 77 percent of children from the top income quintile do. Low access to secondary education for the bottom income quintiles of population in Sudan urge targeted support to the most vulnerable and poor families **.** 13. **Socioeconomic disparities in basic education are large** . While Grade 1 enrollment rates for the wealthiest fifth of households were universal, only 81 percent of children in the poorest fifth of households were enrolled. This socioeconomic gap in primary access widens by the end of the education cycle. Only 34 percent of children from the poorest quintile reach the last grade of primary education compared to 94 percent of children from the wealthiest quintile. The socioeconomic disparities further translate into access to secondary education: only 9 percent of children from the bottom income quintile of households proceed to Form 1 of secondary education, while 77 percent of children from the top income quintile do. Low access to secondary education for the bottom income quintiles of population in Sudan urge targeted support to the most vulnerable and poor families.\n\n14. **Low retention and high dropout rates have undermined Sudan’s effort to implement universal fee-free**\n**basic education** . An analysis of enrollment in 2017 [1] illustrates the large volume of pupils entering Grade 1 gradually\nshrinks while moving to upper grades due to drop out. In general, boys are more likely to drop out than girls. For example, 48 percent of boys enrolled in Grade 1 are likely to reach Grade 8 compared to 53 percent of girls.\nAnecdotal evidence suggests that high drop out of male pupils is associated with the high opportunity cost of attending school, which includes the cost of not working in the household, while female pupils drop out due to early marriage.\n\n15. **The number of out-of-school-children (OOSC) is striking: approximately three million school-age children**\n**are not in the education system.** While 52 percent of those children had never attended school, 48 percent quit.\nThe majority of OOSC (77 percent) are 6- to 13-year-olds, i.e. basic school-age. The system still has late entry until 11 years, with children who do not attend school before turning 12 are likely not to attend ever. According to the results of the National Household Budget and Poverty Survey (NHBPS) conducted in 2014/15, the main reasons for not attending school for children between the age of 6 and 15 are high costs (mentioned by 20 percent of respondents), distance to schools (14 percent), and the need for the child to support the family (6 percent) (World Bank, 2018). There is a significant risk that OOSC will increase further when schools reopen again post COVID-19.\n\n1 Education Sector Analysis, 2018.\n\nPage 9 of 40", "output": {"entities": {"named_data": ["National Household Budget and Poverty Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**Figure 2: Enrollment pyramid and share of illiterate pupils**\n\nA. Enrollment pyramid, thousand (2017) B. Percent of Grade 3 pupils who could not read a single word of a short text in Arabic (2014 or the latest available) 40% 27% Secondary 3 Secondary 2 Secondary 1 Basic 8 Basic 7 Basic 6 Basic 5 Basic 4 Basic 3 Basic 2 Basic 1 185 Male 164 255 341 524 197 Female 177 246 312 464 17% 18% 22% 600 400 200 0 200 400 600 4% Jordan Iraq Morocco Egypt Yemen Sudan _Source:_ Education Sector Analysis, 2018. _Source:_ http://www.earlygradereadingbarometer.org/ _Low and stagnant learning outcomes_ 16. **Learning outcomes in Sudan schools are generally low** . According to the National Learning Assessment (NLA) conducted in 2015 for Grade 3 pupils, the results were low in all domains of the assessment: reading, writing, and numeracy. For example, only 5 percent of pupils could read fluently (more than 60 words per minute) in Arabic, and 40 percent were not able to read at all. Furthermore, the assessment of reading speed among third graders indicated an average speed of 15 words per minute, which is far below the estimated minimum reading speed of 40 words per minute thought to be necessary to gain understanding of and meaning from the text. However, Sudan’s third graders did better in listening and comprehension compared to pupils from other Arabic Countries.\n\n17. **There is sign of slight improvements in learning outcomes at the national level** . The country has recently completed its second round of NLA, and preliminary data indicates some gains in reading scores, with the reduction of non-readers from 40 percent in 2014 to 38 percent in 2017 [2] . Reading comprehension has improved from 36 percent in 2014 to 52 percent in 2017. The share of students able to perform single digit subtraction and addition increased significantly from 40 and 46 percent in 2014 to 43 percent and 52 percent in 2017, respectively. The preliminary results show that schools, where gains were made in raising reading levels in Grade 3 between the first and second NLA’s, also did better overall on Grade 6 tests. The analysis serves as an important source of data for policy dialogue. The data provides for details, which facilitate the understanding of the learning among states and within states.\n\n2 The difference in scores is statistically significant at 0.01 confidence level.\n\nPage 10 of 40", "output": {"entities": {"named_data": ["National Learning Assessment"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**Figure 3: Grade 3 reading performance by state (2014/15 and 2017/18 NLAs)**\n_Share of pupil unable to read a single word (non-readers)_ 80% 70% 60% 50% 40% 30% 20% 10% 0% 2014/15 2017/18 Improved (% of non-readers declined) Stagnant Declined _Inadequate learning environments_ 18. **The poor learning environment in many primary schools affects teacher motivation as well as student**\n**outcomes** . Many schools do not meet norms for teaching and learning materials. While there have been\nimprovements in student textbook ratios recently, mostly due to the efforts made within the recently completed Basic Education Recovery Project (P128644), under which all pupils in Grade 1-4 received a set of textbooks, shortages in specific subjects remain. On average four learners share a science book, while in Math and Arabic language classes, two and three learners, respectively, share one textbook.\n\n_19._ **Some areas of school infrastructure are currently inadequate and continued basic education expansion**\n**will add further pressure** . Existing primary schools have shortfalls in classrooms and other facilities. For example,\n16 percent of public schools including 21 percent in rural areas and 9 percent in urban, have a least one grade without a classroom. Pupils in such classes study outside ‘under a tree’ and are often dismissed during rainy seasons and hot summer months, which contribute to further worsening learning outcomes. The availability of water and sanitation facilities also tend to vary widely across schools and the number of latrines is generally inadequate. This is an important driver of dropout for girls in upper primary school, as girls are entering puberty, particularly given the high number of over-aged children due to repetition (Sperling et al., 2016). Expanding access to amenities in underserved areas will also require more classrooms in basic schools. Since the distance between schools and households is an important factor in explaining school drop-out, it will be important to locate new schools optimally to reduce travel times (World Bank, 2018).\n\nPage 11 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) _20._ **Incomplete primary schools affect the system’s ability to retain children until completion.** A review of the supply of basic education indicates that 6,793 out of the 16,643 schools are incomplete schools that miss at least one grade. When children transition from one school to another, the risk of non-completion increases, because they find it harder to settle in a new environment, and then learning tends to regress.\n\n_Low level of public spending on education and significant contributions from households_ 21. **Low public funding for education is affecting quality services and impeding access.** The education budget as a proportion of the overall budget remained stable at 11 percent between 2009-2017, which is low compared to the GPE recommended 20 percent (GPE, 2016). In the same period, the sector budget increased 2.6 times in nominal terms, from SDG 2.7 trillion to SDG 6.9 trillion. In turn, recurrent spending in education, which represent 90 percent of the budget, more than doubled in current prices from SDG 2.4 trillion in 2009 to SDG 5.4 trillion in 2017. However, in real terms, at 2016 prices, recurrent education expenditure dropped by half. As a share of GDP, spending in education was halved from 2.4 percent in 2009 to 1.2 percent in 2017, which is the lowest in Sub-Saharan Africa.\n\n22. **Families contribute greatly to education costs including goods and services, capital costs, salaries to**\n**volunteer teachers, and food provision to teachers and pupils.** In basic education for instance, on top of the SDG\n2.6 trillion covered by public finances, parents added a total of SDG 496 million in the 2016/17 translating to about 16 percent of the known spending. The current economic situation is likely to affect the ability of families to pay going forward, so there is a need to mobilize more public funding. With the growing inflation affecting the purchasing power of households in Sudan, most of them may lose the ability to pay for goods and services.\n\n23. **External financing of the education sector is limited and unpredictable.** Sudan remains a highly-indebted country with sizeable external arrears and has been in non-accrual status with the World Bank Group (WBG) since 1994. At the end of 2015, its external debt amounted to US$50 billion (61 percent of GDP) in nominal terms, about 84 percent of which was in arrears. Given Sudan’s current lack of access to IDA funding, the World Bank supported program is resourced mainly through trust funds, partnerships including GPE, and the World Bank’s operational budget.\n\n_Impact of COVID-19 on the education sector_ 24. **On March 17, 2020, the Cabinet of Ministers announced the closure of schools, universities and cancelled**\n**all public gatherings due to COVID-19 global pandemic** . The COVID-19 pandemic threatens education progress\nworldwide through two major shocks: (1) the near-universal closing of schools, and (2) the economic recession sparked by the pandemic-control measures. Without major effort to counter their effects, the school closings shock will lead to learning loss, increased dropouts, and higher inequality; the economic shock will exacerbate the damage, by depressing education demand and supply as it harms households; and together, they will exact long-run costs on human capital and welfare.\n\n25. **It is likely that the poorest will be affected the most by economic shocks and school shutdown** . During the Ebola epidemic in Sierra Leone household income fell from US$336 to US$131 and there was an increase in girls getting pregnant (World Bank, 2020). To protect the poorest and most vulnerable and enable them to continue learning, special interventions will be needed.\n\nPage 12 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**C. Relevance to Higher Level Objectives**\n\n26. **The proposed operation will contribute to implementation of the 2018-2022 Education Sector Strategic**\n**Plan.** The Government has developed a sector strategic plan to guide service delivery in general education between\n2018 and 2022. The proposed project supports operationalization of some of the activities and strategies endorsed for improving quality learning and expanding access to basic education.\n\n27. **The proposed project will contribute to the World Bank’s twin goals of ending extreme poverty and**\n**promoting shared prosperity and is consistent with the World Bank Group’s Human Capital Project.** The proposed\nproject promises to improve learning environments which are critical elements in learning adjusted years. The interventions will impact the potential of the children who the project will reach, increasing their chances of excelling in life.\n\n28. **The project will also contribute to Sudan’s progress towards the Sustainable Development Goal 4 on**\n**education.** The proposed operation will contribute to Target 4.1 ‘By 2030, ensure that all girls and boys complete\nfree, equitable and quality primary and secondary education leading to relevant and effective learning outcomes.\nSpecifically, the school grants will contribute to the increase of _Completion rates in Primary education._ 29. **Interim Strategy Note (ISN) 2014-15 for Sudan (Report No: 80051-SD) defines the areas of World Bank**\n**engagements, focusing on basic service delivery** . The project will contribute to the long-term Poverty Reduction\nand Equity Strategy by investing in improving education outcomes across the country, including areas under conflict.\nIt will support ISN Pillar II “address socioeconomic roots of conflict” and will contribute to improved equitable service delivery in education. The operation is also in line with the Government’s Basic Education Strategy and aims to upgrade the learning environment in states of Sudan.\n\n**II.** **PROJECT DESCRIPTION**\n\n**A. Project Development Objective**\n\n**PDO Statement**\n\nTo sustain enrollment in public basic education in Sudan during the transition school year.\n\n**PDO Level Indicators**\n\n1. Student enrollment in public primary schools.\n\nThe PDO indicator will be disaggregated by school grade and gender.\n\nPage 13 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**B. Project Components**\n\n30. The project design is guided by the following principles: (a) rapid response to support schools in light of the deteriorating economic conditions; (b) lessons learned from past education projects, in particular Basic Education Recovery Project (BERP) (P128644); (c) government ownership and priorities aligned to the 2018-2022 Education Sector Strategic Plan (ESSP); and (d) complementarity with other donor funded projects to fill strategic gaps.\n\n31. Attainment of the proposed PDO will be based on the Government’s achievement of results in the first component: (a) school grants program, which will include providing support to schools to improve learning environment and practices. The project will operate at system and school levels, targeting all public schools.\n\n**Component 1: School Grants Program (US$11.275 million).**\n\n32. This component will support provision of school grants to improve learnining environments and school planning. School grants will aim to: _(i) Incentivize parents’ engagement to reduce the risk of students (especially girls) dropping out_ . While basic education is officially free in Sudan, currently, families contribute greatly to education expenditures at the school level. As the economic situation has deteriorated, many vulnerable families may lose the ability to pay for basic services and pull the children out of school (especially girls). Furthermore, families may face challenges to provide the pupils with basic requirements for schooling, such as uniform, school bags, exercise books, etc. School Grants can play an important role in mitigating the expected economic shock on the most vulnerable and help reduce the education cost burdens during the hard time. It can also help provide girls in the upper primary grades with necessary packages such as sanitary napkins to encourage their retention.\n\n_(ii) Support teachers to reduce absenteeism_ . Due to high inflation rates, teacher remuneration has been deteriorating in real terms, posing the risk of teachers leaving schools temporarily or permanently for alternative livelihood pathways. The School Grants may be used to support teachers (in cash or in-kind).\n\n_(iii) Support the learning environment_ . School Grants are expected to be an important source of funding for the targeted schools to support the acquisition of basic learning materials, stationery, notebooks, classrooms furniture and equipment which contribute to improving the learning environment to attract and retain pupils and teachers, especially females in school. A list of eligible items will be developed and provided to the schools.\n\n_(iv) Improve efficiency by strengthening capacity for participatory planning, budgeting and monitoring at the school_ _level_ . School grants can help disadvantaged schools create a participatory management structure at the school level.\nA school profile report that provides information on the school will be provided to each school to support the participatory evidence-based planning process.\n\n_(v) Improve equity in education by helping children in disadvantaged situation including IDPs, refugees, girls_ .\nAccording to the latest Annual School Census, public schools enroll 30 thousand refugee students (in 1,681 schools) and 280 thousand IDPs (in 1,852 schools). While IDP children are concentrated in three Darfur states (68 percent of Page 14 of 40", "output": {"entities": {"named_data": ["Annual School Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) all IDPs), namely, Central, North, and South Darfur, refugee students are distributed among half of Sudan’s states: South Kordofan (17 percent), White Nile (13 percent), West Kordofan (10 percent), East Darfur (10 percent), South Darfur (9 percent), Gadarif (8 percent), North Darfur (8 percent), and Khartoum state (7 percent). Moreover, girls’ retention rates (grade 6 survival rates) vary from type of schools: from 53.0 percent in co-ed schools to 85.9 percent in schools for girls. Surprisingly, girls’ survival rates are higher in schools with refugees or IDPs students compared to schools without them (78.3 vs 70.0 percent).\n\n33. **Overall, 88 percent of public schools in Sudan (14,429 schools) meet one of the disadvantage criteria** : (i) low girls’ retention; (ii) enrollment of IDPs or refugee students; (iii) poor learning environment (absence of water supply, latrines, fences); and (iv) lack of teachers (high pupil-teacher ratios).\n\n34. **Around 16,500 schools from all 18 States will benefit from school grants and training** in evidence planning to improving learning conditions and ultimately promote access, retention and learning. School Improvement Plans will be developed through a participatory process involving Parents and Teachers’ Associations (PTAs) as well as the community surrounding the schools. The plans will be informed by key information on the schools and the locality where they are situated.\n\n35. **Allocation of school grants per school will be based on a formula**, which will include a per capita base and measures to cater for price differences among the states. An estimated US$2 per child will be allocated equally to all schools. The maximum amount per school will be US$1,000 to keep the grants manageable and at a level that the Government can afford to carry on at the end of the program, avoid having schools managing very high budgets, which may not be sustainable in the future. At least 5.4 million pupils will benefit from the School Grants Program.\n\n36. **Localities will be responsible for allocating the grants to schools** ; train the PTAs and school heads on participatory planning and appropriate use of school grants; and supervision of implementation of the grants. The States with support from the Project Coordination Unit (PCU) will be responsible for capacity building at the community level (empowerment, inclusion, gender sensitivity, school safety) – train the localities and prepare them to perform their role in overseeing implementation of the school grants. The PCU will assess capacity of localities and schools in in participatory planning and monitoring of school results.\n\n37. **Key activities will include** :\n\n- Assessing capacity of localities and schools in in participatory planning and monitoring of school results;\n\n- Training of school heads and PTAs in participatory planning and monitoring of school results, including\nlearning;\n\n- Training of locality supervisors to provide support to schools as needed; and\n\n- Providing grants to schools to improve learning environments.\n\n38. **Selection of intervention schools** : The project will target all public primary schools in Sudan. Rich schoollevel data obtained from the School Census in 2015-2019 with support from the BERP will be used for the targeting of project beneficiaries (figure 4).\n\nPage 15 of 40", "output": {"entities": {"named_data": [], "descriptive_data": ["School Census in 2015-2019"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**Figure 4: Heat map of Sudan’s basic education schools**\n\n_Source:_ Based on 2018/19 School Census using Arcgis software.\n\n39. **Component 2 Program coordination and management (US$0.3 million).** This component will support the Federal Ministry of Educaiton (MoE) in overall program coordination, monitoring and evaluation. The PCU will cover functions such as planning, procurement, financial management, environmental and social safeguards and monitoring and evaluation. Technical experts will be mobilized as necessary. The PCU will monitor the progress by collecting and analyzing school-level data under the the Annual School Census.\n\n**C. Project Beneficiaries**\n\n40. Primary beneficiaries are schoolchildren, teachers, and parents. Approximately 5.4 million students will benefit from the project through provision of school grants. Communities in targeted areas will also benefit from enhance participatory school management.\n\nPage 16 of 40", "output": {"entities": {"named_data": ["2018/19 School Census", "Annual School Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**D. Results Chain**\n\n41. The following chart depicts the program theory of change:\n\n**MEDIUM TERM**\n**ACTIVITIES** **INTERMEDIATE OUTCOMES**\n\n**OUTCOMES**\n\nProvide grants to targeted schools to sustain retention Improved availability of school operational and learning resources Train school heads and PTAs in participatory planning & monitoring Train school heads and PTAs in Enhanced participatory participatory planning & management in targeted monitoring schools Build capacity of locality Increased supervision in supervisors in school-based targeted schools planning & management Sustained enrollment in public basic education Increased supervision in targeted schools\n\n**E. Rationale for Bank Involvement and Role of Partners**\n\n42. **The World Bank has been engaged in the education sector in Sudan for more than two decades** . Among Development Partners engaged in the sector, the World Bank has provided strategic leadership in policy dialogue with a focus on education quality, access, and good governance. The World Bank has gained valuable experience through the implementation of projects in the education and numerous other sectors in Sudan. This has provided many lessons about specific characteristics of the country’s implementation environment, particularly understanding of the ability to respond to GoS’s implementation strengths and weaknesses.\n\n43. **The World Bank’s convening authority is well recognized and will be of particular value given the need for**\n**broad-based consensus and alignment among stakeholders for ensuring a successful GPE process** . The World Bank\nwill bring added value through its high level of technical expertise derived from its knowledge gained from operations to support primary education around the globe. It brings the advantage of strong in-country capacity for continual implementation support, particularly to ensure sound fiduciary functioning and management. The World Bank has carried out extensive analytical work on Sudan’s education sector, the findings of which have been fully integrated into the proposed project’s design.\n\n44. The education program in Sudan is built on organic links amongst three operations. This Emergency Support Project (provided by the GPE Accelerated Fund) will ensure schools receive support during the economic crisis through extending school grants. A COVID-19 response project P174220 is being prepared with the aim of helping Sudan to maintain learning continuity during the pandemic and ensure appropriate resumption of teaching and learning when school starts up. The third operation, the Basic Education Support Project P167169(funded by GPE Education Sector Plan Implementation Grant) will support Sudan to continue to sustain the achievement and further Page 17 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) improve the equity, efficiency and learning outcomes of the education system by financing textbooks and reading materials, school grants and teacher training and support as well as data collection, analysis and feedback systesm.\n\n**F. Lessons Learned and Reflected in the Project Design**\n\n45. **The World Bank has successfully supported the recovery and stabilization of basic education sector in**\n**Sudan** . The Basic Education Recovery Project (P128644) (US$76.5 million) supported the improvement of learning\nconditions in basic education, covering classroom construction, provision of school grants disadvantaged schools, provision of a set of core textbook to all students in Grade 1 to Grade 4, and also provided training to teachers teaching Grade 1 to 3 to teach the new curriculum and textbooks. The project was successful in many cases (textbooks and schools in particular) surpassing the original targets, and was rated satisfactory at completion. The success stories - the textbook delivery mechanism and the successful implementation of the school grants - have provided a critical basis for the design of this operation.\n\n46. **The operation aims to build on the lessons learnt of BERP including:** A dual approach to school grants and administration facilitated community capacity building. Several school grant pilots were conducted which produced useful lessons that will be integrated into the scale up of the school grant sub-component. One successful method was using a dual approach to implementation. Government systems were used when capacity was adequate, and Intermediary Support Providers (ISPs) were used when capacity for planning and financial management was weak.\nIn the latter case, ISPs provided capacity building to localities, so they could eventually take over management of their school grants. These efforts included training communities on how to communicate effectively with the project, locality, and local banks (e.g., informing the project and locality executive officers when the grants procedures would be launched, which schools had amounts payable to them and their bank account information).\nIt also included training on how to open accounts and follow up on fund transfers to recipients’ bank accounts and reporting on finalization of grants.\n\n**III.** **IMPLEMENTATION ARRANGEMENTS**\n\n**A. Institutional and Implementation Arrangements**\n\n47. **The implementation will be mainstreamed through the MoE, State Ministries of Education and localities**\n**at the local government level, using the existing government structures in Sudan** .\n\n48. **Component 1 will be implemented by public schools with the support of the locality, state and MoE** . At MoE level, the Department of Planning will provide the overall coordination and support the school grants activities.\nThe implementation arrangement will build on the school grants experience under the BERP and, to the extent possible, government systems will be used to deliver the grants to schools, provide training on participatory planning, budgeting, monitoring and accountability. However, implementation capabilities may vary among the localities, therefore the project may make use of Third Party Providers to support capacity building at the beginning of the program and then phase out as the system mature. The nature and role of such third-party providers shall be defined once a capacity assessment is done to determine their capability to manage school grants.\n\nPage 18 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) 49. **The MoE and States will be supported by a PCU.** The PCU will be led by a Program Manager and include the following key personnel: (i) school grant coordinator; (ii) program monitoring and evaluation specialist; and (iii) procurement, financial management and administrative staff.\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n50. **The project will support the establishment of a robust monitoring and evaluation system to supplement**\n**and complement the Federal and State Ministries of Education structures.** The project will support training of state\nofficers on planning and budgeting to ensure timely development of monitorable annual plans with clear outputs.\nUnder the School Grants Program (Component 1), schools will receive regular supervision and support from localities, which means the latter will also need to strengthen their capacity to perform this task. Localities will train PTAs on participatory planning and use of school data for planning and monitoring purpose at the school level.\nLocalities will receive support for coaching and monitoring the reading program.\n\n**C. Sustainability**\n\n51. **The sustainability of project investments and activities guided project preparation and key elements of**\n**the project design.** First, the PDO and project-supported activities are consistent with national strategies—in terms\nof increasing access and improving the quality of basic education. There is also strong alignment between the indicators to be used to assess progress under the proposed project and the indicators/outcomes defined for the Government’s ESSP. Third, the technical contents of the project are supported by international and national evidence of good practices. Finally, a central focus of the project is strengthening the education system and capacity at all levels, including strengthening the community’s overall role in school management and planning, which is expected to be sustained in the long term. The elements discussed above support the sustainability of the project’s objective.\n\n**IV.** **PROJECT APPRAISAL SUMMARY**\n\n**A. Technical, Economic and Financial Analysis**\n\nTechnical Analysis 52. **The project is designed to support activities that address key issues in basic education identified by the**\n**current Education Sector Analysis and ESSP** : limited and inequitable access to basic education due to supply-side\nand demand-side constraints; poor quality of education service delivery and low student learning levels; and insufficient institutional capacity for efficient education system planning and management.\n\n53. **Project objectives and performance targets are based on detailed financial analysis and simulations** .\nThese objectives and targets are financially feasible if the national education budget is supported by external funding. The selected activities draw on international experiences and best-practice and past projects in Sudan and elsewhere as relevant.\n\nPage 19 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["school data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) 54. **However, continued economic crisis exasperated by the COVID-19 outbreak and hyper-inflation may**\n**hinder achievement of project targets of improved retention in basic schools as more children may start dropping**\n**out to help their families.** Fuel crisis may hamper successful implementation of project activities such as school\ngrant provision as the schools would become harder to reach. Regular monitoring and evaluation of the implementation process and economic and social environment would be required starting from the Project effectiveness date.\n\nEconomic and Financial Analysis 55. **Investments in basic education in Sudan carry high returns.** Sudan is far from reaching the Education SDG 4 of universal primary completion because of the high number of dropouts within the primary cycle. Pupil retention within the basic cycle slightly improved from 48.2 percent in 2014 to 49.3 percent in 2018 but remains largely inadequate. Investment in basic education in Sudan is justified by low NER (69 percent) and completion rate (55 percent) and weak learning levels among enrolled students with 39 percent of Grade 3 pupils unable to read a single word in Arabic. The high share of illiterate pupils means that 39 percent of public resources spent on pupils in Grades 1-3 are wasted in the system, which is equivalent to SDG 473 million (US$14.6 million). See Annex 3 for a full economic analysis.\n\n56. **Use of public funds and external financing for basic education is well justified.** Repetition rates in Sudan’s basic schools are relatively low compared to other countries in the SSA region. Though, an estimated US$10.4 million is used annually to deliver basic education services to repeaters and pupils that drop out before completing the basic education cycle. Given an increasingly tight fiscal environment related to loss of oil revenues, ongoing conflict in some areas, and limited infrastructure, external resources are required for Sudan to meet the SDG targets and to expand and sustain the education developments achieved so far.\n\n57. **Domestic revenues will continue to be the main source of education financing** . Currently, public spending in education is very low with communities bearing much of the non-salary cost. Interventions under the project are justified by the urgent need to support schools during the transition school year to sustain pupil retention, as schools reopen following the COVID-19 closures. The rapid support serves an immediate injection and continuity of services to children and help complement ongoing humanitarian support; while ensuring previous GPE program gains are stabilized and new GPE priorities under the next Education Sector Program Implementation Grant (ESPIG) are established.\n\n**B. Fiduciary**\n\n**(i)  Financial Management (FM)**\n\n58. The project will build on the BERP and will be managed by its PCU. The PCU is placed under the MoE but sits in a separate location. The purpose of keeping the PCU in place for the proposed project was to benefit from the institutional memory of the PCU staff who received training in basic World Bank project implementation and fiduciary procedures, and to inherit the existing investments in office equipment and vehicles. Nevertheless, certain equipment will need to be replaced/repaired/upgraded under the project. The following financial management arrangements will be undertaken by the project management and the Client: Page 20 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) 59. **Budgeting:** The project will prepare an Annual Budget based on an agreed Annual Work and Procurement Plans. The budget will be adopted by the Project Steering Committee (PSC) before the beginning of the year and its execution will be monitored on a quarterly basis. Annual draft budgets will be submitted for the World Bank’s nonobjection before adoption and implementation no later than November 30 every year. The budget monitoring will be conducted at three levels: - (i) at transaction level, there will be checks conducted to ensure that payment requests are approved after checking the availability of budget; (ii) at system level, the accounting system should be able to support the budget monitoring aspect by tracking budget, by enabling easy recording of budgets and commitments, by enabling comparison of actual performance with budget; and (iii) at the report level- where the project will be preparing periodic/ad-hoc financial reports and analyses to follow up on budget utilization and variance analysis prompting management and or the PSC to appropriate actions and mid-way corrections. In addition, quarterly Interim unaudited Financial Reports (IFRs) submitted to the World Bank will include statements that show budget utilization, comparing actual expenditures with budgets and proving justifications/explanations for major variances. These and other budgeting process and monitoring will be clearly defined in the FM Manual.\n\n60. **Internal control and internal audit** : Internal control comprises the entire system of control, financial or otherwise, established by management in order to: (i) carry out the project activities in an orderly and efficient manner; (ii) assure adherence to policies and procedures; (iii) safeguard, manage and control the assets of the project; (iv) ensure completeness and accuracy of the financial transaction/information; (v) ensure proper segregation of FM-related functions; (vi) ensure proper flow of funds; and (vii) ensure adequacy and accuracy and recording of FM data. The details of these procedures will be documented in the Project FM Manual to be prepared.\nThe Internal Audit Chamber will assign a staff to carry out internal audit reviews on the project on a regular basis.\nThe reports of the internal audit will be shared during supervision missions. The project management will ensure that audit findings are timely resolved.\n\n61. **Disbursement Arrangement:** The following disbursement methods may be used under the project: reimbursement, advance, direct payment, and special commitment as will be specified in the Disbursement Letter and in accordance with the World Bank Disbursement Guidelines for Projects, dated February 1, 2017.\nDisbursements will be transactions-based whereby withdrawal applications will be supported with Statement of Expenditures (SOE). Documentation will be retained at the project for review by World Bank staff and external auditors. The Disbursement Letter will provide details of the disbursement methods, required documentation, designated account (DA) ceiling, and minimum application size. No withdrawal shall be made for payments made prior to the Signature Date of the Grant Agreement, except that withdrawals up to an aggregate amount not to exceed US$2,315,000 may be made for payments made prior to this date but on or after May 1, 2020, for Eligible Expenditures. The Closing Date is February 28, 2021. A period of four months (grace period) after the closing date will be allowed to complete processing of disbursement for eligible expenditures incurred up to and until the closing date of the grant.\n\n62. **Banking Arrangements** : The PCU will open a segregated Designated Account on or after May 1, 2020 denominated in Euro at a bank acceptable to the World Bank. Local currency account(s) can also be opened to receive transfer from the Euro account. The details of both accounts (the designated and project account) along with the details of account signatories will be communicated to the World Bank within one month after effectiveness. No disbursements will be made from the World Bank until the segregated bank accounts are opened Page 21 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) for the project. If ineligible expenditures are found to have been made from the Designated Account, the Recipient will be obligated to refund the same. If the Designated Account remains inactive for more than six months, the Recipient may be requested to refund amounts transferred by the World Bank to the Designated Account. These accounts will finance all eligible project expenditures as per the Financing Agreement. It is envisaged that funds will be held at the PCU only. Should a need arise in future to transfer resources to other entities, then a FM assessment for them will be conducted to ensure that adequate FM capacity exists and to mitigate risks.\n\n63. **Fund flow arrangement** : The World Bank will make an initial advance disbursement into the designated account for the project managed by PCU in Euro upon receiving a withdrawal application. Subsequent replenishment of funds from the World Bank to the Designated Account will be made upon evidence of satisfactory utilization of the advance, reflected in SOEs and/or on full documentation for payments above SOE thresholds.\nReplenishment applications would be required to be submitted regularly (preferably monthly). Funds can be transferred from the designated accounts to the project local currency account where payments in relation to project eligible expenditure can be made. In addition, payments could also be affected from the designated account for eligible expenditure. A separate local bank account will be opened for the counterpart fund, normally in the Central Bank of Sudan. Relevant payments will be paid out of this account.\n\n64. **Internal Reporting** : The PCU and specifically the finance officer will prepare financial reports regarding the project, analyze and explain these reports and submit to internal stakeholders or management on a regular and adhoc basis.\n\n65. **Reporting to the World Bank** : The PCU will prepare quarterly IFRs for the project in form and content satisfactory to the World Bank, which will be submitted to the World Bank within 45 days after the end of each quarter to which they relate. The IFR will support the monitoring of project implementation. The IFR format/content will include templates for: (i) Statement of Sources and Uses of Fund stating summary statement of funds received from IDA, expenditures incurred on the project appropriately classified and fund balances including opening and closing balances and the movements there of; (ii) Statement of Use of Funds by Project Activity/Component comparing budgets with actual expenditures/payments for the quarter and cumulative showing variances, budget burnout rates and balances, etc.; (iii) reconciliations of the DA and Project accounts as at the closing date of the reporting period; (iv) Notes to the IFR, advance and retention statements, supporting schedules e.g. aging analysis, bank statements, trial balances; and (v) any other forms and information that may be requested by the World Bank.\nThe Project also will prepare Annual Financial Statements (AFS) in compliance with International Accounting Standards and Bank requirements. The annual Project Financial Statements (PFS) will be prepared within 2 months of the close of the fiscal year to which it relates and will be submitted to the National Audit Chamber for audit. Audit TOR will show the content of the AFS.\n\n66. **External audit arrangement** : The Ministry of Finance and Economic Planning (MoFEP)/PCU will be responsible for having the PFS audited by the National Audit Chamber (NAC). The Annual audited PFS and audit reports (including Management Letters) for the project will be submitted to the World Bank by MoFEP/PCU within six months from the end of the fiscal year. In accordance with the World Bank’s Policy on Access to Information, the World Bank requires that the Recipient disclose the audited financial statements in a manner acceptable to the World Bank. Following the World Bank’s formal receipt of these statements from the Recipient, the World Bank makes them available to the public as per the policy. The audit would be in conformity with the World Bank’s audit Page 22 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) requirements and in accordance with internationally recognized auditing standards. The auditor will express an opinion on the Financial Statements in compliance with International Standards on Auditing (ISA) issued by the International Federation of Accountants (IFAC). The NAC will also prepare a Management Letter giving observations and comments, and providing recommendations for improvements in accounting records, systems, controls and compliance with financial covenants in the Grant Agreement. As noted above, the audit report will be submitted to the World Bank within six months after the end of the accounting period to which the audit relates. The audit Terms of Reference (ToR) for the project audit will be prepared by MoFEP/PCU and will be agreed with the World Bank by negotiations.\n\n**(ii)  Procurement**\n\n67. **The procurement of the project will be implemented by the PCU** . The PCU will have the same fiduciary function as under the BERP implementation.\n\n68. **The procurement arrangement has been assessed and the procurement risk is rated as “High.”** The risk will be mitigated through regular reporting on the progress and implementation of fiduciary activities by the PCU, World Bank supervision, World Bank procurement team hands-on support as required, and further capacity building and training. Risk mitigation measures have been discussed and agreed with the PCU. The measures include intensive trainings of staff from the procurement unit on the use of the World Bank procedures and processes for procurement of works, goods and selection of consultants. The preparation of a procurement plan for the duration of the project would also contribute to alleviate the risks.\n\n69. **Procurement under the proposed operation will be guided by the following documents** : (a) the ‘World Bank Procurement Regulations for IPF Borrowers dated July 1, 2016, revised November 2017 and August 2018 (Procurement Regulations); and (b) the World Bank’s Anticorruption Guidelines ‘Guidelines on Preventing and Combatting Fraud and Corruption’, revised July 1, 2016. The Project Implementation Manual “the Community Contracting Project Implementation Manual” has been revised in accordance with these documents and will include simplified instructions and procedures for procurement in decentralized units (schools and communities) and detailed procedures for administration and handling of procurement-related complaints. The PTAs procurement capacity in handling Community-Driven Development (CDD) procurement and Contract Management need to be revisited and reassessment is important on individual case basis to ensure that PTAs get the required skills and training for the appropriate procurement delivery in line with the IPF guidelines.\n\n70. **As required by the procurement Regulations, the Recipient has already prepared the Project Procurement**\n**Strategy for Development (PPSD) and a draft Procurement Plan covering the first 18 months of** **implementation** .\nThe project will use the World Bank’s online procurement planning and tracking tools to carry out all procurement transactions. The is the Systematic Tracking of Exchanges in Procurement (STEP) which is an end to end and will be used for submission, clearance, to capture procurement data and to update the Procurement Plan. Sudan has procurement, contracting and public asset management regulations (law). Any contract, which will be procured through National Competitive Bidding (NCB) procedures, would be subjected to these national procurement procedures. The project has no complex procurement that may pose a challenge to the Recipient capacity.\nProcurement activities are similar to those under BERP. As such, the Client is familiar with the types of procurement that will be undertaken under this project.\n\nPage 23 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) 71. **Summary of the Project Procurement Strategy for Development (PPSD):** Based on the main conclusions of the PPSD, the environment is considered favorable for the execution of public contracts. The goods including the IT equipment required for the project, including the laptops and desktops can be procured from the domestic market by using “Request for Quotations.” IT equipment with the appropriate software, photocopiers and office furniture will be procured through Direct Selection as well as other printing materials. In the case of vehicle rentals for fieldwork, if needed, a wide domestic market exists, including a number of enterprises capable of fulfilling the contracts. No consultancy firm is expected to be recruited; while individual consulting services will be open to both local and international candidates through advertisement in local and international media. The Implementing Agency has already developed clear procedures for the use of Request for Quotations and Individual Consultants Selection. The complete PPSD has been prepared and is included in the project operational manual. The proposed Procurement and Selection methods in view of the identified activities are all Post Review.\n\n~~.~~ **C. Legal Operational Policies** .\n\n**Triggered?**\n\nProjects on International Waterways OP 7.50 No Projects in Disputed Areas OP 7.60 No ~~.~~\n\n**D. Environmental and Social**\n\n72. **Environmental Risk Rating – Moderate** . School grants are expected to be an important source of funding for schools to support the acquisition of basic learning materials, stationery, notebooks, classrooms furniture, provide services such as water provision and support small reparation, which will contribute for improving the learning environment, which can attract and retain pupils and teachers in school. The school grants may be used to support teachers (in cash or in-kind). An estimated US$2 per child will be allocated equally to all schools, with the maximum amount US$1,000 per school. Due to the possibility that the grants may be used for water supply and sanitation infrastructure (such as supplying water tanks, clay pots) within school boundaries, etc., the environmental risk rating is considered to be moderate. This risk rating can be changed later during implementation according to a re-assessment of environmental risks.\n\n73. **Social Risk Rating (SRR) - Moderate.** The school grant will be used for financing stationery, paying volunteer teachers if the number of teachers is not enough, supporting school meals for very poor children and provision of drinking water for both teachers and students were water isn’t available in the school. The potential social risks may arise from utilization of grant resources. Though in all of themselves, the project activities are low risk, the SRR is considered moderate as the project will support voluntary teachers in schools across Sudan, including in conflict affected areas where the contextual risk to the project is considered moderate. The social risk mitigation measures include, undertaking robust stakeholder engagement, using third party (NGOs) to implement the school grant in conflict affected areas and preparing labor management plans which will help to manage the potential risks. A social assessment will be prepared by September 2020 to deepen the understanding of the issues.\n\nPage 24 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) 74. **Safeguards Management Approach and Capacity** : An Environmental and Social Commitment Plan (ESCP) has been prepared and disclosed on April 23, 2020. Since this project is prepared under emergency procedures (Investment Project Financing Policy Paragraph 12), the ESCP outlines the commitment by the Client to update the ESMF of the Sudan Basic Education Support Project and finalize after project approval. A labor management plan has been prepared and disclosed on April 3 2020. The MoE will continue to serve as the implementation agency for this project. Within the MoE, there is an existing PCU which will hold responsibility for carrying day-today implementation of project activities. The PCU is supported a social mobilization and grass-roots capacity building/school grant coordinator and a safeguards specialist to carry out environmental and social safeguards implementation, monitoring and reporting respectively. National institutional capacity is thus strong. The PCU has a history of engaging with State Ministries and local communities to build capacity, and there is a component in the project dedicated to funding this, especially for the new States being added.\n\n75. **Stakeholder Engagement and Information Disclosure -** The project has prepared a stakeholder engagement plan based on the findings of a stakeholder mapping. This plan was disclosed in country on April 23, 2020 and on the World Bank website on April 30, 2020. There will be continuous stakeholder engagement by the MoE, State Education Offices, implementing entities; such as, partner non-government organizations. The project social mobilizers will closely work with the school level Parent Teacher Association (PTA) in the process of stakeholder engagement and community consultation.\n\n76. **Grievance Redress Mechanism:** in Sudan, customary institutions including community development committees are responsible for managing community grievances. In case of grievances and disputes the communities/tribes typically settle these problems through their traditional system/community committees.\nFurther, the native administration or be heard by local courts, which are staffed with traditional leaders such as Nazir, Omdas, and Sheikhs serving as mediating and ruling out. The customary court can refer cases to the formal court system; however, chiefs and sub-chiefs in many areas continue to arbitrate grievances and disputes arising within the community. The BERP had weak institutionalization, systematic recording and reporting of grievances so the project needs to set up a grievance/complain handling mechanism building on existing local practice with defined procedure, timeline and capacity building to the committee.\n\n77. **Gender** : the project will consider gender sensitive planning, through systematic gender analysis, action, monitoring and reporting. The analysis will consider gender disparities among different states, retention of girls in schools, understanding female teacher situations. The gender aspects differ among pastoralists, geographic locations, agro-pastoralists, which will be accounted in the implementation of the project. The project will also raise awareness and engage communities in making sure girls do not enter early marriage and stay in schools to complete basic education.\n\n78. **Gender Based Violence:** in improving school planning and monitoring the proposed project will help to reduce GBV. To prevent and reduce such risks, the project will engage in awareness and stakeholder engagement campaigns as part of the continuous community consultation that will accompany project activities. For cases of GBV and sexual exploitation and abuse (SEA), the State PCU social mobilization and grass-roots capacity building/school grant coordinator specialist will be the focal person to ensure referral and services. If cases are reported, the project will allocate adequate resources to build awareness of this mechanism for bringing GBV Page 25 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) grievances to the attention of the State focal person. The State focal person will receive training in the basic principles of GBV case management, encompassing confidentiality, a non-judgmental approach, and service referrals for survivors. Adoption and Implementation of the GBV Action Plan will be done within six months of effectiveness and during project implementation and will be maintained throughout the Project life.\n\n**V.** **GRIEVANCE REDRESS SERVICES**\n\nCommunities and individuals who believe that they are adversely affected by a World Bank (WB) supported project may submit complaints to existing project-level grievance redress mechanisms or the WB’s Grievance Redress Service (GRS). The GRS ensures that complaints received are promptly reviewed in order to address project-related concerns.\nProject affected communities and individuals may submit their complaint to the WB’s independent Inspection Panel which determines whether harm occurred, or could occur, as a result of WB non-compliance with its policies and procedures. Complaints may be submitted at any time after concerns have been brought directly to the World Bank's attention, and Bank Management has been given an opportunity to respond. For information on how to submit complaints to the World Bank’s corporate Grievance Redress Service (GRS), please visit\n[http://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service. For information](http://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service)\n[on how to submit complaints to the World Bank Inspection Panel, please visit www.inspectionpanel.org.](http://www.inspectionpanel.org/)\n\n**VI.** **KEY RISKS**\n\n79. The overall risk for the proposed project is rated **Substantial** . While sectoral, technical and stakeholder risks are moderate, political/governance and macro-economic risks are high and fiduciary risks are substantial.\nBelow are the high and substantial risks and mitigation measures: (a) _Political and governance risks (high)_ . The federal system limits the possibility to coordinate and steer the education system to achieve the national educational objectives. To mitigate this risk, the Program Coordination Unit established within the Federal MoE will coordinate the vertical and horizonal project activities. There are capacity constraints in the system, but more than one third of communities and schools have already been trained in the management and implementation of school grants under previous project.\n\n(b) _Macro-economic risks (high)_ . Sudan’s macro-economic situation is erratic and significant fluctuations exist. The ongoing food and fuel crisis also continue dampening investor confidence affecting key economic sectors especially the service sector which is still in its infancy in Sudan. The resultant rising inflation, exchange rate instability and continued currency depreciation greatly weaken economic activity.\nThe poor are particularly affected by rising inflation given their high food share in consumption, and limited mesas to preserve the erosion of the value of their savings. This increases the risks that parents will not be able to afford to send their children to schools and that counterpart funding will not be forthcoming. The COVID-19 crisis is expected to aggravate the situation. The current economic crisis in Sudan has created a large variation between the official exchange rate and the parallel market and the devaluation of the Sudanese currency. The Central Bank of Sudan official exchange rate is 1 USD = 58 SDG however the parallel market has fluctuated greatly reaching as high as 1 USD = 140 SDG; an average of 70 Page 26 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) SDG to the USD is currently calculated. . In light of the urgency of the situation, this emergency support will be conducted in a short project implementation cycle, limiting the impacts of macro-economic fluctuations and providing much needed resources directly to the schools. The project aims to thereby reduce some of the pressure on households to finance education services and ensure that students, especially girls, stay enrolled in schools.\n\n(c) _Fiduciary (substantial)_ . There are risks related to the project’s fiduciary management (i.e., FM and procurement). The MoE has gained substantial experience in managing World Bank funds with the BERP which was rated satisfactory in financial management. The new project will continue to emphasize strong fiduciary controls and checks and balances. Standard controls, such as financial audits and internal audits, as well as systematic monitoring, will be built into the project design to mitigate fiduciary risks.\n\n.\n\nPage 27 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**VII.** **RESULTS FRAMEWORK AND MONITORING**\n\n**Results Framework**\n\n**COUNTRY: Sudan**\n**Sudan Basic Education Emergency Support**\n\n**Project Development Objectives(s)**\n\nTo sustain enrollment in public basic education in Sudan during the transition school year.\n\n**Project Development Objective Indicators**\n\n**RESULT_FRAME_TBL_PDO**\n\n**Indicator Name** **DLI** **Baseline** **End Target**\n\n**To sustain enrollment in public basic education in Sudan**\n\nStudent enrollment in targeted schools (Number) 5,400,000.00 5,535,000.00 Girls enrolment in targeted schools (Number) 2,780,000.00 2,850,000.00\n\n**PDO Table SPACE**\n\n**Intermediate Results Indicators by Components**\n\n**RESULT_FRAME_TBL_IO**\n\n**Indicator Name** **DLI** **Baseline** **End Target**\n\n**School grants program**\n\nNumber of schools receiving grants (Number) 0.00 16,500.00 Share of schools with school-based management committees 0.00 90.00 trained on school grant management (Percentage) Page 28 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**IO Table SPACE**\n\n**UL Table SPACE**\n\n**Monitoring & Evaluation Plan: PDO Indicators**\n\n**Methodology for Data**\n**Indicator Name** **Definition/Description** **Frequency** **Datasource**\n**Collection**\n\n**Responsibility for Data**\n**Collection**\n\nMinistry of Education and PCU Ministry of Education and PCU\n\n**Responsibility for Data**\n**Collection**\n\nPCU Project Coordination Unit Page 29 of 40 Annual School Census Annual School Census Census of schools key data collected yearly Census of school key data collected annually Student enrollment in targeted schools The enrollment will be monitored through the annual school census Number of girls enrolled in Girls enrolment in targeted schools targeted schools\n\n**ME PDO Table SPACE**\n\nAnnual Annual\n\n**Monitoring & Evaluation Plan: Intermediate Results Indicators**\n\n**Methodology for Data**\n**Indicator Name** **Definition/Description** **Frequency** **Datasource**\n**Collection**\n\nReports compiled by Localities and States Reports compiled by Localities and States Number of schools receiving Number of schools receiving grants grants Annual Annual Administrativ e Reports Administrativ e reports Share of schools with school-based management committees trained on school grant management\n\n**ME IO Table SPACE**\n\nShare of schools receiving training on school grant management.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) Page 30 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**ANNEX 1: Costing for the Sudan Basic Education Emergency Support Project**\n\n**COUNTRY: Sudan**\n**Sudan Basic Education Emergency Support Project**\n\n**Table A1-1: Accelerated Funding Framework Budget**\n\n**Number of**\n**Component/Activity** **Unit** **Unit cost, USD** **Comment**\n**beneficiaries**\n\n**Total cost USD**\n**Component 1. School Grants Program**\n\nTraining of schools and public 16,500 75 communities schools Providing school grants 5.4\n\n**Component 2. Program Management**\n\nOperational cost of the Program Coordination Unit (PCU): communication, coordination, monitoring, and reporting students in target schools, million 2 Capacity building of schools and communities on the use of school grants.\n\nUS$2 per student in grants, but not more than USD 1,000 per school.\n\n1,237,500 10,037,500 US$300 300,000 thousand\n\n**TOTAL: Sudan GPE**\n**11,575,000**\n**Accelerated Fund**\n\nPage 31 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**ANNEX 2: Implementation Support Plan**\n\n**COUNTRY: Sudan**\n**Sudan Basic Education Emergency Support Project**\n\n1. **The implementation support plan of the project is consistent with the new Government’s strategy for managing**\n**externally funded programs supporting education** . It also considers challenges in the education sector and risks\nidentified in the Systematic Operations Risk-Rating Tool. It reflects the lessons learned from the past projects in Sudan. The project implementation rests under the responsibility of the MoE with targeted and continuous implementation support and technical advice from the World Bank.\n\n2. **The implementation support strategy is based on several mechanisms** that will enable enhanced implementation support to the Government, on-time and effective monitoring of the Project, and guidance to implementing agencies on technical, fiduciary, environmental and social aspects, as necessary. The implementation support thus comprises: (a) implementation support missions; (b) regular technical meetings and field visits; (c) progress report on Results Framework; (d) M&E; and (e) harmonization among development partners and other stakeholders.\n\n3. **The World Bank’s implementation support will broadly consist of the following:**\n\n- Capacity-building activities to strengthen the ability to implement the project, covering the technical,\nfiduciary, and environmental and social dimensions\n\n- Provision of technical advice and implementation support geared to the attainment of the PDO, PDO-level\nand intermediate outcome results indicators\n\n- Ongoing monitoring of implementation progress, including regularly reviewing key outcome and\nintermediate indicators, and identification of bottlenecks\n\n- Monitoring risks and identification of corresponding mitigation measures\n\n- Close coordination with other DPs to leverage resources, ensure coordination of efforts, and avoid\nduplication 4. **Role of the World Bank.** The World Bank’s implementation support team will be composed of country office (CO) based Task Team Leader (TTL), and both HQ-based and CO-based operations and specialist staff, who will be closely working with the client on a regular basis on implementation monitoring. Consultants will also be engaged for additional support in the key areas of reforms including governance, fiduciary, and safeguard management.\n\n5. **Role of GPE and Local Education Group.** GPE and Local Education Group will be critical in providing oversight of the project implementation and maintaining the policy dialogues on key sector policies under the project. GPE and Local Education Group will join the biannual project implementation support missions.\n\n6. **Fiduciary arrangements.** FM and procurement arrangements will build on and use the capacity developed under the previous projects. The World Bank FM and procurement specialists are based in the World Bank’s CO in Khartoum and will support project implementation through regular reviews and on-time training and capacity building of staff of the client. Formal supervision of fiduciary processes and procedures will be conducted biannually, and implementation support will be provided as required by the World Bank team.\n\n7. **Social and environmental safeguards.** The World Bank environmental and social development specialists will provide regular implementation support to the Government in the implementation of the ESMF.\n\nPage 32 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**Implementation Support Resource Requirements**\n\n8. During the first two years, it is expected that stronger engagement will be required in terms of operational support as well as M&E. Special attention will be paid to the policy development at system level and implementation support for school improvement component. The World Bank team will ensure timely, efficient, and effective implementation support to the client. Tables 2-1 and 2-2 provide the implementation support plan and the skills mix required for the project.\n\n**Table 2-1. Implementation Support Plan**\n\n**Resource Estimate (Staff**\n**Time** **Focus** **Skills Needed**\n\n**Weeks)**\nFirst 12 - Team leadership - Technical expertise for school months - Education specialist improvement interventions,\n\n- Team leadership\n\n- Education specialist\n\n- Education Data specialist\n\n- Implementation support\nand supervision\n\n- Fiduciary support and\nmanagement\n\n- Environmental and social\nsafeguards monitoring and reporting\n\n- Technical expertise for school\nimprovement interventions, teacher management, reading interventions, learning assessment, civil works, governance, and accountability, and gender\n\n- Project supervision, and\nmonitoring and reporting\n\n- Procurement training and\nsupervision\n\n- Environment and social\nmonitoring and reporting\n\n- Institutional capacity building\n\n- Task Team Leader: 30\n\n- Education/operations\nspecialists: 20\n\n- Education data and M&E:\n7\n\n- Procurement: 6\n\n- FM: 5\n\n- Environmental: 5\n\n- Social: 5\n\n- Administrative support: 10\n\n**Team**\n\n**Number of Staff Weeks**\n**Skills Needed** **Number of Trips** **Comments**\n\n**Per Year**\n\nTask Team Leader 30 Field trips as required CO-based HQ-based or based in Education Specialist 10 Field trips as required region Education HQ-based or based in 10 Field trips as required Economist/Data Specialist region Operations Officer 10 Field trips as required HQ-based FM Specialist 6 Field trips as required CO-based Procurement Specialist 5 Field trips as required CO-based Social Development 4 Field trips as required CO-based Specialist Environmental Specialist 4 Field trips as required HQ-based Page 33 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**Number of Staff Weeks**\n**Skills Needed** **Number of Trips** **Comments**\n\n**Per Year**\n\nGender/Social 5 Field trips as required HQ-based Development Specialist Administrative support 10 Co-based CO-based _Note_ : CO=Country Office. HQ = Headquarters.\n\nPage 34 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812)\n\n**ANNEX 3: Economic and Financing Analysis**\n\n**COUNTRY: Sudan**\n**Sudan Basic Education Support Project**\n\n1. This section summarizes the results of the economic and financial analysis carried out to identify the current\nchallenges of the basic education sector in Sudan and to underline the potential economic gains to the society that could be sought through the Project.\n\n**Education Sector Context and Labor Market Outcomes**\n\n2. Sudan is far from the Education SDG 4 of universal primary completion because of the high number of dropouts within the primary cycle. Access to Grade 1 in basic education is relatively high with four out of five six-year-olds being enrolled on time (see table A3-1), however, the dropout rate is very high, leading to 55 percent basic completion rate in 2014. The retention within the basic cycle slightly improved from 48.2 percent in 2014 to 49.3 percent in 2018 but remains largely inadequate to generate larger efficiencies. Grade 6 survival rate improved by 2.3 p.p. from 64.3 percent in 2015 to 66.6 percent in 2018.\n\nTable A3-1: Access, enrollment, and completion rates in basic education in Sudan\n\n**Sudan** **Urban** **Rural**\nNet entry rate (6-year-olds) * 82.8% 90.4% 79.9% NER (6-13-year-olds) * 69.1% 85.8% 62.6% GER (6-13-year-olds) * 73.3% 88.0% 67.4% Grade 4 survival rate** 84.7% 97.7% 76.8% Grade 6 survival rate** 66.6% 83.3% 56.5% Grade 8 survival rate** 49.3% 68.2% 37.8% Completion rate (13-year-olds) * 55.0% 58.2% 52.6% _Source:_ authors’ estimates based on the data from Sudan MICS 2014* and School Census 2018** 3. About seven percent of people never attended school in 2014. Real access increased by eight percentage points from 85 percent in 2009 to 93 percent in 2014. Access at age six increased by 30 percentage points from 40 percent in 2009 to 70 percent in 2014. The increase is driven by an increase in access among the bottom-40 percent of the population. System demonstrated growth between 2009 and 2014 with late entry remaining constant at 11 years. Those who are not ever attended school at age 11 will never attend.\n\n4. The poor retention rate in basic education comes from a lack of school demand, in particular, among the poorest. Economic difficulties and behavior such as early marriage, pregnancy, and economic hardships explain the fragility of school demand. The lack of supply (overcrowded classrooms, ‘open-air’ or temporary classrooms, and incomplete schools) also negatively effects retention rates. According to the School Census data, 16 percent of students are enrolled in a school that does not provide full course of basic education cycle (8 grades). In addition, these students are likely to drop out before completion.\n\nPage 35 of 40", "output": {"entities": {"named_data": ["School Census 2018"], "descriptive_data": ["School Census data", "data from Sudan MICS 2014"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) 5. **Repetition rates are relatively low compared to other countries in the SSA region** . There was a slight improvement in the repetition rates: from 5.3 percent in 2015 to 4.8 percent in 2018. Though, an estimated SDG 336 million (US$10.4 million) is used annually to deliver basic education services to repeaters and pupils that drop out [3] .\n\n6. **Learning levels of students in basic schools in Sudan are generally weak** . Representative evidence from the National Learning Assessment find that on average 39 percent of grade 3 pupils are not able to read a single word and only 5 percent of pupils read fluently (more than 60 words per minute) in Arabic (NLA, 2018).\nFurthermore, the assessment of reading speed among third graders indicated an average speed of 15 words per minute, which is far below the estimated minimum reading speed of 40 words per minute thought to be necessary to gain understanding of and meaning from the text. The high share of illiterate pupils in grade 3 means that 39 percent of public resources spent on pupils in grades 1-3 are wasted in the system, which is equivalent to SDG 473 million (US$14.6 million).\n\n7. **The Internal Efficiency of the system is weak**, particularly due to very high dropout rates and low learning achievements of pupils. The IEC at the primary level is particularly low (39 percent), which implies that more than half of public resources are wasted in paying for repeated grades or schooling for students who dropout before cycle completion.\n\n**Economic Rationale for Public Investment in Sustaining Basic Education Enrollment in Sudan**\n\n8. The rationale for public sector financing of basic education is well established. Investments under the Project would strengthen efficiency and equity at the basic level overall, likely contributing to improved learning outcomes at the school level. The pressing needs and challenges for both improved efficiency and equity warrant public sector support consistent with Sudan’s commitment to providing Universal Primary Education of reasonable quality to all children.\n\n9. Investment in basic education in Sudan is justified by the low NER (69 percent) and completion rate (55 percent) and weak learning levels among enrolled students. National Learning Assessment conducted in all 18 states of Sudan found that Grade 3 students performed very poorly. On average, 40 percent of pupil are not able to read a single word. This suggests that there is not only a large proportion of school-age children out of school but even when in school many students are not learning.\n\n**The Project’s Development Impact**\n\n10. The project is expected to contribute positively to Sudan’s education system and national economic development. It aims to sustain enrollment in public schools during the economic crises and pandemic. To that end, it is expected that the proposed interventions will affect the probability of a child completing primary education and transitioning to the secondary level. This, in turn, will yield gains in labor earnings measured 3 Authors’ estimation based on 2018 School Census data and reported USD/SDG exchange rate (Economist).\n\nPage 36 of 40", "output": {"entities": {"named_data": ["2018 School Census data", "National Learning Assessment"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) over the course of a standard working life. The key project's economic impact is, therefore, estimated as the incremental benefit accruing to a representative child as the result of effects induced by the program's interventions.\n\n11. Provision of school grants under the project is aimed at incentivizing schools to make better use of existing resources to achieve improved learning and efficiency goals through better retention of students. Gains in internal efficiency will lower the cost to the government for providing basic education: as less students fail and repeat grades, government spends fewer resources. Improvements in teacher knowledge and effort shall result in reduced dropouts in the course of the basic education cycle.\n\n**Expected Economic Benefits**\n\n12. The proposed project is likely to yield positive results on the education quality in the medium-run as it: (a) targets an area of intervention, basic education, that is critical for long-term school performance, as measured by standardized assessment; (b) supports and complements the Government’s reforms in this area; and (c) provides instrumental additional funding to support both cost-effective and well-targeted basic education programs.\n\n13. The analysis was therefore restricted to the quantifiable economic impact and benefits. These comprise: (a) impact on internal efficiency estimates and cost savings, that is, government budget savings due to reduction of ‘inefficient’ expenditures on pupils that drop out (Internal Efficiency Gains), and (b) impact of completion probabilities in basic education, that is, direct private returns to schooling (External Efficiency Gains).\n\n14. The importance of schooling and learning to economic growth and development is well documented.\nEducation is central to achieving the goals of eliminating extreme poverty and boosting shared prosperity.\nHigh levels of education are often associated with improved economic opportunities, including higher improved access to jobs and higher lifetime wages. Education is also correlated with healthier life choices and increased voice and agency, the ability to make decisions and act on them. At the country-level, economic benefits include increased rates of economic growth through gains in productivity and a greater capacity to adopt new technologies. But education is not only instrumental in promoting development; it is also by itself an end of development.\n\n15. However, recent evidence suggests that learning is more important for earnings and development than educational attainment (which fails to account for the quality of education). This issue is of particular importance in countries, including Sudan, where school enrollment has increased rapidly and therefore educational attainment but, in some cases, accompanied by a decline in the quality of schooling with adverse consequences for student learning. When student learning levels are low, this provides a strong indication that education systems are not performing as intended.\n\n16. Education is a good predictor of wellbeing. Among Sudan’s population with higher education, two thirds are in the richest quintile of the population compared to only six percent among population without education Page 37 of 40", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) (figure A3-1). Probability of being poor – in the bottom 40 percent – is 61 percent lower for people with basic education compared to people without education.\n\nFigure A3-1: Distribution of population in Sudan by education attainment and wealth quintiles Higher Secondary Primary None 30.4% 4.4% 6.7% 8.8% 10.2% 16.7% 16.1% 20.0% 24.3% 21.8% 28.9% 13.0% 16.5% 68.2% 26.2% 21.7% 42.7% 15.5% 6.0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Poorest Second Middle Fourth Richest _Source:_ estimations based on Sudan MICS 2014/15 data.\n\n17. Educational attainment is highly associated with literacy rates. Even among those that never completed basic education, the share of literate people is above 80 percent after completing at least six grades compared to only 15 percent of people that attended only first grade of basic education (figure A3-2).\n\nFigure A3-2: Women's literacy rates in Sudan, 2014 Women's literacy rates 100% 80% 60% 40% 20% 0% Highest grade of basic education attended Weath index quintile Location _Source:_ estimations based on Sudan MICS, 2014/15 data.\n_Note:_ a woman is literate if she is able to read parts of sentence or able to read whole sentence _Impact on Internal Efficiency Estimates and Cost Savings_ 18. The Project intends to sustain enrollment in public schools in Sudan, which will lead to improved Internal Efficiency of Basic Education through preventing children from dropping out, thereby improving survival rates between Grades 1 and 8.\n\nPage 38 of 40", "output": {"entities": {"named_data": ["Sudan MICS 2014/15 data", "Sudan MICS, 2014/15 data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) 19. The current economic analysis presents estimates of the efficiency gains in basic education to 2021, based on enrollment estimates employing UN population projections, average values from recent years for intake into Grade 1 of basic education (from the School Census) relative to population, and recent trends in promotion and retention in each grade of basic school. The analysis employs the same projections as the current Education Sector Strategic Plan (ESSP) including for the GDP growth (IMF/World Bank), share of domestic resources spent on education (a 0.5 p.p. annual increase from 9.8 percent in 2017/18).\n\n20. _Gains from improved Internal Efficiency_ . The project objective is to sustain enrollment in public schools meaning that the enrollment in target schools is to increase at the rate of the population growth – 2.5 percent per annum: from 5.40 million pupils to 5.54 million in 2021. The analysis is built on the assumption that survival and repetition rates will remain unchanged.\n\n21. We compare inefficient government spending under the expected scenario and a scenario, under which there is no increase in the student enrollment. The following formula is used to estimate inefficient spending: 8 x x\n\n[i] ∗Gi Inefficient Spending = ∑( [D][i] Gi x ) ∗S [x], x i=1 where Dxi is the dropout rates in grade i in year x in target schools; Gxi is the number of pupils enrolled in grade i in year x ; S [x] is the projected government spending per pupil in year x .\n\n22. According to the results of the analysis, the share of inefficient public spending in public schools is equivalent to 16 percent of overall public expenditures in education in Sudan. If all public schools will sustain the current level of survival, the share of inefficient public spending would decrease by 0.6 percentage points by 2021 compared to the scenario where the enrollment numbers are stagnant. This improvements in spending efficiency translate into the Government savings in basic education equivalent to SDG 122 million (US$ 2.6 million) in 2019-2021 [ 4] .\n\n_Impact on External Efficiency: Impact on Completion Probabilities in Basic Education_ xi is the dropout rates in grade i in year x in target schools; Gi where Di 23. The proposed Project is likely to sustain the current level of survival rates and completion probabilities in basic education. In 2014, only 57.4 percent of 10-to-13-year-olders had at least four years of completed basic education, i.e. more than third of that cohort failed to complete lower primary education on time (table A32). We expect that by 2021 there would be 40 thousand more pupils in grades 6-8 in public schools with a high probability of the majority (80 percent) being literate and functionally numerate.\n\n4 Estimations based on the enrollment projections and the current official exchange rate – 47.5 SDG/USD.\n\nPage 39 of 40", "output": {"entities": {"named_data": ["School Census"], "descriptive_data": ["UN population projections"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nSudan Basic Education Emergency Support Project (P172812) Table A3-2: Education Completion Rates in Sudan (2014) Male, Urban Male, Rural Female, Urban Female, Total Rural Grade 4, lower basic (10-13-year-olds) 64.6 50.8 68.9 56.9 57.5 Grade 6 (12-15-year-olds) 48.2 38.0 47.0 45.1 43.3 Grade 8, upper basic (14-17-year-olds) 37.5 29.7 46.1 35.0 35.4 _Source:_ authors’ estimation based on Sudan MICS data 24. _Conclusion_ . The project is designed to support activities that address key issues in basic education identified by the 2018 Education Sector Analysis and the ESSP for 2019-2024: limited and inequitable access to basic education due to supply-side and demand-side constraints; poor quality of education service delivery and low student learning levels; and insufficient institutional capacity for efficient education system planning and management.\n\n25. Project objectives and performance targets are based on the detailed financial analysis and simulations using assumptions for basic education enrollment, student repetition, student-teacher ratios, class size, and economic growth, share of public resource to education (and to basic education) and the official exchange rate. These objectives and targets are financially feasible if the national education budget is supported by external funding. The selected activities draw on international experiences and best-practice and past projects in Sudan and elsewhere as relevant.\n\nPage 40 of 40", "output": {"entities": {"named_data": [], "descriptive_data": ["Sudan MICS data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**FOR OFFICIAL USE ONLY**\n\nReport No: PAD3491 INTERNATIONAL BANK FOR RECONSTRUCTION AND DEVELOPMENT PROJECT APPRAISAL DOCUMENT", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) CURRENCY EQUIVALENTS (Exchange Rate Effective February 29, 2020) Currency Unit = Turkish Lira 5. 96 TL = US$1 US$0.17 = TL 1 6.62 TL = EURO 1 EURO 0.15 = TL 1 Euro 1.0 = US$0.90954568 FISCAL YEAR January 1 - December 31 Regional Vice President: Cyril E Muller Country Director: Auguste Tano Kouame Regional Director: Fadia M. Saadah Practice Manager: Cem Mete Task Team Leader(s): Ahmet Levent Yener; Mattia Makovec", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**ABBREVIATIONS AND ACRONYMS**\n\nCAR Capital Adequacy Ratio CPF Country Partnership Framework CQS Selection based on the Consultants' Qualifications E&S Environmental and Social EC European Commission EBRD European Bank for Reconstruction and Development EFIL Export Finance Intermediation Loan ESA Environmental and Social Assessment ESF Environmental and Social Framework ESMS Environmental and Social Management System ESS Environmental and Social Standard ESSN Emergency Social Safety Net Program EU European Union FRIT Facility for Refugees in Turkey FCC Fully Credit Constrained GRS Grievance Redress Service GRM Grievance Redress Mechanism IBRD International Bank for Reconstruction and Development IDA International Development Association IFI International Financial Institution IT Information Technology İŞKUR Türkiye İş Kurumu LE Large Enterprise KfW _Kreditanstalt für Wiederaufbau_ LMP Labour Management Procedure M&E Monitoring and Evaluation LCC Likely Credit Constrained NPL Nonperforming Loan MoNE Ministry of National Education OECD Organisation for Economic Co-operation and Development PDO Project Development Objective PFI Participating Financial Institution PIU Project Implementation Unit POM Project Operations Manual PPSD Project Procurement Strategy for Development ROA Return on Assets SBIC Small Business Investment Company SEP Stakeholder Engagement Plan SGK Sosyal Güvenlik Kurumu SME Small and Medium Enterprises SOE Statement of Expenses STEP Systematic Tracking of Exchanges in Procurement", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) SuTP Syrians Under Temporary Protection TIMSS Trends in International Mathematics and Science Study TKYB Türkiye Kalkınma ve Yatırım Bankası WDR World Development Report", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**TABLE OF CONTENTS**\n\n**DATASHEET ........................................................................................................................... 1**\n\n**I.** **STRATEGIC CONTEXT ...................................................................................................... 8**\n\n**A. Country Context** ............................................................................................................................... 8\n\n**B. The Sectoral and Institutional Context** ......................................................................................... 10\n\n**C. The Relevance to Higher-Level Objectives** .................................................................................... 14\n\n**II.** **PROJECT DESCRIPTION .................................................................................................. 15**\n\n**A. Project Development Objective** .................................................................................................... 16\n\n**B. Project Components** ...................................................................................................................... 16\n\n**C. Project Beneficiaries** ...................................................................................................................... 19\n\n**D. Results Chain** .................................................................................................................................. 20\n\n**E. Rationale for World Bank Involvement and the Role of Partners** ................................................ 21\n\n**F. Lessons Learned and Reflected in the Project Design** ................................................................... 22\n\n**III.** **IMPLEMENTATION ARRANGEMENTS ............................................................................ 23**\n\n**A. Institutional and Implementation Arrangements** ........................................................................ 23\n\n**B. Results Monitoring and Evaluation Arrangements** ...................................................................... 24\n\n**C. Sustainability** .................................................................................................................................. 25\n\n**IV.** **PROJECT APPRAISAL SUMMARY ................................................................................... 25**\n\n**A. Technical, Economic, and Financial Analysis (if applicable)** ......................................................... 25\n\n**B. Fiduciary** ......................................................................................................................................... 31\n\nC. Legal Operational Policies ............................................................................................................... 32 D. Environmental and Social ............................................................................................................... 32 E. Citizen Engagement, Gender, and Climate Co-Benefits ................................................................. 34\n\n**V.** **GRIEVANCE REDRESS SERVICES ..................................................................................... 37**\n\n**VI.** **KEY RISKS ..................................................................................................................... 37**\n\n**VII.** **RESULTS FRAMEWORK AND MONITORING ................................................................... 41**\n\n**ANNEX 1: Implementation Arrangements and Support Plan .......................................... 47**\n\n**ANNEX 2: Detailed Description of the Project ................................................................ 60**\n\n**ANNEX 3: Financial Intermediary Assessment................................................................ 75**\n\n**ANNEX 4: Additional Sectoral Background ..................................................................... 78**\n\n**ANNEX 5: Review of the Literature and Relevant Projects.............................................. 81**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) DATASHEET\n\n**BASIC INFORMATION**\n~~BASIC~~ ~~INFO~~ ~~TABLE~~ Country(ies) Project Name Turkey Formal Employment Creation Project Environmental and Social Risk Project ID Financing Instrument Process Classification Investment Project P171766 Substantial Financing\n\n**Financing & Implementation Modalities**\n\nUrgent Need or Capacity Constraints (FCC)\n\n[ ] Multiphase Programmatic Approach (MPA) [ ] Contingent Emergency Response Component (CERC)\n\n[ ] Series of Projects (SOP) [ ] Fragile State(s)\n\n[ ] Disbursement-linked Indicators (DLIs) [ ] Small State(s)\n\n[✓] Financial Intermediaries (FI) [ ] Fragile within a non-fragile Country\n\n[ ] Project-Based Guarantee [ ] Conflict\n\n[ ] Deferred Drawdown [✓] Responding to Natural or Man-made Disaster\n\n[ ] Alternate Procurement Arrangements (APA) Expected Approval Date Expected Closing Date 31-Mar-2020 31-Dec-2024 Bank/IFC Collaboration No\n\n**Proposed Development Objective(s)**\n\nThe project objective is to enhance the conditions for formal job creation by firms operating in provinces with high incidence of Syrians under Temporary Protection (SuTP), for the benefit of Turkish citizens and refugees.\n\nPage 1 of 86", "output": {"entities": {"named_data": ["Disbursement-linked Indicators"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Components**\n\n**Component Name** **Cost (US$, millions)**\n\nLoans targeting firms with high potential for job creation 345.77 Grants targeting firms conditional on job creation 76.94 Technical and Institutional Support 7.27\n\n**Organizations**\n\nBorrower: Development and Investment Bank of Turkey Implementing Agency: Development and Investment Bank of Turkey\n\n**PROJECT FINANCING DATA (US$, Millions)**\n\n**SUMMARY-NewFin1**\n\n**Total Project Cost** 430.85\n\n**Total Financing** 347.35\n\n**of which IBRD/IDA** 347.35\n\n**Financing Gap** 83.50\n\n**DETAILS-NewFinEnh1**\n\n**World Bank Group Financing**\n\nInternational Bank for Reconstruction and Development (IBRD) 347.35\n\n**Expected Disbursements (in US$, Millions)**\n\n**WB Fiscal Year** 2020 2021 2022 2023 2024 2025\n\n**Annual** 0.00 141.91 152.62 37.20 15.63 0.00\n\n**Cumulative** 0.00 141.91 294.53 331.73 347.35 347.35\n\n**INSTITUTIONAL DATA**\n\nPage 2 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Practice Area (Lead)** **Contributing Practice Areas**\n\nSocial Protection & Jobs Finance, Competitiveness and Innovation\n\n**Climate Change and Disaster Screening**\n\nThis operation has been screened for short and long-term climate change and disaster risks\n\n**SYSTEMATIC OPERATIONS RISK-RATING TOOL (SORT)**\n\n**Risk Category** **Rating**\n\n1. Political and Governance ⚫ Moderate\n\n2. Macroeconomic ⚫ Substantial 3. Sector Strategies and Policies ⚫ Moderate 4. Technical Design of Project or Program ⚫ Substantial 5. Institutional Capacity for Implementation and Sustainability ⚫ Substantial 6. Fiduciary ⚫ Moderate 7. Environment and Social ⚫ Substantial 8. Stakeholders ⚫ Moderate 9. Other ⚫ Substantial 10. Overall ⚫ Substantial\n\n**COMPLIANCE**\n\n**Policy**\nDoes the project depart from the CPF in content or in other significant respects?\n\n[ ] Yes [✓] No Does the project require any waivers of Bank policies?\n\n[ ] Yes [✓] No Page 3 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Environmental and Social Standards Relevance Given its Context at the Time of Appraisal**\n\n**E & S Standards** **Relevance**\n\nAssessment and Management of Environmental and Social Risks and Impacts Relevant Stakeholder Engagement and Information Disclosure Relevant Labor and Working Conditions Relevant Resource Efficiency and Pollution Prevention and Management Relevant Community Health and Safety Relevant Land Acquisition, Restrictions on Land Use and Involuntary Resettlement Not Currently Relevant Biodiversity Conservation and Sustainable Management of Living Natural Resources Indigenous Peoples/Sub-Saharan African Historically Underserved Traditional Local Communities Not Currently Relevant Not Currently Relevant Cultural Heritage Relevant Financial Intermediaries Relevant\n\n**NOTE** : For further information regarding the World Bank’s due diligence assessment of the Project’s potential\nenvironmental and social risks and impacts, please refer to the Project’s Appraisal Environmental and Social Review Summary (ESRS).\n\n**Legal Covenants**\n\nSections and Description Loan Agreement, Schedule 2, Section I.A.1. The Borrower shall, throughout the implementation of the Project: (a) maintain a Project Implementation Unit (“PIU”); and (b) ensure the PIU functions at all times in a manner and with staffing and budgetary resources necessary and appropriate for the implementation of the Project, acceptable to the Bank, including (i) an environmental expert, (ii) a social expert, and (iii) a focal point for the grievance redress mechanism and stakeholder engagement.\n\nSections and Description Loan Agreement, Schedule 2, Section I.A.2. The Borrower shall, throughout the implementation of the Project, comply with the applicable prudential regulations of the Guarantor.\n\nSections and Description Loan Agreement, Schedule 2, Section I.A.3. By no later than ninety (90) days after the Effective Date, the Recipient Page 4 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) shall execute and deliver the Grant Agreement, and fulfill all conditions precedent to the effectiveness of, or to the right of TKYB to make withdrawals under, said Grant Agreement.\n\nSections and Description Loan Agreement, Schedule 2, Section I.B.1 and 2. The Borrower shall maintain, throughout Project implementation, an Operations Manual for Sub-loans, in substance and manner acceptable to the Bank, and shall carry out the Project, and cause the Project to be carried out, in accordance with the arrangements, procedures and guidelines set forth in the Operations Manual for Sub-loans.\n\nSections and Description Loan Agreement, Schedule 2, Section I.C.1. For the purpose of carrying out of Part 1 of the Project, Large Enterprise and SME firms shall (a) be financially viable, and (b) have a consolidated record of job creation.\n\nSections and Description Loan Agreement, Schedule 2, Section I.C.2. The Borrower shall take reasonable measures to encourage the participation of Large Enterprise and SME firms in Less Developed Sub-regions affected by SuTP influx and WomenInclusive Enterprises.\n\nSections and Description Loan Agreement, Schedule 2, Section I.C.3. For the purpose of carrying out Part 1.A of the Project, the Borrower shall ensure that (a) the selection criteria of Large Enterprise firms, (b) the eligibility criteria for selecting Subprojects, (c) the terms and conditions for the Borrower’s provision of Sub-loans to Large Enterprise firms, and (d) the procedures for approving Sub-projects, set forth and/or referred to in Annex 2 of Schedule 2 to the Loan Agreement, and the Operations Manual for Sub-loans, are followed.\n\nSections and Description Loan Agreement, Schedule 2, Section I.C.4. All Sub-loans extended under Part 1.A of the Project and extended through PFIs under Part 1.B of the Project may be subject to ex-post review by the Bank to verify compliance with the requirements set forth in this Agreement and Operations Manual for Sub-loans.\n\nSections and Description Loan Agreement, Schedule 2, Section I.C.5 (a). For the purposes of carrying out Part 1.B of the Project, the Borrower shall ensure that (i) the eligibility criteria and procedures for selecting PFIs, (ii) the terms and conditions for the Borrower’s provision of Subsidiary Financing to PFIs, (iii) the eligibility criteria and procedures for selecting beneficiary SMEs, (iv) the eligibility criteria and procedures for selecting and approving Sub-projects, and (v) the terms and conditions for the selected PFIs’ provision of Sub-loans to SMEs, as set forth in Annex 1 of Schedule 2 of the Loan Agreement and in the Operations Manual for Sub-loans, are followed.\n\nSections and Description Loan Agreement, Schedule 2, Section I.C.5 (b). For the purposes of carrying out Part 1.B of the Project, the Borrower shall provide that the amount of Subsidiary Financing extended to any single PFI not exceed the equivalent of EUR 40,000,000, or such other amount agreed to by the Bank and set forth in the Operations Manual for Sub-loans.\n\nPage 5 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) Sections and Description Loan Agreement, Schedule 2, Section I.C.6. After the collection from a PFI of at least EUR 20,000,000 in Repayments, or such amount as agreed to in writing by the Bank, the Borrower shall (unless otherwise agreed to in writing by the Bank) utilize the principal Repayments from the first financing cycle to provide additional Subsidiary Financing to the same or other PFIs under Part 1.B of the Project, and additional Sub-loans to the same or other Large Enterprise firms under Part 1.A of the Project (each case, “Reflows”) – for the same purpose – for at least one additional financing cycle, and such Reflows shall be made within twelve (12) months of the principal Repayments reaching a total equivalent to EUR 20,000,000.\n\nSections and Description Loan Agreement, Schedule 2, Section I.D.1 and 2. The Borrower shall ensure that the Project is carried out in accordance with the Environmental and Social Standards, in a manner acceptable to the Bank. and shall ensure that the Project is implemented in accordance with the Environmental and Social Commitment Plan (“ESCP”), in a manner acceptable to the Bank.\n\nSections and Description Loan Agreement, Schedule 2, Section I.E.1. The Borrower shall: (a) prepare and furnish to the Bank not later than February 28th of each year during the implementation of the Project, a proposed Annual Work Plan and Budget; (b) afford the Bank a reasonable opportunity to exchange views on each such proposed Annual Work Plan and Budget, and shall thereafter ensure that the Project is implemented with due diligence during said following year, in accordance with such Annual Work Plan and Budget accepted by the Bank; and (c) make any significant changes, or allow any significant changes to be made, to the Annual Work Plan and Budget that has been accepted by the Bank only after receiving the Bank’s prior written confirmation.\n\n**Conditions**\n\nType Description Effectiveness Loan Agreement, Section 4.01 (a). The Borrower has adopted an Operations Manual for Subloans acceptable to the Bank.\n\nType Description Effectiveness Loan Agreement, Section 4.01 (b). The Borrower has properly staffed its Project Implementation Unit, with positions, terms of reference, and staff qualifications acceptable to the Bank.\n\nType Description Effectiveness Loan Agreement, Section 4.01 (c). The Borrower has designated a senior management representative to have overall accountability for environmental and social performance of Sub-projects.\n\nType Description Effectiveness Loan Agreement, Section 4.01 (d). The Borrower has developed, adopted, and operationalized a grievance redress mechanism for the Project.\n\nPage 6 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) Page 7 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**I.** **STRATEGIC CONTEXT**\n\n**A. Country Context**\n\n1. **Turkey has high growth potential, but recent shocks have affected the sustainability of its**\n**economic gains since the early 2000s** . After the Global Financial Crisis in 2008-2009, growth has been\nincreasingly fueled by credit growth and accumulation of (mostly foreign exchange) private sector debt, together with temporary stimulus policies. These led to declining productivity growth and macroeconomic imbalances in late 2017/early 2018. The situation was compounded by exogenous factors including multiple election cycles, regional conflict, and difficult international relations.\n\n2. **Economic vulnerabilities that had accumulated over the past four years came to a head in mid-**\n**2018.** Policy stimulus in the aftermath of the 2016 failed coup led to economic overheating. Though\ngrowth accelerated to 7.4 percent in 2017, this came at a cost of double-digit inflation and a large current account deficit. A hardening of external economic conditions in mid-2018, together with tense international relations, led to a depreciation in the Turkish lira. This profoundly affected the real and financial sectors. Corporations and banks suffered due to high foreign exchange debt, annual inflation peaked at 25 percent in October 2018, the economy went into recession in the second half of 2018, and unemployment spiked from 10 percent in January 2018 to 14 percent in June 2019.\n\n3. **The Turkish economy over the past 12 months has experienced major adjustments.** Current account imbalances have declined, banks have reduced their external exposure, and portfolio flows have started to recover. These adjustments have lessened external vulnerabilities that had accumulated in the run up to the August 2018 currency shock. They have also contributed to a more stable lira, notwithstanding bouts of currency volatility. There has also been steady disinflation over this period.\nThese developments were supported by selected policy responses and accommodative global monetary conditions. Even so, foreign exchange reserves have eroded over the past two years, exposing Turkey to external market pressure.\n\n4. **Stagnating output, high costs of production, and high consumer prices have led to significant**\n**job losses and falling real wages.** Unemployment among the youth is particularly high, jumping from 19\npercent to 25 percent between May 2018 and May 2019. Average real wages declined by 2.6 percent between 2017 and 2018 but have picked up more recently due to adjustments to the minimum wage.\nPoorer households have been most affected because many low-income workers are employed in construction and agriculture—the sectors that saw the biggest decline in jobs. Moreover, the long-term impact of the real wage effects is greater for the poorest households because they have limited coping mechanisms.\n\n5. **Turkey now faces a two-fold challenge.** In the near-term to extricate itself from a downturn whilst maintaining the disinflation momentum, navigating an uncertain external environment, and addressing corporate debt overhang; and to put in place appropriate policy and institutional settings to support a shift to a sustainable-medium term growth model. The pace and sustainability of Turkey’s recovery will depend on reducing economic uncertainty backed by consistent policy mix. The economy has stabilized in the short-term. The gross domestic product is projected to rebound to 3 percent and 4 percent in 2020 and 2021, respectively. However, given the high degree of uncertainty in the global outlook, restoring Page 8 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) confidence and reducing domestic risk premia with appropriate monetary stance and effective fiscal policy would be key for sustaining recovery. Rigorous progress in advancing structural reforms such as deepening financial markets and completing overdue labor market reforms will help to mitigate vulnerabilities, and support growth in the medium term.\n\n6. **Turkey is both a transit and reception country for migrants and refugees and, globally, the**\n**country hosts the highest number of refugees.** [1] Because of the crisis in its southern border with Syria,\nTurkey has been hosting an increasing number of refugees and foreigners seeking international protection. In addition to hosting more than 3.6 million Syrians, [2] who are under temporary protection, there are an estimated 400,000 asylum seekers and refugees from other nations. The country’s refugee response has been progressive and provides a model to other countries hosting refugees. However, the magnitude of the refugee and migrant influx continues to pose substantial development consequences for not only the displaced but also for the communities into which they settle, contributing to the expansion and overcrowding of settlements; increased demands for urban services (including water supply, sanitation, and solid waste services); additional pressure on infrastructure and the urban environment; conflicts over land; and increased competition for employment, housing, and social services. These stresses stretch the limited capacity of urban local governments, including municipalities and other service providers. Apart from the large cities such as Ankara, Istanbul and Izmir, many of the cities hosting a high concentration of Syrians are already located in the more vulnerable or disadvantaged provinces in Turkey, which exacerbates the development challenges.\n\n7. **The Government of Turkey spent an estimated €31 billion to meet the needs of refugees and**\n**hosting communities from the beginning of the Syrian crisis to 2017.** [3] This includes the provision of free\nhealthcare and education, as well as allowing legal access to the labor market. The international community has also provided over €4 billion since 2016, of which 95 percent is from the European Union (EU). [4] This includes the first tranche of the EU Facility for Refugees in Turkey (FRIT), which is a €3 billion fund launched in 2016, designed to support the Government hosting refugees; €600 million EU support outside FRIT; and over €400 million in bilateral support from EU countries. Other donors, such as United Nations agencies; international, national, and local civil society organizations; and international financial institutions (IFIs), have also been playing an important role in Turkey’s refugee response, implementing a diverse range of programs and projects, accounting for over €200 million. These efforts have been geared 1 Directorate General of Migration Management, 2019. This Project Appraisal Document uses the term refugee regardless of the country of origin, although Syrians are under temporary protection status, and non-Syrians under international protection law.\n_[http://www.goc.gov.tr/icerik6/temporary-protection_915_1024_4748_icerik](http://www.goc.gov.tr/icerik6/temporary-protection_915_1024_4748_icerik)_ .\n2 The terms ‘Syrians’ and ‘refugees’ are used in terms of sociological context and widespread daily use and are independent of the legal context in Turkey and Turkish Law. Turkey is a party to the 1951 Refugee Convention and 1967 Protocol. Turkey retains a geographic limitation to its ratification of the 1951 United Nations Convention on the Status of Refugees, which means that only those fleeing as a consequence of ‘events occurring in Europe’ can be given refugee status. Syrian nationals, as well as stateless persons and refugees from Syria, who came to Turkey due to events in Syria after April 28, 2011, are provided with temporary protection.\n3 European Commission. 2018. _Technical Assistance to the EU Facility for Refugees in Turkey–Needs Assessment Report_ . Specific Contract No. 2017/393359/1, October 2018. _https://ec.europa.eu/neighbourhood-_ _[enlargement/sites/near/files/updated_needs_assessment.pdf](https://ec.europa.eu/neighbourhood-enlargement/sites/near/files/updated_needs_assessment.pdf)_ .\n4 European Commission. 2018. _Technical Assistance to the EU Facility for Refugees in Turkey–Needs Assessment Report_ . Specific Contract No. 2017/393359/1, October 2018. _https://ec.europa.eu/neighbourhood-_ _[enlargement/sites/near/files/updated_needs_assessment.pdf](https://ec.europa.eu/neighbourhood-enlargement/sites/near/files/updated_needs_assessment.pdf)_ .\n\nPage 9 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) primarily toward facilitating refugee access to the existing public services while strengthening the capacity and responsiveness of state institutions at the national and local levels.\n\n**B. The Sectoral and Institutional Context**\n\n8. **The recent worsening of economic and labor market conditions risks exacerbating key structural**\n**challenges in the labor market.** Between 2005 and 2018, the economy managed to create 8.7 million jobs\nowing to sustained growth and the introduction of a wide range of government programs and subsidies aimed at stimulating labor demand. More recently, however, the link between economic growth and job creation, especially formal job creation, has weakened, and the number of formal jobs created was insufficient to absorb all the new cohorts entering the labor market. As a result, the unemployment rate among youth ages 15–24, already one of the highest rates among the countries of the Organisation for Economic Co-operation and Development (OECD), has increased dramatically since the beginning of 2018, reaching almost 25.0 percent, while total unemployment has risen to 13.6 percent.\n\n9. **Creating more and better formal jobs remains a priority because, as a result of the recent**\n**economic downturn, the long-term declining trend in informality has slowed substantially.** Informality\ndecreased from 48 percent in 2005 to 33 percent in 2018, but this trend has been stagnating since 2015.\nInformality gradually decreased until 2015, after which it began to increase slightly, reaching 36 percent in July 2019 (23.2 percent in nonagricultural sectors). [5] Informality reached 42 percent among women in 2018 and 50 percent among people with less than high-school education. Informal employment is more extensive in provinces that have been affected by the Syrian influx, especially the southeastern provinces, reflecting the absorption of Syrians able to work in informal jobs.\n\n10. **The influx of Syrian refugees has added additional pressure on the labor market.** As previously noted, Turkey is the largest refugee-hosting country in the world, with 3.6 million Syrians. Turkey also hosts refugees from other countries (for example, Afghanistan and Iraq). The Syrian inflows to Turkey began in 2011, and the number of Syrian refugees in the country increased dramatically between 2013 and 2016. The surge represented a large labor supply shock to the economy. Many Syrians able to work, mostly low skilled, began taking up informal jobs that paid less than the minimum wage, thereby crowding out native formal workers.\n\n11. **The high unemployment rates in refugee-hosting areas—up to 27 percent—signal the**\n**seriousness of the challenges facing local and refugee job seekers.** Most provinces, hosting high numbers\nof refugees, were already among the most disadvantaged in economic welfare and economic opportunities before the Syrian crisis, including a higher incidence of low-skilled workers, lower labor force participation rates, and high unemployment rates relative to the national average. To prevent further deterioration in labor markets, promoting permanent formal job creation is critical.\n\n12. **The socioeconomic integration of refugees into the economy and improvements in livelihoods**\n**in areas with high incidence of Syrians Under Temporary Protection (SuTP) are a major challenge.** The\nfew available data sources point to the substantial participation of working-age refugees in informal jobs.\nAlthough most refugees are poor and vulnerable, many can work (around 430,000), but work informally (86 percent) for relatively low wages (TL 1,300 [US$220 equivalent] per month on average) (Livelihoods 5 The latest official monthly data refer to July 2019.\n\nPage 10 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) Survey). [6] Only 2 percent of respondents to a recent survey reported that they were working and had work permits. About 65 percent of the beneficiaries of the Emergency Social Safety Net Program (ESSN), a temporary humanitarian program, report that their main source of income is short-term informal work. [7] This will become a more significant problem once the ESSN comes to an end.\n\n13. **One of the most important contextual factors that limits formal job creation is the poor access**\n**to financing among firms.** Credit service provision is less developed in many provinces where refugees\nlive and work. According to the World Bank Enterprise Survey, most respondents (76 percent) in the affected regions assert that access to finance deteriorated loan terms and conditions (interest rates, maturity, and collateral requirements). [8] Poor access to longer-term financing limits enterprises from investing, increasing production capacity, and providing sustainable employment opportunities. After high tax rates, access to finance is perceived as a top constraint on firms, particularly small and medium enterprises (SMEs), seeking to carry out and expand business in Turkey. [9] Limited access to finance can also have a negative impact on labor market outcomes, resulting in higher unemployment, higher workforce informality, and lower employment growth. Limited access to credit is also problematic among large enterprises (LEs) because these have the potential to create more jobs, especially among refugees, including higher-quality formal jobs. [10] 14. **Banks do not usually have adequately structured resources to offer medium- to long-term**\n**maturities to most firms, mostly because of the short term of their liability base, thus leaving firms,**\n**mostly SMEs, open to severe liquidity and interest rate risk.** **[11]** Lack of cash flow-based financing and high\ncollateral requirements constrain access to finance among SMEs. [12] After the global financial crisis and strong rebalancing in the economy after August 2018, major banks have significantly cut their exposure to SMEs and LEs. The banking system has limited access to long‐term financing. It is funded mostly by relatively stable customer deposits that mature in less than three months, while most of the lending is concentrated in loans for more than three months. The result is a negative liquidity gap, that is, more liquid liabilities than assets, or a liquidity mismatch risk, which peaks in the one- to five‐year maturity range. These imbalances are reflected in bank loan portfolios and the liability structure of enterprises. The bank‐dominated financial sector thus has only a limited ability to provide the maturity critical to support SMEs and LEs that need to make long‐term investments, expand production capacity, and increase employment. In order to address the problems mentioned above, the government introduced some measures to improve SMEs access to finance and their entrepreneurial capacities, that could result effective in the medium to long-term: 6 Turkish Red Crescent and World Food Programme. 2019. _Refugees in Turkey. Livelihoods Survey Findings._ Ankara: Turk Kizilay and World Food Programme.\n7 World Bank and World Food Programme. 2019. _Vulnerability and Protection of Refugees in Turkey: Findings from the Rollout of_ _the Largest Humanitarian Cash Assistance Program in the World_ . Washington, DC: World Bank and World Food Programme.\n8 Enterprise Surveys (database), International Finance Corporation and World Bank, Washington, DC, https://www.enterprisesurveys.org/.\n9 Enterprise Surveys (database), International Finance Corporation and World Bank, Washington, DC., https://www.enterprisesurveys.org/.\n10 Ayyagari, M., A. Demirgüç-Kunt, and V. Maksimovic. 2011. “Small vs. Young Firms Across the World: Contribution to Employment, Job Creation, and Growth.” Policy Research Working Paper 5631, World Bank, Washington, DC.\n11 World Bank. 2014. _Turkey’s Transitions: Integration, Inclusion, Institutions_ . Report 90509-TR. Washington, DC: World Bank.\n12 World Bank 2014 and 2018 data of the Survey on the Access to Finance of Enterprises (database), European Central Bank, Frankfurt, https://www.ecb.europa.eu/stats/ecb_surveys/safe/html/index.en.html.\n\nPage 11 of 86", "output": {"entities": {"named_data": ["World Bank Enterprise Survey", "Enterprise Surveys (database)", "Survey on the Access to Finance of Enterprises"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n- The establishment of an Emerging Companies Market at Borsa Istanbul where the shares of\nthe SMEs are exclusively traded. Since 2011 SMEs have access to the capital markets via this channel.\n\n- Angel investment program has been launched in 2013 to encourage angel investment as a\nnew instrument for SMEs at their early stages.\n\n- Promoting SME financing with the regulations that allows the use of movable assets as\ncollateral.\n\n- The Portfolio Guarantee System has been included into the scope of Treasury-backed\nguarantee mechanism enabling Credit Guarantee Fund to access more firms and to perform more efficiently.\n\n- State-backed credit insurance has been launched in order to cover the losses of SMEs.\n\n15. **SMEs make more use of internal funds to finance investments and working capital and face**\n**higher collateral requirements than their peers in Eastern Europe and Central Asia.** [13] The share of SMEs\nin total credit declined by 5 percentage points to slightly more than 20 percent in the aftermath of the global crisis in 2008–2009 and peaked at 28 percent in mid-2018 following the economic upturn and the highly utilized credit guarantee scheme. However, it declined to 23 percent, in 2019, despite several statefinanced policy stimulus programs. This fluctuation demonstrates how SMEs are among the first and most affected frontiers of the financing cycle.\n\n16. **SMEs account for most firms in Turkey, but they have been facing difficulties in growing and**\n**expanding.** Firm composition is highly skewed toward microenterprises and small firms. Firms with fewer\nthan 10 employees represent 84 percent of all firms that have employees; yet, they employ only 20 percent of the workforce. Firms with fewer than 50 employees represent 97 percent of all firms and employ 44 percent of the workforce, while, overall, SMEs (fewer than 250 employees) account for almost all firms (99.5 percent) and employ 66 percent of all formal sector workers. However, 84 percent of all new job creation between 2014 and 2016 was generated by large firms (more than 250 employees); firms with between 50 and 250 employees accounted for 20 percent of job creation, while firms with fewer than 50 employees experienced net employment loss (–4.5 percent). Meanwhile, SMEs are more highly exposed to rising mandatory labor costs, such as the increase in the national minimum wage, which grew by 33 percent in nominal terms between December 2015 and January 2016. Constrained by such costs, SMEs may resort to hiring workers informally or might exit completely from the registered formal sector and continue operations informally.\n\n17. **Another major challenge affecting the capacity of firms to create jobs and expand is the capacity**\n**to find skilled workers.** An inadequately educated labor force is perceived to be among the top five\nconstraints to doing business in Turkey. The analysis of data on more than 7 million job postings at the public employment agency (İŞKUR) between 2016 and 2018 and from İŞKUR’s Labor Market Needs Assessment Survey and the top nine online job search portals shows that the most critical skills sought by employers across provinces are behavioral, socioemotional, and software-related skills. [14] 13 Enterprise Surveys (database), International Finance Corporation and World Bank, Washington, DC, https://www.enterprisesurveys.org/.\n14 https://media.İŞKUR.gov.tr/33412/istihdamda-3i-30-sayi-ek1-2019-yili-isgucu-piyasasi-arastirmasi-sonuclari.pdf Page 12 of 86", "output": {"entities": {"named_data": [], "descriptive_data": ["data on more than 7 million job postings"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) 18. **The labor market in Turkey is still characterized by substantial gender gaps compared with OECD**\n**countries.** The female labor force participation rate, despite a slight increase between 2014 and 2018,\nremains less than half the rate among males (34 percent versus 73 percent). The gap is substantial at all ages. The participation rate among adult women ages 25–64 is 38 percent versus the OECD average of 64 percent. Since 2009, the gap between the female and the male unemployment rates has been widening steadily, and the unemployment rate reached 14 percent among women in 2018. Women also suffer from higher informality in the labor market, which reaches 42 percent of total female employment versus 29 percent in the case of male employment. Women are twice as likely as men to be unpaid family workers (representing 20 percent of total female employment versus 10 percent among men). There is a higher incidence of young women not in education, employment, or training, which reaches 33 percent of the female population ages 15–24, versus 15 percent among males.\n\n19. **By supporting enterprises in gaining access to longer-term financing, through grants and loans**\n**to boost their operational capacity, and on formal job creation that provides decent working conditions**\n**in the case of grant recipients, the project will contribute to improving the conditions to increase formal**\n**employment** **[15]** **opportunities and formalization of workers in selected provinces.** It also contributes to\nthe employability of the beneficiaries through formal on-the-job work experience and skill building. This will directly or indirectly contribute to strengthening the local economy by reducing unemployment and informality and increasing social cohesion.\n\n20. **The proposed project is a part of the World Bank’s engagement in Turkey by focusing firm-led**\n**job creation among the most vulnerable populations.** It targets communities negatively affected by\nrefugee-related risks associated with the Syrian conflict. It does so by supporting the Government of Turkey’s efforts to alleviate certain constraints to access to financing using grants and loans for firms most likely to create formal jobs largely for work-able, low-income, workers. The workers are those who would otherwise be eligible for noncontributory emergency social safety net (income or cash transfers) support in selected provinces with a high number of refugees. These workers are largely ineligible for other interventions supported by the Government of Turkey, the World Bank, or other development agencies. [16] 21. **The project is informed by the frameworks of the World Development Report (WDR) 2013, on**\n**jobs, and the WDR 2011 on conflict, security, and development.** The WDR 2013, on jobs, calls for a threepronged approach to addressing job creation, including (a) ensuring macroeconomic fundamentals, (b)\nexamining labor policies, and, as needed, (c) designing targeted interventions to address structural or systemic exclusion among highly vulnerable populations. [17] Similarly, WDR Report 2011, on conflict, discusses productive employment as a key factor that helps accelerate the shift from fragility-associated welfare risks to greater resilience and social cohesion. [18] The proposed project also seeks to alleviate welfare- and social cohesion-related risks through greater inclusion of vulnerable groups in the formal sector.\n\n15 The definition of formal employment used here is “workers registered in the social security system through their main job.” 16 These include ongoing lending operations focusing on strengthening financial sector conditions and access to finance in traditional banking; other proposed FRIT grant-financed interventions supporting active labor market policies, microenterprises, and agricultural cooperatives for low-skilled and high-skilled populations; and World Bank-financed technical assistance on macroeconomic reforms, labor institutions, employment subsidies, and social assistance policies.\n17 World Bank. 2012. _World Development Report 2013: Jobs_ . Washington, DC: World Bank 18 World Bank. 2011. _World Development Report 2011: Conflict, Security, and Development_ . Washington, DC: World Bank.\n\nPage 13 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**C. The Relevance to Higher-Level Objectives**\n\n22. **The proposed project is a part of and aligned with the Turkish Government’s and development**\n**partners’ broader response to the Syrian refugee crises.** The alignments of the proposed actions with key\nnational and multilateral strategies are as follows: (a) **11th Development Plan (2019–2023).** The project is aligned with the objectives of the development plan to combat informal employment, to enhance the participation of groups requiring special policies in labor force and employment and to encourage (particularly beginning from local level) the participation of women in all levels of economic, social, and cultural life and decision-making mechanisms.\n\n(b) **New Economic Program.** The proposed action is aligned with the key objectives of the New Economic Program in tackling the structural problems of the labor market and combating informal employment.\n\n(c) **Turkey’s Syrian Refugees Response Program.** The action will be part of the larger joint response of the Turkish Government, the European Commission (EC), and the World Bank to the Syrian refugee crisis. The proposed action directly responds to two of the four priority areas of the EC’s response on socioeconomic support: ‘ensuring and increasing formal employment and entrepreneurship’ and ‘improvement of the labor market cohesion’. Also, it is in line with the Government of Turkey’s current response seeking to facilitate integration of refugees into society and economy. The program will complement the Government efforts to create local employment opportunity, reduce regional disparities, and eventually improve social cohesion.\n\n(d) **National ESSN Exit Strategy.** The strategy calls for the graduation of 180,000 SuTP into the labor market making them self-reliant. In this effort, the same number of Turkish citizens shall be supported, adding up to 360,000 beneficiaries. The proposed project takes up the strategy by providing formal employment to work-able Syrians and Turkish citizens alike.\n\n(e) **World Bank Country Partnership Strategy.** The project will seamlessly build on former projects by the World Bank, such as the Inclusive Access to Finance Project (P163225) with the Development and Industrial Development Bank of Turkey (TKYB) and FRIT I-supported projects (Employment Support Project for Syrians Under Temporary Protection and Turkish Citizens [P161670]), Strengthening Economic Opportunities for Syrians under Temporary Protection and Turkish Citizens in Selected Localities Project [P165687], and Development of Businesses and Entrepreneurship for Syrians under Temporary Protection and Turkish Citizens Project [P168731]). The proposed action will build on the knowledge acquired through various employment-related operations in Turkey and elsewhere, especially those that relate to helping refugees integrate in labor markets.\n\n(f) **Complementarity with other initiatives supported by EU. The proposed action is part of**\n**the priority area of the EU FRIT on socioeconomic support for Turkish citizens and refugees**\n**in enhancing livelihoods and access to employment.** It assumes the FRIT’s second tranche’s\ngreater emphasis on socioeconomic activities and includes lessons from the second needs Page 14 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) assessment and internal assessments. This action will leverage ongoing work financed by the EU, which analyses the economy to identify the economic sectors and firms that have large job creation potential and wage growth (the _Kreditanstalt für Wiederaufbau_ [KfW], _Deutsche_ _Gesellschaft für Internationale Zusammenarbeit_, the European Bank for Reconstruction and Development [EBRD], and so on).\n\n23. **The operation is well aligned with the World Bank-Country Partnership Framework (CPF) for**\n**Turkey for FY2018–2021 (discussed by the Board on August 29, 2017, Report No. 11096-TR),** specifically\nFocus Areas I (‘Growth’) and II (‘Inclusion’) of the CPF, with the main objectives of enhancing access to finance within underserved markets, improving competitiveness and employment in selected industries, and increasing labor force participation of women and vulnerable groups. To reach these objectives, the operation will support enterprises in gaining access to longer-term financing, improving capital structures of loan recipients, and creating formal jobs for grant recipients. The operation aims at enhancing the employability of beneficiary employees (both Turkish citizens and refugees) through formal work experience and training programs. The operation will support Focus Area II of the CPF by improving labor market cohesion, as well as social cohesion.\n\n**II.** **PROJECT DESCRIPTION**\n\n24. **The planned operation will support refugees and Turkish citizens in accessing formal**\n**employment opportunities in creditworthy enterprises in provinces with a high incidence of Syrian**\n**refugees.** It will offer beneficiary firms in project provinces [19] greater access to financial resources and\nskills to enhance their capacity to expand business and, ultimately, increase formal employment and decent working conditions, [20] that is, social security and other legally mandated benefits.\n\n25. **The total amount for the proposed interventions is €391.96 million (US$430.85 million**\n**equivalent).** The project will be funded by a €316 million loan from the World Bank and a €75.96 million\ngrant from the EU under the FRIT Program constituting a special measure on health, protection, socioeconomic support, and municipal infrastructure. Financing from each source is parallel and thus the respective activities/investments are financed by either one or the other financing source. [21] Still, the project is conceived as a unique operation, and aims at leveraging the mutually beneficial complementarities between the grant and the loan components to achieve greater results in terms of formal job creation in refugees affected areas.\n\n26. **The loan programs will be implemented by the TKYB with the support of participating financial**\n**institutions (PFIs).** The program will take advantage of the TKYB’s extensive network and loan provision\n\n19 Project provinces include Istanbul, Sanliurfa, Hatay, Gaziantep, Adana, Mersin, Bursa, Izmir, Kilis, Konya, Mardin, Ankara, Kahramanmaras, Kayseri, Kocaeli, Osmaniye, Diyarbakir, Malatya, Adiyaman, Batman, Manisa, Denizli, Tekirdag, and Sakarya.\n20 The International Labour Organization’s decent work involves “opportunities for work that is productive and delivers a fair income, security in the workplace and social protection for families, better prospects for personal development and social integration, freedom for people to express their concerns, organize and participate in the decisions that affect their lives, and equality of opportunity and treatment for all women and men.” 21 The EU financing will be available after an Administrative Agreement is signed between the World Bank and EU, followed by a Grant Agreement signed between the World Bank and the recipient/implementing body.\n\nPage 15 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) activities. The project will leverage the TKYB’s extensive experience in providing access to financial services to LEs and SMEs [22] through intermediaries, such as banks and leasing companies.\n\n**A. Project Development Objective**\n\n**PDO Statement**\n\n27. The project objective is to enhance the conditions for formal job creation by firms operating in provinces with high incidence of Syrians under Temporary Protection (SuTP), for the benefit of Turkish citizens and refugees.\n\n28. To reach this objective, the proposed project will support increased (a) access to finance through loans to firms with an already consolidated record of sustained job creation and greater capacity to expand business and generate markets, (b) access to finance through grants conditional on job creation to firms which are with high job creation potential, and (c) access to skills through participation of beneficiary firms and workers in training programs financed by the project.\n\n**PDO-level indicators**\n\n(a) Ratio of the average maturity of beneficiary firms sub-financing under the project, over the average maturity of borrower's portfolio not financed under the project; (b) Number of formal jobs created by subgrants (for refugees and ESSN beneficiaries, SMEs, and women) and; (c) Increased management skills in loan beneficiary firms and increased employee skills in grant beneficiary firms.\n\n**B. Project Components**\n\n29. **The main components of the project are summarized in the following paragraphs. A detailed**\n**description of project components is presented in annex 2.**\n\n22 SMEs are defined here as firms with less than 250 employees (small enterprises 10–49 and medium enterprises 50–249), while large firms are defined as firms employing equal to or over 250 employees.\n\nPage 16 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) 30. **The project will not impose a priori categorical restrictions on eligible economic sectors or firms**\n**except for the sectors excluded according to World Bank safeguard regulations.** [23]\n\n**Component 1: Loans targeting established firms with high potential for job creation (€314.6 million,**\n**US$345.8 million** **equivalent)**\n\n31. **This component supports loan financing for financially viable firms (a) through direct lending,**\n**and (b) wholesale lending.** The TKYB will be the borrower and implementing agency under the repayment\nguarantee of the Ministry of Treasury and Finance. The TKYB will provide loans to LEs directly (direct lending) and to SMEs through PFIs (wholesale lending), thereby expanding its geographical and sectoral reach. The TKYB will provide a total of loans up to €125.8 million (US$138.3 million equivalent), corresponding to 40 percent of the total to LEs. PFIs will provide a total of loans up to €188.7 million (US$207.5 million equivalent), corresponding to 60 percent of the total to SMEs.\n\n32. **The TKYB will select PFIs, including banks and/or leasing companies, based on PFIs’ financial**\n**health and capacity to implement subprojects.** The selection will be based on a two-step procedure: (a)\na general limit allocation study (asset quality, return on equity, capital adequacy ratios, and so on) conducted and approved by the TKYB and (b) PFIs’ willingness to be part of a specific loan program and their business orientation and operational capacity. The TKYB will assume the credit risk of PFIs and therefore has a strong incentive to assess their financial health and operational capabilities. The PFI selection is also subject to a ‘no objection’ process by the World Bank team, while subsidiary loan agreement covenants between the TKYB and PFIs require compliance with local regulations.\n\n33. **The TKYB has recently carried out a cross-sectoral market research to assess the potential**\n**demand for loans among firms planning to expand business and their workforce with very positive**\n**prospects.** The TKYB will use its marketing tools to target and inform potential beneficiary firms and solicit\nthem to apply. A multipurpose targeting mechanism will be used for outreach.\n\n**Component 2: Grants targeting firms conditional on job creation (€70 million, US$76.9 million**\n**equivalent)**\n\n23 Ineligible sectors and subprojects include commercial activities involving habitats and products prohibited within the framework of the Convention on International Trade in Endangered Species of Wild Fauna and Flora; the release of genetically modified organisms into the wild; the production, distribution, or sale of pesticides that fall under the World Health Organization’s Recommended Classification of Pesticides by Hazard Classes 1a (extremely hazardous) and 1b (highly hazardous) or annexes A and B of the Stockholm Convention on Persistent Organic Pollutants or that are restricted by the Government of Turkey, or herbicides; trawl fishing; radioactive products; hazardous waste storage, processing, and disposal; the production of equipment and materials containing chlorofluorocarbons or other substances regulated under the Montreal Protocol on Substances that Deplete the Ozone Layer; the manufacture of electrical equipment containing more than 0.005 percent polychlorinated biphenyls by weight; the manufacture of products containing asbestos; nuclear reactors and parts; processed or unprocessed tobacco and tobacco processing machinery; the significant conversion or degradation of critical natural habitats; significant damage to nonreplicable cultural property; involuntary land acquisition and any activity on land or affecting land over which the ownership, tenure, or user rights is disputed; any land-based activity that is considered dangerous because of security hazards or the presence of unexploded mines or bombs; weapons including (but not limited to), mines, guns, and ammunition; and any activity that supports drug crop production or the processing of such crops.\n\nPage 17 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) 34. **This component finances grants to targeted firms operating in the project provinces conditional**\n**on formal job creation among refugees and Turkish citizens.** The TKYB will be the implementing agency\nof the grant component. This component targets financially viable firms.\n\n35. **The intended beneficiary firms can apply for a subgrant scheme in which the firms will be asked**\n**to submit an employment plan to recruit refugees (including ESSN beneficiaries) together with a viable**\n**business plan.** The grant scheme will disburse 20 percent of the total amount to small enterprises (€14\nmillion), 32 percent to medium enterprises (€22.4 million), and 48 percent to LEs (€33.6 million).\n\n36. **Applications will be evaluated by a grant evaluation committee including TKYB experts and**\n**independent evaluators.** The committee will be the responsible for preliminary approval of the grants,\nwith the ‘no objection’ of the World Bank. The grant evaluation committee will evaluate the applicant firms based on employment and business plan. The firms will receive a score depending on several factors, including the number of newly created jobs according to the grant amount awarded and whether the firm is women or refugee inclusive.\n\n**Component 3. Technical and institutional support (€6.6 million, US$7.3 million** **equivalent)**\n\n_Subcomponent 3.A: Skills building for loan beneficiary firms (€0.50 million, US$0.55 million_ _equivalent)_ 37. The loan beneficiary firms will be expected to participate in management capacity-building activities, provided by the TKYB and financed by the loan component of the project. This component will finance training for management practices; socioemotional skills (such as leadership, teamwork, and client orientation); and financial literacy.\n\n_Subcomponent 3.B: Skills building for grant beneficiary firms (€1.9 million, US$2.1 million_ _equivalent)_ 38. In addition to submitting their business plans in the request for financing, grant beneficiary firms will be expected to commit to participate in capacity-building activities, provided by the TKYB and financed by the grant component of the project. This component will finance training for employees to build the skills identified in the diagnosis as constraining firms’ capacity to increase formal job creation, including technical and analytical skills (for example, software and information technology [IT] knowledge and data analytics).\n\n_Subcomponent 3.C: Capacity building for loan implementation (€0.1 million, US$0.2 million_ _equivalent)_ 39. This subcomponent will finance consultancy services under/by the Project Implementation Unit (PIU) and training activities for the TKYB and PFIs. This subcomponent will be financed by the loan.\n\n_Subcomponent 3.D: Capacity building for grant implementation (€4 million, US$4.4 million_ _equivalent)_ 40. This subcomponent will finance grant governance body, consultancy services under/by the PIU, goods, non-consulting services (including travel), communication, outreach and visibility activities, and operational expenses to implement the grant component. This component will be financed by the grant.\n\nPage 18 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Table 1. Project Costs (€)**\n\n**All years** **Year 1** **Year 2** **Year 3** **Year 4**\n\n**Costs** **Total Cost** **Total Cost** **Total Cost** **Total Cost** **Total Cost**\n**Component 1** **314,560,000** **125,824,000** **141,552,000** **31,456,000** **15,728,000**\n1.1. Loans to SMEs 188,736,000 75,494,400 84,931,200 18,873,600 9,436,800\n1.2. Loans to LEs 125,824,000 50,329,600 56,620,800 12,582,400 6,291,200\n**Component 2** **70,000,000** **17,500,000** **49,000,000** **3,500,000** **0**\n2.1. Grants to SEs 14,000,000 3,500,000 9,800,000 700,000 0 2.1. Grants to MEs 22,400,000 5,600,000 15,680,000 1,120,000 0 2.2. Grants to LEs 33,600,000 8,400,000 23,520,000 1,680,000 0\n**Component 3** **6,611,538** **2,364,000** **3,649,500** **427,038** **171,000**\n3.A. Skills building for loan beneficiaries 503,000 251,500 251,500 0 0 3.B. Skills building for grant beneficiaries 1,937,000 968,500 968,500 0 0 3.C. Capacity Building for loan implementation 147,000 80,500 45,500 21,000 0 3.C.1. Individual Consultants (Loan) 52,500 10,500 21,000 21,000 0 3.C.2 Training Programs (training for loan officers - Loan) 94,500 70,000 24,500 0 0 3.D. Capacity Building for grant implementation 4,024,538 1,063,500 2,384,000 406,038 171,000 3.D.1. Individual Consultants (Grant) 587,500 88,500 189,000 169,000 141,000 3.D.2. Goods, and non-consulting services (including travel, Grant) 215,000 75,000 75,000 35,000 30,000 3.D.3. Communication and Visibility (Grant) 422,038 200,000 160,000 62,038 0 3.D.5. Operating Expenses (TKYB+PFI, Grant) 2,800,000 700,000 1,960,000 140,000 0\n**Project Budget** **391,171,538** **145,688,000** **194,201,500** **35,383,038** **15,899,000**\nFront end Fee (Loan) 790,000 Direct operational cost of the Bank (Grant) 1,000,000 450,000 200,000 175,000 175,000 Indirect Costs and Remuneration Costs (Grant) 3,038,462 3,038,462 0 0 0\n**Total (Loan)** _316,000,000_ _126,156,000_ _141,849,000_ _31,477,000_ _15,728,000_\n**Total (Grant)** _80,000,000_ _23,020,462_ _52,552,500_ _4,081,038_ _346,000_\n**Grand Total** **395,210,000** **149,176,462** **194,401,500** **35,558,038** **16,074,000**\n\n_Note:_ The World Bank-executed activities and the World Bank’s fees are presented below the project’s budget total line.\n\n**C. Project Beneficiaries**\n\n41. **Firms eligible for loan or grant financing will be the direct beneficiaries of the project.** The planned action will also benefit three groups of stakeholders indirectly: (a) work-able trained refugees and Turkish citizens and their families who will enjoy an improvement in their living standards, (b) financial institutions (TKYB and PFIs), and (c) their loan officers; and the local economy through the increase of formalization and new opportunities for economic interaction.\n\n_Direct Beneficiaries_ 42. **Private enterprises in selected provinces.** An estimated number of 859 (88 loan beneficiaries and 771 grant beneficiaries) [24] enterprises operating in project provinces will be direct beneficiaries of the program. The TKYB and financial sector intermediaries will be identifying, through broad outreach activities, eligible borrowers and grant recipients in line with financial soundness conditions and willingness to abide by the conditionality criteria. These enterprises will benefit by accessing resources that will allow them to realize their long-term investment plans and obtain working capital. Financial resources (grants and loans) will enable firms to undertake new investments, expand production, and improve their business performance and ultimately their productivity. The grant will support additional investments of eligible firms and will incentivize job creation and reward compliance for long-term retention of workers.\n\n24 This reflects the minimum numbers with the assumption that every beneficiary will get the upper limit of the loans and grants.\n\nPage 19 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) _Indirect Beneficiaries_ 43. **Refugees and ESSN beneficiaries.** Refugees residing in the 24 target provinces and those workable refugees who exit from the ESSN, willing and able to find formal employment could directly benefit from the program. Refugees acknowledge that finding better employment opportunities is of utmost importance for them. The increase in demand for refugee labor could incentivize refugees to seek skills training and subsequently seek formal employment opportunities.\n\n44. **Turkish citizens.** Turkish citizens, currently unemployed or entering the labor market and seeking work, would have formal employment opportunities. Because of the conditionality to be imposed on firms accessing grant financing, employment will be formal in nature. Such jobs are likely to directly improve the livelihood of beneficiaries and their families. The increase in demand for labor could incentivize people to obtain industry-relevant skills to fulfil the employment requirements of beneficiary enterprises.\n\n45. **TKYB and PFIs (intermediaries).** The proposed project will also directly support the TKYB and various intermediaries through technical assistance and training of loan officers. For instance, loan officers will be sensitized to service broader client markets (foreign firms). They will also be trained to assess the viability of employment plans and on how to guide applicant enterprises to find suitable workers from institutions such as İŞKUR and others. The TKYB will be supported to draft and sign a memorandum of understanding with the Ministry of Family, Labor, and Social Services to work directly with the Social Security Institution (SGK) and with İŞKUR for distinct functions that relate to the conditionality. The staff involved in project implementation will also be trained on the enforcement of the conditionality, including report drafting, the verification and validation process, sanctions process, and data exchange with SGK.\nThese capacities will be kept by the PIU staff and the mechanism and learning can be used for future projects with similar objectives.\n\n46. **The local economy, including the refugee community, will also enjoy the injection of economic**\n**resources that a large influx of available financing (both grant and loan) makes possible.** All the\ninstitutions involved will benefit from capacity training and improvements in their overall services to clients through the provision of more integrated services.\n\n**D. Results Chain**\n\n47. **The proposed project aims at enhancing the conditions conducive to greater formal job creation**\n**and long-term employability of refugees and Turkish citizens.** The challenges the project aims to deal\nwith are as follows: (a) low access to finance to create formal jobs, (b) high rates of informality and unemployment, and (c) low skilled labor force. Figure 1 describes the expected theory of change, from access to inputs (finance and skilled labor) to the utilization of those inputs to improve firms’ capacity to expand business, create job opportunities, and improve workplace conditions, thereby contributing to the longer-term intended impact on formal job creation. Other outcomes will be higher integration of workable refugees to the workforce and increased skills of the workforce.\n\nPage 20 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Figure 1. Theory of Change**\n\n**E. Rationale for World Bank Involvement and the Role of Partners**\n\n48. **The project will leverage the World Bank’s experience with line of credit interventions in Turkey**\n**with a strong inclusion focus.** The project design builds on recent lines of credit operations supporting\nTurkish exporters, SMEs, and energy efficiency investments, all rated Highly Satisfactory or Satisfactory by the World Bank Independent Evaluation Group. The TKYB has experience working with the World Bank and is willing to explore opportunities to expand formal job creation through its existing and future client base and draw lessons for future activities.\n\n49. **Improved access to finance can lead to significant positive effects on employment growth**\n**among private and formal firms in Turkey.** The empirical evidence shows that firms with greater access\nto finance markets, are more likely to experience higher employment growth. Access to finance constraint has a significant negative effect on employment growth for small firms (5–49 employees) and large firms (250+ employees). The effect is stronger for the large firms (see annex 4). When considering specific forms of finance interventions, lines of credit targeted to SMEs show comparatively greater potential to spur employment growth. Out of 15 studies investigating the impact of lines of credit (see annex 5), 10 had a specific focus on job creation. All 10 studies reported that lines of credit had positive and significant effects on employment generation. For instance, a World Bank-financed onlending program targeted at SMEs resulted in the creation of 7,000 jobs and the preservation of 35,000 jobs among beneficiary firms in Turkey. Also, compared with nonbeneficiary firms, beneficiary firms created more jobs. Employment effects persisted after three years from disbursement. [25] 25 World Bank. 2016. _Second Turkey Access to Finance for Small and Medium Enterprises Project. Implementation Completion_ _Report Review_ ICRR 14918. Washington, DC: World Bank.\n\nPage 21 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) 50. **Since early 2018, the World Bank team, in collaboration with the Ministry of Family, Labor, and**\n**Social Services, İŞKUR, and the Small and Medium Enterprises Development Organization, has designed**\n**and implemented an EU FRIT Employment Support and Small Grants Project for Refugees and Turkish**\n**Citizens.** The project aims at improving entrepreneurship and employment outcomes for refugees and\nTurkish Citizens in select localities which have been affected by the refugee influx. These activities have provided insight to the design and will provide insight into the implementation of this project. Another EU FRIT Project (Strengthening Economic Opportunities for refugees and Turkish Citizens in Selected Localities) will also provide lessons on the occupations and demanded skills in provinces with high incidence of refugees.\n\n51. **The knowledge the World Bank gained through these programs will be used for the planned**\n**action.** Moreover, the proposed action will also leverage previous experience from other EU- and World\nBank-financed projects that aimed to improve the institutional capacity of the Government to implement conditional grant-loan programs and provide beneficiary enterprises more integrated services that encourage them to become more inclusive of foreign workers.\n\n52. **The proposed action is also complementary to other employment generating activities being**\n**submitted by the World Bank under FRIT II.** For instance, the proposed project with IŞKUR and Turkish\nRed Crescent aims to make refugees and Turkish workers more employable and readier to be placed into jobs. [26] This project will have a direct link to that project to ensure that enterprises accessing financing have a direct connection to IŞKUR to hire their trained beneficiaries for immediate placement. A memorandum of understanding will be signed between the TKYB and İŞKUR to ensure such a direct link is actualized on the ground. This project will also establish links with the World Bank’s ongoing assessment with Ministry of National Education (MoNE) to ensure that technical and vocational graduates trained have a smooth school to work transition into employment by providing graduates with more plentiful employment opportunities. [27] Still, with its unique design and innovative approach, the actions in this project proposal maximize complementary with and avoids duplication of existing programs.\n\n**F. Lessons Learned and Reflected in the Project Design**\n\n53. **The project will leverage the World Bank’s comparative advantage in offering competitively**\n**priced long-term funds and builds on and expands the successful experience with implementing line of**\n**credit interventions in Turkey.** The key lessons learned include the need for a: (a) capable borrower or\nimplementing agency; (b) simple and flexible design, allowing for easy operational adjustments if needed; (c) clear definition of target beneficiary enterprises, with minimum overlap between wholesale and direct lending components; (d) intensive monitoring of key indicators that measure the quality of the loan portfolio; (e) use of quantitative eligibility criteria for PFI selection; and (f) availability and use of sound analysis and data on the financial performance of PFIs, along with external audits for verification.\nFurthermore, the project seeks to enhance the development effectiveness of the World Bank’s financial intermediation operations in Turkey by targeting specific segments of the economy that experience particularly severe access to finance constraints. The innovative design also reflects lessons learned from 26 Support for Transition to Labor Market for People under Temporary and International Protection and Turkish Citizens Projec _t_ (P171471).\n27 Turkey's Quality Learning and Inclusion Roadmap (P172472).\n\nPage 22 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) other World Bank credit lines in other regions (for instance, Africa), as well as from recent credit lines by the International Finance Corporation and the EBRD in Turkey.\n\n54. **The project design will reflect the lessons from a series of recent line of credit and FRIT**\n**operations supporting Turkish exporters, SMEs, and energy efficiency investments,** including Export\nFinance Intermediation Loan (EFIL) I (P065188), EFIL II (P082801), EFIL III (P093568), SME I (P082822), EFIL IV (P096858), and Inclusive Access to Finance Project (P163225). Further, the project design will learn from both implementation and political economy challenges, especially related to the difficulties associated to the employability of refugees, from several FRIT I projects, such as Employment Support Project (P161670), Strengthening Economic Opportunities for Syrians under Temporary Protection and Turkish Citizens in Selected Localities Project (P165687), and Development of Businesses and Entrepreneurship for Syrians under Temporary Protection and Turkish Citizens Project (P168731).\n\n**III.** **IMPLEMENTATION ARRANGEMENTS**\n\n**A. Institutional and Implementation Arrangements**\n\n55. **The TKYB will be responsible for the implementation of the project and was selected based on**\n**its demonstrated strong capacity to design and implement complex, innovative projects** . The TKYB has\nexperience in implementing financial intermediation loans funded by the World Bank and other IFIs, including both direct lending and on‐lending through PFIs. The TKYB has undergone a demand assessment during project preparation, including on the willingness of the firms to fulfill the job creation objectives, that shows encouraging results. Additional criteria for selecting the TKYB include the bank’s financial soundness, quality of credit portfolio, and its performance as the Borrower in previous World Bank Projects. The proposed project is being processed under Bank Policy: Investment Project Financing paragraph 12, referring to projects in situations of urgent need of assistance or capacity constraints. The project design will be based on principles to be determined in the Project Operations Manuals (POMs) and derived from a mix of the TKYB’s current and planned implementation modalities. Annex 3 provides background information on the TKYB, and a summary evaluation of the TKYB against the World Bank’s standard criteria for financial intermediaries listed in OP/BP 10.\n\n56. **A PIU will be established under the TKYB, which will coordinate and facilitate relevant project**\n**activities and have fiduciary responsibility.** The PIU will be staffed with individual consultants with\nsafeguards, fiduciary, monitoring and evaluation (M&E) and technical experience, as well as the TKYB staff from different units. The PIU responsibilities will include (a) selecting and onlending to PFIs; (b) monitoring of PFIs to ensure compliance with project criteria including PFIs’ application of the TKYB’s Environmental and Social Management System (ESMS), where needed (projects with moderate risk will require application of the TKYB’s ESMS whereas subprojects with low risk can be conducted based on the environmental and social [E&S] risk assessment procedures of PFIs); (c) coordinating the TKYB’s direct lending to large firms eligible for loans; (d) coordinating extending grants to eligible firms and monitoring their compliance with grant agreements; (e) adhering to all fiduciary and safeguard requirements of the World Bank for final borrowers; (f) providing financial management arrangements for the project; (g) all procurement implementation under Component 3 and overseeing all beneficiary procurement under Component 2; and (h) ensuring M&E based on agreed results indicators.\n\nPage 23 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) 57. **PFIs will be selected by the TKYB based on their financial health.** PFIs will onlend to SMEs under the loan component, thereby increasing the reach of the TKYB to areas and sectors where it currently has no presence. The TKYB will take the credit risk of PFIs and therefore has a strong incentive to carefully assess their financial health and operational capabilities. The PFI selection is also subject to a ‘no objection’ process by the World Bank, while Subsidiary Finance Agreement covenants between the TKYB and PFIs require compliance with standard prudential regulations thereby ensuring the financial health of PFIs.\n\n58. **SGK** **will provide the official employment records to monitor compliance with job creation and**\n**retention.** To assess the compliance of grant-recipient firms with the formal employment creation and\nretention targets specified in the business plan at the moment of application, the PIU at the TKYB will receive regular employment and wage records for beneficiary firms from SGK at grant allocation, every six months thereafter, and periodically following requests for disbursements and claims of conditionality compliance by beneficiary firms.\n\n59. **İŞKUR will participate as public provider of skills and skills-building capacity for prospective**\n**employees of beneficiary firms.** The TKYB staff will guide beneficiary firms in the identification and\nrecruitment of job seekers. This will include the development of guidelines and materials to support loan officers in assisting beneficiary firms in recruiting personnel through various employability programs supported by the Government or the EU, such as İŞKUR, Red Crescent, and the Association for Solidarity with Asylum Seekers and Migrants. Formal links between the TKYB, and various employment or training institutions will be established to ensure that appropriate referrals lead to recruitment.\n\n60. **A FRIT Project Steering Committee will be established for the overall implementation under**\n**Component 2 of the project.** The TKYB will convene and ensure the appropriate functioning of the\nSteering Committee, consisting of relevant stakeholders (i.e. İŞKUR, SGK ), to carry out a semiannual review of implementation progress. The TKYB PIU will carry out the secretariat role of the Steering Committee.\n\n61. **Two POMs which detail the responsibilities of all stakeholders and institutions at the central**\n**and local levels for implementation of the project components will be developed and finalized before**\n**effectiveness.** The POMs will be shared with all stakeholders to ensure full understanding of project\ncontent and implementation.\n\n62. **The PIU will report implementation progress to the World Bank.** There will be semi-annual implementation reports presenting the progress and status of expenditures under each component separately; and quarterly reports presenting the progress with the key indicators in the Results Framework.\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n63. **The PIU will report on progress on the PDO and intermediate indicators (including core**\n**indicators for World Bank-wide monitoring and gender-related indicators) on a semiannual and annual**\n**basis.** The PIU will prepare semiannual project progress reports to be shared with the World Bank, by\ntaking into account different reporting requirements under the grant and loan components. The TKYB is accustomed to collecting such information from PFIs and beneficiary enterprises for previous World Bank Page 24 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) projects. A midterm and end line citizen engagement survey will be conducted by the TKYB to seek feedback from beneficiary firms on their satisfaction with the project. The PIU will discuss the survey results with PFIs and the results will inform project implementation, as appropriate. The financial performance of the TKYB will be monitored through independent auditors’ reports and separate management letters confirming adherence to prudential norms. Monitoring of core intermediate result indicators at the PFI level will enable the TKYB and the World Bank team to take action in case of a significant deviation for a specific PFI, which may affect the progress toward the PDO. Though, it is not included in the Results Framework, the PIU will also report the statistics on formal employment creation in the loan beneficiary firms.\n\n**C. Sustainability**\n\n64. **The project is expected to facilitate greater intermediation by the financial sector in the**\n**currently underserved market segments.** Although the World Bank loan amount is small relative to the\npotential demand for such loans in project provinces, the multiplication effect will be achieved through the TKYB’s channeling of any subfinance repayments to other eligible firms both through PFIs and directly for the same purpose. More broadly, the World Banks’s financing support under the project will go handin-hand with strengthening the capacity of the TKYB and other PFIs to serve the specific needs of beneficiary firms.\n\n65. **To avoid market distortions, the TKYB and PFIs will follow their respective pricing policy**\n**according to market conditions.** The cost of onlending subsidiary financing through PFIs will include, at a\nminimum, the cost of World Bank funds to the TKYB, plus an onlending margin reflecting the TKYB’s administrative costs, a credit risk margin (or risk markup) associated with the PFI, and fees to the Ministry of Treasury and Finance for the guarantee provision. Ultimate beneficiary costs will add, at a minimum, the PFI’s administrative costs and a credit risk margin (or risk markup) associated with the beneficiary enterprise. The only significant market advantage from the World Bank funds is in terms of maturity, facilitating the provision of long‐term finance to enterprises without taking on a significant maturity mismatch. TKYB with the support of the World Bank will also ensure that the grant allocation process is non-distortionary and transparent\n\n**IV.** **PROJECT APPRAISAL SUMMARY**\n\n**A. Technical, Economic, and Financial Analysis (if applicable)**\n\n66. **Economic estimates on the impact of the project components on formal job creation in the short**\n**and medium run show substantial employment effects across different types of firms.** Under the main\nscenario related to the costs associated to workers’ wages, it is estimated that the project will generate approximately 9,000 new formal jobs through the grant.\n\n67. **The parameters used to assess the potential job creation effects of the grant component are in**\n**line with labor market characteristics and employment composition by type of firms in project**\n**provinces.** Potential beneficiary firms are divided into three main groups depending on firm size, which is\na very strong predictor of job creation in Turkey: small firms (below 50 employees); medium firms (between 50 and 250 employees); and large firms: above 250 employees, which account for 30.4, 22.6, Page 25 of 86", "output": {"entities": {"named_data": [], "descriptive_data": ["statistics on formal employment creation"], "vague_data": ["citizen engagement survey", "core intermediate result indicators"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) and 47 percent of total employment in project provinces, respectively. The expected employment growth assumed for large firms is in line with the trend observed between 2010 and 2016: this has been discounted by a penalty factor to account for the fact that grant beneficiary firms might not necessarily be top performing firms, given their financial constraints. The expected growth rate in employment for SMEs reflects the expected effect of the grant on job creation: in fact, SMEs employment yearly growth rate between 2010 and 2016 has been negative, but the findings from the economic literature (see annex 5) suggest that SMEs tend to benefit more than proportionally compared to other type of firms from the alleviation of financial constraints. Another working assumption is that the distribution of the grant across firm types should be consistent with the distribution of employment. Given these conditions, the expected number of jobs to be created for the ‘average’ firm in each group are estimated, and then the total number of potentially beneficiary firms in each group is obtained. In summary, the table shows that around 9,000 jobs are expected to be created under the grant component, of which over 4,500 are in SMEs, by reaching out to approximately 771 beneficiary firms in project provinces (table 2A, scenario 1).\n\n**Table 2A. Parameters Choice for Estimating the Job Creation Effect of the Grant Component (Scenario 1)**\n\n**Firm size** **Expected**\n\n**average**\n**beneficiary**\n\n**firm size**\n\n**Expected**\n**jobs to be**\n**created by**\n\n**average**\n\n**firm**\n\n**Expected**\n**employment**\n\n**(%) increase**\n\n**for average**\n\n**firm**\n\n**Expected**\n**number of**\n**beneficiary**\n\n**firms**\n\n**Expected**\n\n**job**\n**creation**\n\n**(total)**\n\n**Expected**\n**Employment**\n\n**%**\n**composition**\n\n**Total**\n**grant**\n**amount**\n**(million**\n\n**euro)**\n\n**Expected**\n\n**average**\n**grant per**\n\n**firm**\n**(euro)**\n\n[below 50) 30 4 13.3 453 1,812 20 14 31,000\n\n[50-250) 150 14 9.3 204 2,856 32 22 108,000\n\n[250+) 300 38 12.7 114 4,332 48 34 295,000 Total 771 9,000 100 70 90,000\n\n**Table 2B. Parameters Choice for Estimating the Job Creation Effect of the Grant Component (Scenario 2)**\n\n**Firm size** **Expected**\n\n**average**\n**beneficiary**\n\n**firm size**\n\n**Expected**\n**jobs to be**\n**created by**\n\n**average**\n\n**firm**\n\n**Expected**\n**employment**\n\n**(%) increase**\n\n**for average**\n\n**firm**\n\n**Expected**\n**number of**\n**beneficiary**\n\n**firms**\n\n**Expected**\n\n**job**\n**creation**\n\n**(total)**\n\n**Expected**\n**Employment**\n\n**%**\n**composition**\n\n**Total**\n**grant**\n**amount**\n**(million**\n\n**euro)**\n\n**Expected**\n\n**average**\n**grant per**\n\n**firm**\n**(euro)**\n\n[below 50) 30 2 6.7 969 1,938 22 19 19,600\n\n[50-250) 150 10 6.7 367 3,667 41 22 60,000\n\n[250+) 300 24 8.0 141 3,395 38 29 205,000 Total 1477 9,000 100 70 47,000 Page 26 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Table 3. Comparison of parameters choice to assess the cost effectiveness of the grant under different scenarios.**\n\n**Firm size** **Scenario 1:**\n**Cost per job**\n\n**(euro)**\n\n**Scenario 2:**\n**Cost per job**\n\n**(euro)**\n\n**Scenario 1:**\n**Cost per job**\n\n**per month**\n\n**(euro)**\n\n**Scenario 2:**\n**Cost per job**\n\n**per month**\n\n**(euro)**\n\n**[1]** **[2]** **[3]** **[4]**\n\n[below 50) 7780 9803 432 545\n\n**[1]**\n\n**[2]**\n\n**[3]**\n\n[50-250) 7780 6000 432 333\n\n[250+) 7780 8542 432 475 Total 7780 8115 432 451 68. **Cost effectiveness analysis of the grant allocation across firms under different scenarios shows**\n**that the preferred parameters choice ensures more cost-effective results in terms of job creation.** We\ncompare the cost-effectiveness results of the grant allocation across firms in terms of jobs creation using the parameters described in the previous paragraph, with an alternative scenario in which the grant amount per firm is lower (table 2B, Scenario 2). A lower grant per firm allocation under Scenario 2, however, has several less desirable implications. First, it lowers the number of expected jobs created per firm. Second, grant beneficiary firms can find more challenging to meet the conditionality criteria of 18 months of workers retention, since they have less resources to ensure sustainability. Third, there are less resources per beneficiary firm to be used for capital investments, which limit the possibility to further enhance job creation exploiting the complementarities between capital and labor. Fourth, in order to achieve the same expected outcome of jobs created with smaller grants-per-firm, it will be necessary to outreach to a much larger number of firms (almost double), which will increase the transaction costs and the management burden on the project. Table 3 shows that under the preferred parameters (table 2A, scenario 1), the grant allocation is more cost effective than in the alternative scenario based on smaller grant amounts per firm (and particularly so for small and large firms), with an average difference of 335 euro per worker (column 1 vs. column 2 in table 3).\n\n**Table 4. Estimates of the IRR of the Grant Component at 10 Years since the Beginning of the Project**\n\n**IRR (internal rate of return) at 10 years (%)**\n\nDiscount rate = 4%; Firm survival rate: 100% Skill composition of new hires (%) New hires duration in employment (%)\n\n[1] [2] [3] low medium high 100% 80% 60% 75 20 5 17.8 10.5 2.7 low medium high 100% 80% 60% 55 30 15 21.7 13.8 5.5 Discount rate = 4%; Firm survival rate: 100% for first 5 years, 90% after 5 years Skill composition of new hires (%) New hires duration in employment (%) low medium high 100% 80% 60% 75 20 5 16.2 9 1.2 low medium high 100% 80% 60% 55 30 15 20.1 12.3 4 Page 27 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) 69. **Cost-benefits analysis of the economic returns of the grant components shows that the project**\n**is expected to yield positive rates of return at year 10 (starting to produce positive returns around year**\n**5-7 since the beginning), depending on the assumptions on the employment duration of new hired**\n**workers, the extent of firms’ survival, and skills composition of the newly hired labor force.** Costsbenefits analysis has been carried out to assess the economic returns associated to greater tax revenues\nfrom formal work, lower social assistance spending, and greater revenues from corporate income taxes, assuming also an expansion in profits spurred by the grant allocation (table 4). The results depend largely on the skills composition of the new hires, on the expected employment duration, and on beneficiary firm’s survival rates. Under the assumptions implied by the intermediate (and more realistic) scenario under column [2] in Table 4, we can expect substantial returns to the grant component, varying between 9 and 14 percent. These estimations will be regularly updated during implementation, to provide valuable information on the project’s internal rate of return as soon as real data become available.\n\n70. **The above estimates of both job creation and internal rate of return, should be considered as**\n**realistic but conservative estimates for the purpose of assessing the total job creation potential of the**\n**whole project.** In fact, the figures do not include the number of jobs potentially created under the loan\ncomponent. Further, the above estimates do not take into account yet possible multiplier effects, deriving by the linkages and the interaction between loan and grant beneficiary firms operating in the same markets. Finally, the effects of Component 3 of the project, might help making the project more sustainable, for instance by reducing the risk for workers in beneficiary firms of dropping out from formal employment, and by this token increasing the internal rate of return of the project as a whole.\n\n71. **Estimates of the potential employment generation capacity of loan beneficiary firms, show that**\n**the joint impact of both project components in terms of formal job creation can be much larger than**\n**the effect estimated under the grant component alone.** In fact, when estimating the potential number\nof jobs created by loan beneficiary firms throughout the duration of the project, and still under conservative assumptions on their annual employment growth rate, we obtain that up to 3400 jobs could be created under the loan component (table 5). Therefore, the total job creation impact of the project can be estimated in over 12400 jobs. Further, when considering spillovers effects generated by the linkages between loan and grant receiving firms (estimated in around 10 percent for both components), we obtain even higher figures, and we estimate that the project as a whole could lead to the creation of over 13600 formal jobs (table 6). These estimates yield to a cost-per job for the total project ranging between 16000 and 17700 euros (table 7). These results suggest that the project as whole can be significantly cost-effective, if we compare these figures with the average cost-per-job for the largest subsidy program for Small Businesses in the USA (between 12500 and 15000 USD, see Annex 5), and other comparable programs financed by the EU to stimulate job creation and business expansion form SMEs through enhanced access to finance and lending [28] 28 See Annex 5, the reference to Bertoni et al. (2019), and Asdrubali & Signore (2015), showing employment growth ranging between 14and 18 percent for loan beneficiary SME firms, respectively in Central Eastern European and Western European countries, under the EU-financed Multi-Annual Program for Enterprise & Entrepreneurship (MAP) for SME under EU SME Guarantee Facility (SMEG).\n\nPage 28 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Table 5. Estimates of job creation among loan beneficiary firms**\n\n**Loan beneficiary firm**\n**expected average size**\n\n**Expected yearly**\n\n**employment**\n\n**growth**\n\n**Expected Jobs**\n**created during**\n**the project per**\n\n**Expected**\n**number of**\n**beneficiary**\n\n**Expected Jobs**\n**created during**\n\n**the project**\n\n**firm** **firms** **(total)**\n\nDirect lending 300 3.5% 44 60 2655 Lending through PFI 150 3.0% 19 40 753 All firms 100 3408\n\n**firm**\n\n**firms**\n\n**Table 6. Estimates of total job creation among loan and grant beneficiary firms.**\n\nTotal Jobs created Loan beneficiary firms Grant beneficiary firms Total without \"linkages\" effects 3408 9000 12408 with \"linkages\" effects 3749 9900 13649\n\n**Table 7. Cost effectiveness estimates for the overall project.**\n\nCost per job Loan beneficiary firms Grant beneficiary firms Total without \"linkages\" effects 44008 7778 17730 with \"linkages\" effects 40007 7071 16118 _Multiplier Effects_ 72. **The effects of the implementation of sub-loans and subgrants will be measured by the World**\n**Bank both at the firm level and at the regional and sectoral level, including spillover effects.** Potential\nspillovers of the program will be measured through the project-run firm survey (including also firms that are not direct beneficiaries) and by leveraging the richness of the Enterprise Information System (the database of all Turkish firms available at the Ministry of Industry and Technology), including information on the business networks of loan beneficiary firms, and the type and number of subsidiary firms.\n\n73. **The selection of loan beneficiary firms will take into account potential links and spillover effects**\n**in terms of job creation between loan beneficiary firms and grant beneficiary firms.** The project will aim\nto strengthen the links between loan beneficiary firms and grant beneficiary firms and maximize the spillover effects that enhanced conditions for job creation for loan beneficiary firms can have on grant beneficiary firms. With this objective, the TKYB will take into account, among the criteria for the selection of loan beneficiary firms, the value chain generated by the business activities of loan applicants, the ecosystem in which they operate and their business network, and the number of their subsidiary or intermediary firms which potentially could be grant beneficiary firms. Further, successful grant beneficiary firms with high record of job creation, could “graduate” and potentially become loan beneficiary firms.\n\nPage 29 of 86", "output": {"entities": {"named_data": [], "descriptive_data": ["project-run firm survey", "database of all Turkish firms"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) _Methodology and Implementation Approach_ 74. **The implementation support strategy is developed considering the risks and mitigation**\n**measures related to the operation and targets the provision of flexible and efficient implementation**\n**support for the TKYB and other stakeholders.**\n\n_Methods of Implementation_ 75. **The project will offer enterprises an option of accessing grant versus loan financial resources to**\n**undertake investments in existing businesses.** This project combines resources from an EU grant and a\nWorld Bank loan to the TKYB, guaranteed by the Government of Turkey, to increase access to financial resources to enterprises located in provinces affected by a large influx of refugees to expand their production and/or service capacity and as a result increase the workforce needed. Enterprises will be able to access a ‘loan only’ option or a grant option. The project also includes a subcomponent to strengthen the implementation capacity of the staff and the institutional capacity of the TKYB and PFIs. The grant approval process will have a separate governing body to ensure transparency of implementation and enterprise (beneficiary) selection. The project will set up a compliance mechanism which will rely on a partnership between the TKYB and SGK. The project will include activities that focus on training the staff, as well as the PFIs and their staff, to apply and monitor project compliance and ensure the overall achievement of results.\n\n76. **The project will include an M&E function, to be performed by the World Bank, to ensure that**\n**the implementation process is on the right track and impact evaluations are carried out to document**\n**results.** Because the selection criteria for grant beneficiary firms will be based on a scoring system, in the\npresence of a sufficiently large number of applicants and eligible firms, it will be possible to identify the final pool of beneficiary firms by selecting randomly among eligible firms with identical scores and similar characteristics. This approach will allow to build comparable ‘treatment’ and ‘control’ groups and will enable the TKYB to assess the effectiveness of the intervention by monitoring the performance (in terms of employment creation) of beneficiaries versus non-beneficiary firms. Finally, the World Bank team will inform the EU about the list of identified grant beneficiary firms under its no-objection.\n\n_Organizational Structure_ 77. **The TKYB will implement the program within its existing organizational structure.** It will integrate the project in its systems and use its own staff for the overall management of the project.\nAlongside the PIU, the TKYB staff in various departments (loan evaluation, loan marketing, intelligence and financial analysis, and IT) will manage and implement the project as part of their regular routine. The staff assigned to work on the project are highly qualified and experienced. There is a clear separation of duties between the staff with respect to evaluation of applications, review of documents and approvals, and accounting and reporting. The TKYB will document work flows specific to the project in the POMs. To facilitate coordination, experts from technical departments can be assigned to the PIU. The TKYB will ensure that a full-time dedicated team of experts for the implementation of projects’ E&S impacts as well as stakeholder engagement activities and grievance management are assigned before project effectiveness.\n\nPage 30 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) _Role and Participation of the Stakeholders_ 78. **The main groups of stakeholders are—as explained in previous sections—refugees, Turkish**\n**citizens, private enterprises of all sizes, and the TKYB and its intermediaries.** The project assumes that\nall stakeholders will be supportive in project implementation. The broader development agenda in Turkey and the FRIT Program is supported by a wide range of partners, including United Nations agencies, the EBRD, KfW, _Gesellschaft für Internationale Zusammenarbeit_, _Agence Française de Développement_, and other national and international financial institutions. The project design will benefit from consultations with these partners. The project will also interact with other stakeholders like local authorities, nongovernmental organizations working in the field, and other civil society organizations. These stakeholders are expected to be supportive or at least neutral to the implementation. To improve the quality of communication, and collaboration, these stakeholders will be targeted during the outreach activities.\n\n79. **The project’s dual options, grant versus loan financing, aims at maximizing the impact regarding**\n**employment generation and outreach.** Placing an emphasis on SMEs and, to a certain extent, on LEs as\nfinal beneficiaries, the project aims to spur investments and generate good-quality employment by enhancing access to longer-term finance and better training opportunities for underserved segments that might not have secured loans with similar conditions in the absence of the project. Focus on beneficiaries of different sizes (SMEs and LEs) calls for a variety of intermediary institutions and models that complement each other. Therefore, the project’s dual option modality intends to facilitate the outreach to all targeted segments and achievement of the operation’s broader impact.\n\n**B. Fiduciary**\n\n**(i) Financial Management**\n\n80. **The PIU will be responsible for the financial management of all parts of the project.** They will maintain the two Designated Accounts that will be opened for the project (one for the loan and one for the grant). The PIU will ensure that accounting for the project is maintained in the TKYB’s own system and the semi-annual reports are generated automatically through the system. The internal controls for all components of the project will be detailed in the POMs and the acceptance of the POMs will be an effectiveness condition for the Loan and Grant. The PIU will prepare semi-annual interim unaudited financial reports for the project and ensure that the project financial statements are audited on an annual basis by auditors acceptable to the World Bank. The audit reports will be submitted to the World Bank within six months following the end of the year.\n\n**(ii) Procurement**\n\n81. **The World Bank Procurement Regulations for IPF Borrowers dated July 2016 revised in**\n**November 2017 and August 2018 (‘Procurement Regulations’) will apply to Components 2 and 3 of the**\n**proposed project.** The TKYB will assume full credit risk in onlending the financings to private sector\nborrowers under Component 1 of the project and therefore the Procurement Regulations do not apply to the procurement of goods, works, non-consulting services, and consulting services under Component 1 of the project (refer to paragraph 2.2 (b) of the Procurement Regulations). The World Bank's Guidelines on ‘Preventing and Combating Fraud and Corruption in Projects Financed by IBRD Loans and IDA Credits and Page 31 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) Grants’, dated October 15, 2006 and revised in January 2011 and as of July 1, 2016 (‘Anti-Corruption Guidelines’) will also apply to the proposed project. A General Procurement Notice will be published on the World Bank’s external website and United Nations Development Business online.\n\n82. **A Project Procurement Strategy for Development (PPSD) has been prepared by the TKYB as**\n**required by the Procurement Regulations to determine the optimum procurement approach to deliver**\n**the right procurement result under the proposed project.** The PPSD proposed that all procurements\nunder Component 3 of the project be conducted by the TKYB PIU. It is envisaged that the sizes of the procurements in this component are generally small and their risks are also low. The procurements under Component 2 of the project will be performed by the relevant beneficiaries in accordance with the wellestablished commercial practices of the private sector enterprises as stipulated in the POM and/or agreed Procurement Plans. The TKYB will oversee the procurements done by the beneficiaries.\n\n83. **The procurement capacity assessment concluded that the TKYB has adequate resources and**\n**capacity for implementing the credit line operations of the project through their current credit line PIU**\n**established under the ongoing Geothermal Development Project (P151739).** The PIU also developed\nexperience in the selection of consultants in accordance with the World Bank’s procurement procedures under the same project. However, some experienced staff left the PIU when the TKYB headquarters moved from Ankara to Istanbul in 2018. Hence, the TKYB/PIU will be supported with an external procurement specialist for the procurement of goods, technical services, and consultants’ services under Component 3 of the project.\n\n84. **More details on the findings of the procurement assessment, the proposed procurement**\n**supervision arrangements, risks, and relevant mitigation measures to address them are provided in**\n**annex 2.**\n\n~~**.**~~ **C. Legal Operational Policies** .\n\n**Triggered?**\n\nProjects on International Waterways OP 7.50 No Projects in Disputed Areas OP 7.60 No ~~.~~\n\n**D. Environmental and Social**\n\n85. **Project activities are not expected to have large-scale, significant, and/or irreversible E&S**\n**impacts.** On the contrary, the project’s aim to improve conditions for formal job creation and increase the\nmanagerial capacity of grant and loan beneficiary firms is expected to have positive social impacts.\nAlthough there are no restrictions on sectoral basis, except the exclusion list of the World Bank and TKYB, potential sectors that were surveyed during design phase and are likely to be supported under this project, are expected to be manufacturing, motor vehicle repair and maintenance, food processing, education, construction, and real estate. Subprojects of substantial and/or high risk will not be included in project financing; however, due to the complexity and unknown subprojects, there may be risks associated with generation of hazardous wastes from the use, storage, or transportation of hazardous materials. Also, there may be labor risks related to working at heights/confined spaces in these sectors and inherent social cohesion risks stemming from competition in job opportunities within the refugee context. Subprojects Page 32 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) that are likely to have significant impacts on sensitive areas (for example, nationally and internationally protected areas, cultural values, and so on) or ones that may require land acquisition will not be eligible for financing.\n\n86. **It is anticipated that subprojects to be supported under Components 1 and 2 will be mainly**\n**activities of beneficiary firms, not involving large-scale construction works** . The anticipated\nconstruction-related impacts might include habitat disturbance, air and noise emissions, community health and safety (including traffic management-related risks and gender-based violence risks) and occupational health and safety risks, and so on. The operational phase impacts will depend on the investment sector of the beneficiary and will be determined after the subprojects are defined during project implementation. Workplace adaptation trainings incorporated into the project design will reinforce social cohesion between Turkish citizens and refugees. In informal employment circumstances, there is a risk for Syrian refugees to be engaged in informal, low wage work and child labor. These labor risks will be mitigated by formal employment creation, which will be verified by the SGK, requiring SMEs and LEs to implement age verification procedures, and by the requirements to all interested SMEs/LEs to ensure that their business plans are in compliance with national labor and occupational health and safety laws. The TKYB and PFIs will screen SMEs/LEs on a range of labor issues and will have the supervision role through their ESMS to ensure that the business plans are implemented in line with national laws and relevant requirements of Environmental and Social Standards (ESS)2.\n\n87. **The final corporate ESMS of the TKYB was adopted by the TKYB Executive Board on January 17,**\n**2020, and its Environmental and Social Policy together with Exclusion List and summary of ES**\n**procedures were disclosed on TKYB’s website** **[29]** **on February 12 and 28, 2020, respectively.** The World\nBank reviewed the TKYB’s ESMS and concluded that it contains all necessary elements as required by respective Environmental and Social Framework (ESF) and ESSs, and thus, can be used for the assessment and management of E&S risks of subprojects. The ESMS provides screening of all subproject applications and assigning risk ratings and identification and preparation of appropriate site-specific environmental and social assessment (ESA) instruments, such as Environmental and Social Management Plans, to address site-specific impacts as well as impacts of associated facilities (if any). Site-specific ESAs will duly incorporate and comply with the provisions of national legislation, World Bank Group Environmental Health and Safety General and Industry Specific Guidelines, (and Good International Industry Practices as well as relevant sectoral guidelines will be used. The capacity of the TKYB’s PIU will be enhanced by assigning/hiring additional full-time staff to support implementation and monitoring of project activities, as defined in the respective E&S documents, and to ensure compliance with the World Bank’s ESF. The World Bank will conduct ESF training and capacity building for the financial intermediary (TKYB) before implementation. The World Bank will monitor the financial intermediary and the financial intermediary will monitor the PFIs to successfully screen subprojects, and the World Bank will conduct prior review for an initial set of subprojects of the TKYB and from then after conducting post-review of selected subprojects, as part of regular project supervision. Potential risks and impacts related to labor and working conditions are expected to be managed through the project design and the TKYB’s ESMS. The TKYB has prepared a labor management procedure (LMP), which will apply to direct and contracted workers. The LMP also includes a screening questionnaire of loan and grant recipients, on labor and working conditions, 29 Environment and Social Policy of the TKYB, accessed on February 6, 2020, at https://english.kalkinma.com.tr/environmentaland-social-policy.aspx.\n\nPage 33 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) and age verification procedures. The TKYB will also apply a corporate-level Human Resource Policy (dated April 2019), which is in line with national regulations and ESS2 requirements.\n\n88. **In line with ESS9, the TKYB will apply relevant requirements of ESS2 to subprojects and SME and**\n**LE loan and grant recipients.** Grants will be provided conditional on the commitment of firms to formally\nemploy Turkish citizens and refugees in project provinces and sustain the newly created jobs throughout the duration of the grant. The jobs will be formal and will providing decent working conditions consistent with ESS2 requirements, social security, and other legally mandated benefits. SMEs and LEs will adhere to the labor code provisions. These requirements will be included in the legal agreements between the TKYB/PFIs and loan and grant beneficiary firms. To verify formality and duration of employment, the TKYB will receive regular employment and wage records for beneficiary firms from SGK.\n\n89. **The TKYB has prepared, disclosed, and consulted its Stakeholder Engagement Plan (SEP) where**\n**direct stakeholders include potential SMEs and LEs, unemployed work-able Syrian refugees and Turkish**\n**citizens in targeted provinces, and PFIs.** The SEP has also identified indirect stakeholders (project-affected\nparties and other interested parties) including IŞKUR, SGK, local nongovernmental organizations/civil society organizations, community leaders, and local government representatives residing or working in the project areas.\n\n90. **The TKYB will establish a good communications strategy to be implemented throughout the**\n**lifetime of the project.** In disseminating project-related information to potential SME and LE beneficiaries,\nthe TKYB and PFIs will employ experienced loan officers to engage with the direct target group (potential beneficiary firms). In addition, both the TKYB and PFIs will hire individual consultants who will be able to use various engagement tools specified in the project’s SEP. According to ESS10, public consultation meetings will be required for both Turkish citizens and refugees to introduce project information and E&S documents, once the subprojects are known. To mitigate potential risks and address received grievances, the TKYB and PFIs will establish a project-specific grievance redress mechanism (GRM) to address inquiries or concerns in line with project SEP. The project-specific GRM will also benefit from the national-level GRM through Cumhurbaşkanlığı İletişim Merkezi (CIMER) and Yabancılar İletişim Merkezi (YİMER) as defined in the SEP.\n\n**E. Citizen Engagement, Gender, and Climate Co-Benefits**\n\n91. **Citizen engagement activities will be carried out to involve project beneficiaries in two-way**\n**engagement processes.** The project will develop and adopt a set of participatory tools for engaging\nbeneficiaries, tailored to suit firms and employees of beneficiary firms. The project will obtain feedback from (a) loan beneficiary firms (SMEs and LEs) under the loan component, (b) grant beneficiary firms (SMEs and LEs) under the grant component, and (c) employees (refugees and Turkish citizens) of beneficiary firms under the grant component.\n\n(a) The loan component of the project will adopt a set of tools to obtain feedback continuously through the project. Building on the TKYB survey of 600 firms conducted during preparation, beneficiary firms will be involved in the following sequence of activities: (i) **Participatory needs assessments and decision making over training needs and the**\n**design of training programs.** The TKYB, in collaboration with PFIs, will conduct\n\nPage 34 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) participatory needs assessments with loan beneficiary firms to establish their trainings needs at the application stage.\n\n(ii) **Post-training assessments.** The TKYB will conduct a post-training assessment to measure the satisfaction and impact of the trainings, with the results being used by training providers to revise and improve the process in subsequent assessments.\n\n(iii) **Satisfaction surveys.** The TKYB, in collaboration with PFIs, will conduct satisfaction surveys in the midterm and end term with the loan beneficiary firms regarding the subfinance received in terms of their needs. [30] (iv) **Biannual beneficiary workshops, roundtables, and focus group discussions** (targeting different beneficiaries: employers, civil society and end beneficiaries) will be held to discuss the survey results with a view to developing measures that improve the project design (such as the selection criteria of loans beneficiary firms, loan utilization, and choice of training activities). This activity will draw from the World Bank team’s recent experience in carrying out validation workshops in the context of the FRIT I Strengthening Economic Opportunities for Syrians under Temporary Protection and Turkish Citizens in Selected Localities Project (P165687). To ensure that the feedback of beneficiaries is effectively used, the project will establish a review committee tasked with consideration of the findings of each tool and its integration in subsequent activities.\n\n(b) The grant component will engage with the target beneficiary firms, Turkish citizens, refugees and ESSN beneficiaries through the following activities: (i) **Consultations.** Meetings will be held in four project provinces during the calls for grant applications to provide an open platform for interested firms, and other stakeholders to raise questions/comments regarding the project design (for example, selection criteria for beneficiary firms, grant utilization, strictness of employment conditionalities, and impact on Turkish citizens and refugee communities).\n\n(ii) **Participatory needs assessments, post-training assessments, and satisfaction surveys** will also be conducted by the TKYB as described above, and the TKYB will use the same review committee to ensure that the findings and feedback from grant beneficiaries are adopted in subsequent years.\n\n(c) In addition, a GRM will be established and managed by the TKYB PIU as a channel for beneficiary firms and workers to register their complaints. The GRM, which will build on the lessons of the FRIT I Project, will be advertised in the TKYB’s website and will ensure that the beneficiaries receive a response within a stipulated time period.\n\n(d) The project will measure whether the subfinance, training, and employment processes meet the needs of the beneficiaries. The results will be gender disaggregated, presented, and 30 The survey will not include satisfaction with the decisions of the TKYB and PFIs related to the size, terms, and conditions of the loan that need to be market based.\n\nPage 35 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["satisfaction surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) discussed by the review committee to bring about improvement in the targeting and effectiveness of project activities.\n\n92. **The project mainly addresses three specific gender gaps.** First, despite the rising levels of educational attainment among women in Turkey, high rates of low-skilled, informal, unpaid family workers prevail among women. The female labor force participation rate for people ages 15–64 is significantly lower than the OECD average in 2018 (64.6 percent for OECD; 38.3 percent for Turkey).\nSecond, the lack of skills is one of the main obstacles to low-skilled women’s participation in the labor market in Turkey. Evidence shows that training programs can be effective in boosting female employment and earnings. Job and business training programs can fill the skill gap for people who are out of the education system. Third, according to the gender analysis of the United Nations, only 15 percent of refugee women are engaged in income-generating jobs. [31] The same assessment indicated that only a small number of women (7 percent) have taken part in vocational training, and, when they do take part, the most popular areas of study are hairdressing (30 percent) and needlework (27 percent), which are closely related to traditional gender roles and provide limited opportunity for formal employment.\n\n93. **The project proposes several actions to improve gender outcomes.** First, the project will encourage and prioritize both refugee and Turkish women to participate in the labor force. Second, the project will specifically encourage adequate women’s representation at the participatory activities implemented under the project components. Also, the project will ensure that women have equal opportunities in accessing trainings to increase their skills. These actions will be measured through the following indicators: (a) number of formal jobs created for women and (b) number of women-inclusive firms receiving grants.\n\n94. **The potential environmental impacts of the project are anticipated to be predictable and**\n**expected to be temporary and/or reversible, low in magnitude. and site specific.** However, there are no\nrestrictions on sectoral basis, except the exclusion list of the World Bank and TKYB, most of the potential subprojects are expected to be in manufacturing, motor vehicle repair and maintenance, food processing, and construction, which might be associated with the generation of hazardous wastes from the use, storage, or transportation of hazardous materials. Therefore, while the high and substantial risk subprojects will be screened out, given the scale of the overall project and the potential need to handle hazardous materials and address the risk of the generation of hazardous wastes, the proposed environmental risk rating for the project is determined as Substantial.\n\n95. **Climate change is further exacerbating Turkey’s already observed vulnerabilities and**\n**environmental risks.** While the country has no detailed climate change assessments that reflect\nconditions at the municipal level, it faces the risk of climate-related impacts resulting from the interaction of climate-related hazards with the vulnerability and exposure of human and natural systems and their ability to adapt. The project intends to mitigate climate events by promoting national preparedness and capacity to avoid the adverse impacts of climate change identified in Turkey’s Climate Change Action Plan (2011–2023). Specifically, the project will: 31 United Nations Women and the Association for Solidarity with Asylum Seekers and Migrants. 2018. _Needs Assessment of Syrian_ _Women and Girls under Temporary Protection Status in Turkey_ . Ankara: UN Women.\n\nPage 36 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n- Under Components 2 and 3, prioritize financing in sectors (such as energy and water) that\ncan have the most impact in mitigating the impact of climate change on beneficiaries;\n\n- Ensure that those types of projects, when selected, are designed and informed by climate\nchange best practices; and\n\n- Under Component 3, provide trainings for loan officers that will include information on best\npractices to mitigate climate change and sensitize beneficiaries on the need for incorporating them into projects and lifestyles. Such topics could include energy efficiency, sustainable procurement, and recycling, among other things.\n\n**V.** **GRIEVANCE REDRESS SERVICES**\n\n96. Communities and individuals who believe that they are adversely affected by a World Bank supported project may submit complaints to existing project-level grievance redress mechanisms or the World Bank’s Grievance Redress Service (GRS). The GRS ensures that complaints received are promptly reviewed in order to address project-related concerns. Project affected communities and individuals may submit their complaint to the World Bank’s independent Inspection Panel which determines whether harm occurred, or could occur, as a result of World Bank non-compliance with its policies and procedures.\nComplaints may be submitted at any time after concerns have been brought directly to the World Bank's attention, and Bank Management has been given an opportunity to respond. For information on how to submit complaints to the World Bank’s corporate Grievance Redress Service (GRS), please visit\n[http://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service.](http://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service) For information on how to submit complaints to the World Bank Inspection Panel, please visit\n[www.inspectionpanel.org.](http://www.inspectionpanel.org/)\n\n**VI.** **KEY RISKS**\n\n97. The overall risk of the operation is **Substantial** based on the aggregated risk ratings in the categories discussed below.\n\n98. **Macroeconomic risk is rated as Substantial.** The Turkish economy experienced instability between 2018-2019, following a period of growing macro imbalances and economic overheating in 20172018. The economy has stabilized more recently thanks to important external adjustments: a reversal in current account imbalances, declining external debt of banks, and a gradual recovery in forex reserves.\nThese have contributed to currency stability. There are a priori three possible macroeconomic risks to the project. The first is currency risk for municipalities. Though the Lira has been more stable recently, uncertainty in global markets, including from the recent Coronavirus outbreak pose risks for all emerging markets. Some of the risk could be offset by trade diversion from China that benefits Turkey. The second relates to the authorities’ fiscal consolidation path, which assumes a sharp decrease in capital expenditure. This may or may not affect public investment plans under the project as some of this risk may be mitigated by external project financing. Thirdly, the construction sector is among the most severely hit in the current downturn – leverage and exposure to forex debt will affect construction companies’ ability to respond to an increase in specialized construction investment under the project.\n\nPage 37 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) Some of this risk may be mitigated by the availability of spare capacity in the construction sector, that may allow construction companies to respond quickly.\n\n99. **The technical design of the project is rated Substantial.** While the TKYB has implemented several projects funded by the World Bank in the past, based on a similar design, the complexities inherent in working in regions under a crisis context, the blended financing, and the monitoring and enforcement of conditionalities pose additional challenges. The new mandate of the TKYB (as defined in the amended establishment Law 7147, dated October 11, 2018) underlines the importance of providing support to reduce regional development differences and promote economic and social development in the regions affected by domestic and regional migration. The implementation of the proposed operation will be managed by the Loan Department of the TKYB, in close collaboration with other relevant TKYB departments. The TKYB has strong business relations with the potential financial intermediaries. The project relies on the TKYB’s existing organization structure, which is critical for its sustainability. To complement this structure, a governance body for the allocation of grants will be established. The monitoring and enforcement of the compliance with the conditionalities will rely on official records provided by SGK and a formal coordination agreement will be signed between the TKYB, SGK, and intermediaries. The World Bank team will closely monitor the compliance mechanisms to mitigate the risks and implement timely corrective action as needed.\n\n100. **Institutional capacity for implementation and sustainability is rated Substantial.** While the institutional capacity risk is rated Moderate the risk to sustainability is rated Substantial. The TKYB has significant experience in managing similar interventions; however, the mixed approach and the context of the targeted areas require substantial technical, financial, managerial, safeguards, and fiduciary capacity to fully carry out its mandate under the project. Reaching SMEs for the grant component would present an additional challenge, given the TKYB’s limited presence in the target areas. The enforcement of the conditionality for the grant component and the delivery of the core training program requires significant efforts. The enforcement of the PIU with experts in these areas and employing additional fulltime human resources for the implementation and monitoring of E&S issues, including stakeholder engagement and GRM, should help mitigate this risk. Regarding sustainability, training programs for employees of beneficiary firms should help in their increased marketability and the participation on core training by management and employees should contribute to increase their productivity in the long term.\n\n101. **Environmental and social risks are rated Substantial** . The structure of the project is based on providing loans and grants to SMEs and LEs for working capital and investment purposes through the TKYB and selected PFIs. While the project is expected to have positive outcomes on improving access to employment opportunities for refugees and Turkish citizens, there are contextual risks related to the inherent social risks associated with the potential competition for job opportunities among the Syrian and Turkish citizens. The project design included measures to improve social cohesion both through training activities and capacity-building activities for beneficiary firms, employees, and the TKYB staff under Component 3 in addition to the project’s SEP. Although there are no restrictions on sectoral basis, except Page 38 of 86", "output": {"entities": {"named_data": [], "descriptive_data": ["official records provided by SGK"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) the exclusion list of the World Bank [32] and the TKYB, potential subprojects are expected to be in sectors like manufacturing, motor vehicle repair and maintenance, food processing, and construction. While the high and substantial risk subprojects will be screened out, given the scale of the overall project and the potential need to handle hazardous materials and address the risk of the generation of hazardous wastes, moderate labor risks related to working in heights and confined spaces, working with hazardous wastes and chemicals, and so on, the proposed E&S risk rating for the project is determined as Substantial.\nFurthermore, because the grant operations related to a refugee context is not a usual business and new for the TKYB, additional capacity building for the TKYB and employers will be required to ensure that the grants are used in line with its objectives. Subprojects that are likely to have significant impacts on the sensitive areas (for example, nationally and internationally protected areas, cultural values, and so on) and that will require involuntary resettlement and land acquisition will not be eligible for financing. The project will support women-inclusive enterprises. Potential risks and impacts related to labor and working conditions will be managed with measures included in the project design through workplace adaptation trainings and capacity-building activities under Component 3, periodic labor audits, and in the TKYB’s ESMS and LMP. Additionally, workplace-related issues and grievances (including sexual harassment and abuse) will be handled through project’s GRM and a worker’s GRM as defined in the SEP and LMP, respectively. The TKYB will screen SME and LE recipients of loans and grants to ensure consistency with ESS2 and adherence with labor and occupational health and safety laws.\n\n102. **Other risks rated Substantial** **pertain to the** **time frame for loan and the grant approval and**\n**associated financing gap.** The signing of the Administrative Agreement between the World Bank and the\nEU for the grant is subject to finalization of an amendment to the broader EU Framework Agreement with the World Bank, which is under discussion by the parties. As such, the grant financing is reflected as a financing gap in the Project Appraisal Document, even though the EC approved grant financing for the entire program to be administered by the World Bank under the second tranche of the FRIT and authorized the team to negotiate the details for the project design with the EU. To mitigate the risk of a potential delay in finalizing the Framework Agreement amendment, thereby precluding timely signing of the Administrative Agreement, negotiations for the IBRD loan will proceed with Technical Discussions for the Grant. The Technical Discussions would involve a full discussion and agreement between TKYB and the World Bank on the terms and conditions of the Grant Agreement. Conclusion of the negotiations, based on the Technical Discussions for the Grant, will be done after signing of the Administrative Agreement. Once the Administrative Agreement has been signed, a formal Grant Funding request will be processed for the project. TKYB has indicated its preference to sign the loan and grant agreements with 32 Ineligible subprojects: commercial activities involving habitats and products prohibited within the framework of the Convention on International Trade in Endangered Species of Wild Fauna and Flora; the release of genetically modified organisms into the wild; the production, distribution, or sale of pesticides that fall under the World Health Organization’s Recommended Classification of Pesticides by Hazard Classes 1a (extremely hazardous) and 1b (highly hazardous) or annexes A and B of the Stockholm Convention on Persistent Organic Pollutants or that are restricted by the government of Turkey, or herbicides; trawl fishing; radioactive products; hazardous waste storage, processing, and disposal; the production of equipment and materials containing chlorofluorocarbons or other substances regulated under the Montreal Protocol on Substances that Deplete the Ozone Layer; the manufacture of electrical equipment containing more than 0.005 percent polychlorinated biphenyls by weight; the manufacture of products containing asbestos; nuclear reactors and parts; processed or unprocessed tobacco and tobacco processing machinery; the significant conversion or degradation of critical natural habitats; significant damage to nonreplicable cultural property; involuntary land acquisition and any activity on land or affecting land over which the ownership, tenure, or user rights is disputed; any land-based activity that is considered dangerous because of security hazards or the presence of unexploded mines or bombs; weapons including (but not limited to), mines, guns, and ammunition; and any activity that supports drug crop production or the processing of such crops.\n\nPage 39 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) the World Bank at the same time. However, should there be a significant delay in signing the grant, the TKYB would be able to sign the loan agreement and access IBRD funds before accessing the grant.\nMoreover, activities within the project would be separately financed fully from either source under a parallel financed arrangement. The Procurement Plan has been structured accordingly, and it would allow for activities financed from the loan to proceed in the event of a delay in grant effectiveness, should this situation arise.\n\n.\n\nPage 40 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation for Refugees and Turkish Citizens (P171766)\n\n**VII.** **RESULTS FRAMEWORK AND MONITORING**\n\n**Results Framework**\n\n**COUNTRY: Turkey**\n**Formal Employment Creation Project**\n\n**Project Development Objectives(s)**\n\nThe project objective is to enhance the conditions for formal job creation by firms operating in provinces with high incidence of Syrians under Temporary Protection (SuTP), for the benefit of Turkish citizens and refugees.\n\n**Project Development Objective Indicators**\n\n**RESULT_FRAME_TBL_PDO**\n\n**Indicator Name** **DLI** **Baseline** **Intermediate Targets** **End Target**\n\n**1** **2** **3**\n\n**To increase access to finance in loan beneficiary firms**\n\nRatio of the average maturity of beneficiary firms sub-financing under the project, over the average maturity of borrower's portfolio not financed under the project (Text)\n\n1.00 >1 >1 >1 >1\n\n**To increase formal job creation in grant beneficiary firms**\n\nNumber of formal jobs created by Grants (disaggregated by gender) (Number) Number of formal jobs created in SMEs (disaggregated by gender) (Number) 0.00 3,000.00 6,000.00 9,000.00 9,000.00 0.00 1,800.00 3,600.00 4,500.00 4,500.00 Page 41 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**RESULT_FRAME_TBL_PDO**\n\n**Indicator Name** **DLI** **Baseline** **Intermediate Targets** **End Target**\n\n**1** **2** **3**\n\nNumber of formal jobs created 0.00 900.00 1,800.00 2,700.00 2,700.00 for women (Number)\n\n**To enhance skills in beneficiary firms**\n\nIncreased management skills in\n1.00 1.00 >1 >1 >1\nloan beneficiary firms (Text) Increased employee skills in grant\n1.00 1.00 >1 >1 >1\nbeneficiary firms (Text)\n\n**PDO Table SPACE**\n\n**Intermediate Results Indicators by Components**\n\n**RESULT_FRAME_TBL_IO**\n\n**Indicator Name** **DLI** **Baseline** **Intermediate Targets** **End Target**\n\n**1** **2** **3**\n\n**Loans targeting firms with high potential for job creation**\n\nCapital Stock of loan beneficiary\n1.00 >1 >1 >1 >1\nfirms (Text)\n\n**Grants targeting firms conditional on job creation**\n\nNumber of firms receiving grants 0.00 300.00 600.00 770.00 770.00 (Number) Number of women-inclusive firms 0.00 100.00 200.00 255.00 255.00 receiving grants (Number)\n\n**Strengthening capacity of beneficiary firms, TKYB, and PFIs**\n\nMonitoring and Evaluation system No Yes developed (TKYB and SGK) (Yes/No) Training provided to loan officers 0.00 10.00 20.00 25.00 25.00 (in TKYB and PFIs) (Number) Page 42 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**RESULT_FRAME_TBL_IO**\n\n**Indicator Name** **DLI** **Baseline** **Intermediate Targets** **End Target**\n\n**1** **2** **3**\n\nNumber of employers completing 0.00 0.00 60.00 90.00 120.00 training (Number) Number of employees completing 0.00 0.00 1,500.00 3,000.00 4,500.00 training (Number) Citizen engagement: Loan beneficiaries report that project sub-finance reflected their needs (Percentage) Citizen engagement: Grant beneficiaries report that project sub-finance reflected their needs (Percentage) Citizen engagement: Workers in grant beneficiary firms reporting that training and employment processes met their needs (disaggregated by gender) (Percentage)\n\n**IO Table SPACE**\n\n**UL Table SPACE**\n\n0.00 70.00 75.00 80.00 80.00 0.00 70.00 75.00 80.00 80.00 0.00 75.00 80.00 85.00 85.00\n\n**Monitoring & Evaluation Plan: PDO Indicators**\n\n**Methodology for Data** **Responsibility for Data**\n**Indicator Name** **Definition/Description** **Frequency** **Datasource**\n**Collection** **Collection**\n\nRatio of the average maturity of Project beneficiary firms sub-financing under the Annual Firm surveys TKYB and PFIs reports project, over the average maturity of borrower's portfolio not financed under\n\n**Methodology for Data**\n**Indicator Name** **Definition/Description** **Frequency** **Datasource**\n**Collection**\n\nAnnual Project reports Firm surveys TKYB and PFIs Page 43 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) the project Number of formal jobs created by Grants Annual (disaggregated by gender) Number of formal jobs created in Annual SMEs (disaggregated by gender) Number of formal jobs created for women Increased management skills in loan Annual beneficiary firms Increased employee skills in grant Annual beneficiary firms\n\n**ME PDO Table SPACE**\n\nProgress reports Progress reports Progress reports Progress reports Declaration of firms verified by SGK Firm declaration verified by SGK Skills measurement surveys Skills measurement surveys\n\n**Monitoring & Evaluation Plan: Intermediate Results Indicators**\n\n**Methodology for Data**\n**Indicator Name** **Definition/Description** **Frequency** **Datasource**\n**Collection**\n\nTKYB TKYB TKYB and PFIs TKYB\n\n**Responsibility for Data**\n**Collection**\n\nTKYB and PFIs TKYB TKYB Page 44 of 86 Firm survey TKYB administrative data TKYB administrative data Progress reports Progress reports Progress reports Capital Stock of loan beneficiary firms Capital-output ratio will be calculated per firm (and cellbased) Annual Annual Number of firms receiving grants Number of women-inclusive firms Annual receiving grants", "output": {"entities": {"named_data": [], "descriptive_data": ["TKYB administrative data"], "vague_data": ["Skills measurement surveys"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) Monitoring and Evaluation system One time developed (TKYB and SGK) Training provided to loan officers (in TKYB Annual and PFIs) Annual Number of employers completing training Annual Number of employees completing training Progress reports Progress reports Progress reports Progress reports Progress reports Progress reports Progress reports Agreement between TKYB and SGK Satisfaction surveys Satisfaction surveys Satisfaction surveys TKYB TKYB and PFIs TKYB TKYB TKYB TKYB TKYB Page 45 of 86 Citizen engagement: Loan beneficiaries report that project sub-finance reflected their needs Citizen engagement: Grant beneficiaries report that project sub-finance reflected their needs Citizen engagement: Workers in grant beneficiary firms reporting that training and employment processes met their needs (disaggregated by gender)\n\n**ME IO Table SPACE**\n\nMeasures feedback from firms - review committees tasked with utilizing feedback.\n\nMeasures feedback from firms - review committees tasked with utilizing feedback.\n\nMeasures feedback from end beneficiaries – review committees tasked with utilizing feedback.\n\nAnnual Annual Annual", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) Page 46 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**ANNEX 1: Implementation Arrangements and Support Plan**\n\n**Implementation Arrangements**\n\n1. The Government of Turkey, represented by the Ministry of Treasury and Finance, is the Guarantor\nof the loan.\n\n2. The TKYB is the implementing entity of the project. Implementation of the loan part of the project will also involve intermediaries (such as banks, leasing companies) where the TKYB will lend to larger enterprises directly and liaise with intermediaries in lending to SMEs. The grant-funded part of the project will be assigned centrally by the TKYB through the PIU.\n\n3. A PIU will be established at the TKYB. The PIU will be responsible for coordinating implementation of the project in the TKYB and coordinating with PFIs. The PIU will also have the fiduciary responsibility for the project. The PIU will be staffed with consultants (if required) and the TKYB’s technical staff from different units will be assigned to the project. The individual consultants will have expertise in the areas of procurement, financial management, and M&E and will also provide technical support under the project and ensure compliance with World Bank requirements for procurement, reporting, auditing, and monitoring.\n\n4. The PIU will have dedicated personnel who will be assigned with in depth knowledge of the TKYB`s procedures and preferably also those of the World Bank. Under the current Geothermal Development Project, the TKYB has staff assigned for the project with satisfactory experience and skills. The experience of these staff will be leveraged for implementation of this project. In addition to the TKYB’s staff from different units, the PIU will be supported by individual consultants possessing specialized skills explained in annex 2.\n\n5. The World Bank, as the Trust Fund administrator, and IBRD lender, will be responsible for monitoring the implementation of project activities and will ensure compliance with the former`s procedures and guidelines. World Bank’s fiduciary arrangements, including its procurement and FM procedures, as well as its Environmental and Social Framework will apply. Detailed fiduciary and safeguards arrangements will be agreed with the TKYB and will be reflected in the POM.\n\n**Financial Management**\n\n6. The financial management of the project will be fully integrated into the TKYB’s system which facilitates follow-up of loans extended to the companies from the initial application to the approval and monitoring stages. The system has adequate security levels. The system will be tailored to facilitate followup on PFI basis and firm basis.\n\n7. The TKYB will sign the grant and loan agreements with the beneficiary enterprises for which it is directly extending loans or grants. Beneficiary enterprises will provide supporting documentation for expenses incurred and the TKYB will control and ensure that the expenditures incurred comply with the business plans originally submitted and with the conditions of the loan/grant agreements. They will also reconcile the reported expenditures to the supporting documentation that will be submitted by the Page 47 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) beneficiary enterprises. For the loans extended through PFIs, this responsibility will belong to the PFIs and the TKYB will supervise the whole process. These will be reflected in the PFI agreements to be made between the TKYB and PFIs and loan agreements that will be made between the PFI and the beneficiary enterprise. The loan processes and the internal controls that will be conducted by the TKYB and PFIs will be detailed in the relevant POMs that will be prepared before effectiveness.\n\n8. The TKYB has well established credit application procedures which might be minimally altered for the purposes of the current project. In the TKYB’s current procedures, companies wanting to finance their investments or working capital needs from a loan from the TKYB will apply to its Corporate Banking and Project Finance Department. The Corporate Banking and Project Finance and Financial Analysis Departments review the application and the feasibility of the proposed project, conduct the eligibility screening, and prepare a term sheet for loan proposal. Then, the Engineering Departments assess the project and the company from technical, financial, and economical perspectives. The Corporate Banking and Project Finance Department together with Loan Allocation Department will review collateral conditions. The Loan Allocation Department submits the loan proposals to the Credit Committee and the Board of Directors. Upon approval of the Board of Directors, the loan agreement is signed with the beneficiary enterprise. The contract entitles the firm to receive disbursements from its allocated loan account. The invoices are received and entered into the system by the Engineering Department, which will be responsible for the project in coordination with the Corporate Banking and Project Finance Department in coordination with the Development Finance Institutions Department. The Engineering Department, which is responsible of the verification of the receipt of the goods and services outlined in the invoice, usually prepares a progress report after visiting the project site if needed. The Loan Allocation Department reviews the financials related to the project progress in coordination with Engineering Department and executes the control mechanism, wherever deemed necessary. Procedures for the working capital loans and the documentation are like those required by investment loans. The procedures outlined earlier could be adopted for the investment loans for the projects that will be financed directly by the TKYB.\n\n9. The procedures followed for lending through PFIs will be similar to those adopted by investment loans, with the difference that the Customer Value Management and Business Development Department will be the responsible business unit as the PIU. This information will be detailed in the relevant POMs that will be prepared by effectiveness. PFIs will be selected according to selection criteria explained in annex 1, section A and the loan allocation will be approved by the Credit Committee and Board of Directors. Disbursements will be processed by the Customer Value Management and Business Development Department in coordination with other departments. PFIs will be asked a pipeline of 25 percent of first disbursement and they will be provided access to the TKYB’s online monitoring tool for submitting information about subfinances for the TKYB control and approval. The online monitoring tool will also be used for reporting purposes. The internal control requirements for PFIs will also form a part of the relevant POM for the project.\n\n10. Procedures for the working capital loans and the documents, required systems at the PFIs, and documentary flow will be detailed in the relevant POM that will be prepared by effectiveness. The internal control requirements for the PFIs will also form a part of the relevant POM.\n\n11. The TKYB will be responsible for monitoring the compliance of the firms with the grant agreement conditions. Additionally, disbursements to beneficiary enterprises will depend on the compliance of these Page 48 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) companies with the grant conditions. Accordingly, the roles and responsibilities of different departments within the TKYB will be detailed in the POM for implementation of subgrants. The acceptance of this POM by the World Bank will be an effectiveness condition for the Grant. The TKYB could hire the services of an audit company to monitor on-site compliance of the beneficiary enterprises with the grant agreement conditions and these services could be financed by Component 3 of the project.\n\n12. **Internal and external audits.** The TKYB has a department responsible for auditing its business processes. The transactions under the project will also be subject to the internal audit in compliance with the audit program of the department _**.**_ Annual project financial statements (integrating both sources of financing) for the project and the TKYB’s financial statements will be subject to an independent audit by auditors that are acceptable to the World Bank. The TKYB has been submitting audited entity and project financial statements to the World Bank as a part of its obligations under another World Bank-financed project. The TKYB’s financial statements prepared in accordance with International Financial Reporting Standards have been audited by private sector auditors in accordance with International Auditing Standards. The entity audited financial statements had unqualified (clean) audit opinions for the last three years. The TKYB additionally prepares financial statements for the projects it is implementing, and these financial statements are also audited by independent auditors.\n\n13. Under the project, the TKYB will be required to submit its audited entity financial statements prepared in accordance with Turkish Accounting Standards (which are fully compatible with International Financial Reporting Standards) and project financial statements to the World Bank within six months following the end of year. The project financial statements are required to be made publicly available in accordance with the World Bank guidelines. Table 1.1 identifies the audit reports and their due dates.\n\n**Table 1.1. Audit Reports and Due Dates**\n\n**Audit Report** **Due Date**\nEntity financial statements prepared in accordance Within six months after the end of each calendar year with Turkish Accounting Standards or International and at the closing of the project.\nFinancial Reporting Standards (IFRS) Project financial statements for the loan financed part of the project, including Statement of Expenditures and the Designated Accounts Project financial statements for the grant financed part of the Project, including Statement of Expenditures and the Designated Accounts Within six months after the end of each calendar year and at the closing of the project.\n\nWithin six months after the end of each calendar year and at the closing of the project.\n\n14. The TKYB will also receive annual audited financial statements from the PFIs. This requirement will be reflected in the agreements that will be signed between the TKYB and PFIs and will be monitored by the TKYB.\n\n15. **Reporting and monitoring.** The TKYB will prepare and submit IFRs for the project on a semiannual basis. There will be two sets of interim financial reports; one for the loan and one for the grant. The TKYB will also ensure that the reports with financial content are automatically generated from its system.\n\n16. **Disbursement arrangements** _**.**_ The project will be disbursing on the traditional disbursement techniques. Two Designated Accounts will be created for the project (one for the EU grant and one for the Page 49 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) World Bank loan). Two authorized signatories will sign the withdrawal applications. The minimum application size for payments directly from the loan account for the issuance of special commitments, as well as the Statement of Expenditure limits, will be described in the disbursement letters. Full documentation in support of Statement of Expenditures, including completion reports and certificates, will be retained by the TKYB for at least two years after the World Bank has received the audit report for the fiscal year in which the last withdrawal from the loan account was made. This information will be made available for review during supervision visits by World Bank staff and for annual audits.\nDisbursements for expenditures above the Statement of Expenditure thresholds will be made against presentation of full documentation of the expenditures.\n\n**Procurement**\n\n17. **Applicable regulations** _**.**_ The World Bank Procurement Regulations for IPF Borrowers dated July 2016 and revised in November 2017 and August 2018 (‘Procurement Regulations’) will apply to Components 2 and 3 of the project. A General Procurement Notice will be published on the World Bank’s external website and United Nations Development Business online immediately after the negotiations.\n\n18. **Anticorruption Guidelines.** The World Bank's ‘Guidelines on Preventing and Combating Fraud and Corruption in Projects Financed by IBRD Loans and IDA Credits and Grants’, dated October 15, 2006, and revised in January 2011 and as of July 1, 2016 (Anticorruption Guidelines)’ will apply to the proposed project.\n\n19. **PPSD.** The Procurement Regulations requires the borrower to develop a PPSD for the project.\nBecause the TKYB and targeted beneficiaries are considered to be in urgent need of assistance as described under Bank Policy: Investment Project Financing paragraph 12, referring to projects in situations of urgent need of assistance or capacity constraints, a simplified PPSD was prepared by the TKYB. The draft PPSD describes how procurement activities will support project operations under Components 2 and 3 for the achievement of the PDOs and deliver value for money. The PPSD is linked to the overall project implementation strategy by ensuring proper sequencing of procurement activities. It provides information on institutional arrangements for procurement, roles and responsibilities, appropriate procurement methods, procurement due diligence, and other requirements needed for carrying out procurement. The PPSD also includes a detailed description of the procurement capacity needed by the executing agencies for carrying out procurement with specific focus on managing contract implementation, governance structure, and accountability framework. In addition, the PPSD is supported with market research, and analysis assesses market-related risks and opportunities that will affect the preferred procurement approach to market strategy.\n\n20. The PPSD confirmed that the sizes of the procurements in Component 3 are generally small and their risks are also low. The goods, consulting services, and non-consulting services under this component will be procured by the TKYB PIU and the selection methods will be simple and follow streamlined procedures. A market sounding concluded that there are adequate number of suppliers operating in the national market for the envisaged procurement packages. So, the procurements under this component are located as ‘tactical acquisition’ in the supply positioning matrix. The durations of the contracts are expected to be limited with the completion of the logistical arrangements, delivery of goods, and training or visibility materials, and not multiyear. Few consultants’ contracts to support the TKYB for project management purposes most likely will be longer than a year. The market sounding concluded that the Page 50 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) potential suppliers will see the TKYB as a good customer to develop business in their respective field of expertise.\n\n21. The PPSD confirmed that commercial procurement practices of small firms, group of people, or individuals in Turkey follow the general rule that they procure the least-cost goods, works, and services consistent with acceptable quality requirements. In the case of goods, the local practice is to prepare the technical specifications and solicit quotations from the local and/or international market. In the case of medium and large works, the technical specifications are usually prepared by consultant companies and bids are collected from qualified contractors. Minor works are generally tendered on a lump-sum basis by collecting bids from a number of local contractors. In the recent years, the purchasers commonly visit shopping sites in the Internet to find the optimum price for specific goods and seek availability of after sale services from the manufacturers. When equipment and machinery are needed for expansion of the existing facilities, the purchasers usually prefer proprietary goods from a single source for the sake of standardization and minimization of the operation and maintenance cost.\n\n22. The Turkish commercial code provides a modern ‘constitution’ for private sector commercial activity and entrepreneurship, grounded in financial transparency and strengthened corporate governance, and regulates the reporting requirements for intergroup transactions among group companies. In this context, the communique on ‘minimum content for the annual activity report’ requires the group companies to report on the group transactions, which will eventually strengthen arms-length arrangements between them. The World Bank provided finance to various credit line operations in Turkey in which the end users were private sector firms or individuals. All such World Bank-financed credit line projects confirmed that the funds were used by the beneficiaries for the intended purposes in consideration with economy and efficiency. So, the procurement to be done by the beneficiaries under Component 2 of the project will be done in accordance with well-established commercial practices as stipulated in the relevant POM and confirmed by the TKYB that these practices are consistent with the World Bank’s Core Procurement Principles of value for money, economy, integrity, fit for purpose, efficiency, transparency, and fairness.\n\n23. The PPSD proposed to hire the individual experts required for the TKYB PIU in accordance with the individual consultant selection procedures as specified in the Procurement Regulations. These include, but are not limited to, a procurement specialist, a training coordinator, an M&E expert, and a financial management specialist.\n\n24. **Procurement Plan and procurement tracking.** The Procurement Regulations require the borrower to use the World Bank’s Systematic Tracking of Exchanges in Procurement (STEP), an online procurement tracking tool to prepare, clear, and update its Procurement Plans and conduct all procurement transactions. The TKYB will create the Procurement Plan through STEP before initiating any procurement activity. The PPSD and the underlying Procurement Plan will be updated at least annually or as required to reflect the actual project implementation needs. All the procurement-related complaints will be recorded in the STEP complaint module by the TKYB.\n\n25. A list of procurements performed by the beneficiaries under Component 2 will be recorded in a format agreed by the World Bank and specified in the relevant POM and these records will be uploaded into STEP by the TKYB at least annually but not later than closing date of the project. The contracts agreed by the World Bank for financing and included in the approved procurement plan are listed in table 1.2.\n\nPage 51 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Table 1.2. Contracts Agreed by the World Bank**\n\n**Activity Description** **Reference**\n\n**No.**\n\n**Procurement**\n\n**Category**\n\n**Procurement**\n\n**Method**\n\n**Market**\n**Approach**\n\n**Estimated Amount**\n\n**including VAT for**\n\n**the Loan**\n\n**Funding**\n\n**Source**\n\n**Review**\n**Method**\n\n**Estimated**\n\n**Contract**\n**Signing Date**\n\n**Estimated**\n\n**Contract**\n**Completion**\n\n**(€)** **Date**\n\nSelection of a consultant firm to provide online CS-CQS IBRD February 1, and/or face-to face 3A.L – 01, CS CQS National 192,000 Post Loan 2021 2022 trainings for loan 02..\nbeneficiaries (multiple contracts)\n\n**(€)**\n\nCS-CQS3A.L – 01, 02..\n\nIBRD February 1, CS CQS National 192,000 Post Loan 2021 January 30, 2022 Selection of individual consultant/s to provide training (multiple contracts) Organizational and logistical services for face-to-face trainings for loan beneficiaries (multiple contracts) CS-INDV3A.L – 01, 02..\n\nNCS-RFQ3A.L01,02....\n\nIBRD January 30, CS INDV National 8,000 Post April 15, 2021 Loan 2022 IBRD January 30, NCS RFQ National 300,000 Post April 15, 2021 Loan 2022 Printing/publishing documents Printing/publishing NCS-RFQ- IBRD January 15, March 15, NCS RFQ National 3,000 Post documents 3A.L-....... Loan 2021 2021 Selection of a consultant firm to provide online CS-CQS EU March 1, and/or face-to-face 3B.G – 01, CS CQS National 1,080,000 Post April 15, 2021 Grant 2022 trainings for grant 02..\nbeneficiaries (multiple contracts) NCS-RFQ- IBRD January 15, NCS RFQ National 3,000 Post 3A.L-....... Loan 2021 CS-CQS3B.G – 01, 02..\n\nEU March 1, CS CQS National 1,080,000 Post April 15, 2021 Grant 2022 Selection of individual consultant/s to CS-INDV3B.G – 01, 02..\n\nEU March 1, CS INDV National 32,000 Post June 15, 2021 Grant 2022 Page 52 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Activity Description** **Reference**\n\n**No.**\n\n**Procurement**\n\n**Category**\n\n**Procurement**\n\n**Method**\n\n**Market**\n**Approach**\n\n**Estimated Amount**\n\n**including VAT for**\n\n**the Loan**\n\n**Funding**\n\n**Source**\n\n**Review**\n**Method**\n\n**Estimated**\n\n**Contract**\n**Signing Date**\n\n**Estimated**\n\n**Contract**\n**Completion**\n\n**(€)** **Date**\n\nprovide training (multiple contracts) Organizational and logistical services for NCS-RFQ face-to-face trainings EU March 1, 3B.G- NCS RFQ National 780,000 Post June 15, 2021 for grant Grant 2022 01,02....\n\nbeneficiaries (multiple contracts)\n\n**(€)**\n\nNCS-RFQ3B.G01,02....\n\nEU March 1, NCS RFQ National 780,000 Post June 15, 2021 Grant 2022 Printing/publishing documents Printing/publishing NCS-RFQ- EU -March 15, May 15, NCS RFQ National 45,000 Post documents 3B.G-....... Grant 2021 2021 Selection of a part- July 1,2020 CS-INDV- IBRD time procurement CS INDV National 52,500 Post 3C.L - 01 Loan 29, 2023 specialist NCS-RFQ- EU -March 15, NCS RFQ National 45,000 Post 3B.G-....... Grant 2021 CS-INDV- IBRD CS INDV National 52,500 Post 3C.L - 01 Loan July 1,2020 December 29, 2023 Organizational and logistical services for capacity building trainings to loan officers (multiple contracts) Selection of a parttime procurement specialist NCS – RFQ- 3C.L01, 02....\n\nIBRD March 30, NCS RFQ National 94,500 Post June 1,2020 Loan 2021 CS-INDV- EU December CS INDV National 73,500 Post July 1,2020 3D.G - 01 Grant 29, 2023 Selection of a financial management specialist CS-INDV- EU CS INDV National 126,000 Post 3D.G - 02 Grant July 1,2020 December 29, 2023 Selection of a M&E CS-INDV- EU July 1,2020 December CS INDV National 105,000 Post specialist 3D.G - 03 Grant 29, 2023 Selection of a CS-INDV- EU July 1,2020 December CS INDV National 55,000 Post training coordinator 3D.G - 04 Grant 29, 2022 Selection of a M&E specialist CS-INDV- EU July 1,2020 December CS INDV National 55,000 Post 3D.G - 04 Grant 29, 2022 Page 53 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Activity Description** **Reference**\n\n**No.**\n\n**Procurement**\n\n**Category**\n\n**Procurement**\n\n**Method**\n\n**Market**\n**Approach**\n\n**Estimated Amount**\n\n**including VAT for**\n\n**the Loan**\n\n**Funding**\n\n**Source**\n\n**Review**\n**Method**\n\n**Estimated**\n\n**Contract**\n**Signing Date**\n\n**Estimated**\n\n**Contract**\n**Completion**\n\n**(€)** **Date**\n\nSelection of labor CS-INDV- EU September December CS INDV National 51,000 Post auditor 1 3D.G - 05 Grant 1,2020 29, 2023\n\n**(€)**\n\nSelection of labor CS-INDV- EU September December CS INDV National 51,000 Post auditor 1 3D.G - 05 Grant 1,2020 29, 2023 Selection of labor CS-INDV- EU September December CS INDV National 51,000 Post auditor 2 3D.G – 06 Grant 1,2020 29, 2023 CS-INDV- EU September CS INDV National 51,000 Post 3D.G - 05 Grant 1,2020 CS-INDV- EU September CS INDV National 51,000 Post 3D.G – 06 Grant 1,2020 Selection of labor CS-INDV- EU September December CS INDV National 51,000 Post auditor 2 3D.G – 06 Grant 1,2020 29, 2023 Selection of individual consultants for grant CS-INDV EU December implementation; i.e. 3D.G - CS INDV National 126,000 Post July 1, 2020 Grant 29, 2023 communication 07.........\nexpert (multiple contracts) CS-INDV3D.G 07.........\n\nEU December CS INDV National 126,000 Post July 1, 2020 Grant 29, 2023 Selection of a parttime individual consultant for translation services Selection of independent assessors for grant evaluation purposes (1- 4 persons multiple contracts) Selection of a consultant firm for on-site compliance audit, including procurement practices Selection of a consultant firm for the design of visibility and CS-CQS- EU March 30, CS CQS National 50,000 Post August 1, 2020 3D. G – 02 Grant 2021 Page 54 of 86 CS-INDV- EU November CS INDV National 27,500 Post October 1,2020 3D.G – 08 Grant 1, 2020 CS-INDV3D.G – 09, 10....\n\nEU December CS INDV National 40,000 Post March 1,2021 Grant 29, 2021 CS-CQS- EU September 1, CS CQS National 250,000 Post 3D. G – 01 Grant 2021 December 31, 2024", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Activity Description** **Reference**\n\n**No.**\n\n**Procurement**\n\n**Category**\n\n**Procurement**\n\n**Method**\n\n**Market**\n**Approach**\n\n**Estimated Amount**\n\n**including VAT for**\n\n**the Loan**\n\n**Funding**\n\n**Source**\n\n**Review**\n**Method**\n\n**Estimated**\n\n**Contract**\n**Signing Date**\n\n**Estimated**\n\n**Contract**\n**Completion**\n\n**(€)** **Date**\n\ncommunication materials Production of NCS – visibility and EU November 1, December RFQ- NCS RFQ National 30,000 Post communication Grant 2020 31, 2020 3D.G-01 materials\n\n**(€)**\n\nNCS – RFQ3D.G-01 EU November 1, NCS RFQ National 30,000 Post Grant 2020 December 31, 2020 Organizational and logistical services for visibility and communication activities (event organization/s and so on) (multiple contracts) Organizational & logistical services for consultationsmeetings during the calls for grant applications (multiple contracts) NCS – RFQ3D.G-02, 03....\n\nNCS – RFQ3D.G-......\n\nEU December NCS RFQ National 140,000 Post January 1, 2021 Grant 15, 2023 EU November 15, NCS RFQ National 100,000 Post Grant 2020 January 15, 2021 _Notes_ : CS = Consulting Services; NCS = Non-consulting Services; CQS: Consultant’s Qualifications-based Selection; RFQ = Request for Quotations as a selection method; INDV = Selection of Individual Consultant/s.\n\nPage 55 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) 26. **Advance procurement** _**.**_ Procurement Regulations Paragraphs 5.1 and 5.2 (Advance Contracting and Retroactive Financing) permits that the borrower may wish to proceed with the procurement process before signing of the Legal Agreement. In such cases, if the eventual contracts are to be eligible for World Bank financing the procurement procedures, including advertising, shall be consistent with Sections I, II, and III of the Procurement Regulations which cover the World Bank’s Core Procurement Principles of economy, efficiency, transparency, fairness, fit-for purpose, value for money, and integrity _._ With this understanding, the TKYB will initiate the selection of the PIU consultants immediately after project negotiations upon publication of the General Procurement Notice in the United Nations Development Business online.\n\n27. **Procurement methods and standard procurement documents.** While comprehensive selection methods and use of the World Bank’s standard procurement documents will be applicable, due to the emergency response nature of the project, flexible, streamlined, and fit-for-purpose arrangements will also be used in the project procurements. Higher thresholds will be used for the simple selection procedures as specified in the approved Procurement plan. When approaching the national market, as agreed in the Procurement Plan, bidding documents agreed by the World Bank in Turkish language will be used. Determined thresholds and selection methods will be reviewed by the World Bank during the project supervisions and/or when the World Bank decides and will be updated as appropriate.\n\n28. **Procurement risk assessment** _**.**_ The World Bank has conducted a procurement assessment for the project, with a focus on the TKYB in terms of (a) procurement regulatory framework and management capability, (b) integrity and oversight, (c) procurement process and market readiness, and (d) procurement complexity. While the Procurement Regulations will apply in the proposed project, the assessment concludes that (a) applicable procurement policies and the regulatory system are designed broadly to meet Core Procurement Principles of value for money, economy, efficiency, effectiveness, integrity, transparency and fairness, and accountability; (b) the TKYB has a clear system of accountability with clearly defined responsibilities and delegation of authority on who has control of procurement decisions; (c) there is a clear identified target market for all procurements; and (d) the TKYB effectively manages contracts to ensure delivery according to the contract conditions. The assessment was recorded in the Procurement Risk Assessment and Management System of the World Bank.\n\n29. The TKYB will undertake the overall responsibility of the project implementation and coordination through its PIU that was established under the Private Sector Renewable Energy and Energy Efficiency Projects and continued to operate under the Geothermal Development Project. The procurement capacity assessment concluded that the TKYB has adequate resources and capacity for implementing the credit line operations of the project through its current PIU.\n\n30. The TKYB PIU also developed experience in the selection of consultants in accordance with the World Bank’s procurement procedures under the Geothermal Development Project. However, some experienced staff separated from the PIU when the TKYB headquarters moved from Ankara to Istanbul in 2018. In addition, the TKYB does not have experience with the World Bank’s Procurement Regulations.\nHence, the TKYB/PIU will be supported by an external procurement specialist for the procurement of goods, non-consulting services, and consultants’ services under Component 3 of the project.\n\n31. Given the emergency nature of the project and limited implementation time and taking into account that the TKYB will implement the first project under the Procurement Regulations, the overall Page 56 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) procurement risk for the project is assessed as ‘Moderate’. The risk rating can be lowered to ‘Low’ when the agreed actions No. (1) and (2) in table 1.3 have been put in place.\n\n**Table 1.3. Identified Risks and Agreed Action Plan**\n\n**Action**\n\n**No.**\n\n**Identified Risk** **Mitigation Measure** **Responsible**\n\n**Party**\n\n**Time Frame**\n\n1 The TKYB has been implementing the World Bankfinanced Geothermal Development Project simultaneously, and staff assigned to the PIU have their regular work. The PIU may not be able to meet the procurement deadlines.\n\nA dedicated procurement specialist will be employed by the TKYB in the PIU.\n\nTKYB Terms of references will be prepared by the TKYB and the selection will be completed in advance before the earliest of loan or grant effectiveness.\n\nContracts will be The TKYB does not have signed within the experience to work under the first month after Procurement Regulations. Loan and Grant effectiveness, respectively.\n2 Unclear procurement Develop a POM for grant and TKYB Before Loan and procedures may create loan separately. Grant effectiveness, unnecessary questions from the respectively.\nprocurement stakeholders.\n\nThe TKYB does not have experience to work under the Procurement Regulations.\n\nDevelop a POM for grant and loan separately.\n\nTKYB Before Loan and Grant effectiveness, respectively.\n\n3 Incomplete E&S assessment studies may delay commencement of the contract implementation.\n\n4 Misinterpretation of the Procurement Regulations and terms and conditions of the contracts. It may cause noncompliance and also time and cost overruns in the contract implementation.\n\nIf applicable, all E&S assessment studies will be completed before signing of the contracts.\n\nWork closely with World Bank procurement specialist.\n\nTKYB/PIU Throughout the project.\n\nTKYB Throughout the project.\n\n32. **Procurement supervision frequency** _**.**_ The World Bank will review the procurement arrangements performed by the TKYB, including contract packaging, applicable procedures, and the scheduling of the procurement processes, for their conformity with the Legal Agreement. Those procurements that did not have ex ante due diligence by the World Bank will be subject to ex post due diligence on a sampling basis in accordance with the procedures set forth in Paragraph 4 of the Annex II to the Procurement Regulations. A post review of the procurement documents will normally be undertaken annually and/or during the World Bank’s supervision mission, or the World Bank may request to review any contracts at any time. In such cases, the PIU shall provide the World Bank the relevant documentation for its review.\n\n33. Review procedures for the procurements done by beneficiaries under Component 2 will be detailed in the relevant POM. The procurements and physical review of the procured goods, works, and Page 57 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) services will be conducted by an independent procurement audit firm annually, and the resulting report will be shared with the World Bank for its review.\n\n34. **Complaint review.** The procurement complaints, other than covered under Annex III of the Procurement Regulations, are to be handled by the TKYB in accordance with the procedures agreed by the World Bank and stipulated in the relevant POMs. Immediately upon receipt, the complaints will be recorded in the STEP complaint module by the PIU. The TKYB will not proceed with the next stage/phase of the procurement process, including with awarding a contract without satisfactory resolution of the complaint(s).\n\n35. Operational costs will not be considered under procurement implementation, which could be the incremental expenses, including office supplies, vehicles operation and maintenance cost, maintenance of equipment, communication costs, rental expenses, utilities expenses, consumables, transport and accommodation, per diem, cost for procurement advertisements, honorarium for the independent grant evaluation committee members, and salaries of locally contracted support staff.\n\n**Monitoring and Evaluation**\n\n36. **Reporting progress.** The PIU will report on progress on the PDO and intermediate indicators (including core indicators for World Bank-wide monitoring and gender-related indicators) on a semiannual and annual basis. The PIU will prepare semiannual project progress reports to be shared with the World Bank, by taking into account different reporting requirements under the grant and loan components. The TKYB is accustomed to collecting such information from PFIs and beneficiary enterprises for previous World Bank projects. A midterm and end line citizen engagement survey will be conducted by the TKYB to seek feedback from beneficiary firms on their satisfaction with the project. The PIU will discuss the survey results with PFIs and the results will inform project implementation, as appropriate. The financial performance of the TKYB will be monitored through independent auditors’ reports and separate management letters confirming adherence to prudential norms. Monitoring of core intermediate result indicators at the PFI level will enable the TKYB and the World Bank team to take action in case of a significant deviation for a specific PFI which may affect the progress toward the PDO. Though, it is not included in the Results Framework, the PIU will also report the statistics on formal employment creation in the loan beneficiary firms.\n\n**Environmental and Social**\n\n37. The grant and loan programs will be implemented by the TKYB with the support of local PFIs. The project will leverage the TKYB’s extensive experience in providing access to finance services to LEs and, through intermediaries (such as banks and leasing companies), to SMEs. As far as grants are concerned, the TKYB will manage the assignment of grants centrally through the PIU, a team of technical experts, and with the technical assistance of the World Bank.\n\n38. The TKYB will undertake the overall responsibility of the project implementation and coordination through its PIU that was established under the Private Sector Renewable Energy and Energy Efficiency Projects and continued to operate under the Geothermal Development Project. However, the capacity of the former PIU established to administer World Bank projects will be increased by assigning/hiring Page 58 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) additional full-time staff to support implementation and monitoring of the E&S documents and to ensure compliance with the World Bank’s ESF.\n\n39. The corporate ESMS, recently adopted by the TKYB Executive Board on January 17, 2020, found acceptable to the World Bank, will be implemented by the designated E&S focal points in the PIU. The Environmental and Social Policy, including the E&S exclusion list, will be applicable to all the TKYB operations and financial services, and the ESMS will also be applicable to subprojects which are directly financed by the TKYB. The same focal points will be responsible for carrying out the procedure on E&S risk evaluation in credit processes, risk categorization, E&S management of risks defined, and the E&S training process under the capacity-building activities.\n\n40. Similar to the E&S focal points under the PIU, PFIs will have designated staff to implement and monitor low category E&S risks of the subprojects they finance.\n\n41. The TKYB’s communication approach and grievance system will be in the responsibility of another full-time focal point designated to plan, organize, supervise, and monitor the stakeholder engagement activities that will be carried out both by the TKYB and PFIs. This focal point will oversee the implementation of the project’s SEP and the grievance mechanism established for the project. The focal point will ensure that regular progress reports prepared for subprojects financed both by the TKYB and PFIs include progress and actions carried out for engagement activities are in line with the project’s SEP and that grievances related to both internal and external stakeholders (project workers and all other stakeholders) are recorded, addressed on time, and closed and reported according to the World Bank requirements, particularly with ESS10 and ESS2.\n\n**World Bank Project Implementation Support Plan**\n\n42. The Implementation Support Plan (ISP) is tailored to the specific context and characteristics of the project, and existing capacity of the implementing agency and arrangements. The ISP will be reviewed periodically to ensure that it remains fit for purpose and responsive to the project’s implementation support needs over its lifetime.\n\n43. **Project Launch Workshop** . A project launch workshop will be held soon after project approval. It will bring all project stakeholders together to ensure that the project scope, design, process and responsibilities are understood.\n\n44. **Implementation support missions** . The project would be supervised at least twice a year and the recommendations of such supervisions would be presented to the TKYB and recorded in an Aide Memoire.\nThe World Bank would be represented by a task team leader, supported by a team of experts with various skills as needed, including fiduciary (Procurement and financial management), safeguards (Social and environmental), monitoring and evaluation, and technical specialists.\n\n45. The semiannual supervision missions and short/ follow-up technical missions as needed in specific areas, will focus on the following areas: Strategic support, technical support, fiduciary support, and safeguards support.\n\nPage 59 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**ANNEX 2: Detailed Description of the Project**\n\n**A. Detailed Description of Implementation of Sub-loans**\n\n1. **The TKYB will be the borrower and implementing agency under the repayment guarantee of the**\n**Ministry of Treasury and Finance.** The TKYB will provide loans to LEs directly, and to SMEs through PFIs,\nthereby expanding its geographical and sectoral reach. The TKYB will provide a total of loans to LEs directly, and to SMEs through PFIs using subsidiary financing agreements. Although the IBRD loan will be provided in foreign currency, the PFIs and TKYB will be able to provide loans to beneficiary enterprises in local currency, if required, by making use of country’s well-developed currency swap market. The TKYB and PFIs will make use of the loan in accordance with the Council of Minister’s Decree Number 32, which regulates the foreign exchange lending provisions.\n\n2. **The selection of PFIs and all subsidiary financing agreements are subject to prior review and**\n**acceptance by the World Bank.** Before the final selection of PFIs, the TKYB will submit to the World Bank\nthe evaluation report, including financials of the proposed PFIs, together with a request to include the PFIs in the project. The World Bank will review and clear the TKYB’s assessment by conveying a ‘no objection’ for each PFI’s participation. The ‘no objection’ will be based on the criteria included in this section. The TKYB will send the financials of the proposed PFIs to the World Bank every year, within the first six months of each calendar year, to ensure that the selected PFIs continue to meet the required criteria until full repayment of the subfinancing. The ‘no objection’ is not required for the continued participation of the selected PFIs.\n\n3. **The TKYB will rely on its Loan Policy document approved by the Board of Directors to identify**\n**PFIs** . The Loan Policy document, which regulates all loan processes for cash and noncash loan products,\nstates that the credit risk limits are determined using a quantitative methodology for rating the banks and financial leasing companies separately and allocates credit risk limits for both groups. The methodology considers six main criteria: (a) capital adequacy, (b) asset quality, (c) liquidity, (d) profitability, (e) income and expenditure structure, and (f) loan capacity. Further, the methodology analyzes capital structure, credit risk level, operational risk level, market risk level, and market share of each financial institution. The risk limit allocations are monitored continuously and revised annually by the Loan Allocation Department according to year-end independently audited financial statements disclosed by the banks and financial leasing companies. In addition to the quantitative model, financial leasing companies must also have net profits in two of the last three years and their controlling shareholders must be banks. The PFI selection for any program starts with the TKYB’s own risk limit allocation screening and then the eligibility criteria for specific program and/or IFIs are applied.\n\n4. **PFIs will be selected based on their expression of interest in participating in the project,**\n**implementation capacity, capacity to present timely reports on project indicators, and on acceptance**\n**by the TKYB of their credit risk, as well as the following eligibility criteria:**\n\n- For banks, unless agreed otherwise by the World Bank, the following eligibility criteria will\napply: Page 60 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) `o` Total assets during the last two fiscal years to exceed a minimum of €1 billion equivalent on average `o` Compliance with all prudential norms of the Banking Regulation and Supervision Agency `o` General compliance with legal and regulatory requirements applicable to the banking industry, including, but not limited to, such prudential regulations as minimum capital adequacy ratio, maximum foreign currency exposure limits, maximum large exposure to single and connected clients and maximum insider lending limits, and so on, duly certified by the banks’ auditors every year and confirmed by the management as of June 30 every year; in such cases where the year-end audits have already been completed, the bank shall submit a management letter confirming its compliance with prudential norms `o` Audited interim financial reports according to the requirements of the Banking Regulation and Supervision Agency `o` Adequate organization, management, staff, and other resources necessary for its efficient operation `o` Application of their own E&S assessment procedures for screening, monitoring, and reporting as per TKYB ESMS acceptable to TKYB and the World Bank\n\n- For leasing companies, unless agreed otherwise by the World Bank, the following eligibility\ncriteria will apply: `o` Total lease receivables during the last two fiscal years to exceed a minimum of €50 million equivalent on average `o` New lease volume during the last two fiscal years to exceed a minimum of €50 million equivalent on average `o` The leasing company should have been profitable for at least two out of the last three years of operations `o` Compliance with all prudential norms of the Banking Regulation and Supervision Agency `o` General compliance with legal and regulatory requirements applicable to the leasing industry, including, but not limited to, such regulations as minimum equity capital, the total sum of lease exposures, and the total sum of exposures to related parties, duly certified by the leasing companies’ external auditors every year and confirmed by management as of June 30 every year; in such cases where the year-end audits have already been completed, the leasing company shall submit a management letter confirming its compliance with prudential norms Page 61 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) `o` Audited financial statements according to the requirements of the Banking Regulation and Supervision Agency `o` Adequate organization, management, staff, and other resources necessary for its efficient operation `o` Application of their own E&S assessment procedures for screening, monitoring, and reporting as per TKYB ESMS acceptable to TKYB and the World Bank\n\n- **PFIs will be responsible from the following:**\n\n`o` PFIs must start and remain in compliance with the eligibility criteria for PFIs and the Environmental and Social Assessment procedures as defined in the Environmental and Social Commitment Plan.\n\n`o` PFIs will be responsible for ensuring that subbeneficiaries comply with the applicable Turkish environmental legislation and regulations and the World Bank ESF.\n\n`o` PFIs will provide the TKYB with a set of documentation for all subfinance to enable it to maintain all project records and make them available for ex post review by the World Bank or by external auditors as necessary.\n\n`o` PFIs will be required to provide reasonable information for the purpose of monitoring and impact assessment for five years after the project closing date or as may be otherwise requested by the World Bank or the borrower.\n\n5. **If a PFI repays at least €20 million of the World Bank funds, the TKYB will onlend or finance the**\n**reflows to PFIs within one year to be used for a purpose consistent with the PDO.** Total reflows to be\nonlent or financed for at least one financing cycle will be an amount equal to the aggregate of the subsidiary financing, and the selection of PFIs under the reflows will be independent of earlier commitments. Similarly, the TKYB will use the repayments under the direct lending component for new loans for formal job creation for firms operating in provinces with high incidence of refugees through improved access to finance and skills, for at least one financing cycle.\n\n6. **If PFIs do not effectively implement the project, their allocation can be reallocated to other PFIs.** In addition to demonstrating solid financial standing, each prospective PFI will commit to participate in training organized jointly by the TKYB and the World Bank to strengthen their capacity to assess the formal job creation potential of the proposals of viable enterprises operating in areas where refugees are concentrated.\n\n7. **The TKYB will have exposure only to the selected PFIs and will assume the credit risk for**\n**onlending funds to PFIs** . PFIs will assume the credit risk of the subbeneficiaries who will be selected based\non an agreed upon eligibility criteria.\n\n8. **Maturity of funds from the TKYB to PFIs and leasing companies will be at a minimum of six**\n**years. The funds available to PFIs will depend upon the availability of funds to the TKYB from the World**\n**Bank.**\n\nPage 62 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) 9. **Eligibility criteria for beneficiaries of sub-loans, unless agreed otherwise by the World Bank, will**\n**include the following:** final beneficiaries of the loan will be selected based on clearly defined eligibility\ncriteria in 24 provinces including (i) firms that are financially viable, and (ii) firms with a consolidated record of job creation. TKYB will encourage women-inclusive enterprises [33] and enterprises in less developed sub-regions affected by SuTP influx to expand its pool of potential beneficiaries. The loans should be allocated with a longer maturity than the maturity of TKYB’s and PFI’s representative lending portfolio. The firms will be selected on a first come, first served basis. The criteria above will be implemented according to the loan application evaluation procedure as defined in annex 2.\n\n10. **Terms and conditions of subfinance between the TKYB and LEs, unless agreed otherwise by the**\n**World Bank, include the following:**\n\n- Subfinance will be evaluated in accordance with the TKYB’s normal project and finance\nevaluation guidelines. The TKYB will ascertain the eligibility of the subfinance to ensure that they meet the project requirements.\n\n- The cost of the targeted direct financing to LEs by the TKYB will include (a) the cost of World\nBank funds going to the TKYB, (b) an onlending margin reflecting the TKYB’s administrative costs, (c) a credit risk margin associated with the enterprises, and (d) fees for the Treasury’s guarantee provision.\n\n- The amount of an individual subfinance will not exceed €5 million equivalent for LEs except\nas the World Bank shall otherwise agree. Total exposure, the aggregate amount of outstanding subfinance to any one LE, shall not exceed €15 million equivalent.\n\n- Subfinance to LEs may be made for working capital and investment purposes. Total working\ncapital subfinance extended directly by the TKYB shall not amount to more than 20 percent of Component 1 proceeds.\n\n- All subfinance to LEs must have at least two-year maturity for working capital loans and at\nleast five-year maturity for investment loans and those maturities expected to exceed the TKYB’s representative loan portfolio maturity.\n\n- For all subfinance above €1 million equivalent, subbeneficiaries must submit a cash flow\nstatement following a format agreed with the TKYB.\n\n- For subfinance above €1 million equivalent, subbeneficiaries must have a financial\ndebt/equity ratio of not more than 85:15 after the receipt of the subfinance, unless agreed otherwise by the World Bank.\n\n- For subfinance above €1 million equivalent, subbeneficiaries should, after the receipt of the\nsubfinance, be projected to maintain a financial debt service coverage ratio of at least 1.1:1 Page 63 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) and calculated on an average basis over the subfinance life, unless agreed otherwise by the World Bank.\n\n- The first subfinance by the TKYB, irrespective of size, will be subject to prior review by the\nWorld Bank. Subfinance to be provided to a large enterprise, exceeding €3 million equivalent, will require prior approval by the World Bank.\n\n- All subfinance not subject to prior review may be subject to ex post review by the World\nBank to verify compliance with the terms and conditions.\n\n- The relevant authorities must certify that the LEs (subbeneficiaries) and subprojects meet\nenvironmental laws and standards in force in Turkey. The World Bank policy on environmental assessment will also be complied with.\n\n- Subprojects classified with a risk rating of High and Substantial according to the World Bank’s\nESF will not be financed.\n\n- Subprojects that trigger ESS5 (Land Acquisition, Restrictions on Land Use, and Involuntary\nResettlement) and goods, works, non-consulting services, and consultant services on the World Bank’s negative list will not be eligible for financing;\n\n- Subprojects having significant impacts on sensitive areas and cultural heritage will not be\neligible for financing within the scope of the project.\n\n- Contracts from subbeneficiaries, where the contracted firms are on the World Bank’s lists of\ndebarred or suspended firms, will not be eligible for financing.\n\n- LEs will be required to keep copies of invoices for all expenses financed with working capital\nand investment finance received under the project. LEs will be required to send invoices and other documentation for subfinance to the borrower, except in the case of working capital expenditures. For working capital expenses, which will be financed by working capital loans, LEs will send a list of working capital expenses financed through the working capital loan to the borrower or, based on a due diligence undertaken by the World Bank, borrower’s calculations on the working capital gap will be accepted as documentation on working capital expenditures. Ultimately the invoices and documentation for all expenses will be kept by the subbeneficiary and made available to the borrower and the World Bank if requested.\n\n- Subbeneficiaries are required to comply with the World Bank's Guidelines on Preventing and\nCombating Fraud and Corruption in Projects Financed by IBRD Loans and IDA Credits and Grants (revision of July 1, 2016) (Anticorruption Guidelines) as part of its general obligations relating to the receipt and use of such proceeds of the loan. The Turkish translation of the anticorruption guidelines is available on the World Bank website in Turkey (http://www.worldbank.org.tr). The English language version will apply in case of any inconsistency between the Turkish and the English versions of the guidelines.\n\nPage 64 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) 11. The eligibility criteria for lending may be different under direct lending to larger enterprises and lending through PFIs, to reflect the differences in operating models between the TKYB (as a development bank) and commercial banks. Implementation guidelines will be included in the relevant POM.\n\n12. **Terms and conditions of subfinance between PFIs and SMEs, unless agreed otherwise by the**\n**World Bank, include the following:**\n\n- Subfinance will be evaluated in accordance with PFIs’ normal project and finance evaluation\nguidelines. The TKYB will ascertain the eligibility of the subfinance provided by PFIs to ensure that they meet the project requirements but will not conduct its own evaluation of subfinance.\n\n- The cost of subfinance by PFIs to SMEs will include, at a minimum, the cost of the project\nfunds to PFIs plus an onlending margin reflecting (a) PFI’s administrative costs and (b) a risk premium.\n\n- The amount of an individual subfinance will not exceed €3 million equivalent for SMEs,\nexcept as the World Bank shall otherwise agree. Total exposure, the aggregate amount of outstanding subfinance to any one SME, shall not exceed €6 million equivalent.\n\n- Subfinance to SMEs may be made for working capital and investment purposes.\n\n- For bank PFIs all subfinance to SMEs must have at least two-year maturity for the working\ncapital loans and at least five-year maturity on investment loans, and those maturities are expected to exceed the respective loan portfolio maturities of the TKYB and PFIs. For leasing PFIs, subfinance to SMEs must have at least two-year maturity for working capital loans and three-year maturity for investment loans, and those maturities are expected to exceed the respective loan portfolio maturities of those leasing PFIs.\n\n- For all subfinance above €1 million equivalent, subbeneficiaries must submit a cash flow\nstatement following a format agreed upon with the TKYB.\n\n- For subfinance above €1 million equivalent, subbeneficiaries must have a financial\ndebt/equity ratio of not more than 85:15 after the receipt of the subfinance, unless agreed otherwise by the World Bank.\n\n- For subfinance above €1 million equivalent, subbeneficiaries should, after the receipt of the\nsubfinance, be projected to maintain a financial debt service coverage ratio of at least 1.1:1 and calculated on an average basis over the subfinance life, unless agreed otherwise by the World Bank.\n\n- The first subfinance by each PFI, irrespective of size, will be subject to prior review by the\nWorld Bank. Subfinance exceeding €2.5 million equivalent to be provided to an SME will require prior approval by the World Bank.\n\nPage 65 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n- All subfinance not subject to prior review may be subject to ex post review by the TKYB\nand/or by the World Bank to verify compliance with the terms and conditions.\n\n- The relevant authorities must certify that the SMEs (subbeneficiaries) and subprojects meet\nenvironmental laws and standards in force in Turkey. The World Bank’s ESF will be complied with.\n\n- Subprojects classified as Low risk according to the TKYB’s ESMS and World Bank’s ESF will be\nfinanced by PFIs.\n\n- Subprojects involving dams and international waterways will not be financed.\n\n- Subprojects that trigger ESS5 (Land Acquisition, Restrictions on Land Use and Involuntary\nResettlement) and goods, works, non-consulting services, and consultant services on the World Bank’s negative list will not be eligible for financing.\n\n- Subprojects having significant impacts on cultural heritage will not be eligible for financing\nwithin the scope of the project.\n\n- Contracts from subbeneficiaries where the contracted firms are on the World Bank lists of\ndebarred or suspended firms will not be eligible for financing;\n\n- SMEs will be required to keep copies of invoices for all expenses financed with working\ncapital and investment finance received under the project. SMEs will be required to send, to their respective PFIs, invoices and other documentation for subfinance, except in the case of working capital expenditures. For working capital expenses, which will be financed by working capital loans, SMEs will send a list of working capital expenses financed through a working capital loan to the PFIs or, based on due diligence undertaken by the World Bank, eligible financial institution calculations on a working capital gap will be accepted as documentation on working capital expenditures. Ultimately the invoices and documentation for all expenses will be kept by SMEs and made available to PFIs, the TKYB, and the World Bank if requested.\n\n- Subbeneficiaries will be required to provide reasonable information for the purpose of\nmonitoring and impact assessment for five years after the project closing date, or as may be requested by the World Bank or the borrower.\n\n- Subbeneficiaries are required to comply with the World Bank's Guidelines on Preventing and\nCombating Fraud and Corruption in Projects Financed by IBRD Loans and IDA Credits and Grants (revision of July 1, 2016) (Anticorruption Guidelines) as part of its general obligations relating to the receipt and use of such proceeds of the loan. The Turkish translation of the anticorruption guidelines is available on the World Bank website in Turkey (http://www.worldbank.org.tr). The English language version will apply in case of any inconsistency between the Turkish and the English versions of the guidelines.\n\nPage 66 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**B. Detailed Description of Implementation of Subgrants**\n\n13. **TKYB will be the implementing agency of the grant component.** **[34]** 14. **The intended beneficiary firms can apply for a subgrant scheme in which the firms will be asked**\n**to submit an employment plan to recruit new employees together with a viable business plan.** The grant\nscheme will disburse 20 percent to small enterprises, 32 percent to medium enterprises, and 48 percent to LEs.\n\n15. **Applications will be evaluated by a Grant Evaluation Committee including the TKYB experts and**\n**independent evaluators.** The committee will be responsible for preliminary approval of the grants, with\nthe ‘no objection’ of the World Bank.\n\n16. **Firms applying for a grant are required to submit a business plan, also including job creation**\n**targets for the new employees during the period covered by the grant.** Applicant firms will be evaluated\nunder each grant scheme by the grant evaluation committee based on the evaluation grid presented in box 2.1.\n\n17. **The grant amount can vary between EUR 15,000 and EUR 45,000 for SEs, EUR 25,000 and EUR**\n**125,000 for MEs, and EUR 40,000 and EUR 300,000 for LEs.** It is expected – on average across beneficiary\nfirms - the grant to cover up to 70 of the estimated gross median wage [35] of workers for 18 months (EUR 7.780), conditional to hiring at least 2, 3, 5 new employees in SEs, ME, and LEs respectively. The average grant amount per firm is calculated as EUR 31,000, EUR 108,000, and EUR 295.000 for SEs, ME, and LEs respectively\n\n**Table 1. Estimated Grant Amounts**\n\nEstimated total Grant Amount by firm type (Million EUR) Estimated total Grant Amount by firm type (%) Estimated average grant amount per firm (EUR) Estimated number of grant beneficiary firms Estimated new jobs created per firm Estimated Total Jobs Created by firm type Total Cost per new job in EUR (for 18 months) Small (Below 50) 14 20% 31,000 453 4 1,812 7,780 Medium (50-250) 22 31% 108,000 204 14 2,856 7,780 Large (250+) 34 49% 295,000 114 38 4,332 7,780 All firms 70 100% 90,000 771 9,000 7,780 34 The TKYB already has grant management experience in implementing several grant programs. Most notably Economic and Commercial Cooperation of Organization of Islamic Countries (COMCEC) Project Funding in which the TKYB partners with the Standing Committee for COMCEC for an internationally implemented grant program since 2014. The TKYB is responsible for financing and M&E of this grant program and acts as a contracting entity. The grant program finances institutional capacitybuilding projects of member countries in cooperation areas of agriculture, financial cooperation, poverty alleviation, tourism, trade and transport, and communications (http://www.comcec.org/en/pcm).\n35 This corresponds on average to EUR 432.2 per month, an amount very close to the national Minimum Wage. According to the OECD, the national Minimum Wages in Turkey equals to 70 percent of the median wage (see OECD.stat: https://stats.oecd.org/Index.aspx?DataSetCode=MIN2AVE).\n\nPage 67 of 86", "output": {"entities": {"named_data": ["OECD.stat"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Box 2.1. Evaluation Grid for the Grant Scheme**\n\n**Scoring**\n\nThe evaluation criteria are divided into sections and subsections. Each subsection must be given a score between 1 and 5 in accordance with the following guidelines: 1 = very poor, 2 = poor, 3 = adequate, 4 = good, 5 = very good. The applications with the highest scores will be given priority when grants are awarded.\n\n**Note on Section 3. Methodology (employment)**\n\nIf the total score lower than ‘good; 20 points’ in section 3, the proposal will not be evaluated. Also, proposals with a total score below 50 will not be evaluated.\n\nIf a score for subsection 3.2, 3.3, 3.4, and 3.5 is 1 (very poor), the multiplier will be applied as ‘zero’ (5 x 0)\n\n**The awarding criteria** evaluates the quality of the applications in relation to the objectives and priorities set\nforth in the guidelines and awards grants to projects which maximise the overall effectiveness of the call for proposals. The criteria will help select applications which the contracting authority can be confident will comply with its objectives and priorities, cover the relevance of the action, and be consistent with the objectives of the call for proposals, quality, expected impact, sustainability, and cost-effectiveness.\n\n**Provisional selection.** After evaluation, the applications will be ranked according to their score. The highest\nscoring applications will be provisionally selected until the available budget for this call for proposals is reached. In addition, a reserve list will be drawn up following the same criteria. This list will be used if more funds become available during the validity period of the reserve list.\n\n**Grant Evaluation Grid**\nEnterprise size of the applicant firm (small/medium/large) Small/Medium/Large\n**Section** **Maximum Score**\n**1. Relevance** **10**\n1.2. How relevant is the proposal to the objectives (formal employment of target 5\ngroups) and one or more of the priorities of the call for proposals? Have the needs of the target groups proposed and the final beneficiaries been clearly defined and does the proposal address them appropriately?\n\n1.3. Does the proposal contain specific elements of added value, such as innovative\napproaches, models for good practice, promotion of refugee employment, gender equality and equal opportunities, environmental protection? (refugee- and womeninclusive firms will be given higher points).\n\n5\n\n**2. Financial and Operational Capacity** **15**\n2.1. Does the applicant have operational capacity sufficient technical expertise, 7.5 management capacity and financial capacity? (notably knowledge of the issues to be addressed.) 2.2. Is the proposed business plan appropriate, clear and feasible, and consistent with the objectives and expected results?\n\n7.5\n\n**3. Methodology (Formal Employment)** **45**\n3.1. How coherent is the overall design of the action? (5 X 2) 7.5 (in particular, does it reflect the analysis of the problems involved, take into account external factors, competition, market trends and create employment according to program objectives?) 3.2. Does the proposal include sufficient number of additional employments of ESSN beneficiaries? (5 X 2) 7.5 Page 68 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) % of additional employment; very poor (1): none / poor (2): <10% / adequate (3): <30% / good (4): <50% / very good (5): ≥50% 3.3. Does the proposal include additional employment of any male refugees? (5 X 2) 7.5 % of additional employment; very poor (1): none / poor (2): <10% / adequate (3): <30% / good (4): <50% / very good (5): ≥50% 3.4. Does the proposal include employment of any female refugees? (5 X 3) 10 % of additional employment; very poor (1): none / poor (2): <10% / adequate (3): <30% / good (4): <50% / very good (5): ≥50% 3.5. Does the proposal include any female Turkish citizen employment? (5 X 2) 7.5 % of additional employment; very poor (1): none / poor (2): <10% / adequate (3): <30% / good (4): <50% / very good (5): ≥50% 3.6. Does the proposal include male Turkish citizen employment? (5 X 1) 5 % of additional employment; very poor (1): none / poor (2): <10% / adequate (3): <30% / good (4): <50% / very good (5): ≥50%\n\n**4. Cost-effectiveness** **15**\n4.1. Is the ratio between the estimated costs and the expected results satisfactory? 7.5 (*) employment numbers will be proportionated with the total grand amount (new employment over grant amount 4.2 Are the proposed activities (investment and/or working capital) appropriately reflected in the budget?\n\n7.5\n\n**5. Sustainability** **15**\n5.1. Is the action likely to have a tangible impact on its target groups and sustainable 7.5 (financially, institutionally, and environmentally)\n\n- Financially (how will the activities be financed after the EU funding ends?)\n- Institutionally (will structures allowing the activities to continue be in place at the\nend of the action?\n\n- Environmentally (if applicable) (is the action environmentally sustainable)\n5.2. Is the proposal likely to have multiplier effects? (including scope for replication and extension of the outcome of the action and dissemination of information.) 7.5\n\n**Maximum total score** **100**\n\n18. **The PIU will serve as the secretariat of the Grant Evaluation Committee and administer the grant**\n**financing tool together with the relevant departments in the TKYB (such as Customer Value**\n**Management and Business Development Departments).**\n\n19. **There will be one grant application call happening in all 24 provinces at the same time, possibly**\n**followed by a second round in case of insufficient demand.** Grant applications will be received online\nthrough the TKYB website, where related implementation and submission guidelines will also be available.\nApplications will be evaluated by a grant evaluation committee including TKYB experts as well as one to four independent evaluators. The committee will be the responsible for preliminary approval of the grants, with the ‘no objection’ of the World Bank. After approval, a grant agreement will be prepared together with the World Bank and will be signed by the beneficiary firm and the TKYB. The grant agreement will include financial, operational, and reporting requirements for the grant. The details of the Page 69 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) format of the grant evaluation committee meetings and assessment criteria, as well as the rules, duties, and responsibilities of that committee, will be defined in the relevant POM.\n\n20. **The grant disbursement will be done in three tranches, based on the firm’s continual**\n**compliance with the employment creation and retention conditionalities under the grant scheme.** [36] The\nPIU will consider a cutoff baseline date (for example, three months before the grant award date) to assess the firms’ progress toward complying with the proposed employment targets, depending on the latest official data sources available on the firm’s formal employment. Grant beneficiary firms will have six months to hire new employees from the date of grant award. The first tranche will be disbursed only when the TKYB verifies the existence of new hires using SGK administrative data. The tranche will be weighted by the share of the actual new hires to the total new hires proposed in the business plan (for example, if a beneficiary firm reports that it will hire six new employees but hires three of them within first couple of months, then the firm will be awarded 50 percent of the grant amount). This grant amount will be treated as a subgrant for the firm, and the following subgrants will be awarded once the remaining employment conditions are met at every nine-month interval). The second employment conditionality check will take place nine months after the first tranche disbursement for the beneficiaries under the grant scheme. The second tranche will be disbursed only after the second conditionality check is met and will be weighted again by the proportion of new employees (on the total employment target) hired for at least nine months at the date of the second conditionality check for beneficiaries. The third employment conditionality check will happen nine months after the second tranche disbursement for the beneficiaries under the scheme.\nThe third tranche will be disbursed proportional to the number of new employees hired for at least 18 months for the scheme beneficiaries. If some of the firms fail to fulfill the employment conditions, the TKYB will provide an additional period (up to six months) for compliance. The Bank will support TKYB to develop a case management system. The next conditionality check will be conducted once the employment condition is met. If some firms cannot fulfill the conditions or leave the grant program, then the remaining resources will be used to finance new firms.\n\n21. **Monitoring compliance with the conditionalities.** Monitoring of compliance with the conditionalities will rely on official employment data from SGK. To assess the compliance of grantbeneficiary firms with the formal employment creation and retention targets specified in the business plan at the moment of application (including the consent of the employers and employees to be taken in line with the requirements of the Law on Protection of Personal Data, Law no. 6698), the PIU at the TKYB will receive regular employment and wage records for beneficiary firms from SGK at grant allocation, every six months thereafter, and periodically following requests for disbursements and claims of conditionality compliance by beneficiary firms. The PIU will also monitor the compliance of working conditions of the employees in accordance with labor law and labor and working conditions defined in ESS2 of ESF of the 36 The three tranches will be cumulative 20 percent, cumulative 60 percent, and cumulative 100 percent of the awarded grant amount.\n\nPage 70 of 86", "output": {"entities": {"named_data": [], "descriptive_data": ["SGK administrative data", "official employment data from SGK"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) World Bank. [37] For reporting and evaluation purposes, the World Bank team will regularly monitor creation of employment and retention of employees in grant beneficiary firms even after the expiration of the grant.\n\n22. **Communication and visibility.** As the grant component is funded through an EU-funded trust fund, the EU’s support shall be visible to all direct beneficiaries. All communications and visibility actions executed under the grant component will comply with the Communication and Visibility Manual for EU External Actions [38] and the FRIT Facility Visibility Guidelines. [39] The EU-Turkey cooperation logo will appear on all visibility materials and at all events, accompanied by the following text: ‘This project is funded by the EU’.\n\n23. The objectives of communication activities to be carried out under the grant component are to facilitate outreach and engagement of firms operating in provinces with high incidence of SuTPs accessing financial resources provided by the grant component and to inform and communicate to beneficiaries and stakeholders about the project’s objectives by sharing results and impacts of project interventions.\n\n24. The goods, services, and operational expenditures to implement a communication and outreach strategy across various geographical locations and economic sectors will be funded by the grant.\n\n25. A detailed communication and visibility plan will be drafted by the PIU, containing overall and specific communication objectives, clearly identified target groups, main communication activities and tools, indicators of achievement, and information on human and financial resources required to implement the grant component.\n\n**C. Detailed Description of the Capacity Building Component**\n\n26. **Skills building for loan beneficiary firms.** The loan beneficiary firms will be expected to participate in management capacity-building activities provided by the TKYB and financed by the loan component of the project. The coverage and scope of the training activities will be determined by the TKYB by conducting surveys of the participating firms. The training activities will be designed under a general framework and will include face-to-face and distant learning modules. At least 60 firms are expected to use loans under the project. The World Bank team expects that all the firms will participate in the training activities. The participants (around 120) will attend five days of face-to-face programs, followed by 80 hours of distant learning activities. Face-to-face programs will be held in one province (that is, Istanbul), and the travel 37 In August 2016, the World Bank’s Board of Executive Directors approved the ESF, which came into effect in 2018 and progressively replaced the World Bank’s Safeguards. The ESF protects people and the environment from potential adverse impacts that could arise from World Bank-financed projects and promotes sustainable development. Within the ESF, 10 ESSs set out responsibilities for borrowers. The standards are designed to help borrowers manage project risks and impacts as well as improve E&S performance, consistent with good international practice and national and international obligations. The Environmental and Social Standard on Labor and Working Conditions (ESS2), requires that employers (a) promote safety and health at work; (b) promote the fair treatment, nondiscrimination, and equal opportunity of project workers; (c) protect project workers, with emphasis on vulnerable workers; (d) prevent the use of all forms of forced labor and child labor; (e) support the principles of freedom of association and collective bargaining of project workers in a manner consistent with national law; and (f) provide project workers with accessible means to raise workplace concerns.\n38 European Union. 2018. _Communication and Visibility in EU -Financed External Actions:_ _Requirements for Implementing Partners_ _(Projects)._ _[https://ec.europa.eu/international-partnerships/comm-visibility-requirements_en](https://ec.europa.eu/international-partnerships/comm-visibility-requirements_en)_ 39 Communication concerning the European Union's External Action and Visibility Manual Appendix EU Aid for Refugees in Turkey.\n_[visibilityguidelines_may2017_frıt_en_20170605_final_tr.docx](https://www.avrupa.info.tr/sites/default/files/2017-06/VisibilityGuidelines_May2017_FRIT_EN_20170605_Final_TR.docx)_ Page 71 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) and accommodation costs of the participants will be covered under the loan component. The World Bank team will help the TKYB to prepare the terms of references of the individual consultants and/or training providers. [40] The training providers will be selected through the Selection based on the Consultants' Qualifications (CQS) method based on their experiences, technical capabilities, and coverage of the existing training programs (including distant learning modules). Surveys will be developed to assess the effectiveness of the training activities with the technical support of the World Bank team.\n\n27. **Skills building for grant beneficiary firms.** The grant beneficiary firms are expected to let the newly hired employees under the grant scheme participate in capacity-building activities, provided by the TKYB and financed by the grant component. Training activities will focus on building new (or improving the existing) skills of the employees, such as computer literacy, basic accounting bookkeeping, data compiling, and so on. The coverage and scope of the training activities will be determined by the TKYB by conducting surveys of the participating firms. The training activities will be designed under a general framework and will include face-to-face and distant learning modules. A total of 4,500 new employees are expected to be recruited under the grant scheme. The World Bank team expects that one-third of the new hires will participate in two days of face-to-face programs, and all the new hires will participate in 80 hours of distant learning activities. The face-to-face programs will be held in one province (that is, Istanbul), and the travel and accommodation costs of the participants will be covered under the project.\nThe World Bank team has examined the training programs of several training providers and will help the TKYB prepare the terms of references of the individual consultants and training provider firms. Surveys will be developed to assess the effectiveness of the training activities with the technical support of the World Bank team. The project will also seek opportunities to engage with IŞKUR, the MoNE, and other institutions to use the existing training programs.\n\n28. **Capacity building for implementation of sub-loans.** Capacity-building activities will finance consultancy services under/by the PIU and training activities for the TKYB and PFIs.\n\n29. **Project management** _._ As a first step, a PIU will be established to ensure that the appropriate skills mix is available (including E&S safeguards and fiduciary experts) and build capacity to monitor compliance with the job creation conditionality, provide implementation support to PFIs, and measure project results.\nThe PIU will be composed of TKYB personnel and individual consultants. The PIU will be staffed with adequate capacity to ensure that all World Bank procedures are followed during project implementation.\nA procurement specialist will be hired under part contracts funded by the loan and grant separately and will provide required services for loan and grant implementation. TYKB will assign appropriate staff for financial management, M&E and Training services for Loan implementation. A financial management specialist an M&E specialist, and a training coordinator will be hired under Grant to provide required services for grant implementation. The World Bank team will also conduct a process evaluation during the project implementation and an impact evaluation at the end of the project. The terms of references of the individual consultants will be presented in the POMs. The cost of goods, equipment, travel, and operating expenses of the PIU will be financed by the grant. The PIU, with support from the World Bank, will create an implementation and compliance toolkit for the grant implementation to ensure that all 40 International Education Center and Counseling Center, Aydin Academy, Etkin Academy, Global Career, TOBB University of Economics and Technology, Turkiye Girisim, Gelisim ve Teknolojileri Dernegi, Baskent University Training and Counselling Services Center, Business Management Institute, Ankara University Training Center, Deloitte Academy, PWC Business School, AB Academy, KOSGEB e-Academy, and Kadir Has University Life Long Learning Center.\n\nPage 72 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) relevant documents are collected on-time, the employment conditionality is properly applied and monitored, and tranche disbursements are properly carried out. Details on the toolkit, including implementation guidelines, will be included in the POMs. The PIU will also be responsible for the regular reporting to the World Bank and other stakeholders.\n\n30. **Capacity** **building in PFIs.** Each prospective PFI will be supported in conducting a survey of loan officers after the signature of a subsidiary finance agreement with the TKYB, based on a standard methodology agreed with the World Bank, to identify capacity deficiencies among staff. The capacity building in PFIs is necessary to guide interested firms in filling out employment plans, reviewing eligibility of applications, and assessing firms’ potential beyond their financial capacities. PFIs will commit to participating in the series of training events during project implementation (organized jointly by the TKYB and the World Bank) to improve the capacity for lending to prospective beneficiaries and borrowers. The TKYB loan officers will also benefit from these services. These activities will be financed by the loan component.\n\n31. **Capacity building for grant implementation.** Capacity-building activities will finance the services of individual consultants and/or consultancy firms to be recruited under/by the PIU, grant governance body, goods, non-consulting services (including travel), communication, outreach and visibility activities, and operational expenses to implement the grant component.\n\n32. **Project management** _**.**_ In addition to the individual consultants, that is, a financial management specialist, an M&E specialist, and a training coordinator to be hired for implementation of subgrants, a procurement specialist will be hired under part time contracts funded by the loan and grant separately.\nOne or more individual consultants will be recruited to conduct labor audits in the grant beneficiary firms under the grant component.\n\n33. **Facilitation services for the grant beneficiary firms.** This will include assisting beneficiary firms in recruiting personnel through various employability programs supported by the Government or the EU, such as İŞKUR, Red Crescent, and the Association for Solidarity with Asylum Seekers and Migrants. Formal links between the TKYB and various employment or training institutions will be established to ensure that appropriate referrals lead to recruitment. The staff involved in the project implementation will be trained on the enforcement of the conditionality, including report drafting, the verification and validation process, the sanctions process, and data exchange with SGK. These capacities will be maintained by the PIU staff and the mechanism and learning can be used for future projects with similar objectives.\n\n34. **Conducting communications and outreach services.** The goods, services, and operational expenditures to implement a communication and outreach strategy across various geographical locations, economic sectors, and implementing institutions will be funded by the grant. Materials will be created to reach out to eligible firms (and potential workers, if needed), including information on the application process, eligibility criteria, time line, and options to get support for the preparation of applications.\nCommunications materials in a format appropriate for targeted firms and beneficiaries will be included.\n\n35. **Enhancing job creation conditions in the ecosystem in which loan and grant beneficiary firms**\n**operate.** This will be done by maximizing the links and spillovers between the loan and grant beneficiary\nfirms. This will be achieved on the one hand by prioritizing, among loan beneficiary firms, firms with larger networks of intermediary and subsidiary SMEs (potential grant beneficiaries). On the other hand, in the Page 73 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) initial stages of the project, the TKYB will facilitate, owing to its extended business network and with the technical support of the World Bank team, outreaching events, in the form of ‘job fairs’, where potential loan and grant beneficiaries will be convened to incentivize the formation of business networks and links.\n\nPage 74 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**ANNEX 3: Financial Intermediary Assessment**\n\n1. **An assessment of the TKYB took place at the reappraisal stage based on eligibility criteria in**\n**accordance with Bank Policy: Investment Project Financing paragraph 12, referring to projects in**\n**situations of urgent need of assistance or capacity constraints.** Eligibility criteria included the following:\n\n(a) The bank must be duly licensed and have been in operation for at least two years.\n\n(b) The bank’s owners and managers must be considered ‘fit and proper’. It must have qualified and experienced management and adequate organization and institutional capacity for its specific risk profile.\n\n(c) The bank must be in ‘good standing’ with its supervisory authority (that is, it should meet all pertinent prudential and other applicable laws and regulations) and remain in compliance at all times.\n\n(d) The bank must maintain capital adequacy prescribed by prudential regulations.\n\n(e) The bank must have adequate liquidity.\n\n(f) The bank must have positive profitability and acceptable risk profile. It must maintain the value of its capital.\n\n(g) The bank must have well-defined policies and written procedures for management of all types of financial risks (liquidity, credit, currency, interest rate, and market risk, and risks associated with balance sheet and income statement structures) and operational risk.\n\n(h) The bank must classify its assets and off-balance-sheet credit risk exposures (at least four times per year) and make adequate provisions. It must have adequate portfolio quality. The bank should not have more than 10 percent of criticized assets (that is, classified as doubtful and loss).\n\n(i) The bank must have adequate internal audits and controls.\n\n(j) The bank must have adequate management information systems.\n\n2. **A detailed confidential appraisal report has been internally filed with summary results**\n**presented in the table 3.1.** The appraisal is based on the following sources of information: (a) audited\nfinancial statements as of September 30, 2019, (b) written information provided by the TKYB, and (c) interviews with senior TKYB management.\n\n**Table 3.1. Summary of the TKYB Appraisal**\n\n**Criterion** **Comments/Actions**\n1. License Criterion met\n2. Owners/managers ‘fit and proper’ and governance quality Criterion met 3. Good standing with Bankacılık Düzenleme ve Denetleme Kurumu Criterion met (BRSA) 4. Capital adequacy Criterion met 5. Liquidity Criterion met Page 75 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Criterion** **Comments/Actions**\n6. Profitability Criterion met 7. Policies and risk management functions Criterion met 8. Asset quality and provisions Criterion met 9. Internal audit and controls Criterion met 10. Adequate management information systems Criterion met\n\n**Background of the TKYB**\n\n3. **The TKYB is a state-owned development bank (99 percent of the shares owned by Turkish**\n**Treasury) which was established in 1975 with the goal of channeling the remittances of Turkish**\n**expatriate workers to finance private sector development in Turkey.** Over the years, the mandate of the\nTKYB was broadened to support the development of joint stock companies in various sectors, provide technical assistance to investors, and promote the development of capital markets.\n\n4. **Since its establishment in 1975, the TKYB has been providing investment and working capital**\n**loans to private companies operating mainly, in industry, tourism, and energy sectors, in accordance**\n**with the development needs of Turkey.** However, as an additional product, wholesale banking (apex)\nloans have been introduced in 2008 to better serve small and medium companies who are the driving force of growth and employment. For this project, the TKYB will provide direct lending to viable LEs.\n\n5. **The TKYB’s asset size is TL 18.8 billion (0.46 percent of the banking system in terms of asset size)**\n**as of Q3 2019.** It has 315 employees and its head office recently has been moved in Istanbul. The TKYB’s\nfunding structure is 91 percent medium and long-term liabilities, mostly funding from the IFIs. While loans/total asset ratio is 78 percent, its gross nonperforming loan (NPL) ratio is below 1 percent, it operates with 2.7 percent return on assets (ROA) ratio, and capital adequacy ratio (CAR) is 22.4 as of Q3 2019. The TKYB’s net foreign exchange position is balanced. The TKYB’s sectoral loan engagements include energy and commodities (48 percent), manufacturing (12.8 percent), tourism (11 percent), leasingfactoring (9 percent), construction and real estate (0.13 percent), retail (0.05 percent), and others (19.8 percent).\n\n6. **To meet financing needs of SMEs more effectively, apex loans provided through lending**\n**intermediary financial institutions with extensive branch networks are preferred by the TKYB in recent**\n**years.** As for apex loan programs developed in line with its mission, the TKYB also identifies the needs of\nthe target market segments and matches appropriate resources borrowed from IFIs, mostly obtained with the guarantee of the Turkish Government, and channels funds through lending commercial banks and leasing companies. The main objectives for apex projects are identified as contributing to job creation and increasing the competitiveness of SMEs in Turkey.\n\n**Background on World Bank Projects with the TKYB**\n\n7. **The TKYB is the recipient of one active and three closed lines of credit from the World Bank in**\n**the last decade.** The TKYB has reached 62 percent disbursement rate as of December 2019 under the\nTurkey Geothermal Development Project (P151739), expecting full disbursement by the closing date in December 2022. Some recent World Bank engagements include Second Turkey Access to Finance for Small and Medium Enterprises Project (P118308 - US$100 million, onlending, closed in 2012), Private Sector Page 76 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) Renewable Energy and Energy Efficiency Project (P112578 - US$1 billion, also with Türkiye Sınai Kalkınma Bankası (TSKB) and TKYB, closed in 2010); and Renewable Energy Project (P072480 - US$201 million, also with TSKB and TKYB, closed in 2009).\n\n**Table 3.2. Simplified Consolidated Balance Sheet and Income Statement for the TKYB**\n\n**(TL, millions)** **December 31, 2018** **December 31, 2019**\n**TL** **Foreign** **Total** **TL** **Foreign**\n**Currency** **Currency**\n\n**Total**\n\n**Cash and banks** 102 319 **421** 1,596 741 **2,337**\n**Securities** 133 135 **268** 494 1,349 **1,843**\n**Loans** 427 13,214 **13,642** 244 14,661 **14,905**\n**Subsidiaries** 15 58 **73** 16 0 **16**\n**Others** 1,028 284 **1,311** 178 97 **275**\n**Total** **1,705** **14,009** **15,715** **2,528** **16,848** **19,376**\n**Short term funds** 0 0 **0** 0 0 **0**\n**Long term funds** 17 13,571 **13,588** 18 1,5364 **15,382**\n**Repo*** 1 0 **1** 50 0 **50**\n**Other** 317 392 **709** 122 1,465 **1,587**\n**Equity** 1,406 11 **1,417** 2,353 4 **2,357**\n**Total** **1,740** **13,975** **15,715** **2,543** **16,833** **19,376**\n\n**Table 3.3. Simplified Income Statement for the TKYB**\n\n**January 1–**\n**December 31,**\n\n**2019**\n\n**(TL, millions)**\n\n**January 1–**\n**December 31,**\n\n**2018**\n\n**Change**\n\n**(%)**\n\n**Net interest income** **457** **708** **55**\nNet fees + commissions 26 14 -46 Dividend income 2 5 150 Net trading income -5 35 -800 Other operating income 16 23 44\n**Total operating income** 496 785 58\nProvisions (+/-) 155 72 -54 Other operating expenses 96 127 32\n**Net operating income** **245** **586** **139**\nProfit before tax 245 586 139 Tax provisions 85 139 64\n**Net profit** **160** **47** **179**\n\n**Table 3.4. TKYB’s Key Financial Ratios on Consolidated Basis**\n\n**(Percentage)** **Q3 2018** **Q4 2018** **Q1 2019** **Q2 2019** **Q3 2019** **Q4 2019**\n**CAR** 12.79 14.18 13.95 21.32 22.36 **22.29**\n**NPL** 0.86 0.90 0.82 0.79 0.81 **0.82**\n**ROE** 3.02 12.40 29.67 24.63 24.68 **22.9**\n**ROA** 0.33 1.27 2.89 2.67 2.72 **2.49**\n**Cost to income** 0.94 0.79 0.58 0.61 0.60 **0.64**\n\nPage 77 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**ANNEX 4: Additional Sectoral Background**\n\n1. The World Bank’s enterprise surveys have been collected to understand what firms experience in\nthe private sector. It follows a global methodology and provides a wide range of business environment topics including access to finance indicators.\n\n2. To estimate the effects of access to finance constraints on firms’ employment growth, both subjective and objective measures of the investment climate (that is, access to finance) were used. The regression results are presented in tables 4.1 to 4.4 based on the models where employment growth is the dependent variable and the independent variables are those measuring access to finance constraints (such as industry, sector, firm’s ownership and firm’s age).\n\n3. The primary variable of interest is having access to finance obstacle, which is the access to finance constraint perceived by the firms as the top obstacle for business environment. If the access to finance is the top obstacle by the firms for employment growth, it will have a negative sign. The results presented in tables 4.1 and 4.2 show that the access to finance constraint has a significant negative effect on employment growth for small firms (5–49 employees) and large firms (more than 250 employees). The effect is stronger for the large firms.\n\n**Table 4.1. Effect of Access to Finance on Employment Growth, Using Subjective Measure of Access to Finance**\n\nDependent variable: Average growth in employment Pooled cross sections: 2008, 2013-14 and 2015-16 Firm size: Less than Firm size: Firm size: Firm size: 5 5-49 50-249 250+ VARIABLES All firms employees employees employees employees Biggest Business Environment Obstacle is: Access to Finance -0.0076* 0.0042 -0.0205*** 0.0152 -0.1343** (0.004) (0.003) (0.007) (0.010) (0.059) Constant 0.1110*** 0.0473* 0.1328*** 0.1612*** 0.3617*** (0.011) (0.026) (0.014) (0.022) (0.124) Observations 6,562 2,185 2,530 1,220 627 R-squared 0.109 0.045 0.133 0.101 0.519 Standard errors in parentheses\n*** p<0.01, ** p<0.05, * p<0.1\n_Source_ : Enterprise Survey, 2008, 2013–14, and 2015–16.\n_Note_ : Explanatory variables include firm size and age, firm’s ownership status, industry, region, and year.\n\n5-49 employees 50-249 employees VARIABLES All firms 5 employees\n\n**Table 4.2. Effect of Access to Finance on Employment Growth, Using Subjective Measure of Access to Finance**\n\nDependent variable: Growth in employment Pooled cross sections: 2008, 2013-14 and 2015-16 Firm size: 5-49 employees Firm size: 50-249 employees Firm size: 5 5-49 50-249 250+ VARIABLES All firms employees employees employees employees Biggest Business Environment Obstacle is: Access to Finance -0.1279 0.0192 -0.0977*** 0.0694 -4.5589*** (0.087) (0.012) (0.034) (0.053) (1.762) Constant 1.3017*** 0.2374** 0.6930*** 0.8355*** 6.4888* (0.224) (0.098) (0.063) (0.118) (3.715) Observations 6,562 2,185 2,530 1,220 627 R-squared 0.056 0.041 0.143 0.091 0.491 Standard errors in parentheses\n*** p<0.01, ** p<0.05, * p<0.1\n_Source:_ Enterprise Survey, 2008, 2013–14, and 2015–16.\n_Note:_ Explanatory variables include firm size and age, firm’s ownership status, industry, region, and year.\n\nVARIABLES All firms Firm size: Less than 5 employees Page 78 of 86", "output": {"entities": {"named_data": [], "descriptive_data": ["enterprise surveys"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) 4. The objective measure of access to finance uses the definition of credit constrained status of Kunchev et al. (2013). [ 41] The variable has the following four categories: (a) fully credit constrained (FCC), [42] (b) partially credit constrained (PCC), [43] (c) likely credit constrained (LCC), [44] and (d) non-credit constrained (NCC). The regression results reveal that credit constraint status has a significant positive effect on employment growth among private and formal firms in Turkey. If a firm has greater access to finance markets, it will experience higher employment growth. Experiencing a credit constraint is a more serious problem for microenterprises and SMEs compared to large firms.\n\n**Table 4.3. Effect of Access to Finance on Employment Growth, Using Objective Measure of Credit Constraint**\n\n**Status**\n\nDependent variable: Average growth in employment Pooled cross sections: 2008, 2013-14 and 2015-16 Firm size: 5-49 employees Firm size: 50-249 employees Firm size: 5 5-49 50-249 250+ All firms employees employees employees employees Partially Credit Constrained (PCC) 0.0114* 0.0051 0.0263** 0.0160 0.1771 (0.006) (0.005) (0.011) (0.017) (0.135) Likely Credit Constrained (LCC) 0.0160*** 0.0128*** 0.0182* 0.0558*** 0.1158 (0.006) (0.005) (0.010) (0.010) (0.096) Not Credit Constrained (NCC) 0.0134*** 0.0061* 0.0240*** 0.0168* 0.0196 (0.004) (0.003) (0.008) (0.010) (0.082) Constant 0.1032*** 0.1067*** 0.0729*** 0.1007*** 0.7243*** (0.012) (0.033) (0.015) (0.021) (0.160) Observations 5,984 2,412 2,046 992 534 R-squared 0.085 0.038 0.072 0.123 0.568 Standard errors in parentheses\n*** p<0.01, ** p<0.05, * p<0.1\n_Source_ : Enterprise Survey, 2008, 2013–14, and 2015–16 _Note_ : Explanatory variables include firm size and age, firm’s ownership status, industry, region, and year. Control group for credit constraint status is FCC.\n\nAll firms Firm size: Less than 5 employees 41 Kuntchev, V., Ramalho, R., Rodriguez-Meza, J., Yang, J.S., 2013. What have we learned from the Enterprise Surveys regarding access to finance by SMEs? Policy Research Working Paper 6670. World Bank, Washington D.C.\n42 The firms in the FCC group applied for a loan and were rejected and do not have any type of external finance. Firms in the FCC group meet all the following conditions simultaneously: (a) did not use external sources of finance for both working capital and investments during the previous year, (b) applied for a loan during the previous year, and (c) do not have a loan outstanding at the time of the survey that was disbursed during the last fiscal year or later.\n43 The firms in the partially credit constrained group meet the following conditions: (a) used external sources of finance for working capital or investments during the previous fiscal year or have a loan outstanding at the time of the survey; (b) did not apply for a loan during the previous fiscal year and the reason for not applying for a loan was other than having enough capital for the firm’s needs; some of the reasons may indicate that firms self-select out of the credit market because of prevailing terms and conditions; thus some degree of rationing is assumed; or (c) applied for a loan but was rejected.\n44 The firms in the LCC group have had access to external finance and there is evidence of them having bank finance and the LCC group includes firms that (a) used external sources of finance for working capital or investments during the previous fiscal year or have a loan outstanding at the time of the survey or (b) applied for a loan during the previous fiscal year.\n\nPage 79 of 86", "output": {"entities": {"named_data": ["Enterprise Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**Table 4.4. Effect of Access to Finance on Employment Growth, Using Objective Measure of Credit Constraint**\n\n**Status**\n\nDependent variable: Growth in employment Pooled cross sections: 2008, 2013-14 and 2015-16 Firm size: 5-49 employees Firm size: 50-249 employees Firm size: 5 5-49 50-249 250+ All firms employees employees employees employees Partially Credit Constrained (PCC) 0.0865 0.0199 0.1157** 0.0703 5.9283 (0.134) (0.018) (0.050) (0.073) (4.044) Likely Credit Constrained (LCC) 0.0816 0.0639*** 0.0866* 0.2174*** 3.4668 (0.130) (0.018) (0.045) (0.045) (2.869) Not Credit Constrained (NCC) 0.1264 0.0226* 0.0963*** 0.0645 0.0900 (0.097) (0.013) (0.036) (0.042) (2.441) Constant 1.9124*** 0.4297*** 0.4076*** 0.5504*** 16.8318*** (0.264) (0.129) (0.067) (0.090) (4.765) Observations 5,984 2,412 2,046 992 534 R-squared 0.062 0.036 0.071 0.113 0.546 Standard errors in parentheses\n*** p<0.01, ** p<0.05, * p<0.1\n_Source_ : Enterprise Survey, 2008, 2013–14, and 2015–16 _Note_ : Explanatory variables include firm size and age, firm’s ownership status, industry, region, and year. Control group for credit constraint status is FCC.\n\nAll firms Firm size: Less than 5 employees Page 80 of 86", "output": {"entities": {"named_data": ["Enterprise Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766)\n\n**ANNEX 5: Review of the Literature and Relevant Projects**\n\n1. This section presents a summary of the existing evidence on the effectiveness of finance\ninterventions (both World Bank and non-World Bank) aimed at improving access to credit and employment generation among microenterprises and SMEs. The analysis reviewed 38 impact evaluation studies covering four main types of finance interventions: (a) lines of credit, (b) microcredit, (c) cash and in-kind grants and transfers, and (d) formalization interventions. A total of 17 studies focused on lines of credit, 7 studies focused on microcredit, 6 studies covered cash and in-kind grants, while 4 studies centered on formalization interventions. Four studies investigated ‘access to credit’ but without specifying the type of credit product.\n\n2. **Overall, improving access to finance appears to have a positive and significant impact on**\n**employment generation** . Out of 22 interventions targeted at employment generation, 20 show positive\nand statistically significant impacts, while the remaining 2 report no effects. A cross-country analysis reviewed shows that, when provided with access to credit, microenterprises and SMEs are found to experience 1 percent to 4 percent increase in employment. [45] Expanding financing helps microenterprises and SMEs expand their operations, grow in size, and also preserve existing jobs, especially in periods of economic and financial downturn. Evidence from the cases under review, however, reveals that the impact of access to credit is not homogenous, as it varies with specific firm characteristics as well as the form of finance interventions.\n\n3. **Firm size and age stand as the major demand-side constraints to access to credit** . Smaller and younger firms appear to be more in need of credit but also more financially constrained than larger and older firms. In Turkey, the probability of accessing credit is 1.8 times higher for medium than micro firms, and 2.8 times higher for large than micro firms. [46] This is consistent with the findings from China, where large firms may have a 52 percent to 65 percent higher probability of investing relative to only 19 percent to 35 percent of small firms. This suggests a positive association between size and access to credit. [47] 4. **Despite their credit needs, however, younger and smaller enterprises are also more likely to**\n**hold off from applying for loans** . Reticence seems to be associated with fear of rejection, perception of\nunfavorable collateral requirements and high interest rates; management inexperience may also account as a possible reason for failing to apply. [48] This implies that combining finance interventions with business development services or entrepreneurship training may have a greater and more positive impact on employment generation.\n\n45 Ayyagari, Juarros, Martinez Peria, and Singh. 2016. “Access to Credit and Job Growth: Firm-Level Evidence across Developing Countries.” Policy Research Working Paper 7604, World Bank, Washington, DC.\n46 Mutluer, K., and S. Tiryaki. 2016. “How Credit-Constrained Are Firms in Turkey? A Survey-Based Analysis.” _Applied Economics_ _Letters_ 23 (6): 420–423.\n47 Regis, Paolo Jose. 2018. “Access to Credit and Investment Decisions of Small- and Medium-Sized Enterprises in China.” _Review_ _of Development Economics_ 22: 766–786.\n48 Cole, R., and A. Dietrich. 2014. “SME Credit Availability Around the World: Evidence from the World Bank’s Enterprise Survey.” World Bank, Washington, DC.\n\nPage 81 of 86", "output": {"entities": {"named_data": ["World Bank’s Enterprise Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) 5. **The implication is that access to credit has a greater impact on expanding credit availability and**\n**spurring employment generation for younger and smaller firms than for older and larger firms** . [49] When\nprovided with adequate capital, ‘gazelle’ firms and young firms show a higher job growth potential than larger and older counterparts. Based on a cross-country analysis included in this review, access to credit generated a 6 percent to 24 percent increase in employment among microenterprises and SMEs compared with large firms (Ayyagari, Peria, and Singh 2016). Only one study in the United States found that large firms generate a greater number of jobs than smaller firms. In particular, a loan of US$1 million was found to generate 2.2 jobs in small firms, 3.0 jobs in medium firms, 4.0 jobs in large firms, and 5.9 jobs in the largest firms. However, the study also highlighted that start-ups created 5.3 jobs because of the loan and that large firms were the least financially constrained. Therefore, the marginal impact of credit was greater for start-ups and small firms. [50] In line with these results, evidence grants awarded to Nigeria high-growth entrepreneurs in a business plan competition generated a 23 percent increase in the probability for new winning firms of employing more than 10 workers after 18 months from grant reception and a 20.8 percent increase in the same probability for existing firms. [51] 6. **Ceteris paribus, women-owned firms appear to face more severe financial constraints than their**\n**men-owned counterparts** . They also tend to have lower incentives to find external sources of funding, as\n‘discouraged firms’ as well as denied firms are found to be more likely to be run by women rather than by men (Cole and Dietrich 2014). As a result, women-owned firms seem less likely to invest than men-owned firms. In Uganda, capital drops in the form of microloans failed to generate business profits for womenowned microenterprises, while microloans were associated with 50 percent increase in business profits for men-owned businesses. Women also tend to suffer more from limited access to credit. Credit constraints for Nigerian men-owned microenterprises and SMEs translated into a 16.4 percent to 24 percent lower output per worker, 17 percent to 23 percent lower capital per worker, and a 28 percent to 30 percent lower investment in fixed assets. However, results were much larger for women-owned firms: respectively, 64.7 percent, 60.0 percent, and 48.4 percent (Nwosu and Orji 2017). This suggests not only that women tend to be more credit constrained than men, but also that credit-constrained women face other limitations than just credit in their ability to invest in their businesses.\n\n7. **When considering specific forms of finance interventions, lines of credit targeted to SMEs show**\n**comparatively greater potential to spur employment growth** . Out of 15 studies investigating the impact\nof lines of credit, 10 had a specific employment focus. All 10 studies reported that lines of credit had positive and significant effects on employment generation. For instance, a World Bank-financed onlending program targeted at SMEs resulted in the creation of 7,000 jobs and the preservation of 35,000 jobs among beneficiary firms in Turkey. Also, compared with nonbeneficiary firms, beneficiary firms created more jobs. Employment effects persisted after three years from disbursement (World Bank 2016).\nSimilarly, in the United States, the Small Business Investment Company (SBIC) program generated nearly 3 million jobs and preserved roughly 7 million jobs among beneficiaries. [52] 49 Paniagua, G., and A. Denisova. 2012. “Meta-Evaluation on Job Creation Effects of Private Sector Interventions.” World Bank Working Paper 82000: 1-42, World Bank, Washington, DC.\n50 Brown, D., J. Earle, and Y. Morgulis. 2015. “Job Creation, Small vs. Large vs. Young, and the SBA.” IZA DP No. 9489 _._ 51 Nwosu, E., and A. Orji. 2017. “Addressing Poverty and Gender Inequality through Access to Formal Credit and Enhanced Enterprise Performance in Nigeria: An Empirical Investigation.” _African Development Review_ 29 (S1): 56-72.\n52 Paglia, J., and D. Robinson. 2017. “Measuring the Role of the SBIC Program in Small Business Job Creation.” Library of Congress, Washington, DC.\n\nPage 82 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) 8. **Among different credit line products, public credit guarantee schemes tend to be more**\n**systematically associated with improved firm performance, particularly job growth** . Importantly, such\ngrowth appears to be sustained over time, even several years upon obtaining the loan _._ In Colombia, the positive employment effects of access to a credit line were found to be increasing from the year of disbursement through the following two years (respectively, 3.7 percent, 5.5 percent, and 6.6 percent). [53] In another study in Brazil, the average increase by 24 employees among firms with access to a line of credit from the Brazilian Development Bank was sustained for six years after the disbursement of the credit. [54] Across Central, Eastern and South Eastern Europe, the EU MAP (Multi-Annual Program) targeted to SMEs led to a 14 percent to 18 percent increase in employment at the aggregate level, with effects sustained over the first five years after the signature date. [55] The same program was found to have positive and significant impacts also in Italy, the Benelux, and the Scandinavian countries. Particularly, it was reflected in a 16.9 percent increase in employment among beneficiary SMEs relative to nonbeneficiary SMEs. Moreover, beneficiary firms saw a 19.6 percent increase in assets, a 14.8 percent increase in sales, a 1.0 percent increase in innovation, and a 30.0 percent decrease in the probability of default. Stronger program effects were observed for smaller and younger firms. [56] Finally, a government-backed guarantee scheme in Malaysia generated an average net increase of 103 jobs across beneficiary SMEs. [57] 9. **Loans channeled through state-owned banks or financial intermediary institutions tend to**\n**include better credit conditions than loans from other institutions** . In the case of Colombia’s Bancoldex,\nloans from this institution to micro, small, and medium enterprises and LEs resulted in an improved credit structure and increased access to credit for beneficiaries, in the form of 24.4 percent and 18.2 percent increases in loan size in the year of disbursement and in the following year, respectively. [58] Positive and significant results were also observed for employment creation and other business outcomes. Specifically, beneficiaries registered a 11 percent increase in employment, a 24 percent increase in output, a 70 percent increase in investment, and a 10 percent increase in productivity. Such increases in output, investment, and productivity tended to be associated with longer-term loans (more than five years) than shorter-term loans (less than five years) and were seen to persist up to four years after disbursement. [59] 10. **While further work is needed to understand why state-owned banks may offer better credit**\n**conditions, existing evidence suggests that those loans do not simply substitute credit that private**\n**institutions would be willing to offer under similar conditions** . The second-tier design, however, implies\nthat the intermediaries take on the risk of default. As a result, they face greater incentive to screen 53 Arráiz, I., M. Meléndez, and R. Stucchi. 2012. \"Partial Credit Guarantees and Firm Performance: Evidence from the Colombian National Guarantee Fund.\" IDB Publications (Working Papers) 4089, Inter-American Development Bank.\n54 De Negri, J., A. Maffioli, A. Rodríguez, and C. Gonzalo. 2012. \"The Impact of Public Credit Programs on Brazilian Firms.\" IDB Publications (Working Papers) 3826, Inter-American Development Bank.\n55 Asdrubali, P., and S. Signore. 2015. \"The Economic Impact of EU Guarantees on Credit to SMEs Evidence from CESEE Countries,\" European Economy - Discussion Papers 2015 - 002, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.\n56 Bertoni, F., J. Brault, M. Colombo, A. Quas, and S. Signore. 2019. \"Econometric Study on the Impact of EU Loan Guarantee Financial Instruments on Growth and Jobs of SMEs,\" EIF Working Paper Series 2019/54.\n57 Boocock, G., and M. Shariff. 2005. “Measuring the Effectiveness of Credit Guarantee Schemes. Evidence from Malaysia.” _International Small Business Journal_ 23, no. 4: 427-453.\n58 Eslava, M., A. Maffioli, and M. Melendez. 2012. “Second-tier Government Banks and Access to Credit: Micro-Evidence from Colombia.” IDB Working Papers, Series No. IDB-WP-308.\n59 Eslava, M., A. Maffioli, and M. Melendez. 2012. “Second-tier Government Banks and Firm Performance. Micro-Evidence from Colombia.” IDB Working Papers, Series No. IDB-WP-294.\n\nPage 83 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) profitable from unprofitable businesses to minimize risks. The implication is that funds can be mostly provided to sounder or higher-potential enterprises which are not necessarily the most credit constrained, rather than those firms that face higher obstacles to accessing credit and which, as mentioned above, tend to benefit the most from accessing credit in terms of higher employment growth.\n\n11. **This raises the question of whether funds may be most effective in generating a positive impact**\n**on performance if they are directed at firms that face the severest difficulties in accessing credit** .\nTargeting the most promising and higher-potential firms carries undeniable benefits. However, this strategy could be integrated with an effort to target firms that are the most credit constrained, such as young firms that lack a credit history or collateral requirements, but which are entering promising lines of business, to maximize the impact of the access to credit.\n\n12. **High-income countries provide a more mixed evidence on the effectiveness of public credit**\n**guarantee schemes in generating jobs** . In high-income countries, employment growth may benefit from\nmeasures targeted at improving relationship banking and to ease procyclicality, especially in economies where SMEs constitute the bulk of economic and productive activity. Across 11 OECD countries, neither direct government loans nor government-backed loans were found to have an impact on employment.\nRather, reinforcement of relationship banking and financial stability steps to ease procyclicality were observed to be more successful, generating a 12.8 percent to 18.2 percent increase and a 15.0 percent increase in employment, respectively. [60] Government-backed credit guarantees may be associated with greater risk-taking behavior and moral hazards by lending institutions. Nevertheless, they also seem to be associated with a larger size of loans as well as less stringent standards and lower cost of borrowing. As several studies reviewed here show that higher-value loans also tend to have greater and more positive impacts on employment generation, the success of public credit guarantee program may depend on finding the right balance between employment and financial stability goals. [61] 13. **Public credit guarantee schemes seem to have no impact on labor productivity** . In Brazil, loans from Brazilian Development Bank from micro, small, and medium enterprises and LEs were shown to have no significant impact on productivity, with the latter measured as real wages (De Negri et al. 2012). While this result may depend upon how productivity is measured, it proves consistent across the reviewed cases in both developed and developing countries. This suggests that borrowing firms employ extra capital mainly as working capital (when this is allowed by the lender’s criteria), rather than investing it in capital stock.\n\n14. **Albeit popular, microloans are found to hardly increase employment and profits** . Only three microloan programs reviewed were specifically targeted at employment generation. Two of these programs, implemented in Bosnia and Herzegovina, increased the likelihood for beneficiaries of being self-employed and owning a business by 6 percent. However, effects were not observed beyond 14 months from disbursement. Moreover, the effects of microloans were heterogeneous, depending upon the characteristics of the borrowers at baseline. In particular, the impact on self-employment and business creation was mainly driven by highly educated beneficiaries, who tend to open their business in urban 60 Seo, Ji‐Yong. 2017. \"A Study of Effective Financial Support for SMEs to Improve Economic and Employment Conditions: Evidence from OECD Countries.\" _Managerial and Decision Economics_ 38 (3): 432-442.\n61 Gropp, Reint, et al. 2014. “The Impact of Public Guarantees on Bank Risk-Taking: Evidence from a Natural Experiment.” _Review_ _of Finance_ 18 (2): 457-488.\n\nPage 84 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) sectors, rather than lower-educated beneficiaries, who instead were more likely to start a business in the rural sector. [62] 15. **Conventional microloan contracts, typically including short repayment periods or no grace**\n**periods, often fail to generate growth at the extensive margins** . Often the reason behind such limitation\nis that the rigidity of conventional debt contract terms discourages capital-constrained or poor entrepreneurs from engaging in riskier but higher-return investments. By contrast, different contract structures, including grace periods and/or individual liability, may promote firm growth by allowing for a more balanced risk- and revenue-sharing between the investors and the borrowers. [63] While repayment flexibility or grace periods in the debt contract terms have been found to have positive and significant impacts on business outcomes such as profits, revenues, and business assets, their effects on employment generation is ambiguous at best. In one study in Bangladesh, repayment flexibility had no employment impacts for collateral-free loan borrowers, whereas it led to a 42 percent increase for collateral-backed microloans borrowers. However, treatment effects were associated with a larger loan size and higher schooling levels among collateral-backed clients. [64] In sum, microloans seem to have clearer impacts on stabilizing income and in securing the survival and continuation of existing businesses than on creating new employment. This implies that increased access to capital through microloans may contribute more to employment protection than employment creation.\n\n16. **Similarly, the impact on firm growth of capital alone, when provided through cash or in-kind**\n**grants, remains unclear** . While a few studies underscore positive results on profits, other studies shed\nlight on the short-term or insignificant nature of those impacts. Especially, grants appear to be more successful in enhancing firm survival, provided that they are distributed in kind and paired with other accompanying measures, such as training programs. In Sri Lanka, unconditional one-time cash or in-kind grants resulted in a 4.6 percent to 5.3 percent increase in monthly returns to capital among beneficiary microenterprises, although returns were higher for in-kind grants. [65] A follow-up study showed that combining a savings incentive program either with a wage subsidy to hire more employees or a business training program led, respectively, to a 0.23 and 0.19 increase in the number of workers only in the short term (one year). [66] In Ghana, positive impacts on monthly profits (US$21–US$31) among micro firms were associated only with in-kind grants but no effect was observed for cash grants. [67] Finally, in Northern Uganda providing unconditional, unsupervised lump-sum cash transfers to microentrepreneurs to pay for vocational training, tools, and business start-up costs produced 25 percent increase in employment 62 Augsburg, Britta, de H. Ralph, H. Heike and M., Costas. 2014, “Microfinance at the Margin: Experimental Evidence from Bosnia and Herzegovina”, WZB Discussion Paper, No. SP II 2014-304, Wissenschaftszentrum Berlin für Sozialforschung (WZB), Berlin; and Augsburg, Britta, et al. 2015. “The Impacts of Microcredit: Evidence from Bosnia and Herzegovina.” _American Economic Journal:_ _Applied Economics_ 7, 1 (January): 183–203.\n63 Field, Erica, P., Rohini, P. John, and R., Natalia. 2013. “Does the Classic Microfinance Model Discourage Entrepreneurship Among the Poor? Experimental Evidence from India.” _American Economic Review_ 103 (6): 2196–2226.\n64 Battaglia, M., S. Gulesci, and A. Madestam. 2019. “Repayment Flexibility and Risk Taking: Experimental Evidence from Credit Contracts.” Working Paper. Centre for Economic Policy Research (CEPR), London.\n65 De Mel, S., D. McKenzie, and C. Woodruff. 2008. “Returns to Capital in Microenterprises: Evidence from a Field Experiment.” _The Quarterly Journal of Economics_ 123 (4): 1329–1327.\n66 De Mel, S., D. McKenzie, and C. Woodruff. 2013. “What Generates Growth in Microenterprises? Experimental Evidence on Capital, Labor, and Training.” CAGE Online Working Paper Series 212.\n67 Fafchamps, M., D. McKenzie, S. Quinn, and C. Woodruff. 2011. “When Is Capital Enough to Get Female Microentrepreneurs Growing? Evidence from a Randomized Experiment in Ghana.” NBER Working Paper No. 17207.\n\nPage 85 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nFormal Employment Creation Project (P171766) outside home for men and 50 percent increase for women, as well as a monthly net income increase by US$9. [68] 17. **Reductions in payroll taxes, costs of registration, and social security contributions, and financial**\n**incentives can boost employment growth** . Especially reduction in taxes and social security contributions\nare associated with positive, large, and statistically significant impacts on both formal employment creation and firm registration. A business regulation reform in Mexico was associated with 3.8 percent increase in firm formalization, amounting to 902 firms per municipality, and with a 2.8 percent increase in employment in eligible firms [69] Following the introduction of two employment subsidy schemes in Turkey, formal employment increased by 5 percent to 13 percent under the first scheme and by 11 percent to 15 percent under the second scheme. [70] Lastly, among Integrated System for the Payment of Taxes and Social Security Contributions of Micro and Small Enterprises (SIMPLES) beneficiaries in Brazil, the number of formal firms increased by 7.5 percent, while tax registration increased by 7.2 percent.\nFurthermore, newly registered firms showed a 57 percent increase in revenues and a 49 percent increase in profits as a result of formalization. [71] 18. **Whether the increase is caused by creation of new economic activity or registration of existing**\n**firms and workers, however, greatly depends on the macroeconomic, legal, and fiscal context of a**\n**country.** In particular, finance-based formalization interventions show lesser effectiveness under\nconditions of high unemployment, slow growth, weak legal enforcement institutions, complex business registration procedures and high labor taxes, especially for low-wage workers. Under these circumstances, employment generation is more likely to be driven by formalization of existing businesses and workers than by newly created jobs and start-ups.\n\n68 Blattman, C., N. Fiala, and S. Martinez. 2012. “Employment Generation in Rural Africa. Mid-Term Results from an Experimental Evaluation of the _Youth Opportunity Program_ in Northern Uganda.” DIW Berlin Discussion Paper no. 1201 (August): 1-76.\n69 Bruhn, Miriam. 2008. “License to Sell: The Effect of Business Registration Reform on Entrepreneurial Activity in Mexico.” World Bank Policy Research Working Paper 4538. World Bank, Washington, DC.\n70 Betcherman, G. et al. 2010. “Do Employment Subsidies Work? Evidence from Regionally Targeted Subsidies in Turkey.” _Labor_ _Economics_ 17 (4): 710–22.\n71 Fajnzylber, P., W. Maloney, and G. Montes-Rojas. 2011. “Does Formality Improve Micro-Firm Performance? Evidence from the Brazilian SIMPLES Program.” _Journal of Development Economics_ 94: 262–276.\n\nPage 86 of 86", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**FOR OFFICIAL USE ONLY**\n\nReport No: PAD00159 INTERNATIONAL DEVELOPMENT ASSOCIATION PROJECT APPRAISAL DOCUMENT", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "CURRENCY EQUIVALENTS (Exchange Rate Effective September 30, 2023) Currency Unit = Ethiopian Birr (ETB) US$1 = ETB 55.72 US$1 = SDR 0.76 FISCAL YEAR July 8 - July 7 Regional Vice President: Victoria Kwakwa Regional Director: Christine Zhenwei Qiang Country Director: Ousmane Dione Practice Manager: Maria Isabel A. S. Neto Task Team Leaders: Luda Bujoreanu, Jonathan Marskell", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**ABBREVIATIONS AND ACRONYMS**\nAML Anti-Money Laundering API Application Programming Interface AWPB Annual Work Plan and Budget CBA Cost-Benefit Analysis CGD Centre for Global Development DA Designated Account DE4A Digital Economy for Africa DFIL Disbursement and Financial Information Letter DPI Digital Public Infrastructure e-KYC Electronic Know Your Customer ESF Environmental and Social Framework ESMF Environmental and Social Management Framework ESS Environmental and Social Standards FCV Fragility, Conflict, and Violence FM Financial Management FMM Financial Management Manual GDP Gross Domestic Product GoE Government of Ethiopia GRF Global Refugee Forum GRM Grievance Redress Mechanism GRS Grievance Redress Service ICS Immigration and Citizenship Service ICT Information and Communication Technology ID4D Identification for Development IDP Internally Displaced Person IFMIS Integrated Financial Management Information System IFR Interim Financial Report IMF International Monetary Fund IPF Investment Project Financing IRR Internal Rate of Return IT Information Technology KYC Know-Your-Customer LMP Labor Management Procedures M&E Monitoring and Evaluation MFD Maximizing Finance for Development MInT Ministry of Innovation and Technology MIS Management Information System MoF Ministry of Finance MoU Memorandum of Understanding NBE National Bank of Ethiopia NDC Nationally Determined Contribution NIDP National ID Program NPV Net Present Value PC Policy Commitment PDO Project Development Objective", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "PMO Prime Minister's Office PMU Project Management Unit PP Procurement Plan PPSD Project Procurement Strategy for Development PSC Project Steering Committee PSNP Productive Safety Net Program RRS Refugees and Returnees Service SEA/SH Sexual Exploitation and Abuse/Sexual Harassment SEP Stakeholder Engagement Plan SESRE Socioeconomic Survey of Refugees in Ethiopia SPD Standard Procurement Document STEP Systematic Tracking of Exchanges in Procurement TC Technical Committee UNHCR United Nations High Commissioner for Refugees WHR Window for Host Communities and Refugees", "output": {"entities": {"named_data": ["SESRE Socioeconomic Survey of Refugees in Ethiopia"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**TABLE OF CONTENTS**\n**DATASHEET ........................................................................................................................... I**\n\n**I.** **STRATEGIC CONTEXT ..................................................................................................... 1**\n\nA. Country Context ............................................................................................................................... 1 B. Sectoral and Institutional Context ................................................................................................... 2 C. Relevance to Higher Level Objectives .............................................................................................. 7\n\n**II.** **PROJECT DESCRIPTION .................................................................................................. 8**\n\nA. Project Development Objective ....................................................................................................... 8 B. Project Components ........................................................................................................................ 8 C. Project Beneficiaries ......................................................................................................................15 D. Results Chain ..................................................................................................................................15 E. Rationale for Bank Involvement and Role of Partners ...................................................................15 F. Lessons Learned and Reflected in the Project Design ....................................................................16\n\n**III.** **IMPLEMENTATION ARRANGEMENTS ........................................................................... 17**\n\nA. Institutional and Implementation Arrangements ..........................................................................17 B. Results Monitoring and Evaluation Arrangements ........................................................................18 C. Sustainability ..................................................................................................................................18\n\n**IV.** **PROJECT APPRAISAL SUMMARY .................................................................................. 19**\n\nA. Technical, Economic and Financial Analysis ..................................................................................19 B. Fiduciary .........................................................................................................................................21 C. Legal Operational Policies ..............................................................................................................22 D. Environmental and Social ..............................................................................................................22\n\n**V.** **GRIEVANCE REDRESS SERVICES .................................................................................... 24**\n\n**VI.** **KEY RISKS .................................................................................................................... 24**\n\n**VII.** **RESULTS FRAMEWORK AND MONITORING .................................................................. 26**\n\n**ANNEX 1: Implementation Arrangements and Support Plan** ..........................................................32\n\n**ANNEX 2: Risk Mitigation for Fayda Implementation** ......................................................................36\n\n**ANNEX 3: Gender Analysis and Action Plan** .....................................................................................38", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) @#&OPS~Doctype~OPS^dynamics@padbasicinformation#doctemplate\n**DATASHEET**\n\n**BASIC INFORMATION**\n\nProject Operation Name Beneficiary(ies) Ethiopia Ethiopia Digital ID for Inclusion and Services Project Environmental and Social Risk Operation ID Financing Instrument Classification Investment Project P179040 Substantial Financing (IPF) @#&OPS~Doctype~OPS^dynamics@padprocessing#doctemplate\n\n**Financing & Implementation Modalities**\n\n[ ] Multiphase Programmatic Approach (MPA) [ ] Contingent Emergency Response Component (CERC)\n\n[ ] Series of Projects (SOP) [✓] Fragile State(s)\n\n[ ] Performance-Based Conditions (PBCs) [ ] Small State(s)\n\n[ ] Financial Intermediaries (FI) [ ] Fragile within a non-fragile Country\n\n[ ] Project-Based Guarantee [✓] Conflict\n\n[ ] Deferred Drawdown [ ] Responding to Natural or Man-made Disaster\n\n[ ] Alternative Procurement Arrangements (APA) [ ] Hands-on Expanded Implementation Support (HEIS) Expected Approval Date Expected Closing Date 14-Dec-2023 07-Jan-2029 Bank/IFC Collaboration No\n\n**Proposed Development Objective(s)**\n\nThe Project Development Objective is to establish an inclusive digital ID ecosystem and improve service delivery for registered persons in Ethiopia.\n\n**Components**\n\n**I**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**Component Name** **Cost (US$)**\n\nBuilding institutions and trust 21,000,000.00 Establishing scalable and secure Fayda ICT infrastructure 68,000,000.00 Inclusive ID issuance 214,000,000.00 Improving service delivery 35,000,000.00 Project management and coordination 12,000,000.00 @#&OPS~Doctype~OPS^dynamics@padborrower#doctemplate\n\n**Organizations**\n\nBorrower: Federal Democratic Republic of Ethiopia Implementing Agency: Prime Minister's Office @#&OPS~Doctype~OPS^dynamics@padfinancingsummary#doctemplate\n\n**PROJECT FINANCING DATA (US$, Millions)**\n\n**Maximizing Finance for Development**\n\n**Is this an MFD-Enabling Project (MFD-EP)?** Yes\n\n**Is this project Private Capital Enabling (PCE)?** No\n\n**SUMMARY**\n\n**Total Operation Cost** **350.00**\n\n**Total Financing** **350.00**\n\n**of which IBRD/IDA** **350.00**\n\n**Financing Gap** **0.00**\n\n**DETAILS**\n\n**World Bank Group Financing**\n\nInternational Development Association (IDA) 350.00 IDA Credit 300.00 IDA Grant 50.00\n\n**IDA Resources (US$, Millions)**\n\n**II**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) National Performance-Based Allocations (PBA) Window for Host Communities and Refugees (WHR)\n\n**Guarantee**\n**Credit Amount** **Grant Amount** **SML Amount** **Total Amount**\n\n**Amount**\n\n300.00 0.00 0.00 0.00 300.00 0.00 50.00 0.00 0.00 50.00\n\n**Total** **300.00** **50.00** **0.00** **0.00** **350.00**\n\n@#&OPS~Doctype~OPS^dynamics@paddisbursementprojection#doctemplate\n\n**Expected Disbursements (US$, Millions)**\n\n**WB**\n**Fiscal**\n**Year**\n\n2024 2025 2026 2027 2028 2029 2030 2031 2032\n\n**Annual**\n40.00 60.00 80.00 90.00 80.00 0.00 0.00 0.00 0.00\n\n**Cumulati**\n40.00 100.00 180.00 270.00 350.00 350.00 350.00 350.00 350.00\n**ve**\n\n@#&OPS~Doctype~OPS^dynamics@padclimatechange#doctemplate\n\n**PRACTICE AREA(S)**\n\n**Practice Area (Lead)** **Contributing Practice Areas**\n\nDigital Development\n\n**CLIMATE**\n\n**Climate Change and Disaster Screening**\n\nYes, it has been screened and the results are discussed in the Operation Document @#&OPS~Doctype~OPS^dynamics@padrisk#doctemplate\n\n**SYSTEMATIC OPERATIONS RISK- RATING TOOL (SORT)**\n\n**III**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**Risk Category** **Rating**\n\n1. Political and Governance ⚫  Substantial\n\n2. Macroeconomic ⚫ Substantial 3. Sector Strategies and Policies ⚫ Moderate 4. Technical Design of Project or Program ⚫ Moderate 5. Institutional Capacity for Implementation and Sustainability ⚫ Substantial 6. Fiduciary ⚫ Substantial 7. Environment and Social ⚫ Substantial 8. Stakeholders ⚫ Moderate 9. Other ⚫ Moderate 10. Overall ⚫ Substantial @#&OPS~Doctype~OPS^dynamics@padcompliance#doctemplate\n\n**POLICY COMPLIANCE**\n\n**Policy**\nDoes the project depart from the CPF in content or in other significant respects?\n\n[ ] Yes [✓] No Does the project require any waivers of Bank policies?\n\n[ ] Yes [✓] No\n\n**ENVIRONMENTAL AND SOCIAL**\n\n**Environmental and Social Standards Relevance Given its Context at the Time of Appraisal**\n\n**E & S Standards** **Relevance**\n\nESS 1: Assessment and Management of Environmental and Social Risks and Relevant Impacts ESS 10: Stakeholder Engagement and Information Disclosure Relevant ESS 2: Labor and Working Conditions Relevant ESS 3: Resource Efficiency and Pollution Prevention and Management Relevant ESS 4: Community Health and Safety Relevant\n\n**IV**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) ESS 5: Land Acquisition, Restrictions on Land Use and Involuntary Resettlement Not Currently Relevant ESS 6: Biodiversity Conservation and Sustainable Management of Living Natural Not Currently Relevant Resources ESS 7: Indigenous Peoples/Sub-Saharan African Historically Underserved Relevant Traditional Local Communities ESS 8: Cultural Heritage Not Currently Relevant ESS 9: Financial Intermediaries Not Currently Relevant NOTE: For further information regarding the World Bank’s due diligence assessment of the Project’s potential environmental and social risks and impacts, please refer to the Project’s Appraisal Environmental and Social Review Summary (ESRS).\n\n@#&OPS~Doctype~OPS^dynamics@padlegalcovenants#doctemplate\n\n**LEGAL**\n\n**Legal Covenants**\n\n**Sections and Description**\n\nThe Recipient shall establish, not later than sixty (60) days after the Effective Date, a Project Steering Committee (“PSC”) at the federal level, and thereafter maintain such PSC at all times during the implementation of the Project, with a composition, mandate, resources and functions satisfactory to the Association.\nThe Recipient shall establish, not later than sixty (60) days after the Effective Date, a Technical Committee at the federal level, and thereafter maintain such Technical Committee at all times during the implementation of the Project, with a composition, mandate, resources and functions satisfactory to the Association.\nThe Recipient shall, through the NIDP and not later than the Effective Date, prepare and thereafter adopt Interim Personal Data Protection Guidelines, in form and substance satisfactory to the Association.\nThe Recipient shall, through the NIDP and not later than thirty (30) days after the Effective Date, prepare and thereafter adopt a template of the Memorandum of Understanding, in form and substance satisfactory to the Association.\n\n@#&OPS~Doctype~OPS^dynamics@padconditions#doctemplate\n**Conditions**\n\n**Type** **Citation** **Description** **Financing Source**\n\nThe Association is satisfied Effectiveness 5.01.(a) Effectiveness 5.01.(b) that the Recipient has an adequate Refugee Protection Framework.\n\nThe Recipient has adopted the Project Operations Manual in accordance with the provisions of Section I.B.1 of Schedule 2 to this Agreement.\n\nIBRD/IDA, Trust Funds IBRD/IDA, Trust Funds\n\n**V**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) Recipient has, through Effectiveness 5.01 (c) Disbursement B.1.(b) Disbursement B.1.(c) MINT, pending the ratification of the Draft Personal Data Protection Proclamation by the House of People's Representatives, adopted Interim Personal Data Protection Guidelines for registration and data sharing activities under the Project, in form and substance satisfactory to the Association.\n\nNo withdrawal shall be made unless and until the Recipient has, through its House of Representatives, ratified the Personal Data Protection Proclamation.\n\nNo withdrawal shall be made unless and the Recipient has adopted and disclosed an environmental and social management framework including the following annexes: (i) a gender-based violence/sexual exploitation and abuse/sexual harassment action plan; (ii) e-waste management plan; and (iii) a security risk assessment and management plan, all in accordance with the provisions of the Environmental and Social Commitment Plan IBRD/IDA, Trust Funds IBRD/IDA IBRD/IDA\n\n**VI**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**I.** **STRATEGIC CONTEXT**\n\n**A. Country Context**\n\n1. **Ethiopia, the second most populous country in Sub-Saharan Africa, has an estimated population of 123 million,**\n**with 77 percent living in rural areas.** It has 98 ethnicities with roughly 93 languages spoken, as well as three major religious\ngroups. There are 13 regions and two federal cities, under which there are more than 1,000 _woredas_ (districts) and around 16,000 _kebeles_ (wards). There are significant opportunities and challenges arising from a rapidly rising working-age population in Ethiopia and its landlocked and strategic location of being surrounded by six countries.\n\n2. **Despite Ethiopia’s success over the past decade with economic growth and reduction of extreme poverty from**\n**55 percent in 2000 to 25 percent in 2020, the country continues to face major challenges,** including the impacts of the\nCOVID-19 pandemic, natural disasters and climate-related events, internal conflicts, food insecurity, and high inflation.\nReal gross domestic product (GDP) growth is projected by the International Monetary Fund (IMF) to be 6.1 percent in 2023 [1], down from 9 percent in 2019. Poor and vulnerable households are facing losses in real income and challenges in food affordability. Despite this, the Government of Ethiopia (GoE) aims to continue to provide basic services to people and to continue supporting and protecting the most vulnerable populations.\n\n3. **Ethiopia has frequently experienced extreme climate events like droughts and floods, rainfall variability and**\n**increasing temperature among others,** **[2]** **which have adversely impacted livelihoods.** **[3]** In 2022, almost 12 million people\nneeded food assistance from five consecutive failed rainy seasons, and floods displaced more than half a million Ethiopians. [4] Communities, especially in climate hot spots of Afar, Somali, and Oromia regions, which are often hit by severe droughts and floods, [5] do not have access to digital connectivity and e-services, hampering the Government’s response to climate-related and other emergencies.\n\n4. **Ethiopia aims to reach lower-middle-income status by 2025 and to reduce poverty to 7 percent by 2030.** The latest 10-Year Development Plan aims to sustain growth while shifting toward a more private sector-driven economy. It also aims at fostering efficiency and competition in key growth-enabling sectors (for example, telecommunications), improving business climate, and addressing macroeconomic imbalances. The Government is also devoting a high share of its budget to pro-poor programs and investments, notably to the Productive Safety Net Program (PSNP) launched in 2005.\n\n5. **Gender disparities in Ethiopia, including in access to economic opportunities for women, are profound.** The 2022 Global Gender Gap report ranked Ethiopia 74 out of 146 countries and 15 in Africa. The rank drops to 112 for economic participation and opportunities and to 133 for educational attainment. [6] The 2022 Global Findex Survey [7] found 1 International Monetary Fund, 2023. Website, accessed November 14th: imf.org/en/Countries/ETH#featured 2 Drought is the most destructive climate-related natural hazard. Through 2100, there is a likely 20 percent increase in extreme high rainfall events.\nFlash floods and seasonal river floods are becoming more frequent and widespread. World Bank, 2021, Ethiopia Climate Risk Profile.\n3 This is particularly due to dependence on key sectors that are highly affected by climate change such as agriculture, water, tourism, and forestry (World Food Programme. Ethiopia Annual Country Report 2022).\n4 Ethiopia Country Climate Development Report, 2023, draft, World Bank.\n5 World Bank, 2021, Ethiopia Climate Risk Profile.\n6 World Economic Forum. 2022. Global Gender Gap Report, published July 2022. URL: https://www3.weforum.org/docs/WEF_GGGR_2022.pdf.\n7 World Bank. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the age of COVID-19.\nhttps://www.worldbank.org/en/publication/globalfindex#sec1.\n\nPage 1 of 39", "output": {"entities": {"named_data": ["2022 Global Findex Survey", "Global Findex Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) that only 39 percent of women had a financial account, compared to 55 percent of men, with the gap growing continuously since 2014 when the rates were 21 percent and 23 percent, respectively.\n\n6. **Ethiopia hosts many forcibly displaced persons, including refugees and asylum seekers.** Ethiopia is the third largest refugee-hosting country in Africa, with nearly 1 million refugees. [8 ] The World Bank, following consultation with the United Nations High Commissioner for Refugees (UNHCR), confirmed that the protection framework for refugees is adequate. Most refugees live in 24 camps across five regional states (Gambella, Somali, Benishangul-Gumuz, Afar, and Amhara). About 53 percent of refugees and asylum seekers are female, and 56 percent are children; together, women and children constitute some 80 percent of the refugee population. The unexpected arrival of additional thousands of refugees from Somali and Sudan starting in February/April 2023 have exacerbated socioeconomic challenges that had already become significant, with the UNHCR making supplementary appeals of US$41.7 million and US$19.3 million, respectively, to cover the needs of these refugees and host communities. Most refugee populations and host communities are in fragile border areas with limited services and economic potential. These communities remain vulnerable to conflict and climaterelated crises, with acute needs and limited opportunities for self-reliance.\n\n7. **As of June 2023, nearly 3.4 million internally displaced persons (IDPs) were displaced across 2,750 accessible**\n**sites in Ethiopia, excluding Tigray region,** **[9]** largely resulting from the ongoing conflict in northern Ethiopia and localized\nconflicts and tensions in different parts of the country [10] . Stateless persons and persons at risk of statelessness, especially long-term refugees, also face challenges in exercising their rights and accessing services because of problems with obtaining adequate proof of identity. By implementing an accessible identification system, these challenges can be addressed by promoting inclusivity, economic empowerment, and integration for stateless populations.\n\n8. **The internal conflicts have limited economic growth and reduced access to basic services and infrastructure.** In Tigray, efforts began after the peace deal to restore basic services and rebuild infrastructure. Improving access to services and economic opportunities, including those facilitated by the Federal Government, can help ensure that peace can be maintained. Despite progress in northern Ethiopia following the cessation of hostilities agreement signed in November 2022, internal conflicts continue to be a concern in other regions such as Amhara, Gambela, and Oromia.\n\n**B. Sectoral and Institutional Context**\n\n9. **Foundational ID systems** **[11]** **are broadly recognized as key enablers for inclusive digitalization and development.** For people, the ability to establish and verify their identity is often a prerequisite for access to services and economic opportunities, such as social protection, healthcare, education, financial services, and employment. Proof of legal identity is also the basis for exercising rights, such as property ownership, and nationality. For governments and businesses, ID systems can serve as a platform for more effective and efficient service delivery by enabling the unique identification and verification of persons. Importantly, ID systems can promote greater inclusion by de-risking and reducing the costs of 8 UNHCR's Ethiopia Update on the Total Number of Refugees and Asylum Seekers as of August 31, 2023.\n9 In Tigray, new internal displacement data has been reported, including 1,021,798 IDPs (250,468 households) in 643 sites across six zones (excluding 20 _woredas_ /districts hard to reach due to security or environmental factors).\n10 IOM. 2023. Ethiopia National Displacement Report 16 - Site Assessment Round 33 and Village Assessment Survey Round 16: Nov 2022 - Jun 2023.\nhttps://reliefweb.int/report/ethiopia/ethiopia-national-displacement-report-16-site-assessment-round-33-and-village-assessment-survey-round16-november-2022-june-2023.\n11 Foundational ID systems are primarily created to provide credentials to the general population as proof of identity for a wide variety of public and private sector transactions. Common types of foundational ID systems include civil registries, national ID systems, and population registers.\n\nPage 2 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["internal displacement data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) service delivery. It is for these reasons that, “ _by 2030, provide legal identity for all, including birth registration_,” was included as target 16.9 in the Sustainable Development Goals.\n\n10. **Well-designed and universally accessible foundational ID systems can promote reconciliation and a sense of**\n**national unity and identity.** Qualitative research by the World Bank’s Identification for Development (ID4D) initiative in\nCôte d'Ivoire, Rwanda, the Philippines, and Timor-Leste, which has engaged vulnerable citizens and residents, has shown that one of the key benefits of ID systems is the sense of belonging that is attached to having an ID issued by the State that has their identity displayed on it. There is the notable case of Peru, which the Centre for Global Development (CGD) described as, “...a remarkable example of a country that established civil identification as a national priority in response to the need to re-integrate the state after a serious insurgency.” [12] ID agents in the field are often one of, if not the most, visible representation of the State in many communities, and identification can be the only universally-used service in a country. ID systems can help establish the presence of the State and show its value in the minds of the population.\n\n11. **Foundational ID systems is one of the three main building blocks of digital public infrastructure (DPI).** [13] As more transactions move online and the importance of the digital economy and digital government rapidly grows, there is also a need for digital ID systems to equip people with the ability to securely prove their identity online, without requiring physical presence and allow transactions to be done completely online. Traditional credentials, such as the paper Kebele ID currently in use in Ethiopia and birth certificates, cannot support such remote transactions.\n\n12. **Ethiopia is one of the few countries in the world that does not have a national-scale foundational ID system**\n**with digital capabilities.** Despite enacting the Registration of Vital Events and National Identity Card Proclamation in 2012,\nthe national identity card that it provided for has never been implemented. A paper-based ID issued by _kebeles_ (and known as the Kebele ID) is the most common proof of identity in Ethiopia, even though its primary purpose is proof of address and residence. A Kebele ID is often required to access public and private services, obtain other IDs such as driver’s licenses and passports, and formal procedures like proving land ownership. [14] The features of the Kebele ID vary by _kebele_, but they generally display handwritten demographic information and address and include a stapled photo.\n\n13. **According to the 2017 ID4D-Findex Survey, 36 percent of the population aged 18 and older lack a Kebele ID,**\n**with a significant gender gap of 46 percent of women lacking one compared to 25 percent of men, creating barriers for**\n**a large portion of people to access services and economic opportunities.** Kebele ID coverage reaches 70 percent for\nadults older than 25 and 80 percent for the highest income quintile. Obtaining a Kebele ID often requires residing in a location for a minimum period (for example, six months), which leads to exclusion of internal migrants and refugees. Being tied to residence also means that a Kebele ID cannot serve as a continuous identification throughout the life of an individual, as one may move. Most Kebele IDs display holder’s ethnicity, which is a potential source of discrimination. The lack of uniformity among the forms of Kebele ID cards and the ease of forgery add substantial identity risks for service providers. Records are often paper ledgers vulnerable to damage, tampering, unauthorized use, identity theft, and loss due to fires or natural disasters like floods.\n\n14. **The current ID landscape in Ethiopia has four main weaknesses.** First, inefficiency: the absence of identity verification capabilities for online and even for in-person service delivery adds transaction costs through manual 12 CGD. 2017. “Identification as a National Priority: The Unique Case of Peru - Working Paper 454.”\n[https://www.cgdev.org/publication/identification-national-priority-unique-case-peru.](https://www.cgdev.org/publication/identification-national-priority-unique-case-peru) 13 DPI refers to a set of foundational systems and their organizational frameworks (for example, laws and institutions) that enable core functions in today's digital age, including the ability to verify identities, to send and receive money, and to exchange data.\n14 World Bank. 2022. _Voluntary Migration in Ethiopia: In Search for Work and Better Opportunities_ .\n\nPage 3 of 39", "output": {"entities": {"named_data": ["2017 ID4D-Findex Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) processes. Second, exclusion: gaps in Kebele ID coverage among adults and low birth registration coverage (around 20 percent in 2022 [15] ). Third, fraud-related risks: inability to reliably verify an individual’s unique identity, particularly for use cases that require higher levels of assurance, such as cash transfers and obtaining credit. Finally, lack of interoperability: the lack of common standards for identity information and population-scale unique identification makes it difficult for public and private sector service providers to share and reuse data to improve outcomes for people.\n\n15. **Exclusion from access to IDs and services particularly affects IDPs and women who live in rural areas and migrate**\n**for marriage and domestic labor jobs, as well as refugees.** Kebele IDs are commonly associated with the ‘head of\nhousehold’ (a traditionally male role) and are mainly required for opening a financial account or getting formal employment outside the home (both of which men are more likely to do). Consequently, women are less likely to pursue getting a Kebele ID. Indeed, early results from qualitative research by ID4D reveal that although there are no legal obstacles for women to access Kebele IDs, there are symbolic and practical barriers. Moreover, not having an ID can also lead to harassment and arrest by police officers as a migrant may be considered as an illegal resident. During displacement, IDPs may seek refuge elsewhere in the country and face challenges to renew their Kebele ID. The low birth registration rate in the country (20 percent) further enhances the risk of de facto statelessness for children.\n\n16. **Though Ethiopia has an inclusive policy environment for refugees, they face similar challenges with regard to**\n**documentation in Ethiopia, which hampers their access to services and economic opportunities.** The Government has\naffirmed its commitment to supporting long-term inclusion through its 2019 Refugee Proclamation, a 10-year National Comprehensive Refugee Response Strategy, an Out of Camp Policy, 2019 Global Refugee Forum (GRF) pledges, [16] and an April 2023 Strategy Note provided to the World Bank. Through these, refugees have the right to work, access national services, and register vital events, including births and marriages, directly with national authorities. However, implementation of these policies faces challenges. During the conflict in northern Ethiopia, refugee registration was paused but now has been largely resumed. One of the main challenges is that special identification issued to refugees by the Refugees and Returnees Service (RRS) and UNHCR are not widely recognized by service providers such as banks and mobile network operators, hindering their integration and access to services. The GoE’s decision to provide the same kind of identification to refugees (Fayda) [17] as it will to nationals, is a significant step for inclusion and long-term solutions.\n\n17. **The GoE has sought to resume previously paused registration of refugees with support from UNHCR and has**\n**collaborated across government to include refugees at the outset within Fayda.** In line with Government policy\ncommitments (PCs), RRS, the National ID Program (NIDP), and UNHCR signed a Data Sharing Agreement in October 2023 to “help refugees easily access basic services such as health insurance, education, banking, and driving, among other things.” NIDP concretized its commitment to include refugees in their Fayda IDs target for which the Data Sharing Agreement is a key prerequisite, and RRS participated in the preparation of this project. Concurrently, RRS and UNHCR have signed a Registration Multi-Year Joint Strategy for Refugees in Ethiopia to further facilitate the registration of refugees in the country. Political instability in the region, which remains unpredictable because of national and neighboring countries’ political instability, conflict, and humanitarian crisis, risks future refugee inflows into Ethiopian territory, and these agreements will strengthen Ethiopia’s ability to prepare for these.\n\n15 World Bank. _ET - Health SDG Program for Results (P123531) Implementation Completion Report (ICR) Review_ .\n[https://documents1.worldbank.org/curated/en/099041923195522835/pdf/P1235310c601d00940959307b7687b4eccf.pdf.](https://documents1.worldbank.org/curated/en/099041923195522835/pdf/P1235310c601d00940959307b7687b4eccf.pdf) 16 The GoE (RRS) is now fine-tuning its draft pledges for the upcoming second GRF to be held in December 2023. It is expected that refugee inclusion\n[in the Fayda Digital ID system will contribute toward filling identification-related gaps in the pledge implementation process.](https://x.com/RRSEthiopia/status/1712048845373186450?s=20) 17 Fayda is the official brand name of the Ethiopian digital identification initiative.\n\nPage 4 of 39", "output": {"entities": {"named_data": ["Fayda Digital ID system"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) 18. **According to the latest data available from UNICEF, only 3 percent of children** **under 5 years of age have their**\n**birth registered.** **[18]** Through the World Bank-funded Health SDG Program (P123531), the percentage of births occurring in\na given year that were registered attained 20.9 percent in 2022. [19] In 2016, kebeles introduced paper-based and manual recording of births, deaths, marriages, and divorces. In May 2021, the Immigration, Nationality and Vital Events Agency— now known as Immigration and Citizenship Services (ICS)—unveiled a Civil Registration and Vital Statistics Improvement Plan for 2022–2026. [20] Digitalization is a major component, supported by the World Bank’s Program for Results (Hybrid) for Strengthening Primary Health Care Services (P175167) project.\n\n19. **In line with the Digital Ethiopia 2025 Strategy,** **[21]** **the GoE began an initiative in 2019 to establish a foundational**\n**ID system as a complement—not a replacement—to the existing Kebele IDs.** The intention of the new ID system is to\nprovide a trusted source of identity, including for online transactions, while Kebele IDs will continue to provide proof of address and residence. In the same year, the GoE developed the Principles and Governance Structure of the National Identity Program, which emphasized inclusion for all residents (including refugees) of Ethiopia and alignment with the Principles on Identification for Sustainable Development. [22] Responsibility was transferred from the Ministry of Innovation and Technology (MInT) to the Ministry of Peace in 2020 and subsequently to the Prime Minister's Office (PMO) in 2021, where it became NIDP. The NIDP will be transferred to a permanent ID Institution once the latter is established.\n\n20. **During March–June 2022, NIDP carried out a registration pilot, enrolling 124,233 people using open-source**\n**software that was integrated in-house.** The pilot registrations focused on workers at Addis Bole Lemi and Hawassa\nIndustry Parks, higher education students, customers of several participating banks, and PSNP beneficiaries in five _woredas_ located across two regions (Oromia and Sidama). Following an evaluation by NIDP, it was decided to proceed with the open-source software approach. By November 2023, the registration pilot had reached over 3 million individuals who had voluntarily registered in the system. NIDP is also close to signing a tripartite memorandum of understanding (MoU) with RRS and UNHCR to be able to share data for the issuance of a Fayda ID to refugees and asylum seekers.\n\n21. **Following extensive stakeholder engagement and preparatory work, the Digital ID Proclamation was**\n**promulgated in March 2023 to create legal grounds for the foundational ID system, now known as Fayda, meaning value**\n**in several local languages, and an ID Institution to implement it.** Fayda is envisioned to provide a unique ID to all nationals\nand residents from birth. It is designed to encompass these key elements: being nationwide in scope; being accessible to Ethiopian nationals and residents; providing a unique and random number (with the possibility to tokenize it for enhanced data protection); leveraging digital technologies; and maintaining the security of personal data. The ID system collects minimal data, including only four mandatory fields (full name, gender, date of Birth, and current address), while excluding sensitive information like ethnicity and religion. It also incorporates three biometric modalities (face, fingerprint, and irises) for enhanced security, with exceptions to handling procedures in case of disability. While the ID Proclamation is largely aligned with international good practices, there are some areas that need clarity and elaboration, such as the institutional and governance arrangements for the ID Institution and its relationship with a future Data Protection 18 UNICEF. 2019. Fact Sheet Birth Registration. Ethiopia, Child Protection\n[https://www.unicef.org/ethiopia/media/1651/file/Birth%20registration%20factsheet%20.pdf](https://www.unicef.org/ethiopia/media/1651/file/Birth%20registration%20factsheet%20.pdf) 19 Health SDG Program for Results, 2023\n[https://documents1.worldbank.org/curated/en/099041923195522835/pdf/P1235310c601d00940959307b7687b4eccf.pdf](https://documents1.worldbank.org/curated/en/099041923195522835/pdf/P1235310c601d00940959307b7687b4eccf.pdf) 20 Immigration, Nationality and Vital Events Agency, Civil Registration and Vital Statistics Systems Improvement Strategy and Costed Action Plan of Ethiopia: 2021/22–2022/26, April 2021.\n21 For more information about _Digital Ethiopia 2025_, see [https://www.lawethiopia.com/images/Policy_documents/Digital-Ethiopia-2025-Strategy-english.pdf](https://www.lawethiopia.com/images/Policy_documents/Digital-Ethiopia-2025-Strategy-english.pdf)\n[22 For more on the principles, see http://idprinciples.org](http://idprinciples.org/) Page 5 of 39", "output": {"entities": {"named_data": [], "descriptive_data": ["latest data available from UNICEF"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) Proclamation, among others. The Data Protection Proclamation approved by the Council of Ministers in October 2023 and expected to be ratified by Parliament in 2024, is critical for further mitigating personal data protection risks.\n\n22. **NIDP has identified high-impact use cases for Fayda, especially links with and between financial inclusion and**\n**social protection, to improve lives and livelihoods.** It has been engaging and establishing partnerships with various\ngovernment agencies and businesses to understand their requirements and problems, as well as to engage with other countries to learn lessons and good practices. To this end, NIDP has signed several MoUs with banks, Ethio Telecom, and Safaricom and has engaged with the Ministry of Agriculture, Ministry of Education, and Ministry of Health, to collaborate on awareness raising and pilots. In the area of financial inclusion, the National Bank of Ethiopia (NBE) released a new directive in 2021 to strengthen know-your-customer (KYC) requirements across the financial sector to discern individual customer uniqueness for all transaction types. [23] This new requirement is overwhelming for both the banks and the people, both with and without a Kebele ID. Fayda, by providing a digitally verifiable ID, is envisioned to provide enough assurance to waive these controls, hence reducing costs, time, and risks associated with account opening and credit applications. This is particularly important as social assistance programs like the PSNP (which can be gateways for financial inclusion) are currently being digitalized with the transition to digital payments and a new management information system (MIS) to replace the current manual processes used to target and enroll beneficiaries. Fayda can also improve the integrity and transparency of this rather complex and currently paper-based safety nets delivery chain. There is also interest from the GoE to use Fayda to support microfinance for farmers and for providing ID to students ages 16 and older. This is being done to certify diplomas, community health insurance, public and private pensions, use of mobile money and microloans, and SIM card registration.\n\n23. **Fayda could also play an important role in realizing peace dividends in Ethiopia.** First, at the social level, Fayda will enable all citizens and residents to exercise their rights related to having proof of their legal identity, and it will be the first universally accessible ID system that only focuses on individual identity, without collecting information on ethnicity nor religion. Furthermore, as a national system (compared to a collection of _kebele_ systems), it can create a sense of belonging and will have a consistent look and feel, providing equal access to services for all registered persons. Second, the Fayda ID system can foster inclusion and shared prosperity by improving access to services and economic opportunities. As a digital system, Fayda will enable the Government, businesses, and civil society to harness digital technologies to make products and services more inclusive and human centered.\n\n24. **The World Bank, through ID4D, has been providing technical assistance to the GoE on ID issues since 2016, and**\n**modest financing for upstream activities through Digital Foundations Project (P171034) since 2021.** An ID4D Diagnostic\nwas completed in 2017 [24] and updated in 2019. In 2020, a legal assessment was carried out, which contributed to the Principles and Governance Structure of the National Identity Program published by the GoE. The ID4D technical assistance also contributed to the development of the Digital ID Proclamation, now adopted. In 2021, ID4D worked with NIDP to do a costing for a Fayda rollout, which came up with (a) registering adults ages 18 and older only (US$283 million or US$3.8 per registrant), (b) registering adults and children ages 14 and older only (US$308 million or US$3.5 per registrant), and (c) registering adults and children ages 5 and older only (US$334 million or US$2.8 per registrant). The World Bank has also worked with NIDP to map use cases for Fayda, including identifying complementary World Bank engagements. During 2022 pilots, ID4D conducted an exit survey and focus group discussions among PSNP beneficiaries who registered for Fayda to get insights on any shortcomings of registration processes to fine-tune registration during scale-up. A conflict analysis and end-user survey have contributed to the design and risk mitigation measures for this project.\n\n23 NBE. 2021. _Requirements for Undertaking Account Based Transactions and Ensuring of Regulatory Limits Directive No. FIS/04/2021_ .\n24 World Bank. 2016. _ID4D Country Diagnostic: Ethiopia_ . https://id4d.worldbank.org/country-action/id4d-diagnostics.\n\nPage 6 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["end-user survey"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**C. Relevance to Higher Level Objectives**\n\n25. **The project contributes to all three Focus Areas of the Ethiopia Country Partnership Framework (FY18-FY22,**\n**Report 119576-ET).** Fayda will improve access to and efficiency of economic and financial services (Objective 1.1), social\nprotection (Objective 2.1), health care and continuous care (Objective 2.2), education for all (Objective 2.4), management of job employment programs (Objective 2.6), and child protection (Objective 2.5). Furthermore, the project will build capacity and increase transparency for the delivery of public services (Objective 3.1).\n\n26. **The project is aligned with the World Bank’s Evolution and new mission to end extreme poverty on a livable**\n**planet.** The project will support Ethiopia to better harness digital technologies, including through public and private sector\nsolutions, to enhance access to services and economic opportunities, while strengthening capabilities to manage risks related to exclusion and digital security. With respect to the Evolution, the project’s design and implementation is and will continue to draw on the World Bank’s knowledge and research. Digital public infrastructure, including digital ID and data sharing platforms that are supported by this project, are one of three elements of the Global Challenge Program on Accelerating Digitalization.\n\n27. **The project is contributing to four other World Bank important strategies and initiatives:** a. **Gender Strategy.** The project will close gender gap in ID coverage in Ethiopia that exists today (21 percent for Kebele IDs) and will ensure that Fayda’s systems and processes are gender sensitive, thereby improving their access to services and economic opportunities. Furthermore, the project will contribute to improving human endowments, removing constraints to more and better jobs for women, and removing barriers to women’s ownership and control over assets (Annex 3) **.** b. **IDA-20 policy commitments (PCs).** This includes closing gaps in digital technology for women (Gender PC5), expanding adaptive social protection (Human Capital PC4), expanding access to services for persons with disabilities (Human Capital PC6), and enabling digital government services (Governance PC3).\n\nc. **Mobilizing finance for development (MFD).** [25] By removing binding constraints (such as the current inability to effectively verify the identities of people) the project is supporting sustainable private sector solutions.\nThis will be achieved by leveraging the competitive advantage of the private sector to provide Fayda registration activities in hard-to-reach areas through the super-agent model and enabling private sector service providers to verify identities, which in turn would incentivize the expansion of private sector-led service offerings and corresponding investments.\n\nd. **Fragility, Conflict, and Violence (FCV).** By ensuring that Fayda is accessible for refugees, host communities, and IDPs, the project will be contributing to fulfilling the principle of inclusion and mitigating the spillovers of FCV, as well as helping Ethiopia to transition out of fragility. Impactful results are expected especially to those who do not have access to pre-Fayda forms of identification that are required for access to services and economic opportunities.\n\n28. **In terms of Paris alignment, the project is aligned with Ethiopia’s 2021 Updated Nationally Determined**\n**Contribution (NDC),** **[26]** **which outlines the need to build a climate-resilient green economy as one of its strategic pillars.**\n\n25 The MFD objective is to mobilize private finance enabled by upstream reforms and public funding, where necessary, to address market failures and other constraints to private investments.\n26 Federal Democratic Republic of Ethiopia. Updated Nationally Determined Contribution, July 2021.\n[https://unfccc.int/sites/default/files/NDC/2022-06/Ethiopia%27s%20updated%20NDC%20JULY%202021%20Submission_.pdf](https://unfccc.int/sites/default/files/NDC/2022-06/Ethiopia%27s%20updated%20NDC%20JULY%202021%20Submission_.pdf) Page 7 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) This necessitates climate-proofing digital investments but also leveraging digital technologies, such as digital ID, to strengthen the resilience of vulnerable communities. Digital ID facilitates resiliency-enhancing schemes, such as safety nets programs that cushion vulnerable individuals against impacts of climate change induced food insecurity. The project is aligned with Ethiopia’s Long-term Low Emissions and Climate Resilient Development Strategy (2020–2050) and the National Adaptation Plan (2019) that recognize the role of data management for climate action.\n\n29. **The project supports Ethiopia’s implementation of key African digital transformation initiatives.** The project supports operationalization of the World Bank’s Digital Economy for Africa (DE4A) strategy in Ethiopia and the African Union’s Digital Transformation Strategy for Africa and Digital ID Interoperability Framework for Africa.\n\n**II.** **PROJECT DESCRIPTION**\n\n**A. Project Development Objective**\n\n**PDO Statement**\n\n30. The Project Development Objective (PDO) is to establish an inclusive digital ID ecosystem and improve service delivery for registered persons in Ethiopia.\n\n**PDO Level Indicators**\n\n31. The achievement of the PDO will be measured by the following results indicators: (a) Number of people in Ethiopia who have received a Fayda ID (i) Percentage of whom are women and girls.\n(ii) Number of whom are refugees.\n(iii) Number of whom are individuals living in refugee host communities.\n(iv) Number of whom registered in remote and hard to reach areas.\n\n(b) Number of successful digital ID authentications by Fayda ID holders to access public and private sector services.\n\n(i) Number of which are in areas hosting refugees.\n\n**B. Project Components**\n\n32. **The project design provides comprehensive support for Fayda implementation** **[27]** **in accordance with**\n**international good practices for inclusion and accessibility, nondiscrimination, personal data protection and privacy,**\n**digital security, accountability, and good governance.** Fayda will comply with the 10 Principles on Identification for\nSustainable Development enshrined in the Ethiopian Digital ID Proclamation. [28] This alignment (elaborated in Annex 2) will maximize the socioeconomic benefits and development impacts that stem from trusted and inclusive ID systems, while also mitigating risks related to exclusion, discrimination, personal data protection, and technology lock-in.\n\n27 To refer to the implementing entity in this section, the term “NIDP” is used synonymously with “ID Institution” (once the latter is established).\n[28 The full text of the Parliament-endorsed proclamation can be found on https://id.et/law](https://id.et/law) Page 8 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**Table 1. Overview of Components, Subcomponents, and Budget Allocation**\n\n**IDA Credit**\n\n**WHR**\n**Grant**\n\n**(US$,**\n**millions)**\n\n**Total**\n**(US$,**\n**millions)**\n\n**Component/Subcomponent**\n\n**(US$,**\n**millions)**\n\n**1. Building institutions and trust** 16 5 21\n\n1.1. Supporting stakeholder engagement and communications 9 5 14\n1.2. Establishing and operationalizing the Digital ID Institution 4 - 4\n1.3. Establishing and operationalizing the Data Protection Commission of Ethiopia 3 - 3\n**2. Establishing scalable and secure Fayda ICT infrastructure** 60 8 68\n2.1. Supporting the design, development and maintenance of Fayda software 40 4 44 2.2. Supporting development of data infrastructure 15 3 18 2.3. Strengthening digital security 5 1 6\n**3. Inclusive ID issuance** 194 20 214\n3.1. Supporting mass registration and issuance of physical and digital IDs to Ethiopian residents 190 - 190 3.2. Supporting mass registration and issuance of physical and digital IDs to Ethiopian residents - 20 20 in host communities and refugees 3.3. Supporting integration of Fayda and digital civil registration systems 4 - 4\n**4. Improving service delivery** 20 15 35\n4.1. Supporting integration of Fayda 12 15 27 4.2. Developing an Ethiopia digital stack 8 - 8\n**5. Project management** 10 2 12\n**Total** **300** **50** **350**\n\n_Note:_ WHR = Window for Host Communities and Refugees.\n\n**Component 1 - Building institutions and trust (US$21 million equivalent – US$16 million IDA, US$5 million WHR)**\n\n33. This component will invest in the ‘analogue’ foundations, including stakeholder engagement, legal frameworks, and the institutions that are key for the successful implementation of Fayda and use cases.\n\n34. **Subcomponent 1.1 – Supporting stakeholder engagement and communications (US$9 million IDA, US$5 million**\n**WHR).** The subcomponent will support a two-way engagement with the population (especially vulnerable groups),\nregional governments and _kebeles_, service-providing agencies, civil society, academia, the private sector, and other stakeholders to inform the implementation of all aspects of Fayda, notably to implement corrective measures if needed to ensure that Fayda remains inclusive for all and to raise awareness about Fayda and its use, promote registration and usage, and address misinformation. A special focus will be on including and addressing host communities and refugees with adapted communication strategies, including in their own languages, and using culturally sensitive methods. The activities will consider regional contextualities, languages and cultural norms, and the need for information, education, and communication to be accessible (for example, for persons with disabilities and those with lower literacy). Funds will be used for provision of goods, consulting services, non-consulting services, training, and operating costs for (a) carrying out information, education, and communication activities and engagement with various stakeholders to raise awareness about Fayda and its use, to promote registration and usage, and to address misinformation; (b) scaling up grievance redress mechanisms (GRMs) through in-person and online help desks, and Fayda mobile app; and (c) developing information systems and processes including, digital and physical media production, workshops, events, and advertising.\n\nPage 9 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) 35. **Subcomponent 1.2 – Establishing and operationalizing the ID Institution (US$4 million IDA).** This will support the establishment of the ID Institution, as provided for in the Digital ID Proclamation, and its presence in all regions and two chartered cities to coordinate registration and ID issuance operations, use case development, communications, stakeholder engagement, and grievance redress. The local presence of the ID Institution may be co-located with Fayda centers and regional offices of ICS and/or other federal authorities. Additionally, through the WHR fund, interoperability between existing refugee registration (RRS and UNHCR) will be worked on to make refugee registration as efficient as possible in the already existing registration environment (triangle data sharing agreement RRS/UNHCR/NIDP).\n\n36. **Subcomponent 1.3 – Establishing and operationalizing the Data Protection Commission of Ethiopia (US$3**\n**million IDA).** Subject to the passage of the Personal Data Protection Proclamation, this will support the establishment of\nthe federal data protection Commission and its local presence.\n\n**Component 2 - Establishing scalable and secure Fayda ICT infrastructure (US$68 million equivalent - US$60 million**\n**IDA, US$8 million WHR)**\n\n37. This component will invest in developing Fayda’s software and hardware with a view to ensuing high performance and scalability, system integrity and security, interoperability, and vendor and technology neutrality.\n\n38. **Subcomponent 2.1 - Supporting the design, development and maintenance of appropriate software for Fayda**\n**(US$40 million IDA, US$4 million WHR).** This subcomponent will support NIDP in developing, maintaining, and enhancing\nopen-source software for Fayda and NIDP’s back-office operations, including upgrading and scaling up Fayda systems developed as part of the pilots to date and developing a mobile ID application that will include a consented data-sharing function and a digital wallet to store, present, and share data and verifiable credentials [29] as well as integration and interoperability with other sectoral systems, notably RRS and UNHCR systems for refugees specifically. Funds will be used for supporting: (i) the design, development, and maintenance of appropriate software for Fayda and back-end operations, including upgrading and scaling up existing Fayda systems; (ii) developing a mobile ID application incorporating a consented data-sharing function and a digital wallet; (iii) supporting system integration and interoperability with other sectoral systems; iv) procuring software, licenses, and subscriptions for automated biometric identification systems; (v) software development kits for registration and authentication processes; (vi) ID card personalization and lifecycle management; (vii) the short message service; (viii) public key infrastructure for encrypting and digitally-signing data; and (ix) back-office systems such as enterprise resource planning tools, business intelligence and data analysis tools for monitoring and evaluation (M&E), and x) various collaboration tools.\n\n39. **Subcomponent 2.2 – Supporting development of data infrastructure (US$15 million IDA, US$3 million WHR).** Funds will be used for the development of data infrastructure such as ID data storage and computing capabilities for Fayda through server equipment expected to be co-located in the existing data centers, as well as business continuity through disaster recovery and backup systems, procurement of data center software, minor renovations to existing data center facilities, cloud computing subscriptions, and related licenses. The primary data center to house Fayda server equipment is expected to be in the national data center being financed by the Digital Foundations Project (P171034). The secondary data center(s) will be at existing co-location facilities to be identified by NIDP. The purchase of the energy-efficient server equipment for the existing data centers will be in accordance with internationally recognized best practices on energy 29 For instance, by using the World Wide Web Consortium’s (W3C) Verifiable Credentials standard.\n\nPage 10 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) efficiency. [30] The project will undertake a climate risk assessment to assess risks (typically floods). **[31]** This will inform the location of IT equipment and other relevant mitigation measures. The project will also embed automated backup and disaster recovery system to avoid data loss in the event of climate calamities.\n\n40. **Subcomponent 2.3 - Strengthening digital security (US$5 million IDA, US$1 million WHR).** This subcomponent will support NIDP to strengthen its operational, technological, and policy capabilities for information and cybersecurity aspects of Fayda, which is particularly important due to the vulnerability of refugees and host communities. NIDP will collaborate with MInT and other parties on various cybersecurity aspects that are being funded from the Digital Foundations Project (P171034) and the new regional operation Eastern Africa Regional Digital Integration Project SOP-II (P180931). Funds will be used for strengthening digital security including operational, technological, and policy capabilities for information and cybersecurity aspects of Fayda as well as the development of strategies and standard operating procedures; procurement of appropriate equipment and subscriptions for network and security operations centers; capacity building; third-party security and software code audits and associated services; and subscriptions and tools for monitoring, preventing, and responding to cyber threats.\n\n**Component 3 - Inclusive ID issuance (US$214 million equivalent - US$194 million IDA, US$20 million WHR)**\n\n41. **This component will finance the voluntary registration and issuance of physical and digital IDs to at least 90**\n**million** **[32]** **Ethiopian nationals and nonnationals** (for example, refugees, other migrants, and stateless persons) in line with\nthe strategies described in **Error! Reference source not found.** and good practices elaborated in Annex 2. The targeted registered population of 90 million is in addition to registrations that are being financed by the Government or the registration partners such as banks, estimated to reach at least 5 million people before effectiveness. The planned ID registration processes have been enhanced by pilots and proofs of concept, including for refugees, and are expected to be easy and accessible, notably to persons with disabilities and other marginalized/vulnerable groups, with information available in a variety of formats and from a range of trusted sources. To avoid exclusion, registrants will be able to present over 30 official documents, including a Kebele ID or birth certificate. Any persons without these could have their claimed identity vouched for by a trusted ‘introducer’ who is already registered in Fayda, such as a husband introducing his wife or a village chief introducing an elderly person.\n\n**Box 1. Fayda Registration and Credential Strategies**\n\nTo have accessible and scalable reach, there will be four channels for registration and ID issuance (Figure 1). Two of these channels (registration partners and super agents) will be outsourced for a fixed fee between US$0.60 and US$3.80 per registration (subject to inflation), depending on the location. [33] NIDP will supervise the implementation of these two outsourced channels through audits, spot checks, and other methods. The four channels are as follows: (a) **Registration partners.** These are government agencies and state-owned enterprises selected by NIDP based on client profile, geographic coverage, and technical and fiduciary capacity. The intention is to provide convenient registration and to leverage existing client relationships with partners, such as beneficiaries of social safety net programs and license applicants. NIDP will provide registration kits, software, and training, notably to ensure compliance with nondiscrimination principles and special measures for individuals with disabilities or vulnerability. The partners will provide facilities, staffing, and\n\n[30 For example the EU Code of Conduct on Data Centre Energy Efficiency or International Telecommunication Union recommendations on green data](https://e3p.jrc.ec.europa.eu/sites/default/files/documents/publications/jrc114148_best_practice_guide_2019_final.pdf)\n[centers.](https://www.itu.int/en/action/environment-and-climate-change/Documents/ITU-TRecDataCentresList.pdf) 31 Flash floods and seasonal river floods are becoming more frequent and widespread and are projected to increase alongside extreme rainfall events.\nWorld Bank, 2021, Ethiopia Climate Risk Profile.\n32 This is in addition to the estimated 5 million persons registered by NIDP at the time of project approval.\n33 Fees were calculated based on cost models by NIDP as part of project preparation, in line with the costs observed in other developing countries.\n\nPage 11 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) consumables and have the possibility of procuring kits (for example, banks). The fund flows will be elaborated in the Project Operations Manual.\n\n(b) **Super agents.** These are private companies procured through a competitive process. They will cover all geographies, including urban centers, nonurban, remote, and special zones/use cases benefiting from the private sector’s ability to scale operations. NIDP will provide registration kits, software, and training to ensure inclusion and nondiscrimination. The agents will provide facilities, staffing, and consumables and may use their own hardware that meet standards set by NIDP.\n\n(c) **Fayda Centers.** These are permanent, temporary, and mobile centers to be established and operated by NIDP, typically at regional and zonal levels, to offer registration, data update, and grievance services. For the initial mass registration, the focus will be on temporary and mobile centers for populations, such as those living in remote areas and persons with disabilities, who are unlikely to be covered by registration partners and super agents. Permanent centers will be gradually established, including through co-location with offices of ICS and other government agencies, to take over steady state responsibilities.\n\n(d) **Civil registration.** This channel involves Fayda registration at the time of birth registration for children below an age to be agreed by NIDP and ICS, facilitated by system-to-system integration.\n\n**Figure 1. Fayda Registration and ID Issuance Channels**\n\nThe IDs issued by Fayda will use credentials that will use open technologies (for example, open standard QR codes) for security and verification (including with offline capabilities) and will not display data that could cause risk of discrimination. NIDP will also consider using global and regional standards for cross-border interoperability of the credentials. The two types of credentials are as follows: (a) **Physical credentials.** Low-cost plastic or paper ID cards with basic physical security features will be issued for free in the first instance with the possibility of charging nominal fees for replacements and for these fees to be waived for vulnerable populations. These credentials will be distributed through the registration channels and partnerships with EthioPost, regional governments, and _kebeles_ .\n\n(b) **Digital credentials.** A Fayda smartphone application will be developed along with the issuance of digital credentials that can be stored in other applications and emerging standardized digital wallets. These credentials will be free for all to use.\n\n42. Kebele ID paper-based records are stored locally across the country, with no aggregated backups in a centralized repository. This makes them vulnerable to damage or loss during climate disasters or conflict. [34] Recent floods in Somali,\n\n[34 Lucas, Kitzmüller. 2020. Let’s Get Digital? Policy Options for Ethiopia’s ID System. https://lucaskitzmueller.medium.com/lets-get-digital-policy-](https://lucaskitzmueller.medium.com/lets-get-digital-policy-options-for-ethiopia-s-id-system-ef2b02468942)\n\n[options-for-ethiopia-s-id-system-ef2b02468942](https://lucaskitzmueller.medium.com/lets-get-digital-policy-options-for-ethiopia-s-id-system-ef2b02468942) Page 12 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) Oromia, and Afar have flooded fields, schools, and public institutions. [35] The roll out of Fayda and the planned interoperability with civil registration will improve the Government’s ability to offer digital services that require proof of vital events, such as for emergency cash transfers following climate shocks (floods and drought) [36] . The provision of unique identifier will assist the layering of services and support packages (cash plus) on climate change vulnerable households by various partners which will better facilitate building the resilience of households from the impact of climate change.\n\n43. **Subcomponent 3.1 – Supporting mass registration and issuance of physical IDs to Ethiopian residents (US$190**\n**million IDA).** This subcomponent will support a mass registration through registration partners, super agents, and Fayda\ncenters to produce and distribute IDs to up to 90 million nationals and nonnationals, including disadvantaged groups, such as women, girls, and persons with disabilities (except for refugees and host communities, which are funded by Subcomponent 3.2). It will also support the transition to a steady state through the establishment of permanent Fayda centers. In line with the project’s commitment for inclusion, the registration strategies and processes will be designed to be universally accessible. The planned Fayda registration processes have been enhanced by pilots and are expected to be easy and accessible for Ethiopians and residents, with information available in a variety of formats and from a range of trusted sources. To avoid exclusion, the registrants will have the option to present many possible official documents, including a Kebele ID or birth certificate. Any persons without either of these or other identity documents, in the last resort, would have their claimed identity vouched for by a trusted ‘introducer.’ Funds will be used to support mass registration and issuance of physical and digital IDs to Ethiopian residents (excluding refugees and host communities) through registration partners, super agents, and Fayda centers as well as the establishment of permanent Fayda centers and registration partners. The project will promote the use of renewable energy by procuring solar-powered registration kits for the mass registration (90 million people). [37] 44. **Subcomponent 3.2 – Supporting mass registration and issuance of physical IDs to Ethiopian residents in host**\n**communities and refugees (US$20 million WHR).** This subcomponent will support NIDP, in collaboration with UNHCR and\nRRS, to prioritize registration and ID issuance, through registration partner and Fayda center channels, to host communities and refugees in regions where large populations of refugees exist, namely Gambella, Somali, BenishangulGumuz, Afar, Tigray, [38] and Amhara, as well as urban refugees in Addis Ababa. This is estimated to cover up to 1.7 million persons in host communities and up to 924,000 refugees. [39] NIDP will work closely with RRS and UNHCR to utilize existing and upcoming initiatives for issuing and renewing refugee ID cards. This involves reusing biographic and biometric data collected by RRS through the UNHCR ProGres system for Fayda registration. NIDP will also develop registration strategies for individuals who require to be ‘introduced’ by a witness in the absence of supporting documentation (for example, due to delay in issuance of refugee cards). Fayda will not substitute existing documents issued to refugees (refugee ID card, proof of registration, and so on) but will be used as a complementary form of identification.\n\n35 Floodlist 2023. Ethiopia-Flooding Continues in Several Regions, Displacing Thousands and Threatening Food Security.\nhtps://floodlist.com/africa/ethiopia-floods-may-2023 36 Governments must respond quickly to climate or other shocks and provide emergency assistance. When Pakistan was hit by floods in 2010, it used the foundational ID to facilitate emergency cash grants: beneficiaries used their cards to make a quick application at centers across the country, and their address was validated (to check whether they lived in the affected areas). Over 2.7 million people applied, but 1.1 million were deemed ineligible, saving up to US$248 million. Having such reliable systems enables a country to respond at speed and with transparency.\n37 The process will require thousands of registration kits.\n38 Numbers of refugees in Tigray require confirmation as RRS is currently in the process of re-engaging in the region following the recent conflict.\n39 The target for refugees tackles almost the whole population of registered refugees in Ethiopia as of October 2023. However, this number might change due to continuing inflows and outflows of refugees. The same is valid for the target seizure of the host community, which is until now an estimate provided by UNHCR, for _woredas_ with a great number of host communities. It should be noted that the per-person cost of registering refugees is expected to be nearly three times that of host communities, which has been considered in the costing and allocations of WHR.\n\nPage 13 of 39", "output": {"entities": {"named_data": ["UNHCR ProGres system"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) 45. **Subcomponent 3.3 – Supporting integration of Fayda and digital civil registration (US$4 million IDA).** This subcomponent will be carried out in coordination with the Ethiopia Program for Results (Hybrid) for Strengthening Primary Health Care Services Project (P181399), which is supporting ICS to digitalize its civil registration system. The project’s funds will be used for supporting the integration of Fayda and digital civil registration systems at federal and regional levels, including Fayda registration for children whose births have been registered by ICS, Fayda-based identity verification of parents and other notifiers of vital events, and notification of registered marriages and deaths to Fayda, all through the provision of goods, consulting services, non-consulting services, training, and operating costs for the purpose. The integration of Fayda to the civil registration system managed by ICS provides for long-run operational sustainability, ensuring a continuous data collection mechanism from birth. Furthermore, significant efforts are being made under Component 1 to enable the legal and institutional reforms necessary for long-term institutional legitimacy.\n\n**Component 4 – Improving service delivery (US$35 million equivalent - US$20 million IDA, US$15 million WHR)**\n\n46. This component will support carrying out the following program of activities designed to increase usage of Fayda and to transform service delivery by integrating authentication and e-KYC into priority sectors, as well as developing an Ethiopia Digital Stack of platforms and APIs to support public and private sector service providers build better systems.\n\n47. **Subcomponent 4.1 – Supporting the integration of Fayda (US$12 million IDA, US$15 million WHR).** This subcomponent will fund the integration of Fayda authentication and e-KYC, and the data exchange platform supported under Subcomponent 4.2 into priority services in the public and private sectors, which will be determined by the Project Steering Committee (PSC) based on the variables of population reach, impact, and feasibility. The initial priority sectors have been defined as social protection (registration and identity verification of safety nets beneficiaries, financial inclusion (opening of a bank account and e-KYC), education (student ID), and health (links with civil registration, health records, and insurance). The ability to verify the identity of beneficiaries combined with financial inclusion schemes are core enablers for public cash transfers responding to climate and other shocks.\n\n48. **Subcomponent 4.2 -Developing an Ethiopia Digital Stack (US$8 million IDA).** This subcomponent will support NIDP, in collaboration with other government agencies (such as NBE, EthioSwitch, and MInT) and private sector, to develop a whole-of-government data exchange platform and a set of open APIs. This will make sharing of data across the Government and with the private sector more secure and seamless, while ensuring consent of the data subject facilitated by Fayda authentication. Funds will be used for developing an Ethiopia Digital Stack including a whole-of-government data exchange platform and a set of open APIs, and development tools to facilitate the integration of Fayda payment systems, and other DPI to enable better service design and delivery and to unlock innovation across the public and private sectors.\n\n**Component 5 - Project management (US$12 million equivalent - US$10 million IDA, US$2 million WHR)**\n\n49. **This component will facilitate effective implementation of the project by supporting the establishment and**\n**functioning of the Project Management Unit (PMU)** to undertake financial management (FM), procurement, risk\nmanagement, ESF management, and reporting responsibilities, as well as act as the secretariat for the PSC and augment technical expertise in more complex activities. Funds will be used to support the establishment and operationalization of the project management office for the implementation, coordination, supervision, and overall management of the project (including procurement, FM, environmental and social standards management, M&E, carrying out of external audits, communication and reporting of project activities and results, and grievance redress), all through the provision of goods, non-consulting services, consulting services, training, and operating costs for the purpose.\n\nPage 14 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**C. Project Beneficiaries**\n\n50. **All Ethiopian nationals and residents, including refugees, are expected to benefit from the project.** The project will enhance capacity of the public and private sectors to plan, delivery, and monitor services, indirectly benefiting the whole population, such as because of more efficient and transparent public spending. The project will directly benefit at least 90 million people who will obtain secure identification, making it easier for them to prove their identity for various purposes, including exercising their rights and accessing services. These benefits will be especially accrued by the estimated 36 percent of Ethiopia’s adult population who lack a Kebele ID, as well as women who disproportionately lack a Kebele ID, and persons with disabilities. Both populations will benefit from greater agency through enhanced financial inclusion and other economic opportunities. Refugees, asylum seekers, and stateless persons will also benefit from having access to widely accepted form of identification. This includes the target of the entire refugee and host community population of around 1.7 million. Many interventions are planned in those areas where refugee and host community populations are large (Gambela and Somali), comprising over 50 percent of Ethiopia’s total refugee population.\n\n51. **The other beneficiaries are public and private service providers.** Fayda and the Ethiopia Digital Stack will enable public and private sector service providers to reduce the costs and risks of delivering services. More innovative products and services will be built by leveraging new functionalities that would allow a shift to online channels instead of depending on brick-and-mortar service delivery channels. The GoE will benefit from reduced fraud and leakages, including in social protection and subsidy programs, through the ability, for the first time, to uniquely identify and securely verify individuals.\n\n**D. Results Chain**\n\n**Figure 2. Project Results Chain**\n\n**E. Rationale for Bank Involvement and Role of Partners**\n\n52. **The World Bank brings deep knowledge and international experience related to the development of inclusive**\n**and trusted identification and civil registration systems and harnessing these for development.** Through the ID4D\n\nPage 15 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) initiative, the World Bank has provided technical assistance to dozens of client countries and has studied systems in many more with international good practices. As of September 2023, the World Bank is funding or preparing to fund identification and civil registration system development in more than 45 countries. As a result, it has an excellent understanding of international good practices, standards, and emerging technologies. Complementing this, the World Bank also has a thorough understanding of Ethiopia’s identification and civil registration ecosystem and the opportunities and challenges that the project will address, based on ID4D’s partnership with the GoE that began in 2016, as well as the Program for Results (Hybrid) for Strengthening Primary Health Care Services (P175167) project. As part of the ID4D technical assistance to NIDP, the World Bank has worked closely with the Bill & Melinda Gates Foundation, the United Nations Economic Commission for Africa, and the Tony Blair Institute for Global Change, which have been providing complementary technical assistance to NIDP, as well as the United Nations Children’s Fund, which is supporting ICS to improve birth registration. The World Bank also collaborated with the _Agence Française de Développement_ (French Development Agency) and the United States Agency for International Development (USAID), which supported the inception of Fayda. The World Bank’s broader engagement in Ethiopia will enable a strong link between this project and those implemented in other sectors, such as social protection, financial services, health care, agriculture, disaster risk management, gender, education, digital government, data governance, and digital economy.\n\n**F. Lessons Learned and Reflected in the Project Design**\n\n53. The project draws on and contextualizes lessons from the World Bank’s global experience related to identification and civil registration systems, including the following:\n\n- **Having a strong legal and regulatory framework as an enabler for success.** The identification systems must\nbe underpinned by legitimate, comprehensive, and enforceable laws and regulations that promote inclusion and trust, including ensuring data protection and privacy, accountability, and transparency in how data are managed and reducing risks of abuse such as unauthorized surveillance in violation of due process.\n\n- **Ensuring universal accessibility of registration.** A key lesson from Fayda pilots conducted by the Government\nin 2022-2023 and international experiences is to accept a wide range of documents as supporting evidence while encouraging (but not requiring) the presentation of a Kebele ID or birth certificate. Even persons without any documentation will be able to register for Fayda through a qualified 'introducer'.\n\n- **To build trust and mitigate the risk of misuse, it is important to also strengthen cybersecurity and protect**\n**people’s privacy through system design.** Some good practices include ensuring software and firewalls are\npatched and up to date, monitoring network activity and cyber threats, ensuring strong identity and access control for internal systems (including multifactor authentication), and educating staff on how they can prevent phishing and ransomware attacks. Data protection measures include limiting the collection and exposure of data—particularly sensitive personal data—and ensuring that authentication discloses only the minimum data necessary to ensure appropriate levels of identity assurance and providing people with choice, oversight, and consent over how their data are shared.\n\n- **A standards-based, technology, and vendor-neutral approach promotes financial and operational**\n**sustainability.** Vendor and technology lock-in reduce the sustainability of identification system investments\nand create high costs that are often passed on to users. To mitigate these risks, the project will minimize dependency on a single vendor and technology while ensuring extensible system design (for example, open and modular architecture and open standards and open-source software, where appropriate).\n\nPage 16 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n- **The design of ID systems should be driven by user needs and use cases.** The design of systems should be\ndone collaboratively with people and institutional users to be responsive to their needs. Fayda benefits from broad and deep engagement with these stakeholders to understand what services they need and how.\n\n- **Ensure refugees and their host communities are given specific consideration.** Refugees often face a\ndifferential experience regarding issuance of documentation other than refugee IDs, sometimes perceiving that such identification threatens their status or entitlements as refugees. This was experienced to some level on the issuance of work permits for refugees under the World Bank-supported Ethiopia Economic Opportunities Program (P163829). Social cohesion and/or tensions within host communities must also be considered. As such, and as evidenced by other refugee inclusion operations, communications, GRM, and transparent and equitable targeting must be tailored for these specific circumstances, in coordination with stakeholders on the ground with experience with these unique communities.\n\n- **Reinforcing existing committees within refugee camps simplifies and streamlines refugee inclusion.** Refugee\ncamps and settlements are usually well organized, with representative and sectoral committees covering different aspects of life, such as livelihood and conflict management. Refugee Central Committees and Refugee Camp Development Committees are common important interlocuters. Experience from refugee host community projects demonstrate the value of working with these existing bodies to promote inclusion into project implementation arrangements, rather than creating new institutions. Through collaboration with RRS and UNHCR, the Digital ID project will work with and strengthen these existing structures to represent refugee interests in the project, though some new committees can be considered where there might be a need.\n\n**III.** **IMPLEMENTATION ARRANGEMENTS**\n\n**A. Institutional and Implementation Arrangements**\n\n54. **Until the ID Institution is established as the permanent home for Fayda, the PMO (specifically, NIDP) will be the**\n**implementation agency and will host the PMU.** The FM and procurement assessment of the PMO has been carried out\nby the World Bank, with results shared and action items discussed and agreed. The PMU will be staffed with core positions, such as project coordinator, procurement specialist, FM specialist, and environmental and social specialists as well as M&E experts. These positions will be funded by the Digital Foundations Project (P171034), until effectiveness.\n\n55. **The GoE is still evaluating options for the permanent home of Fayda.** It is expected that the ID Institution will be set up within two years from effectiveness date. Options being considered include (a) establishing a new GoE agency, (b) integrating Fayda as a function of an existing GoE agency, and (c) integrating different aspects of Fayda as a regulatory entity into an existing GoE agency and a business wing of a state-owned enterprise. Considerations include public trust, local presence, business model, and financial and operational sustainability. Under Subcomponent 1.1, the project will support NIDP to conduct the necessary studies and consultations to inform the GoE decision, as well as develop the foundational aspects of the ID Institution. Once the ID Institution is established, the project may need to be restructured.\n\n56. **Strategic guidance and oversight will be provided by a Project Steering Committee (PSC).** The PSC will be cochaired by the Minister of Finance and Minister of Cabinet Affairs and will comprise representatives from the Ministry of Finance (MoF), MInT, Ministry of Justice, Ministry of Planning and Development, NBE, RRS, and ICS. It will be established not later than 60 days after effectiveness and will serve concurrent to project implementation, meeting as often as required but at least once a quarter. The PSC will be responsible for approving work plans, budgets, and reports to the World Bank, as well as deciding on priority use cases under Subcomponent 4.1.\n\nPage 17 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) 57. **A multi-stakeholder technical committee (TC) will be established to support the implementation of Components**\n**3 and 4.** The TC will be chaired by NIDP which will comprise the representatives from registration partners and super agents,\nas well as from the MoF, MInT, ICS, RRS, academia and civil society, and international and national ID experts. The TC will form working groups that will undertake technical oversight of the key performance indicators (KPIs) of the ID Program, that is, the progress of Fayda registration and inclusive ID issuance and improving service delivery in the form of use cases.\nInternational and local development and humanitarian partners can also be invited to join these TCs. The TCs will meet as often as required but at least once a quarter.\n\n**Figure 3. Implementation Arrangements**\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n58. **The PMU housed at NIDP will be responsible for monitoring project implementation, tracking budget utilization,**\n**tracking results (against the Results Framework), and identifying and mitigating challenges.** This includes organizing\nregular consultations and disclosing relevant project information to stakeholders, as part of the project’s ESF commitment.\nThe project, particularly through Component 5, will support NIDP to carry out surveys (for example, satisfaction surveys among people and relying parties) and develop M&E systems (for example, real-time registration updates dashboards).\nThe PMU will compile regular progress reports, as well as formal project annual and midterm reports.\n\n**C. Sustainability**\n\n59. **The project incorporates various measures to ensure institutional, operational, and financial sustainability.** It will invest in developing internal capacity of NIDP as the implementing agency, as well as development of the regulatory framework under the Digital ID Proclamation and other policies, guidelines, and standard operating procedures. There is a clear commitment for a timeline for the program office to mature into a permanent and autonomous ID institution, and the project will invest in building its capacity and sustainability. The technology deployed by the project will also adopt good practices to avoid technology and vendor lock-in and to promote extensibility and adaptability of systems.\n\n60. **Financial sustainability will be assured by ensuring constant improvements to the business model** (for example, fees and public-private partnerships) for Fayda authentication and e-KYC services that balances the need to earn some revenue to sustain operations and the need to ensure that set fees identity verification do not create a barrier for Page 18 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) adoption. For example, authentication and e-KYC will be free for public and social impact services. Likewise, the project will continue to document generated savings and other benefits of adopting Fayda that will strengthen the case for the GoE to invest in continuous operating expenses.\n\n**IV.** **PROJECT APPRAISAL SUMMARY**\n\n**A. Technical, Economic and Financial Analysis**\n\n61. **The project is expected to contribute to sustainable economic growth, through long-term cost savings,**\n**efficiency, and productivity gains, fueled by greater digital adoption by residents and businesses.** **[40]** Digital ID holds the\npromise of enabling economic value creation by fostering increased inclusion, increasing formalization, and promoting digitalization of services. The project activities will ease access to identification for millions of Ethiopians and residents, including women, people with disabilities, refugees, and IDPs. The ability to prove one’s identity is often a prerequisite for accessing many public and private sector services. By addressing the gap of 36 percent in ID ownership (the population lacking a current version of paper based Kebele ID, according to the 2018 ID4D-Findex Survey), the project will contribute to removing some of the most basic barriers that people face.\n\n62. **The public sector is expected to benefit from cost savings, on the back of the productivity gains.** Use of digital technologies is expected to reduce transaction costs by lowering the time spent on manual processing, allowing civil servants to focus on higher-value tasks. Numerous examples of savings from a reduction in errors, fraud, and corruption [41] are also expected to apply. A _‘build once, reuse always’_ approach will reduce the incremental costs of offering each new service, through capital investments in shared Digital Public Infrastructure such as Digital ID.\n\n63. **The project is expected to make service delivery more efficient for government agencies and businesses,**\n**including by accelerating financial inclusion, universal health insurance coverage, and will reduce identity-related fraud**\n**and leakages in public programs.** Current identity verification processes are relatively inefficient and insecure. A sample\nstudy on the digital delivery of financial entitlements as part of the PSNP found that fraud is more frequent with manual cash payments (0.23 percent) than electronic transfers (0.15 percent). [42 ] Through the introduction of automated verification mechanisms—including biometric, demographic, and SMS one-time password verification—service providers will be able to streamline the identity verification processes. In India, for example, biometric-based e-KYC contributed to increasing financial inclusion from 35 percent in 2011 to 80 percent in 2017 [43] and reduced customer onboarding costs for firms from US$23 per customer to as low as US$0.15. [44 ] This and other examples are relevant to Ethiopia, given that current validation exercises focus on using demographic rather than biometric verification and are thus prone to imposters using real information (for example, that of deceased persons). [45] 64. **The economic and financial analysis’ model to appraise the project is based on the economic impact of Fayda,**\n**following a twofold approach: savings from digitization of service delivery and revenue streams from transaction fees**\n**and add-on services made possible by Fayda.** To estimate the savings that arise from transitioning from paper-based to\ndigital-enabled service delivery, the model first estimated the savings from using Fayda versus the current Kebele IDs, for 40 World Bank. 2016. _World Development Report: Digital Dividends_ .\n41 World Bank. 2018. _Public Sector Savings and Revenue from Identification Systems: Opportunities and Constraints_ .\n42 World Bank. 2017. _Advancing Electronic Food Security Payments in Ethiopia_ .\n43 World Bank. 2018. _Findex Survey_ .\n44 World Bank. 2018. _Private Sector Economic Impacts from Identification Systems_ .\n45 World Bank. 2018. _Public Sector Savings and Revenue from Identification Systems: Opportunities and Constraints_ .\n\nPage 19 of 39", "output": {"entities": {"named_data": ["2018 ID4D-Findex Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) all residents, including refugees. These savings range from the reduction in administrative, transaction, and liability costs of holding personal data, to the elimination of redundancy in identification systems and gaining efficiencies. [46] The model also estimated the savings for residents and businesses, in terms of productivity, transportation time, and savings in fuel and reduction of CO2 emission due to services being digitized and thus made accessible remotely. [47] In addition to Fayda versus Kebele ID savings, the model also estimate the economic impact in sector-specific use cases (that is, financial services, social protection, health care, education, and taxation) by boosting access to finance; by providing unique identification, proof of life, or identity verification in pension plans; or by reducing ghost workers in education and health sectors, among others. A study on savings for financial institutions observed that the cost went from US$4.25 per client for in-person service to US$0.10 for online service delivery. [48] Regarding revenues, the economic analysis model estimated revenue streams from charging fees both to individuals and third parties for identity-related services. [49 ] In addition, given Ethiopia’s exchange rate distortions, the model included alternate exchange rates for sensitivity analysis. [50] These calculations were made based on an estimated 90%-10% distribution for USD and BIRR project spending respectively. The Bank and IMF continue to work closely in collaboration with the authorities on addressing Ethiopia’s exchange rate and other market distortions in an orderly and sequenced way to minimize social and poverty effects.\n\n65. **The economic and financial analysis performed for this project followed a standard Cost-Benefit Analysis (CBA)**\n**approach and revealed a positive net present value (NPV) of US$ 210.10 million and an internal rate of return (IRR) of**\n**19 percent in the lower bound sensitivity analysis, and an NPV of US$ 194.81 million and an IRR of 18% in the higher**\n**bound.** The twofold approach (that is, savings from digitization of service delivery and improved government efficiencies\nfor all residents, including refugees, and revenue streams from e-KYC and authentication) relied on the available secondary data from other countries and reasonable assumptions, as well as additional evidence sourced from the business cases developed by NIDP. The financial model was used to run a cash flow and financial analysis for three different scenarios (optimistic, neutral, and pessimistic), plus an additional sensitivity analysis was performed based on alternate exchange rates for lower and higher bound NPV. As a result, the overall lower bound NPV for the project in the optimistic scenario was estimated at US$386.42 million and is expected to demonstrate an IRR of 27 percent over 10 years. In the neutral scenario, the NPV is expected to be US$210.06 million, and the IRR is expected to be 19 percent. In the pessimistic scenario, the NPV is expected to be US$27.17 million, and the IRR is expected to be 9 percent. The analysis for the higher bound NPV in the optimistic scenario is estimated at US$371.13 million and is expected to demonstrate an IRR of 26 percent over 10 years. In the neutral scenario, the NPV is expected to be US$194.81 million, and the IRR is expected to be 18 percent. In the pessimistic scenario, the NPV is expected to be US$11.88 million, and the IRR is expected to be 8 percent. [51] 66. **The project is aligned with the goals of the Paris Agreement on both mitigation and adaptation.**\n\n- **Assessment and reduction of mitigation risks.** The project will not finance any physical digital connectivity\nor large ICT infrastructure, except the purchase of energy-efficient server equipment for the existing data centers, in accordance with internationally recognized best practices on energy efficiency. These measures together ensure that the carbon lock-in and the transition risks associated with the project are low.\n\n46 Government of Moldova. 2017. “Feasibility Study on Enhancing Citizens Access to Administrative Services at Local Level.” 47 Digital ID in India helped reduce the average firm onboarding cost from US$23 to US$0.15.\n48 “Mobile Banking Adoption: Where Is the Revenue for Financial Institutions?” Fiserv White Paper, 2016.\n49 The Government of Rwanda charges US$0.72 for a basic virtual ID, while the Government of Peru charges US$10 for a basic ID and US$14 for an electronic ID.\n50 Exchange rates used in the sensitivity analysis are 80 ETB/USD as the lower bound, and 110 ETB/USD as the higher bound.\n51 The discount rate used for the calculations is 7 percent, which is the current (November 2023) savings deposit rate in Ethiopia.\n\nPage 20 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["secondary data"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n- **Assessment and reduction of adaptation risks.** The main climate risks are likely to be floods and intense\nrainfall which could potentially affect servers and registration activities. A climate risk assessment will be done to select the most appropriate locations of IT equipment to prevent data loss in the face of extreme weather events. These adaptation measures will reduce risks from climate hazards to an acceptable level **.** 67. **The project will contribute to gender equality and particularly women’s economic empowerment.** There is a gender coverage gap for the existing Kebele ID of 21 percent, one of the largest gender gaps for identification systems worldwide. [52 ] Women leaving their communities following their marriage and those who migrate for domestic labor jobs are particularly at risk of exclusion from the Kebele ID system as the requirements to apply for one in their new _kebele_ are usually cumbersome. Beyond systemic barriers, there is a lack of demand which exasperates the gap. Recent ID4D research on women’s ID ownership in Ethiopia found that women do not see the Kebele ID as salient to their daily lives and therefore do not pursue applying for one even if it is accessible. [53] By streamlining and, in some cases, automating the updating of information, the project will be making it easier for women to assert their associated rights and entitlements.\nThe new Fayda system can also embed use cases and value propositions that are tailored to women, to increase demand for identification. Furthermore, a digital identification system will create more opportunities for Ethiopia’s many womenowned small and medium enterprises to do business online, and more generally access employment, and enroll in maledominated education sectors such as science, technology, engineering, and mathematics education.\n\n68. **The authentication component of a digital ID system can also increase the security of funds transfers for both in-**\n**person and remote environments, particularly as Ethiopian legislation enables and helps increase payment**\n**interoperability between financial service providers.** This is particularly important in the Ethiopian context where\nremittances were estimated in 2017 to represent over 5 percent of the country’s GDP and one-quarter of its foreign exchange earnings, which corresponds to a little over US$4 billion. [54] International transfers often serve as a vital lifeline for households and are sometimes the only means of income for many individual recipients. Unfortunately, in some corridors, 78 percent of total remittances are still sent through informal channels, which can represent a risk for individuals and undermine the ability of public authorities to implement anti-money laundering and combating the financing of terrorism policies.\n\n**B. Fiduciary**\n\n69. **An FM assessment was carried out at NIDP at the PMO** in accordance with the FM Manual (FMM) for World Bank Investment Project Financing Operations reissued on September 7, 2021, effective March 1, 2010. The project’s FM arrangements meet minimum requirements under the World Bank Policy and World Bank Directive on Investment Project Financing (IPF) and the FMM. The FM risk is ranked as substantial. Action plans were developed to mitigate identified risks.\nThe PMO will coordinate and manage all FM aspects of the project through a PMU to be established under NIDP and staffed with core positions, including FM experts. The project FM largely follows the government procedures but will address the peculiarities of this project. The project budgeting will follow the government budget procedures and will be included and proclaimed as part of the PMO budget on an annual basis. In addition, NIDP will submit the project’s annual work plan and budget (AWPB) to the World Bank for ‘no objection’. The project will establish sets of accounts and maintain an adequate accounting system. The internal audit unit of the PMO will conduct an internal audit of the project. Quarterly unaudited interim financial reports (IFRs) will be submitted within 45 days of the end of the fiscal quarter end according to agreed IFR\n\n[52 Metz and Clark. 2019. https://documents.worldbank.org/en/publication/documents-reports/documentdetail/727021583506631652/global-id-](https://documents.worldbank.org/en/publication/documents-reports/documentdetail/727021583506631652/global-id-coverage-barriers-and-use-by-the-numbers-an-in-depth-look-at-the-2017-id4d-findex-survey)\n[coverage-barriers-and-use-by-the-numbers-an-in-depth-look-at-the-2017-id4d-findex-survey](https://documents.worldbank.org/en/publication/documents-reports/documentdetail/727021583506631652/global-id-coverage-barriers-and-use-by-the-numbers-an-in-depth-look-at-the-2017-id4d-findex-survey) 53 Irish Aid. 2019. _Coverage of Older People in Ethiopia’s Social Protection System_ . [https://socialprotection.org/sites/default/files/publications_files/](https://socialprotection.org/sites/default/files/publications_files/Coverage%20of%20older%20people%20in%20Ethiopia%E2%80%99s%20social%20protection%20system.pdf)\n[Coverage%20of%20older%20people%20in%20Ethiopia%E2%80%99s%20social%20protection%20system.pdf.](https://socialprotection.org/sites/default/files/publications_files/Coverage%20of%20older%20people%20in%20Ethiopia%E2%80%99s%20social%20protection%20system.pdf) 54 IOM. 2017. _Scaling up Formal Remittances to Ethiopia_ [. https://www.iom.int/sites/g/files/tmzbdl486/files/press_release/file/iom-ethiopia-](https://www.iom.int/sites/g/files/tmzbdl486/files/press_release/file/iom-ethiopia-executive-summary-21.pdf)\n[executive-summary-21.pdf](https://www.iom.int/sites/g/files/tmzbdl486/files/press_release/file/iom-ethiopia-executive-summary-21.pdf) Page 21 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) formats. All disbursement methods are available to the project. Channel 2 fund flow mechanisms are to be used where funds from IDA will flow to accounts managed by the PMO. A Segregated Designated Account (DA) will be opened for the project at the NBE managed by the PMO. For advances to the DA and reimbursements, the project will use report-based disbursement, with submission of six months forecast as part of quarterly IFR. The PMO will be responsible for having project financial statements audited annually by an independent auditor acceptable to IDA and submit audit reports to the World Bank within six months of the fiscal year-end.\n\n70. **Procurement will be carried out in accordance with the World Bank’s Procurement Regulations for IPF**\n**Borrowers, dated September 2023;** the Guidelines on Preventing and Combating Fraud and Corruption in Projects\nFinanced by IBRD Loans and IDA Credits and Grants revised as of July 1, 2016; and other provisions stipulated in the Financing Agreement. The procurement risk is ranked as Substantial. The PMO/NIDP will be responsible for procurement and will utilize the institutional structure of the PMO, including the Tender Endorsing Committee. NIDP has prepared a Project Procurement Strategy for Development (PPSD), which includes market conditions, risks, and corresponding approaches resulting in an 18-month Procurement Plan (PP). Systematic Tracking of Exchanges in Procurement (STEP) will be used to prepare, clear, and update PPs and to conduct all procurements. The ones not uploaded or agreed in STEP will not be eligible for project financing. Annex 1 details project procurement arrangements.\n\n71. **The main risks identified** are (a) no institutional experience in managing World Bank-financed projects by PMO/NIDP; (b) unavailability of qualified procurement staff with experience in World Bank-financed projects and possible inability to retain qualified staff due to low salary levels imposed by the MoF; and (c) challenges in translating the project scope into specific procurable activities because of complexity and uniqueness of this project, particularly in Component 3. These risks will be mitigated by recruiting two dedicated procurement specialists with experience in World Bank procurement and agreeing with the World Bank on all aspects of the ID registration and credential distribution arrangements that are fit for purpose, including confirmation of the list of identified government-owned registration partners with adequate justification.\n\n**C. Legal Operational Policies**\n\n@#&OPS~Doctype~OPS^dynamics@padlegalpolicy#doctemplate Legal Operational Policies **Triggered?** Projects on International Waterways OP 7.50 No Projects in Disputed Area OP 7.60 No\n\n**D. Environmental and Social**\n\n72. **The project is processed under the World Bank ESF, and the overall environmental and social risk is classified as**\n**Substantial.** Six of the ten Environmental and Social Standards (ESS) are relevant for this project: ESS1 - Assessment and\nManagement of Environmental and Social Risks and Impacts; ESS10 - Stakeholder Engagement and Information Disclosure; ESS2\n\n- Labor and Working Conditions; ESS3 - Resource Efficiency and Pollution Prevention and Management; ESS4 - Community\nHealth and Safety; ESS7 - Indigenous Peoples/Sub-Saharan African Historically Underserved Traditional Local Communities. A combination of ESF instruments defines and addresses the needs of vulnerable populations who may potentially experience more barriers in obtaining legal identity documents, such as the poor, women, people who live in remote Page 22 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) areas, the elderly, people with disabilities, residents with nonpermanent domiciles or high mobility, IDPs, refugees, asylum seekers, or stateless population, and those who are living in orphanages and detention houses.\n\n73. **Environmental risk is moderate.** According to ThinkHazard and the Climate and Disaster Risk Screening (CDRS) conducted for this operation, Ethiopia is at high climate and geophysical risk of floods (river flood, urban flood), landslide, volcano, extreme heat, and wildfire. The country is also exposed to medium risk of earthquakes and water scarcity and a low risk of cyclone. However, when considering the lifetime of the project's investments and with the risks from volcanoes and earthquakes averaging return periods of 35 years, the level of potential impact of these climate and geophysical risks to the project is Moderate. The overall environmental impact is anticipated to be minimal with no adverse risks. The project’s main risks are from the increased use of various IT equipment, namely ID registration kits, laptops, ID card printers, and servers. The anticipated introduction of authentication capability, including through mobile phones, may eventually lead to increased mobile phone ownership by the population. Studies estimate that the lifespan for the IT equipment may range from 4 to 5 years. Therefore, the project may result in an increase of e-waste in the long run which could have various environmental health and safety risks and community health and safety concerns if not properly managed. An e-waste management plan will be prepared as part of the Environmental and Social Management Framework (ESMF), which includes recycling and up-cycling of the electronic components for reuse. Given the limited capacity in ewaste management and lack of ESF experience of the PMU and other implementing entities, the environmental risk of the project is rated as Moderate.\n\n74. **The social risk is rated Substantial.** The project will be implemented nationwide including in the conflict-affected areas, which can result in risks to the safety of the project workers tasked with Fayda registration. Potential social risks are related to possible exclusion of marginalized groups, improper use or sharing of data, creation or reinforcement of social conflict or stakeholder perception. Given the sensitivity of identity politics, unless properly managed, it may create new or exacerbate existing historical tensions and conflicts of an ethnic or religious nature. However, since Fayda excludes the collection of information about ethnicity and religion, which could potentially be used to discriminate against an individual, this new Digital ID system is expected to positively contribute to reducing the potential risks of intensifying ethnic or religious conflicts. Other inherent risks may include the possible exclusion of vulnerable groups from services, including due to technology gaps among various layers of the population and possible biases. The issue of personal data protection has also become an important public concern in the face of high ethnic and religious-based conflicts around the world. The National ID Proclamation approved in March 2023 has specific guidance on protecting ID-related data.\nEthiopia is also making progress towards putting in place an overall data protection legislation. In October 2023, the Council of Ministers approved a draft Personal Data Protection Proclamation, with ratification by the Parliament included as one of the disbursement conditions.\n\n75. **The project may induce risks related to labor and working conditions, labor-induced issues, and community**\n**health and safety risks such as spread of communicable diseases and sexual exploitation and abuse/sexual harassment**\n**(SEA/SH) or other forms of gender-based violence.** Since infrastructure investments or civil works that require land\nacquisition are not envisaged, there are no risks around involuntary resettlement. Due to the national implementation, it is likely to affect areas where historically underserved communities reside. There will be a potential risk of social exclusion if the equitable distribution of project benefits is not applied among underserved communities in emerging regions.\nAccessible and inclusive access to services requires an identification system that can address the concerns of historically underserved and other vulnerable groups who are mostly at risk of being excluded and the most in need of the protection and benefits that identification can provide. There are potential risks that identification could be used as a tool for discrimination or to infringe on or deny individual or collective rights. Due to the digital divide, there may be potential social risks where some groups can be denied identification or associated services and rights because they lack internet Page 23 of 39", "output": {"entities": {"named_data": ["Climate and Disaster Risk Screening"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) connectivity/devices and digital literacy/digital skills or due to technology bias. Making sure that Fayda registration and use are widely accessible and inclusive are the top priorities for the Government. The project design ensures that the entire population, including migrants, daily laborers, and the poor will have an opportunity to receive Fayda ID. The provision of accessible handling procedures and GRMs, Toll-free telephone hotline, SMS, online help desk portal\n[(www.id.et/help) and self-service function on the Fayda ID app are already being piloted by NIDP. In addition, physical](https://nam11.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.id.et%2Fhelp&data=05%7C01%7Clbujoreanu%40worldbank.org%7C043e37c2b2554735bb3e08dbe6c44b4f%7C31a2fec0266b4c67b56e2796d8f59c36%7C0%7C0%7C638357503019492597%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=EME9%2F6GM24y9t0%2F5Ch54WffedGc1eNdn0V2QCDShHEc%3D&reserved=0) help desks will be established at Fayda registration sites to address and register complaints from walk-in residents through a grievance logbook and a suggestion box. The scale up of these online and offline mechanisms will be essential to avoid any exclusion, both in terms of Fayda registration and access to services that require proof of identification.\n\n76. **The environmental and social risks and impacts are addressed in the ESMF, Stakeholder Engagement Plan (SEP),**\n**and Labor Management Procedures (LMP).** The ESMF outlines the guiding principles of environmental screening,\nassessment, review, management, and monitoring procedures for all envisaged activities. An e-waste management plan will be developed to reduce the digital sector’s carbon and environmental footprint through e-waste collection, dismantling, refurbishing, and recycling. The SEP and LMP have been developed and disclosed on NIDP’s website [55] and on the World Bank Project website on October 25, 2023, and November 16, 2023 respectively. A security risk assessment and management plan, and a SEA/SH risk assessment and management plan will be developed as part of the ESMF, finalization of which is a disbursement condition. The ESMF, once acceptable to the World Bank, will be shared with all stakeholders and disclosed nationally and on the World Bank’s external website.\n\n**V.** **GRIEVANCE REDRESS SERVICES**\n\n77. Communities and individuals who believe that they are adversely affected by a project supported by the World Bank may submit complaints to existing project-level grievance mechanisms or the Bank’s Grievance Redress Service (GRS). The GRS ensures that complaints received are promptly reviewed in order to address project-related concerns.\nProject affected communities and individuals may submit their complaint to the Bank’s independent Accountability Mechanism (AM). The AM houses the Inspection Panel, which determines whether harm occurred, or could occur, as a result of Bank non-compliance with its policies and procedures, and the Dispute Resolution Service, which provides communities and borrowers with the opportunity to address complaints through dispute resolution. Complaints may be submitted to the AM at any time after concerns have been brought directly to the attention of Bank Management and after Management has been given an opportunity to respond. For information on how to submit complaints to the\n[Bank’s Grievance Redress Service (GRS), visit http://www.worldbank.org/GRS. For information on how to submit](https://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service)\n[complaints to the Bank’s Accountability Mechanism, visit https://accountability.worldbank.org.](https://www.worldbank.org/en/programs/accountability)\n\n**VI.** **KEY RISKS**\n\n78. The overall risk rating for the project is Substantial.\n\n79. **Political and governance risk is Substantial.** The ongoing civil conflicts in Ethiopia could create risks of low trust in Government’s good intentions to roll out Fayda and thus low adoption and possible opposition. Key design features for Fayda, including many codified into the Digital ID Proclamation, such as special provisions related to data protection for data collected at registration and its use, as well as data minimization principles, can mitigate these risks. Unlike existing Kebele IDs, Fayda will not collect information concerning ethnicity and religion. Furthermore, there are conditions on who 55 [https://id.gov.et/policies](https://id.gov.et/policies) Page 24 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) can access the data and under what circumstances. However, the overall trust will be improved by having in place a Data Protection Proclamation, now presented to the Parliament following approval by Council of Ministers in October 2023.\nThe institutional and governance arrangements for Fayda are still shaping up, with the ID Institution and Data Protection Commission yet to be set up. To further mitigate these risks, the project will invest heavily in communications, stakeholder engagement, and GRMs. Refugee inclusion can also be a political risk in some cases, and in Ethiopia, certain regions like Gambella or regions hosting Eritrean refugees in the north might be susceptible to political risks. The project will work closely with RRS and UNHCR to ensure localized understanding of refugee and host communities’ relationships and accordingly modulate project implementation to account for the potential sensitivities.\n\n80. **Macroeconomic risk is Substantial.** Ethiopia is severely affected by the recent jump in food, fertilizer, and fuel prices. As of end of 2023, the country faces a high risk of debt distress and has been unable to secure relief from creditors or agree on an IMF program. The country faces significant balance of payment issues and reserves currently cover less than one month of imports. Shortage of foreign currency and the existence of a parallel foreign exchange market are affecting a wide range of imports, which could affect specialized items not available locally, or take an unusually long time to be delivered. To mitigate these risks, the government is advancing the publication of major requests for proposal (RFPs) in anticipation of possible delivery delays, with plans in place to go for international open bids, lower ceilings for special commitments, use direct payment methods of disbursements, allow payments in foreign currency for local suppliers, and working with MoF and the National Bank of Ethiopia to allow for project items planned to be procured under National Competitive Bids to be given a priority designation so that the local suppliers are allowed to open Letters of Credit to enable the import of goods for the project.\n\n81. **Institutional capacity for implementation and sustainability risk is Substantial.** ID4D has been providing IDrelated technical assistance since 2016. As a result, there is an advanced capacity in the GoE, including at both leadership and technical levels, and knowledge of good practices for digital ID systems. However, the MoF has recently imposed a salary ceiling for individual consultants that is well below market rates which makes it difficult to hire highly skilled and motivated consultants. The project will attempt to mitigate this risk by leveraging firm contracts, where possible. The World Bank will continue its advocacy for a change in policy by the MoF. Furthermore, establishing proper coordination may be challenging; therefore, special attention will be paid to facilitate interlinks between government agencies.\n\n82. **Fiduciary risk is Substantial.** On FM, neither the NIDP team nor the PMO has any experience in managing a World Bank-funded project. Internal audit capacity is also weak at most public bodies. Delays in the preparation of annual financial statements and internal control weaknesses are risks observed at the PMO. On procurement, capacity limitations and process delays are the main risks to effective implementation. To mitigate procurement risks, several bidding documents have already been prepared, representing a significant portion of the project budget.\n\n83. **Environment and social risk is substantial.** This is due to potential for exclusion, possible personal data breaches and misuse of data. Exclusion risks are mitigated by Fayda being accessible to all nationals and residents and inclusive Fayda registration and authentication processes that have been informed by pilots, ID4D technical assistance, and risk assessments done during project preparation. Data protection risks will be addressed by: (i) data minimization; (ii) data protection provisions in the Digital ID Proclamation; (iii) an effectiveness condition for acceptable interim data protection guidelines; (iv) ratification of the Proclamation on Personal Data Protection as a disbursement condition for integration of Fayda into services; and (v) a legal remedy for suspension if the Data Protection Commission is not established within three years of effectiveness. Also, to ensure that social risks are properly addressed through project design and implementation, the ID4D supported the end user research to understand the potential negative social impacts of Fayda, notably on vulnerable groups, and agreed with the government on a series of mitigation measures detailed in Annex 2.\n\nPage 25 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**VII.** **RESULTS FRAMEWORK AND MONITORING**\n\n@#&OPS~Doctype~OPS^dynamics@padannexresultframework#doctemplate\n\n**PDO Indicators by PDO Outcomes**\n\nBaseline Period 1 Period 2 Period 3 Period 4 Closing Period\n**An inclusive digital ID ecosystem is established**\n**Number of people in Ethiopia who have received a Fayda ID (Number)**\nSep/2023 Dec/2024 Dec/2025 Dec/2026 Dec/2027 Dec/2028 3,000,000 10,000,000 30,000,000 50,000,000 70,000,000 93,000,000.00 ➢Percentage of whom are women and girls (Number) Sep/2023 Dec/2028 48 50 ➢Number of whom are individuals living in refugee host communities (Number) Nov/2023 Dec/2024 Dec/2025 Dec/2026 Dec/2027 Dec/2028 0 100,000 500,000 7,000,000 1,000,000 1,700,000.00 ➢Number of whom are refugees (Number) Sep/2023 Dec/2024 Dec/2025 Dec/2026 Dec/2027 Dec/2028 0 10,000 50,000 100,000 500,000 924,000 ➢Number of whom registered in remote and hard to reach areas (Number) Nov/2023 Dec/2024 Dec/2025 Dec/2026 Dec/2027 Dec/2028 0 50,000 100,000 1,000,000 5,000,000 7,200,000\n**Service delivery for registered persons in Ethiopia is improved.**\n**Number of successful digital ID authentications by Fayda ID holders to access public and private sector services (Number)**\nNov/2023 Dec/2024 Dec/2025 Dec/2026 Dec/2027 Dec/2028 0 0 1,000,000 10,000,000 50,000,000 100,000,000.00 ➢Number of which are in areas hosting refugees (Number) Nov/2023 Dec/2024 Dec/2025 Dec/2026 Dec/2027 Dec/2028 0 0 10,000 100,000 500,000 1,000,000\n\n**Intermediate Indicators by Components**\n\nPage 26 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) Baseline Period 1 Period 2 Period 3 Period 4 Closing Period\n**Building institutions and trust**\n**ID institution is established (Yes/No)**\nNov/2023 Dec/2025 No Yes\n**Proportion of population satisfied with Fayda services (Percentage)**\nNov/2023 Dec/2028 50 90\n**Proportion of complaints resolved successfully (Percentage)**\nNov/2023 Dec/2028 0 90\n**Establishing scalable and secure Fayda ICT infrastructure**\n**Proportion of up-time (over a one-year period) for the registration module of the ID system (Percentage)**\nNov/2023 Dec/2028 0 99.90\n**Number of penetration tests conducted to prevent cyber attacks and loss of data (Number)**\nNov/2023 Dec/2024 Dec/2025 Dec/2026 Dec/2027 Dec/2028 0 4 8 12 16 20\n**Inclusive ID issuance**\n**Percentage of population within 10km of a permanent, semi-permanent, or mobile registration site at least once a year (Percentage)**\nNov/2023 Dec/2028 5.00 70.00\n**Improving service delivery**\n**Number of entities using Fayda services for improved delivery of benefits and services (Number)**\nNov/2023 Dec/2024 Dec/2025 Dec/2026 Dec/2027 Dec/2028 6 20 30 50 70 100 ➢Number of whom are public entities (Number) Nov/2023 Dec/2028 3 40 ➢Number of who are private entities (Number) Nov/2023 Dec/2028 3 60\n**Number of government agencies integrated with the data exchange platform (Number)**\n\nPage 27 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) Nov/2023 Dec/2028 0.00 10.00\n**Project management and coordination**\n\nPage 28 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**Monitoring & Evaluation Plan: PDO Indicators by PDO Outcomes**\n\n**An inclusive digital ID ecosystem is established**\n**Number of people in Ethiopia who have received a Fayda ID (Number)**\nDescription A number of unique individuals registered in the Fayda system and who have been assigned a Fayda number Frequency Biannual Data source Fayda registration data Methodology for Data Collection Fayda data analytics platform Responsibility for Data Collection NIDP\n**Percentage of whom are women and girls (Number)**\n\nPercentage of women and girls among the total number of unique individuals registered in the Fayda system Description and who have been assigned a Fayda number Frequency Biannual Data source Fayda registration data Methodology for Data Collection Fayda data analytics platform Responsibility for Data Collection NIDP\n**Number of whom are individuals living in refugee host communities (Number)**\nDescription Number of individuals who have been registered in areas tagged as a refugee host community Frequency Biannual Data source Fayda registration data Methodology for Data Collection Fayda data analytics platform Responsibility for Data Collection NIDP\n**Number of whom are refugees (Number)**\nDescription Number of individuals registered in Fayda ID recognized as refugees by UNHCR/RRS Frequency Biannual Data source Fayda registration data and data from UNHCR Methodology for Data Collection API shared between Fayda data analytics platform and reporting from UNHCR/RRS Responsibility for Data Collection NIDP\n**Number of whom registered in remote and hard to reach areas (Number)**\n\nNumber of people registered in areas classified by NIDP as \"remote and difficult to reach\" in their registration Description strategy. These areas are characterized by a combination of limited infrastructure, geographical location, population density, weather conditions, and restrictions due to conflicts or social conditions.\nFrequency Bi-annual Data source Fayda registration data and reporting from registration partners Methodology for Data Collection Fayda data analytics platform Responsibility for Data Collection NIDP\n\n**Service delivery for registered persons in Ethiopia is improved.**\n**Number of successful digital ID authentications by Fayda ID holders to access public and private sector services (Number)**\n\nNumber of successful authentications by individuals using their Fayda ID to access either public or private Description services.\nFrequency Biannual Data source Fayda services usage data - number of authentication requests received by the system Methodology for Data Collection Fayda data analytics platform Responsibility for Data Collection NIDP Page 29 of 39", "output": {"entities": {"named_data": [], "descriptive_data": ["Fayda services usage data"], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**Monitoring & Evaluation Plan: Intermediate Results Indicators by Components**\n\n**Building institutions and trust**\n**ID institution is established (Yes/No)**\n\nThe regulation establishing an institution to implement the powers and responsibilities of the Description Digital Identification System stipulated in the Proclamation 1284/2023 is adopted.\nFrequency Annual Data source Federal Negarit Gazette Methodology for Data Collection Project progress report Responsibility for Data Collection NIDP\n**Proportion of population satisfied with Fayda services (Percentage) (Percentage)**\n\nThe percentage of people who rated the different Fayda services they have received as ‘satisfied Description and ‘very satisfied’ out of a sample of the population that has registered to Fayda in a given month or year Frequency Annual Data source Survey via Fayda GRM system Methodology for Data Collection Project progress report Responsibility for Data Collection NIDP\n**Proportion of complaints resolved successfully (Percentage)**\n\nThe percentage of complaints submitted in the GRM that have been satisfactorily resolved out of Description the total complaints received Frequency Annual Data source Fayda GRM system Methodology for Data Collection Project progress report Responsibility for Data Collection NIDP\n**Establishing scalable and secure Fayda ICT infrastructure**\n**Proportion of up-time (over a one-year period) for the registration module of the ID system (Percentage) (Percentage)**\n\nThe proportion of time that the Fayda registration module is available and working over a oneDescription year period Frequency Annual Data source Fayda system KPI Methodology for Data Collection Fayda data analytics platform Responsibility for Data Collection NIDP\n**Number of penetration tests conducted to prevent cyber-attacks and loss of data (Number)**\n\nAt least 4 penetration tests conducted each year during the 5-year project duration, intended to Description proactively identify and address gaps in security (cyber and physical) of personal data to prevent unauthorized access to or loss of data.\nFrequency Annual Data source Test reports by specialized firms Methodology for Data Collection Project progress report Responsibility for Data Collection NIDP\n**Inclusive and sustainable ID issuance**\n**Percentage of population within 10 km of a permanent, semi-permanent, or mobile registration site at least once a year**\n**(Percentage)**\n\nThe proportion of people who have access to a permanent, semipermanent, or mobile Fayda registration center at least once a year within less than 10 km from their residence. The indicator Description will be measured using population estimates per area using satellite data compared to the global positioning system coordinates of Fayda registration centers and mobile units.\nFrequency Annual Data source Fayda registration centers global positioning system coordinate and data population from online Page 30 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) open-source data set Methodology for Data Collection Heat map of all registration locations Responsibility for Data Collection NIDP\n**Improving service delivery**\n**Number of entities using Fayda services for improved delivery of benefits and services (Number)**\n\nNumber of entities that are using at least one of the several services offered by Fayda to verify Description the identity of their beneficiaries/clients, including but not only unique number seeding, yes/no authentications, and sharing of ID attributes Frequency Biannual Data source Number of API connections to the Fayda Platform Methodology for Data Collection Fayda data analytics platform Responsibility for Data Collection NIDP\n**Number of whom are public entities (Number)**\n\nNumber of public entities that are using Fayda services that are a state, regional, or local Description authority; a body governed by public law; or a private entity mandated to provide public services Frequency Biannual Data source Number of API connections to the Fayda Platform Methodology for Data Collection Fayda data analytics platform Responsibility for Data Collection NIDP\n**Number of who are private entities (Number)**\nDescription Number of private entities using Fayda ID services Frequency Biannual Data source Number of API connections to the Fayda Platform Methodology for Data Collection Fayda data analytics platform Responsibility for Data Collection NIDP\n**Number of government agencies integrated with the data exchange platform (Number)**\n\nNumber of government agencies integrated with the data exchange platform as either users or Description providers of data Frequency Biannual Data source Number of API connections to the data exchange platform Methodology for Data Collection Data exchange analytics platform Responsibility for Data Collection NIDP\n**Project management and coordination**\n\nPage 31 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**ANNEX 1: Implementation Arrangements and Support Plan**\n\n**Implementation Arrangements**\n\n_Financial Management_\n\n1. **An FM assessment** was carried out for the Digital ID for Inclusion and Services Project per the FMM for World\nBank Investment Project Financing Operations reissued on September 7, 2021, effective March 1, 2010, and supporting guidance note as well as considering the requirements of World Bank Policy and World Bank Directive on IPF.\n\n2. **Budget arrangement for the project.** The project will follow the Federal Government of Ethiopia’s budgeting procedure and calendar. The PMU to be established under NIDP at the PMO will prepare a consolidated AWPB, based on the project’s objectives and resources. The project budget preparation should be prudent, realistic, detailed, and implementable. The AWPB, prepared and approved by the PSC, will be submitted to the World Bank for no objection before May 31 of each year. The project's annual budget will be proclaimed under the PMO. The working budget will detail components, subcomponents, activities, and categories of expenditures. It will also have a quarterly breakdown to allow for proper budget monitoring. The PMU will ensure that robust budget monitoring and control mechanisms are in place.\nThe budget monitoring system will be at the transaction, system, and reporting levels. The budget control in the Integrated Financial Management Information System (IFMIS) will be applied based on the Government budget code. The accounting system to be used would enable budget controls and monitoring, budget tracking, and periodic reporting. Expenditures will also be compared to the budgets regularly, explanations will be sought for significant variances, and remedial actions will be taken as appropriate. IFRs would include a variance report along with explanations of material variances.\nManagement will take midway corrective measures based on the reports and explanations.\n\n3. **Accounting and staffing arrangement for the project.** The GoE’s accounting policies (modified cash basis) and procedures will apply to the project. Separate accounts for the project will be maintained at the PMO. NIDP will develop a project specific FMM, which follows the government procedures and addresses the peculiarities of the project.\nPreparation of the FMM will be completed within three months of effectiveness. The chart of accounts of the PMO will be updated to accommodate the project. The project is expected to use an accounting system that captures project records at the component, subcomponent, and activity levels. In addition, to comply with government reporting requirements, the project will have to maintain records through IFMIS. The PMO will coordinate and manage the FM aspects of the project through a PMU to be established. It is noted that recruitment for core positions, including FM experts, is currently under way, using the funds from the ID component of the Digital Foundation Project (P171034), until this project becomes effective. The recruited FM experts will work as a team and in close collaboration with the finance and procurement unit of the PMO. Accounting staff capacities will be reviewed and increased as appropriate during implementation.\n\n4. **Internal control and internal audit arrangements.** All the applicable Government internal control policies and procedures will be applied for the project. In addition, the project’s FMM will incorporate detailed control procedures specific to the project. The internal audit unit of the PMO will include the project in its annual work program, conduct audits, prepare reports, and share them with the World Bank. The management will take the necessary action on the internal audit findings and update the World Bank on the status of implementation of the findings as part of the quarterly IFRs. Copies of project documents (PAD, PIM, Financing Agreement, and FMM) will be provided to the internal auditors for reference. FM and related training will be provided to the internal auditors to enhance their capacity.\n\n5. **Financial reporting arrangements.** Quarterly IFRs will be required for the project. The PMU will prepare the quarterly IFR and submit it to the World Bank within 45 days after the end of the quarter. The template of the IFR was Page 32 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) agreed and annexed to the Disbursement and Financial Information Letter (DFIL). The World Bank will provide the required support after effectiveness and during project implementation to ensure quality reporting. In addition, the project team will prepare the project’s annual accounts/financial statements within three months after the end of the accounting year per accounting standards acceptable to the World Bank and submit to the project’s external auditors.\n\n6. **External audit arrangements.** The PMO will ensure that the project accounts are audited annually. Annual audited financial statements and audit reports (including management letters and audited financial statements, with audit opinion) of the project will be submitted to the World Bank within six months from the end of the fiscal year using auditors acceptable to the World Bank. The auditor will be appointed within three months of effectiveness. The annual financial statements will be prepared within three months of the end of the fiscal year per standards and provided to the auditors to enable them to carry out and complete their audit on time. The audit will be carried out per the International Standards of Auditing issued by the International Federation of Accountants. The auditor will also provide a Management Letter, which will, among others, outline deficiencies or weaknesses in systems and controls and recommendations for improvement and report on compliance with key financial covenants. By the World Bank’s policies, the World Bank requires that the borrower disclose the audited financial statements in a manner acceptable to the World Bank. After formally receiving these financial statements from the PMO, the World Bank will make them available to the public in accordance with the World Bank Policy on Access to Information.\n\n_Disbursement Arrangements_ 7. **Funds flow and disbursement arrangements.** The project will follow the Government’s channel two fund flow mechanisms, where IDA funds will be made available directly to NIDP at the PMO. Funds will not flow to other beneficiary institutions. The PMO will open a DA denominated in US dollars at the NBE. This account shall be opened by the credit and grant effectiveness date. The authorized ceiling of the DA would be two quarters forecasted cash requirement based on the approved AWPB. The PMO may also open an Ethiopian birr bank account for making payments in local currency to suppliers of goods and services. NIDP at the PMO will manage the US dollar and local currency bank accounts. Details of the DA once it is opened and the signatories appointed would be communicated to the World Bank. The fund flow arrangement for the project is summarized in Figure 1.1.\n\n**Figure 1.1. Disbursement Arrangement**\n\n8. **Disbursement methods.** The project may follow one or a combination of these disbursement methods: Advance to Designated Account, Direct Payment, Reimbursement, and Special Commitment. For Advance to the Designated Account and Reimbursement methods, the project will use IFRs as report-based disbursement method. Disbursement will be made quarterly to the DA to cover cash requirements for the next six months based on a six-monthly expenditure and cash requirement forecast (prepared based on the approved AWPB) which is expected to be reported as part of the Page 33 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) quarterly IFRs. Further details about disbursements to the project will be included in the DFIL. Initial advance will be made based on six months forecast prepared from the approved AWPB. Further replenishments will be made upon submission of the quarterly IFRs. The IFR will be used to document or settle past advances to the DA and request future resources using their respective cash forecast statement. The project’s eligible expenditures are costs incurred for activities agreed upon and included in the Financing Agreement and as included in the approved AWPB.\n\n9. The FM risks management plan will be included in the project’s Implementation Manual.\n\n_Procurement Management_ 10. **Procurement rules and procedures** will be carried out in line with the World Bank’s Procurement Regulations for IPF Borrowers, dated September 2023 (amended from time to time) (Procurement Regulations); the Guidelines on Preventing and Combating Fraud and Corruption in Projects Financed by IBRD Loans and IDA Credits and Grants, revised as of July 1, 2016; and any other provisions stipulated in the Financing Agreement. The World Bank’s Standard Procurement Documents (SPDs) shall be used for all international and national competitive procurements.\n\n11. **The Procurement Plan**, as agreed between the World Bank and the recipient, will specify the procurement methods and their applicable thresholds, as well as activities that will be subject to the World Bank review. The implementing agency will submit the PP through STEP, and it will be disclosed to the public once the PP is approved. The PP will be revised as needed to reflect the project implementation needs.\n\n12. **The PPSD** include a description of market conditions, risks, and corresponding market approaches for identified procurable items resulting in the first 18 months PP. The project will include the procurement of an automated biometric identification system for biometrics duplication; ID registration kits; and use cases in support of financial inclusion, social protection, and education as well as the development of various options for online and offline authentication.\n\n13. **Institutional arrangements.** The PMO/NIDP will be responsible for procurement implementation on behalf of the project beneficiary institutions. The decision-making process will utilize the internal institutional structure of the PMO, including the Tender Endorsing Committee. NIDP will recruit and assign two procurement specialists with experience in World Bank-financed projects and one contract management officer.\n\n14. **Procurement risk assessment has been carried out by the World Bank** in accordance with the Procurement Risk Assessment and Management System. The procurement risk is high. The main risks are (a) overall fragile and conflict environment affecting stable functioning of government institutions; (b) no institutional experience in managing World Bank-financed projects, which may lead to slow procurement processing and decision-making with potential implementation delays; (c) unavailability of adequate number of procurement staff with experience in World Bankfinanced projects and possible inability to retain such staff because of low levels of salaries imposed by the MoF; (d) limited technical capacity to lead, manage, prepare, and evaluate technical aspects of information systems-related procurement activities, including possible delays in the preparation of bid documents; (e) limitations in producing proper evaluation reports backed with due diligence; (f) the national Standard Bidding Documents (SBDs) not updated with requirements for social, environmental, health, safety, and sexual exploitation risks; (g) challenges in translating project scope into discreet procurable activities because of the complexity and uniqueness of the project; and (h) poor contract management systems with potential time and cost overruns and poor-quality deliverables.\n\n15. **To mitigate the risks, the following are recommended:** (a) closely monitor planned procurements; (b) recruit two qualified procurement specialists at NIDP dedicated to this project; (c) recruit technical experts with experience in managing information system-related activities; (d) ensure goods delivery, inspection, and receipt documents are issued Page 34 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) on time and copies are uploaded in STEP at contract completion road map stage; (e) utilize Standard Evaluation Report Format for all evaluations; (f) ensure relevant staff undertake training on the World Bank’s procurement procedures; (g) use the World Bank's SPDs for national market approach procurements as appropriate; (h) pre-agree with the World Bank on unique ID registration and distribution arrangements that are fit for purpose with adequate justification; and (i) establish a contract monitoring system, hire a qualified contract management officer, and deliver monitoring reports regularly.\n\n16. **The overall procurement risk rating is High.** Once the procurement mitigation actions have been implemented, the residual risk will be Substantial.\n\n17. **STEP will be used** to prepare, clear, track, and update PPs and conduct all procurement transactions. All priorand post-review procurements will need to be uploaded to STEP. Those not uploaded in STEP will not be eligible for project financing. The STEP system will also be used for handling and closure of all procurement complaints. NIDP will assign specific staff as contract managers in STEP’s Contracts Management Module. Regardless of value, all consultancies will be prior reviewed.\n\n18. **National procurement arrangements.** The country’s procurement procedures may be used for the domestic market. When the recipient uses its own national open competitive procurement procedures as outlined in the Public Procurement and Property Administration Proclamation No. 649/2009, such arrangements will be subject to the provisions of paragraph 5.4 of the Procurement Regulations. However, since the national SBDs are not yet modified to reflect social, environmental, health, safety, and sexual exploitation requirements, the project will use the World Bank’s appropriate SPDs for national open market approach procurement activities.\n\n19. **Implementation support and World Bank reviews.** The World Bank will review contracts based on the risk and complexity of activity, which will be indicated in the PP in STEP. The prior-review contracts will be updated in the PP as necessary during implementation, based on the procurement capacity assessment. The World Bank will carry out post reviews of procurement activities to determine whether they comply with the requirements of the Legal Agreement.\n\n20. **Selection methods.** The project will utilize available selection methods and approaches in the World Bank Procurement Regulations. The selection methods and World Bank review thresholds have been determined in the PPSD and to be reflected in PPs in STEP. The thresholds will be determined based on activity risks and whether an activity will be prior reviewed or post reviewed will be reflected in STEP.\n\nPage 35 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**ANNEX 2: Risk Mitigation for Fayda Implementation**\n\n1. NIDP and the GoE have demonstrated a firm commitment to alignment with the Principles on Identification for\nSustainable Development, including through comprehensive stakeholder engagement and a very high level of transparency. Table 2.1 describes the potential social risks arising from Fayda and how these risks have been or will be mitigated through the Fayda system and project design.\n\n**Table 2.1. Key Social Risks and Mitigation Measures**\n\n**Social Risks** **Mitigation Measures**\n**Exclusion**\n\nSituations where the design of an ID system makes it challenging for specific groups or individuals, especially those who are vulnerable or disadvantaged, to access or use the system effectively\n\n**Personal data**\n**breaches and misuse**\n\nID systems involve the collection, storage, and processing of personal data. These data are vulnerable to theft, misuse, loss, or Today, about 40 percent of Ethiopia's population lack proof of identity and almost 100 percent lack a form of identity that offers a digital trail that enables trusted transactions and services. Project interventions significantly reduce the risks of exclusion compared to the status quo by creating a more inclusive ID system (open to all residents) and catering to the most vulnerable, including women and refugees. Specific measures include the following:\n\n- Digital ID Proclamation (March 2023) makes Fayda a legal and acceptable form of digital ID to all\nresidents of Ethiopia, and for any resident that wishes to have it, the legal responsibility of ensuring access to the ID falls on the ID institution implementing Fayda.\n\n- Large number of legal documents accepted (30+) and specific protocols are in place (that is,\nintroduction mechanism) to enable the individuals to prove their identity at registration centers, even allowing undocumented people to register through an introducer system.\n\n- Tailored enrollment strategies are planned for harder-to-reach regions and populations, including\nmobile Fayda registration centers.\n\n- Refugees and asylum seekers can access Fayda the same as nationals, including the same introducer\nprocesses for undocumented persons. Considering that Fayda only issues identification and not refugee status determination (which is done separately by RRS), this will mean less bureaucratic processes.\n\n- The registration campaign will utilize locals to ensure adequate coverage of the language and to build\ntrust with the local population.\n\n- Special protocols will be applicable for registration of people with disabilities (for example, biometric\nexemptions and accessibility of centers).\n\n- Participatory and human-centered design approaches will be used to inform the implementation and\nuse of Fayda to access services.\n\n- Processes have been enhanced by lessons from numerous pilots, such as ensuring information\navailability (including in accessible formats, multiple languages, and disseminated by trusted sources), having physically accessible facilities, and having multiple registration and ID issuance channels.\n\n- The ID laws ensure sufficient check and balance with strong complaints and grievance redress\nmechanisms.\n\nThe current, paper based Kebele IDs or civil registrations in Ethiopia do not have adequate policies and controls on how personal data are collected, stored, and used. Kebele IDs also collect a significant amount of personal data, including ethnicity. However, the limited digitization and decentralized management of these data limit the scale of risk of breaches and misuse, but the manual nature means there are limited audit controls and logs, if any. On the other hand, Fayda collects less data than existing Kebele IDs and raises standards for governing how personal data are collected, stored, and processed, as well as information and cybersecurity. Specific measures include the following: Page 36 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**Social Risks** **Mitigation Measures**\nmismanagement, - Minimal data collection with only five mandatory fields (full name, gender, date of birth, nationality, which can lead to and current address), including no collection of racial or ethnic origins, genetic data, political opinion, negative physical or mental health condition, religion, and other sensitive data that could lead to discrimination, consequences for and three biometrics modalities (face, fingerprint, and irises).\nindividuals. The risks\n\n- Fayda incorporates several privacy-by-design measures, such as tokenization of the Fayda ID number,\n\nbecome even more limitation of the sharing of personal data unless necessary (for example, implementation of yes/no significant when replies rather than sharing of personal data unless required by court or law), and user empowerment sensitive or personal tools (for example, consent and notification of data sharing) that would prevent unauthorized data, such as correlation of information between different data sets, covenant to the Financing Agreement that the biometrics, are GoE will abide by the Principles on Identification for Sustainable Development, including in subsequent collected.\n\ndetailing regulations (notably minimal data collection).\n\n**Low trust**\n\nThere is a risk of negative perception surrounding Fayda ID, which can be influenced by political economy factors such as current conflicts.\n\n- The Digital ID Proclamation includes specific measures for personal data protection, including details\nabout an auditable consent mechanism when sharing data with any third party, processing, retention, and sanctions. Sharing personal data to any third parties against the provisions of the proclamation will lead to criminal liability.\n\n- The project will support the operationalization of the Personal Data Protection Proclamation once\nadopted.\n\n- The project will strengthen information and cybersecurity capacity with due emphasis given to both\nlegal and technical safeguards.\n\nAlthough they are decentralized, there is limited transparency in how existing Kebele ID systems operate, including in terms of access to data. Fayda will operate in a much more transparent manner, including by using open-source software, which provides for more accountability on the technology, stakeholder engagement, and publication of documentation. These measures will go a long way toward reducing the risks of low trust.\n\nSpecific measures include the following:\n\n- The Government will be asked to adopt the Data Protection Proclamation, in addition to the ID\nProclamation.\n\n- NIDP has developed a comprehensive communication strategy based on the findings of the pilot\nevaluation, tailored to specific demographics.\n\n- NIDP is publishing all relevant documentation, regulations, directives, MoUs, and information about\n[the systems developed on its website, which is constantly being updated at https://id.gov.et/.](https://id.gov.et/)\n\n- The website contains frequently asked questions that address the risks related to some\nmisconceptions.\n\n- NIDP organized a series of stakeholder consultations with civil society organizations, human and digital\nrights groups, and legislative and executive wings of the Government among others during 2021–23.\nThe program will be able to further expand these activities once the project is operational, notably further engaging with communities and local leaders.\n\n- Based on the registrant’s request, all logs and audit trails of the user’s personal data and their\nauthentication history can be accessed and/or deleted. There are also facilities for the user to lock their personal data so that it cannot be used for authentication.\n\nPage 37 of 39", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040)\n\n**ANNEX 3: Gender Analysis and Action Plan**\n\n_**Problem statement.**_ In Ethiopia, there is a notable gender gap in the existing ID system (Kebele ID) coverage. According to the ID4D-Findex Survey (2017), 36 percent of the population ages 18 and older lack a Kebele ID, with significant gender gap of 46 percent of women lacking one, compared to 25 percent of men.\n\n**ANALYSIS:**\n**Gender gaps identified**\n\n**ACTIONS:**\n**Proposed actions Taken to address gaps**\n\n**INDICATORS** :\n**How bridging the gap**\n\n**will be measured**\n**Women have less knowledge about benefits** **Subcomponent 1.1** Number of people in\n**of having an ID.** Country-specific research, Ethiopia who\n\n- The project will support implementation of a\n\nincluding a Social Risk Analysis and a Gender received a Fayda ID, a communications strategy encompassing an Gap in ID Study, outline low literacy, a general percentage of whom intersectional segmentation approach that lack of awareness on the day-to-day use of ID, are women and girls directly addresses factors impeding low uptake to perceived irrelevance of formal identification, ID and includes tailored approaches for women and limited knowledge of individual rights as and girls (such as having female registration key factors contributing to lower Kebele ID agents) in both urban and rural areas and across enrolment by women. Based on the most socioeconomic segments. The communication recent data from 2017, the adult (age 15 and campaign will also be tailored to men who can above) literacy rate for men is 59 percent, support women’s ID enrolment, such as head of compared to 44 percent for women (World households, spouses, and family members.\n\nBank 2022), which can make it harder for\n\n- The reach-out efforts will also include community\n\nwomen to navigate the ID registration sessions or tap into existing women's groups to process.\n\n**Subcomponent 1.1**\n\n- The project will support implementation of a\ncommunications strategy encompassing an intersectional segmentation approach that directly addresses factors impeding low uptake to ID and includes tailored approaches for women and girls (such as having female registration agents) in both urban and rural areas and across socioeconomic segments. The communication campaign will also be tailored to men who can support women’s ID enrolment, such as head of households, spouses, and family members.\n\n- The reach-out efforts will also include community\nsessions or tap into existing women's groups to more directly increase awareness among women.\n\nNumber of people in Ethiopia who have received a Fayda ID, a percentage of whom are women and girls\n\n**ID registration processes lack equity.** For\nadults willing to enroll for an ID, barriers to access disproportionately affect women.\nLong lines and waiting times for enrollment, for example, can add a burden to women who are pregnant and/or must be always with their young children. Far distances for registration can also present tangible barriers for female enrolment, particularly for women who must attend to domestic duties or revenue-generating responsibilities and forgo income to register for an ID. In addition, women from low-income households in urban and especially in rural areas often lack mobile devices, including lower-end feature phones, highlighting potential challenges with participating in ID registration that is linked to mobile devices (such as text notifications). In 2021, internet use by women as a total of the female population stood at 14 percent, compared to 20 percent for men (ITU 2021), surfacing gender gaps in online participation, therefore showing the\n\n**Subcomponents 3.1 and 3.2**\n\n- The project will also strive to ensure an inclusive\nID enrolment by leveraging the omni-channel registration strategy which includes registration options that would enable registration in locations that are convenient and safe for women.\n\n- The project will procure mobile registration kits\nthat enable registration close to home. These will be brought to places where women usually congregate to ensure convenient registration for women, including the elderly, ill, or those caring for young children.\n\n- The project will support initiatives to enhance\nboth effectiveness and efficiency. This includes efficiencies in registration that reduce wait times, including upgrades to biometric equipment that lower the attempts required for capture, the availability of female enrolment officers and private spaces for women wearing religious coverings (e.g., niqab), and availability of registration centers within a 6 km radius.\n\nPercentage of population within 6 km of a permanent, semi-permanent, or mobile registration site at least once a year Number of people registered in remote and hard to reach areas) Page 38 of 39", "output": {"entities": {"named_data": ["ID4D-Findex Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEthiopia Digital ID for Inclusion and Services Project (P179040) lack of a need to prove identity for e-services for example.\n\n**A sizeable female refugee population is**\n**particularly at risk of lacking an ID and being**\n**denied access to essential services as a**\n**consequence.** Ethiopia hosts over 823,000\nrefugees and asylum seekers, mostly from South Sudan, Somalia, and Eritrea, 47 percent of whom are women and 59 percent of whom are children (UNHCR 2022). Women and girls without official documentation face challenges with accessing food rations, education, and legal services, the latter of which increases their vulnerability to genderbased violence.\n\n**Lack of sex-disaggregated ID data.** The\ncurrent Kebele ID system does not produce any aggregated data about ID ownership, and sex-disaggregated data are not collected. The lack of data, and especially of sexdisaggregated data, inhibits the GoE’s ability to design evidence-based and genderresponsive ID policy and programming. It also limits policy makers’ ability to track progress over time.\n\n**Subcomponents 3.1 and 3.2**\n\n- In collaboration with stakeholders such as the\nRRS and UNHCR, the project will advance inclusive ID issuance and distribution to provide a unique and verifiable digital identification to all residents, including refugees. The project will also deepen an analysis of the case of vulnerable women in Ethiopia, including female refugees and IDPs.\n\n**Component 5**\n\nEnsure the collection of sex-disaggregated data on ID enrolment and use, including through;\n\n- Training on M&E with a gender lens, including for\nthe M&E officer housed within the Digital ID Institution to enable the collection of data concerning the enrolment and usage of ID by women and girls and\n\n- Collection of gender-disaggregated data as part\nof the project-level M&E.\n\nNumber of people in Ethiopia who have received a Fayda ID, a percentage of whom are women and girls Number of people in Ethiopia who have received a Fayda ID, number of whom are refugees or individuals living in refugee host communities The proportion of population satisfied with Fayda services Page 39 of 39", "output": {"entities": {"named_data": ["Kebele ID system"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**FOR OFFICIAL USE ONLY**\n\n\nINTERNATIONAL DEVELOPMENT ASSOCIATION\n\n\nPROJECT APPRAISAL DOCUMENT\n\n\n\nReport No: PAD4830", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "CURRENCY EQUIVALENTS\n\n\n(Exchange Rate Effective April 30, 2022)\n\n\nCurrency Unit =  Ethiopian Birr\n\n\n51.86 Ethiopian Birr = US$1\n\n\nUS$1.34 = SDR 1\n\n\nGOVERNMENT OF ETHIOPIA FISCAL YEAR\n\nJuly 8 – June 7\n\n\nRegional Vice President: Hafez M. H. Ghanem\n\n\nCountry Director: Boutheina Guermazi\n\n\nRegional Director: Catherine Signe Tovey\n\n\nPractice Manager: Helene Monika Carlsson Rex\n\n\nTask Team Leaders: Matthew Stephens, Esayas Nigatu Gebremeskel", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "ABBREVIATIONS AND ACRONYMS\n\n3R-4-CACE Response-Recovery-Resilience for Conflict-Affected Communities in Ethiopia Project\nBoA Bureau of Agriculture\nBoF Bureau of Finance\nAWPB Annual Work Plan and Budget\nCAC Community Audit Committee\nCBO Community-Based Organization\nCDD Community-Driven Development\nCERC Contingent Emergency Response Component\nCFT Community Facilitation Team\nCHS Community Health and Safety\nCIF Community Investment Fund\nCIG Common Interest Group\nCOVID Coronavirus Disease\nCPC Community Procurement Committee\nCPF Country Partnership Framework\nCPMC Community Project Management Committee\nCRRF Comprehensive Refugee Response Framework\nDA Designated Account\nDRDIP Development Response to Displacement Impacts Project\nE&S Environment and Social\nEHS Environmental Health and Safety\nEIRR Economic Internal Rate of Return\nERM Emergency Response Manual\nESCP Environmental and Social Commitment Plan\nESF Environmental and Social Framework\nESMF Environmental and Social Management Framework\nESRM Environmental and Social Risk Management\nESRS Environmental and Social Risk Summary\nESS Environmental and Social Standards\nETB Ethiopian Birr\nEU European Union\nFA Financing Agreement\nFAO Food and Agriculture Organization\nFCV Fragility, Conflict and Violence\nFLID Farmer-Led Irrigation Development\nFM Financial Management\nFPCU Federal Project Coordination Unit\nFSC Federal Steering Committee\nFTC Federal Technical Committee\nFTC Farmers Training Center\nFY Financial Year\nGBV Gender-based Violence\nGCR Global Compact on Refugees\nGEMS Geo-enabling initiative for Monitoring and Supervision", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "GoE Government of Ethiopia\nGP Global Practice\nGRF Global Refugee Forum\nGRM Grievance Redress Mechanism\nGRS Grievance Redress Service\nHoA Horn of Africa\nHoAI Horn of Africa Initiative\nHROC High Risk of Ongoing Conflict\nIBEX Integrated Budget and Expenditure\nIBM Iterative Beneficiary Monitoring\nIBRD International Bank for Reconstruction and Development\nIDA International Development Association\nIDP Internally Displaced Person\nIFMIS Integrated Financial Management Information System\nIFR Interim Financial Report\nIGAD Intergovernmental Authority on Development\nIPF Investment Project Financing\nIPV Intimate Partner Violence\nIWUA Irrigation Water Users Association\nM&E Monitoring and Evaluation\nMoA Ministry of Agriculture\nMoF Ministry of Finance\nMOU Memorandum of Understanding\nNBE National Bank of Ethiopia\nNPV Net Present Value\nNRM Natural Resource Management\nNROC Non-High Risk of Ongoing Conflict\nOFAG Office of the Federal Auditor General\nOP Operational Policy\nPCU Project Coordination Unit\nPDO Project Development Objective\nPFM Public Financial Management\nPIM Project Implementation Manual\nPP Procurement Plan\nPPSD Project Procurement Strategy for Development\nPTC Pastoralist Training Center\nPWDs Persons with Disabilities\nRCC Refugee Central Committee\nRF Resettlement Framework\nRMEAL Results Monitoring, Evaluation and Learning\nRPCU Regional Project Coordination Unit\nRRS Refugees and Returnees Service\nRSW IDA 18 Sub-window for Refugees and Host Communities\nSA Social Assessment\nSACCO Savings and Credit Cooperative\nSEA/SH Sexual Exploitation and Abuse/Sexual Harassment", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "SEP Stakeholder Engagement Plan\nSIF Strategic Investment Fund\nSOP Series of Projects\nSORT Systematic Operations Risk-Rating Tool\nSRAMP Security Risk Assessment and Management Plan\nSSI Small-scale Irrigation\nSTEP Systematic Tracking of Exchanges in Procurement\nTPM Third-Party Monitoring\nTPMA Third-Party Monitoring Agent\nUNHCR United Nations High Commissioner for Refugees\nUNOPS United Nations Office for Project Services\nUSD United States Dollar\nWBG World Bank Group\nWHR IDA 19 Window for Host Communities and Refugees\nWNCCA Woreda Needs, Conflict and Capacity Assessment\nWoFED Woreda Office of Finance and Economic Development\nWPCU Woreda Project Coordination Unit", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nDevelopment Response to Displacement Impacts Project in the Horn of Africa Phase II (P178047)\n\n\nTABLE OF CONTENTS\n\n\n**DATASHEET ........................................................................................................................... 1**\n\n\n**I.** **STRATEGIC CONTEXT ...................................................................................................... 7**\n\n\nA. Country Context................................................................................................................................ 8\n\n\nB. Sectoral and Institutional Context .................................................................................................. 10\n\n\nC. Relevance to Higher Level Objectives ............................................................................................. 14\n\n\n**II.** **PROJECT DESCRIPTION .................................................................................................. 15**\n\n\nA. Project Development Objective ..................................................................................................... 15\n\n\nB. Project Components ....................................................................................................................... 16\n\n\nC. Project Beneficiaries ....................................................................................................................... 33\n\n\nD. Results Chain .................................................................................................................................. 33\n\n\nE. Rationale for Bank Involvement and Role of Partners ................................................................... 34\n\n\nF. Lessons Learned and Reflected in the Project Design .................................................................... 35\n\n\n**III.** **IMPLEMENTATION ARRANGEMENTS ............................................................................ 36**\n\n\nA. Institutional and Implementation Arrangements .......................................................................... 36\n\n\nB. Results Monitoring and Evaluation Arrangements......................................................................... 37\n\n\nC. Sustainability ................................................................................................................................... 38\n\n\n**IV.** **PROJECT APPRAISAL SUMMARY ................................................................................... 39**\n\n\nA. Technical, Economic and Financial Analysis ................................................................................... 39\n\n\nB. Fiduciary .......................................................................................................................................... 40\n\n\nC. Legal Operational Policies ............................................................................................................... 43\n\n\nD. Environmental and Social ............................................................................................................... 43\n\n\n**V.** **GRIEVANCE REDRESS SERVICES ..................................................................................... 47**\n\n\n**VI.** **KEY RISKS ..................................................................................................................... 47**\n\n\n**VII.** **RESULTS FRAMEWORK AND MONITORING ................................................................... 50**\n\n\n**Annex 1 : Implementation Arrangements and Support Plan .......................................... 70**\n\n\n**Annex 2: Map ............................................................................................................... 80**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nDevelopment Response to Displacement Impacts Project in the Horn of Africa Phase II (P178047)\n\n\nDATASHEET\n\n\n**BASIC INFORMATION**\n~~BASIC~~ ~~INFO~~ ~~TABLE~~\n\n\nCountry(ies) Project Name\n\n\nDjibouti, Ethiopia,\nDevelopment Response to Displacement Impacts Project in the Horn of Africa Phase II\nKenya, Uganda\n\n\nProject ID Financing Instrument Environmental and Social Risk Classification\n\n\nInvestment Project\nP178047 High\nFinancing\n\n\n**Financing & Implementation Modalities**\n\n\n[ ] Multiphase Programmatic Approach (MPA) [✓] Contingent Emergency Response Component (CERC)\n\n\n[✓] Series of Projects (SOP) [✓] Fragile State(s)\n\n\n[ ] Performance-Based Conditions (PBCs) [ ] Small State(s)\n\n\n[ ] Financial Intermediaries (FI) [ ] Fragile within a non-fragile Country\n\n\n[ ] Project-Based Guarantee [ ] Conflict\n\n\n[ ] Deferred Drawdown [ ] Responding to Natural or Man-made Disaster\n\n\n[ ] Alternate Procurement Arrangements (APA) [ ] Hands-on Enhanced Implementation Support (HEIS)\n\n\nExpected Approval Date Expected Closing Date\n\n\n06-Jun-2022 31-Dec-2027\n\n\nBank/IFC Collaboration\n\n\nNo\n\n\n**Proposed Development Objective(s)**\n\n\nTo improve access to basic social and economic services, expand livelihood opportunities and enhance\nenvironmental management for host communities and refugees in the target areas.\n\n\nPage 1 of 80", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nDevelopment Response to Displacement Impacts Project in the Horn of Africa Phase II (P178047)\n\n\n**Components**\n\n\n**Component Name** **Cost (US$, millions)**\n\n\nSocial and Economic Services and Infrastructure 83.60\n\n\nSustainable Environmental Management 13.60\n\n\nLivelihoods Program 64.80\n\n\nProject Management, Monitoring and Evaluation and Learning 18.00\n\n\nContingent Emergency Response 0.00\n\n\n**Organizations**\n\n\nBorrower: Federal Democratic Republic of Ethiopia\n\n\nImplementing Agency: Ministry of Agriculture\n\n\n**PROJECT FINANCING DATA (US$, Millions)**\n\n\n**SUMMARY-NewFin1**\n\n\n**Total Project Cost** 180.00\n\n\n**Total Financing** 180.00\n\n\n**of which IBRD/IDA** 180.00\n\n\n**Financing Gap** 0.00\n\n\n**DETAILS-NewFinEnh1**\n\n\n**World Bank Group Financing**\n\n\nInternational Development Association (IDA) 180.00\n\n\nIDA Grant 180.00\n\n\n**IDA Resources (in US$, Millions)**\n\n\n**Credit Amount** **Grant Amount** **Guarantee Amount** **Total Amount**\n\n\n**Ethiopia** 0.00 180.00 0.00 180.00\n\n\nPage 2 of 80", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nDevelopment Response to Displacement Impacts Project in the Horn of Africa Phase II (P178047)\n\n\nNational PBA 0.00 34.07 0.00 34.07\n\n\nRegional 0.00 45.93 0.00 45.93\n\n\nRefugee 0.00 100.00 0.00 100.00\n\n\n**Total** **0.00** **180.00** **0.00** **180.00**\n\n\n**Expected Disbursements (in US$, Millions)**\n\n\n**WB Fiscal Year** 2022 2023 2024 2025 2026 2027 2028\n\n\n**Annual** 0.00 18.00 40.00 65.00 41.00 16.00 0.00\n\n\n**Cumulative** 0.00 18.00 58.00 123.00 164.00 180.00 180.00\n\n\n**INSTITUTIONAL DATA**\n\n\n**Practice Area (Lead)** **Contributing Practice Areas**\n\nAgriculture and Food, Environment, Natural Resources & the\nSocial Sustainability and Inclusion\nBlue Economy, Fragile, Conflict & Violence, Water\n\n\n**Climate Change and Disaster Screening**\n\n\nThis operation has been screened for short and long-term climate change and disaster risks\n\n\n**SYSTEMATIC OPERATIONS RISK-RATING TOOL (SORT)**\n\n\n**Risk Category** **Rating**\n\n\n1. Political and Governance ⚫ High\n\n\n2. Macroeconomic ⚫ High\n\n\n3. Sector Strategies and Policies ⚫ Moderate\n\n\n4. Technical Design of Project or Program ⚫ Substantial\n\n\n5. Institutional Capacity for Implementation and Sustainability ⚫ Substantial\n\n\n6. Fiduciary ⚫ Substantial\n\n\n7. Environment and Social ⚫ High\n\n\n8. Stakeholders ⚫ Moderate\n\n\nPage 3 of 80", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nDevelopment Response to Displacement Impacts Project in the Horn of Africa Phase II (P178047)\n\n\n9. Other ⚫ Substantial\n\n\n10. Overall ⚫ High\n\n\n**COMPLIANCE**\n\n\n**Policy**\nDoes the project depart from the CPF in content or in other significant respects?\n\n[ ] Yes   [✓] No\n\n\nDoes the project require any waivers of Bank policies?\n\n[ ] Yes   [✓] No\n\n\n**Environmental and Social Standards Relevance Given its Context at the Time of Appraisal**\n\n\n**E & S Standards** **Relevance**\n\n\nAssessment and Management of Environmental and Social Risks and Impacts Relevant\n\n\nStakeholder Engagement and Information Disclosure Relevant\n\n\nLabor and Working Conditions Relevant\n\n\nResource Efficiency and Pollution Prevention and Management Relevant\n\n\nCommunity Health and Safety Relevant\n\n\nLand Acquisition, Restrictions on Land Use and Involuntary Resettlement Relevant\n\n\n\nBiodiversity Conservation and Sustainable Management of Living Natural\nResources\n\n\nIndigenous Peoples/Sub-Saharan African Historically Underserved Traditional\nLocal Communities\n\n\n\nRelevant\n\n\nRelevant\n\n\n\nCultural Heritage Relevant\n\n\nFinancial Intermediaries Not Currently Relevant\n\n\n**NOTE** : For further information regarding the World Bank’s due diligence assessment of the Project’s potential\nenvironmental and social risks and impacts, please refer to the Project’s Appraisal Environmental and Social Review\nSummary (ESRS).\n\n\nPage 4 of 80", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nDevelopment Response to Displacement Impacts Project in the Horn of Africa Phase II (P178047)\n\n\n**Legal Covenants**\n\n\nSections and Description\n\nThe Recipient shall no later than three (3) months after the Effective Date, appoint a Third-Party Monitoring Agent\n(“TPMA”), with terms of reference, mandates, staffing, and other resources satisfactory to the Association, to\nmonitor the implementation and supervision of Project activities in high risk of conflict areas.\n\n\n**Conditions**\n\n\nType Financing source Description\nEffectiveness IBRD/IDA The Recipient has adopted an updated Project Implementation\nManual in accordance with the provisions of Section I.B.1 of\nSchedule 2 to the Financing Agreement.\n\n\nType Financing source Description\nEffectiveness IBRD/IDA The Recipient, through its Ministry of Agriculture, has contracted an\nimplementing agency with terms of reference, mandate, staffing,\nand other resources satisfactory to the Association, to be\nresponsible for implementing Project activities in high risk of\nongoing conflict areas.\n\n\nType Financing source Description\nEffectiveness IBRD/IDA The Association is satisfied that the Recipient has an adequate\nrefugee protection framework.\n\n\nType Financing source Description\nDisbursement IBRD/IDA No withdrawal shall be made for payments made prior to the\nSignature Date.\n\n\nType Financing source Description\nDisbursement No withdrawal shall be made for Emergency Expenditures under\nCategory (2), unless and until all of the following conditions have\nbeen met in respect of said expenditures:\n(i) (A) the Recipient has determined that an Eligible Crisis or\nEmergency has occurred, and has furnished to the Association a\nrequest to withdraw Financing amounts under Category (2); and (B)\nthe Association has agreed with such determination, accepted said\nrequest and notified the Recipient thereof; and\n(ii) the Recipient has adopted the CERC Manual and Emergency\nAction Plan, in form and substance acceptable to the Association.\n\n\nType Financing source Description\nEffectiveness IBRD/IDA The Recipient has adopted and disclosed: (i) a resettlement\n\n\nPage 5 of 80", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nDevelopment Response to Displacement Impacts Project in the Horn of Africa Phase II (P178047)\n\n\nframework; (ii) an environmental and social management\nframework; (iii) a social assessment; (iv) labor management\nprocedures; (v) gender-based violence/sexual exploitation and\nabuse/sexual harassment action plan; and (vi) a security risk\nassessment and management plan, all in accordance with the\nprovisions of the Environmental and Social Commitment Plan.\n\n\nPage 6 of 80", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nDevelopment Response to Displacement Impacts Project in the Horn of Africa Phase II (P178047)\n\n\n**I.** **STRATEGIC CONTEXT**\n\n\n1. **Any engagement in Ethiopia at present must consider the fluid conflict-related developments in the country**\n\n**and the rapid pace at which these developments could lead to changes in the country’s context.** The proposed\nDevelopment Response to Displacement Impacts Project (DRDIP) in the Horn of Africa (HoA) Phase II (DRDIP II) was\nprepared with this understanding, with a flexible design that can be adapted to the country’s evolving needs. DRDIP\nII includes several important new design features compared to the ongoing Phase I, as follows: (a) refugees are\nincluded as direct beneficiaries; (b) the geographic scope is expanded to cover all communities in the country that are\naffected by the refugee presence; (c) deeper support to help the government of Ethiopia implement its policies for\nrefugee inclusion; (d) integration of refugee concerns into local development planning; and (e) enhanced focus on\nwomen’s economic and social empowerment. Furthermore, the Government has agreed to third-party\nimplementation and third-party monitoring in areas at high risk of ongoing conflict.\n\n2. **Global forced displacement presents significant humanitarian and development challenges.** Over 84 million\n\npeople are now forcibly displaced because of conflict, persecution, and natural disasters. This includes 26.6 million\nrefugees – the highest number ever recorded – and 51 million internally displaced persons (IDPs). [1] Forced\ndisplacement remains largely concentrated in low- and middle-income countries. The HoA and the Great Lakes\nRegions in Africa host 20 percent of the world’s refugees and asylum-seekers (five million) and 25 percent of the IDPs\n(12.4 million). [2] This is an increase of two million refugees and 500,000 IDPs since DRDIP in the HoA (P152822)\ncommenced in 2016. Displacement is protracted in the HoA and is driven by natural events and human actions, with\nclimate change and environmental degradation acting as threat multipliers.\n\n3. **Displacement has emerged as a regional phenomenon in the HoA, with spillover effects in neighboring**\n\n**countries, posing major challenges for poverty reduction, stability and community resilience.** Despite its rich\nendowment in human, social and natural capital, the HoA is affected by a complex history of marginalization, pockets\nof poverty and insecurity and increasing environmental degradation. Conflict remains endemic, compounded by\ndemographic shifts because of population growth and movement of people; imbalanced service provision; increasing\ncompetition for scarce natural resources; and harsh climatic conditions, including frequent droughts and floods.\n\n4. **In line with the growing importance of regional integration goals to combat fragility, five countries (Djibouti,**\n\n**Eritrea, Ethiopia, Kenya and Somalia) launched the Horn of Africa Initiative (HoAI) in 2019 to forge closer economic**\n**ties in the sub-region. Subsequently, Sudan also joined the HoAI.** Highlighting the importance of regional\ncooperation in resilience building, the initiative includes four pillars: (i) Regional Infrastructure Networks; (ii) Trade\nand Economic Integration; (iii) Resilience; and (iv) Human Capital. Developed with support from the African\nDevelopment Bank, the European Union (EU) and the World Bank, the HoAI agreed on priority projects and programs\nfor the region requiring financing of up to US$15 billion. Responding in part to requests at the October 2021 HoAI\nMinisterial meeting to increase support for resilience considering the heightened fragility risks faced by the region\nthat spill over national boundaries, this project supports the Resilience Pillar.\n\n\n5. **The proposed project is the second phase of an ongoing operation benefiting Ethiopia and is part of a series of**\n\n**ongoing regional projects—also covering Djibouti, Kenya and Uganda.** Designed as a series of projects (SOP), DRDIP\naddresses the regional spillover effects of conflict and forced displacement. DRDIP supports a development response\n\n\n1 _[https://www.unhcr.org/en-us/mid-year-trends.html](https://www.unhcr.org/en-us/mid-year-trends.html)_ .\n2 https://data2.unhcr.org/en/situations/rbehagl\n\n\nPage 7 of 80", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nDevelopment Response to Displacement Impacts Project in the Horn of Africa Phase II (P178047)\n\n\nthat helps refugee-hosting countries to overcome the negative impacts of forced displacement, while maximizing the\npositive opportunities it can also present, thus creating a refugee space contributing to a regional “public good”. The\nfirst phase of DRDIP commenced in 2016, supporting Djibouti, Ethiopia and Uganda, with Kenya joining in 2017. Both\nprojects include grants to the Inter-governmental Authority on Development (IGAD) for regional coordination and\nlearning. DRDIP II follows the World Bank’s people-centric approach for the HoA. The project will support\napproximately 2.5 million people, of whom 1.76 million are host community members and 740,000 are refugees. The\nSOP allows for other countries in the HoA to opt into the program at a later date, or for second phases and Additional\nFinancing for existing DRDIP countries, as in the case of this operation.\n\n**A. Country Context**\n\n\n6. **With a population of over 115 million people (2020), Ethiopia is the second most populous nation in Africa**\n\n**after Nigeria, and one of the most diverse** . Ethiopia’s location in the center of the HoA and close to the Middle East\ngives it high strategic importance. It is physically connected to six other countries, namely Eritrea, Kenya, Somalia,\nSouth Sudan, Sudan and – essential in terms of global trade – to Djibouti and its deep-sea port. Ethiopia hosts about\n86 ethnic groups and 90 spoken languages and is also religiously and geographically diverse. [3]\n\n\n7. **Consistent economic growth and poverty reduction have underpinned significant development progress in**\n**Ethiopia over the last two decades** . Driven by average Gross Domestic Product (GDP) growth of more than ten percent\nannually over the last 15 years, Gross National Income per capita increased from US$140 in 2004 to an estimated\nUS$890 by 2020. Poverty reduced from 38.4 percent in 2004 to 23.5 percent in 2016. This sustained growth has,\nhowever, exacerbated inequality, with the Gini coefficient increasing from 29.8 in 2004 to 35 in 2015. The rise in\ninequality is largely due to increasing urban-rural disparity, with 88 percent of the country’s poor living in rural areas. [4]\n\n8. **Ethiopia’s development gains are being challenged by a combination of the global pandemic, natural disasters**\n**and violent conflict.** The Coronavirus Disease 2019 (COVID-19) has had severe impacts on the country’s economy. [5]\nGrowth slowed to 6.3 percent in 2020/21 and is expected to decline to 3.3 percent in 2021/22. World Bank projections\nestimate that the pandemic has caused a significant increase in the poverty headcount – particularly in urban areas –\nand has also exacerbated inequality. [6]\n\n9. **Flooding and a locust infestation over the past two years have seriously affected food security** **and livelihoods**\n**and highlighted the risks of climate change.** The number of people requiring food assistance has increased to as many\nas 13.2 million. [7] Ethiopia is one of the most vulnerable countries to climate variability and climate change due to its\nhigh dependence on rain-fed agriculture and natural resources. The country’s relatively low adaptive capacity to deal\nwith current and expected changes amplifies resource scarcity and the risk of instability.\n\n\n10. **Climate change is expected to cause severe damage to infrastructure, increase the risk of water scarcity and**\n**increase crop land exposure to drought** . This will increase demand for water, raising the potential for conflict and\n\n\n3 According to Ethiopia’s 2007 Census.\n4 World Bank Poverty and Equity Brief for Ethiopia, October 2021.\n5 As of May 11, 2022, Ethiopia had registered 470,760 COVID cases and 7,510 fatalities: _[https://covid19.who.int/region/afro/country/et](https://covid19.who.int/region/afro/country/et)_\n6 World Bank analysis suggests that the poverty headcount in the 23.5th percentile (the national poverty rate) increased by 11.2 percent\nand for the bottom 40 [th] percentile by 7.7 percent between 2018/19 and October 2020. Inequality is estimated to have increased, with\nthe Gini coefficient rising to 42 in October/November 2020. See Christina Wieser et al (2021) “Poverty projections and profiling based on\nEthiopia’s High Frequency Phone Surveys of households using a SWIFT-COVID-19 package” World Bank: Washington DC.\n7 2021 Humanitarian Response Plan. _[https://www.wfp.org/countries/ethiopia](https://www.wfp.org/countries/ethiopia)_\n\n\nPage 8 of 80", "output": {"entities": {"named_data": ["Ethiopia’s 2007 Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nDevelopment Response to Displacement Impacts Project in the Horn of Africa Phase II (P178047)\n\n\nforced displacement. [8] Higher temperatures, which will cause increased aridity, may also lead to livestock stress and\nreduced crop yields. This is likely to result in significant economic losses, damage to agricultural lands and\ninfrastructure, as well as human casualties. [9] Investments will be needed in climate-resilient infrastructure such as\ntransport networks and public institutions for basic services.\n\n11. **The war in Ukraine is exacerbating economic and food security challenges** . Russia and Ukraine account for close\nto 30 percent of the global wheat market. As a result of anticipated shortages due to the war, the Food and Agriculture\nOrganization (FAO) global Food Price Index reached an historical high in March 2022. While the index dropped slightly\nin April 2022, it remains 30 percent higher than one year previously. [10] In Ethiopia the latest official data shows that\nthe price of food has increased by 43 percent over the past year. [11] Across the HoA, the impact of the price increases\nand food shortages will be felt acutely by the poor, particularly refugees, who are highly dependent on dwindling\nhumanitarian aid for livelihood and food security. [12]\n\n12. **The last five years have seen an increase in violent conflict which has carried a major human and economic cost**\n**to Ethiopia** . The most significant recent manifestation of conflict is the Northern Ethiopian Crisis, which began in Tigray\nin November 2020 and has since spilled over to the adjoining regions of Amhara and Afar. The current wave of conflict\nhas been triggered by a complex web of drivers and grievances, including political rivalries, contestation over natural\nresources, and perceptions of regional and historical inequalities. These triggers have been aggravated by unfulfilled\nemployment expectations among youth, shrinking availability of land, and the impacts of climate change. [13] Some of\nthese conflicts have long histories (for instance, between different ethnic groups over control of local resources such\nas water or pasture) and have re-emerged during the recent political transition. Political competition and rivalry\nbetween elites has intensified over this period – locally, and at the national and regional levels. These tensions have\nmanifested across both rural and urban areas and have largely been organized along ethnic lines. Conflict is affecting\nvarious parts of the country, complicating access to several refugee-hosting areas, raising risks for both humanitarian\nand development programming and affecting social cohesion in different regions. It has also forced refugees to move\nto new areas, creating a new set of humanitarian and development needs for displaced populations.\n\n13. **The conflict has triggered a humanitarian crisis, particularly in northern Ethiopia.** The United Nations estimates\n\nthat 5.2 million people in the north, or more than 75 percent of the region’s population, need humanitarian\nassistance. [14] There are now more than four million IDPs in the country, 50 percent of whom have been displaced in\nthe past six months alone. Overall, half are female and 18 percent are children. [15] Approximately 60,000 Ethiopians\n\n\n8 Africa Center for Strategic Studies (2021) “Climate Change Amplifies Instability in Africa”, April 21, 2021. See also German Federal\nMinistry for Economic Cooperation and Development “Climate Risk Profile: Ethiopia”.\n9 Average temperatures in Ethiopia have increased by an average of 1°C since 1960s, increasing evapotranspiration and reducing soil\nmoisture. The incidence of drought has increased and the rains in central and northern areas occurring in February to May have become\nless predictable. Projections of future change indicate increased temperatures, and as much as a 20 percent decline in spring and summer\nrainfall in southern and central regions and an increase in southwest and southeast areas. Projected warming trends for the entire\ncountry are expected to exacerbate observed declines in rainfall, leading to increased water stress.\n10 https://www.fao.org/worldfoodsituation/foodpricesindex/en/\n11 https://www.statsethiopia.gov.et/wp-content/uploads/2022/04/7.CPI-Mar-2022-HB.pdf\n12 United Nations Conference on Trade and Development (2022) The Impact on Trade and Development of the War in Ukraine.\n13 ‘Across Africa, the effects of climate change are shaping conflict patterns, in particular the pattern of violence arising from forced\n[migration into contested space…’: https://aoav.org.uk/2021/how-is-climate-change-driving-conflict-in-africa/](https://aoav.org.uk/2021/how-is-climate-change-driving-conflict-in-africa/)\n[14 OCHA https://reports.unocha.org/en/country/ethiopia](https://reports.unocha.org/en/country/ethiopia)\n15 IOM. 2021. Ethiopia National Displacement Report 10 (August-September 2021), published 13 December 2021:\n[https://dtm.iom.int/ethiopia](https://dtm.iom.int/ethiopia)\n\n\nPage 9 of 80", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**FOR OFFICIAL USE ONLY**\n\nReport No: PAD5029 INTERNATIONAL BANK FOR RECONSTRUCTION AND DEVELOPMENT PROJECT APPRAISAL DOCUMENT", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "CURRENCY EQUIVALENTS (Exchange Rate Effective April 27, 2022) Currency Unit = Jordanian dinar (JOD) JOD 1 = US$1.41 US$ 1 = JOD 0.71 FISCAL YEAR January 1 - December 31 Regional Vice President: Ferid Belhaj Country Director: Saroj Kumar Jha Regional Director: Ayat Soliman Practice Manager: Marianne Grosclaude Task Team Leader(s): [Svetlana Edmeades, Mohamed Hassan Abdulkader, ] Tobias Baedeker", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "ABBREVIATIONS AND ACRONYMS ACG Anti-Corruption Guidelines ARDI Agriculture Resilience, Value Chain Development and Innovation Program CBJ Central Bank of Jordan CCDR Country Climate and Development Report COVID Coronavirus disease CPI Consumer Price Index DA Designated Account DFIL Disbursement and Financial Information Letter E&S Environmental and Social ECT Emergency Cash Transfers EHS Environmental, Health, and Safety Guidelines ESF Environmental and Social Framework ESCP Environmental and Social Commitment Plan ESMF Environmental and Social Management Framework ESIA Economic and Social Impact Assessment EU European Union FAO Food and Agriculture Organization FM Financial Management FO Financial Officer GDP Gross Domestic Product GFMIS Government Financial Management Information System GFRP Global Food Crisis Response Program GIIP Good International Industry Practice GOJ Government of Jordan GRM Grievance Redress Mechanism IFRs Interim Financial Reports INT Integrity Vice Presidency (INT) IMF International Monetary Fund JACPA Jordan Association of Certified Public Accountants LMP Labor Management Plan MENA Middle East and North Africa MOA Ministry of Agriculture MOF Ministry of Finance MOITS Ministry of Industry, Trade and Supply MOPIC Ministry of Planning and International Cooperation MOSD Ministry of Social Development mVAM mobile Vulnerability, Analysis and Monitoring NAF National Aid Fund OHS Occupational Health and Safety OP/BP Operational Policies/Bank Procedures PCT Project Coordination Team PDO Project Development Objective PforR Program for Results", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "POM Project Operations Manual PPSD Project Procurement Strategy for Development SEA/SH Sexual Exploitation and Abuse and Sexual Harassment SEP Stakeholder Engagement Plan SMEs Small- and Medium Enterprises SSN Social Safety Net STEP Systematic Tracking of Exchanges in Procurement TA Technical Assistance TOR Terms of Reference UN United Nations UNHCR The United Nations High Commissioner for Refugees US United States US$ United States Dollar WA Withdrawal Application WBG World Bank Group WFP World Food Programme yoy Year-on-year", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEmergency Food Security Project (P178936) TABLE OF CONTENTS\n\n**DATASHEET ........................................................................................................................... 1**\n\n**I.** **STRATEGIC CONTEXT ...................................................................................................... 6**\n\nA. Country Context................................................................................................................................ 6 B. Sectoral and Institutional Context .................................................................................................... 8 C. Relevance to Higher Level Objectives ............................................................................................. 12\n\n**II.** **PROJECT DESCRIPTION .................................................................................................. 15**\n\nA. Project Development Objective ..................................................................................................... 15 B. Project Components ....................................................................................................................... 15 C. Project Beneficiaries ....................................................................................................................... 20 D. Results Chain .................................................................................................................................. 20 E. Rationale for Bank Involvement and Role of Partners ................................................................... 22 F. Lessons Learned and Reflected in the Project Design .................................................................... 22\n\n**III.** **IMPLEMENTATION ARRANGEMENTS ............................................................................ 24**\n\nA. Institutional and Implementation Arrangements .......................................................................... 24 B. Results Monitoring and Evaluation Arrangements......................................................................... 24 C. Sustainability ................................................................................................................................... 25\n\n**IV.** **PROJECT APPRAISAL SUMMARY ................................................................................... 25**\n\nA. Technical, Economic and Financial Analysis (if applicable) ............................................................ 25 B. Fiduciary .......................................................................................................................................... 27 C. Legal Operational Policies ............................................................................................................... 31 D. Environmental and Social ............................................................................................................... 31\n\n**GRIEVANCE REDRESS SERVICES ............................................................................................ 33**\n\n**KEY RISKS ............................................................................................................................ 33**\n\n**RESULTS FRAMEWORK AND MONITORING .......................................................................... 36**\n\n**ANNEX 1: Implementation Arrangements and Support Plan .......................................... 44**\n\n**ANNEX 2: Financial Management .................................................................................. 45**\n\n**ANNEX 3: Summary of Wheat and Barley Value Chains ................................................. 53**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEmergency Food Security Project (P178936) DATASHEET\n\n**BASIC INFORMATION**\n~~BASIC~~ ~~INFO~~ ~~TABLE~~ Country(ies) Project Name Jordan Emergency Food Security Project Environmental and Social Risk Project ID Financing Instrument Process Classification Investment Project P178936 Substantial Financing\n\n**Financing & Implementation Modalities**\n\nUrgent Need or Capacity Constraints (FCC)\n\n[ ] Multiphase Programmatic Approach (MPA) [ ] Contingent Emergency Response Component (CERC)\n\n[ ] Series of Projects (SOP) [ ] Fragile State(s)\n\n[ ] Performance-Based Conditions (PBCs) [ ] Small State(s)\n\n[ ] Financial Intermediaries (FI) [ ] Fragile within a non-fragile Country\n\n[ ] Project-Based Guarantee [ ] Conflict\n\n[ ] Deferred Drawdown [✓] Responding to Natural or Man-made Disaster\n\n[ ] Alternate Procurement Arrangements (APA) [ ] Hands-on Enhanced Implementation Support (HEIS) Expected Approval Date Expected Closing Date 13-May-2022 31-Dec-2024 Bank/IFC Collaboration No\n\n**Proposed Development Objective(s)**\n\nTo ensure the availability of basic grains and mitigate the impact of high commodity prices in Jordan Page 1 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEmergency Food Security Project (P178936)\n\n**Components**\n\n**Component Name** **Cost (US$, millions)**\n\nSecuring food and feed to reduce short term vulnerability to supply shocks 460.00 Addressing food security to mitigate commodity risks 30.00\n\n**Organizations**\n\nBorrower: Ministry of Planning and International Cooperation (MOPIC) Implementing Agency: Ministry of Industry, Trade and Supply (MoITS)\n\n**PROJECT FINANCING DATA (US$, Millions)**\n\n**SUMMARY-NewFin1**\n\n**Total Project Cost** 490.00\n\n**Total Financing** 490.00\n\n**of which IBRD/IDA** 490.00\n\n**Financing Gap** 0.00\n\n**DETAILS-NewFinEnh1**\n\n**World Bank Group Financing**\n\nInternational Bank for Reconstruction and Development (IBRD) 490.00\n\n**Expected Disbursements (in US$, Millions)**\n\n**WB Fiscal Year** 2022 2023 2024 2025\n\n**Annual** 0.50 480.00 8.00 1.50\n\n**Cumulative** 0.50 480.50 488.50 490.00\n\n**INSTITUTIONAL DATA**\n\n**Practice Area (Lead)** **Contributing Practice Areas**\n\nPage 2 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEmergency Food Security Project (P178936) Agriculture and Food Finance, Competitiveness and Innovation\n\n**Climate Change and Disaster Screening**\n\nThis operation has been screened for short and long-term climate change and disaster risks\n\n**SYSTEMATIC OPERATIONS RISK-RATING TOOL (SORT)**\n\n**Risk Category** **Rating**\n\n1. Political and Governance ⚫ Moderate\n\n2. Macroeconomic ⚫ Substantial 3. Sector Strategies and Policies ⚫ Substantial 4. Technical Design of Project or Program ⚫ Moderate 5. Institutional Capacity for Implementation and Sustainability ⚫ Substantial 6. Fiduciary ⚫ Substantial 7. Environment and Social ⚫ Substantial 8. Stakeholders ⚫ Substantial 9. Other 10. Overall ⚫ Substantial\n\n**COMPLIANCE**\n\n**Policy**\nDoes the project depart from the CPF in content or in other significant respects?\n\n[ ] Yes [✓] No Does the project require any waivers of Bank policies?\n\n[✓] Yes [ ] No Page 3 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEmergency Food Security Project (P178936) Have these been approved by Bank management?\n\n[✓] Yes [ ] No Is approval for any policy waiver sought from the Board?\n\n[ ] Yes [✓] No\n\n**Environmental and Social Standards Relevance Given its Context at the Time of Appraisal**\n\n**E & S Standards** **Relevance**\n\nAssessment and Management of Environmental and Social Risks and Impacts Relevant Stakeholder Engagement and Information Disclosure Relevant Labor and Working Conditions Relevant Resource Efficiency and Pollution Prevention and Management Relevant Community Health and Safety Relevant Land Acquisition, Restrictions on Land Use and Involuntary Resettlement Not Currently Relevant Biodiversity Conservation and Sustainable Management of Living Natural Resources Indigenous Peoples/Sub-Saharan African Historically Underserved Traditional Local Communities Not Currently Relevant Not Currently Relevant Cultural Heritage Relevant Financial Intermediaries Not Currently Relevant\n\n**NOTE** : For further information regarding the World Bank’s due diligence assessment of the Project’s potential\nenvironmental and social risks and impacts, please refer to the Project’s Appraisal Environmental and Social Review Summary (ESRS).\n\n**Legal Covenants**\n\nSections and Description The Borrower, through MoITS, shall prepare by May 31, 2022 an interim Project Operations Manual (POM) for Component 1, which will also constitute a condition for disbursement of proceeds from Component 1.\n\nSections and Description Page 4 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEmergency Food Security Project (P178936) The Borrower shall by no later than two (2) months after the Effectiveness Date prepare a complete Project Operations Manual for both components.\n\nSections and Description The Borrower shall by no later than two (2) months after the Effectiveness Date designate a Project Steering Committee that includes representatives of MOPIC, MOF, MOITS, and Ministry of Agriculture.\n\nSections and Description The Borrower shall by no later than two (2) months after the Effectiveness Date prepare an annual work plan and budget acceptable to the World Bank.\n\n**Conditions**\n\nPage 5 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEmergency Food Security Project (P178936)\n\n**I.** **STRATEGIC CONTEXT**\n\n**A. Country Context**\n\n**1.** **Over the past decade, Jordan has faced a number of external shocks that directly affected economic**\n**growth and the economy’s ability to create enough productive jobs for its young and fast-growing population.**\nHeadwinds started with the global financial crisis in 2008 leading to a dampening of economic activity in 2009, while the accompanying policy response—including lower taxes and debt accumulation— was not enough to stimulate growth. Second, regional conflicts erupting in 2011 disrupted trade routes to key trade partners, including Turkey and the European Union (EU). For example, exports have been adversely affected by the closure of the border with Iraq in 2014. World Bank estimates show that the Syrian conflict alone has led to a 1.6 percentage point lower growth during 2010-18. [1] Moreover, Jordan experienced a large refugee influx and is currently hosting almost 1.3 million Syrians (representing almost 13 percent of the total population, of which 674,268 are registered refugees with United Nations High Commissioner for Refugees (UNHCR) [2], which has put tremendous pressure on public services, and on food security [3] . Third, the disruption of favorably priced natural gas supplies from Egypt (also in 2011), created another major shock and had a serious implication for Jordan’s energy intensive manufacturing sector, negatively impacting competitiveness [4], economic growth and public debt. [5] Fourth, the economic slowdown in Gulf countries (following depressed oil prices in 2014) led to a significant drop in external inflows. These external shocks have dampened Jordan’s growth momentum leading to persistently sluggish growth dynamics along with increased macroeconomic vulnerability.\n\n**2.** **The COVID-19 pandemic has had significant economic impact on Jordan, given the country’s small and**\n**open economy with strong linkages with the rest of the world.** Jordan’s real Gross Domestic Product (GDP)\ncontracted by 1.6 percent in 2020, compared to 2.0 percent growth in 2019. The pandemic has had particularly profound impacts on key sectors for the Jordanian economy, such as the service sector, including travel and tourism. Jordan’s unemployment rate, which marginally increased from 18.3 percent to 19.1 percent between 2017 and 2019, rose significantly as a result of the pandemic shock, reaching 23.2 percent in 2020. Female unemployment, which had been declining between 2017 and 2019, from 31.2 percent to 27 percent, rose sharply to 30.7 percent in 2020. Moreover, youth unemployment (15-24 years) soared from 40.6 percent in 2019 to an unprecedented high of 46.0 percent in 2020. The COVID-19 pandemic has also had severe impacts on Small and Medium Enterprises (SMEs), which provide 52 percent of private sector employment in the country, [6] including a decline in demand, reduced supply, tightening credit conditions, and a fall in investment.\n\n**3.** **Jordan’s economy nonetheless has weathered the COVID-19 shock better than many peers.** In global\ncomparison, Jordan’s GDP contraction in 2020 remained relatively muted, in part due to the Government of Jordan 1 World Bank. 2020. _The Fallout of War: The Regional Consequences of the War in Syria_ . Washington, DC: World Bank.\n_[https://www.worldbank.org/en/region/mena/publication/fallout-of-war-in-syria.](https://www.worldbank.org/en/region/mena/publication/fallout-of-war-in-syria)_ 2 As of 31 March 2022.\n3 Food security exists when all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life”. (World Food Summit, 1996) 4 By 2015, although, the balance between NEPCO revenues and the generation cost has been restored but electricity cost had become the binding constraint to growth and product diversification. (Hausmann, R., et al., 2019. \"Jordan: The Elements of a Growth Strategy,\" CID Working Papers 346, Center for International Development at Harvard University.).\n5 Government of Jordan caped the tariff response to spare the bulk of the population from large tariff increases and produced an accumulated energy sector debt of about 18 percent of GDP, held by the Central Government and NEPCO (World Bank, 2018: “Jordan First Equitable Growth Job Creation Programmatic Development Policy Financing Project”).\n6 Jordan Loan Guarantee Corporation (JLGC). June 2019.\n\nPage 6 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEmergency Food Security Project (P178936) (GOJ)’s timely fiscal and monetary stimuli, efficient management of the health crisis as well as a substantial improvement in terms of trade (as a result of the decline in international oil prices during 2020). In 2021, real GDP grew by 2.2 percent reflecting reasonably strong bounce back. Nevertheless, despite this turn around, high unemployment rates and unresolved structural challenges remain a concern. According to the World Bank’s Jobs Diagnostic (2019) [7] the economy needs at least six percent growth to make a dent in the unemployment rate, which is three times the level experienced since 2010. Thus, the current rate of unemployment at 23.3 percent (last quarter of 2021) and of labor force participation rate (33.5 percent total, 53.8 percent for men and 13.6 percent for women), present a sizeable challenge for Jordan. Furthermore, Jordan’s foreign direct investment (FDI) flows have notably declined over the past decade, from 6.1 percent of GDP in 2010 to 1.5 percent of GDP in 2019—foregoing a potential source for productivity gains, dynamism, and external funding for the economy. [8]\n\n**4.** **Jordan’s economy is facing increased price pressures.** Even before the war in Ukraine, significant\ndisruptions to global supply chains caused by the COVID-19 pandemic have further affected trade, increasing the economic vulnerability of Jordan. Being an import-dependent small open economy, unfavorable changes in Jordan’s terms-of-trade have negatively impacted domestic price settings during 2021. This trend was further accentuated by the rise in global transportation costs. [9] Consequently, headline CPI inflation clocked in at an average 1.3 percent in 2021 - the highest rate in 3 years. However, in regional comparison, inflation in Jordan remained low. In 2021, the central government’s overall fiscal deficit (including grants) reached 5.7 percent of GDP, or 1.6 percent of GDP lower than 2020 and 0.4 percent of GDP below the budget target. Timely donor support, the International Monetary Fund (IMF) Extended Fund Facility (EFF) program [10] (including program augmentation as well as front loading) have helped alleviate pressure from the external sector. At end 2021, the Central Bank of Jordan’s gross official foreign reserves reached US$19 billion or almost US$2 billion higher than the previous year.\n\n**5.** **The war in Ukraine has caused a major shock to global commodity markets and poses a major threat to**\n**global food security** . With Ukraine and the Russian Federation being major global producers of basic agricultural\ncommodities (wheat, maize, oilseeds), fertilizer and fuel, the war in Ukraine has introduced significant instability and uncertainty in global agricultural commodity, energy and fertilizer markets. The disruptions in production and trade caused by the war have pushed international prices in these markets to new all-time highs in recent months.\nThe most recent World Bank Food Security Update (April 7, 2022) highlighted that since January 2021 the agricultural price index is up 29 percent, the cereals price index 11 percent, and the export price index 64 percent.\nWheat prices retreated somewhat from the peak reached on March 7, 2022, but they are still nearly 36 percent higher than in early February 2022, before the war in Ukraine, and 60 percent higher since January 2021. Maize prices are about 20 percent above the early February level and 48 percent higher than in January 2021, while rice prices have continued to remain remarkably stable. Meanwhile, fertilizer prices surged in March 2022, up nearly 20 percent since January and almost 3 times higher compared to a year ago, whereas energy prices had surged 63.4 percent year on year.\n\n7 Winkler, Hernan; Gonzalez, Alvaro. 2019. “Jordan Jobs Diagnostic.” Jobs Series; No. 18. World Bank, Washington, DC.\n8 In 2019, FDI inflows fell to the lowest point over the past two decades, accounting for only 1.5 percent of GDP.\n9 International freight charges peaked around US$11,109 in September 2021 from US$1,500 pre-pandemic. These currently stand around US$ 9,430 (source: Bloomberg).\n10 Jordan’s fiscal adjustment remains supported by the IMF. The Third Review of the IMF EFF program was completed on December 20, 2021. This allowed IMF to disperse almost US$335 million (or cumulatively US$1.23 billion) under the program). According to the IMF, sound policies have helped maintain macroeconomic stability, while the government remains on track to narrow its fiscal deficit, and reserves remain at a comfortable level.\n\nPage 7 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEmergency Food Security Project (P178936)\n\n**6.** **Jordan’s fragility to external shocks and internal factors can exacerbate food security in the country.**\nJordan’s structural impediments in a context of needs for regional stability and response to imminent global risk factors arising from the war in Ukraine represent key challenges for immediate recovery. Jordan is positioned at the epicenter of one of the most volatile regions in the world. The government has consistently preserved its stability, drawing from its unique geopolitical positioning and socio-political resilience. The country has been highly exposed to exogenous shocks, particularly the spillovers from regional conflicts, including Syria, Iraq and the West Bank and Gaza, dependency on foreign aid and inflow of capital, and shifts in geopolitical relations, all of which have compounded the country’s existing vulnerabilities. Regional insecurity and any subsequent displacement of refugees into Jordan coupled with the pandemic pose a risk for the food security of the country and stress its food system. Although direct trade links with Ukraine and the Russian Federation are limited, a continuous and accelerated surge in commodity prices (especially those of wheat and barley) and stronger slowdown in global growth represent imminent downside risks to the economy and food security.\n\n**B. Sectoral and Institutional Context**\n\n**7.** **Globally and in the region, Jordan is one of the most import dependent countries for covering its**\n**national grains consumption needs.** The country is highly vulnerable to price volatility and supply disruptions in\nglobal markets for basic agricultural commodities. Jordan does not produce significant volumes of basic agricultural commodities domestically. For example, annual domestic wheat and barley production averaged 21,499 tons and 54,234 tons respectively between 2018 and 2020 compared to monthly domestic consumption levels of 90,000 tons of wheat and 80,000 tons of barley [11] . In the period 2018-2020, total annual import volumes of wheat and barley averaged 913,334 tons and 770,068 tons respectively, which amounted to an average annual import bill of US$ 227 million for wheat and US$ 177 million for barley (UN Comtrade, 2022). The Black Sea region is the principal origin for the bulk of Jordan’s grain imports (given lower freight costs compared to other origins), with the region representing 98 percent of Jordanian wheat imports and 78 percent of barley imports. Yet the supply shock caused by the war in Ukraine has changed the geopolitics and economics of grain trade where small buyers, like Jordan, are competing in a tight market with large countries and fewer suppliers, that are geographically farther from the region, and where not only price, but also grain quality play an important role in the purchasing decisions of the GOJ, further reducing market options for the country. This could further exacerbate the ability of the GOJ to secure the required grain purchases on time to meet domestic consumption levels at socially acceptable prices.\n\n**8.** **Jordan’s vulnerability to shocks in global commodity markets is further compounded by the**\n**demographic pressures facing the country** . In addition to the structural challenges of sluggish growth, growing\ndebt, high unemployment, declining FDI and trade disruptions due to regional conflicts, Jordan is confronted with growing demographic, climate and environmental pressures. Between 2000 and 2020 the Jordanian population (excluding refugees) grew at rates between 1.37 and 5.00 percent. When adding the influx of 1.3 million refugees from Syria into Jordan since the start of the civil war, Jordan’s effective population growth in 2020 amounted to 14 percent.\n\n**9.** **Moreover, climate change is significantly reducing the opportunities to respond to growing domestic**\n**consumption and improve resilience to external shocks through increased domestic production of grains** .\nClimate change poses a serious challenge to the Jordanian agriculture sector. Not only is Jordan endowed with limited natural resources (Jordan is the second most water scarce country in the world with only 97 cubic meters 11 As a result, Jordan is entirely dependent on global markets to meet domestic consumption needs for basic commodities such as cereals (95 percent overall and more than 95 percent for wheat and barley), vegetable oil (79 percent), rice (100 percent) and sugar (100 percent).\n\nPage 8 of 54", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEmergency Food Security Project (P178936) of water available per capita per year – well below the absolute water scarcity threshold of 500 cubic meters per capita per year), but those resources are increasingly vulnerable to climate-related hazards (Jordan ranks 72 out of 182 countries in the ND-GAIN index for climate vulnerability in 2019), including droughts [12], extreme temperature, storms, landslides and flash floods [13] . These changes are likely to have negative effects on crop production as they will decrease the availability of water for irrigation, diminishing the suitability of key crops and increase the vulnerability of smallholder farmers who are already extremely vulnerable to the impacts of climate change due to their low-incomes, poor access to technical capacities and lack of technology to increase productivity under extreme weather conditions [14] . In the livestock sub-sector, rising drought risks reduce the carrying capacity of rangelands and increase the dependency on grains for fodder, thereby contributing to import dependency and vulnerability to external price shocks. Finally, adaptation options to reduce vulnerability along grain import supply chains and the grain import system and physical grain storage will need to be pursued to reflect increasingly hot and dry conditions, as well as heavy precipitation events.\n\n**10.** **High and volatile food prices due to shocks in global commodity markets disproportionately impact**\n**poor and vulnerable households in Jordan.** In February 2022, annual food price inflation trended at 2.7 percent\n\n- the highest rate recorded over the past 12 months. Growing inflationary pressures as a result of higher food\nprices pose a significant threat to vulnerable households. Importantly, short-term demand for wheat products, which account for the largest share in the food basket, is relatively inelastic in Jordan as in the Arab world in general (World Bank, 2012). Many Jordanian households hover around the national poverty line making poverty numbers very sensitive to even small increases in the cost of living (Figure 1). [15] Moreover, households are affected by higher food prices in different ways, with women, including female-headed households resorting to harmful coping strategies, such as reducing their own nutrition consumption to feed members of their family, or taking on risky jobs to acquire food as a result of having limited access to assets and markets, and fewer pathways out of the crisis as compared to their male counterparts (Oxfam, 2019). Furthermore, poor and near-poor households in Jordan tend to spend a larger share of their income on food. Food purchases account for over one-third of poor and near poor Jordanian household budgets and a quarter among the richest 20 percent of the population (Figure 2). Similarly, food purchases account for over one-third of refugee household budgets. Within Jordanians’ food basket basic agricultural commodities account for most of the caloric intake, in particular wheat products (28 percent), sugar (14 percent), rice (8 percent), sunflower seed oil (7 percent) and soybean oil (4 percent) (Department of Statistics of Jordan, 2022).\n\n12 In 2021, Jordan experienced one of the most severe droughts in its history.\n_13_ [Jordan - Vulnerability | Climate Change Knowledge Portal (worldbank.org)](https://climateknowledgeportal.worldbank.org/country/jordan/vulnerability#:~:text=Climate%2Drelated%20hazards%20in%20Jordan,include%20periodic%20earthquakes%20and%20epidemics.)\n14 IPCC projections for the future indicate that by 2030 temperatures are projected to increase by 1–2oC and that there will be a significant\nincrease in the number of crop heat stress days throughout Jordan. Climate models from the Coupled Model Intercomparison Project Phase\n5 (CMIP5) ensemble project a decline in future precipitation rates of 6 percent, 11.5 percent, and 19 percent by 2030, 2050, and 2070,\nrespectively (RCP 8.5). The whole country is expected to experience a decrease in precipitation. The northern region and King’s Highway\nare projected to experience the largest precipitation declines by 2030: 12.5 percent and 10.4 percent respectively. Rainfall decreases in the\nEastern and Southern desert are projected to be 9.4 percent and 3.1 percent, respectively. Jordan’s Eastern and Southern Desert regions,\nwhich are primarily rangelands, are likely to experience more warming than the Northern region and the King’s Highway, which currently\nrely mostly on rainfed and irrigated agriculture.\n15 World Bank analysis indicates that if food prices were to increase by 10 percent in Jordan (a smaller increase than in the 2007-2008 food\nprice crises), poverty would increase by an additional 1.3 percentage points and the poverty gap would rise by half a percentage point,\nwhile a 20 percent increase in food prices would increase the poverty rate from 15.7 percent to 19.4 percent (43,000 new poor families).\n\nPage 9 of 54", "output": {"entities": {"named_data": ["ND-GAIN index for climate vulnerability"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nEmergency Food Security Project (P178936)\n\n**Figure 1: Jordan 2017-18 household per capita consumption**\n**distribution relative to the national poverty line**\n\n**Figure 2: Share of food expenditure in total expenditure p.c. (%)**\n\nSource: 2017-18 HEIS and World Bank calculations. Source: World Bank\n\n**11.** **Existing food insecurity levels are particularly high among Jordan’s refugee population.** According to the\nmost recent mobile Vulnerability Assessment and Mapping (mVAM) [16] completed by the World Food Program (WFP) in Jordan, 7 percent of Jordanian households (representing 535,559 individuals) were found to be food insecure as of February 2021 and another 51 percent of households (representing 3,843,701 individuals) were vulnerable to food insecurity, meaning that they were very likely to experience an acute decline in food access or consumption levels below minimum survival needs. WFP’s mVAM also found that food insecurity levels among Jordan’s refugee community (which are not covered by Jordan’s social security net system) are significantly higher than those registered at the level of Jordanian households. Of the more than 750,000 refugees [17] registered in Jordan (89 percent of whom came from Syria), an estimated 17 percent live in the Za’atari and Azraq refugee camps, while the remaining 83 percent are mostly in Jordan’s urban areas. Throughout the COVID-19 pandemic, food security has been a key concern for refugees in both camps and in host communities mainly due to the loss of income from temporary and informal labor activities. More than 80 percent of labor activities performed by non-Jordanians are estimated to take place in the informal economy versus 40 percent for Jordanian citizens (MOSD, 2019). February 2021 mVAM data showed that 23.3 percent of refugee households in host communities are food insecure (over 154,777 individuals), while another 63.7 percent of refugee households (equivalent to approximately 423,344 individuals) are vulnerable to food insecurity.\n\n**12.** **Ensuring food security and social stability are at the core of the urgent need to ensure availability of**\n**and access to staple food** . Bread is an essential part of the diet in Jordan and represents the main caloric source\nfor the poorest Jordanians and the many refugees in the country. Similarly, barley import dependent livestock herding is the mainstay of the rural economy and the livelihoods of about 200,000 people in traditional nomadic and semi-nomadic Bedouin tribal communities. While trade in basic food commodities (such as sugar, rice, cooking oil, milk powder) is managed mainly by the private sector in Jordan, the GOJ maintains full control of grain reserves by directly managing the purchase, import, storage and domestic sales of wheat and barley. In 2021, the GOJ discontinued the bread subsidy and, as a response to the COVID-19 outbreak, it fixed the domestic price of bread to ensure its affordability (at 0.32 JOD per kilogram) for the entire population of Jordan. This intervention model (in combination with the effective appreciation of the Jordanian dinar) has helped control inflationary pressures.\nHowever, maintaining this status quo in a context of all time high international grain prices is costly, also coinciding with Bond maturity obligations of the GOJ, further reducing the availability of financial resources at a critical time.\n\n**13.** **Public procurement of strategic agricultural commodities is at the core of Jordan’s food security strategy**\n**as rising food prices pose a significant threat to social stability** . Historically, Jordan has played a key role as an\n\n_16_ [WFP | Jordan: Mobile Vulnerability Analysis and Mapping Dashboard | As of June 2021 (arcgis.com)](https://unwfp.maps.arcgis.com/apps/MapSeries/index.html?appid=7210a3ee33b14c5b9a989590345cb49a)\n_17_ _As of July 2021_\n\nPage 10 of 54", "output": {"entities": {"named_data": ["mobile Vulnerability Assessment and Mapping"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**FOR OFFICIAL USE ONLY**\n\nReport No: PAD3866 INTERNATIONAL DEVELOPMENT ASSOCIATION PROJECT APPRAISAL DOCUMENT ON A", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "CURRENCY EQUIVALENTS (Exchange Rate Effective March 31, 2020) Currency Unit = Central African Franc (XAF) US$1.00 = SDR 0.73 SDR1.00 = US$1.36 FISCAL YEAR January 1 - December 31 Regional Vice President: Hafez M. H. Ghanem Country Director: Soukeyna Kane Regional Director: Amit Dar Practice Manager: Magnus Lindelow Task Team Leader: Andy Chi Tembon", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) ABBREVIATIONS AND ACRONYMS AFD _Agence Française de Développement_ (French Agency for Development) AMR Anti-microbial Resistance BFP World Bank Facilitated Procurement CDC Center for Disease Control CERC Contingency Emergency Response Component COVID-19 Coronavirus Disease CPIA Country Policy and Institutional Assessment DA Designated Account DFIL Disbursement and Financial Information Letter DHIS District Health Information System DLI Disbursement-linked Indicators ECHO European Civil Protection and Humanitarian Aid Operations EID Emerging Infectious Disease ESCP Environmental and Social Commitment Plan ESF Environmental and Social Framework ESMF Environmental and Social Management Framework ESMP Environmental and Social Management Plan FA Financing Agreement FCV Fragility, Conflict and Violence FTCF Fast Track COVID-19 Facility FM Financial Management GAVI Global Alliance for Gavi and Immunization GBV/SEA Gender Based Violence / Sexual Exploitation & Abuse GDP Gross Domestic Product GEMS Geo-enabled Monitoring Systems GHSA Global Health Security Agenda GoC Government of Chad GRS Grievance Redress Service HCI Human Capital Index HEIS Hands-on Expanded Implementation Support IBRD International Bank for Reconstruction and Development ICR Implementation Completion and Results Report ICU Intensive Care Unit IDA International Development Association IDSR Integrated Disease Surveillance Response IEC/BCC Information Education Communication/Behavior Change Communication IFA International Federation of Accountants IFC International Financial Corporation IFR Interim Financial Report IHR International Health Regulation IMF International Monetary Fund IPF Investment Project Financing ISA International Standards on Auditing JEE Joint External Evaluation LIC Low-income Country M&E Monitoring and Evaluation MDP Mandatory Direct Payment", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) MMR Maternal Mortality Ratio MOPH Ministry of Public Health MPA Multiphase Programmatic Approach MTR Medium-term Review NGO Non-governmental Organizations NPF New Procurement Framework OIE World Organization for Animal Health PAD Project Appraisal Document PCU Project Coordination Unit PDO Project Development Objective PIM Project Implementation Manual PPE Personal Protective Equipment PPSD Project Procurement Strategy for Development PVS Performance of Veterinary Services REDISSE Regional Disease Surveillance Systems Enhancement Project SBC Social and Behavior Change SDG Sustainable Development Goals SDR Special Drawing Rights SEA/H Sexual Exploitation and Abuse / Harassment SEP Stakeholder engagement plan SOE Statements of Expenses SORT Systematic Operations Risk-rating Tool SPRP Strategic Preparedness and Response Program STEP Systematic tracking of Exchanges in Procurement TA Technical Assistance TFR Total Fertility Rate TOR Terms of Reference UNICEF United Nations Children's Fund UN United Nations UNOPS United Nations Office for Project Services WB World Bank WBG World Bank Group WHO World Health Organization", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) TABLE OF CONTENTS\n\n**DATASHEET ........................................................................................................................... 1**\n\n**I.** **PROGRAM CONTEXT ....................................................................................................... 7**\n\nA. MPA Program Context .................................................................................................................. 7 B. Updated MPA Program Framework .............................................................................................. 8 C. Learning Agenda ........................................................................................................................... 8\n\n**II.** **CONTEXT AND RELEVANCE ............................................................................................. 8**\n\nA. Country Context ............................................................................................................................ 8 B. Sectoral and Institutional Context ................................................................................................ 9 C. Relevance to Higher Level Objectives ......................................................................................... 12\n\n**III.** **PROJECT DESCRIPTION .................................................................................................. 12**\n\nA. Development Objectives ............................................................................................................. 12 B. Project Components ................................................................................................................... 13 C. Project Beneficiaries ................................................................................................................... 15 D. Results Chain ............................................................................................................................... 15 E. Complementarities with REDISSE IV and the support from other Development Partners ........ 16\n\n**IV.** **IMPLEMENTATION ARRANGEMENTS ............................................................................ 18**\n\nA. Institutional and Implementation Arrangements ....................................................................... 18 B. Results Monitoring and Evaluation Arrangements ..................................................................... 20 C. Sustainability ............................................................................................................................... 20\n\n**V.** **PROJECT APPRAISAL SUMMARY ................................................................................... 21**\n\nA. Technical, Economic and Financial Analysis................................................................................ 21 B. Fiduciary ...................................................................................................................................... 21 C. Legal Operational Policies ........................................................................................................... 26 D. Environmental and Social Standards .......................................................................................... 26\n\n**VI.** **GRIEVANCE REDRESS SERVICES ..................................................................................... 28**\n\n**VII.** **KEY RISKS ..................................................................................................................... 28**\n\n**VIII.** **RESULTS FRAMEWORK AND MONITORING ................................................................... 30**\n\n**ANNEX 1: Project Costs ............................................................................................................. 35**\n\n**ANNEX 2: Implementation Arrangements and Support Plan ....................................................... 36**\n\n.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) DATASHEET\n\n**BASIC INFORMATION**\n~~BASIC~~ ~~INFO~~ ~~TABLE~~ Country(ies) Project Name Chad Chad COVID-19 Strategic Preparedness and Response Project Project ID Financing Instrument Environmental and Social Risk Classification Investment Project P173894 Substantial Financing\n\n**Financing & Implementation Modalities**\n\n[ ] Multiphase Programmatic Approach (MPA) [ ] Contingent Emergency Response Component (CERC) ✓\n\n[ ] Series of Projects (SOP) [ ] Fragile State(s) ✓\n\n[ ] Disbursement-linked Indicators (DLIs) [ ] Small State(s)\n\n[ ] Financial Intermediaries (FI) [ ] Fragile within a non-fragile Country\n\n[ ] Project-Based Guarantee [ ] Conflict\n\n[ ] Deferred Drawdown [ ] Responding to Natural or Man-made Disaster ✓\n\n[ ] Alternate Procurement Arrangements (APA) Expected Project Approval Date Expected Project Closing Date Expected Program Closing Date 24-Apr-2020 30-Dec-2022 31-Mar-2025 Bank/IFC Collaboration No\n\n**MPA Program Development Objective**\n\nThe Program Development Objective is to prevent, detect and respond to the threat posed by COVID-19 and strengthen national systems for public health preparedness\n\n**Financing**\n**MPA Financing Data (US$, Millions)**\n\nPage 1", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) MPA Program Financing Envelope 4,251.75\n\n**Proposed Project Development Objective(s)**\nTo prevent, detect and respond to the threat posed by COVID-19 and strengthen national systems for public health preparedness in Chad.\n\n**Components**\n\n**Component Name** **Cost (US$, millions)**\n\nComponent 1: Emergency COVID-19 Preparedness and Response 13.45 Component 2. Community Engagement and Social and Behavior Change 2.50 Communication Component 3: Implementation Management, Monitoring and Evaluation and\n1.00\nCoordination\n\n**Organizations**\n\nBorrower: Republic of Chad Implementing Agency: Ministère de la Santé Publique\n\n**MPA FINANCING DETAILS (US$, Millions)**\n\n**MPA FINANCING DETAILS (US$, Millions)** Approved\n**Board Approved MPA Financing Envelope:** 4,251.75\n\n**MPA Program Financing Envelope:** 4,251.75\n\n**of which Bank Financing (IBRD):** 2,696.10\n\n**of which Bank Financing (IDA):** 1,555.65\n\n**of which other financing sources:** 0.00\n\n**PROJECT FINANCING DATA (US$, Millions)**\n\nFIN_SUMM_NEW\n\n**-NewFin1**\n**SUMMARY**\n\n**Total Project Cost** 16.95\n\n**Total Financing** 16.95\n\nPage 2", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**of which IBRD/IDA** 16.95\n\n**Financing Gap** 0.00\n\n**-NewFinEnh1**\n**DETAILS**\n\n**World Bank Group Financing**\n\nInternational Development Association (IDA) 16.95 IDA Grant 16.95\n\n**IDA Resources (in US$, Millions)**\n\n**Credit Amount** **Grant Amount** **Guarantee Amount** **Total Amount**\n\n**Chad**\n0.00 16.95 0.00 16.95 Crisis Response Window (CRW) 0.00 16.95 0.00 16.95\n\n**Total** **0.00** **16.95** **0.00** **16.95**\n\n**Expected Disbursements (in US$, Millions)**\n\n**WB Fiscal**\n2020 2021 2022 2023\n**Year**\n\n**Annual** 13.48 2.32 0.82 0.34\n\n**Cumulative** 13.48 15.79 16.61 16.95\n\n**INSTITUTIONAL DATA**\n\n**Practice Area (Lead)** **Contributing Practice Areas**\n\nHealth, Nutrition & Population\n\n**Climate Change and Disaster Screening**\n\nThis operation has not been screened for short and long-term climate change and disaster risks Explanation Not applicable for COVID-19 Page 3", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**SYSTEMATIC OPERATIONS RISK-RATING TOOL (SORT)**\n\n**Risk Category** **Rating**\n\n1. Political and Governance  High\n\n2. Macroeconomic  High 3. Sector Strategies and Policies  Moderate 4. Technical Design of Project or Program  Moderate 5. Institutional Capacity for Implementation and Sustainability  High 6. Fiduciary  Substantial 7. Environment and Social  Substantial 8. Stakeholders  Moderate 9. Other 10. Overall  Substantial\n\n**Overall MPA Program Risk**  High\n\n**COMPLIANCE**\n\n**Policy**\nDoes the project depart from the CPF in content or in other significant respects?\n\n[ ] Yes [ ] No ✓ Does the project require any waivers of Bank policies?\n\n[ ] Yes [ ] No ✓ Page 4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**Environmental and Social Standards Relevance Given its Context at the Time of Appraisal**\n\n**E & S Standards** **Relevance**\n\nAssessment and Management of Environmental and Social Risks and Impacts Relevant Stakeholder Engagement and Information Disclosure Relevant Labor and Working Conditions Relevant Resource Efficiency and Pollution Prevention and Management Relevant Community Health and Safety Relevant Land Acquisition, Restrictions on Land Use and Involuntary Resettlement Not Currently Relevant Biodiversity Conservation and Sustainable Management of Living Natural Resources Not Currently Relevant Indigenous Peoples/Sub-Saharan African Historically Underserved Traditional Local Communities Not Currently Relevant Cultural Heritage Not Currently Relevant Financial Intermediaries Not Currently Relevant\n\n**NOTE** : For further information regarding the World Bank’s due diligence assessment of the Project’s potential\nenvironmental and social risks and impacts, please refer to the Project’s Appraisal Environmental and Social Review Summary (ESRS).\n\n**Legal Covenants**\n\nSections and Description Schedule 2, section 1 (B): Not later than 30 days after the Effective Date (or such later date as agreed to by the Association), the Recipient, through the MoH, shall: (a) prepare and adopt a manual acceptable to the Association (“Project Implementation Manual” or “PIM”); and (b) thereafter, implement the Project in accordance with the PIM. Except as the Association shall otherwise agree, the Recipient shall not amend or waive the PIM.\n\nSections and Description Schedule 2, section 1, D.7: The Recipient shall recruit an environmental specialist and social specialist for the PIU before the commencement of Project activities with potential social or environmental impacts, and shall thereafter ensure that said specialists be retained throughout Project implementation.\n\nPage 5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**Conditions**\n\nPage 6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) I. **PROGRAM CONTEXT**\n\n1. This Project Appraisal Document (PAD) describes the emergency response of The Republic of Chad under\nthe coronavirus disease (COVID-19) Strategic Preparedness And Response Program (SPRP) (P173894) using the Multiphase Programmatic Approach (MPA), approved by the World Bank’s Board of Executive Directors approved by the World Bank’s Board of Executive Directors on April 2, 2020 (PCBASIC0219761) with an overall Program financing envelope of up to US$6.00 billion.\n\n2. Chad is exceeding its IDA Fast Track COVID-19 Facility (FTCF) allocation by 50 percent, and the exceeded amount will be returned to the FTCF from the country’s FY21 Performance-based Allocation (PBA) envelope.\n\n**A.** **MPA Program Context**\n\n3. An outbreak of the COVID-19 caused by the 2019 novel coronavirus (SARS-CoV-2) has been spreading rapidly across the world since December 2019, following the diagnosis of the initial cases in Wuhan, Hubei Province, China. Since the beginning of March 2020, the number of cases outside China has increased thirteenfold and the number of affected countries has tripled. On March 11, 2020, the World Health Organization (WHO) declared a global pandemic as the coronavirus rapidly spreads across the world. As of April 20, 2020, the outbreak has resulted in an estimated 2,420,439 cases and 166,205 deaths in 213 countries and territories. [1] 4. COVID-19 is one of several emerging infectious diseases (EID) outbreaks in recent decades that have emerged from animals in contact with humans, resulting in major outbreaks with significant public health and economic impacts. The last moderately severe influenza pandemics were in 1957 and 1968; each killed more than a million people around the world. Although countries are now far more prepared than in the past, the world is also far more interconnected, and many more people today have behavior risk factors such as tobacco use [2] and pre-existing chronic health problems that make viral respiratory infections particularly dangerous. [3] With COVID-19, scientists are still trying to understand the full picture of the disease symptoms and severity. Reported symptoms in patients have varied from mild to severe, and can include fever, cough and shortness of breath. In general, studies of hospitalized patients have found that about 83 percent to 98 percent of patients develop a fever, 76 percent to 82 percent develop a dry cough and 11 percent to 44 percent develop fatigue or muscle aches. [4] Other symptoms, including headache, sore throat, abdominal pain, and diarrhea, have been reported, but are less common. While 3.7 percent of the people worldwide confirmed as having been infected have died, WHO has been careful not to describe that as a mortality rate or death rate. This is because in an unfolding epidemic it can be misleading to look simply at the estimate of deaths divided by cases so far. Hence, given that the actual prevalence of COVID-19 infection remains unknown in most countries, it poses unparalleled challenges with respect to global containment and mitigation. These issues reinforce the need to strengthen the response to COVID-19 across all IDA/IBRD countries to minimize the global risk and impact posed by this disease.\n\n5. This project is prepared under the global framework of the World Bank COVID-19 Response financed under 1 _[https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6](https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6)_ 2 Marquez, PV. 2020. “Does Tobacco Smoking Increases the Risk of Coronavirus Disease (Covid-19) Severity? The Case of China.” _[http://www.pvmarquez.com/Covid-19](http://www.pvmarquez.com/Covid-19)_ 3 Fauci, AS, Lane, C, and Redfield, RR. 2020. “Covid-19 — Navigating the Uncharted.” New Eng J of Medicine, DOI: 10.1056/NEJMe2002387 4 Del Rio, C. and Malani, PN. 2020. “COVID-19—New Insights on a Rapidly Changing Epidemic.” JAMA, doi:10.1001/jama.2020.3072 Page 7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) the FTCF.\n\n**B.** **Updated MPA Program Framework**\n\n6. Table-1 provides an updated overall MPA Program framework for the proposed project for Chad.\n\n**Table 1. MPA Program Framework**\n\n**Estimated**\n**IDA**\n**Amount**\n**(US$**\n**million)**\n\n**Estimated**\n**Other**\n**Amount**\n**(US$**\n**million)**\n\n**Estimated**\n**Environme**\n**ntal &**\n**Social Risk**\n**Rating**\n\n**Sequential or**\n**Phase #** **Project ID**\n**Simultaneous**\n\n**Phase’s**\n**Proposed DO***\n\n**IPF, DPF**\n**or PforR**\n\n**Estimated**\n**IBRD**\n**Amount**\n**(U$**\n**million)**\n\n**Estimated**\n**Approval**\n**Date**\n\nPlease see April 23, 2 P173894 Simultaneous **IPF** 16.95 Substantial relevant PAD 2020\n\n**Board Approved Financing**\nTotal 16.95\n**Envelope**\n\n**C.** **Learning Agenda**\n\n7. The country project under the MPA Program will support adaptive learning throughout the implementation, as well as from international organizations including WHO, International Monetary Fund (IMF), Center for Disease Control (CDC), United Nations Children's Fund (UNICEF), and others. Chad aims to support the following:\n\n- Forecasting: modeling the progression of the pandemic, both in terms of new cases and deaths, as well as\nthe economic impact of disease outbreaks under different scenarios.\n\n- Supply chain approaches: Assessments may be financed on options for timely distribution of medicines and\nother medical supplies.\n\n**II.** **CONTEXT AND RELEVANCE**\n\n**A.** **Country Context**\n\n8. **Chad is a low-income country (LIC) in the African Sahel region with a population of over 13 million people.** Chad is a landlock, sparsely populated country with a high share of the population living in rural areas.\nEconomic growth in Chad has been volatile over the last decade and its economy has been highly impacted by the changes in oil prices of 2014 and 2015. The subsequent recovery in oil prices and the increase in oil and agricultural production have contributed to a slow but positive growth of real gross domestic product (GDP) since 2018 (World Bank, 2020). Despite recent economic growth, poverty rates in Chad remain high and nearly half of the population (47 percent) lives below the poverty line (World Bank, 2020).\n\n9. **Chad has the lowest Human Capital Index (HCI) in the world** . A child born in Chad today will be 29 percent as productive when she grows up as she could be if she enjoyed full health and complete education (World Bank, 2018). This weak performance is driven largely by high infant mortality rates and poor quality of Page 8", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) education. An underlying driver of Chad’s Human Capital (and broader development) challenges is its high population growth. With a Total Fertility Rate (TFR) of 6.4 Chad is among the fastest growing countries in the world (DHS 2014/15). Nutrition outcomes, in turn, are very poor with 40 percent of children under five being stunted (World Bank, 2018). Further, while Human Capital outcomes are poor for both boys and girls, girls are particularly vulnerable and perform lower on the HCI, particularly in terms of expected years of schooling and survival rates.\n\n10. **Chad is highly vulnerable to the impact of climate change and it has repeatedly experienced security**\n**threats over the last decade** . With a Country Policy and Institutional Assessment (CPIA) of 2.7 in 2018, Chad\nis classified as a Fragility, Conflict and Violence (FCV) country. Regional security risks destabilize the country and lead to severe humanitarian needs. As of January 2020, there were 442,672 refugees settled in 19 camps in the East, the South and Lake Chad regions. [5] Climate change has contributed to the region’s social fragility (particularly in the Lake Chad Region and the pastoral areas in the Sahelian part of the country) and it seriously affected Chadians’ livelihoods.\n\n11. **There is a World Bank-financed Refugees and Host Communities Support Project (P164748 / 172255)**\n**being implemented in Chad.** This project will finance projects in water and sanitation (water points, latrines,\nwells, rainwater harvesting facilities) and provide shock-response cash transfers to approximately 14,000 households. These project activities will go a long way to also help in the COVID-19 control. The COVID-19 project will complement these activities in the camps. Working with non-governmental organizations (NGOs) and civil society, it will provide prevention supplies and the design of targeted communication campaigns for refugees and displaced populations.\n\n12. **The direct impact of COVID-19 and the anticipated slowdown in the global economy, will likely reduce**\n**trade and disrupt supply chains of basic goods** . The effects of a pandemic-driven global economic downturn\nand its impact on Chad’s economy are difficult to predict at this stage.\n\n**B.** **Sectoral and Institutional Context**\n\n13. **Chad’s performance in terms of human capital outcomes is closely linked to the structural weaknesses of**\n**its health sector** . Mortality rates are among the highest in the world with a maternal mortality ratio (MMR)\nof 860 deaths per 100,000 live births and under-five child mortality rate of 131 per 1,000 live births in 2015.\nInfant mortality is estimated at between 87 to 90 per 1,000 live births. Several factors help explain the performance of Chad’s health sector, including: (i) limited financial resources; (ii) salient shortages of health workers and inadequate infrastructure; and (iii) significant geographic barriers to the delivery of health services. In addition, Chad’s Joint External Evaluation (JEE) conducted in 2017 revealed important capacity constraints in all 19 technical areas. This shows the country’s vulnerability to health security threats.\n\n14. **Health financing in Chad is insufficient and highly inequitable** . In 2017, Chad spent 4.5 percent of its GDP on health. In per capita terms, the country only spent US$32. This is less than countries of similar levels of income and less than other countries in the region. [6] Furthermore, health has not been sufficiently prioritized in Chad’s public budget and the share of health to general government spending has declined over the last decade from over 12 percent in 2009 to less than 4 percent in 2018, well below the Abuja target of 15 percent. In fact, the largest share (61.2 percent) of Chad’s total health spending is financed by households 5 United Nations High Commissioner for Refugees (UNHCR) Refugee Protection Assessment for Chad, January 2020.\n6 LICs countries spent on average US$35, while Sub-Saharan African countries spent US$82 (World Bank 2020) Page 9", "output": {"entities": {"named_data": ["Country Policy and Institutional Assessment", "DHS 2014/15", "Joint External Evaluation"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) through out-of-pocket payments. This poses major challenges in terms of the equity, the efficiency and the sustainability of the country’s health financing architecture.\n\n15. **Health facilities have low readiness levels to deliver quality health services** . The number of health facilities in Chad is low and more than 3,000 facilities are needed to reach WHO target of two facilities per 10,000 inhabitants. Further, according to the most recent SARA survey, one in three health facilities had access to electricity and two in three had access to improved water sources. The availability of essential medical equipment (scales, thermometers, stethoscopes, etc.) and laboratory capacity were also substandard (WHO, 2019). In terms of health professionals, in 2017 there were less than 10,000 professionals in all Chad.\nShortages are particularly acute for doctors and specialized health professionals (0.38 per 10,000 population), and there are important disparities in the distribution of health professionals between provinces.\n\n16. **The coverage of essential health services is low** . The above-mentioned constraints, together with the significant geographic barriers enhanced by the poor transport infrastructure, lead to the low coverage of essential health services such as reproductive, maternal, neonatal and child health services and nutrition.\nIn 2017, one in four children under five received all required vaccines. According to the DHS 2014/2015, only 25 percent of women attended at least four antenatal care visits and less than 30 percent delivered at a health facility. These coverage rates reflect a low demand for health services and great difficulties delivering health services through outreach.\n\n17. **Weakness in core capacities to enforce International Health Regulation (IHR) can increase the risk of**\n**emergencies** . Chad signed on to the International Health Regulations (RSI, 2005) in 2012. Five years later\n(August 2017), there was a JEE. [7] This evaluation highlighted the capacities and skills of Chad for the 19 technical fields. It was found that out of the 19 technical areas assessed on a scale of 1 (no capacity) to 5 (sustainable capacity), there was no area with a favorable rating of 4 or 5. Most of the technical areas were rated as 1 (no capacity), [8] particularly, Coordination, communication and promotion of IHR; anti-microbial resistance (AMR); emergency response operations; preparedness; biosafety and biosecurity; medical countermeasures; and Point of entry. Only one technical area was rated as 3 (capacity) for all their indicators: vaccination. Based on the recommendations of the Joint External Assessment, a National Plan for Health security has been developed and validated whose implementation is slow to be effective for lack of availability of resources.\n\n18. **The first COVID-19 case in the country was diagnosed on March 19, 2020** . Since then, thirty-two more cases have been confirmed, eight recovered cases and no deaths caused by COVID-19 have been reported as of April 20, 2020. In response to the COVID-19 outbreak, the Government initially introduced surveillance measures at the airport. The international airport was subsequently closed for all passenger flights, all educational facilities have been closed and leisure venues such as restaurants, bars and casinos have been closed as well.\n\n7 Joint External Evaluation of IHR Core Capacities of the Republic of Togo. Geneva: World Health Organization; 2018. License: CC BY-NCSA 3.0 IGO.\n8 The following technical areas were rated as 1 (no capacity) for all of their indicators: IHR coordination, communication and advocacy; AMR; biosafety and biosecurity; linking public health and security authorities; medical countermeasures and personnel deployment; and point of entry. Some technical areas were rated as 2 (limited capacity) or below for all their indicators: national laboratory system, zoonotic diseases, reporting, preparedness, and emergency response operations, among others.\n\nPage 10", "output": {"entities": {"named_data": ["SARA survey", "DHS 2014/2015"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) 19. **Generally, hospitals in Chad lack critical equipment to effectively respond to complications from COVID-**\n**19.** A mobile laboratory based in Ndjamena is available for testing, and an isolation center has been\nidentified at the airport. All suspected cases identified in Ndjamena will be kept at the Farcha Provincial hospital and others identified in provinces will be kept in provincial/district hospitals. However, isolation capacity at provincial and district hospitals will need to be developed. The country does not have enough quantities of basic personal protective equipment (PPE) in case of an increase in the number of cases. In addition, the country is currently experiencing a polio outbreak putting additional pressures on the PPE stocks.\n\n20. **The proposed project will support activities identified by the Government in their response plan** . The Ministry of Public Health (MOPH), in close collaboration with the WHO, has prepared a COVID-19 National Action Plan (“ _Plan National de Contingence pour la Preparation et la Riposte a l’Épidémie de la Maladie a_ _Coronavirus Covid-19_ ”), which takes into consideration a scenario of significant local transmission. The Plan consists of the following six strategies: (1) Strengthening coordination of preparedness and management of epidemics; (2) strengthening health surveillance; (3) strengthening laboratories; (4) strengthening of case management; (5) strengthening of communication and community engagement; and (6) infection prevention and control (hygiene and sanitation).\n\n21. **The Government of Chad (GoC) has already started the implementation of activities from the National**\n**Action Plan which include the following** :\n\n- **Coordination:** Government put in place a Health Security Committee and a Technical Committee to\ncoordinate the response. The first one is chaired by the State Minister, Secretary General at the Presidency and has an executive function. The second one is chaired by the Director General of the MOPH and it is responsible for day to day follow-up of the evolution of the epidemic.\n\n- **Surveillance:** Surveillance was reinforced at the points of entry, particularly at the international Hassan\nDjamouss airport where a isolation and treatment centers were identified and equipped. The airport has been closed since March 20, 2020 for all passenger flights.\n\n- **Laboratory:** A mobile laboratory from the MOPH and WHO is available as a site for diagnosis of COVID19.\n\n- **Communication:** Information and sensitization messages are made via the media, flyers and posters.\n\n- **Rapid Intervention Team:** Rapid intervention teams have been put in place to investigate, follow up\npassengers from endemic countries and their contacts.\n\n- **Preparation of quarantine and isolation centers** : Government has proposed to use Ibis Hotel and Hotel\nChari for the 14-day quarantine of suspected cases. As for isolation, the Farcha Provincial hospital is being used as an isolation center for cases identified. Suspected cases identified in the regions will be isolated in District/Provincial hospitals. These sites will need to be renovated and equipped.\n\n- **Financing:** a Special Fund was created to pool resources for the COVID-19 response.\n\n22. **The Regional Disease Surveillance Systems Enhancement Project – Phase IV (REDISSE IV; P167817) will**\n**contribute to further strengthen national and regional cross-sectoral capacity for collaborative disease**\n**surveillance and epidemic preparedness.** The selection of activities to be financed under this operation\nconsiders the support available under REDISSE IV and the support planned by other development partners, including the Agence Française de Développement (French Agency for Development, AFD) and Global Alliance for Gavi and Immunization (GAVI). A more detailed description of the complementarities between Page 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) this project and REDISSE IV is provided in Section D, paragraphs 43-47.\n\n23. **In Chad, two major challenges were identified to be addressed to build the capacity** required to prevent, detect and respond to public health emergencies. Among these are:\n\n- Insufficient and inadequate equipment to prevent and control infections, as well as to manage COVID19 complications.\n\n- Misinformation and limited social and behavior change (SBC) campaigns to promote the hygiene\nmeasures required to prevent the spread of COVID-19.\n\n**C.** **Relevance to Higher Level Objectives**\n\n24. The project is aligned with World Bank Group (WBG) strategic priorities, particularly the WBG’s mission to end extreme poverty and boost shared prosperity. The project’s focus on preparedness is also critical to achieving Universal Health Coverage. It is also aligned with the World Bank’s support for national plans and global commitments to strengthen pandemic preparedness through three key actions under Preparedness: (i) improving national preparedness plans including organizational structure of the government; (ii) promoting adherence to the IHR; and (iii) utilizing international framework for monitoring and evaluation (M&E) of IHR. The economic rationale for investing in the MPA interventions is strong, given that success can reduce the economic burden suffered both by individuals and countries. The project complements both WBG and development partner’s investments in health systems strengthening, disease control and surveillance, attention to changing individual and institutional behavior, and citizen engagement. Further, as part of the proposed IDA19 commitments, the World Bank is committed to “support at least 25 IDA countries to implement pandemic preparedness plans through interventions (including strengthening institutional capacity, technical assistance (TA), lending and investment).” The project contributes to the implementation of IHR (2005), Integrated Disease Surveillance and Response (IDSR), and the World Organization for Animal Health (OIE) international standards, the Global Health Security Agenda (GHSA), the Paris Climate Agreement, the attainment of Universal Health Coverage and of the Sustainable Development Goals (SDG), and the promotion of a One Health approach.\n\n25. The WBG remains committed to providing a fast and flexible response to the COVID-19 epidemic, utilizing all WBG operational and policy instruments and working in close partnership with government and other agencies. Grounded in OneHealth, which provides for an integrated approach across sectors and disciplines, the proposed WBG response to COVID-19 will include emergency financing, policy advice, and TA, building on existing instruments to support IDA/IBRD-eligible countries in addressing the health sector and broader development impacts of COVID-19. The WBG COVID-19 response will be anchored in the WHO’s COVID-19 global Strategic Preparedness and Response Plan (SPRP) outlining the public health measures for all countries to prepare for and respond to COVID-19 and sustain their efforts to prevent future outbreaks of emerging infectious diseases.\n\n**III.** **PROJECT DESCRIPTION**\n\n**A.** **Development Objectives**\n\n26. The project objectives are aligned to the results chain of the COVID-19 Strategic Preparedness and Response Program (SPRP).\n\nPage 12", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) 27. **Project DO statement** : To prevent, detect and respond to the threat posed by COVID-19 and strengthen national systems for public health preparedness in Chad.\n\n28. While the project is structured around the government’s response to COVID-19, activities implemented through the project will have a broader, long-lasting impact that will contribute to overcome structural challenges in Chad’s health system. For example, the project will address critical needs in terms of equipment needed to respond to the COVID-19 outbreak. Most of this equipment is also utilized to treat other diseases. The project will also increase knowledge on infection prevention and control therefore promoting better quality of health services. Finally, better emergency preparedness is critical to ensure that service delivery is not disrupted by emergency response efforts.\n\n29. **PDO level indicators** : The PDO will be monitored through the following PDO level outcome indicators:\n\n- Number of designated laboratories with COVID-19 functioning diagnostic equipment, test kits, and\nreagents per MOPH guidelines;\n\n- Percentage of targeted acute healthcare facilities with isolation capacity;\n\n- Number of suspected cases of COVID-19 cases reported and investigated based on national guidelines;\n\n- Number of laboratory-confirmed cases of COVID-19 treated per approved protocol.\n\n**B.** **Project Components**\n\n30. This operation addresses critical country-level needs for preparedness and response for COVID-19, and other diseases with epidemic potential. This project has been designed to support the implementation of Chad’s National Action Plan against COVID-19, benefiting from the agile procedures established under the FTCF. This project will therefore prioritize those activities that will benefit from the added agility in fiduciary procedures, including the procurement of essential equipment and the engagement of United Nations (UN) agencies to support the immediate response.\n\n31. While the project will help strengthen the national capacity to respond to the COVID-19 emergency, the distribution of equipment and supplies and the training of staff will prioritize the seven provinces identified by the WHO as high-risk provinces. These provinces are those where there are points of entry to the country and include: N’Djamena, Lake Chad, Mayo Kebbi East, Mayo Kebbi West, Logone Oriental, Moyen-Chari and Ouaddai. In addition, the project will support activities in selected refugee camps. The camps will be selected based on their proximity to points of entry and the frequency and size of inflows of new refugees. Activities targeted to refugee camps include the provision of prevention supplies (e.g., hand-washing stations and PPE for health staff) and the design of targeted communication campaigns.\n\n32. **The project has three components** : (1) Emergency COVID-19 Preparedness and Response; (2) Community Engagement and Social and Behavior Change Communication; and (3) Implementation Management, Monitoring and Evaluation, and Coordination.\n\n33. **Component 1. Emergency COVID-19 Preparedness and Response (US$13.45 million equivalent)** : This component will support the country’s ability to promote an integrated preparedness and response to COVID-19 through improved prevention measures, laboratory capacity and surveillance, case detection and contact tracing, case management and treatment. Furthermore, this component will support coordinated Page 13", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) efforts that will enable the country to mobilize surge response capacity through trained, motivated and well-equipped frontline health workers. The component will also finance provisions for emergency response activities targeted at migrant and displaced populations in fragile, conflict or humanitarian emergency settings compounded by COVID-19. This component has four subcomponents: 34. _Subcomponent 1.1. COVID-19 Prevention and Preparedness Planning (US$4.24 million)_ : Given the limited number of COVID-19 cases confirmed in Chad, it is of utter importance to strengthen prevention measures that can help control the spread of the disease. Further, it will be important to develop robust plans to ensure that the country is prepared to manage the response. For that purpose, this subcomponent will finance: (i) TA to build the government’s capacity on preparedness planning, including TA to strengthen fiduciary mechanisms under the COVID-19 Special Fund; (ii) the purchase of all infection prevention and control commodities, consumables and equipment including masks, gloves, gowns, cleaning supplies, autoclaves, etc., as well as strengthening medical waste management and disposal systems; and (iii) the provision of prevention supplies for refugees and displaced populations. Given the potential exacerbation of Chad’s social fragility through the implementation of certain prevention measures (e.g. the use of force to ensure compliance with restrictions to movement), the TA delivered under this subcomponent will integrate an analysis of the social implications of such policies and it will include mitigating measures to such risks.\n\n35. _Subcomponent 1.2. Improving Case Detection, Confirmation, Contact Tracing, Recording and Reporting_ _(US$2.86 million):_ This sub-component will finance the following activities: (i) disease surveillance activities including early detection, investigation, active contact tracing, risk assessment, on-time data and information collection and utilization; (ii) the reinforcement of human resources through mobilization of additional health personnel; (iii) the purchase of ambulances for the rapid intervention team; (iv) the establishment and/or upgrade of laboratory capacity including purchase of equipment, specimen collection and transport, as well as training of personnel; (v) procurement of laboratory tests and related consumables for the national laboratory; (vi) organization of screening at all points of entry into the country; (vii) support to strengthen health management information systems to facilitate recording and real-time sharing of information; (viii) hardware and software needs such as internet connection and telephone communication of health facilities at operational, regional and central levels.\n\n36. _Subcomponent 1.3. Improving Case Management of COVID-19 Patients (US$5.80 million)_ : This subcomponent will build Chad’s capacity to provide quality supportive treatment for COVID-19 patients. It will finance the establishment of specialized and intensive care units (ICU) and beds in selected primary care facilities and hospitals. This includes the purchase and installation of medicalized tents and rehabilitation of existing infrastructure, the provision of medical equipment and supplies to comply with WHO standards for COVID-19 supportive treatment, drugs, and other operational expenses. In addition, this subcomponent will finance the development and validation of treatment guidelines and the clinical training of health personnel.\n\n37. _Subcomponent 1.4. Food and basic supplies to households and patients (US$0.55 million_ ): This component aims to address the significant negative economic impact on COVID-19-affected households. Using the services of civil society and NGOs, it will provide emergency support that include basic hygiene supplies/toiletries such as soap and a towel, to quarantined households and those of people in isolation (i.e.\nsuspected cases being monitored/isolated at health facilities), and treatment centers, including food and basic supplies. This project did not offer cash transfers for affected households because there is a Safety Net Page 14", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) Project (P156479/168685) in Chad, and the Refugees and Host Communities Project (P164748/172255) also has cash transfers for refugees.\n\n38. **Component 2. Community Engagement and Social and Behavior Change Communication (US$2.5 million**\n**equivalent):** This component will support the development and testing of SBC messages and materials to\nraise awareness, knowledge and understanding among the general population about the risk and potential impact of the pandemic and to promote prevention measures, including hand-washing, hygiene and social distancing. Targeted messages will be developed for vulnerable groups, including refugees living in refugee camps and internally displaced persons, as social distancing and other prevention measures will need to be adapted to the different realities of refugees living in refugee camps and people on the move. Partnerships with organizations with experience addressing such vulnerabilities in Chad will be explored.\n\n39. Communication activities will cover the entire country using cost effective channels of communications such as radio, television and social media as appropriate, as well as SBC campaigns in schools, workplaces, and through ongoing outreach activities of various ministries and sectors, especially ministries of health, education, agriculture, and transport. This will be done after a rapid community behavior assessment to gather information about the knowledge, attitudes, beliefs and challenged related COVID-19 response. This component will primarily finance the production of SBC and mass media products as well as buying the airtime of mass media. These materials will be translated to French, Arabic and local languages. Advocacy communication and community mobilization activities through civil society organizations including religious and tribal leaders, community health worker and community organization will also be supported, especially in rural areas.\n\n40. **Component 3. Implementation Management, Monitoring and Evaluation and Coordination (US$1 million**\n**equivalent):** This component will finance operational costs of the Project Coordination Unit (PCU). These\ninclude equipment, additional staff and other operational expenses needed to implement the project. This component will also finance coordination activities. These include meetings of Technical Coordination committees, Coordination’s meetings at different level of the health system and operation costs of Emergency Operation Center.\n\n**C.** **Project Beneficiaries**\n\n41. The expected project beneficiaries will be the population at large given the nature of the disease, infected people, at-risk populations, particularly the elderly, refugees and people with chronic conditions, medical and emergency personnel, medical and testing facilities, and public health agencies engaged in the response in participating countries.\n\n**D.** **Results Chain**\n\n42. Swift detection of an outbreak, assessment of its epidemic potential and rapid emergency response can reduce avoidable mortality and morbidity and reduce the economic, social, and security impacts. Failure in the rapid mobilization of financing and coordination of response results in unnecessary casualties and significant socioeconomic consequences. By focusing on the containment, diagnosis and treatment of patients, the proposed project seeks to control the disease outbreak and limit socioeconomic losses.\n\nPage 15", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["rapid community behavior assessment"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) 43. Critical interventions are needed to reduce morbidity and mortality rates from existing and emerging infectious diseases, curtail the spread of COVID-19 and mitigate the social impacts of the outbreak. The development of national COVID-19 preparedness and response plan which includes strengthening of the MOPH capacity for efficient emergency response for multiple hazards, strengthening of surveillance and information systems, increased laboratory capacity, improved infection and prevention control and case management will improve disease surveillance and emergency response in the country. Health care workers trained in critical skills involved in disease detection and response will increase the health system’s effectiveness whereas risk communication and behavior change interventions including social distancing measures will contribute to slowing the spread of COVID-19 and other disease outbreaks.\n\n**E.** **Complementarities with REDISSE IV and the support from other Development Partners**\n\n44. Chad’s fiscal situation significantly limits its capacity to quickly mobilize resources to respond to COVID-19.\nCommitments made by the GoC are targeted to several activities in the plan, but they are insufficient to fully fund any of the activities targeted. In this context, there is a great need for external financing to cover the funding gap.\n\n45. The GoC has requested the World Bank and other development partners to assist in the emergency response to COVID-19. WHO is providing technical support to the development and update of preparedness plans. Intentions to support the National Action Plan have been expressed by other technical and financial partners that include the AFD, GAVI, the Global Fund and the European Civil Protection and Humanitarian Aid Operations (ECHO). Budget support to respond to the economic slowdown caused by COVID-19 is being discussed with the African Development Bank and with the IMF. In addition, some development partners with ongoing projects (e.g. the Swiss Development Cooperation and the AFD) are reallocating unprogrammed funds to respond to the emergency.\n\nPage 16", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) 46. The COVID-19 Strategic Preparedness and Response Project will coordinate with and leverage the new REDISSE IV project. Since March 2020, Chad is part of the REDISSE IV project. REDISSE IV focuses on strengthening the country’s capacity for pandemic preparedness and response. It aims to strengthen national and regional cross-sectoral capacity for collaborative disease surveillance and epidemic preparedness in Central Africa. REDISSE IV uses the One Health approach to recognize the connectedness of human, animal and environmental health and the need to address challenges in a collaborative, multisectoral and trans-disciplinary approach. REDISSE IV strengthens not only the human health Joint External Evaluation, but also the evaluation of the Performance of Veterinary Services (PVS) and epidemiological surveillance network for animal health, to improve analytical capacity and exchange of information. In Chad, REDISSE IV will finance surveillance and laboratory capacity development, emergency planning and management, workforce development, and institutional capacity building. An important contribution from REDISSE IV will be the support to the roll-out of District Health Information System (DHIS2) and the overall strengthening of the country’s health management information system.\n\n47. To avoid overlaps with REDISSE IV, the COVID-19 SPRP project will tackle activities that support the immediate needs identified by the National Action Plan, i.e. activities prioritized in the 3-month Action Plan and urgent needs in the National Action Plan. Given additional agility in fiduciary procedures, the Fast Track Facility is more suitable to ensure that the activities prioritized are implemented and completed within a short period of time. REDISSE IV will carry on and enhance the support provided by this project and build Chad’s capacity to effectively address similar pandemics in the future. Furthermore, early lessons from the global COVID-19 response highlight the utter importance of social and behavior change communication campaigns and the need to strengthen surveillance and case management functions. Based on this, the COVID-19 SPRP project includes specific support to strengthen these functions. Table 2 shows the complementarity between COVID-19 SPRP and REDISSE IV for the core functions of REDISSE IV.\n\n**Table 2. Complementarities between COVID-19 SPRP and REDISSE IV by REDISSE IV’s core functions**\n\n**Function** **COVID-19 SPRP** **REDISSE IV**\nSurveillance and laboratory capacity Focus on human health. One-Health approach.\ndevelopment.\n\nOne-Health approach.\n\nCapacity gaps will be assessed with regards to Chad’s overall burden of disease.\n\nNational level support.\n\nEmergency planning and management.\n\nEmergency planning and TA will be delivered to regularly All relevant departments of the management. update COVID-19 response plans MOPH will participate in capacity and to evaluate gaps in the building activities.\ncountry’s response.\n\nThe OneHealth approach is used to Directors from units with leading address human and animal health roles in the COVID-19 National needs. This means that other Action Plan will be supported with relevant ministries will also benefit TA. from this support.\n\nWorkforce development. Clinical training will focus Capacity building covers a wide exclusively on infection prevention range of themes but focuses Focus on human health.\n\nSupport will be limited to the equipment and commodities necessary to diagnose COVID-19.\n\nOnly laboratories in the seven atrisk provinces will be supported.\n\nTA will be delivered to regularly update COVID-19 response plans and to evaluate gaps in the country’s response.\n\nDirectors from units with leading roles in the COVID-19 National Action Plan will be supported with TA.\n\nCapacity building covers a wide range of themes but focuses Page 17", "output": {"entities": {"named_data": ["District Health Information System"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**Function** **COVID-19 SPRP** **REDISSE IV**\nand control and case management of COVID-19.\n\nAdditional staff will be hired to support rapid response teams.\n\nprimarily on disease surveillance.\n\nInstitutional capacity building. Support to the coordination of the Resources available to conduct COVID-19 response. external evaluations to identify core capacity gaps.\n\n48. It should be noted that both projects will be implemented by the same PCU. This approach will promote the alignment between them, facilitate the exchange of information and reduce the potential duplication of activities.\n\nInstitutional capacity building. Support to the coordination of the COVID-19 response.\n\n**IV.** **IMPLEMENTATION ARRANGEMENTS**\n\n**A.** **Institutional and Implementation Arrangements**\n\n49. **The GoC has established several working groups to coordinate the response to COVID-19.** The Health Security Committee is a high-level committee formed by ministers and chaired by the State Minister, Secretary General at the Presidency. The Technical Committee is formed by Directors from the MOPH and key stakeholders, including development partners, and it is chaired by the Director General of the MOPH.\nTechnical units have been organized to cover critical areas of the response plan, including laboratory, rapid interventions teams, supportive treatment, and M&E. While the Health Security Committee has an executive function, the Technical Committee provides regular advice and coordinates the implementation of the National Action Plan.\n\n50. **The MOPH plays a key role in the implementation of the COVID-19 response** . In coordination with other ministries (civil aviation, education, agriculture), the MOPH leads many activities, particularly those related to surveillance, prevention and management of COVID-19 cases. The Technical Directorate for Disease Control and Health Promotion is the unit within the MOPH leading most of these efforts.\n\n51. **The project will be implemented by the REDISSE IV PCU** . The PCU has experience working on projects financed by the World Bank. Prior to the REDISSE IV project, which became effective on March 13, 2020, the PCU managed the implementation of the Mother and Child Health Service Strengthening Project (P148052). That project included the engagement of UN agencies, which means that the PCU has experience managing this kind of contracts. Furthermore, the Project Coordinator is a member of the above-mentioned Technical Committee and participates in their meetings. The PCU is already staffed with a procurement and a financial management (FM) specialist, a M&E officer and a communications officer. The PCU is in the process of recruiting an environmental specialist and a social specialist. An emergency liaison officer (fulltime) will be hired to coordinate all emergency response efforts and a part-time public health officer will be recruited to support the implementation of Component 2. The emergency liaison officer will also be responsible for the oversight of the contract with UN agencies and for ensuring that information related to the implementation of activities carried out by UN agencies is shared with the government’s COVID-19 governance structures in a timely manner. The public health officer will support activities under Component 2 and contribute to capacity building activities on advocacy and communication.\n\nPage 18", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) 52. **The PCU will be responsible for the daily management, implementation, administration, project**\n**coordination, and M&E of the project** . The PCU is responsible for: (i) procurement and project FM; (ii)\nimplementing of a communication program to inform the public of project activities and obtain feedback; (iii) preparing annual work plans, quarterly and annual implementation and results reports; (iv) monitoring overall project implementation and ensuring compliance with safeguard policies; and (v) oversee service contracts with UN agencies and NGOs.\n\n53. **Figure 2 summarizes the linkages between the government’s COVID-19 governance structures, the MOPH**\n**and the project’s PCU** . The Project Coordinator – supported by the emergency liaison officer – will play a\nkey role in the coordination with the Technical Committee and the MOPH. He will continue to participate in the Technical Committee’s meetings to update its members on progress made and to coordinate with other stakeholders involved in the response. The Project Coordinator will also report directly to the Director General at MOPH and update him on issues that require MOPH’s attention. Relevant members of the PCU will also advise their respective department at MOPH and they will ensure that the project is implemented in line with MOPH’s technical specifications. Particularly important will be the coordination between the technical team at the PCU and the Technical Directorate for Disease Control and Health Promotion, as well as the respective Technical Unit.\n\n**Figure 2.– Institutional Set-up**\n\n54. **The project will explore innovative approaches to overcome the logistical challenges posed by the COVID-**\n**19 pandemic and the difficulties of operating in a fragile environment** . The immediate response will be\nmostly executed through relevant UN agencies and NGOs (roughly 80 percent of activities, which represent 80 percent of the project’s financial envelop), and, where possible, other stakeholders like the private sector will be considered. These organizations will be selected based on their expertise and their ability to respond to this crisis in a timely manner. Organizations will also be selected based on their previous performance implementing similar projects in Chad. The project will engage UN agencies to (i) provide TA/support to prepare and respond to the pandemic; (ii) implement effective Information Education Communication/Behavior Change Communication (IEC/BCC) strategies; (iii) strengthen case management functions, and (iv) procure and deliver essential commodities and equipment. These activities will be funded out of the Financing Agreement (FA) with Chad.\n\nPage 19", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**B.** **Results Monitoring and Evaluation Arrangements**\n\n55. **The M&E activities will be the responsibility of REDISSE IV’s PCU at the MOPH** . This unit will be responsible for collecting and compiling all the data related to the PDO and intermediary indicators in the results framework. It will evaluate the results and report them to the World Bank before each of the two annual support missions. Given that REDISSE IV results framework focuses on the JEEs, reporting under this project will be complemented, when possible, by reports on REDISSE IV’s indicators.\n\n56. **The M&E activities will support M&E efforts from the MOPH** . The project’s results framework has been designed to provide useful information on the implementation of the National Action Plan. As a result, M&E activities under this project will strengthen MOPH’s M&E efforts to track and manage information. The project will closely collaborate with REDISSE IV which will support the roll-out of DHIS2 and the overall strengthening of the country’s health management information system, as well as to facilitate recording and real-time sharing of information. Lastly, the project will use Geo-enabled Monitoring Systems (GEMS) to supervise project implementation despite the travel restrictions.\n\n57. **It should be noted that given that standard reporting standards from UN agencies are not adequate** for the context of an emergency response, agencies engaged under this project will be required to produce weekly reports and to share these with all relevant stakeholders involved in the COVID-19 response in Chad.\n\n58. **Large volumes of personal data, personally identifiable information and sensitive data are likely to be**\n**collected and used in connection with the management of the COVID-19 outbreak under circumstances**\n**where measures to ensure the legitimate, appropriate and proportionate use and processing of that data**\n**may not feature in national law or data governance regulations, or be routinely collected and managed**\n**in health information systems.** In order to guard against abuse of that data, the Project will incorporate\nbest international practices for dealing with such data in such circumstances. Such measures may include, by way of example, data minimization (collecting only data that is necessary for the purpose); data accuracy (correct or erase data that are not necessary or are inaccurate), use limitations (data are only used for legitimate and related purposes), data retention (retain data only for as long as they are necessary), informing data subjects of use and processing of data, and allowing data subjects the opportunity to correct information about them, etc. In practical terms, operations will ensure that these principles apply through assessments of existing or development of new data governance mechanisms and data standards for emergency and routine healthcare, data sharing protocols, rules or regulations, revision of relevant regulations, training, sharing of global experience, unique identifiers for health system clients, strengthening of health information systems, etc.\n\n**C.** **Sustainability**\n\n59. **This project is an emergency operation to respond to a crisis due to the pandemic of Covid-19.** The sustainability is ensured through the strong alignment between this project, the core objectives of REDISSE IV and the National Action Plan. Further, all activities in this plan, regardless of their emergency nature, will contribute to strengthen the country’s pandemic preparedness and response.\n\nPage 20", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**V.** **PROJECT APPRAISAL SUMMARY**\n\n**A.** **Technical, Economic and Financial Analysis**\n\n60. **There are very significant gaps in knowledge of the scope and features of the COVID-19 pandemic.** It is apparent that one main set of economic effects will derive from increased sickness and death among humans and the impact this will have on the potential output of the global economy. In the Spanish Influenza pandemic (1918-19) 50 million people died -about 2.5 percent of the then global population of 1.8 billion.\nThe most direct impact would be through the impact of increased illness and mortality on the size and productivity of the world labor force. The loss of productivity as a result of illness which, even in normal influenza episodes is estimated to be ten times as large as all other costs combined will be quite significant.\n\n61. **Another significant set of economic impacts will result from the uncoordinated efforts of private**\n**individuals to avoid becoming infected or to survive the results of infection** . The SARS outbreak of 2003\nprovides a good example. The number of deaths due to SARS was estimated at “only” 800 deaths and it resulted in economic losses of about 0.5 percent of annual GDP for the entire East Asia region, concentrated in the second quarter. The measures that people took resulted in a severe demand shock for services sectors such as tourism, mass transportation, retail sales, and increased business costs due to workplace absenteeism, disruption of production processes and shifts to more costly procedures. Prompt and transparent public information policy can reduce economic losses.\n\n62. **A last set of economic impacts are those associated with governments’ policy efforts to prevent the**\n**epidemic, contain it, and mitigate its harmful effects on the population** . These policy actions can be\noriented to the short, medium or long-term or, in spatial terms to the national, regional or global levels.\n\n**B.** **Fiduciary**\n\n**Financial Management**\n\n63. **The proposed project is a US$16.95 million grant which will support the implementation of the national**\n**COVID-19 Plan endorsed by the Minister of MOPH.** The COVID-19 Preparedness and Response Project will\nbe implemented by the REDISSE IV PCU and selected UN Agencies. The REDISSE IV PCU will provide integrated and coordinated project management interventions in collaboration with the existing Technical Committee, which is responsible for overall coordination of the implementation and monitoring of the national COVID-19 National Action Plan. The Technical Committee will therefore provide strategic guidance for overall project implementation.\n\n64. **The FM arrangements currently in place at the REDISSE IV PCU meet the minimum fiduciary requirements**\n**under World Bank Policy and Directive for Investment Project Financing (IPF)** . However, implementing a\nproject in a situation of urgent need raises additional risks for which mitigating measures are described in the paragraphs that follow. The FM risk, after implementation of mitigating measures, remains Substantial.\n\n65. **FM arrangements.** Although the PCU has experience in coordinating World Bank-financed projects, shortcomings were identified in the past and implementing a project in a situation of urgent need poses additional risks. The Recipient will contract with UN Agencies (UNOPS, WHO and UNICEF) for the purchase Page 21", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) of equipment, TA and various non-consulting services. Activities that represent more than 75 percent of the project budget allocation will be implemented by or contracted through UN Agencies. The World Bank has obtained, through a fiduciary assessment, assurance over the capacity of UNOPS, WHO and UNICEF to properly implement World Bank-financed projects. Hence, UN FM procedures will apply. For the remaining activities that will be implemented by the REDISSE IV PCU, internal controls will be strengthened by effective internal audit activities.\n\n66. **Internal control and internal audit arrangements.** FM procedures as documented in the REDISSE IV FM manual will apply to the proposed project. Given the Recipient limited capacity in internal audit, the internal audit activities will be outsourced to an audit firm whose qualifications and experience will be deemed satisfactory by IDA. The internal auditor will propose and execute an audit plan based on a risk-based approach, and in accordance with the International Standards for the Professional Practice of Internal Auditing (IIA’s Standards). An internal audit report will be issued on a quarterly basis.\n\n67. **Banking arrangements** . As the coordinating agency of the project, the PCU will maintain the project Designated Account (DA). The Segregated DA denominated in Central African CFA Franc (CFAF) will be opened in a commercial bank on terms and conditions acceptable to IDA. The project’s DA will function under the co-signature of the project Coordinator and the FM Specialist of the PCU. UN Agencies will not be required to open segregated accounts. The allocated funds will be transferred to each UN Agency Bank Account as an Advance.\n\n68. **Disbursements arrangements** . For activities to be implemented by the PCU, the methods that will be used under this project will be based on the Disbursement Guidelines for IPF, dated February 2017. Precisely, the following four methods will be used: (a) an initial advance into the DA to finance eligible expenditures as they are incurred; (b) direct payments to a third party for works, goods and services upon the Recipient’s request; (c) special commitments; and (d) reimbursements for expenditures incurred under the project. For disbursements to UN Agencies, the full contract amount as indicated in the agreement between the Recipient and the UN Agency will be disbursed as an advance, through direct payments. Further details about disbursements to the Project will be included in the procedures described in the Disbursement and Financial Information Letter (DFIL).\n\n69. **Flow of funds arrangements** . Funds flow arrangements for the project are as follows: a. **PCU.** Upon receiving a withdrawal application from the Recipient, IDA will disburse into the DA an initial advance for activities being implemented by the PCU. The DA ceiling for advances will be fixed-.\nReplenishment of funds from IDA to the DA will be made upon evidence of satisfactory utilization of the advance, reflected in Statements of Expenses (SOE). Replenishment applications will be required to be submitted monthly. Payments for all contracts for works, goods, non-consulting services and consulting service procured or selected through international open or limited competition or direct selection, as set out in the procurement plan, will be made only through Mandatory Direct Payment (MDP) and/or Special Commitment disbursement methods.\n\nb. **UN Agencies.** Upon receiving the withdrawal application from the Recipient with the signed agreement between the Recipient and the UN Agency, IDA will disburse the full amount of the contract to the UN Agency bank account. The amount advanced will be documented through the quarterly unaudited interim Page 22", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) financial reports (IFRs) (Fund Utilization Reports) as actual expenditures are incurred by the UN Agency.\n\n70. **Financial Reporting Arrangements** . The PCU will prepare combined quarterly IFRs. The IFRs will be submitted to the World Bank within forty-five (45) days of the end of the quarter. The format and the content, consistent with the World Bank’s standards, will be agreed between the World Bank and the Recipient. At a minimum, the financial report will include: (a) a statement of sources and uses of funds and opening and closing balances for the semester and cumulative; (b) a statement of uses of fund that shows actual expenditures appropriately classified by main project activities (categories, sub-components) including comparison with budget for the quarter and cumulative; (c) a statement on movements (inflows and outflows) of the project DA including opening and closing balances; (d) a statement of expenditure forecast for the next quarter together with the cash requirement; (e) Fund Utilization Reports prepared quarterly by UN Agencies; (f) notes and explanations; and (g) other supporting schedules and documents.\n\n71. **For activities being implemented by UN Agencies**, each UN Agency will submit Fund Utilization Reports as part of their quarterly progress reports and submit them to the PCU within 30 days after the end of the reporting period. The formats and content of progress reports will be agreed between the World Bank, the Recipient and the UN Agencies. Fund Utilization Reports prepared quarterly by UN Agencies will serve to document the utilization of the advance and be used in the preparation of the combined IFR to be submitted by the PCU.\n\n72. **External audit arrangements** . The annual audited financial statements of the project and audit reports (including the Management Letter) will be submitted by the PCU to the World Bank no later than six (6) months from the end of the fiscal year. The scope of the audit will exclude funds allocated to and activities implemented by UN Agencies. The audit will be carried out in accordance with the International Standards on Auditing (ISA) issued by the International Federation of Accountants (IFA), based on terms of references (TORs) acceptable to IDA. The auditor will be an independent external auditor with qualification and experience satisfactory to IDA.\n\n73. **For the UN implemented activities,** the World Bank will rely on UNOPS, WHO and UNICEF external audit arrangements to fulfill the fiduciary requirement (waiver for audit requirement is outstanding). The UN Agencies will ensure that their audited accounts and the External Auditors’ Reports are posted on their websites within ten (10) days of their becoming public documents. However, the Recipient will retain the responsibility of ensuring that good and services acquired are delivered to the intended beneficiaries during implementation. Where and when deemed appropriate, —for example, if a UN Agency progress report show weaknesses or deficiencies— the Recipient may conduct physical inspections of goods and services delivered by the UN Agency.\n\n74. **Retroactive financing** . The retroactive financing may be applied to the contracts procured in advance for the purpose of this Project objective using procurement procedures consistent with Sections I, II and III of the World Bank’s Procurement Regulations and consistent with the FA of this Project. Withdrawals up to an aggregate amount not to exceed US$2,000,000 under the grant may be made for payments made prior to this date but on or after January 25, 2020, for eligible expenditures. The World Bank will provide procurement hands-on expanded implementation support to the Recipient to expedite procurement, if required.\n\nPage 23", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**Procurement**\n\n75. **Procurement for this project will be carried out in accordance with the World Bank’s Procurement**\n**Regulations for IPF Recipients for Goods, Works, Non-Consulting and Consulting Services, dated July 1,**\n**2016 (revised in November 2017 and August 2018).** The projects will be subject to the World Bank’s\nAnticorruption Guidelines, dated October 15, 2006, revised in January 2011, and as of July 1, 2016. Countries will use the Systematic tracking of Exchanges in Procurement (STEP) to plan, record and track procurement transactions.\n\n76. **The major planned procurement** includes: (i) medical/laboratory equipment and consumables; (ii) PPE in facilities and triage; (iii) refurbishment and equipment of medical facilities; (iv) TA for updating or reviewing national plans and costs; (vi) ambulances; (v) human resources for response; and (vi) expertise for development and training of front-line responders. Chad will prepare streamlined project procurement strategy for development (PPSD) during project implementation and Procurement plans will be agreed with the GoC.\n\n77. **The proposed procurement approaches in Chad will utilize the flexibility provided by the World Bank’s**\n**Procurement Framework for fast track emergency procurement.** Key measures to fast track procurement\nwill include: (i) procurement from UN Agencies enabled and expedited by World Bank procedures and templates; (ii) use of simple and fast procurement and selection methods fit for an emergency situation including direct contracting; (iii) streamlined competitive procedures with shorter bidding time; iv) force account, as needed; and (iv) increased thresholds for Requests For Quotations and national procurement.\n\n78. **Chad COVID-19 project may be significantly constrained in purchasing critically needed supplies and**\n**materials due to significant disruption in the supply chain, especially for PPE** . The supply problems that\nhave initially impacted PPE are emerging for other medical products (e.g., reagents and possibly oxygen) and more complex equipment (e.g. ventilators) where manufacturing capacity is being fully allocated by rapid orders from developed countries. Recognizing the significant disruptions in the usual supply chains for medical consumables and equipment for COVID-19 response **,** the Recipient (the GoC), has requested the World Bank to provide procurement hands-on expanded implementation support to help the Government expedite all stages of procurement – from help with supplier identification, to support for bidding/selection and/or negotiations to contract signing and monitoring of implementation. Once the suppliers are identified, the World Bank could proactively support the GoC with negotiating prices and other contract conditions. The GoC will remain fully responsible for signing and entering into contracts and implementation, including assuring relevant logistics with suppliers such as arranging the necessary freight/shipment of the goods to their destination, receiving and inspecting the goods and paying the suppliers, with the direct payment by the World Bank disbursement option available to them.\n\n79. **World Bank Facilitated Procurement (BFP) in accessing available supplies may include aggregating**\n**demand across participating countries, whenever possible, extensive market engagement to identify**\n**suppliers from the private sector and UN agencies** . The BFP would constitute additional support to GoC\nover and above usual Hands on Expanded Implementation Support which will remain available. The World Bank could also provide hands-on support to GoC in contracting to outsource logistics. The World Bank is coordinating closely with the WHO and other UN agencies (specifically UNOPS, WHO and UNICEF) that have Page 24", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) established systems for procuring medical supplies and charge a fee which varies across agencies and type of service and can be negotiated (around 5 percent on average.) 80. All the procurement approach options mentioned above remain available depending on Chad’s preference in order to provide the most efficient and effective support to project in the specific circumstances. For this project, there are discussions going on for procurement from UN Agencies, enabled and expedited by World Bank procedures and templates. The GoC has adopted tracking emergency COVID-19 procurement activities, and to support this, a new Decree No. 498/PR/2020 was signed on April 1, 2020.\n\n81. **All procurement under the project will be undertaken by the REDISSE IV PCU** under the MOPH. The REDISSE IV project became effective on March 13, 2020. Streamlined procedures for approval of emergency procurement to expedite decision making and approvals under country projects have been agreed for implementation.\n\n82. **Key procurement risks include** : (i) Failed procurement due to lack of sufficient global supply of essential medical consumables and equipment needed to address the health emergency as there is significant disruption in the supply chain, especially for PPE; (ii) lack of realistic planning and weak contract management capacity for emergency procurement and limited knowledge of the World Bank’s New Procurement Framework (NPF); (iii) Recipient import restrictions in place for goods/service providers/consultants/contractors from certain countries, which can result in fraud and corruption. The mitigation measures to the above risks are presented in Table 3.\n\n**Table 3. Major Risks to Procurement and Proposed Mitigation Measures**\n\n**Procurement Risks** **Mitigation Measures**\nCapacity of the market and supply chain to meet the demand. proactively assist them in accessing existing supply chains.\n\nCapacity of the market and supply chain to World Bank will provide, at Recipient’s request, support through BFP to meet the demand. proactively assist them in accessing existing supply chains.\n\nProposed mobilization of existing service providers consisting in the possibility to proceed with contracts extension for additional activities through contract amendment are expected to address emergency medical service requirements.\n\nMeasures for supplier preferences, such as direct payments by World Bank, advance payments, etc. will be applied on need basis.\nLimited capacity to conduct emergency REDISSE IV PCU will maintain and reinforce staff with the appropriate procurement. capacity dedicated to the COVID-19 response.\n\nREDISSE IV PCU will maintain and reinforce staff with the appropriate capacity dedicated to the COVID-19 response.\n\nREDISSE IV PCU will utilize rapid disbursement procedures and simplified procurement processes.\n\nWorld Bank will provide, at Recipient’s request, support through Hands-on Expanded Implementation Support (HEIS) to help expedite all stages of procurement – from help with supplier identification, to support for bidding/selection and/or negotiations to contract signing and monitoring of implementation.\n\nPage 25", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**.**\n\n**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**Procurement Risks** **Mitigation Measures**\nManaging fraud and corruption and _Ex ante_ due diligence of firms being selected will be attempted using noncompliance. databases available in country and externally.\n\nPost review of contracts will be scheduled immediately on award of contracts for all contracts that would have been usually prior reviewed.\n\n83. **The procurement risk is Substantial and will be mitigated by the following measures:** (i) the Recipient will utilize rapid disbursement procedures and simplified procurement processes in accordance with emergency operations norms; (ii) the World Bank will provide BFP leveraging its comparative advantage as convener with the objective of facilitating borrowers’ access to available supplies at competitive prices, as described in the procurement section of this document. BFP in identifying suppliers and facilitating contracting between them and borrowers may bring a perception that the World Bank is acting beyond its role as a financier with greater reputational and potentially litigation risks – these would relate to questions of transparency, equity in terms of which borrowers get access to what and when, issues with quality, timeliness of delivery, value for money, and any other issues of contractual non-performance by the suppliers identified by the World Bank. To partially mitigate these risks, the World Bank and the Recipient will clearly delineate the roles and responsibilities of the World Bank and the Recipients for whom the World Bank facilitates access to available supplies. Moreover, BFP is provided to mitigate the greater risk that the World Bank could be providing financing for medical supplies that may not be readily available to developing countries. This more proactive approach in assisting borrowers is justified as an effective way to complement other procurement options and help clients achieve COVID-19 projects’ development objectives on a fit-for-purpose basis.\n\n**C.** **Legal Operational Policies**\n\n84. The table below indicates whether legal operational policies will be triggered.\n\n**Table 4. Legal operational policies triggered**\n\n**Triggered?**\n\nProjects on International Waterways OP 7.50 No Projects in Disputed Areas OP 7.60 No .\n\n**D.** **Environmental and Social Standards**\n\n**Environmental aspects:**\n85. **The project’s environmental risk rating is Substantial** . The main environmental risks include: (i) environmental and community health related risks from inadequate storage, transportation and disposal of infected medical waste; (ii) occupational health and safety issues related to the availability and supply of PPE for healthcare workers and the logistical challenges in transporting PPE across the country in a timely manner; and (iii) community health and safety risks given close social contact and limited sanitary and hygiene services (clean Page 26", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) water, soap, disinfectants) and isolation capabilities at health facilities across the country. The project will finance the rehabilitation and equipping of selected primary health care facilities and hospitals to improve their ability to deliver critical medical services including testing, treatment and hospitalization. The Government has elaborated the Environmental and Social Commitment Plan (ESCP) which sets out material measures and actions as well as the timing for each of these. This document was disclosed in the World Bank on April 9, 2020 and in-country on April 17, 2020. While it is expected that the negative risks and adverse impacts related to the implementation of project activities are likely to be limited, the PCU will develop an Environmental and Social Management Framework (ESMF) to provide clear guidance regarding the treatment of medical waste and the preparation of subproject Environmental and Social Management Plans (ESMPs) when required by the ESMF screening approach. The ESMF will contain provisions for storing, transporting, and disposing of contaminated medical waste and outline guidance in line with good international industry practice and WHO standards on COVID-19 response on limiting viral contagion in healthcare facilities. The relevant parts of the WHO COVID-19 quarantine guidelines and COVID-19 biosafety guidelines will be reviewed so that all relevant occupational and community health and safety risks and mitigation measures will be covered. In addition, the ESMF will include measures to address gender-based violence and sexual exploitation and abuse (GBV/SEA) and outline the principles for the establishment of a functioning grievance redress mechanism (GRM). The ESMF will be finalized and publicly disclosed and consulted – taking into account the necessary precautions for physical distancing – no later than 30 days after Project effectiveness.\n\n86. **An additional risk that will also need to be mitigated is the fact that the existing PCU for the World Bank-**\n**funded Regional Disease Surveillance Systems Enhancement (REDISSE IV, P167817)** which is expected to\nsupport the implementation of this project, does not currently have a qualified environmental specialist and a social specialist that have experience working with the World Bank. The recruitment process is ongoing and needs to be terminated before the commencement of project activities with potential social or environmental impacts. As soon as qualified staff is on board, capacity building will be supported by the project including training on the Environmental and Social Framework (ESF), recruitment of specialized staff and support from third party entities to assist with M&E. In addition, full time specialists for the communication strategy, public health awareness and implementation of the Stakeholder Engagement Plan (SEP) will be necessary given the highly specific nature of this effort. These specialists will need to be contracted no later than one month after Project effectiveness.\n\n**Social aspects:**\n\n87. **The social risk rating of the project is Substantial.** One key social risk related to the COVID-19 operations in general is that vulnerable social groups (poor, disabled, refugees, elderly, isolated communities) may be unable to access facilities and services, which could increase their vulnerability and undermine the general objectives of the project. Other social risks include the rise of social tensions that could be exacerbated by the lack of awareness regarding the behavior change required to decrease transmission (social physical distancing, hand washing and hygiene), stigma associated with victims of COVID-19 and their families, perceived exclusion from key health facilities and services and misinformation regarding how COVID-19 is transmitted and prevented.\nSome of these risks will be addressed under Component 1 that will finance community engagement activities and information sharing and these activities are reflected in the draft SEP that has been prepared and disclosed in-country on April 17, 2020 and in the World Bank since April 9, 2020. After project approval, the SEP will be updated to include more information regarding the methodologies for information sharing in fragile and conflict settings, including in refugee camps, stakeholder mapping and identification of existing community Page 27", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) based platforms that can be used to facilitate effective community engagement and participation. The SEP will be consulted upon and disclosed per the requirements of the policy no later than one month after Project effectiveness.\n\n**VI.** **GRIEVANCE REDRESS SERVICES**\n\n88. Communities and individuals who believe that they are adversely affected by a World Bank supported project may submit complaints to existing project-level grievance redress mechanisms or the Bank’s Grievance Redress Service (GRS). The GRS ensures that complaints received are promptly reviewed in order to address projectrelated concerns. Project affected communities and individuals may submit their complaint to the Bank’s independent Inspection Panel which determines whether harm occurred, or could occur, as a result of Bank non-compliance with its policies and procedures. Complaints may be submitted at any time after concerns have been brought directly to the World Bank's attention, and Bank Management has been given an opportunity to respond. For information on how to submit complaints to the Bank’s corporate Grievance Redress Service\n[(GRS), please visit: http://www.worldbank.org/en/projects-operations/products-and-services/grievance-](http://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service)\n[redress-service. For information on how to submit complaints to the World Bank Inspection Panel, please visit](http://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service)\n[www.inspectionpanel.org.](http://www.inspectionpanel.org/)\n\n**VII. KEY RISKS**\n\n89. **The overall project risk rating is substantial** . The project is supporting the Government effort to prevent and control COVID-19. One of the World Bank’s principal concern is to ensure that project funds are used economically and efficiently for the intended purpose. Risks in three of the eight categories are High due to political and governance factors, macroeconomic challenges and weak institutional capacity for implementation and sustainability. Fiduciary and environmental and social risks are rated Substantial. Risks related to sector strategies and policies, technical design and stakeholders are all rated Moderate.\n\n90. **Political and governance risk is rated as high** . This is due to a lack of accountability measures to ensure that resources supporting COVID-19 activities reach intended health care facilities and beneficiaries. As mitigation measures, the project will ensure that there is internal and external auditing, as well as publication of audit reports and achievement.\n\n91. **Macroeconomic risk is rated as high.** The fiscal capacity of the Government is going to be weakened due to global economic disruption and slowdown, and potential unavailability of fiscal resources. This would negatively impact public health service delivery with respect to COVID-19 prevention, mitigation, and treatment, in addition to other essential health service delivery. To mitigate the impacts, the World Bank is supporting long-term the capacity of the Government to undertake disease surveillance by funding a REDISSE project. Furthermore, the World Bank is also financing another project to strengthen maternal and child health care services.\n\n92. **Institutional capacity risk is rated as high** . The human resource capacity in Chad is weak and there is inadequate institutional capacity to manage project and perform effectively to contain and mitigate the impact of COVID-19. As mitigation measures, the project will work with UN agencies and local organizations to provide TA to the project.\n\nPage 28", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) 93. **The residual Fiduciary risk is rated as substantial.** The PCU has experience in coordinating World Bank-financed projects, shortcomings were identified in the past and implementing a project in a situation of urgent need poses additional risks. Internal controls will be strengthened by effective internal audit activities. The Recipient will contract with UN Agencies (UNOPS, WHO and UNICEF) and the World Bank has undertaken a fiduciary assessment, hence UN FM procedures will apply. Section I above provides details on the main factors justifying the substantial risk rating and the mitigation measures.\n\n94. **The project’s environmental and social risks rating are substantial** . Section D on Environmental and Social above provides details on the main factors justifying the substantial risk rating and the mitigation measures.\n\n.\n\nPage 29", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) VIII. **RESULTS FRAMEWORK AND MONITORING**\n\n**Results Framework**\n\n**COUNTRY: Chad**\n**Chad COVID-19 Strategic Preparedness and Response Project**\n\n**Project Development Objective(s)**\n\nTo prevent, detect and respond to the threat posed by COVID-19 and strengthen national systems for public health preparedness in Chad.\n\n**Project Development Objective Indicators**\n\n**RESULT_FRAME_TBL_PDO**\n**Indicator Name** **DLI** **Baseline** **End Target**\n\n**Prevent, detect and respond to the threat posed by COVID-19 and strengthen national system**\n\nNumber of designated laboratories with COVID-19 functioning diagnostic equipment, test kits, and reagents per MOH guidelines (Number)\n\n1.00 5.00\n\nPercentage of targeted acute healthcare facilities with isolation 0.00 100.00 capacity (Percentage) Number of suspected cases of COVID-19 cases reported and 9.00 5,000.00 investigated based on national guidelines (Number) Number of laboratory confirmed cases of COVID-19 treated per 3.00 3,000.00 approved protocol (Number)\n\n**PDO Table SPACE**\n\nPage 30", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**Intermediate Results Indicators by Components**\n\n**RESULT_FRAME_TBL_IO**\n**Indicator Name** **DLI** **Baseline** **End Target**\n\n**Component 1: Emergency COVID-19 Preparedness and Response**\n\n1.1. Update of National COVID-19 Response Action Plan (Yes/No) No Yes\n\n1.2 Percentage of healthcare workers trained in surveillance and\n0.00 80.00 investigation (Percentage)\n\n1.2 Number of health workers trained on case definition,\nmanagement, infection prevention and control for COVID-19 (Number) 0.00 2,000.00\n\n1.3 Percentage of healthcare facilities with triage capacity\n0.00 80.00 (Percentage)\n\n1.3 Percentage of targeted health facilities with isolation capacity\n0.00 80.00 (Percentage)\n\n1.4 Number of eligible households provided with food and basic\n0.00 8,000.00 supplies within quarantined populations (Number)\n\n**Component 2. Community Engagement and Social and Behavior Change Communication**\n\nNumber of communication campaigns about COVID-19 broadcast 0.00 7.00 to communities (Number) National COVID-19 risk communication and community No Yes engagement strategy established (Yes/No)\n\n**Component 3: Implementation Management, Monitoring and Evaluation and coordination**\n\nNumber of technical crisis coordination meetings issuing an 0.00 52.00 official report on epidemic surveillance and response (Number) Number of treatment, isolation & quarantine centers preparing 0.00 10.00 daily report (Number) Number of centers assessed monthly (using check list) 0.00 10.00 treatment, isolation & quarantine (Number) Establishment of monitoring and evaluation system for COVID-19 No Yes (Yes/No) Page 31", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**IO Table SPACE**\n\n**UL Table SPACE**\n\n**Monitoring & Evaluation Plan: PDO Indicators**\n\n**Methodology for Data** **Responsibility for Data**\n**Indicator Name** **Definition/Description** **Frequency** **Datasource**\n**Collection** **Collection**\n\nNumber of designated laboratories with Number of existing COVID-19 functioning diagnostic laboratories with effective quarterly MOPH report routine data MOH equipment, test kits, and reagents per capacity for testing COVIDMOH guidelines 19\n\n**Methodology for Data**\n**Indicator Name** **Definition/Description** **Frequency** **Datasource**\n**Collection**\n\nNumber of existing laboratories with effective capacity for testing COVID19 quarterly MOPH report routine data MOH Percentage of targeted acute healthcare facilities with isolation capacity Number of suspected cases of COVID-19 cases reported and investigated based on national guidelines Number of laboratory confirmed cases of COVID-19 treated per approved protocol\n\n**ME PDO Table SPACE**\n\nNumber of available targeted acute healthcare facilities with isolation capacity for COVID-19 patients as a percentage of all the target acute healthcare facilities.\n\nNumber of suspected effectively cases tested Number of laboratory confirmed cases of COVID19 treated per approved protocol/ number of laboratory confirmed cases of COVID-19 weekly weekly weekly COVID-19 report COVID-19 report COVID-19 report routine data routine data routine data MOPH MOPH MOPH Page 32", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**Monitoring & Evaluation Plan: Intermediate Results Indicators**\n\n**Methodology for Data**\n**Indicator Name** **Definition/Description** **Frequency** **Datasource**\n**Collection**\n\n**Responsibility for Data**\n**Collection**\n\nMOPH MOPH MOPH MOPH MOPH MOPH Page 33 once weekly weekly weekly weekly weekly\n\n1.1. Update of National COVID-19\nResponse Action Plan\n\n1.2 Percentage of healthcare workers\ntrained in surveillance and investigation\n\n1.2 Number of health workers trained on\ncase definition, management, infection prevention and control for COVID-19\n\n1.3 Percentage of healthcare facilities\nwith triage capacity\n\n1.3 Percentage of targeted health\nfacilities with isolation capacity\n\n1.4 Number of eligible households\nprovided with food and basic supplies within quarantined populations Updating of national action plan to respond to COVID19 Number of trained healthcare workers as a percentage of all the health care workers that could be trained Number of healthcare facilities with capacity to conduct triage as a percentge of all the health facilities.\n\nNumber of health facilities with isolation capacity as a percentage of all the targeted health facilities Number of households provided with food and basic supply while their members are in quarantine, isolation at health facilities and treatment centers.\n\nCOVID-19 report COVID-19 report COVID-19 report COVID-19 report COVID-19 report COVID-19 report routine data routine data routine data routine data routine data routine data Number of trained healthcare workers", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894) Number of communication campaigns about COVID-19 broadcast to communities National COVID-19 risk communication and community engagement strategy established Number of technical crisis coordination meetings issuing an official report on epidemic surveillance and response Number of treatment, isolation & quarantine centers preparing daily report Number of centers assessed monthly (using check list) treatment, isolation & quarantine Establishment of monitoring and evaluation system for COVID-19\n\n**ME IO Table SPACE**\n\n~~.~~ Number of awareness communications campaigns conducted COVID-19 report COVID-19 report COVID-19 report COVID-19 report COVID-19 report COVID-19 report routine data routine data routine data routine data routine data routine data MOPH MOPH MOPH MOPH MOPH MOPH Establishment of a risk communication and engagement strategy in Chad Number of meetings conducted/official reports issued by the emergency crisis committee Number of centers preparing daily reports Number of centers assessed monthly Establishment of a COVID19 M&E system weekly once weekly weekly monthly once Page 34", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**ANNEX 1: Project Costs**\n\n**COUNTRY: Chad**\n**Chad COVID-19 Strategic Preparedness and Response Project**\n\n**COSTS AND FINANCING OF THE COUNTRY PROJECT**\n\n**Project**\n**Program Components**\n\n**Cost**\n\n**IDA**\n**Financing**\n\n**Trust**\n**Funds**\n\n**Counterpart**\n\n**Funding**\n\nEmergency COVID-19 preparedness and response 13.45 13.45 Increase access to health care services 2.50 2.50 Implementation management, monitoring and evaluation\n1.00 1.00                                     -                                     and coordination\n\n**Total Costs** 16.95 16.95 -\n\nTotal Costs 16.95 Front End Fees\n\n**Total Financing Required** **16.95**\n\nPage 35", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**ANNEX 2: Implementation Arrangements and Support Plan**\n\n**COUNTRY: Chad**\n**Chad COVID-19 Strategic Preparedness and Response Project**\n\n1. The project will require support throughout project implementation, but it is anticipated that more\nintense support will be needed at two points of project implementation; during the first 12 months (from approval to effectiveness and through early implementation) and at mid-term review (MTR). A broad range of skills is required for the World Bank to effectively support project implementation. The implementation support team will include specialists in administrative and FM, procurement; public health (including disease surveillance, and laboratory specialists); safeguards, management; social mobilization/advocacy, training, M&E, and operations.\nThe procurement specialist will carry out annual _ex-post_ review of procurement that falls below prior review thresholds and will have separate focused missions depending on the procurement needs that arise. There will also be training in the use of the STEP and the new World Bank Procurement Framework. The FM specialist will review all FM reports and audits and take necessary follow-up actions according to World Bank procedures. World Bank team members will also identify capacity building needs to ensure successful project implementation. With only a limited allocation of budget to the project expected in 2020, implementation support in the first year of the project will focus on creating project implementation momentum through institutional capacity strengthening and TA.\n\n2. The World Bank team will undertake implementation support missions (supervision missions) related to the timely coordination of the pandemic response. A MTR will be organized by the World Bank to take stock of project implementation and to identify corrective actions or adjustments as necessary. At project end, the World Bank team will prepare an Implementation Completion and Results Report (ICR) that summarizes achievements made under the project. Alternative and adaptive ways to supervise the project will be considered to take into account the potential travel restrictions occasioned by the ongoing COVID-19 pandemic. These include, among other, the use of GEMS.\n\n3. Development partners are expected to provide TA, and procurement operational support, to strengthen the implementation of select project activities, in line with their respective mandates. The WHO, with its incountry expertise across a wide range of relevant topics (including non-communicable diseases management, various communicable diseases, information systems, and health systems) will continue to be an important technical partner. UNICEF and UNOPS will have both a technical and an operational role with respect to the procurement. The World Bank team will coordinate its implementation support with these partners to get the most value-for-money, avoid duplication, and exploit synergies.\n\n4. Generally, progress monitoring will focus on: (i) key performance indicators, as identified in the Results Framework; (ii) progress of implementation of the project components; (iii) progress in relation to implementation and procurement plans; (iv) whether estimated project costs are sufficient to cover planned activities and whether reallocation of credit funds is required; (v) compliance with World Bank FM and disbursement provisions; and (vi) compliance with environmental and social safeguards.\n\nPage 36", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nChad COVID-19 Strategic Preparedness and Response Project (P173894)\n\n**Table 2.1. Skills Needed**\n\n**Timeline** **Focus** **Skills Needed** **Resource Estimate**\n0-24 months Implementation monitoring, Project management, Two formal implementation operational and TA to support operational, technical (incl. support missions; just-in-time implementation M&E), fiduciary, environment, TA Two formal implementation support missions; just-in-time TA 0–12 months Creating project implementation momentum through institutional capacity strengthening, preparation for first procurement packages and TA for implementation.\n\nProject management, operational, technical (incl.\nM&E), fiduciary, environment, social Project management, operational, technical (including M&E), fiduciary, environment, and social Project management, operational, technical (including M&E), fiduciary, environment, and social Project management, operational, technical (including M&E), fiduciary, environment, social 12–24 months Continued institutional capacity enhancement, implementation monitoring, operational and TA to support implementation At minimum, two formal implementation support missions. All relevant sights to be visited at least once. Just-intime TA.\n\nTwo formal implementation support missions; just-in-time TA Comprehensive MTR mission MTR MTR and identification of midcourse adjustments Completion phase ICR and final payments Project management, fiduciary ICR mission Page 37", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**FOR OFFICIAL USE ONLY**\n\nReport No: PCBASIC0178487 INTERNATIONAL BANK FOR RECONSTRUCTION AND DEVELOPMENT PROJECT APPRAISAL DOCUMENT", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "CURRENCY EQUIVALENTS (Exchange Rate Effective February 29, 2020) Currency Unit = Turkish Lira 5. 96 TL = US$1 US$0.17 = TL 1 6.62 TL = EURO 1 EURO 0.15 TL 1 Euro 1.0 US$0.9096 FISCAL YEAR January 1 - December 31 Regional Vice President: Cyril E. Muller Country Director: Auguste Tano Kouame Regional Director: Steven N. Schonberger Practice Manager: David Michaud Task Team Leader(s): Sanyu Lutalo, Canan Yildiz", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**ABBREVIATIONS AND ACRONYMS**\n\nAF Additional Financing AFAD Disaster and Emergency Management Authority (in Turkish) AFD French Development Agency (in French) ASA Advisory Services and Analytics ASKI Adana Su Ve Kanalizayson Idaresi (Adana General Directorate of Water and Wastewater Administration) BRSA Banking Regulation and Supervision Agency CAB Community Ablution Block CBA Cost-Benefit Analyses CPF Country Partnership Framework DGMM Directorate General of Migration Management DMA District Metering Area DSI Devlet Su Isleri EBRD European Bank for Reconstruction and Development EC European Commission ESF Environmental and Social Framework ESCP Environmental and Social Commitment Plan ESIA Environmental and Social Impact Assessment ESMP Environmental and Social Management Plan EU European Union EUD European Union Delegation FCV Fragility, Conflict and Violence FIRR Financial Internal Rate of Return FM Financial Management FRIT Facility for Refugees in Turkey FY Fiscal Year GHG Green House Gas GoT Government of Turkey GRM Grievance Redress Mechanism GRS Grievance Redress Service GWSP Global Water Sector Partnership ILBANK Iller Bankasi Anonim Sirketi IBRD International Bank for Reconstruction and Development IDA International Development Association IDP Internally Displaced Person IFI International Financial Institution IPF Investment Project Financing IRR Internal Rate of Return ISKUR Turkish Employment Agency (in Turkish)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "ISP Implementation Support Plan KASKI Kayiseri Su Ve Kanalizayson Idaresi (Kayiseri General Directorate of Water and Wastewater Administration) KFW Credit Institute for Reconstruction (in German) KOSGEB Small and Medium Industry Development Organization (in Turkish) M&E Monitoring and Evaluation MoFLSS Ministry of Family, Labour and Social Services (in Turkish) MoTF Ministry of Treasury and Finance MoNE Ministry of National Education (in Turkish) MSP Municipal Services Project MSP-AF Municipal Services Project – Additional Financing NEP New Economic Program NGO Non-Governmental Organization NWMP & AP National Waste Management Plan and Action Plan NRW Non-Revenue Water O&M Operational and Maintenance PCN Project Concept Note PDO Project Development Objective PIAP Performance Improvement Action Plan PID Project Identification Document PIU Project Implementation Unit PMU Project Management Unit POM Project Operational Manual PPSD Project Procurement Strategy Document SCADA Supervisory Control and Data Acquisition SCP Sustainable Cities Program SDG Sustainable Development Goal SKI Su Ve Kanalizayson Idaresi (General Directorate of Water and Wastewater Administration) SOE Statement of Expenditure SUTP Syrians under Temporary Protection SCD Systematic Country Diagnostic SWM Solid Waste Management TA Technical Assistance TURKSTAT Turkish Statistical Institute (in Turkish) UN United Nations UNHCR United Nations High Commissioner for Refugees UOTF Utility of the Future WASH Water Sanitation and Hygiene WBG World Bank Group WSS Water Supply and Sanitation WWTP Wastewater Treatment Plant", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project(P169996) TABLE OF CONTENTS\n\n**DATASHEET ........................................................................................................................... 1**\n\n**I.** **STRATEGIC CONTEXT ...................................................................................................... 7**\n\nA. Country Context................................................................................................................................ 7 B. Sectoral and Institutional Context .................................................................................................... 8 C. Relevance to Higher Level Objectives ............................................................................................. 12\n\n**II.** **PROJECT DEVELOPMENT OBJECTIVE .............................................................................. 13**\n\n**III.** **PROJECT DESCRIPTION .................................................................................................. 14**\n\nA. Project Components ....................................................................................................................... 14 B. Results Chain................................................................................................................................... 19 C. Rationale for Bank Involvement and Role of Partners ................................................................... 20 D. Lessons Learned and Reflected in the Project Design .................................................................... 21\n\n**IV.** **IMPLEMENTATION ARRANGEMENTS ............................................................................ 22**\n\nA. Institutional and Implementation Arrangements .......................................................................... 22 B. Results Monitoring and Evaluation Arrangements......................................................................... 24 C. Sustainability ................................................................................................................................... 24\n\n**V.** **PROJECT APPRAISAL SUMMARY ................................................................................... 25**\n\nA. Technical, Economic and Financial Analysis ................................................................................... 25 B. Fiduciary .......................................................................................................................................... 27 C. Legal Operational Policies ............................................................................................................... 28\n\n**VI.** **GRIEVANCE REDRESS SERVICES ..................................................................................... 34**\n\n**VII.** **KEY RISKS ..................................................................................................................... 34**\n\n**VIII.** **RESULTS FRAMEWORK AND MONITORING ................................................................... 37**\n\n**ANNEX 1: Implementation Arrangements and Support Plan .......................................... 47**\n\n**ANNEX 2: Detailed Project Description .......................................................................... 55**\n\n**ANNEX 3: Economic and Financial Analysis .................................................................... 67**\n\n**ANNEX 4: Procurement ................................................................................................. 77**\n\n**ANNEX 5: Communications and Visibility Plan ............................................................... 88**\n\n**ANNEX 6: Team List ...................................................................................................... 93**\n\n**ANNEX 7: Project Map .................................................................................................. 94**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project(P169996) DATASHEET\n\n**BASIC INFORMATION**\n~~BASIC~~ ~~INFO~~ ~~TABLE~~ Country(ies) Project Name Turkey Municipal Services Improvement Project Project ID Financing Instrument Environmental and Social Risk Classification Investment Project P169996 Substantial Financing\n\n**Financing & Implementation Modalities**\n\n[ ] Multiphase Programmatic Approach (MPA) [ ] Contingent Emergency Response Component (CERC)\n\n[ ] Series of Projects (SOP) [ ] Fragile State(s)\n\n[ ] Disbursement-linked Indicators (DLIs) [ ] Small State(s)\n\n[✓] Financial Intermediaries (FI) [ ] Fragile within a non-fragile Country\n\n[ ] Project-Based Guarantee [ ] Conflict\n\n[ ] Deferred Drawdown [✓] Responding to Natural or Man-made Disaster\n\n[ ] Alternate Procurement Arrangements (APA) Expected Approval Date Expected Closing Date 31-Mar-2020 31-Dec-2024 Bank/IFC Collaboration No\n\n**Proposed Development Objective(s)**\n\nThe Project Development Objective (PDO) is to improve host and refugee communities access to safely managed water supply, sanitation and solid waste services in selected municipalities affected by the influx of Syrians Under Temporary Protection in Turkey.\n\nPage 1 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996)\n\n**Components**\n\n**Component Name** **Cost (US$, millions)**\n\nEnvironmental Infrastructure Investments 280.34 Technical Assistance for Project Management and Supervision, Capacity Building, 16.04 Communication and Citizen Engagement\n\n**Organizations**\n\nBorrower: ILLER BANKASI ANONIM SIRKETI (ILBANK) Implementing Agency: ILLER BANKASI ANONIM SIRKETI (ILBANK)\n\n**PROJECT FINANCING DATA (US$, Millions)**\n\n**SUMMARY-NewFin1**\n\n**Total Project Cost** 296.76\n\n**Total Financing** 148.80\n\n**of which IBRD/IDA** 148.80\n\n**Financing Gap** 147.96\n\n**DETAILS-NewFinEnh1**\n\n**World Bank Group Financing**\n\nInternational Bank for Reconstruction and Development (IBRD) 148.80\n\n**Expected Disbursements (in US$, Millions)**\n\n**WB Fiscal Year** 2020 2021 2022 2023 2024 2025\n\n**Annual** 0.00 8.30 15.00 25.00 40.00 60.50\n\n**Cumulative** 0.00 8.30 23.30 48.30 88.30 148.80\n\n**INSTITUTIONAL DATA**\n\nPage 2 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996)\n\n**Practice Area (Lead)** **Contributing Practice Areas**\n\nWater Urban, Resilience and Land\n\n**Climate Change and Disaster Screening**\n\nThis operation has been screened for short and long-term climate change and disaster risks\n\n**SYSTEMATIC OPERATIONS RISK-RATING TOOL (SORT)**\n\n**Risk Category** **Rating**\n\n1. Political and Governance ⚫ Moderate\n\n2. Macroeconomic ⚫ Substantial 3. Sector Strategies and Policies ⚫ Moderate 4. Technical Design of Project or Program ⚫ Moderate 5. Institutional Capacity for Implementation and Sustainability ⚫ Moderate 6. Fiduciary ⚫ Moderate 7. Environment and Social ⚫ Substantial 8. Stakeholders ⚫ Substantial 9. Other ⚫ Substantial 10. Overall ⚫ Substantial\n\n**COMPLIANCE**\n\n**Policy**\nDoes the project depart from the CPF in content or in other significant respects?\n\n[ ] Yes [✓] No Does the project require any waivers of Bank policies?\n\n[ ] Yes [✓] No Page 3 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996)\n\n**Environmental and Social Standards Relevance Given its Context at the Time of Appraisal**\n\n**E & S Standards** **Relevance**\n\nAssessment and Management of Environmental and Social Risks and Impacts Relevant Stakeholder Engagement and Information Disclosure Relevant Labor and Working Conditions Relevant Resource Efficiency and Pollution Prevention and Management Relevant Community Health and Safety Relevant Land Acquisition, Restrictions on Land Use and Involuntary Resettlement Relevant Biodiversity Conservation and Sustainable Management of Living Natural Resources Indigenous Peoples/Sub-Saharan African Historically Underserved Traditional Local Communities Relevant Not Currently Relevant Cultural Heritage Relevant Financial Intermediaries Relevant\n\n**NOTE** : For further information regarding the World Bank’s due diligence assessment of the Project’s potential\nenvironmental and social risks and impacts, please refer to the Project’s Appraisal Environmental and Social Review Summary (ESRS).\n\n**Legal Covenants**\n\nSections and Description Schedule 2, Section I.A.1(a). The Borrower shall maintain, until the completion of the Project, a Project Management Unit (“PMU”), established within its International Relations Department, and responsible for coordinating and supervising Project implementation, and for providing implementation support to Selected Municipalities and SKIs.\n\nSections and Description Schedule 2, Section I.A.1(b). The Borrower shall ensure that the PMU functions at all times in a manner and with staffing, budgetary resources, and authority necessary and appropriate for satisfactory Project implementation, and all of which shall be acceptable to the Bank (said staffing shall include (i) an environmental expert, (ii) a social expert, and (iii) a communications and stakeholder specialist).\n\nSections and Description Page 4 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) Schedule 2, Schedule 1, Section I.A.2. By no later than ninety (90) days after the Effective Date, the Borrower shall execute and deliver the Grant Agreement, and shall fulfill all conditions precedent to the effectiveness of, or to the right of the Borrower to make withdrawals under, said Grant Agreement.\n\nSections and Description Schedule 2, Section I.A.3. By no later than thirty (30) days after the signing of the first Sub-loan Agreement or Subgrant Agreement between the Borrower and a Selected Municipality or SKI, the Borrower shall: (a) ensure that the Selected Municipality or SKI establishes a Project implementing unit to carry out its respective Sub-project activities; and (b) cause the respective Selected Municipality or SKI to ensure that its Project implementing unit functions at all times during Project implementation in a manner and with staffing, budgetary resources, and authority necessary and appropriate for satisfactory Project implementation, and all of which shall be acceptable to the Bank.\n\nSections and Description Schedule 2, Section I.B.1. The Borrower shall maintain, throughout Project implementation, a Project Operational Manual (“POM”), in substance and manner acceptable to the Bank.\n\nSections and Description Schedule 2, Section I.B.2. The Borrower shall carry out the Project, and cause the Project to be carried out, in accordance with the arrangements, procedures and guidelines set forth in the POM.\n\nSections and Description Schedule 2, Section I.C.1. With respect to Part 1 of the Project, the Borrower shall make Sub-loans to Selected Municipalities and SKIs for the carrying out of Sub-projects, all in accordance with eligibility criteria and procedures, and on terms and conditions, acceptable to the Bank and as set forth in the Loan Agreement’s Schedule 2, Section I.C, the Loan Agreement’s Annex to Schedule 2, and the POM.\n\nSections and Description Schedule 2, Section I.C.2. Unless otherwise agreed to between the Bank and the Borrower, the eligibility of any said Sub-project investment proposed for financing under Part 1 of the Project shall be appraised and selected in accordance with standards, criteria and procedures acceptable to the Bank, which shall include determining that the proposed investment: (a) is aligned with the Project objective; (b) serves an area or areas that has/have a significant presence of SuTP; (c) is technically feasible; (d) is economically and financially viable; (e) is demand and needs driven; (f) demonstrates substantial readiness; (g) is environmentally and socially sustainable; and (h) is in compliance with, and can be designed and implemented in a manner in compliance with the Bank’s fiduciary requirements, Bank’s Environmental and Social Standards, the Loan Agreement’s Schedule 2, Section I.D, the Environmental and Social Commitment Plan, and the Environmental and Social Instruments, and all other relevant terms of the Loan Agreement, including the exclusion of Excluded Activities.\n\nSections and Description Schedule 2, Section I.C.3. The Borrower shall obtain the Bank’s approval of a proposed Sub-project and Sub-loan prior to the execution of a Sub-loan Agreement with the Selected Municipality or SKI concerned.\n\nPage 5 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) Sections and Description Schedule 2, Section I.C.4. The Borrower shall extend each Sub-loan under a Sub-loan Agreement with the respective Selected Municipality or SKI on terms and conditions approved by the Bank, including, without limitation, the terms and conditions set forth in the Loan Agreement’s Annex to Schedule 2 and the POM.\n\nSections and Description Schedule 2, Section D.1 and 2. The Borrower shall, and shall cause the Selected Municipalities and SKIs to, ensure that the Project is carried out in accordance with the Environmental and Social Standards, and implemented in accordance with the Environmental and Social Commitment Plan, in a manner acceptable to the Bank.\n\nSections and Description Schedule 2, Section E. The Borrower shall prepare and furnish to the Bank not later than April 15 of each year during the implementation of the Project, a proposed Annual Work Plan and Budget, afford the Bank a reasonable opportunity to exchange views on each such proposed Annual Work Plan and Budget, and ensure that the Project is implemented with due diligence during said following year, and not make or allow to be made any change to the approved Annual Work Plan and Budget without the Bank’s prior written approval.\n\n**Conditions**\n\nType Description Effectiveness The Borrower has adopted a Project Operations Manual acceptable to the Bank (Article IV: Section 4.01 (a)).\n\nType Description Effectiveness The Borrower has properly staffed its Project Management Unit, with positions, terms of reference, and staff qualifications acceptable to the Bank (Article IV: Section 4.01 (b)). .\n\nType Description Effectiveness The Borrower has properly adjusted its existing grievance redress mechanism for the purposes of the Project, and operationalized said mechanism, all in manner and form acceptable to the Bank (Article IV: Section 4.01 (c)).\n\nPage 6 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996)\n\n**I.** **STRATEGIC CONTEXT**\n\n**A. Country Context**\n\n1. **Turkey has high growth potential, but recent shocks have affected the sustainability of its economic gains since**\n\n**the early 2000s.** After the Global Financial Crisis in 2008-2009, growth has been increasingly fueled by credit booms\nand rapid accumulation of (mostly foreign exchange) private sector debt, together with short-term stimulus policy.\nThese led to declining productivity and growing macroeconomic imbalances in late 2017/early 2018. The situation was compounded by exogenous factors including multiple election cycles, regional conflict, and difficult international relations. The ensuing volatility in growth has affected the sustainability of Turkey’s economic gains.\n\n2. **Economic vulnerabilities that had accumulated over the past 4 years came to a head in mid-2018.** Policy stimulus in the aftermath of the 2016 failed coup attempt led to economic overheating. Though growth accelerated to 7.4 percent in 2017, this came at a cost of double-digit inflation and a large current account deficit. A hardening of external economic conditions in mid-2018, together with tense international relations, led to a collapse in the Lira.\nThis profoundly affected the real and financial sectors. Corporations and banks suffered due to high foreign exchange debt, annual inflation peaked at 25 percent in October 2018, the economy went into recession in 2018, and unemployment spiked from 10 percent in January 2018 to 14 percent in June 2019 (World Bank 2019a, 2019b).\n\n3. **The Turkish economy over the past 12 months has experienced major adjustments.** Current account imbalances have declined, banks have reduced their external exposure and portfolio flows have started to recover. These adjustments have lessened external vulnerabilities that had accumulated in the run up to the August 2018 currency shock. They have also contributed to a more stable Lira, notwithstanding bouts of currency volatility. There has also been steady disinflation over this period. These developments were supported by selected policy responses and accommodative global monetary conditions. Even so, foreign exchange reserves have gotten eroded over the past two years, exposing Turkey to external market pressure.\n\n4. **Stagnating output, high costs of production, and high consumer prices have led to significant job losses and falling**\n\n**real wages.** Unemployment among the youth is particularly high, jumping from 19 percent to 25 percent between\nMay 2018 and May 2019. Average real wages declined by 2.6 percent between 2017 and 2018, though have picked up more recently due to adjustments to the minimum wage. Poorer households have been most impacted because many low-income workers are employed in construction and agriculture – the sectors that saw the biggest decline in jobs. Moreover, the long-term impact of the real wage effects is greater for the poorest households since they have limited coping mechanisms.\n\n5. **Turkey now faces a two-fold challenge:** in the near-term to extricate itself from a downturn while inflation is high (albeit declining), the external environment is uncertain, and firms are struggling with debt; and to put in place appropriate policy and institutional settings to support a shift to a sustainable-medium term growth model. The pace and sustainability of Turkey’s recovery will depend on reducing economic uncertainty backed by consistent policy mix. The economy has stabilized in the short-term. GDP is projected to rebound to 3 percent and 4 percent in 2020 and 2021, respectively. However, given the high degree of uncertainty in the global outlook, restoring confidence and reducing domestic risk premia with appropriate monetary stance and effective fiscal policy would be key for sustaining recovery. Rigorous progress in advancing structural reforms such as deepening financial markets and completing overdue labor market reforms will help to mitigate vulnerabilities, and support growth in the medium term.\n\nPage 7 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) 6. **Turkey is both a transit and reception country for migrants and refugees and, globally, the country hosts the**\n\n**highest number of refugees.** **[1]** As a result of the crisis in its southern border with Syria, Turkey has been hosting an\nincreasing number of refugees and foreigners seeking international protection. In addition to hosting more than 3.6 million Syrians [2], who are under temporary protection, there are an estimated 400,000 asylum seekers and refugees from other nationalities. The country’s refugee response has been progressive and provides a model to other countries hosting refugees. However, the magnitude of the refugee and migrant influx continues to pose substantial development consequences for not only the displaced but also the communities into which they settle, contributing to the expansion and overcrowding of settlements, increased demands for urban services (including water supply, sanitation and solid waste services), additional pressure on infrastructure and the urban environment, conflicts over land, and increased competition for employment, housing, and social services. These stresses stretch the limited capacity of urban local governments, including municipalities and other service providers. Apart from the large cities such as Ankara, Istanbul and Izmir, many of the cities hosting a high concentration of Syrians are already located in the more vulnerable or disadvantaged provinces in Turkey, which exacerbates the development challenges.\n\n7. **The Government of Turkey (GT) spent an estimated EUR 31 billion to meet the needs of refugees and hosting**\n\n**communities from the beginning of the Syrian crisis to 2017.** **[3]** This includes the provision of free healthcare and\neducation possibilities, as well as allowing legal access to the labor market. The international community has also provided over EUR 4 billion since 2016, of which 95 percent is from the European Union (EU). [4] This includes the first tranche of the EU Facility for Refugees in Turkey (FRiT), which is an EUR 3 billion fund launched in 2016, designed to support the GoT hosting refugees, EUR 600 million EU support outside of the FRiT, and over EUR 400 million in bilateral support from EU countries. Other donors, UN agencies, international, national and local civil society organizations, as well as International Financial Institutions (IFIs), have also been playing an important role in Turkey’s refugee response, implementing a diverse range of programs and projects, accounting for over EUR 200 million.\nThese efforts have been geared primarily towards facilitating refugee access to existing public services while strengthening the capacity and responsiveness of state institutions at the national and local levels.\n\n**B. Sectoral and Institutional Context**\n\n8. **A legal framework was established to tackle challenges due to the influx of refugees in Turkey by issuing Law No.**\n\n**6458 on Foreigners and International Protection in 2013 and Regulation No. 29153 on Temporary Protection of**\n**Syrians in 2014.** While the primary responsibility for emergency response and coordinating humanitarian needs,\nincluding management of the refugee camps, are fulfilled by the Disaster and Emergency Management Authority (AFAD), relevant ministries and local authorities, depending on their respective area of jurisdiction, assume responsibility to provide registered refugees with access to municipal services, education, healthcare, social services, and labor markets.\n\n1 DGMM. 2019. This PAD uses the term refugee regardless of country of origin, although Syrians are under temporary protection status, and non-Syrians under international protection law.\n_[http://www.goc.gov.tr/icerik6/temporary-protection_915_1024_4748_icerik](http://www.goc.gov.tr/icerik6/temporary-protection_915_1024_4748_icerik)_ 2 The terms “Syrians” and “refugees” are used in terms of sociological context and widespread daily use, and independent of the legal context in Turkey and Turkish Law. Turkey is a party to the 1951 Refugee Convention and 1967 Protocol. Turkey retains a geographic limitation to its ratification of the 1951 UN Convention on the Status of Refugees, which means that only those fleeing as a consequence of “events occurring in Europe” can be given refugee status. Syrian nationals, as well as stateless persons and refugees from Syria, who came to Turkey due to events in Syria after 28 April 2011 are provided with temporary protection.\n3 European Commission (2018): Technical Assistance to the EU Facility for Refugees in Turkey – Updated EU Needs Assessment. October 2018.\nRefer to: _[https://ec.europa.eu/neighbourhood-enlargement/sites/near/files/updated_needs_assessment.pdf](https://ec.europa.eu/neighbourhood-enlargement/sites/near/files/updated_needs_assessment.pdf)_ 4 Ibid.\n\nPage 8 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) 9. **The scope of the current project is primarily limited to infrastructure and technical assistance relating to water**\n\n**supply and sanitation (WSS) and solid waste management, even though municipal services in general** **cover a**\n**broader range of sub-sectors** **[5]** **, based on priority action areas identified under the FRiT Municipal Infrastructure**\n**Window in the 2018 Needs Assessment** **[6]** **prepared by the EU.** Four out of five priority action areas focused on\nactivities in the mentioned environmental infrastructure sub-sectors, while the fifth area focused on other municipal services which included transport, recreational area needs and migrant information and coordination centres, and community and culture centres. The Bank in its proposal to the EU to access the grant made a strategic choice to limit the focus areas to the WSS and solid waste sub-sectors to reduce the complexity of the project scope given the limited implementation period and available resources. Other development partners were subsequently invited by the GoT through ILBANK to address the other municipal services under the fifth priority area.\n\n10. **The WSS sector has undergone several reforms over the years and roles are currently distributed among several**\n\n**institutions.** The 1926 Water Law places the overall responsibility for water resources management at the state level.\nThis was confirmed in the Constitution of 1982 that provided that the state owns the right to explore and operate these natural resources but can transfer this right to private institutions for a defined period. Municipalities are responsible for WSS services in their respective areas. In 1984, starting with Istanbul, Turkey created 16 “metropolitan municipalities [7] ” (MM) by consolidating the municipalities in the main urban areas. The _Su Ve_ _Kanalizayson Idaresi_ (General Directorate of Water and Wastewater Administration) “SKI” is established in every metropolitan municipality to manage water supply and sanitation (WSS) services in accordance with the provisions of Law No 2560. SKIs are public entities or utilities that are affiliated with the metropolitan municipality and have an autonomous budget. A new law that came into effect on March 31, 2014, created 14 new metropolitan municipalities and SKIs and extended the service area of all metropolitan municipalities to cover the entire province. As a result, there are 30 SKIs responsible for providing WSS services to about 77 percent of the population. Other smaller municipalities provide WSS services through municipal departments. Special provincial administrations (SPAs) provide services in non-municipal areas. Responsibility for solid waste management rests with the respective municipalities.\n\n11. **Goal 6 of the UN 2030 Agenda for Sustainable Development Goals (SDG 6) calls for** _**Ensuring Availability and**_ _**Sustainable Management of Water and Sanitation for All**_ **.** The goal addresses water supply, sanitation and hygiene (SDG Targets 6.1 and 6.2), treatment, recycling and reuse of wastewater (Target 6.3), increasing efficiency and ensuring sustainable withdrawals (Target 6.4), and protection of water-related ecosystems (Target 6.6) as part of an integrated approach to water resources management (Target 6.5). It also focuses attention on the links between development outcomes and means of implementation (6a and 6b). The Goal specifically seeks to achieve universal and equitable access to safe and affordable drinking water for all by 2030. While Turkey is considered to be advanced in terms of availability of water and sanitation services for most of its population in terms of overall access, sustainable management [8] of these services remains a challenge in many municipalities. Moreover, climate change is expected to compound this challenge. [9] 5 [Municipal services may cover a broad range of basic services that include, inter alia, sanitation (both wastewater/excreta management](https://en.wikipedia.org/wiki/Public_services)\n[and solid waste), water supply, recreational facilities, street cleaning services, public libraries, schools, food inspection, fire services, law](https://en.wikipedia.org/wiki/Water)\n[enforcement, ambulance and other health department services, and transportation..](https://en.wikipedia.org/wiki/Ambulance) 6 EU Facility for Refugees in Turkey - Updated Needs Assessment 2018: _[https://ec.europa.eu/neighbourhood-](https://ec.europa.eu/neighbourhood-enlargement/sites/near/files/updated_needs_assessment.pdf)_ _[enlargement/sites/near/files/updated_needs_assessment.pdf](https://ec.europa.eu/neighbourhood-enlargement/sites/near/files/updated_needs_assessment.pdf)_ 7 Metropolitan municipalities represent municipalities with population in excess of 750,000 people.\n8 Sustainable WSS services are able to effectively deliver adequate consumer needs and are economically and financially viable, socially acceptable, technically and institutionally appropriate, and protect the environment and natural resources.\n9 A 2016 Systematic Country Diagnostic (SCD) for Turkey, prepared by the World Bank, concluded that, given its large population and high Page 9 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) 12. **The sixth target under SDG 11 focuses on reducing the adverse per capita environmental impact of cities, including**\n\n**by paying special attention to air quality and municipal and other waste management by 2030.** Progress towards\nthis target is to be monitored though measurement of the proportion of urban solid waste regularly collected and with adequate final discharge. Municipal solid waste is one of the major environmental problems in Turkey. While most of the population receives waste collection services, the majority of solid waste is not disposed in accordance with the relevant legal requirements and standards, and about 44 percent of the collected municipal waste is disposed to dump sites. Uncontrolled and illegal disposal methods like dumping and discharge into rivers and lakes also occur in several parts of the country.\n\n13. **A World Bank Sector Note on Sustainable Urban Water Supply in Turkey** **[10]** **cited increasing water demand and**\n\n**scarcity in Turkey, with the cumulative water demand for domestic, industrial, and irrigation uses expected to**\n**exceed the water available before the end of the century.** Water demand is expected to increase from 43 billion m [3]\nin 2015 to 54 billion m [3] by 2020 and to 62 billion m [3] by 2100, with models predicting that the decrease in water availability will be more severe in central provinces and provinces located on the southern and western shores of Turkey. There is therefore a need for increased efficiency in the use of water resources especially in these regions.\n\n14. **While piped water coverage is relatively high in Turkey, more than 40 percent of water is distributed untreated,**\n\n**which increases risks linked to poor water quality, including to public health.** According to Municipal Water\nStatistics prepared by TURKSTAT in 2018, 99 percent of the population living in municipalities has access to piped water supply, however only 60 percent are served by a water treatment plant. The municipalities targeted in this project face significant water supply service challenges, including poor quality water due to inadequate water treatment facilities. Thus, based on the SDG definition of a safely managed drinking water service as one located on premises, available when needed and free from contamination, the municipalities are out of compliance with these service requirements, putting Turkey at risk of not fulfilling the SDG goal if not addressed. Service efficiency is also an issue, with utilities having high Non-Revenue Water (NRW), in some cases with over 50 percent losses, due to ageing or sub-optimally maintained transmission and distribution infrastructure. NRW in utilities averages about 36 percent country-wide.\n\n15. **Similarly, while access to sewage networks is relatively high, a significant proportion of wastewater is discharged**\n\n**untreated into the environment.** According to TURKSTAT Municipal Wastewater Statistics for 2016, 90 percent of\nthe population living in municipalities are served with a sewage network. However, only 70 percent of the population is served with a wastewater treatment plant. In individual municipalities coverage is lower, and the quality of sewerage infrastructure is inadequate, resulting in sewage leakages. These conditions not only impact the environment, but also lead to significant increases in operation and maintenance costs of the systems. With higher populations, impacts on the environment are increased. The current situation in the areas with no wastewater treatment puts the affected municipalities out of compliance with the SDGs, which define safely managed sanitation, as the use of an improved sanitation facility which is not shared with other households and where excreta is safely disposed in-situ or is transported and treated off-site. Access to safely managed [11] water supply and sanitation services at the neighborhood level in individual municipalities, especially in the lower income neighborhoods in urban level of water consumption, the country faces a significant water security threat from climate change—to manifest itself as potential droughts associated with the rising temperatures, changes in precipitation patterns, and reduced seasonal snow storage.\n10 Republic of Turkey - Sustainable Urban Water Supply and Sanitation, World Bank, 2016.\n11 According to the definition from the United Nations Sustainable Development Goals (SDGs), a safely managed drinking water service is defined as one located on premises, available when needed and free from contamination. Goal: By 2030 achieve universal and equitable access to safe and affordable drinking water for all.\n\nPage 10 of 94", "output": {"entities": {"named_data": ["TURKSTAT Municipal Wastewater Statistics", "Municipal Water\nStatistics prepared by TURKSTAT"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) areas and peri-urban agricultural areas, which are inhabited by both Turkish citizens and SUTPs is a major issue.\n\n16. **In addition to those challenges, the rapid influx of refugees has generated a significant additional burden on**\n\n**already strained WSS services.** While the Government has embarked on an extensive rehabilitation and\ndevelopment process nationwide to improve water quality nationally and to ensure that all rural and urban residential and industrial areas have access to safe drinking water, the sizeable population increase due to the refugee influx in many municipalities is further exacerbating pressure on existing infrastructure, requiring additional investments in water treatment and distribution systems. The provinces located in the South Eastern part of Turkey in particular, which remain the prime target of the Turkish Government’s development program, have seen the greatest negative impact in this regard. In individual municipalities, access to safely managed [12] water supply services at the neighborhood level is an issue, especially in the lower income neighborhoods and settlements which are inhabited by both Turkish citizens and refugees or SUTPs.\n\n17. **There is evidence that existing solid waste disposal capacities in some affected municipalities are being strained**\n\n**due to the refugee influx in affected areas.** This increase in waste generation has an impact on the lifetime of waste\ncollection vehicles and equipment, as well as disposal sites. While solid waste collection rates in Turkey are relatively high, a significant proportion of the waste is not disposed properly in landfills. According to the 2016 National Waste Management Plan and Action Plan (NWMP&AP) data, just over 60 percent of the municipal waste is sent to sanitary landfills, 28 percent is dumped into municipal dumpsites, and 11 percent was reported as recycled, composted or disposed of by other methods.\n\n18. **Turkey’s rapid industrialization and urbanization growth during the last two decades has caused its environmental**\n\n**footprint to increase rapidly, with total GHG emissions rising from 208 MtCO2e (in 1990) to 526 MtCO2e (in 2017)** **[13]** **.**\nAt the same time, climate change is further exacerbating already observed vulnerabilities and environmental risks, such as the intense flash floods in the Marmara region in September 2009, with 32 human losses and more than US$100 million of economic damage, as well as the recent floods in July-August 2019 in Northeastern Turkey. While there are no detailed climate change assessments that reflect conditions at each individual municipality level, Turkey has the highest risk of water safety in the arid and semi-arid regions including the Southeast, Central Anatolia, Aegean and Mediterranean. Most of the projections suggest a temperature increase of 3–4 °C on average across Turkey (from 2041 to 2070) [14] and uneven changes in the precipitation patterns, which are in turn expected to impact a number of sectors, including the availability of water resources across the country and the incidence of floods, associated with intense precipitation and coastal storms. Climate change impacts are expected to lead to more frequent and severe climate extreme events, including droughts (as highlighted in footnote 9), which could result in a disruption of critical municipal services and pose additional challenges to the existing urban infrastructure and well-being of the urban population. In addition, water pollution (caused by untreated industrial wastewater being discharged in rivers, among 12 According to the definition from the United Nations Sustainable Development Goals (SDGs), a safely managed drinking water service is defined as one located on premises, available when needed and free from contamination. Goal: By 2030 achieve universal and equitable access to safe and affordable drinking water for all.\n13 Sources: _[http://www.turkstat.gov.tr/PreHaberBultenleri.do?id=30627 and OECD Environmental Performance Reviews: Turkey 2019](http://www.turkstat.gov.tr/PreHaberBultenleri.do?id=30627)_ _(_ [https://www.oecd-ilibrary.org/environment/oecd-environmental-performance-reviews-turkey-2019_9789264309753-en](https://www.oecd-ilibrary.org/environment/oecd-environmental-performance-reviews-turkey-2019_9789264309753-en) _)_ 14 Sources: _[OECD Environmental Performance Reviews: Turkey 2019 ); (https://www.oecd-ilibrary.org/environment/oecd-environmental-](https://www.oecd-ilibrary.org/environment/oecd-environmental-performance-reviews-turkey-2019_9789264309753-en)_ _[performance-reviews-turkey-2019_9789264309753-en](https://www.oecd-ilibrary.org/environment/oecd-environmental-performance-reviews-turkey-2019_9789264309753-en)_ ; Turkey’s National Climate Change Adaptation Strategy and Action Plan, T.R. Ministry of Environment and Urbanization, November 2011, Ankara (1st edition) and T.R. Ministry of Environment and Urbanization, August 2012, Ankara (2nd edition) ( _[http://www.dsi.gov.tr/docs/iklim-degisikligi/turkeys-national-climate-change-adaptation-strategy-](http://www.dsi.gov.tr/docs/iklim-degisikligi/turkeys-national-climate-change-adaptation-strategy-and-action-plan.pdf?sfvrsn=2)_ _[and-action-plan.pdf?sfvrsn=2](http://www.dsi.gov.tr/docs/iklim-degisikligi/turkeys-national-climate-change-adaptation-strategy-and-action-plan.pdf?sfvrsn=2)_ ) ; Page 11 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) other things) could significantly reduce in water quality. Against this background, conserving the quantity and quality of water resources is essential for the country’s long-term growth and sustainability.\n\n19. **Turkey’s Climate Change Action Plan (2011–2023) identified actions aimed at increasing national preparedness and**\n\n**capacity to avoid the adverse impacts of climate change and to adapt to its impacts.** In 2015, Turkey submitted its\nIntended Nationally Determined Contribution to the United Nations Framework Convention on Climate Change, committing to reduce its greenhouse gas (GHG) emissions by up to 21 percent by 2030 compared to the business-asusual scenario, to be achieved through several new policies and measures, including those related to energy efficiency improvements.\n\n20. **Currently, municipalities have limited financial capacity to design and implement climate- and resilience-related**\n\n**investments, which is recognized as one of the key constraints for climate action in Turkey.** The Project provides\nthe opportunity to build capacity for screening, preparing, and implementing sub-projects which consider climate and disaster resilience, particularly in terms of addressing increasing risks of extreme weather events such as floods and droughts, and increasing difficulties in managing urban water resources during more intense and lengthy drought periods. It also provides the means to invest in mitigation and strengthening a range of such climate adaptation measures in municipalities, which are increasingly susceptible to climate change risks.\n\n21. **Given the strain that the refugee influx has generated on water, wastewater and solid waste services, grant**\n\n**financing has been extended in the amount of EUR 139,812,400** **[15]** **by the European Development Fund (EDF) under**\n**the Municipal Infrastructure Window of the FRiT and the ILBANK has requested an IBRD loan in the amount of EUR**\n**135,355,000 for a project to support municipal services improvements in several municipalities impacted by**\n**refugees.** The targeted municipalities were selected from a list of ten affected areas identified through the 2018 EU\nNeeds Assessment, based on criteria such as the number of refugees and the magnitude and scope of municipal infrastructure needs. At the time of appraisal, sub-projects in five municipalities: Adana, Kahramanmaraş, Osmaniye, Kayseri and Konya, were identified to participate in the project based on eligibility criteria described under Section III below (Project Description). These municipalities had a total population of about 7.4 million people in 2018, including about 540,000 registered refugees. They already suffer from significant operational problems such as high water losses, inadequate water treatment facilities, ageing WSS infrastructure, inadequate solid waste management, and lack of wastewater treatment.\n\n**C. Relevance to Higher Level Objectives**\n\n22. **The World Bank Group views the development challenge of forced displacement as a corporate priority and a long-**\n\n**term challenge that must be addressed to attain the Bank’s twin goals of reducing poverty and achieving shared**\n**prosperity.** The project would leverage the important partnership with the European Union which is providing grant\nsupport from the EU FRIT to be blended with proposed IBRD to support targeted municipalities in Turkey in addressing the increased pressure from Syrian refugees. Other development partners, such as the French 15 The project is expected to receive grant funding from the second tranche of the European Commission’s Facility for Refugees in Turkey (FRIT), in the amount of about Euro 134,219,904, excluding World Bank fees, remuneration and Bank executed operational costs. An application for the grant financing was submitted by the World Bank on March 15, 2019, and the EC through a letter dated May 27, 2019 authorized the Bank to formally negotiate the details of the project with the EU delegation in Turkey, to lead to a formal Contribution Agreement. Signing of the Administration Agreement (AA) between the World Bank and the EC for the Grant is subject to finalization of an update to the 2016 World Bank Group-EU Framework Agreement, which is under discussion by the parties. Accordingly, the grant financing is reflected as a financing gap in the Project documentation until the AA has been signed.\n\nPage 12 of 94", "output": {"entities": {"named_data": ["2018 EU Needs Assessment"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) Development Agency (AFD), United Nations Development Programme (UNDP) and European Investment Bank (EIB) will also engage in supporting implementation of municipal infrastructure projects in other municipalities not covered by the World Bank.\n\n23. **The proposed project is consistent with the World Bank Country Partnership Framework (CPF) for Turkey for the**\n\n**FY 2018-2021** **[16]** **period, discussed by the Board on August 29, 2017 (Report No. 11096-TR), and is aligned with the**\n**objectives of Turkey’s 11th Development Plan (2019–2023).** The CPF is based on the findings of the Systematic\nCountry Diagnostic (finalized in 2016) that highlighted water availability and sustainable use as being key challenges for Turkey’s future development. The CPF has three focus areas: (1) Growth; (2) Inclusion; and (3) Sustainability.\nUnder Focus Area 2 (“Inclusion”) the WBG support in this area aims to consolidate Turkey’s success towards achieving the twin goals while also supporting efforts to reach those who are left behind. An important evolution of the WBG program in this CPF area has been and will continue to be the introduction of new investment operations financed by the EU’s FRiT which specifically sets the target to improve the situation of Syrians under Temporary Protection (SuTPs) (or refugees [17] ) in Turkey. Under Focus Area 3 (“Sustainability”) the Project will support the Strategic Objective 8 to improve sustainability and resilience of cities, specifically against the climate change-related risks of floods and droughts, and Strategic Objective 9, to increase sustainability of infrastructure assets and natural capital.\n\n**II.** **PROJECT DEVELOPMENT OBJECTIVE**\n\n**.Project Development Objective**\n\n24. The project development objective is to improve host and refugee communities access to safely managed [18] water supply, sanitation [19] and solid waste services in selected municipalities affected by the influx of Syrians Under Temporary Protection in Turkey.\n\n**B. PDO Level Indicators**\n\n25. The key results (PDO level indicators) are presented below:\n\n- Total number of people benefitting from safely managed drinking water services in the selected municipalities as\na result of the project (out of which female (%); host population (number); refugees (number))\n\n- Total number of people benefitting from safely managed sanitation services in the selected municipalities as a\nresult of the project (out of which female (%); host population (number); refugees (number))\n\n- Total number of people benefitting from safely managed solid waste services in the selected municipalities as a\nresult of the project (out of which female (%)); host population (number); refugees (number)) 15 Refer to Report No. 11096-TR: _[http://documents.worldbank.org/curated/en/585411504231252220/pdf/Turkey-CPF-08072017.pdf](http://documents.worldbank.org/curated/en/585411504231252220/pdf/Turkey-CPF-08072017.pdf)_ 17 Refugees in this context include both SUTPs and other refugees from other countries living among host communities includes Iraq, Afghanistan, Iran and others, who are under the status of international Protection.\n18 Safely managed water supply focuses on ‘accessibility’, ‘availability’, and ‘quality’, and is defined as drinking water from an improved water source which is located on premises, available when needed and free of faecal and priority contamination. ‘Improved’ sources are those that are potentially capable of delivering safe water by nature of their design and construction, including piped water, boreholes or tube wells, protected dug wells, protected springs, and rainwater.\n19 Safely managed sanitation refers to the use of improved facilities which are not shared with other households and where excreta are safely disposed in-situ or transported and treated off-site before disposal. Sanitation in this context mainly refers to wastewater and excreta management services.\n\nPage 13 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996)\n\n**C. Project Beneficiaries**\n\n26. A total of about 3.3 million people, including refugees and host communities in the municipalities targeted under this project (out of which about 50 percent are women) are expected to benefit from the project. Of these, approximately 301,890 refugees (or 9 percent of the total beneficiaries) and 3,021,000 of the host population are expected to benefit directly from the municipal investments. Other beneficiaries will include the municipalities and municipal service providers (water and wastewater utilities) in the project areas, who will benefit from capacity building efforts financed through Component 2 to improve their capacity to more effectively manage the operation and maintenance of the environmental infrastructure, as well as commercial operations such as handling all their customers in an equitable and socially sensitive manner, in order to provide sustainable services that take into consideration climate change mitigation and adaptation aspects. ILBANK will also benefit from training in overall project management of operations involving refugee affected areas. The project is an opportunity for continued learning to the Government of Turkey, the World Bank, and other key stakeholders on an adequate business model for improving municipal service interventions in the context of situations of urgent need due to forced urban migration or displacement.\n\n**III.** **PROJECT DESCRIPTION**\n\n**A. Project Components**\n\n27. The Project will finance environmental infrastructure investments and technical assistance (TA) through the provision of sub-loans and/or grants to support selected municipalities in dealing with the increased pressure on the defined municipal services, in particular water supply, sanitation and solid waste management, in a sustainable [20] and equitable manner, taking into account the needs of the entire population including the most vulnerable among host and SUTP populations. The activities will also aim to enhance the resilience of the infrastructure, as well as that of the targeted communities to climate change–exacerbated risks such as droughts, floods, and degraded water quality, and also raise awareness of the mitigation measures to be derived from improved efficiency of the water supply and sanitation service delivery and improved solid waste management. The Project design ensures that careful attention is paid to gender and citizen engagement corporate priorities in the World Bank, both of which are central to the development of an inclusive and responsive approach to development among the host and SUTP communities.\n\n28. **Municipality and Sub-project eligibility.** The Project funds will be channeled through a public, national-level financial intermediary banking institution, ILBANK, which will in turn on-lend or on-grant it, as applicable to a number of municipalities or utilities (SKIs) based on a framework approach for sub-projects that meet eligibility criteria for participating in the project. Eligible municipalities were identified in the EU Needs Assessment. At appraisal, subprojects in five municipalities: Adana, Kahramanmaraş, Osmaniye, Kayseri and Konya, meeting the above criteria were identified to participate in the project. The eligibility criteria for sub-projects will be defined in the Project Operational Manual (POM), and include, _inter alia_ : (a) a significant presence of SUTP in the host municipality as identified in the EU Needs Assessment; and (b) municipal investments aligned with the PDO that are: (i) technically feasible; (ii) economically and financially viable; (iii) demand and needs driven, and that demonstrate substantial readiness, including having approved feasibility studies, detailed designs and draft safeguards documentation in line with World Bank ESF requirements; (iv) environmentally sustainable and in compliance with, and can be designed and implemented in manner incompliance with the Bank’s fiduciary (including financial and procurement) 20 Sustainability in this case addresses technical, environmental, economic/financial, and social dimensions of the project.\n\nPage 14 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) requirements, and the Bank’s Environmental and Social Standards, consistent with the Environmental and Social Commitment Plan for the project (ESCP), and the Environmental and Social Instruments; (v) that do not trigger World Bank Operational Policy (OP) 7.50 (Projects on International Waterways); and (iv) that involve no new dams or dams under construction. All sub-projects to be financed from the grant and loan are subject to approval by ILBANK and the World Bank and EUD, as applicable [21] . ILBANK follows the financial eligibility criteria in accordance with the existing _Law on Regulating Public Finance and Debt Management (Law No. 4749)_ which restricts borrowing by any institution if it has overdue payments to Treasury for its on-lending to each municipality. Municipalities/utilities would be required to provide a Municipal Resolution/Utility Board decision stating that project financing would be sought for the proposed subprojects. Where the sub-borrower is a utility/SKI, a guarantee from the metropolitan municipality is also required. In the solid waste management sector, the grant beneficiary/ loan sub-borrower will be the municipality in line with existing institutional roles, while the beneficiary / sub-borrower for the WSS sectors will either be the municipality or utility /SKI with a guarantee provided by the municipality. Proposed activities are summarized below and the indicative breakdown of activities by financing source is presented in Table 2.1 (Annex 2).\n\n29. **The IBRD loan and EU FRIT grant will co-finance** **[22]** **the project as a single project, although each financier will finance**\n\n**separate individually identifiable activities as indicatively detailed in Annex 2 (Table A 2.1).** An indicative list of\nactivities to be financed from the loan and the grant was provided by ILBANK at appraisal. Activities financed from both sources will be implemented seamlessly in parallel under a single Procurement Plan prepared by ILBANK and approved by the World Bank during appraisal, and a single Project Implementation Plan to be prepared by ILBANK no later than 30 days after loan effectiveness. Both Plans will be reviewed and updated annually as needed. The Procurement Plan will identify the source of funding for each activity with prior approval by the Bank, although the rules for implementation will be exactly the same for both sources of funds.\n\n30. **Component 1. Environmental Infrastructure Investments (Euro 254.98 million (US$280.34 million equivalent), of**\n\n**which EU Grant is Euro 132.33 million** **[23]** **(US$145.49 equivalent) and IBRD loan is Euro 122.65 (US$134.83**\n**equivalent):** This component will finance environmental infrastructure investments through the provision of subloans and/or grants to selected municipalities for goods, works, non-consulting and consulting services required to\ncarry out construction and rehabilitation works for water supply, sanitation, and solid waste management infrastructure in those municipalities to achieve improvements in access, service quality and continuity of municipal services; as well as smaller scale critical facilities to address immediate municipal service needs of vulnerable SUTP and host communities at the neighborhood or settlement level, to be identified in a participatory manner by the beneficiaries. Individual infrastructure facilities will, where appropriate, consider adaptive designs that would allow for a scaling up or down of capacity in the event of unanticipated changes in the refugee population or climate change-related extreme weather events. An indicative program of proposed sub-projects with investments to be financed through this component was pre-identified in the selected municipalities at appraisal (see Table 1 in Section V, A) of this document). Envisaged investments in water supply, sanitation, and solid waste management are described below.\n\na) **Water Supply Investments (Estimated Cost Euro 145.93 million of which IBRD loan is Euro 104.65 million**\n\n**and EU Grant is Euro 41.28 million).** This activity will finance water supply investments, including for the: (i)\n\n21 EUD approval will be required for grant financed sub-projects 22 Please see footnote 15 which is applicable for all further references to the grant financing in this document.\n23 An additional contingency amount for investments in the amount of about Euro 984,904 is allowed for in the grant amount, bringing the total grant amount to Euro 134,219,904. A front-end fee for the IBRD loan in the amount of US$372,040 is included in the project cost.\n\nPage 15 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) construction and/or rehabilitation of water treatment plants; (ii) construction or rehabilitation of water transmission and distribution networks; (iii) expansion of water reservoir capacities; (iv) Non-Revenue Water (NRW) reduction activities such as installation of SCADA systems and development of district metering areas (DMAs), leakage control measures; and (v) provision of priority people-facing public hygiene facilities or interventions at the individual settlement/neighborhood level to ensure that all people, including the most vulnerable, have access to safely managed water supply services; and (vi) other demand-driven water supply investments as approved by the Bank. All these activities will increase the supply of (treated) water and, combined with sanitation investments, will decrease the volume of untreated wastewater discharged into water bodies, thereby raising the targeted communities’ resilience to droughts and poor-quality water. The priority interventions at the household or neighborhood level under (v) above will be identified in a participatory manner with the beneficiary communities and the respective municipalities early on during implementation to respond to specific people-facing priorities in terms of spatial or facility targeting, and could include facilities such as provision of additional access points, laundry facilities, etc. Investments under this component are expected to contribute to improved access to safely managed water supply in the targeted areas by augmenting existing capacities, improving quality of services among host and refugee populations, and raising the targeted communities’ resilience to droughts and poor-quality water. Water supply and sanitation investments will be designed to address climate change adaptation and mitigation through measures aimed at increased energy efficiency, such as: installation of energy-efficient pumping systems; reduction of non-revenue water (NRW); and system monitoring and regulation, with automation through SCADA systems. All three of these activities are expected to result in energy efficiency gains.\n\nb) **Sanitation investments (Estimated Cost Euro 91.07 million of which IBRD loan is Euro 18.00 million and EU**\n\n**Grant Euro 73.07 million)** . This activity will finance sanitation investments in selected municipalities, including\nfor the: (i) construction of new wastewater treatment plants (WWTPs) or interventions to increase their capacity; (ii) construction of new or rehabilitation or extension of existing sewerage collection networks; or (iii) targeted priority sanitation/or related public hygiene facilities to address immediate needs such as toilets in the most vulnerable settlements among host and refugee communities, to be identified in consultation with the communities, municipalities and SKIs; and (iv) other demand-driven sanitation investments as approved by the Bank. Extension or construction of the wastewater collection network is expected to decrease the amount of uncontrolled discharges to receiving bodies and the construction of wastewater treatment plants (WWTPs) to improve the quality of discharges to receiving bodies. These activities will thus not only reduce the volume of contaminated floodwaters in case of flooding but also reduce the likelihood of wastewater infrastructure becoming overwhelmed or damaged during a flood, thereby increasing the targeted communities’ resilience to floods. Priority sanitation facilities at the neighborhood level will be identified in a participatory manner with the beneficiary communities and the municipalities early during implementation, and appropriate designs will be prepared in an adaptive manner to respond to specific people-facing priorities in terms of spatial or facility targeting, use of appropriate technologies such as semi-permanent structures that respond to land tenure constraints, etc. Investments under this component are expected to contribute to improved access to safely managed sanitation resulting in reduced environmental degradation and improved living conditions and environment in the targeted areas.\n\nc) **Solid waste Investments (Estimated Cost Euro 17.00 million of which IBRD loan is Euro 0 and EU Grant is**\n\n**Euro 17.00 million).** Identified investments under this component will include construction of a new solid\nwaste landfill in one municipality, closing of existing dumpsites, and provision of waste collection and transfer equipment and construction of two transfer stations in targeted municipalities, and other demand driven solid Page 16 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) waste management investments as approved by the Bank. At appraisal solid waste activities were mainly envisaged in Kahramanmaraş Municipality, however other municipalities may identify limited investments in solid waste management, such as waste containers and collection vehicles as prioritized.\n\n31. **Component 2. Technical Assistance for Project Management and Supervision, Capacity Building, Communication**\n\n**and Citizen Engagement (Euro 14.59 million (US$16.03 million equivalent), of which EU Grant is Euro 1.885 million**\n**(US$2.07 million equivalent) and IBRD Loan is Euro 12.71 million (US$13.97 million equivalent).** This component\nwill finance goods and consultancy services for the following activities: a) **Project Management (Euro 2,605,000; Euro 780,000 from the EU Grant and Euro 1,825,000 from the IBRD**\n\n**loan).** This activity will finance: **(i) Incremental operating costs** **(Euro 500,000 from the loan)**, including goods\nand non- consulting services required to support ILBANK for day to day project coordination and supervision, and implementation of procurement, financial management, monitoring and evaluation, and safeguards functions and project audits; it will also finance workshops and other events such as steering committee meetings required to facilitate project management; **(ii) Consulting services for project management (Euro**\n**780,000 from the grant)**, including consultant services required to support implementation of procurement\nand financial management aspects, management of social and environmental safeguards and climate changerelated aspects, including dam safety requirements associated with the project, technical and contract management, monitoring and evaluation (M&E), and project reporting and communications, as agreed with the Bank; **(iii) Procurement of Video Conference System/s for ILBANK HQs and Regional Directorates (Euro**\n**1,325,000 from the Loan)** .\n\nb) **Consultancy services for design review and supervision of environmental** **infrastructure (Euro 10,250,000**\n\n**financed from the IBRD loan).** This activity will finance consultancy services for design review and preparation\nof bidding documents, taking into account climate change considerations, and supervision of construction and rehabilitation works for proposed environmental infrastructure investments under Component 1.\n\nc) **Institutional Capacity Building activities targeting the participating municipalities, utilities and ILBANK (Euro**\n\n**630,000 financed from the IBRD loan).** This activity will finance capacity building activities, including interalia, training, workshops and other learning events targeted at ILBANK, Municipalities and SKIs. Capacity\nbuilding activities will be aimed at, inter alia, improving performance efficiency, and increasing capacity within municipalities and SKIs/utilities to further modernize their operations to optimize efficiency and effectiveness in municipal service delivery while embracing principles such as resilience, financial and environmental sustainability, and inclusion. These activities will also seek to contribute to increased water security through more efficient delivery, and thus increased resilience to climate change. This activity will leverage World Bank tools and resources as well as synergies with other FRIT projects managed by the Bank to identify and prioritize capacity building needs in a participatory manner and identify and agree on selected actions to be implemented and monitored during the project’s lifetime. An Institutional Capacity Development Plan will be prepared for the project in consultation with relevant institutions at the start of the project, and an Action Plan, including specific capacity building activities will be developed and updated annually defining planned actions, to be confirmed on a no objection basis with the Bank. Agreed actions by the SKIs and municipalities to improve their performance will be defined in Performance Improvement Action Plans (PIAPs) comprising a set of feasible actions identified in a participatory manner by the relevant participating institutions at the start of the project. Relevant Bank activities and resources to be leveraged may include: a _Utility for the Future_ _(UOTF)_ initiative with support from the Global Water Security Partnership (GWSP) Multi-donor trust fund.\n\nPage 17 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) d) **Citizen/Community engagement activities and trainings to facilitate effective two-way engagement among**\n\n**stakeholders, including Turkish citizens and SUTPs, Municipalities and SKI’s to identify the needs and**\n**priorities for enhancing service delivery to all people in the beneficiary communities (Euro 370,000 financed**\n**from the EU Grant)** . Detailed activities will be planned and implemented in a participatory manner and will\nseek to identify and respond within the context of the current project to grievances and concerns relating to planning, implementation, and delivery of municipal services; affordability and other pertinent issues. The Bank will take advantage of the outcome of the citizen engagement activities of ILBANK under the relevant FRIT funded activities of the Bank.\n\ne) **Public Communication and Visibility activities (EURO 735,000 fully financed from the EU Grant).** This activity will finance Communications and Visibility activities for the project, including preparation of a detailed communication and visibility plan, information and outreach campaigns; brochures, promotional and audiovisual materials, and awareness-raising events. The objectives of project communications and visibility activities are to: (i) facilitate outreach and engagement of refugees and host communities in project activities; and (ii) share results and disseminate project lessons with key audiences for broader impact. Communications and visibility activities are also intended to communicate to project beneficiaries and stakeholders information about the EU’s financial contribution to support refugees and host communities in Turkey. [24] Activities will be detailed in a Communications and Visibility Plan that will be included in the POM.A detailed Communication and Visibility Plan will be developed at the start of implementation, in line with EU Communications and Visibility Manual for EU External Actions [25], as well as the World Bank’s external communications guidance.\nCommunications activities will also seek to target critical public education messaging to different stakeholder groups, including children and youths, and adults in relevant languages, specifically Turkish, English and Arabic.\n\n32. **The project has significant potential to enhance the resilience of environmental infrastructure and services, and of**\n\n**the targeted communities, to climate change-exacerbated risks, and to increase climate change mitigation from**\n**improved efficiency of WSS service delivery and SWM.** Efficiency gains, including NRW reduction and energy savings\n(which are both considered climate-smart activities through their potential to reduce emission), as well as the modernization/rehabilitation of WSS infrastructure under Component 1, are expected to result in large mitigation benefits, while the management of waste via a sanitary landfill rather than the current disposal sites that typically lack environmental controls is expected to have strong climate benefits. NRW reduction is expected to support utilities better withstand climate shocks to water supply, including water scarcity and drought, while reducing GHG emissions from pumping and treatment activities. Given the existing and future climate vulnerabilities, including the risk river and urban floods, and water scarcity, all project investments will account for climate aspects in their design.\nThe WSS activities, especially those addressing wastewater, will be designed in a flood-resilient manner to ensure environmentally sound and safe transport and treatment of wastewater. For the SWM activities, a regional solid waste management approach creates a more resilient solid waste management system since transport and waste disposal will be streamlined, following the operations and maintenance manual, and will follow safe, environmentally-sound practices to withstand rain and natural disasters. By transitioning from non-sanitary landfills to sanitary landfills, the waste system is expected to significantly improve how heavy storms and flooding affect the 24 The Delegation of the European Union to Turkey will be consulted on communications and visibility activities during implementation to ensure that the Communications and Visibility Plan is implemented in accordance with the Communications and Visibility Manual for EU External Actions as well as EU FRiT Visibility Guidelines.\n\n25 https://ec.europa.eu/europeaid/work/visibility/_en Page 18 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) surrounding environment. The leachate collection and treatment systems in the sanitary landfills will be designed to prevent contamination of surrounding areas during periods of heavy rain.\n\n33. **GHG Emissions:** The total net emissions of the project based on defined investments at appraisal were estimated at -1,139,559 tCO2-eq over the 30-year economic lifetime of the project. The average annual net emissions are 37,983 tCO2-eq. The total lifetime gross emissions are 671,988 tCO2-eq. The net emissions for the water supply investments in Osmaniye under subcomponent 1a are -990,458 tCO2-eq due to a reduction in tanker use as beneficiaries are switched to a piped potable water supply system. The wastewater collection and treatment investments in Osmaniye under subcomponent 1b are expected to see net emissions of -95,643 tCO2-eq due to a net reduction in methane and nitrous oxide emissions from introducing wastewater treatment. The wastewater system improvements in Kayseri under subcomponent 1b are expected to see net emission reductions of -53,458 tCO2-eq due to improved treatment standards leading to a net reduction in methane missions, greater energy efficiency for treatment, and a partial switch to gravity-based systems for collection. The solid waste investments under subcomponent 1c are estimated to have net emissions of -63,962 tCO2-eq leading to to a reduction in net emissions with a shift from unmanaged to managed landfills.\n\n**B. Results Chain**\n\n34. The results chain is presented in Figure 1. _Theory of Change_ below. Although the Theory of change diagram indicates the linkage with envisaged impacts, the project impacts will not be assessed under the project.\n\n**Figure 1. Theory of Change** **[26]**\n\n26 As highlighted through the patterned background, the ‘Impact level’ outcomes will not be measured as part of the project.\n\nPage 19 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996)\n\n**C. Rationale for Bank Involvement and Role of Partners**\n\n35. **The Bank has substantial global experience in supporting programs and managing policy dialogue to improve**\n\n**service delivery in situations of urban forced displacement.** The Bank has successfully mobilized new financing to\nsupport situations involving conflict-induced forced displacement and crowded-in funding from other donors, recognizing that forced displacement creates specific vulnerabilities for refugees and internally displaced persons (IDPs), who often lose rights, assets, livelihoods, and social capital when they flee their homes. Bank support has been in the form of financial support, Advisory Services and Analytics (ASA) services, and operational support towards implementation of multi-donor funded programs. Globally, the Bank has supported the preparation of needs assessments for refugee host countries, ASA on affected communities, and implementation of activities for provision and expansion of basic services to accommodate the increased pressure on existing systems due to refugees.\n\n36. **The Bank’s comparative advantage in involvement in interventions in situations of forced displacement also stems**\n\n**from its strong partnerships with a wide range of actors in this space** . The Bank has partnered with various host\nstates, UN agencies, donor countries, regional organizations and the private sector, providing grant or loan resources to support critical operations in several countries. It has also used its convening power to bring together different stakeholders to share lessons across sectors. As the Bank gets more involved in refugee contexts, it is increasingly supporting the application of development approaches to nationally-led responses such as the proposed. Globally, the Bank has implemented projects tackling socio-economic challenges for very vulnerable populations, as well as displaced and refugee populations, including in Turkey, Romania, Ukraine, Kazakhstan, Bosnia and Herzegovina, Russia, Lebanon, Jordan, Iraq, Afghanistan and Palestine. The Bank is also working in the socio-economic space in countries affected by conflict such as Uganda, Angola, Sierra Leone, Liberia, Congo, Chad, Rwanda, Burundi, Myanmar, Pakistan, Yemen, and West Bank and Gaza. The World Banks’ concessional funding [27] portfolio (of US$7 billion in 2017) targeted to help conflict affected countries with large displaced and refugee populations to tackle the integration of refugees and displaced populations while also benefiting host communities.\n\n37. **In Turkey, the Bank is currently managing a diverse US$4 billion IBRD loan portfolio of investment and ASA work,**\n\n**with strong synergies to the proposed project, and is preparing five other new operations in various sectors**\n**including employment, agriculture and social cohesion to be financed under the EU FRIT 2** **[28]** . The Bank intends to\nleverage geographical and thematical synergies between these projects to develop an integrative and innovative approach, especially in municipalities which will have interventions from multiple Bank administered FRIT projects.\nSome of the relevant activities include: (a) Projects in the municipal sector with a focus on urban sustainability and resilience such as the ongoing Sustainable Cities Series of Projects 1, 2, and the Additional Financing (SCP 1, SCP 2 and SCP1-AF); (b) Analytical Work such as the Turkey Urbanization Review (April 2015), the City Creditworthiness Academy (April 2016), and Sustainable Urban Water Supply and Sanitation (June 2016); (c) the Education Infrastructure for Resilience Project (Euro 150 million) financed under FRIT I, which is working with the construction and real estate department of MoNE to construct and equip schools in provinces with a large presence of Syrian refugees; (d) technical assistance to DG Migration Management, Ministry of Interior (DGMM), on the design and implementation of the harmonization strategy for migrants; preparation of a project concept to establish harmonization centers that can serve as one-stop service centers for Syrian refugees and migrants to access legal 27 International Development Association (IDA) 28 The other four Bank projects that have been approved by the EUD for financing under FRIT II: 1. Financing to enterprises conditional on employment creation for Refugees and Turkish Citizens (P171766); 2. Employment Support and Activation of Work-Able Refugees and Turkish Citizens; 3. Agricultural Employment Support for Refugees and Turkish Citizens through Enhanced Market Linkages (P171543); 4.\nPromoting Economic Opportunities and Social Cohesion through Social Enterprises for Refugees and Host Communities (P171456) Page 20 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) services and get referrals to other services; and work with DGMM and Turkish Red Crescent to provide inclusion support services targeting the refugees residing in temporary accommodation centers; (e) Bank collaboration with MoFLSS and İŞKUR implementing an EU financed operation (under FRIT I) aimed to improve employment outcomes for Syrian refugees and Turkish Citizens in select localities affected by the influx of Syrian refugees; (f) the Bank’s Social Development and Labor and Skills teams piloting social entrepreneurship as a model to help women who are locality bound but want to be economically active; (g) the Bank’s Finance and Competitiveness team implementing a lending project that facilitates access to credit to enterprises in localities that experienced a large influx of Syrian refugees, to catalyze job creation; and (h) the Bank’s Labor and Skills team working with MoFLSS and İŞKUR on demand-side analytics, to identify critical occupations and skills in demand in the 10 provinces with the largest influx of Syrian refugees.\n\n38. **The Bank has established good collaboration with development partners working in Turkey, including the EU,**\n\n**which is co-financing the current operation, the French Development Agency (AFD), which is preparing to**\n**implement similar projects in different affected municipalities, and several international and locally based NGOs,**\n**and will work in close collaboration with these partners throughout project implementation.** The EUD has allocated\na total of Euro 6 billion in two tranches over a period of six years (2016-2023) to Turkey to support the country in managing the influx of SUTPs, with about Euro 380 million allocated to improvement of municipal infrastructure services, to be administered by various development partners including the Bank.\n\n**D. Lessons Learned and Reflected in the Project Design**\n\n39. The project will incorporate important lessons captured from the implementation of World Bank-financed projects across the globe and in Turkey. Lessons learned that are reflected in the project design are described in the following paragraphs.\n\n- **Having a strong pre-identified investment pipeline ensures project readiness that is critical for successful**\n**implementation.** The project included 15 sub-projects at appraisal stage, for which the majority of the feasibility\nstudies and technical designs for entire municipal investments were available.\n\n- **An integrated approach that targets both host communities and refugees and combines** _**place-based**_ **and**\n_**people-based**_ **methods is critical when designing an emergency response, which in this case spills over into the**\n**development stage given increasing integration of refugees with host communities.** Given the integration of\nmost of the refugees among host communities and the shift in locating refugees from dedicated camps to regular urban settlements, the project has adopted a development-oriented ‘place-based’ response for improving services and infrastructure in the urban areas that are most affected by the forced displacement crisis. Thus, it will specifically identify and prioritize in consultation with municipalities the most affected neighborhoods or settlements, combined with ‘people-facing’ interventions in environmental infrastructure facilities and services that will be identified by host and refugee populations to address the most pressing priorities.\n\n- **Defining the scope of projects clearly while allowing for some flexibility in design by involving stakeholders in**\n**decisions concerning their needs has facilitated achievement of better results in implementing local solutions**\n**in similar situations.** While the project has a pre-defined scope of activities for the larger environmental\ninfrastructure based on the municipal investment priorities of participating municipalities, it will include a consultative decision-making approach to identify the specific needs and priorities of host and refugee communities at the neighborhood level, as well as for capacity building activities.\n\nPage 21 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996)\n\n- **Both quick wins and mid-/long-term improvements are necessary to restore trust and the social contract.** Based\non experience from similar contexts in countries such as Lebanon where there was mounting pressure and perceived competition over resources and services, quick visible improvements in municipal services were critical for building trust in local authorities. On the other hand, mid-size, developmental solutions, though taking longer time to prepare, provided larger, more lasting impacts. The project will seek to adopt this two-pronged approach to improving services combining the larger infrastructure investments with the more immediate people-facing activities identified with municipalities and communities in a participatory manner.\n\n- **Social interventions engaging both host communities and refugees are as important as physical infrastructure**\n**interventions for ensuring the socio-economic recovery of communities affected by refugees.** Some social\ninterventions focusing on behavior change and public education will therefore be included in the project design, along with those targeted at improving service delivery to increase sustainability and reduce social tensions. The project is also exploiting synergies with other FRIT projects that more directly address some of the key drivers of social issues, such as skills training and learning programs that would help in mitigating such tensions.\n\n**IV.** **IMPLEMENTATION ARRANGEMENTS**\n\n**A. Institutional and Implementation Arrangements**\n\n**Project Implementation Arrangements**\n\n40. **ILBANK.** ILBANK is the Borrower for the IBRD loan and intermediary recipient of the grant, and the project implementing agency, serving as a Financial Intermediary to municipalities and SKIs. ILBANK is a state-owned development and investment bank based in _[Ankara](https://en.wikipedia.org/wiki/Ankara)_ [, relevant to the Ministry of Environment and Urbanization](https://en.wikipedia.org/wiki/Ministry_of_Environment_and_Urban_Planning_(Turkey)) _._ The Government of Turkey represented by the Ministry of Treasury and Finance is the guarantor of the loan **.** The project will be implemented by an existing Project Management Unit established under ILBANK’s International Relations Department, as is the case under the ongoing SCP series of projects at the central level, and by Project Implementing Units (PIUs) to be set up within the participating municipalities and/or SKIs at the local level. ILBANK has a long experience with managing implementation of municipal infrastructure investments as a financial intermediary and has demonstrated its capacity for managing World Bank loans through the Municipal Services Project (2004-2016) and the ongoing Sustainable Cities Series of Projects. ILBANK establishes the creditworthiness of all local governments in Turkey, provides loans (or grants for small municipalities and local governments) and guarantees, channels funding from International Finance Institutions (IFI), and carries out all required due diligence. ILBANK’s performance on past and ongoing operations has consistently been rated in the ‘satisfactory’ range by the Bank in its Implementation Status reports (ISRs) and its Implementation Completion Reports (ICRs).\n\n41. ILBANK PMU shall be responsible for day-to-day management and implementation of the Project, in accordance with the provisions of the POM, including the responsibility for financial management, procurement, disbursement, monitoring of environmental and social safeguards, reporting, monitoring and evaluation of the Project activities.\nThe existing PMU has qualified staff responsible for management of core project management functions, including procurement, financial management, safeguards in line with ESF requirements [29], and M&E. At least one additional 29 Responsibilities for management of safeguards are further outlined in relevant ESF documents, and will also include the municipal level Page 22 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) designated individual consultant/staff will be hired by ILBANK to support specific core functions, including procurement and financial management, as well as safeguards given the large portfolio. The PMU will also be supported by several specialized departments in ILBANK including the Project department, Investment Appraisal department, Infrastructure Implementation department, as well as the four regional directorates covering the proposed municipalities. The PMU is led by a department head and unit managers, and it has staff capacity in procurement, financial management, safeguards, and technical sectors such as water supply, wastewater, and transport. ILBANK PMU staff have been beneficiaries of intensive training sessions on World Bank procurement, safeguard implementation and other topics. The experience gained during implementation, together with the efforts of the GoT to reform ILBANK have made it a more attractive institution for other International Financing Institutions (IFIs) and laid the groundwork for harmonizing development financing in the sector.\n\n42. **Municipality Project Implementation Units (PIUs)** . At the local level, the project will be implemented by PIUs at the municipal/utility level to manage subproject implementation. A total of six PIUs will be established for the project at the local level, including PIUs in the Water and Wastewater utilities/SKIs in all five selected municipalities, to be responsible for implementation of the WSS sub-projects at the local level and one PIU in Kahramanmaras Municipality to be responsible for implementation of the solid waste sub-project. The PIUs will be primarily staffed by designated municipal/utility employees with the skills and qualifications to carry out fiduciary functions, as well as technical/contract management supervision with support from the supervision consultants. The local PIUs will have designated staff responsible for the core functions, including procurement, financial management, environmental and social safeguards, with minimum requisite qualifications as will be defined in the POM. In general, municipalities/utilities have staff on hand who can manage all aspects of sub-project implementation, and these may be assigned to the respective PIUs. However, where specific skills are required if not locally available, the PIUs could be supplemented by individual consultants as deemed necessary. The PIU staff will benefit from capacity building activities to enhance relevant skills as deemed necessary.\n\n43. The PIUs, which will be responsible for effective management of the subprojects as described in the various sub project financing agreements, will be established within no later than 30 (thirty) calendar days after the respective municipality or utility has signed its first Sub-financing Agreements with ILBANK. ILBANK PMU will prepare and evaluate the tender documents for the investment and TA components, with representation from the respective municipalities on the evaluation committees. However, the municipalities/utilities will be the contracting authorities for relevant sub-projects. While most of the Metropolitan and many Provincial municipalities in Turkey have experienced and dedicated departments to run capital projects financed by IFIs, institutional capacities for sustaining outcomes through sound operations and maintenance will require further strengthening. The project therefore includes institutional strengthening activities to build their capacity during implementation under Component 2.\n\n44. **Feasibility studies, designs, and construction supervision.** The feasibility reports of the proposed investments will be reviewed and approved by ILBANK and the Strategy and Budgeting Presidency (SBP). Design review and construction supervision will be performed by independent consultancy firms financed through the project. ILBANK’s regional offices will monitor the sub-project implementation progress monthly and will prepare monitoring reports.\nConsequently, Regional Directorates will support the PMU by providing relevant information on time and coordinating with the municipalities/ utilities.\n\n45. **Project Operations Manual (POM).** ILBANK will develop by project effectiveness a detailed POM for purposes of project implementation setting out: (i) policies and procedures relating to implementation of project components PIUs and contractors.\n\nPage 23 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) and eligibility of sub-projects; (ii) Financial management and procurement arrangements and procedures; and (ii) safeguards obligations and procedures.\n\n**B. Results Monitoring and Evaluation Arrangements**\n\n46. ILBANK PMU will be responsible for Monitoring and Evaluation (M&E) under the project at the central level, and it will coordinate all M&E activities with the PIUs. ILBANK has experience in M&E through the previous and ongoing projects. The ILBANK PMU will collect data on results indicators from the participating municipalities/utilities. Project Progress reports will be prepared and submitted quarterly by ILBANK. M&E activities, including monitoring of implementation progress and results, will be reviewed by the World Bank as part project supervision missions to be carried out at least twice a year. The incremental costs for the project M&E arrangements will be covered under Component 2 Project Management.\n\n47. A mid-term review of the project will be carried out around two years after commencement of the project to assess overall implementation progress and identify and resolve any key issues affecting implementation. An evaluation will also be carried out at the end of the project as an input to the Implementation Completion and Results Report to evaluate final results, assess overall performance, and capture key lessons.\n\n**C. Sustainability**\n\n48. **Sustainability of the municipal investments and service delivery will depend on the utilities ability to mobilize the**\n\n**necessary technical and financial resources to manage the new assets effectively.** The financial sustainability of\nutilities and/or municipalities in Turkey is underpinned by their ability to: (a) report positive operating balances (recurring operating revenues – recurring operating expenditures > 0); and (b) fund at least part of their capital expenditure from their own funds (positive operating balance). The four SKIs that will receive loans under the project all reported positive operating margins in FY2018: 15.6 percent for Adana SKI, 32.2 percent for Kahramanmaraş SKI, 33.7 percent for Kayseri SKI, and 20 percent for Osmaniye Municipality. The dialogue concerning SKI or municipality performance and the Performance Improvement Action Plans to be supported through the TA Component of the project will seek to ensure that issues of sustainability, including financial aspects are addressed through specific actions identified by the respective institutions.\n\n49. **Institutionally, sustainability will also be enhanced through implementation of the proposed technical assistance**\n\n**measures aimed at targeted performance improvement for municipalities/utilities under the project.** The TA\ncomponent will seek, inter-alia, to: (i) promote more effective and efficient utility management in line with good practices; (ii) provide support to the ILBANK Project Management Unit (PMU) and municipalities/utilities in embedding local-level actions that link with EU policy directives on climate change, energy efficiency, water, solid waste management, and in building awareness on international best practices to promote and implement sustainable utility operations; and (iii) support provision of training and capacity building to municipalities; with an aim to improve operational efficiency, financial sustainability and customer satisfaction. The Bank will also draw upon additional resources in line with initiatives such as the proposed _Utilities for the Future_ Technical Assistance Program financed by GWSP to support utilities in improving their overall performance. Under the proposed project, the participating utilities will attend trainings on utility performance improvement and financial management to improve their knowledge capacity to improve recovery of Operational and Maintenance (O&M) and investment costs.\n\nPage 24 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) 50. **Technical sustainability of the physical assets will also depend on quality assurance provided through effective**\n\n**management of the design review taking into account climate change considerations and construction supervision**\n**process.** Since all the proposed investments and technical assistance measures have been developed in line with the\nrelevant national policies and international best practice, expected results of the proposed project will contribute to sustainability of the actions at policy level.\n\n**V.** **PROJECT APPRAISAL SUMMARY**\n\n**A. Technical, Economic and Financial Analysis**\n\n51. **Technical Analysis.** At appraisal stage it was expected that the project will finance a total of fifteen environmental infrastructure sub-projects in the five municipalities, spanning three environmental infrastructure sub-sectors, water supply, wastewater management and solid waste management, in addition to the Technical Assistance activities. A summary of the status of identified sub-project interventions by municipality at appraisal is provided in Table 1 below.\nWhile feasibility studies and technical designs were prepared for nine of the municipal sub-projects identified at appraisal, design studies are ongoing for the additional six subprojects. The scope of investments and respective project footprints may change as part of a design review process to be carried out for all sub-projects prior to bidding for the construction works. The design review will, among other things, ensure that individual infrastructure facilities where appropriate consider adaptive designs that would allow for a scale up or scale down of capacity in the event of changes in the refugee population. The design review will be carried out by independent consultancy firms to be commissioned by ILBANK in each municipality on behalf of the municipalities. The same consultants will also be responsible for preparation of the bidding documents and construction supervision to ensure works quality control.\nMost of the available engineering designs have been reviewed and conform to good practice, first by ILBANK and by the World Bank technical team and comments have been provided where gaps have been identified. Terms of reference for the design review and supervision consultants have been prepared by ILBANK and the selection process for the consultants will be initiated as soon as the terms of reference are cleared by the World Bank. Although the consultants will be procured by ILBANK, the respective municipalities shall be invited into the evaluation process and will sign the contracts once the sub-financing agreements are signed between ILBANK and the municipalities/utilities.\nA summary of the status of each sub-contract is given below. ILBANK may also request the Bank’s no objection to pre-finance the consultants and be reimbursed through retroactive financing with the World Bank if deemed necessary.\n\n**Table 1. Indicative List of Environmental Infrastructure Sub-Projects identified at appraisal**\n\n**Est. Financing**\n**Sub-Project Description**\n**Need (m EUR)**\n\n**Target**\n**Municipality**\n\n**Sub-Project**\n\n**No.**\n\n**Status of technical**\n\n**designs (at**\n\n**appraisal)**\nAdana A.1 Kozan İmamoğlu Yedigöze Drinking Water 21.85 Available with Transmission Line sufficient quality A.1 Kozan İmamoğlu Yedigöze Drinking Water 21.85 Available with Transmission Line sufficient quality A.2 Yedigöze Water Treatment Plant 13.15 Ongoing A.3 Kozan Pınargözü Drinking Water 28.07 Available with Transmission Line and Network sufficient quality B.1 Kahramanmaraş Northern Districts 17.00 Available with Integrated Solid Waste Project sufficient quality B.2 Kahramanmaraş (Centrum) Drinking Water, 17.00 Available with Sewerage and Stormwater Project West Part sufficient quality A.3 Kozan Pınargözü Drinking Water 28.07 Available with Transmission Line and Network sufficient quality Kahramanmaraş B.1 Kahramanmaraş Northern Districts 17.00 Available with Integrated Solid Waste Project sufficient quality 17.00 Available with sufficient quality Page 25 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996)\n\n**Est. Financing**\n**Sub-Project Description**\n**Need (m EUR)**\n\n**Target**\n**Municipality**\n\n**Sub-Project**\n\n**No.**\n\n**appraisal)**\nB.3 Ceyhan Basin Wastewater Treatment Plants 8.40 Available for 3 WWTPs; Ongoing for\n\n**Status of technical**\n\n**designs (at**\n\n1 WWTP B.4 Elbistan Drinking Water Network Project 8.00 Ongoing B.5 Kahramanmaraş (Centrum) Drinking Water, 18.80 Available Sewerage and Stormwater Project East Part with sufficient quality B.6 Elbistan Drinking Water Transmission Line 25.00 Ongoing review Kayseri C.1 Kayseri Wastewater Treatment Plant 25.00 Available Konya D.1 Akşehir Water Supply Project 4.71 Available but needs improvement D.2 Ilgin Wastewater Treatment Plant 3.90 Available and under revision D.3 Cumra Wastewater Treatment Plant 4.56 Available and under revision Osmaniye E.1 Osmaniye (Centrum) Drinking Water Project 27.65 Available with sufficient quality E.2 Osmaniye (Centrum) Sewerage Project 29.91 Ongoing review\n\n**Economic and Financial Analysis**\n\n52. The financial and economic analyses focus on sub-projects in the municipalities that will benefit from the loan proceeds, namely: Adana: for drinking water transmission lines and network; Kahramanmaras solid waste system, wastewater treatment plants (WWTP) and sewerage network projects; Kayseri WWTP project; and Osmaniye drinking water and sewerage network projects. Konya Aksehir water supply project and wastewater treatment plants have been left out as they are to be fully grant financed. The integrated solid waste management (ISWM) project in Kahramanmaras is justified based on financial and economic returns.\n\n53. Financial Internal Rates of Return (FIRR) were estimated for the productive subprojects resulting generally in negative Financial Net Present Values (NPV) thus reflecting non-viability on solely commercial basis. Nonetheless, Component 2, TA component includes capacity building activities to utilities to improve operational efficiency in order to operate and maintain investments and deliver services in a sustainable manner. Such support will create conditions to move towards commercial viability. The Bank will also support the utilities in identifying and implementing performance improvements through other initiatives such as the Utilities for the Future Trust Program financed through a GWSP TF and implemented by the Water Global Practice and the ECA Water team. These types of support will foster better management practices for enhanced performance in the future of these utilities.\n\n54. The economic Net Present Values (NPV) of the entire project’s lifetime is estimated at EUR 19.4 million on the basis of the present values of costs and benefits produced by the project. Economic IRR (E-IRR) was 11 percent with benefits to cost ratio of 1.09 for A) Adana Drinking Water and Transmission Sub-project, 10.4 percent and 1.02 for B) Kahramanmaras Wastewater Treatment Plants and 15.5 percent and 1.56 for Kahramanmaras solid waste, 12.2 percent and 1.64 for C) Kayseri WWTP project 23.82 percent and 9.17 for E) Osmaniye drinking water and sewerage network projects. Although estimated E-IRRs are slightly above the discount rate of 10 percent, they are comfortably Page 26 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) above the recommended discount rate of 6 percent per WB guideline [30] .\n\n55. Sensitivity analyses have been carried out measuring change in the E-NPV due to changes in critical parameters including investment costs, personnel costs and power costs. Avoided GHG emissions improving economic benefits to the project due to more efficient wastewater collection, lower energy use for wastewater treatment, shift in solid waste management to a sanitary landfill from current disposal sites and energy efficiency gains from NRW reductions, were also considered as part of economic assessment. The benefits from estimated net emissions of the project based on defined investments at appraisal were estimated at EUR 57.5 million over the 30-year economic lifetime of the project [31] .\n\n56. As the investments will be implemented under affiliated water utilities and/or municipalities, a baseline assessment of their financial position has been carried, to ensure financial sustainability based on 2018 and past three years’ trend data. Financial sustainability of utilities and/or municipalities are underpinned by their ability to: (a) report positive operating balances (recurring operating revenues – recurring operating expenditures > 0); and (b) fund at least part of their capital expenditure from their own funds (positive operating balance as explained above). Current margin reported in FY2018 by Adana SKI was 15.6 percent, 32.2 percent for Kahramanmaras SKI and 33.7 percent for Kayseri SKI. Osmaniye Municipality closed FY2018 with an operating margin of 20 percent. With increased capital investments, utilities are projected to report balanced overall budget results, supported with debt financing and grant contribution as foreseen under the sub-projects. Results of financial baseline assessments are provided in the Annex 3, Financial Estimate section. Detailed financial management and creditworthiness analyses for sub-borrower water utilities and municipalities incorporating projections on fiscal performance, expenditure coverage and indebtedness indicated that sub-borrowers will have capacity to meet their financial commitments while remaining exposed to adverse economic conditions.\n\n**B. Fiduciary**\n\n**Financial Management**\n\n57. The financial management arrangements for the project are satisfactory. The PMU established under the International Relations Department of ILBANK will be responsible for the financial management arrangements for the Project. The PMU has satisfactory arrangements for the Sustainable Cities Project I, II and its AF and the same arrangements will be adopted for Municipal Services Improvement Project in Refugee Affected Areas.\n\n58. ILBANK will on-lend proceeds of the IBRD loan and on-grant the grant proceeds to the participating municipalities/utilities and will provide technical support to these municipalities/utilities in overseeing implementation of the investments. The procurements of the project will be conducted centrally by ILBANK with the participation of the Municipalities/utilities in the bid evaluation committees and concluded contracts will be signed by the relevant Municipalities/utilities. Payments to the suppliers will be registered directly by ILBANK upon submission of acceptable approval documents. The PMU in ILBANK will be responsible for the management of the Designated Accounts and project accounting and reporting. The project accounts will be subject to independent audit on an annual basis by the Treasury Controllers of the Ministry of Treasury and Finance of independent auditors. The 30 Additionally, there are other components that would generate tangible and intangible benefits not measured because of i) inability due to methodological reasons and ii) limited data to estimate benefits at the time of drafting the analysis into the PAD.\n31 Using World Bank GHG Accounting Tool for Water sector lending projects average shadow price of carbon converted to Euro using EUR/US$ conversion rate of 0,90 for calculation in monetary terms.\n\nPage 27 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) project audit report will be made publicly available according to the World Bank’s Access to Information Policy.\n\n**Procurement**\n\n59. ILBANK as the financial intermediary, as well as the beneficiary municipalities are public entities. Thus, the World Bank Procurement Regulations for IPF Borrowers – July 2016 revised in November 2017 and August 2018 (“Procurement Regulations”) will apply to the proposed Project. The World Bank's Guidelines on Preventing and Combating Fraud and Corruption in Projects Financed by IBRD Loans and IDA Credits and Grants, dated October 15, 2006 and revised in January 2011 and as of July 1, 2016 (“Anti-Corruption Guidelines”) will also apply to the proposed project. A General Procurement Notice will be published on the World Bank’s external website and United Nations Development Business online.\n\n60. A draft Project Procurement Strategy for Development (PPSD) has been prepared by ILBANK as required by the Procurement Regulations to determine the optimum procurement approach to deliver the right procurement result under the proposed project. The PPSD was agreed between ILBANK and the World Bank. The PPSD proposed that all procurements will be conducted by the PMU with the support of ILBANK Investment Coordination Department as needed. The relevant municipalities/utilities will be invited to participate in the bid evaluation committees, sign the contracts and they will be responsible from the contract implementation through their PIUs supported with experts in the procurement and contract management. Considering four years project implementation period, the PPSD proposed to initiate procurements as early as possible for the timely implementation of the contracts.\n\n61. The selection of the “Design Review and Supervision Consultant(s)” has been identified as the strategically important procurement although their contract values are relatively low. It is envisaged that the consultants will support the PMU for the preparation of the bidding documents for the infrastructure investments and consultants’ timely input is very critical for the initiation of the relevant procurements planned in the procurement plan. Following the Bank’s guidance for procurements in situations of urgent need of assistance, the selection of the consultants will be accelerated by adapting fit-for-purpose principle, and accordingly higher thresholds will be applied for the “consultant’s qualification-based selection” method to deliver the project development objectives within the duration of the project. All other infrastructure contracts are “strategic critical” for the municipalities. Hence, the selected consultant will also provide services to relevant municipalities/utilities for the quality assurance of the works and goods and timely completion of the contracts within their original contract prices. More details on the findings of the procurement assessment, the proposed procurement supervision arrangements, risks and relevant mitigation measures to address them are provided in Annex 4.\n\n~~.~~ **C. Legal Operational Policies** .\n\n**Triggered?**\n\nProjects on International Waterways OP 7.50 No Projects in Disputed Areas OP 7.60 No ~~.~~ Page 28 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996)\n\n**D. Environmental and Social**\n\n**Environment**\n\n62. The project is categorized as “ _Substantial Risk_ ” within the World Bank’s Environmental and Social Framework (ESF), given that at appraisal stage it consisted of 15 identified infrastructure investments (subprojects) in 5 municipalities and target provinces which are host to among the largest numbers of refugees in urban settlements. The project is expected to have positive impacts on improving environmental infrastructure and hence, contributing to resilience in communities impacted by refugee influx. It is not expected that sub-projects will have significant adverse effects to human health and/or the environment. The project will not result in significant adverse cumulative and does not include activities that might involve transboundary impacts. Moreover, the areas likely to be affected by the project are not of high value and sensitivity.\n\n63. Environmental and Social Standards (ESS) 1-10, with the exception of ESS 7 (the standard does not apply, as there are no indigenous or SSAHUTLC in Turkey) apply to the Project. The project also does not necessitate the application of OP 7.50: Projects on International Waterways [32] and OP 7.60: Projects in Disputed Areas. However, the urgent need for assistance to support the Government of Turkey's efforts to mitigate rising pressures on service delivery from the continual influx of people across the border and from camps to cities has required the Bank policy OP 10, para 12 – Projects prepared in a Situation of Urgent Need of Assistance or Capacity Constraints to be applied.\n\n64. The water supply subprojects will include the construction of water treatment plants, construction of new and extension of existing water distribution networks and transmission line, expansion of water reservoir capacities, and non-revenue water reduction activities such as installation of SCADA systems and development of water metering areas. Wastewater investments will include construction of new wastewater treatment plants or expanding their capacity, construction of new and extension of existing sewerage collection networks. Solid waste facilities will include new waste collection and transfer equipment, construction of two transfer stations and a landfill (including composting and mechanical separation facilities) in Afsin District of Kahramanmaraş, and rehabilitation or closure of existing dumpsites. These subprojects have potential environmental risks both during construction and operation phases.\n\n65. In line with E&S standards that apply to the project, potential environmental and social risks associated with the construction and operation of the wastewater treatment plants and solid waste landfills are: (i) generation of noise, dust, wastewater, excess material and other waste in the construction phase; (ii) emission of dust, bio-aerosols, odors, and vehicle exhaust during waste collection and transportation; (iii) contaminated runoff, leachate generation and landfill gas emissions; groundwater contamination; (iv) noise and vibration from the operation of waste processing equipment; (v) fire and explosion risks due to landfill gas; (vi) community health and safety impacts such as visual, dust and odor problems, as well as scavenging related impacts and physical, chemical and biological hazards; (vii) occupational health and safety impacts such as accidents and injuries, chemical exposure, noise and vibration exposure and exposure to pathogens and vectors; (viii) closure and post-closure management of the landfills; (ix) discharge of treated wastewater to receiving bodies; (x) sludge and solids generation from wastewater treatment plants; (xi) emissions of hydrogen sulfide, methane, ozone, gaseous or volatile chemicals associated with WWTPs; and (xii) ecological impacts on the nearby receptors. Although ILBANK - the project implementing entity - has extensive experience in applying the Bank’s safeguard policies under MSP 1 and 2 and SCP 1 and 2, it lacks experience in managing complex and substantial-risk projects in Fragility-Conflict-Violence (FCV) contexts.\n\n32 Based on sub-project selection criteria, projects triggering OP 7.50 are not eligible for participation under this project.\n\nPage 29 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) 66. The World Bank and ILBANK teams have agreed on the environmental and social risk categorization and assessment requirements of each subproject as presented in Table 2 below.\n\n67. ILBANK has prepared an Environmental and Social Management Framework (ESMF) for managing environmental and social impacts and risks of subprojects, and an Environmental and Social Commitment Plan (ESCP). The ESMF was consulted with stakeholders on November 26, 2019 and was disclosed on December 4, 2019. Subproject and sitespecific environmental impacts will be discussed in detail in the subproject specific environmental assessment documents (such as Environmental and Social Impact Assessment (ESIA) and Environmental and Social Management Plan (ESMP)) to be prepared by ILBANK, on behalf of related municipalities, during the project implementation phase.\nILBANK already completed the selection of Environmental and Social Consultancy Company for the preparation of subproject specific environmental and social assessment documents and initiated the preparation of documents accordingly. One of the key actors in the implementation of the ESMF will be ILBANK Project Management Unit (PMU). ILBANK will recruit additional environmental and social specialists (at least one environmental and one social) to increase PMU capacity during project preparation and implementation.\n\n**Table 2. E&S Risk Categorization of Proposed Subprojects**\n\n**E&S** **Risk**\n**No** **Subproject**\n**Categorization**\n\n**E&S** **Risk** **E&S Assessment Requirements at**\n**No** **Subproject**\n**Categorization** **appraisal**\n\n1-1 Kozan Imamoglu Yedigoze Drinking Water Substantial ESIA; SEP; Dam safety assessment; ExTransmission Line Post Social Audit for subproject 1-2 1-1 Kozan Imamoglu Yedigoze Drinking Water Substantial ESIA; SEP; Dam safety assessment; ExTransmission Line Post Social Audit for subproject 1-2 1-2 Yedigoze Water Treatment Plant 1-3 Kozan Pinargozu Drinking Water Transmission Line and Network 2-1 Kahramanmaras Northern Districts Integrated Solid Substantial ESIA; SEP; LARAP Waste Project Substantial Substantial ESIA; SEP; LARAP 2-2 Kahramanmaraş (Centrum) Drinking Water, Sewerage and Stormwater Project West Part 2-2 Kahramanmaraş (Centrum) Drinking Water, Moderate ESMP; SEP _(covering subprojects 2-2,_ Sewerage and Stormwater Project West Part _2-3, 2-4, 2-5, and 2-6)*_ ; Ex Post Social 2-3 Kahramanmaraş (Centrum) Drinking Water, Audit for subproject 2-2 Sewerage and Stormwater Project East Part 2-4 Ceyhan Basin Wastewater Treatment Plants Substantial ESIA; SEP _(covering subprojects 2-2,_\n\n- Ekinozu WWTP and Collectors _2-3, 2-4, 2-5, and 2-6)*_ ; LARAP\n\n- Caglayancerit WWTP\n\n- Andirin WWTP and Collectors\n\n- Goksun WWTP and Collectors\n\n2-5 Elbistan Drinking Water Network Project Moderate ESMP; SEP _(covering subprojects 2-2,_ 2-6 Elbistan Drinking Water Transmission Line _2-3, 2-4, 2-5, and 2-6)*_ ; LARAP 3-1 Extension of Kayseri Wastewater Treatment Plant Substantial ESIA; SEP Substantial ESIA; SEP _(covering subprojects 2-2,_ _2-3, 2-4, 2-5, and 2-6)*_ ; LARAP 2-5 Elbistan Drinking Water Network Project Moderate ESMP; SEP _(covering subprojects 2-2,_ 2-6 Elbistan Drinking Water Transmission Line _2-3, 2-4, 2-5, and 2-6)*_ ; LARAP 3-1 Extension of Kayseri Wastewater Treatment Plant Substantial ESIA; SEP 4-1 Aksehir Water Supply Project Moderate ESMP; SEP 4-2 Ilgin Wastewater Treatment Plant _Assessment pending at appraisal_ 4-3 Cumra Wastewater Treatment Plant _Assessment pending at appraisal_ 5-1 Osmaniye (Centrum) Drinking Water Project Moderate ESMP; SEP 5-2 Osmaniye (Centrum) Sewerage Project _* For subprojects in Kahramanmaras, 2 SEPs will be prepared in total; one for No 2-1, and one for No 2-2, 2-3, 2-4, 2-5, and 2-6._ 68. **Safety of Dams.** One sub-project in Adana includes the 34 km long Kozan İmamoğlu Yedigöze Drinking Water Page 30 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) Transmission Line, which will be fed by the Yedigöze reservoir on the Yedigoze dam/hydropower plant. The Yedigöze hydropower plant was built on the Seyhan River in Adana province between 2006 and 2010. The main purposes of the plant are hydropower and irrigation. It is owned and operated by SANKO, a private Turkish company, under a Build-Operate-Transfer project licensed by the Ministry of Energy and Natural Resources (MENR). The Bank Dam Safety Expert visited Adana to conduct a due diligence on dam safety aspects for Yedigöze dam as part of project preparation. Yedigoze dam and its appurtenant works were built 10 years ago, and they are well operated and maintained by SANKO, which is responsible for the safety of its facilities. Devlet Su Isleri (DSI) the State agency responsible for water resources management and irrigation ensures the state supervision in accordance with the applicable legislation, and was directly involved in all project stages, from the feasibility study to the reservoir impoundment, and it ensures that the operator implements all required safety measures. In this context, DSI examines all reports related to dam operation and safety and carries out regular dam inspections (twice a year).\n\n69. DSI confirmed the sound state and behavior of the dam and its appurtenant structures. Moreover, the board of experts under the Ministry of Agriculture and Forest carried out an inspection in December 2015 and issued a positive opinion regarding the safety conditions of the Yedigöze dam. This report, which confirms the general good conditions and performance of the dam, can be accepted by the WB as previous dam safety assessment, this is in accordance with OP4.37 requirements. SANKO personnel in charge of the surveillance of the dam are well organized and perform daily routine inspections and maintenance checks. The dam and its appurtenant structures are equipped with 272 monitoring instruments divided into 15 different categories; measurements are automatic and/or manual. Analysis and interpretation of the measurements are made by both SANKO and DSI, and dam surveillance reports are issued quarterly. The instrumentation Plan, the Operation & Maintenance Plan and the Emergency Preparedness Plan are regularly updated by SANKO and submitted to DSI for approval. Overall, the Bank expert’s general impression was positive, with both the operator and the state supervisory authority aware of the importance of the safety and the maintenance of its facilities. The project will finance technical assistance under Component 2 to have independent dam safety specialists, including competent dam safety specialists of DSI, acceptable to the Bank carry out a safety inspection of the above-mentioned dams, based on terms of reference acceptable to the Bank, at intervals of not less than once every five years according to Good International Industry Practice, during the implementation and after completion of the Project. The first of said safety inspections shall be carried out not later than December 2020.\n\n**Social**\n\n70. **Social risk is rated as Substantial.** Social risks and impacts intrinsic to the project are comparable to other municipal investments carried out under the Bank financed Sustainable Cities Projects 1 and 2 with ILBANK, which are assessed as Moderate. Contextual risks are rated Substantial because the sub-projects to be financed under the project are designed in such manner to enable broad population to benefit, regardless of their nationality or any vulnerability.\nSuch approach should allow the management of contextual risks, which may otherwise be considered High. Main social impacts are expected to be manageable and limited to stakeholder engagement with various host communities and refugees, economic displacement with limited livelihood impacts, limited labor influx impacts expected during construction phase, and community health and safety impacts caused by investments in wastewater treatment and solid waste facilities. The ESMF and site – specific ESMPs will address such impacts and propose mitigation measures.\nMajor civil works are expected in association with the construction of water and solid waste treatment plans, while other investments will mainly include the construction of linear structures. SEA (sexual exploitation and abuse) risks are assessed as Low. The project will carry out a set of training activities on SEA prevention, which will target ILBANK and participating municipalities/utilities staff and civil works construction contractors. Project workers will be subject to the Code of Conduct, which will be included in the contracts for the civil works and project labor management Page 31 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) procedure. ILBANK prepared a project labor management procedure (LMP), which applies to project workers, including ILBANK staff and workers engaged in sub-projects including civil works contractors. Civil works contractors shall adopt the project LMP, which includes proposed Code of Conduct.\n\n71. ILBANK prepared a Resettlement Framework (RF) as required by ESS5, which was disclosed on November 26, 2019 and consulted in conjunction with the ESMF and SEF on December 2, 2019. The majority of the sub-projects requiring land acquisition have already completed acquisition works. However, designs are currently still under review and revision, and may change the project footprint and thus the scope of land acquisition. Ex-post social audits of previous land acquisitions will identify any gaps with ESS 5 which need to be addressed and will be completed before the start of civil works of relevant sub projects. Resettlement Plans, and any other site-specific E&S documents will be finalized and approved by the Bank and disclosed prior to the beginning of tendering of works of each sub-project.\n\n72. **Stakeholder Engagement and Citizen Engagement:** In line with ESS 10, ILBANK prepared a Stakeholder Engagement Framework, outlining general principles and a collaborative strategy to identify stakeholders and plan for an engagement process in accordance with ESS10. In addition, the SEPs for each municipality will be prepared. The borrowing municipalities and utilities will implement SEPs including timely information disclosure in accessible manner, engaging stakeholders across host and refugee communities in the project areas, with an overall aim to prevent any social tensions and perceptions of unequal access to project benefits. Before project implementation, Municipalities will develop information booklet/leaflets in Turkish and Arabic describing subproject intended outcomes including the need of land expropriation, which will be shared among the project affected parties (PAPs) during resettlement plan preparation work. The SEF/SEP includes a two-level project level Grievance Redress Mechanism (GRM): the first at the municipal level and the second at ILBANK level. Municipalities will ensure that the GRMs are functional and accessible to the refugee and host communities in the languages they speak for any issues that they would like to address. Municipalities will report on the percentage of grievances resolved within a stipulated time frame and the project will utilize Citizen Report Cards at the beginning and end-term to assess if the municipal investments improved the cities’ current infrastructure and addressed the prior needs of both host and refugee communities. Additional citizen engagement activities are described under Component 2 c (ii).\n\n73. **Additionally, citizen engagement activities to facilitate effective two-way engagement among stakeholders,**\n\n**including Turkish citizens and refugees, municipalities, and SKIs, will seek to identify the needs and priorities of**\n**beneficiary groups to improve access to effective municipal services.** These activities will be planned and\nimplemented by ILBANK in a participatory manner with the support of non-governmental organizations (NGO)’s selected by ILBANK. They will be identified through the project and be described in the Project Operations Manual.\nThe activities will seek to address concerns on issues relating to planning, implementation, and delivery of municipal services, particularly environmental infrastructure services covered under the project, as well as any related issues affecting social cohesion among host and refugee communities. Citizen engagement activities will where possible leverage and be carried out in close collaboration with other World Bank managed activities, such as the proposed _Social Entrepreneurship, Empowerment, and Cohesion Project_ (P171456), which will develop and apply a set of participatory citizen engagement tools to facilitate engagement with host and refugee populations, and the proposed _State Building Fund (SPF) Project’s Information, Counseling and Referral Support Program,_ which will cover, inter-alia, referral to municipality-run support activities, as appropriate. Leveraging of citizen engagement activities with the mentioned activities will be carried out in selected municipalities where there is an overlap between the individual projects, such as Adana, Kahramanmaras, and Osmaniye. Experience from these activities will however help to inform activities in other areas. Activities under the SPF will, inter alia, include information related to municipal services. The specific interactions between these operations will be further defined and agreed in close collaboration with the Page 32 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) respective projects early during implementation. The Bank will facilitate collaboration on the FRIT projects as possible with the citizen engagement activities of ILBANK.\n\n74. **Gender:** Turkey ranks 130th out of 149 countries in the World Economic Forum’s Global Gender Gap Report [33] . As part of project preparation, gender analysis was undertaken, together with a detailed literature review and focus group discussions with women from both refugee and host communities, to identify specific gender gaps among host and refugee populations in the project areas. While individual living conditions of SUTPs vary with factors such as socioeconomic status and location of settlements, evidence from the analysis suggests that the poor housing conditions, including overcrowding and associated access to services such as safely managed water and sanitation for a large proportion of the lower income refugee communities is an issue.\n\n75. The project scope narrows down to three specific gender gaps that could be addressed through project implementation. First, there is a clear lack of voice for both Turkish and refugee women. Research on women’s representation in local level governance shows one of the lowest rates in the world. In 2014, women constituted 2.8 percent of the Mayors and 10.7 percent of the municipal Councilors. [34] According to the Urban Refugee Network [35], many Syrian refugee women lack a forum to discuss needs, share information and exercise community leadership. A UN Needs Assessment of Syrian women refugees carried out in 2018 found that nearly half of the refugee women in Adana had no social relationships with the Turkish host community due to seclusion (15 percent did not leave their homes), and the lack of social ties was greater for women with less education [36] . Second, refugee women have limited economic opportunities. According to the gender analysis, only 15 percent of refugee women are engaged in income generating jobs [37] . The same assessment indicated that only a small number of women (7 percent) have taken part in vocational training, and when they do take part the most popular areas of study are hairdressing (30 percent) and needlework (27 percent), which are closely related to traditional gender roles and provide limited opportunity for formal employment. And finally, there is lack of access to information that also includes language barriers. While women are mainly responsible for management and use of services such as water, sanitation, hygiene and waste management at the household level, they have limited interaction with host communities if they do not work outside.\nFrom the UN Women’s Needs Assessment, Syrian women are unaware of various support services: for example, 68 percent of women do not know about free legal counselling; 63 percent about home care, 59 percent about psychosocial support and 57 percent about childcare services. This applies to the project areas. In Kahramanmaraş, for example, the Municipality has introduced a subsidized water-use scheme for families in need. However, women seem to have insufficient knowledge and awareness of such schemes, which highlight a need for enhanced communication and information sharing in a manner and language accessible to them.\n\n76. While there are other on-going projects in the country that promote female employment [38], this project will promote three main actions to improve gender outcomes. First, the project will encourage refugee women to engage more actively with host communities and participate in decision-making to identify their priorities for support through the 33 World Economic Forum’s Global Gender Gap Report for 2018. _[http://www3.weforum.org/docs/WEF_GGGR_2018.pdf](http://www3.weforum.org/docs/WEF_GGGR_2018.pdf)_ 34 Savas-Yavuzchere, P. and Cigeroglu-Oztepe, Misra 2016. “the Representation of Women in Turkish Local Governments.\n[http://journals.euser.org/files/articles/ejis_jan_apr_16/Pinar.pdf](http://journals.euser.org/files/articles/ejis_jan_apr_16/Pinar.pdf) 35 Urban Refugee Women’s Network – Turkey. _[https://www.refworld.org/pdfid/5a38e20a4.pdf](https://www.refworld.org/pdfid/5a38e20a4.pdf)_ 36 UN Women. 2018 Needs Assessment of Syrian Women and Girls. https://www2.unwomen.org/[/media/field%20office%20eca/attachments/publications/country/turkey/the%20needs%20assessmentengwebcompressed.pdf?la=en&vs=](https://www2.unwomen.org/-/media/field%20office%20eca/attachments/publications/country/turkey/the%20needs%20assessmentengwebcompressed.pdf?la=en&vs=3139)\n[3139](https://www2.unwomen.org/-/media/field%20office%20eca/attachments/publications/country/turkey/the%20needs%20assessmentengwebcompressed.pdf?la=en&vs=3139) 37 Source: United Nations Needs Assessment of Syrian Women and Girls under Temporary Protection Status in Turkey, June 2018 38 For example, the Social Entrepreneurship. Empowerment and Cohesion In refugee and Host communities in Turkey Project Page 33 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) project at the neighborhood level. This will include specific actions to promote women voices and empower them to make decisions concerning their needs and priorities within the scope of the project. The project will specifically encourage adequate women’s representation at the community level decision-making forum under component 2d).\nThe project will help create a safe space for refugee and host country women to share their views, needs, and suggestions by setting-up of separate meetings for women only to respect existing cultural norms. Decisions made will be recorded and shared with the participating municipalities to be reflected in further planning, decision-making and implementation of the project throughout the project implementation. Staff of the participating municipalities and utilities are trained to be sensitized for existing gender bias and norms, so that they could respond to these gender-disaggregated specific needs and requests from the community level decision-making forums. Second, the project will encourage municipalities to provide information for decision-making on their policies, plans, investments and available services in a format and language (Arabic and Turkish) that is user-friendly and accessible to women and the organizations that represent them. In addition, the project will undertake an education and awareness campaign, support WASH initiatives that are sensitive to household gender inequalities and promote behavior change. Third, the project will support and promote the formation of women-led community level groups, where possible, to manage operation and maintenance for hygiene and sanitation infrastructure at the neighborhood level in the selected low-income communities with significant refugee and host populations, and will provide training to empower them to do so. This will extend women’s household role as water managers to the neighborhood and give them voice. The project will encourage monitoring and management of municipal services to be identified in collaboration with relevant municipalities. Combining Turkish and refugee women on the committees could support social cohesion as well enabling them to better understand each other’s cultures.\n\n77. These actions will be measured through the following indicators: (i) percentage of women’s needs and investment choices that were implemented by selected municipalities and SKIs (baseline 0%; target 70%); and (ii) The satisfaction rate of female users of WSS services and facilities provided by the project and their perception of responsiveness of the project to their needs and preferences (baseline 0%; target 70%). A survey will be conducted for a sample of communities at the baseline stage and at the end of project to assess the satisfaction with project outcomes by host and refugee communities.\n\n**VI.** **GRIEVANCE REDRESS SERVICES**\n\n78. Communities and individuals who believe that they are adversely affected by a World Bank (WB) supported project may submit complaints to existing project-level grievance redress mechanisms or the WB’s Grievance Redress Service (GRS). The GRS ensures that complaints received are promptly reviewed in order to address project-related concerns.\nProject affected communities and individuals may submit their complaint to the WB’s independent Inspection Panel which determines whether harm occurred, or could occur, as a result of WB non-compliance with its policies and procedures. Complaints may be submitted at any time after concerns have been brought directly to the World Bank's attention, and Bank Management has been given an opportunity to respond. For information on how to submit complaints to the World Bank’s corporate Grievance Redress Service (GRS), please visit _[http://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service](http://www.worldbank.org/en/projects-operations/products-and-services/grievance-redress-service.%20For)_ . For information on how to submit complaints to the World Bank Inspection Panel, please _[visit www.inspectionpanel.org.](http://www.inspectionpanel.org/)_\n\n**VII.** **KEY RISKS**\n\nPage 34 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project (P169996) 79. **The overall project risk is rated as Substantial** and risks in this category are briefly described below.\n\n80. **Political and Governance risks are rated Moderate.** These risks are primarily related to the overall political context of the Syrian refugee crisis and its broader implications on stability in Turkey and the broader region. Specific political risks relate to the possibility of changes in the refugee population in form of either further increase or reductions depending on prevailing conditions, and potential impacts on project design. Water and sanitation investments will as far as possible be designed to accommodate different populations using phased or modular systems where appropriate. Further political risks are linked to local political and social cohesion risks associated with the refugee influx and potential implications on support for the project. The project through the citizen engagement component will seek to promote dialogue among host and refugee communities to improve social cohesion.\n\n81. **Macroeconomic risk is rated as Substantial.** The Turkish economy experienced instability between 2018-2019, following a period of growing macro imbalances and economic overheating in 2017-2018. The economy has stabilized more recently thanks to important external adjustments: a reversal in current account imbalances, declining external debt of banks, and a gradual recovery in forex reserves. These have contributed to currency stability. There are a priori three possible macroeconomic risks to the project. The first is currency risk for municipalities. Though the Lira has been more stable recently, uncertainty in global markets, including from the recent Coronavirus outbreak pose risks for all emerging markets. Some of the risk could be offset by trade diversion from China that benefits Turkey.\nThe second relates to the authorities’ fiscal consolidation path, which assumes a sharp decrease in capital expenditure. This may or may not affect public investment plans under the project as some of this risk may be mitigated by external project financing. Thirdly, the construction sector is among the most severely hit in the current downturn – leverage and exposure to forex debt will affect construction companies’ ability to respond to an increase in specialized construction investment under the project. Some of this risk may be mitigated by the availability of spare capacity in the construction sector, that may allow construction companies to respond quickly.\n\n82. **Technical design of project risk.** There is a moderate potential risk that the implementation of activities financed from one source is given priority by certain municipalities over activities from another source. ILBANK and the selected municipalities will be required to clearly reflect in the Project Implementation Plan the concurrent parallel implementation of project activities financed from both sources, and to report on this aspect of implementation s as part of progress reporting to the Bank, as will be defined in the Project Operations Manual.\n\n83. **Institutional Capacity for Implementation and Sustainability risk:** There is an increased implementation capacity risk given ILBANK’s expanding project portfolio with currently about US$730 million for Sustainable Cities series of project alone. This risk is to be mitigated through strengthening of ILBANK’s PMU capacity with additional staff in accordance with a Staffing Plan proposed by ILBANK. The list of additional staff and timing for their appointments was agreed with the Bank during appraisal and timeframes have been included as effectiveness conditions even though most of the staff are expected to be appointed before this date. ILBANK has requested retroactive financing from the IBRD loan to finance appointment of the additional PMU staff prior to effectiveness.\n\n84. **Stakeholder risk is rated as Substantial.** This risk reflects past history where municipalities/utilities have withdrawn from similar projects either immediately before negotiations or subsequent to loan signing due to internal political and financial considerations. To mitigate this risk, municipalities/utilities are required to provide Municipal Executive Board/Utility Executive Board Decisions in support of their participation in the project and are also required to prepare feasibility studies that include a financial assessment of the sub-project and the municipality. The risk rating also reflects possible community resistance to certain investments. To mitigate this risk, required Environmental and Page 35 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": ".\n\n**The World Bank**\nMunicipal Services Improvement Project (P169996) Social Impact Assessments (ESIAs) or Environmental and Social Management Plans (ESMPs) will be prepared for each sub-project investment and a grievance redressal mechanism will be set up within the project. In addition, other citizen engagement mechanisms will also be exploited during the implementation period.\n\n85. **The project’s Environmental risk has been rated Substantial.** The environmental risk has been downgraded to Substantial following the Concept stage after site visits carried out by Bank team. It is not expected that sub-projects will have significant adverse effects to human health and/or the environment and that the project will not result in transboundary impacts since there are no sub-projects in transboundary basins. Moreover, the areas likely to be affected by the project are not of high value and sensitivity. Appropriate mitigation measures for identified risks are highlighted in the relevant Safeguards documents.\n\n86. **The Social Risk Rating has been rated Substantial.** The project will have overall positive social impacts on improving environmental infrastructure in provinces known to host the largest numbers of refugees in urban settlements. Social risks and impacts intrinsic to the project are comparable to other municipal investment carried out under the Bank financed Sustainable Cities Projects 1 and 2 with ILBANK which are assessed Moderate. Social risks are rated Substantial because the sub projects to be financed under the project are designed to benefit a broad population regardless of nationality or any vulnerability, which is considered to allow management of contextual risks which may otherwise be High. Risks related to the capacity of implementation agency (ILBANK) to manage social impacts is considered Substantial because ILBANK will be applying ESF for the first time. This will be mitigated by the hiring of an additional social specialist in the PMU as well as training of staff on the ESF.\n\n87. **Other risks rated as substantial pertain to the following.** 88. **Timeframe for EU grant approval and associated financing gap.** The signing of the Administrative Agreement (AA) between the World Bank and the EU for the grant is subject to finalization of an update to the 2016 World Bank Group-EU Framework Agreement, which is under discussion by the parties. Accordingly, the project is presented to the Board with a financing gap for the grant, subject to signature of the AA. The European Commission (EC), in May 2019, invited the World Bank to enter into formal discussions with the EU Delegation in Turkey on the project design, to lead to a Contract between the Parties in the form of the AA. The Bank team has conducted such discussions with the EUD throughout the project preparation process and has reflected key aspects in the project design. To mitigate the risk of a potential delay in finalizing the update of the WBG-EU Framework Agreement, Technical Discussions for the grant were conducted along with the negotiations for the IBRD loan, to reduce the timeframe for Grant approval and signature once the Administration Agreement is signed. The Technical Discussions involved a full discussion and agreement between ILBANK and the World Bank on the terms and conditions of the draft Grant Agreement.\nConclusion of the grant negotiations, based on the Technical Discussions, will be done after signing of the AA. ILBANK has indicated its preference to sign the loan and grant agreements with the Bank at the same time. However, should there be a significant delay in signing the grant, or non-approval of the grant, ILBANK would be able to sign the loan agreement and access IBRD funds prior to accessing the grant. Project activities have been structured to separate loan and grant financed activities in the Procurement Plan to allow implementation of the former to proceed while waiting for the AA to be signed, should it become necessary.\n\nPage 36 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**VIII.** **RESULTS FRAMEWORK AND MONITORING**\n\n**Results Framework**\n\n**COUNTRY: Turkey**\n**Municipal Services Improvement Project**\n\n**Project Development Objectives(s)**\n\nThe Project Development Objective (PDO) is to improve host and refugee communities access to safely managed water supply, sanitation and solid waste services in selected municipalities affected by the influx of Syrians Under Temporary Protection in Turkey.\n\n**Project Development Objective Indicators**\n\n**RESULT_FRAME_TBL_PDO**\n\n**Indicator Name** **DLI** **Baseline** **End Target**\n\n**To improve host and refugee communities access to environmental infrastructure services**\n\nPeople benefitting from safely managed drinking water services 0.00 1,950,193.00 in the selected municipalities as a result of the project (Number) People benefitting from safely managed drinking water services in the selected municipalities (out of which female) (Percentage) People benefitting from safely managed drinking water services in the selected municipalities (out of which host community) (Number) People benefitting from safely managed drinking water services in the selected municipalities (out of which refugees) (Number) 0.00 51.00 0.00 1,728,142.00 0.00 222,051.00 People benefitting from safely managed sanitation services in 0.00 2,402,023.00 the selected municipalities as a result of the project (Number) Page 37 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**RESULT_FRAME_TBL_PDO**\n\n**Indicator Name** **DLI** **Baseline** **End Target**\n\nPeople benefitting from safely managed sanitation services in 0.00 50.00 the selected municipalities (out of which female) (Percentage) People benefitting from safely managed sanitation services in the selected municipalities (out of which host community) (Number) 0.00 2,165,834.00 People benefitting from safely managed sanitation services in 0.00 236,189.00 the selected municipalities (out of which refugees) (Number) People benefitting from safely managed solid waste services in 0.00 320,685.00 the selected municipalities as a result of the project (Number) People benefitting from safely managed solid waste services in the selected municipalities (out of which female) (Percentage) People benefitting from safely managed solid waste services in the selected municipalities (out of which host community) (Number) People benefitting from safely managed solid waste services in the selected municipalities (out of which refugees) (Number)\n\n**PDO Table SPACE**\n\n**Intermediate Results Indicators by Components**\n\n0.00 49.00 0.00 288,616.00 0.00 32,069.00\n\n**RESULT_FRAME_TBL_IO**\n\n**Indicator Name** **DLI** **Baseline** **End Target**\n\n**Environmental Infrastructure Investments**\n\nWater supply network constructed, rehabilitated and/or 0.00 2,062.00 extended (Kilometers) Page 38 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**RESULT_FRAME_TBL_IO**\n\n**Indicator Name** **DLI** **Baseline** **End Target**\n\nWater treatment plants constructed or rehabilitated (Number) 0.00 1.00 Water reservoirs constructed or rehabilitated (Number) 0.00 43.00 Water pumping stations constructed or rehabilitated (Number) 0.00 16.00 Sewerage network constructed, rehabilitated and/or extended 0.00 495.00 (Kilometers) Wastewater treatment plants constructed or rehabilitated 0.00 7.00 (Number) Landfills constructed or rehabilitated (Number) 0.00 1.00 Satisfaction rate of female users of environmental infrastructure 0.00 70.00 services and facilities provided by the project (Percentage)\n\n**Technical Assistance**\n\nNumber of municipal authorities and SKIs provided with capacity 0.00 5.00 building support through the Project (Number) Number of municipal authorities and SKIs that have prepared a Performance Improvement Action Plan through the project (Number) 0.00 4.00 Grievances registered related to delivery of municipal services 0.00 90.00 that are actually addressed and recorded (Percentage) Percentage of women’s needs and investment choices that were 0.00 70.00 implemented by selected municipalities and SKIs (Percentage)\n\n**IO Table SPACE**\n\n**UL Table SPACE**\n\nPage 39 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Monitoring & Evaluation Plan: PDO Indicators**\n\n**Methodology for Data**\n**Indicator Name** **Definition/Description** **Frequency** **Datasource**\n**Collection**\n\n**Responsibility for Data**\n**Collection**\n\nMunicipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Page 40 of 94 People benefitting from safely managed drinking water services in the selected municipalities as a result of the project People benefitting from safely managed drinking water services in the selected municipalities (out of which female) People benefitting from safely managed drinking water services in the selected municipalities (out of which host community) People benefitting from safely managed drinking water services in the selected municipalities (out of This indicator measures the cumulative number of people benefitted from safely managed drinking water services in the selected municipalities that have been provided through this Project.\n\nThis indicator measures the percentage of female benefitted from safely managed drinking water services in the selected municipalities that have been provided through this Project.\n\nThis indicator measures the percentage of host community benefitted from safely managed drinking water services in the selected municipalities that have been provided through this Project.\n\nThis indicator measures the percentage of refugees benefitted from safely Semiannually Semiannually Semiannually Semiannually Reports from PMU Reports from PMU Reports from PMU Reports from PMU Data to be compiled by municipal PIUs and recorded in PMU progress reports, based on updated beneficiary’s records from respective SKIs.\n\nData to be compiled by municipal PIUs and recorded in PMU progress reports, based on updated beneficiary’s connection records from respective SKIs.\n\nData to be compiled by municipal PIUs and recorded in PMU progress reports, based on updated beneficiary’s records from respective SKIs.\n\nData to be compiled by municipal PIUs and recorded in PMU", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) which refugees) managed drinking water services in the selected municipalities that have been provided through this Project.\n\nMunicipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Page 41 of 94 People benefitting from safely managed sanitation services in the selected municipalities as a result of the project People benefitting from safely managed sanitation services in the selected municipalities (out of which female) People benefitting from safely managed sanitation services in the selected municipalities (out of which host community) People benefitting from safely managed sanitation services in the selected municipalities (out of which This indicator measures the cumulative number of people benefitted from safely managed sanitation services in the selected municipalities that have been provided through this Project.\n\nThis indicator measures the percentage of female benefitted from safely managed sanitation services in the selected municipalities that have been provided through this Project.\n\nThis indicator measures the percentage of host community benefitted from safely managed sanitation services in the selected municipalities that have been provided through this Project.\n\nThis indicator measures the percentage of refugees benefitted from safely Semiannually Semiannually Semiannually Semiannually Reports from PMU Reports from PMU Reports from PMU Reports from PMU progress reports, based on updated beneficiary’s records from respective SKIs.\n\nData to be compiled by municipal PIUs and recorded in PMU progress reports, based on updated beneficiary’s records from respective SKIs.\n\nData to be compiled by municipal PIUs and recorded in PMU progress reports, based on updated beneficiary’s records from respective SKIs.\n\nData to be compiled by municipal PIUs and recorded in PMU progress reports, based on updated beneficiary’s records from respective SKIs.\n\nData to be compiled by municipal PIUs and recorded in PMU", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) refugees) managed sanitation services in the selected municipalities that have been provided through this Project.\n\nMunicipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Page 42 of 94 People benefitting from safely managed solid waste services in the selected municipalities as a result of the project People benefitting from safely managed solid waste services in the selected municipalities (out of which female) People benefitting from safely managed solid waste services in the selected municipalities (out of which host community) People benefitting from safely managed solid waste services in the selected municipalities (out of which This indicator measures the cumulative number of people benefitted from safely managed solid waste services in the selected municipalities that have been provided through this Project.\n\nThis indicator measures the percentage of female benefitted from safely managed solid waste services in the selected municipalities that have been provided through this Project.\n\nThis indicator measures the percentage of host community benefitted from safely managed solid waste services in the selected municipalities that have been provided through this Project.\n\nThis indicator measures the percentage of refugees benefitted from safely Semiannually Semiannually Semiannually Semiannually Reports from PMU Reports from PMU Reports from PMU Reports from PMU progress reports, based on updated beneficiary’s records from respective SKIs.\n\nData to be compiled by municipal PIUs and recorded in PMU progress reports, based on updated beneficiary’s records from respective SKIs.\n\nData to be compiled by municipal PIUs and recorded in PMU progress reports, based on updated beneficiary’s records from respective SKIs.\n\nData to be compiled by municipal PIUs and recorded in PMU progress reports, based on updated beneficiary’s records from respective SKIs.\n\nData to be compiled by municipal PIUs and recorded in PMU", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) refugees) managed solid waste water services in the selected municipalities that have been provided through this Project.\n\n**ME PDO Table SPACE**\n\nprogress reports, based on updated beneficiary’s records from respective SKIs.\n\n**Monitoring & Evaluation Plan: Intermediate Results Indicators**\n\n**Methodology for Data**\n**Indicator Name** **Definition/Description** **Frequency** **Datasource**\n**Collection**\n\n**Responsibility for Data**\n**Collection**\n\nMunicipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Page 43 of 94 Reports from PIUs Reports from PIUs Reports from PIUs Reports from PIUs Reports from PIUs Water supply network constructed, rehabilitated and/or extended Water treatment plants constructed or rehabilitated Water reservoirs constructed or rehabilitated Water pumping stations constructed or rehabilitated Sewerage network constructed, rehabilitated and/or extended Length of water supply network constructed, rehabilitated and/or extended due to project activity.\n\nNumber of water treatment plants constructed or rehabilitated due to project activities.\n\nNumber of water reservoirs constructed or rehabilitated due to project activities.\n\nNumber of water pumping stations constructed or rehabilitated due to project activities.\n\nLength of sewerage network constructed, rehabilitated and/or extended due to project activities.\n\nSemiannually Semiannually Semiannually Semiannually Semiannually", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) Reports from PIUs Reports from PIUs Targeted beneficiary satisfaction survey Reports from PIUs Reports from PIUs Wastewater treatment plants constructed or rehabilitated Landfills constructed or rehabilitated Satisfaction rate of female users of environmental infrastructure services and facilities provided by the project Number of municipal authorities and SKIs provided with capacity building support through the Project Number of municipal authorities and SKIs that have prepared a Performance Improvement Action Plan through the project Number of wastewater treatment plants constructed or rehabilitated due to project activities.\n\nNumber of landfills constructed or rehabilitated due to project activities.\n\nSatisfaction rate of female users of environmental infrastructure services and facilities provided by the project and their perception of responsiveness of the project to their needs and preferences.\n\nThis indicator measures the number of municipal authorities and SKIs benefitted from capacity building activities in the selected municipalities that have been provided through this Project.\n\nThis indicator measures the number of municipal authorities and SKIs that have prepared a Performance Improvement Semiannually Semiannually Start and end of the project.\n\nSemiannually Annually A targeted beneficiary satisfaction survey will be carried out among the targeted community groups at the start of the project and at an appropriate time after the services have been provided, towards the close of the project.\n\nMunicipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU The survey will be carried out by selected NGOs and consultants supported through the project, and results will be reported to the municipalities and ILBANK and the World Bank and EU.\n\nMunicipal PIUs and ILBANK PMU Municipal PIUs and ILBANK PMU Page 44 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) Action Plan through this Project.\n\nSemiannually Before the hygiene and sanitation facilities are selected and after the facilities are implement ed.\n\nGrievances registered related to delivery of municipal services that are actually addressed and recorded Percentage of women’s needs and investment choices that were implemented by selected municipalities and SKIs\n\n**ME IO Table SPACE**\n\nThis indicator measures the number of grievances registered related to delivery of municipal services that are actually addressed and recorded in the selected municipalities through the Project.\n\nMeasuring women’s needs or investment choices regarding the hygiene and sanitation facilities under Component 2d that were implemented by selected municipalities and SKIs (baseline 0%; target 70%).\n\nReports from PIUs Needs assessment, Post-survey.\n\nA survey will be conducted among women who participated in needs assessment activities after facilities are constructed. The survey will measure if their needs or investment choices are reflected in decisions made by municipality and SKI, and the built facilities are constructed in the way that they requested.\n\nMunicipal PIUs and ILBANK PMU Ilbank PMU and Municipalities/SKIs.\n\nPage 45 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) Page 46 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**ANNEX 1: Implementation Arrangements and Support Plan**\n\n**Project Implementation Roles and Responsibilities**\n\n**1.** The roles and responsibilities for the implementation of the project are outlined below:\n\n2. **The Government of Turkey**, represented by the Ministry of Treasury and Finance, is the Guarantor of the loan.\n\n3. **ILBANK** . ILBANK is the Borrower for the IBRD loan and will also be the primary recipient of the EU FRIT grant when it is approved. ILBANK is a state-owned development and investment bank, subordinated to the _[Ministry of](https://en.wikipedia.org/wiki/Ministry_of_Environment_and_Urban_Planning_(Turkey))_ _[Environment and Urbanization](https://en.wikipedia.org/wiki/Ministry_of_Environment_and_Urban_Planning_(Turkey))_ . ILBANK, as the national development and investment bank providing financial intermediary services for municipal infrastructure in Turkey, will also be the implementing agency for the Project.\n\n4. ILBANK will carry out its roles as implementing agency through an existing Project Management Unit (PMU), under the bank’s International Relations Department. The PMU was originally established in 2005 for a Bank financed Municipal Services Project (MSP). It has since been responsible for implementing subsequent operations, including MSP-II (Additional Loan), Sustainable Cities Project (SCP), and SCP-II and SCP-II Additional Loan, and it has demonstrated its capacity for managing World Bank loans through these operations. Relevant technical and financial departments of ILBANK, as well as the regional directorates supporting the targeted municipalities will support the PMU in project implementation. ILBANK will procure or support municipalities/utilities to procure works, TA services and goods as required and will manage the implementation of project activities. The PMU has professional staff responsible for various aspects of project management, including procurement, financial management, social and environmental safeguards, communications, technical/engineering and monitoring and evaluation. Detailed operational modalities will be described in the Project Operational Manual. The PMU’s existing capacity will however be strengthened through hiring of additional staff and/or consultants in line with a staffing plan agreed with the Bank before project effectiveness.\n\n5. **Local/Municipal Level PIUs.** The selected municipalities and utilities/SKIs as applicable based on the specific sub project will be responsible for sub-project investment implementation and will set up municipal project implementation units (PIUs) at local level to ensure effective implementation. The local PIUs will comprise competent staff responsible for technical, procurement, financial and safeguards aspects of the sub-project activities. The staff may be seconded from the respective local institutions or may include individual consultants hired by these entities if existing capacity is not sufficient.\n\n6. **World Bank:** The World Bank, as the EDF Trust Fund administrator, and IBRD lender, will be responsible for monitoring the implementation of project activities and will ensure compliance with the former`s procedures and guidelines. World Bank’s fiduciary arrangements, including its procurement and FM procedures, as well as its Environmental and Social Framework will apply. Detailed fiduciary and safeguards arrangements will be agreed with the implementing institutions, namely ILBANK and the municipalities/ water and wastewater utilities and will be reflected in the relevant legal documents and the Project Operations Manual.\n\nPage 47 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Figure 1.1: Project implementation roles and responsibilities**\n\n7. **European Union Delegation:** The EDF will co-finance the project through the FRIT Grant. The World Bank will enter into an Administration Agreement (AA) with the EUD to set up a Recipient-Executed Trust Fund. The AA will provide for the establishment of a Trust Fund to be administered by the World Bank, in the amount of EUR 139.8 million, which the World Bank will provide as a grant to ILBANK. After the AA is signed, the World Bank will enter into a Trust Fund Grant Agreement with ILBANK, which will be the recipient of the funds. ILBANK will be responsible for implementing the funds in accordance with overall project design, including fiduciary and safeguard arrangements, as set out in the POM. The implementation time frame of the EU grant is 48 months from AA signing.\n\n8. **Contractors:** Qualified contractors will be competitively selected to implement sub-projects as per the needs of construction and/or rehabilitation works described within the activities.\n\n9. **Design Review and Supervision Consultants:** A design review and supervision consultant will be awarded to provide supervision services for the construction and rehabilitation works. These Consultants will also support review of existing design documents and preparation of tender documents.\n\n10. **Capacity Building Consultants:** Consultants will be hired to provide trainings and other capacity building activities as required for target groups. Capacity Building events will be organized for the staff of target Page 48 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) municipalities/utilities to develop required skills in the municipalities to ensure the sustainability of project outcomes.\n\n**Financial Management and Disbursement**\n\n11. Banks in Turkey are subject to strict regulations and supervision by the Banking Regulation and Supervision Agency (BRSA). BRSA’s regulatory and supervisory framework meets modern standards preserving the solidity of the system. Banks in Turkey are required to prepare financial statements in compliance with the Turkish Accounting Standards which are based on and correspond to IFRS. The BRSA also issues rules governing the external audit of bank financial statements, and only auditors approved by the BRSA may carry out such audits. The external auditor is required to report to the BRSA on the internal control and risk management systems of banks as well as being obliged to report directly to the BRSA with respect to certain issues which may threaten the going concern nature of a Bank.\n\n**Implementing Entity**\n\n12. ILBANK first established as Belediyeler Bankasi in 1933 to finance the reconstruction activities of municipalities.\n\nILBANK law (Law 4759) was updated in 2011 making it a joint stock development and investment bank that is subject to the banking law. Its objectives are meeting the financing needs of special provincial authorities, municipalities and their affiliated organizations and of local administrative associations and to provide consultancy services to these organizations on urban projects. ILBANK is a related establishment of Ministry of Environment and Urbanization of Turkey. The shareholders of the bank are the municipalities and special provincial administrations. Law 5779 on Allotments of General Budget Revenues to be allocated to Special provincial Authorities and Municipalities (2008) requires 2 percent of total tax allotment revenues distributed by the Ministry of Treasury and Finance through ILBANK to be deducted to contribute as capital to Iller Bank.\n\n**Implementation Arrangements**\n\n13. ILBANK has a Project Management Unit (PMU) under its International Relations Department responsible for foreign financed projects including SCP I, SCP II and SCP II Additional Financing (SCP II AF). The PMU is organized according to functions and its financial management department is responsible for such arrangements under SCP I, II and II AF. They will continue with this responsibility under the FRIT MSP. ILBANK will provide technical support to qualifying municipalities and utilities in identifying and appraising investments. ILBANK will also provide funding for the eligible investment projects both from the EU grant and WB loan. ILBANK will be responsible for the procurement of all activities under component 2 including the consultancy services for design review, support for preparation of bidding documents and supervision of construction and rehabilitation works under Component 1.\nProcurement of investments will also be done by ILBANK with the representatives from the beneficiary municipalities and utilities in the bid evaluation committees and the contracts will be signed by the municipality/utility and the firm. Payments to the suppliers will be registered directly by ILBANK upon submission of acceptable approval documents. PMU in ILBANK will be responsible for the management of the Designated Accounts and project accounting and reporting.\n\n**Staffing**\n\n14. The PMU at the ILBANK has a dedicated financial management unit responsible for FM functions of all foreign financed projects. There are currently 11 staff working in this department and ILBANK will assign at least one Page 49 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) financial management specialist to work solely on the financial management aspects of the MSP FRIT Project.\nILBANK has the option to hire consultants to enhance its implementation capacity since Component B of the FRIT MSP has funding available for this purpose. The qualifications and experience of the current staff are satisfactory to the Bank. They have worked in the implementation of the Municipal Services project, its additional financing and currently working for SCP I, II and AF. The experience they have gained in the implementation of these projects will be utilized in conducting the financial management arrangements of the FRIT MSP.\n\n**Accounting Policies and Procedures**\n\n15. ILBANK has a web-based information system (IL_BIS) that links all departments of the institution, allowing them to execute, monitor and report using the same data source. All the regional offices are also connected to the central IL_BIS system. Project accounting for the SCP I and II are integrated into this system using sub-accounts that were created under the Bank`s main chart of accounts. The PMU staff prepares the payment orders and the accounting entries into the Bank`s main accounting system are made by the Accounting Department`s staff. Financial Interim Unaudited Financial Reports are also generated automatically through the system. FRIT MSP will also rely on the same systems and the accounting and reporting for the project will also be fully integrated into the IL_BIS system.\nILBANK will conduct the necessary modifications/additions to the IL_BIS system and these arrangements are expected to be in place before project effectiveness.\n\n16. ILBANK has robust systems, manuals and guidelines regulating the internal controls environment. The accounting and reporting systems at ILBANK are geared toward producing statements and information as required by Turkish laws and regulations. Additionally, lLBANK has developed and executed specific internal control procedures for the implementation of the foreign financed projects including the SCP Program and these procedures are clearly defined in the project financial management manual which is available in the ILBANK web-site.\n\n**17.** FRIT MSP will disburse through sub-loans and grant agreements that will be made between ILBANK and qualifying\n\nmunicipalities and utilities. The municipalities will submit the payment requests to the PMU after verifying completeness of all documentation is complete will prepare the payment order through its financial management department. The payment will be made directly from the designated accounts to the constructer’s bank account.\n\n18. The PMU has been utilizing detailed checklists that are completed and signed by the relevant staff before processing the payments. Those checklists include financial controls on advance payments made for works in progress, financial controls on payments to individual consultants and corporate consultants, financial controls on works progress payments, financial controls on goods purchases. These checklists with a few modifications to enable funding source identification will also be utilized for MSP FRIT.\n\n**Internal** **Audit**\n\n19. ILBANK has an Internal Controls Department, Risk Management Department and an Inspection Department. All three departments report directly to the Board of Directors. The Internal Controls Department has identified “standard control points” for foreign financed loans. This is standard for all departments and controls points are defined for each function of ILBANK. The International Relations Department, like other departments of ILBANK, is required to complete the form monthly and provide assurance through self-declaration that all control points have been complied with. As a part of its normal procedures, the Department conducts quarterly on-site review of compliance with the control points. There have not been any irregularities observed in foreign financed loans as a part of the internal control review. MSP FRIT will also be a part of regular review of internal controls department.\n\nPage 50 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) The Risk management Department reviews loans before they are granted and through their effectiveness. ILBANK, through its special status, is required to provide provisions for its loan portfolio. The Risk management Department conducts continuous monitoring and reports to the Board on the status of ILBANK`s loan portfolio. Loans that will be granted to municipalities /utilities under MSP FRIT will be monitored by the Risk Management Department. The Inspection Department is responsible for investigating irregularities, as well as conducting regular reviews of ILBANK systems.\n\n**External Audit**\n\n20. Annual project financial statements (integrating both sources of financing) for the project, as well as ILBANK financial statements will be subject to independent audit by auditors that are acceptable to the Bank. ILBANK has been submitting audited entity and project financial statements to the Bank for MSP and SCP. ILBANK`s entity financial statements prepared in accordance with International Financial Reporting Standards (IFRS) have been audited by private sector auditors in accordance with International Auditing Standards. The entity audited financial statements had unqualified (clean) audit opinions for the last three years. ILBANK additionally prepares financial statements for the projects it is implementing, and these financial statements are audited by the Treasury Controllers who are the government auditors who are responsible for the audit of World Bank financed projects implemented by government institutions. The project financial statements for SCP (separately prepared for the loan and the EU grant) have been received on time and include unqualified (clean) audit opinions as well. (SCP II audit report has not yet been submitted as ILBANK did not utilize any funds from the project in 2018 which is the year with the most recent audit reports available). No material issues were identified in the management letter either.\n\n21. Under the MSP FRIT project ILBANK will be required to submit its audited entity financial statements prepared in accordance with Turkish Accounting Standards (which are fully compatible with International Financial Reporting Standards) and project financial statements to the Bank within 6 months following the end of year. The project financial statements are required to be made publicly available in accordance with the World Bank guidelines. The following chart identifies the audit reports and their due dates:\n\n**Table 1.1 Audit Reporting Requirements**\n**Audit Report** **Due Date**\nEntity financial statements (FI) prepared in accordance with Turkish Accounting Standards and also at the closing of the project.\n\nEntity financial statements (FI) prepared in accordance Within six months after the end of each calendar year with Turkish Accounting Standards and also at the closing of the project.\n\nProject financial statements (PFS) for the loan part Within six months after the end of each calendar year including SOEs and the designated accounts. and also at the closing of the project.\n\nProject financial statements (PFS) for the loan part Within six months after the end of each calendar year including SOEs and the designated accounts. and also at the closing of the project.\n\nProject financial statements (PFS) for the grant part Within six months after the end of each calendar year including SOEs and the designated accounts. and also at the closing of the project.\n\nProject financial statements (PFS) for the grant part Within six months after the end of each calendar year including SOEs and the designated accounts. and also at the closing of the project.\n\n22. Currently the financial statements prepared by the municipalities are not subject to independent external audit except the annual audit conducted by the Turkish Court of Accounts (TCA), the supreme audit institution in Turkey.\nAnnual TCA audit reports will be reviewed by the project team as a part of project supervision.\n\n**Reporting and Monitoring**\n\n23. ILBANK will prepare Interim Unaudited Financial Statements (IFRs) for the project following the same format utilized for the SCP I and II. The agreed formats of the IFRs are attached to the negotiations and technical discussion Page 51 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) minutes. ILBANK will also ensure that the reports with financial content are automatically generated from IL_BIS system.\n\n**Disbursement Arrangements**\n\n24. The project will be disbursing on the traditional disbursement techniques. Two Designated Accounts will be created for the project (one for EU grant and one for the WB loan). ILBANK will open the designated accounts at a commercial bank that is acceptable to the World Bank. Designated accounts for SCP I and SCP II are at Ziraat Bankasi. Two authorized signatures who will be specified in the project FM manuals will sign the withdrawal applications. The minimum application size for payments directly from the loan account for the issuance of Special Commitments, as well as the Statement of Expenditure (SOE) limits, will be described in the disbursement letters.\nFull documentation in support of SOEs, including completion reports and certificates, would be retained by the ILBANK for at least two years after the World Bank has received the audit report for the fiscal year in which the last withdrawal from the loan account was made. This information will be made available for review during supervision visits by World Bank staff and for annual audits. Disbursements for expenditures above the SOE thresholds will be made against presentation of full documentation of the expenditures.\n\n**Figure 1.2. Funds Flow**\n\n**World Bank Project Implementation Support Plan**\n\n25. The Implementation Support Plan (ISP) is tailored to the specific context and characteristics of the project, based on the dispersed geographic scale of investments, and existing capacity of the implementing agencies and arrangements. The ISP will be reviewed periodically to ensure that it remains fit for purpose and responsive to the project’s implementation support needs over its lifetime.\n\nPage 52 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) 26. **Project Launch Workshop.** A project launch workshop will be held soon after project approval. It will bring all project stakeholders together to ensure that the project scope, design, process and responsibilities are understood.\n\n27. **Implementation support missions.** The project would be supervised at least twice a year and the recommendations of such supervisions would be presented to ILBANK and recorded in an Aide Memoire. The World Bank would be represented by a task team leader, supported by a team of experts with various skills as needed, including fiduciary (Procurement and financial management), safeguards (Social and environmental), monitoring and evaluation, and technical specialists. The goal would be to: (a) undertake required policy dialogue with the Government of Turkey, ILBANK, and local governments, related to the project and the sector; and (b) coordinate with other donors.\n\n28. The semiannual supervision missions and short/ follow-up technical missions as needed in specific areas, will focus on the following areas: a) **Strategic support.** Supervision missions will meet with ILBANK PMU and relevant municipality PIU representatives to: (i) review progress on the project’s activities; (ii) discuss strategic alignment of the project’s different activities, especially at the planning level, between the relevant stakeholders; and (iii) evaluate progress on cross-cutting issues such as M&E, training, communication, knowledge exchange, innovation, dissemination of project results and experiences, and coordination between the relevant stakeholders.\n\nb) **Technical support.** Supervision will concentrate on ensuring the technical quality of bidding documents, TORs, evaluation reports, construction plans, products delivered by consultants, and investment activities/targets, working in close collaboration with ILBANK and the design review and construction supervision consultants.\nDuring construction and commissioning, technical supervision will be provided to ensure that technical contractual obligations are met. Regular site visits will be carried out during project implementation and will involve technical specialists from the World bank, ILBANK and consultant teams as needed.\n\nc) **Fiduciary support.** Periodic supervision of procurement and FM support will be carried out by the World Bank semiannually to (i) perform desk reviews of project interim financial reports and audit reports, following up on any issues raised by auditors, as appropriate; (ii) assess the performance of control systems and arrangements; (iii) update the FM rating in the FM Implementation Support and Status Report as needed; (iv) provide training and guidance on carrying out procurement processes in compliance with the Procurement and Anticorruption Guidelines and the POM; (v) review procurement documents and provide timely feedback to the PIU; (vi) carry out the post review of procurement actions; and (vii) help monitor project progress against the Procurement Plan and identified performance indicators of the contracts.\n\nd) **Safeguards support.** The coordination that began during preparation will continue throughout project implementation, especially to ensure that the relevant safeguards concerns are included in the works financed under the project through due diligence from applications of the site-specific ESIAs, ESMPs, and RAPs and effective mitigation measures. Supervision from the World Bank safeguard specialists will take place at least twice a year.\n\n29. **Mid-term review.** A mid-term review of the project will be carried out about two years into implementation to assess overall progress towards meeting the development objective, and to address any changes to project design or implementation required to meet the objectives.\n\n30. **Implementation Resource Requirements.** Table 1.2 below reflects estimates of skill requirements, timing, and resource requirements over the life of the project. these projections are subject to modification as needed over the course of implementation.\n\nPage 53 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Table 1.2. Implementation Support Resource Estimates**\n\n**Resource Estimates**\n**Time** **Focus** **Skills Needed**\n\n**(Staff Weeks)**\nFirst 12 Project rollout, management, and implementation Task team leads 6 per task team leader months support coordination per year Project rollout, management, and implementation support coordination Project rollout, management, and implementation Task team leads 6 per task team leader support coordination per year Refine subcomponent activities and ensure quality Task team leads/technical 2 per task team leader of detailed designs specialists per year Refine subcomponent activities and ensure quality Task team leads/technical 2 per task team leader of detailed designs specialists per year Social and environmental safeguards, including risk Social and environmental 4 per year mitigation measures specialist Task team leads/technical specialists Social and environmental specialist 4 per year Technical and procurement review of TORs and bidding document Task team leads, technical specialists, procurement specialists 6 per year Fiduciary arrangements and FM systems FM specialist 3 per year Promoting innovation in the project Task team leads/technical 3 per year specialists 12 to 72 months Procurement review and feedback of bidding documents and consultant contracts Technical review of TORs, technical reports, and bidding documents Non-lending TA, capacity and institutional strengthening efforts Procurement specialist 6 per year Task team leads, technical specialists Task team leads, technical specialists 6 per year 4 per year FM supervision FM specialist 3 per year Social safeguards – supervision Social Development specialist 4 per year Environmental safeguards – supervision Environmental specialist 4 per year Project management, M&E, and project Task team leads, technical 8 per year supervision coordination specialists Task team leads, technical specialists 8 per year Operational support, M&E, lessons learned, progress and final reporting.\n\nTechnical specialists and operations officer 6 per year\n\n**Table 1.3. Skill Mix Requirements**\n**Skill Needs for Supervision** **Comment**\nTask team leader Headquarters based Co-Task team leader Region based Co-Task team leader Country based WSS/technical specialist Country based (including consultant) Solid Waste Management Specialist Region based (including consultant) FM specialist Region based Procurement specialist Region based Social Development specialist Country based Gender Specialist Country/HQ based Environmental specialist Country based Lawyer Region based Disbursement officer Region based Page 54 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**ANNEX 2: Detailed Project Description**\n\n1. **Background.** The Project aims to improve host and refugee communities access to safely managed water supply,\n\nsanitation and solid waste services in targeted municipalities affected by the influx of Syrians Under Temporary Protection in Turkey. The project focuses on five municipalities or provinces (i.e. Adana, Kahramanmaras, Kayseri, Konya and Osmaniye) out of the ten most impacted by the refugee influx in Turkey as identified in the EU Needs Assessment. The selection of the five municipalities to be supported through the current project was based on not just the identified needs, but also eligibility for World Bank financing of sub-projects that do not trigger _World Bank_ _OP 7.50 Projects on International Waterways_ . Sub-projects triggering this policy are to be supported by other development partners, including AFD.\n\n2. **Target Group and Final Beneficiaries.** The final beneficiaries comprise the populations in the affected municipalities, including the refugees and host populations. Only a small share (less than 10 percent) of the refugee population resides in camps, while the majority live outside camps, mostly in urban areas. There are two designated camps in the targeted provinces: Kahramanmaras' Merkez Container Camp which hosts about 14,811 refugees, and Adana Sancam Container Camp which hosts about 26,700 refugees. The primary project beneficiaries include both the refugees and host communities in the five municipalities of Adana, Kahramanmaraş, Osmaniye, Kayseri and Konya targeted under this project and is estimated to be more than 3.3 million.\nApproximately 301,890 refugees (or 9 percent of the total beneficiaries) and 3,021,000 of the host population will be direct beneficiaries of the environmental infrastructure investments under Component 1.\n\n3. **Project Components and Estimated Costs** **[39]** **.** The Project will comprise two components (i) **Component 1. Environmental Infrastructure Investments:** This component will finance the construction and rehabilitation works for water supply, sanitation, and solid waste management infrastructure in the targeted municipalities to achieve improvements in access, service quality and continuity of municipal services; and smaller scale critical facilities to address immediate municipal service needs of vulnerable SUTP and host communities at the neighborhood or settlement level, to be identified in a participatory manner by the beneficiaries. Individual infrastructure facilities will, where appropriate, consider adaptive designs that would allow for a scale up or down of capacity in the event of unanticipated changes in the refugee population. The component may finance goods, works, non-consulting and consultant services towards these activities.\n\n(ii) **Component 2. Technical Assistance for Project Management and Supervision, Capacity Building,**\n\n**Communication and Citizen Engagement.** This component will finance goods and consultancy services for\nthe following activities: a) **Project Management.** This activity will finance goods and consulting services required for coordination and day-to-day project management, including management of procurement and financial management aspects, management of social and environmental safeguards, including dam safety requirements associated with the project, technical and contract management, monitoring and evaluation (M&E), and project reporting and communications.\n\nb) **Consultancy services for design review and supervision of environmental infrastructure investments.** This 39 Please see footnote 15, which is applicable for all references to the grant in this Annex.\n\nPage 55 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) activity will finance consultancy services for design review and preparation of bidding documents, taking into account climate change considerations, and supervision of construction and rehabilitation works for proposed environmental infrastructure investments under Component 1.\n\nc) **Citizen Engagement, Public Communication and Visibility activities.** This activity will finance: (i) **Communications and Visibility activities for the project.** A detailed Communication and Visibility Plan will be developed at the start of implementation, in line with EU Communications and Visibility Manual for EU External Actions [40], as well as the World Bank’s external communications guidelines.\nCommunications activities will also seek to target critical public education messaging to different stakeholder groups, including children and youths, and adults in relevant languages.\n\n(ii) **Citizen/Community engagement activities** to facilitate effective two-way engagement among stakeholders, including Turkish citizens and SUTPs and municipalities and SKIs to identify the needs and priorities for enhancing service delivery to all people in the beneficiary communities.\n\nd) **Institutional Capacity Building activities targeting the participating municipalities, utilities and ILBANK.** This activity will finance capacity building activities, including inter-alia, training and workshops aimed to improve performance efficiency, positively change behavior, and increase capacity within municipalities and SKIs/utilities to further modernize their operations to optimize efficiency and effectiveness in service delivery while embracing principles such as resilience, financial and environmental sustainability, and inclusion. These activities will also seek to contribute to increased water security through more efficient delivery, and thus increased resilience to climate change.\n\nA breakdown of detailed cost estimates for activities to be financed under the project, as tentatively agreed at appraisal stage, is presented in Table 2.1 below.\n\n**Table 2.1 Indicative Detailed Activities and Cost Estimates** **[41]**\n\n**FRIT Grant**\n**Category**\n\n**FRIT Grant (%**\n**IBRD (EUR)**\n**(EUR)** **of total (EUR))**\n\n**Total Cost**\n\n**(in EUR)**\n\n**Total Cost**\n\n**(EUR)** **of total (EUR))** **(in EUR)** **(in US$)**\n\n**Component 1: Environmental**\n**Infrastructure**\n\n**1a. Water Supply Investments**\n**Adana**\nKozan İmamoğlu Yedigöze Drinking Water Transmission Line 0 21,850,000 0 21,850,000 24,253,500 Yedigöze Water Treatment 0 13,150,000 0 13,150,000 14,596,500 Plant Kozan Pınargözü Drinking Water Transmission Line and Network\n\n**Kahramanmaras**\nKahramanmaraş (Centrum) Drinking Water, Sewerage and Stormwater Project West Part 28,070,000 0 100 28,070,000 31,157,700 0 17,000,000 0 17,000,000 18,870,000 40 https://ec.europa.eu/europeaid/work/visibility/_en 41 The US$ amounts are subject to changes due to exchange rate changes.\n\nPage 56 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**FRIT Grant**\n**Category**\n\n**FRIT Grant (%**\n**IBRD (EUR)**\n**(EUR)** **of total (EUR))**\n\n**Total Cost**\n\n**(in EUR)**\n\n**Total Cost**\n\n**(EUR)** **of total (EUR))** **(in EUR)** **(in US$)**\n\nElbistan Drinking Water 8,000,000 0 100 8,000,000 8,880,000 Network Project Elbistan Drinking Water 0 25,000,000 0 25,000,000 27,750,000 Transmission Line\n\n**Konya**\nAkşehir Water Supply Project 4,710,000 0 100 4,710,000 5,228,100\n**Osmaniye**\nOsmaniye (Centrum) Drinking 0 27,650,000 0 27,650,000 30,691,500 Water Project\n\n**Priority public hygiene**\n**investments at neighbourhood**\n**level**\n\n500,000 0 100 500,000 555,000\n\n**1a. TOTAL Water Supply**\n**41,280,000** **104,650,000** **28** **145,930,000** **161,982,300**\n**Investments**\n\n**1b. Sanitation Investments**\n**Kahramanmaras**\nCeyhan Basin Wastewater Treatment Plants (Goksun & Ekinozu) Ceyhan Basin Wastewater Treatment Plants (Caglayancerit & Andirin) Kahramanmaraş (Centrum) Drinking Water, Sewerage and Stormwater Project East Part 0 5,500,000 0 5,500,000 6,105,000 2,900,000 0 100 2,900,000 3,219,000 18,800,000 0 100 18,800,000 20,868,000\n\n**Kayseri**\nKayseri Wastewater Treatment 12,500,000 0 100 12,500,000 13,875,000 Plant - Phase 1 Kayseri Wastewater Treatment 0 12,500,000 0 12,500,000 13,875,000 Plant - Phase 2\n\n**Konya**\nIlgin Wastewater Treatment 3,900,000 0 100 3,900,000 4,329,000 Plant Çumra Wastewater Treatment 4,560,000 0 100 4,560,000 5,061,600 Plant\n\n**Osmaniye**\nOsmaniye (Centrum) Sewerage 29,910,000 0 100 29,910,000 33,200,100 Project\n\n**Priority public hygiene**\n**investments at neighborhood**\n**level**\n\n500,000 0 100 500,000 555,000\n\n**1b. TOTAL Sanitation**\n**73,070,000** **18,000,000** **80** **91,070,000** **101,087,700**\n**Investments**\n\n**1c. Solid Waste Investments**\nKahramanmaraş Northern Districts Integrated Solid Waste Project 17,000,000 0 100 17,000,000 18,870,000\n\n**Category** **FRIT Grant** **IBRD (EUR)** **FRIT Grant (%** **Total Cost** **Total Cost**\n\nPage 57 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**(EUR)** **of total (EUR))** **(in EUR)** **(in US$)**\n**1c. TOTAL Solid Waste**\n**17,000,000** **0** **100** **17,000,000** **18,870,000**\n**Investments**\n\n**Sub-total (Component 1:**\n**131,350,000** **122,650,000** **52** **254,000,000** **279,230,000**\n**Environmental Investments)** **[42]**\n\n**Component 2: Technical**\n**Assistance for Project**\n**Management, Supervision,**\n**Capacity Building,**\n**Communication and Citizen**\n**Engagement**\n\n**2a. Project Management**\ni) Operating Costs 0 500,000 0 500,000 550,000 ii) Consultancy services for 780,000 0 100 780,000 857,500 PMU iii) Procurement of Video Conference System/s for ILBANK HQs and Regional Directorates 0 1,325,000 0 1,325,000 1,456,600\n\n**2b. Design Review and**\n0 10,250,000 0 10,250,000 11,268,000\n**Supervision**\n\n**2c. Institutional capacity**\n0 630,000 0 630,000 692,600\n**building activities**\n\n**2d. Citizen and Community**\n370,000 0 100 370,000 407,000\n**Engagement Activities**\n\n**2e. Communications and**\n735,000 0 100 735,000 808,000\n**Visibility activities**\n\n**Sub-total (Component 2:**\n**Technical Assistance for**\n**Project Management,**\n**Supervision, Capacity Building,**\n**Communication and Citizen**\n**Engagement)**\n\n**1,885,000** **12,705,000** **52** **14,590,000** **16,039,700**\n\n**TOTAL** **133,235,000** **[43]** **135,355,000** **[44]** **50** **268,590,000** **295,269,700**\n\n**Detailed description of sub-project investments at Appraisal Stage**\n\n**A: Adana Municipality**\n\n4. **Adana context.** During the Syrian war Adana became one of the most preferable arrival points for Syrian refugees.\n\nThe refugee population in Adana mostly includes Syrians and Iraqis majority of whom are living in central districts of Adana. Adana province has a total population of about 2,220,125 people out of which 238,556 (or 10.8 percent of the total population) are SuTP’s [45] . There is one refugee camp, the Saricam Container Camp, in the province of 42 A contingency amount for investments in the amount of about Euro 984,904 will be included in the grant amount for component 1 bringing the total grant amount for the entire project to Euro 134,219,904.\n43 See 42 above i.e. total Grant amount includes the contingency will add up to Euro 134, 219, 904.\n44 This amount includes the front-end fee of US$372,040 shall be included in the project cost.\n45 As per the address-based census45 for 2018 Page 58 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) Adana which hosts about 20,700 SuTP’s [46] . The increased population due to the high influx of refugees resulted in additional demand for municipal and infrastructural services including water systems.\n\n5. The water supply and sanitation services are provided by Adana Water and Sewerage Administration General Directorate (ASKİ). The service provision area of ASKI has increased considerably with the change of Adana to a metropolitan municipality. Majority of the settlements in the region use well water. In drought years and seasons, well yields significantly reduces. The use of groundwater wells and associated pumping in these areas have caused high energy expenditures and operational costs for ASKI. The ageing water supply and distribution networks in the service areas are characterized by high NRW (61.6 percent) and water losses (57.7 percent) [47] . In order to solve water deficiency problems in some settlements of Adana, ASKI has initiated a design for Yedigöze Drinking Water Transmission Line and Water Treatment Plant Project.\n\n6. **Adana Sub-Project.** This Sub-Project aims to supply water to 141 settlements from Yedigöze Dam Reservoir which will eliminate well usage in these settlements. The total project beneficiaries are estimated at 506,636 people out of which 49,423 (or 10 percent of the total population) are SuTP’s. Investments in Adana cover:\n\n- Kozan Imamoglu Yedigoeze Drinking Water Transmission Line (35.4 km transmission line),\n\n- Yedigoeze Water Treatment Plant (capacity: 115,776 m3/day), and\n\n- Kozan Pinargoezu Drinking Water Transmission Line and Network (28.12 km transmission line and 310 km\nnetwork).\n\n7. Through the Project, water will be taken from the existing Yedigöze Dam reservoir (in Ceyhan Basin) and transferred to a new Water Treatment Plant (with a planned capacity of 115,776 m3/day) which will be constructed through the project. The treated water from the Yedigöze WTP will be used to supply safe drinking water to the 4 districts of Imamoglu, Kozan, Ceyhan and Yumurtalik and 137 neighborhoods in the Ceyhan basin. The new system is expected to eliminate the existing use of groundwater wells. Energy costs of the system are expected to decrease significantly as the water from the reservoir will be distributed by gravity. In Kozan, existing asbestos pipes will be replaced with ductile iron and high-density polyethylene (HDPE) pipes which will lead to significant decrease in water losses. The proposed investments in Adana metropolitan municipality are summarized in table below.\n\n**Table A2.2: Adana investment components**\n**No** **Investment name** **Scope** **Budget (million**\n\n**EUR)**\nA-1 Kozan İmamoğlu Yedigoeze - 35.4 km transmission line to Kozan (Ø700 mm-Ø1,400 21.85 Drinking Water Transmission mm ductile iron pipes) with auxiliary structures Line (washout and air relief valve chambers, etc.) 21.85 A-2 Yedigoeze Water Treatment Plant A-3 Kozan Pınargoezue Drinking Water Transmission Line and Network\n\n- 35.4 km transmission line to Kozan (Ø700 mm-Ø1,400\nmm ductile iron pipes) with auxiliary structures (washout and air relief valve chambers, etc.)\n\n- Construction of a new WTP with a capacity of 115,776\nm3/day (1,340 l/s)\n\n- Renewal of existing 28.12 km transmission line;\n\n- 310 km water distribution network including\n\n- 8 Pumping stations, water reservoirs (4 new), 2\ncollection tanks and auxiliary structures 13.15 28.07\n\n**TOTAL** 63.07\n\n**B: Kahramanmaraş Municipality**\n\n46 UNHCR Turkey: Syrian Refugee Camps and Provincial Breakdown of Syrian Refugees Registered in South East Turkey (January 2020): _[https://data2.unhcr.org/en/documents/details/73300](https://data2.unhcr.org/en/documents/details/73300)_, accessed on January 15, 2020 47 According to PID for Adana Page 59 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) 8. **Kahramanmaraş context.** Kahramanmaraş is one of the 10 provinces in Turkey with the highest Syrian refugee population. The Syrian population in the Province has increased by 78 percent in the last six years, 2012-2018. In 2018, Kahramanmaraş province had a total population of about 1,144,851 people out of which 92,630 (or 8.1 percent of the total population) were SuTP’s [48] . There is one refugee camp, the Merkez Container Camp, in the province of Kahramanmaraş which hosts about 10,859 SuTP’s [49] . Kahramanmaraş became a Metropolitan Municipality following the administrative reforms, and the water supply and wastewater service utility, Kahramanmaraş Water and Sewerage Administration General Directorate (KASKİ) was established in 2014. In accordance with the changes in municipal legislation, the service area of the Metropolitan Municipality and KASKI is defined by provincial boundaries. Accordingly, the personnel, assets, investments, liabilities, and receivables related to the provision of water and wastewater services carried out before 2014 by the sub-provincial municipalities, the Special Provincial Administration (SPA) and other service providers within the provincial boundaries, were transferred to KASKİ. The provision of solid waste services in terms of waste collections remains with sub-province municipalities, while the disposal and/or recovery of the collected municipal waste is the responsibility of the Metropolitan Municipality.\n\n9. **Kahramanmaraş Sub-Projects** . The proposed subprojects are aimed to improve access to safely managed water supply, sanitation and solid waste services in the proposed project areas in Kahramanmaraş metropolitan municipality. At appraisal stage, six investment sub-projects were identified:\n\n- Kahramanmaraş Northern Districts Integrated Solid Waste Project\n\n- Kahramanmaraş (Centrum) Drinking Water Project\n\n- Kahramanmaraş (Centrum) Sewerage and Storm Water Project\n\n- Elbistan Drinking Water Network Project\n\n- Elbistan Drinking Water Transmission Line\n\n- Ceyhan Basin Wastewater Treatment Plants\n\n10. **Kahramanmaraş Northern Districts Integrated Solid Waste Project.** The sub-project is composed of construction of a sanitary landfill (including a mechanical separation facility of 757 tons/day and a composting plant of 410 tons/day), two transfer stations (in Goksun and Elbistan) and rehabilitation of 24 wild dumpsites. Rehabilitation of 22 wild dumpsites (mainly of former small towns) will be done mainly by transferring dumped wastes to the new landfill site and 2 wild dumpsites (Afsin and Elbistan) will be rehabilitated on site.\n\n11. **Kahramanmaraş (Centrum) Drinking Water Project.** The sub-project includes rehabilitation and construction works in water distribution network including establishment of pressure zones, DMAs, SCADA system and installation of flow meters and pressure reduce valves. Within the scope of this sub-project, there are 59 water reservoir areas of which 38 are planned to be constructed. For the remaining, some of them will be abandoned or rehabilitated. Currently, varying pressure in the distribution network cause problems and the NRW in city is above 60 percent.\n\n12. **Kahramanmaraş (Centrum) Sewerage and Storm Water Project.** The sub-project includes rehabilitation and construction of 400 km sewerage network and 100 km storm water network. The sub-project aims to solve the problematic areas in terms of sewerage and storm water networks in the city center, specifically Onikişubat and 48 As per the address-based census48 for 2018 49 Calculations based on PID for Adana and projected estimates for the year 2020 Page 60 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) Dulkadiroğlu neighborhoods. The city center is located to foothill of Ahır Mountain. The rainwater from the high slopes of the mountain arrives to city center and causes flooding in streets since the closed storm water boxes are not capable of carrying the surface water flow. Recently in June 2018, a storm disaster, caused 3 losses in the city center besides the economic losses. It is reported that some of the storm water manhole lids and grids are covered or remain under roads which also prevents storm water entering through these grids/lids.\n\n13. **Ceyhan Basin Wastewater Treatment Plants.** Ceyhan basin is one of the 25 basins in Turkey extends from İskenderun Bay to inner Anatolia region. Ekinözü, Çağlayancerit, Andırın and Göksun are sub-provinces of Kahramanmaraş and part of Ceyhan basin. According to the Ceyhan Basin Pollution Prevention Plan prepared in 2016, the wastewater treatment plants for Ekinözü, Çağlayancerit, Andırın and Göksun are parts of the measures required to prevent pollution in the basin. The increased population with the addition Syrian refugees increases the pressure on the river basin due to untreated wastewater discharges. The project aims to enable KASKI (General Directorate of Kahramanmaraş Water and Sewage Administration) to provide local population and Syrian refugees with improved wastewater services. The project will also contribute to the prevention of pollution in Ceyhan River Basin.\n\n14. The following 4 WWTPs and relevant collector line constructions are included under the sub-project:\n\n- Ekinoezue WWTP and Collectors: 0.95 km of Ø400 Concrete Pipes, 1.1 km of Ø200 Concrete Pipes, 23\nManholes, 1 pumping station, 1 WWTP\n\n- Çağlayancerit WWTP: 1 WWTP\n\n- Andırın WWTP and Collectors: 4.23 km of Ø400 Concrete Pipes, 3.51 km of Ø200 Concrete Pipes, 163\nManholes, Pumping Station and 600 m Pressure Line (600 m), 1 WWTP\n\n- Goeksun WWTP and Collectors: 4.617 km of (Ø600) HDPE Pipes, 107 Manholes, 1 WWTP\n\n15. **Elbistan Drinking Water Network Project.** Under this sub-project, rehabilitation of 255 km water distribution network, 4,128 km house connections, 3 km pressure lines, six new water reservoirs and five pumping stations will be rehabilitated/constructed. Elbistan city centre’s water is supplied by a group of caisson wells drilled near Ceyhan River. Water from these wells are pumped to 5 existing reservoirs that feed 5 pressure zones. The existing drinking network was first constructed in 1956-1957, then in 1984-1987 and finally in 2009. The pipeline constructed before 2009 is in poor condition. The water losses are high, estimated as 50 percent. Thus, some of the lines need to be renewed and new pipes need to be constructed for extending the distribution network to new developing areas.\n\n16. **Elbistan Drinking Water Transmission Line.** Elbistan Drinking Water Transmission Line includes 120 km water transmission line and 6 water reservoirs (volumes between 100-2000 m3). First part of the transmission line will be constructed by DSI.\n\n17. The proposed investments in Kahramanmaraş are summarized as follows (Table 2.3):\n\n**Table 2.3: Kahramanmaraş investment components**\n**No** **Investment name** **Scope** **Budget**\n\nPage 61 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**(million EUR)**\nB-1 Kahramanmaraş Northern - Construction of a new landfill in Afşin. A composting facility 17.00 Districts Integrated Solid and collection and separation facility in landfill site. Two Waste Project transfer stations in Elbistan and Goeksun.\n\n- Construction of a new landfill in Afşin. A composting facility\nand collection and separation facility in landfill site. Two transfer stations in Elbistan and Goeksun.\n\n- Provision of waste collection equipment (container and pickup trucks).\n\n- Rehabilitation or closure of existing dumpsites.\n\n17.00 B-2 Kahramanmaraş (city centre) Drinking Water Project B-3 Kahramanmaraş (city centre) Sewerage Project B-4 Ceyhan Basin Wastewater Treatment Plants B-5 Elbistan Drinking Water Network Project B-6 Elbistan Drinking Water Transmission Line\n\n**Kayseri Municipality**\n\n- Rehabilitation and construction of water distribution network\nincluding:\n\n- Establishment of pressure zones and DMAs; introduction of\nSCADA systems, installation of flow meters and pressure reduce valves;\n\n- Rehabilitation of water reservoirs and pumping stations;\n\n- Construction of one new water reservoir.\n\n- Rehabilitation and construction work of sewerage network\n(400 km)\n\n- Construction of stormwater network (100 km)\n\n- Ekinözü WWTP and Collectors: 0.95 km of Ø400 Concrete\nPipes, 1.1 km of Ø200 Concrete Pipes, 23 Manholes, 1 pumping station, 1 WWTP (for 12,350 people; in 2037 capacity 889 m3/day, 14,750 people in 2052 capacity 1,062 m3/day)\n\n- Çağlayancerit WWTP: 1 WWTP (for 15,000 people; in 2038\ncapacity 1,650 m3/day, 17 600 people in 2053 capacity 1,936 m3/day)\n\n- Andırın WWTP and Collectors: 4.23 km of Ø400 Concrete\nPipes, 3.51 km of Ø200 Concrete Pipes, 163 Manholes, Pumping Station and 600 m Pressure Line (600 m), 1 WWTP\n\n- Goeksun WWTP and Collectors: 4.617 km of (Ø600) HDPE\nPipes, 107 Manholes, 1 WWTP (for 25 000 people; in 2037 capacity 5 508 m3/day, 30 000 people in 2052 capacity 6480 m3/day)\n\n- Rehabilitation of water distribution network (255 km) and\nhouse connections (4128), Pressure lines (3 km), Construction of water reservoirs (6 new), 5 Pumping stations\n\n- Construction of water transmission line (120 km) and 6 water\nreservoirs (volumes b/w 100-2000 m3) 10.80 25.00 8.4 8.00 25.00 18. **Kayseri context.** The refugee population in Kayseri consists mainly of Syrians, Afghans and Iraqis. Most of the refugees are living in Melikgazi district. In 2018, Kayseri province had a total population of about 1,389,680 people out of which 79,758 (or 5.74 percent of the total population in Kayseri province) were SuTP’s. There are no refugee camps in Kayseri province [50] .\n\n19. The existing WWTP (with a capacity of 110,000 m3/day) in Kayseri started operation in 2004 and was designed to 50 UNHCR Turkey: Syrian Refugee Camps and Provincial Breakdown of Syrian Refugees Registered in South East Turkey (January 2020): _[https://data2.unhcr.org/en/documents/details/73300](https://data2.unhcr.org/en/documents/details/73300)_, accessed on January 15, 2020 Page 62 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) serve the central districts of Kayseri, namely Kocasinan and Melikgazi, until 2025. However, three more districts (Talas, Hacilar and Incesu) were connected to the WWTP which led to an additional population of 160,000. In addition, the increased population in the city center with refugees, the capacity of the WWTP became insufficient.\nIn 2016, the average wastewater received to the WWTP was 163,200 m3/day [51] .\n\n20. **Kayseri Sub-Project.** Under this proposed sub-project, the capacity of the existing WWTP will be increased and improved through the installation of screens, aeration tanks, and primary and secondary sedimentation tanks. The sludge dewatering unit will also be renewed by replacement of the existing belt filter press with decanters. Through the added units, the capacity of the WWTP is planned to increase up to 183,000 m3/day for a PE of 1.4 million. In addition, a solar sludge drying facility will be constructed. The expected beneficiaries are 1,330,500 people out of which 79,830 (or 6 percent of the total population) are SuTP’s.\n\n21. The proposed investments in Kayseri are summarized as follows (Table 2.4):\n\n**Table 2.4: Kayseri province investment components**\n**No** **Investment name** **Scope** **Budget**\n**(million EUR)**\nC-1 Extension of Kayseri - Extension of existing WWTP (2nd phase) and installation of solar sludge 25 Wastewater drying facility; replacement of existing belt filters with decanter units.\nTreatment Plant\n\n- Extension of existing WWTP (2nd phase) and installation of solar sludge\ndrying facility; replacement of existing belt filters with decanter units.\n\n25\n\n**.Konya Municipality**\n\n22. **Konya context.** The refugee population in Konya consists mainly of Syrians and Afghans. In 2018, Konya province had a total population of about 2,205,609 [52] people out of which 109,124 (or 4.95 percent of the total population) were SuTP’s [53] . There are no refugee camps in Konya [54], refugees are mainly living in the central districts of Konya.\nSince 2014, water supply and sanitation services for the metropolitan municipality of Konya are provided by Konya Water and Sewerage Administration General Directorate (KOSKİ). Due to high seasonal variations in the availability of water resources and the high NRW (57 percent) [55], KOSKİ has developed the following investment project for Akşehir district.\n\n23. **Konya Sub-Project.** The proposed Akşehir Water Supply Project will finance the construction of:\n\n- 29 km of water transmission lines,\n\n- 97 km of distribution network including house connections,\n\n- three water reservoirs (3,000, 1,000 and 500 m3) and pressure reducing chambers.\n\n24. Existing water distribution network was constructed in 1990 and the capacity of the system is already reached.\n\nThus, the project aims to renew the existing water system for the city of Aksehir in order to provide healthier and sustainable water services to customers. In Aksehir, drinking water is currently supplied from groundwater wells.\n\n51 According to PID for Kayseri.\n52 As per the address-based census for 2018 53 Calculations based on PID for Konya and projected estimates for the year 2020.\n54 UNHCR Turkey: Syrian Refugee Camps and Provincial Breakdown of Syrian Refugees Registered in South East Turkey (January 2020): _[https://data2.unhcr.org/en/documents/details/73300](https://data2.unhcr.org/en/documents/details/73300)_, accessed on January 15, 2020 55 According to PID for Konya Page 63 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) Through the project, spring waters in Gemen (15 lt/s), Sucikan (50 lt/s) and Ilica (20 lt/s) will be transmitted and distributed to Aksehir for drinking water purposes. The Project is expected to lead to a significant decrease in energy consumption as spring water will be transmitted by gravity. The total project beneficiaries are estimated at 134,524 people out of which 16,270 (or 12.1 percent of the total population) are SuTP’s [56] .\n\n25. At appraisal stage, two further sub-projects in Konya metropolitan municipality were identified:\n\n- Ilgin Wastewater Treatment Plant\n\n- Cumra Wastewater Treatment Plant\n\n26. The proposed investments for Konya are summarized as follows (Table 2.5):\n\n**Table 2.5 Konya province investment components**\n**No** **Investment name** **Scope** **Budget (million**\n\n**EUR)**\nD-1 Akşehir Water Supply - Construction of water transmission lines (29 km). 4.71 Project - Rehabilitation/construction of 3 water reservoirs (3000, and\n\n- Construction of water transmission lines (29 km).\n\n- Rehabilitation/construction of 3 water reservoirs (3000, and\n500 m3) and installation of pressure reducing chambers construction of network pipelines (97 km) including house connections will be prioritized according to available budget.\n\n4.71 D-2 Ilgin Wastewater Treatment Plant Project D-3 Cumra Wastewater Treatment Plant Project\n\n- Construction of Ilgin Wastewater Treatment Plant Project 3.85\n\n- Construction of Cumra Wastewater Treatment Plant Project 5.38\n\n**Osmaniye Municipality**\n\n27. **Osmaniye context.** In 2018, the total population in Osmaniye province was 534,415 people out of which 51,160 (or 9.6 percent of the total population) are SuTP’s. There is one refugee camp, the Cevdetiye Container Camp, in the province of Osmaniye which hosts about 12,610 SuTP’s [57] .\n\n28. The city center of Osmaniye municipality receives drinking water from three main sources, which are: Zorkun Springs (400 l/s; by gravity), Yenikoey Wells (360 l/s; by pump) and 7 groundwater wells (270 l/s; by pump). The design report indicates that the existing resources are only capable to meet water demand until 2026. For this reason, Osmaniye Municipality has contacted DSİ and 786 l/s water allocation from Aslantaş Dam is received.\n\n29. The existing water distribution network has high NRW (56 percent) and real losses (49 percent) in the system [58] .\n\nThere are no ‘as built drawings’ of the existing water distribution network available, hence, the Municipality receives around 200 system failure calls per month due to failure of pipes (mainly PCV, asbestos and cast-iron pipes exist) in the system or house connections. As there are no ‘as built’ drawings the site operations are carried as trial and error actions. Most of the cast iron pipes have corrosion problems and collapse. These issues extend the repair times and create secondary contamination risks to the system.\n\n56 Calculations based on PID for Konya and projected estimates for the year 2020.\n57 UNHCR Turkey: Syrian Refugee Camps and Provincial Breakdown of Syrian Refugees Registered in South East Turkey (January 2020): _[https://data2.unhcr.org/en/documents/details/73300](https://data2.unhcr.org/en/documents/details/73300)_, accessed on January 15, 2020 58 According to PID for Osmaniye Page 64 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) 30. The existing wastewater system has a length of around 400 km and consists of concrete and reinforced concrete pipes with diameters ranging from 200 to 1000 mm. The wastewater collection network was mainly constructed in 1985 therefore it is aged, and the pipe material and connection type used are reported in the Project Identification Document (PID) as not suitable to the area specific soil characteristics. Due to the high groundwater level in the project area, most of the existing pipes are full of groundwater and soil granules brought by groundwater infiltration. The Municipality receives 250 failure calls monthly as a result of sediments causing pipe clogging or failures/collapses of the pipes. The municipality cleans annually 250 km of wastewater pipelines by vacuum trucks from sediments and clogging. These problems make operation of the system inefficient due to economic circumstances and staff capacity.\n\n31. **Osmaniye Sub-Project.** The two proposed sub-projects aim at improving the existing water supply and sewerage systems in the city center of Osmaniye. The total project beneficiaries in the proposed project area are estimated at 334,298 people out of which 57,109 (or 17.1 percent of the total population) are SuTP’s [59] .\n\n32. **Osmaniye Centrum Drinking Water Project.** Osmaniye drinking water project will focus on the distribution network and will include construction of approximately 598 km water distribution network (pipe diameters changing from 110 to 900 mm), 5 water reservoirs, pressure release valves, DMAs and auxiliary infrastructure.\nThese activities seek to address critical water supply service constraints faced by the municipalities in delivering effective and efficient services, including: (a) frequent pipe breakage and subsequent contamination of water due to severe corrosion of existing pipes (cast iron, asbestos cement), and high pressure fluctuations – water utility records show about 200 calls per month, with about 30 percent of these related to the main network and 60 percent to customer connections; and (b) non-revenue water is very high at 56 percent, of which 49 percent are real losses.\n\n33. **Osmaniye Centrum Sewerage project.** Osmaniye sewerage project aims renewal of 403 km of wastewater collection network. The existing collection network was mainly constructed in 1985 and is prone to deficiencies due to aging and connection types used at that time. The high groundwater level also creates problems such as high amount of infiltration to the pipes and entrance of soil granules to network, which creates additional costs for operation. It is stated that approximately 100 failure calls are received from customers due to failure and/or collapse of pipes.\n\n34. The project is expected to lead to significant efficiency improvements for the operation of water distribution network and wastewater collection system for Osmaniye Municipality. The investments in the water distribution network will decrease energy consumption, water losses and non-revenue water. This will also allow the Municipality to meet water demand until water is received from Aslantaş dam. Also, failure calls received for both water and wastewater pipelines will be decreased. The improvement in wastewater collection network is also expected to reduce possible groundwater infiltration to the system thus may reduce the wastewater flow to the WWTP. The proposed investments for Osmaniye are summarized as follows (Table 2.6):\n\n**Table 2.6: Osmaniye investment components**\n**No** **Investment name** **Scope** **Budget**\n\n59 Calculations based on PID for Adana and projected estimates for the year 2020.\n\nPage 65 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["water utility records"]}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**(million EUR)**\nE-1 Osmaniye (Centrum) Drinking Water Project - Construction of water distribution network (598 27.65 km; 110-900 mm) to be prioritized, 5 water reservoirs, pressure release valves, DMAs and auxiliary structures E -2 Osmaniye (Centrum) Sewerage Project - Wastewater collection network (403 km) will be prioritized according to available budget.\n\n29.91\n\n**Priority Water Supply and Sanitation Investments**\n\n35. Priority water and sanitation investments at neighborhood or settlement level in low income areas most affected by vulnerable SUTPS and host communities may range from facilities such as, inter alia, household water facilities, including taps and laundry facilities, and appropriate toilet facilities, which may be introduced semi-permanent solutions in informal settlements that can be relocated if desired. The following paragraphs conceptually describe one form of potential priority investments that have been used in other countries in similar situations where environmental infrastructure services are urgently required in informal settlings. In this case, priority investments are required to address kay concerns such as risks to public health, high levels of environmental degradation and pollution due to grey water run-off or lack of toilets, and social risks to the most vulnerable, including women and children who are often most impacted by lack of access to safe water and sanitation services.\n\n36. **Non-permanent or movable Community Ablution Blocks (CABs) for informal settlements.** CABs comprise blocks of water and sanitation facilities such as toilets and showers that can be made out of lower cost but durable materials such as refurbished shipping containers to provide services in informal low-income settlements. Various CAB models have used successfully elsewhere in similar situations in countries such as South Africa. The project will support the development of a conceptual design for CABs made from locally available facilities, to be piloted in selected low-income settlements in collaboration with municipalities.\n\n37. Required considerations to implement CABs shall include: (i) land availability to implement a CAB project, with landownership needs to be formally agreed on with municipalities; (ii) space consideration; ideally sites or plots must be greater than 250 m2; (iii) ideally the average slope of the ground must be less than 1:3 for the ease of installation; (iv) environmental considerations, including connection to the main sewer and further treatment etc.; and (v) site having a basic level of water supply. Containers may be designed or tailor-made for the local context and may be equipped with water meters to monitor. They should also be designed in a gender sensitive manner with separated male and female units in different blocks, and fitted with other gender appropriate facilities such as menstrual hygiene facilities for females, facilities for disabled people, and women with small children.\n\n38. Development and introduction of CABs will require engagement of both municipalities and selected recipient communities with vulnerable SUTP and host communities, to be identified by the municipalities, initially on a pilot scale with potential to scale up the model. Community members will continue to be involved in operating and maintenance of the CABs to ensure their proper use and management. Management of such facilities may provide employment / income generating activities for local community members, including women. However, appropriate design of the operation and maintenance arrangements will be determined in collaboration with the respective municipalities and communities during development of the facilities. Below is an example of a conceptual layout of CABs.\n\nPage 66 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**ANNEX 3: Economic and Financial Analysis**\n\n**Rationale for Public Sector Investment**\n\n1. The rationale for public sector financing under the proposed project is mainly linked to the important social\n\nand political goals of the Government of Turkey and the EU to address the impacts of the Syrian refugee crisis in the most affected parts of the country. Direct fiscal impact depends on what the state would have done in the absence of the project financing for the subprojects. In this case the fiscal savings are direct and significant given cost savings arising from the reduced state and local governments financing to provide certain services, owing to project investments. Limited capacity to attract private sector financing by the municipalities in the targeted investment areas due to the overall perceived risks, necessitates public sector financing.\n\n**Rationale for Bank’s Involvement**\n\n2. The World Bank has been a long-term partner of Turkey in the water and sanitation, and water resources management sectors. In this instance, the Bank’s involvement occurs at the backdrop of severe service pressures on the municipal services due to the refugee situation, providing contingent funding for critical works to preserve adequate service provision. In this context the knowledge and global experiences of the Bank are highly valuable to the GoT. The Bank has substantial global experience in supporting programs and managing policy dialogue to improve service delivery in areas affected by refugees. Bank support has been provided in form of financial support, Advisory Services and Analytics (ASA) services, and operational support towards implementation of multi-donor funded programs. Globally, the Bank has supported the preparation of needs assessments for refugee host countries, AAA on affected communities, and implementation of activities for provision and expansion of basic services in various sectors to accommodate the increased pressure on existing systems due to refugees. The Bank has also used its convening power in the past to bring together different stakeholders, including municipalities affected by refugees and others to share lessons across sectors.\n\n**Cost Benefit Analysis**\n\n3. The financial and economic analysis focus on the project’s subcomponents with specific investments that materialize direct or indirect economic benefits and costs. The economic viability of subprojects will assess the value from project infrastructure investments without considering sub-project’s benefits from technical assistance aimed at building capacity or improving knowledge and technical expertise.\n\n**Methodology**\n\n4. Costs and benefits are estimated at constant prices for 2018. All benefits and costs were appraised measuring their flow of costs and benefits for the lifetime of the sub-projects, ranging from 25 to 33 years. Costs and benefits were expressed in constant prices as of 2018 at an exchange rate of 5.4852 TL per Euro. The discount recommended by the World Bank guideline is 6 percent, and the analysis used a higher discount rate of 10 percent to weigh in economic risks in discounting flows to present values. The main approach used for this economic analysis is to compare the costs and benefits with and without project scenarios using European Commission CBA Guide.\n\nPage 67 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**A) Adana Sub-projects**\n\n5. Kozan Drinking Water and Transmission Line Project: This activity includes the renewal of water network pipes including house connections, construction of 28 km gravity and pressure transmission lines, pump stations, reservoirs, collection tanks and auxiliary structures. These investments will ensure effective operation of the whole water supply system, contribute to the reduction of non-revenue water to regulation limits and increase service delivery quality levels.\n\n6. A Cost Benefit Analysis (CBA) was carried out as part of the Feasibility Study in line with the Guide to Cost Benefit Analysis of Investment Projects, EC Directorate General Regional Policy, 2014. This reflected an ENPV of EUR1.9 million and EIRR of 11.0 percent. The financial analysis resulted in a FRR/C of -3.29 percent and a FRR/K of 18.36 percent. The social affordability analysis showed that no affordability constraints will be faced throughout the analysis period as the full cost recovery tariffs (EUR 0.96/m3) are below maximum affordable tariff which is set at 2.5 percent of median household income equating to EUR 1.05/m3 (both figures are excluding VAT).\n\n7. Sensitivity analysis has been carried to see the impact of the variations of the critical variables including investment, personnel and power costs as well as health benefits on the economic performance of the project. Accordingly, 1 percent change in investment costs and health benefits led to a change above 1 percent in the ENPV in absolute terms at -1.98 percent and 2.41 percent respectively.\n\n8. Proposed Kozan Drinking Water Project will significantly contribute to the reduction of non-revenue water (app. 61 percent) to the Regulation limits and provide benefits for the efficient operation of the whole network afterwards. Pınargözü Transmission Line Project will ensure safe gravity water supply to District centrum in the short term. Both investments will increase service delivery quality levels of the consumers, which suffer from frequent water interruptions and low pressure.\n\n9. Yedigöze Drinking Water Transmission Line and Yedigöze Treatment Plant Project will serve 141 settlements of Adana city. These settlements include 4 sub-provinces (Kozan, İmamoğlu, Ceyhan and Yumurtalık districts) and 137 surrounding neighborhoods on the route of the transmission line.\n\n10. Specific objectives of these two proposed projects are; Construction of a treatment plant with a capacity of 115K m3/day introducing a conventional treatment process including coagulation, flocculation, filtering and disinfection, construction of 35.4 km ductile iron transmission line with auxiliary structures (washout and air relief valve chambers, etc.). Therefore, lack of safe water sources problem for 4 Sub-provinces and 137 neighborhoods (total 141 settlements) shall be solved. It will eliminate well usage in these settlements and so contribute reduction of the energy costs. Improvement of the underground water levels shall be achieved which also suffer from high withdrawal rates caused from irrigation and water supply activities.\n\nPage 68 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) 11. The financial analysis resulted in an FNPV of EUR -23.1 million an economic analysis was performed taking into account pollution reduction benefits, health benefits, GHG emission savings and resource cost savings, resulting in ENPV of EUR -24.5. million and an EIRR of 6.4 percent.\n\n**B) Kahramanmaras Sub-projects**\n\n12. 12.Construction of Andırın and Göksun (Ceyhan Basin) WWTPs is developed as part of the plan to prevent pollution in Ceyhan Basin and alleviate the extra pressure due to the increased population as a result of the Syrian refugee population influx on the infrastructure services in the province.\n\n13. WTP (Willingness to Pay) is used to estimate shadow price of project output, or in the absence of WTP data, the commonly accepted practice is to take a conservative assumption for the monetization of the project benefits due to uncertainties of the estimation and also to calculate avoided costs for users to get this service from an alternative source. The feasibility classified the benefits of Kahramanmaras WWTP Project into 3 categories detailed below:\n\n- Monetarization of project benefits: A per capita benefit value estimated by the consultants has been\nset over the reference period of the project yielding an economic benefit of EUR 29.671.122.\n\n- Disposable income for new employment: According to the TURKSTAT data, Marginal Propensity to Save\nin January 2019 in Turkey is 19.40 percent which means Marginal Propensity to Consume is 80,60 Page 69 of 94", "output": {"entities": {"named_data": ["TURKSTAT data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) percent. The direct economic benefit is calculated as EUR 1.457.517 over the operational period (20222052) of the project.\n\n- Avoided Cost of Opening Septic Wells: The connection ratio to sewer system of Kahramanmaras\nhouseholds is 80 percent which, by assumption, means that 20 percent of the households should open a septic tank to get rid of wastewater they generate. At the first year of operation period the consultant assumes that 20 percent of not connected households avoid investing in septic tanks for the wastewater generated by themselves which in return if the cost of opening a septic tank is assumed to be EUR 500 per household then the total saving will be calculated as EUR 9.500 in the first year, 2022. For the following years of project reference period, 20 percent of additional subscribers will do the same saving and at the end of project’s reference period, total savings from avoided costs of opening sewer wells become EUR 382.100.\n\n14. The Economic NPV of the Project is EUR 170.145,97 and Economic IRR is 10.43 percent which is greater than the social discount rate of 10 percent which is used in economic analysis. Benefit-Cost ratio is calculated as 1,02 at 10 percent of social discount rate.\n\n**Table 3.1: Kahramanmaras Andırın and Göksun WWTP**\n\n**Economic Benefit, NPV and IRR Calculation** **EUR**\n\nNPV of Monetization of Direct Benefit (Instead of WTP) Per Inhabitants 6.851.827 NPV of Avoided Cost of Opening Septic Wells 84.817 NPV of Disposable Income Addition 368.324 NPV of Total Economic Benefits 7.304.967 NPV of Investments 4.061.870 NPV of O&M and Replacement Costs 3.072.952 NPV of Total Economic Costs 7.134.821 Economic NPV (EUR) 170.145,97 Economic IRR 10,43% Benefit/Cost Ratio 1,02 15. The Project will contribute to the prevention of pollution in Ceyhan River Basin comprising several water resources. The project will lead to increasing the overall effectiveness and efficiency of wastewater management in the region. The health standards of the public will be improved. Project is expected to have a positive impact on the environment by protecting natural resources.\n\n16. Kahramanmaraş Northern Districts Integrated Solid Waste Management (ISWM) System Project intends to upgrade the existing waste management practices through the implementation and operation of a sanitary landfill incorporating optimal routing to minimize transport and emissions, and safe recovery and disposal of Page 70 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) waste with environmental controls for the collection and treatment of leachate and landfill gas. ISWM system project is justified based on financial and economic returns through generation of benefits by closing the existing non-sanitary landfill and switching to a coordinated regional system with collection, separation and composting facilities.\n\n17. In calculating the tariffs for the future ISWM system, the average tariff has been set to comply with the polluter pays principle within the limit of household affordability. The maximum affordable tariff has been defined as 1 percent of disposable income for an average household. Evaluation period has been taken as 2017- 2047 coinciding with the bulk of the planned operational lifetime of the landfill – the largest component of the proposed project.\n\n**Table3. 2: Kahramanmaras ISWM Project**\n\n**Value**\n**Financial Analysis**\n**Discounted (NPV)**\n\nFinancial discount rate (%), real 10% Total investment cost (in EUR, discounted) (*) 21,407,525 Residual value (in EUR, not discounted) (**) 2,909,431 Residual value (in EUR, discounted) (**) 166,735 Revenues (in EUR, discounted) (**) 27,941,825 Operating costs (in euro, discounted) (**) 22,809,183 Net revenue (in euro, discounted) = (7) - (8) + (6) 5,299,377 18. The social discount rate of 10 percent is assumed when considering the economic welfare implications of the proposed investment for the provision of ISWM services for society in the Kahramanmaraş Metropolitan area. In order to convert the project costs from financial costs – both investment and operating costs – based on market prices to economic costs based on the impacts to society, the following steps were made:\n\n- Taxes and subsidies are removed from the cost estimates. Transfer payments (such as social security\ntaxes) are also removed from cost estimates (both investment and operating costs)\n\n- Costs based on domestic prices are converted to be comparable with costs based on international prices\nas internationally traded goods are assumed to reflect actual economic value, whereas domestic prices can be distorted by market imperfections.\n\n**Table 3.3 Kahramanmaras ISWM Project**\n**Economic Cost Benefit Analysis** **Values**\n\n1. Social discount rate (%) 10.0%\n\n2. Economic rate of return (%) 11.5% 3. Economic NPV (in Euro) 3,832,084 4. Benefit-cost ratio 1.15 19. Due to collection and (future) utilization of landfill gas, the negative effects of greenhouse gases will be Page 71 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) reduced. Using the World Bank GHG accounting tool Climate Action for Urban Sustainability (CURB), it was estimated that the project avoided about 63,962 tCO2 of being emitted into the atmosphere over the next 30 years. Using data on the waste production, it was found that the GHG emission reduction was positive because of the shift from open dumping to management of waste in a landfill with landfill gas capture.\nEmissions decreased because of landfill gas capture, which prevents the methane generated by waste within landfills from entering the environment. Using the market price of EUR 24.6/tCO2, the social benefits of the avoided GHG are worth about EUR 1.6 million for the entire project life.\n\n**Table 3.4 Methane Emissions**\n\n**Methane emissions impact calculation*** **2017** **Project�Life**\n\n**30�years**\nWith�action For�Managed�landfill�(tons�CO2e)=�A 19.189 Baseline For�Unmanaged�landfill�-�less�than�5m�deep�(tons�CO2e)=�B 21.321 Avoided�methane�emissions�(CO2e)=�B-A 2.132 63.962 Avoided�GHG�Monetary�Benefits�(EUR)** 52.470 1.574.107\n\n*World�Bank�Climate�Action�for�Urban�Sustainability�GHG�Accounting�Tool\n**Price�data�from�https://origin.markets.businessinsider.com/commodities/co2-european-emission-allowances\n\n**C) Kayseri Sub-project**\n\n20. Kayseri WWTP Capacity Extension Project 21. Kayseri WWTP project is designed to meet the needs of the city with increasing population and expanding industrial facilities. Year of 2025 is selected as target year as of when all the wastewater will be discharged after being treated. Kayseri has a reported Syrian guest percentile at 4.3 percent of its local population. Over its lifetime of 30 years, project will have a FNPV of EUR3,545,000 with an FIRR of 12.2 percent.\n\n22. The project will yield an E-NPV of 10,918,408 with an ERR of 25.6 percent and has a cost/benefit ratio calculated as 1.62. According to the World Bank Water GP GHG Calculation Model, project’s gross emissions were 369,064 tons CO2e, net emissions were 118,894 tons CO2e and net average annual emissions were 3,963 tons CO2e.\n\n**E) Osmaniye Sub-project**\n\n23. Osmaniye (Centrum) Drinking Water and Sewerage Network Projects 24. Osmaniye needs to enlarge its sewerage and drinking water networks to meet the legislative requirements for the collection of urban wastewaters and for the supply of good quality water. In accordance with Turkey’s physical planning guidelines for water and wastewater infrastructure, the Osmaniye wastewater collection system is designed using a 35-year horizon in order to respond to the needs of increasing population with an over 20 percent ratio of Syrian guests.\n\n25. The project will produce a negative FNPV of -EUR10.6 million with an FIRR of 7.5 percent. Economic analysis yields ENPV of EUR 65,019,026 with an ERR of 23.82 percent and benefit/cost ratio of 9.17 thus justifying the project on economic basis. Using World Bank Water GP GHG Calculation Model, project’s gross emissions were 244,082 tons CO2e, net emissions were 990,958 tons CO2e and net average annual emissions were Page 72 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) 28,299 tons CO2e.\n\n**Financial Estimates of Utilities and Municipality**\n\n26. Financial budget results for the last three years covering 2016-2018 and estimates for the next three years of the water utilities and for Adana, Kahramanmaras and Kayseri together with Municipality of Osmaniye are provided below. These constitute a baseline assessment of their financial position reflecting level of financial sustainability. Based on their past budget results all three utilities have generated positive current balances enabling them to finance part of their capital expenditure after debt interest payment. Municipality of Osmaniye has also been reporting positive current balances though with narrow margins reflecting its relatively limited revenue potential due to its non-metropolitan status.\n\n27. For three-year projections operating revenue from water services and other tax related central government transfers are assumed to increase at least by consumer inflation rate with some real tariff adjustments in 2020 and 2021 above expected inflation. These would amount to a combined tariff increase of 30 percent over the three-year period still enabling the affordability rates remaining below 2.5 percent of median household income.\n\n**Table 3.5 Adana SKI Budget Results**\n\n**Adana Water and Sewerage Entity**\n**Budget Results (TRY/EUR ave. rate)**\n**(EURm)**\nWater and Other Tariffs Other Revenues Transfers Grants Received\n**Total revenues**\n\nPersonnel Expenditure Other operating expenditure\n\n**Operating balance**\n\nInterest paid\n\n**Current balance**\nCapital Expenditure\n**Surplus/(Deficit) Before Debt**\nNew Borrowing Debt repayment\n\n**Overall results for the year**\nDebt (EUR equivalent) Source: Budget results and consultant calc **u** 0,2691 0,2211 0,1652 0,1652 0,1652 0,1652\n**2016** **2017** **2018** **2019F** **2020F** **2021F**\n91,9 93,3 81,6 91,4 105,1 113,5 0,0 0,0 2,4 2,7 3,1 3,4 23,0 21,5 16,7 18,7 21,6 23,3\n**114,9** **114,7** **100,8** **112,9** **129,8** **140,2**\n\n-20,0 -17,4 -14,0 -15,6 -18,0 -19,4\n\n-73,8 -74,6 -66,4 -74,4 -85,5 -92,3\n\n**21,1** **22,7** **20,5** **22,9** **26,3** **28,5**\n\n-1,5 -1,1 -4,7 -5,5 -6 -7,5\n\n**19,6** **21,6** **15,7** **17,4** **20,3** **21,0**\n-20,3 -35,8 -40,3 -35 -58 -65\n**-0,7** **-14,2** **-24,6** **-17,6** **-37,7** **-44,0**\n0,0 0,0 31,0 35 42 30 0,0 0,0 -6,1 -8 -9,5 8\n\n**19,6** **21,6** **40,6** **44,4** **52,8** **59,0**\n44,3 47,7 58,7 85 110 130 lations Page 73 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Table 3.6 Kahramanmaras SKI Budget Results**\n\n**Kahramanmaras Water and Sewerage Entity**\n**Budget Results (TRY/EUR ave.rate)** 0,2691 0,2211 0,1652 0,1652 0,1652 0,1652\n**(EURm)** **2016** **2017** **2018** **2019F** **2020F** **2021F**\nWater and Other Tariffs 27,1 26,7 21,9 24,5 28,2 30,5 Other Revenues 7,4 9,6 5,1 5,7 6,6 7,1 Transfers Grants Received 10,3 9,7 8,1 9,1 10,5 11,3\n**Total revenues** **44,8** **46,0** **35,1** **39,4** **45,3** **48,9**\n\nPersonnel Expenditure -7,6 -7,1 -5,2 -5,8 -6,7 -7,2 Other operating expenditure -18,7 -20,1 -17,4 -19,5 -22,5 -24,3\n\n**Operating balance** **18,5** **18,7** **12,5** **14,0** **16,1** **17,4**\n\nInterest paid -1,7 -1,1 -1,2 -2,0 -3,0 -5,0\n\n**Current balance** **16,8** **17,6** **11,3** **12,0** **13,1** **12,4**\nCapital Expenditure -20,8 -23,7 -26,1 -30,8 -31,5 -35,2\n**Surplus/(Deficit) Before Debt** **-4,1** **-6,1** **-14,9** **-18,9** **-18,4** **-22,9**\nNew Borrowing 0,0 12,0 29,5 22,0 34,0 27,0 Debt repayment -2,8 -9,4 -11,4 -14,0 -16,4 -17,0\n\n**Overall results for the year** **14,0** **20,2** **29,3** **20,0** **30,7** **22,4**\nDebt (EUR equivalent) 24,5 23,2 24,6 35,1 52,7 62,7 Source: Budget results and consultant cal **c** ulations\n\n**Table 3.7 Kayiseri SKI Budget Results**\n\n**Kayseri Water and Sewerage E**\n**Budget Results**\n**(EURm)**\nWater and Other Tariffs Other Revenues Transfers Grants Received\n**Total revenues**\n\n**ntity**\n\nPersonnel Expenditure Other operating expenditure\n\n**Operating balance**\n\nInterest paid\n\n**Current balance**\nCapital Expenditure\n**Surplus/(Deficit) Before Debt**\nNew Borrowing Debt repayment\n**Before Debt**\n**Overall results for the year**\nDebt (EUR equivalent) Source: Budget results and consult **a** 0,2691 0,2211 0,1652\n**2016** **2017** **2018** **2019F** **2020F** **2021F**\n47,3 44,1 38,2 42,8 49,2 53,2 1,5 1,2 1,4 1,6 1,8 2,0 14,2 14,3 11,8 13,2 15,1 16,4\n**63,0** **59,6** **51,4** **57,6** **66,2** **71,5**\n\n-11,6 -9,6 -8,0 -9,0 -10,3 -11,2\n\n-32,9 -29,5 -24,0 -26,9 -31,0 -33,4\n\n**18,4** **20,4** **19,3** **21,6** **24,9** **26,9**\n\n-1,5 -2,6 -2,1 -2,4 -3 -3,2\n\n**16,9** **17,9** **17,3** **19,2** **21,9** **23,7**\n-24,4 -25,7 -23,6 -27,8 -28,4 -31,8\n**-7,5** **-7,8** **-6,3** **-8,6** **-6,5** **-8,1**\n31,4 31,1 45,3 35 42,5 28\n-6,0 -7,4 -9,8 -10 -12 -14\n\n**17,9** **15,9** **29,2** **16,4** **24,0** **5,9**\n51,6 51,1 45,2 70 100 114 nt calculations Page 74 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Table 3.8 Osmaniye SKI Budget Results**\n\n**Municipality of Osmaniye**\n\n**Budget Realizations**\n\n**(EUR million)**\n\nTRY/EUR Taxes Transfers received Fees, fines and other operating revenue\n\n**Operating revenue**\n\n**ACTUAL** **ACTUAL** **ACTUAL** **PROJECT** **PROJECT** **PROJECT**\n\n**2016** **2017** **2018** **2019** **2020** **2021**\n\n0,2691 0,2211 0,1652 0,1652 0,1652 0,1652 4,1 3,4 4,1 4,5 5,1 5,7 29,6 29,1 34,0 36,7 41,1 46,0 7,8 7,1 6,8 7,8 8,7 9,4\n\n**41,5** **39,5** **44,9** **49,0** **54,9** **61,1**\n\nOperating expenditure -37,5 -32,6 -35,8 -38,7 -43,4 -48,6\n\n**Operating balance**\n\n**Operating Margin**\n\nFinancial revenue Interest paid\n\n**Current balance**\n\nCapital revenue Capital expenditure\n\n**4,0** **7,0** **9,0** **10,3** **11,5** **12,6**\n\n_**10%**_ _**18%**_ _**20%**_ _**21%**_ _**21%**_ _**21%**_ 0,6 0,6 0,4 0,4 0,4 0,4\n\n-6,6 -4,0 -8,3 -6,6 -5,3 -5,7\n\n**-** **1,9** **3,5** **1,1** **4,1** **6,7** **7,3**\n\n0,8 5,3 0,1 0,2 1,8 2,7\n\n-7,2 -24,5 -17,7 -8,8 -14,4 -24,3\n\nCapital balance -6,4 -19,2 -17,6 -8,6 -12,6 -21,7\n\n**Surplus (deficit) before debt variation** **-8,4** **-15,7** **-16,5** **-4,5** **-5,9** **-14,4**\n\n28. Further detailed financial management assessments and creditworthiness analysis have been carried out for sub-borrowers Adana, Kahramanmaras and Kayseri water utilities along with municipalities of Kahramanmaras Metropolitan and Osmaniye. These have covered qualitative financial management areas as well as fiscal performance, expenditure coverage and indebtedness for the last five years and a projection period of five years.\n\n29. The capacity of Adana SKI to clear its debts using the operating surplus is 1.1 years in 2014 and increased to 2.2 years in 2018 with an average of 2 years over the period from 2014 to 2018. The number of years for the 2019 – 2021 budget period is expected to be less than a year. The capacity of Kahramanmaras SKI to clear its debts using the operating surplus is 3.5 years in 2014 and decreased to 0.9 years in 2018 with an average of\n1.2 years. The number of years for the 2019 – 2021 budget period is expected to be 1.5 years. The capacity\nof Kayseri SKI to clear its debts using the operating surplus is 1.4 years in 2014 and increased to 2.3 years in 2018 with an average of 2.1 years over the period from 2014 to 2018. The number of years for the 2019 – 2021 budget period is expected to be 1.96 on the average.\n\n30. Financial projections for the water utilities and municipalities have indicated that these entities will incur additional debt in the project implementation period with debt outstanding to current revenue peaking at 32 percent for Adana SKI, 45 percent for Kahramanmaras SKI, and 21 percent for Kayseri SKI. For the municipalities debt service burden will increase and self-funding ability from current balance will be stretched with debt service to current revenue reaching 43 percent for Kahramanmaras MM and 42 percent for Osmaniye. Sub-borrowers are forecast to maintain ability to meet financial commitments nonetheless remain exposed to adverse economic conditions.\n\nPage 75 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Table 3.9: Summary Economic Analysis Results**\n\nNPV of Total Costs NPV of Total Benefits E-IRR (%) (EUR) Adana Kozan Drinking Water and Transmission Line Adana Yedigöze Drinking Water Transmission and WWTP Kahramanmaras Ceyhan Basin (Andırın&Goksun) WWTP Kahramanmaras Northern Districts Integrated Solid Waste Management Kayseri WWTP Expansion Osmaniye Water and Sewerage Network\n\n**Conclusion**\n\nEconomic NPV 1,913,523 22,009,027 23,922,550 11.0\n\n-24,497,839\n\n170,146 3,832,084 10,918,408 65,019,026 44,492,053 7,134,821 26,118,978 20,369,926 7,956,038 19,994,214 7,304,967 29,951,062 30,955,334 72,975,064 6.4 10.4 11.5 25.6 23.8 31. The influx of Syrian guests to several Municipalities of Turkey over the past years has led to an increase in infrastructural requirements caused by increasing water demands, wastewater generation and solid waste generation. This has burdened Municipalities’ ability to provide effective level of service both to their local residents, as well as to the guest population.\n\n32. The entire project comprising various sub-projects by different implementing authorities will not produce commercially profitable results in each instance. Nonetheless, it will create viable results underpinned by net economic benefits to be accrued in total. With the implementation of the proposed projects, the capacity of the water entities (SKI) and their municipalities will be strengthened in terms of infrastructure and technical capacities, enabling them to better respond to the significant increase in demands for municipal services.\nThrough the technical assistance components, SKIs are also expected to prepare Performance Improvement Action Plans, which will, among other things focus on improving their performance efficiency and financial viability through appropriate actions targeting results such as improved revenue collection, cost reduction, non-revenue water reduction, etc.\n\nPage 76 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**ANNEX 4: Procurement**\n\n1. **Applicable regulations.** The World Bank Procurement Regulations for IPF Borrowers – July 2016 revised in November\n\n2017 and August 2018 (“Procurement Regulations”) will apply to the proposed Project. A General Procurement Notice will be published on the World Bank’s external website and United Nations Development Business online immediately after the project negotiations.\n\n2. **Anti-Corruption Guidelines** . The World Bank's “Guidelines on Preventing and Combating Fraud and Corruption in Projects Financed by IBRD Loans and IDA Credits and Grants”, dated October 15, 2006 and revised in January 2011 and as of July 1, 2016 (Anti-Corruption Guidelines)” will apply to the proposed Project.\n\n3. **Project Procurement Strategy for Development (PPSD).** The Procurement Regulations requires the Borrower to develop a PPSD for the Project. Since ILBANK and targeted municipalities are considered urgent need of assistance described under paragraph 12 of OP10.00, a simplified Project Procurement Strategy for Development (PPSD) a has been prepared by ILBANK. The draft PPSD describes how procurement activities will support project operations for the achievement of the PDOs and deliver value for money. The PPSD is linked to the overall project implementation strategy by ensuring proper sequencing of procurement activities. It provides information on institutional arrangements for procurement, roles and responsibilities, appropriate procurement methods, procurement duediligence, and other requirements needed for carrying out procurement. The PPSD also includes a detailed description of the procurement capacity needed by the executing agencies for carrying out procurement with specific focus on managing contract implementation, governance structure, and accountability framework. In addition, PPSD is supported with a market research and analysis assesses market-related risks and opportunities that will affect the preferred procurement approach to market strategy.\n\n4. PPSD confirmed that the selection of “Design Review and Supervision Consultant(s)” are located as “ **strategic**\n\n**security** ” procurement in the supply positioning matrix. It is envisaged that these consultants will support ILBANK\n(PMU)/municipalities for the preparation of the bidding documents for the infrastructure investments, and therefore, consultants’ timely input is very critical for the initiation of the relevant procurements planned in the procurement plan. Considering the limited time for the implementation project and urgent need of the services, PPSD proposed to use “consultant’s qualification-based selection” method in the procurement of the six consultant’s contracts in line with the guidance on fit-for-purpose principle. Accordingly, use of higher thresholds proposed for the “consultant’s qualification-based selection” method. It was envisaged that the proposed approach will accelerate the selection of “Design Review and Supervision Consultant(s)” and will support to deliver the project development objectives. The selected consultant will also provide services to relevant municipalities/utilities for the quality assurance of the works and timely completion on the contracts within their original contract prices.\n\n5. In the selection of six “Design Review and Supervision Contract(s)”, a streamlined procedure of single simultaneous international advertisement has been foreseen to collect the interests of the consultants. The firms with best qualifications and relevant experience determined, and based on their capacity and interest, eligible firms will be invited to submit their technical and financial proposals for the relevant contracts. In order to mitigate risks attributable to the perception of integrity matters, PPSD proposed ILBANK to engage independent Probity Assurance Providers to be present during the contract negotiation stages of the relevant contracts.\n\n6. PPSD discussed that contracting of individuals in accordance with the Procurement Regulations will give flexibility in decision making to the project implementation teams in ILBANK and in the beneficiary municipalities in addition to Page 77 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) maintaining uniformity in the followed selection procedures under the project. Therefore, PPSD proposed to hire the individual experts required for ILBANK PMU and municipal project implementation units in accordance with the individual consultant selection procedures as specified in the Procurement Regulations. These include but not limited to Procurement Specialist, Contract Management Consultant, Financial Management Specialist, Environmental Specialist, Social Specialist, Monitoring and Evaluation Expert and Communication Specialist.\n\n7. The PPSD further analyzed the risks against the size of the proposed infrastructure contracts and thus all works procurements were positioned as “ **strategic critical** ” in the supply positioning matrix. And thus, the procurements of these contracts were packaged carefully considering the outcome of the market sounding i.e. availability of the capable contractors in the market who may respond to the ILBANK needs. As a conclusion, PPSD proposed to use National Procurement Procedures as specified in the Procurement Regulations by increasing the thresholds normally used in Turkey for similar contracts. The market sounding concluded that there are more than 50 national firms in each of the water, waste-water and solid waste sectors who may be qualified alone or in association with others for the proposed contracts. Also, it has been observed in the procurements of Bank financed MSP-I, MSP-II and SCP projects that the interest of the foreign bidders was very limited especially in city water and wastewater network construction works.\n\n8. **Procurement Plan and Procurement Tracking.** The Procurement Regulations require the Borrower to use the Bank’s Systematic Tracking of Exchanges in Procurement (STEP) online procurement tracking tool to prepare, clear and update its procurement plans, and conduct all procurement transactions. ILBANK will create the procurement plan through STEP prior to initiating any procurement. The PPSD and the underlying Procurement Plan will be updated at least annually or as required to reflect actual project implementation needs. Only ILBANK will be given STEP access in the Project portal to safeguard confidentiality of the contract information recorded by different municipalities/utilities. All the procurement related complaints will be recorded in the STEP complaint module by ILBANK.\n\n9. The contracts agreed by the Bank for financing and included in the approved procurement plan are listed in Table 4.1 below:\n\n**Table 4.1: Contracts Agreed by the Bank**\n\n**Estimated**\n\n**Contract**\n**Completion**\n**Date (including**\n**defects liability**\n\n**period)**\n\n**Estimated**\n\n**Contract**\n\n**Signing**\n\n**Date**\n\n4-Jan-21 20-Dec-23 1-Feb-21 17-Jan-24 Page 78 of 94\n\n**Activity**\n**Description**\n\nConstruction of Kozan İmamoğlu Yedigöze Water Transmission Line Construction of Yedigöze Water Treatment Plant\n\n**Reference**\n\n**No.**\n\n**Estimated**\n\n**Amount**\n**(in EUR) (*)**\n\nIBRD 21,850,000 Loan IBRD 13,150,000 Loan RFB – Single ADANA-W1 Works Stage RFB – Single ADANA-W2 Works Stage\n\n**Market**\n**Approach**\n\nOpen National Procurement Procedure (NPP) Open National Procurement Procedure (NPP)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Estimated**\n\n**Contract**\n**Completion**\n**Date (including**\n**defects liability**\n\n**period)**\n\n**Estimated**\n\n**Contract**\n\n**Signing**\n\n**Date**\n\n**Activity**\n**Description**\n\nConstruction of Kozan Water Network Project and Pınargözü Transmission Line Construction of Kahramanmaraş Northern Districts Integrated Solid Waste Project Construction of Elbistan Water Network Project Construction of Elbistan Water Transmission Line\n\n**Reference**\n\n**No.**\n\nRFB – Single ADANA-W3 Works Stage\n\n**Estimated**\n\n**Amount**\n**(in EUR) (*)**\n\n28,070,000 EU Grant 14,000,000 EU Grant 8,000,000 EU Grant IBRD 25,000,000 Loan IBRD 17,000,000 Loan RFB – Single KMARAS-W1 Works Stage RFB – Single KMARAS-W2 Works Stage RFB – Single KMARAS-W3 Works Stage\n\n**Market**\n**Approach**\n\nOpen National Procurement Procedure (NPP) Open National Procurement Procedure (NPP) Open National Procurement Procedure (NPP) Open National Procurement Procedure (NPP) 1-Mar-21 14-Feb-24 7-Feb-21 19-Sep-24 14-Jul-21 25-Dec-24 15-May-21 26-Sep-24 Kahramanmaraş (Centrum) Water Supply and Sewerage and Stormwater Project - West Part (Lot 1) RFB – Single KMARAS-W4 Works Open National Procurement KMARAS-W4 Works Procurement 7-Feb-21 23-Jan-24 Kahramanmaraş Stage Procedure (Centrum) Water (NPP) Supply and Sewerage and 18,800,000 EU Grant Stormwater Project - East Part (Lot 2) Stage Procedure (NPP) 18,800,000 EU Grant Construction of Ceyhan Basin Wastewater Treatment Plants (Goksun & Ekinozu) Construction of Ceyhan Basin Wastewater Treatment Plants (Caglayancerit & Andirin) Construction of Kayseri Wastewater Treatment Plant RFB – Single KMARAS-W5 Works Stage 7-Mar-21 25-Feb-23 7-Mar-21 25-Feb-23 12-Jul-21 26-Jun-24 Page 79 of 94 RFB – Single KMARAS-W6 Works Stage RFB – Single KAYSERİ-W1 Works Stage Open National Procurement Procedure (NPP) Open National Procurement Procedure (NPP) Open National Procurement Procedure IBRD 5,500,000 Loan 2,900,000 EU Grant 12,500,000 EU Grant", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Estimated**\n\n**Contract**\n**Completion**\n**Date (including**\n**defects liability**\n\n**period)**\n\n**Estimated**\n\n**Contract**\n\n**Signing**\n\n**Date**\n\n**Activity**\n**Description**\n\n**Reference**\n\n**No.**\n\n**Market**\n**Approach**\n\n(Phase 1) (Lot 1) (NPP) Construction of Kayseri Wastewater Treatment Plant (Phase 2) (Lot 2)\n\n**Estimated**\n\n**Amount**\n**(in EUR) (*)**\n\nIBRD 12,500,000 Loan 4,710,000 EU Grant 3,900,000 EU Grant 4,560,000 EU Grant IBRD 27,650,000 Loan Construction of Akşehir Water Supply Project Construction of Ilgin Wastewater Treatment Plant Construction of Cumra Wastewater Treatment Plant RFB – Single KONYA-W1 Works Stage RFB – Single KONYA-W2 Works Stage RFB – Single KONYA-W3 Works Stage Open National Procurement Procedure (NPP) Open National Procurement Procedure (NPP) Open National Procurement Procedure (NPP) 4-Apr-21 25-Mar-23 4-Apr-21 19-Mar-24 4-Apr-21 19-Mar-24 Construction of Osmaniye (Centrum) Water Supply and Sewerage and Stormwater Project - West Part (Lot 1) OSMANİYE RFB – Single Works W1 Stage Open National Procurement Procedure Works Procurement 6-Mar-21 23-Aug-23 Construction of W1 Stage Procedure Osmaniye (NPP) (Centrum) Water Supply and 29,910,000 EU Grant Sewerage and Stormwater Project - East Part (Lot 2) Stage (NPP) 29,910,000 EU Grant Minor Works for Incidental Infrastructure Investments (Mobile toilets, small water and sewerage network/s, septic tanks and etc.) (Multiple Contracts) 1,000,000 MW 1, 2.... Works RFQ National EU Grant TBD TBD Page 80 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Estimated**\n\n**Contract**\n**Completion**\n**Date (including**\n**defects liability**\n\n**period)**\n\n**Estimated**\n\n**Contract**\n\n**Signing**\n\n**Date**\n\n14-Jul-21 12-Oct-21 14-Jul-21 12-Oct-21\n\n**Activity**\n**Description**\n\nKahramanmaraş Northern Districts Integrated Solid Waste Project Vehicle Supply Kahramanmaraş Northern Districts Integrated Solid Waste Project Container Supply Procurement of Video Conference System/s for ILBANK HQs and Regional Directorates (Multiple Contracts) Review of Existing Designs, Preparation of Bidding Documents and Construction Supervision Services Review of Existing Designs, Preparation of Bidding Documents and Construction Supervision Services Review of Existing Designs, Preparation of Bidding Documents and Construction Supervision Services Review of Existing Designs, Preparation of Bidding Documents and Construction Supervision Services\n\n**Reference**\n\n**No.**\n\nRFB – Single KMARAS-G1 Goods Stage KMARAS-C2 1,800,000 3-Aug-20 15-Jan-24 KAY-C1 1,450,000 3-Aug-20 10-Dec-24 Page 81 of 94\n\n**Estimated**\n\n**Amount**\n**(in EUR) (*)**\n\n2,000,000 EU Grant 1,000,000 EU Grant RFB – Single KMARAS-G2 Goods Stage\n\n**Market**\n**Approach**\n\nOpen National Procurement Procedure (NPP) Open National Procurement Procedure (NPP) 1,325,000 ILBANK G1 Goods RFQ National IBRD Loan 15-Sep-20 9-Nov-21 ADANA-C1 2,000,000 13-Jul-20 23-Feb-24 KMARAS-C1 2,250,000 3-Aug-20 10-Dec-24 CS CQS International IBRD Loan", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Estimated**\n\n**Contract**\n**Completion**\n**Date (including**\n**defects liability**\n\n**period)**\n\n**Estimated**\n\n**Contract**\n\n**Signing**\n\n**Date**\n\n**Activity**\n**Description**\n\nReview of Existing Designs, Preparation of Bidding Documents and Construction Supervision Services Review of Existing Designs, Preparation of Bidding Documents and Construction Supervision Services Project Visual Design and Social Media Management\n\n**Reference**\n\n**No.**\n\n**Market**\n**Approach**\n\n**Estimated**\n\n**Amount**\n**(in EUR) (*)**\n\nKON-C1 1,000,000 5-Oct-20 18-Mar-24 OSMANİYE 1,750,000 21-Sep-20 6-Sep-23 C1 ILBANK C1 CS CQS National 120,000 EU Grant Public Awareness ILBANK C2 CS CQS National 300,000 EU Grant Campaign Individual consultants (in the fields of procurement, finance, technical and environment etc.) and Probity Assurance Providers for ILBANK, Municipalities and Utilities (Multiple Contracts) Project Printed Materials Production Producing the Project Visual Production & Materials ILBANK C3, National CS INDV C4.........\n\n780,000 EU Grant 7-Dec-20 27-Mar-23 7-Dec-20 1-Apr-22\n\n-\n\n24-Jan-21 24-Apr-21 24-Jan-21 24-Apr-21 Page 82 of 94 ILBANK NCS 1 NCS RFQ National 40,000 EU Grant ILBANK NCS 2 NCS RFQ National 150,000 EU Grant", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Estimated**\n\n**Contract**\n**Completion**\n**Date (including**\n**defects liability**\n\n**period)**\n\n**Estimated**\n\n**Contract**\n\n**Signing**\n\n**Date**\n\n**Activity**\n**Description**\n\nLogistical & Organizational Services for Capacity Building Activities for ILBANK and Final Beneficiaries (Workshops, seminars, symposiums, and trainings); and citizen satisfaction, baseline and endline surveys (Multiple Contracts) Logistical & Organizational Services for Capacity Building Activities for ILBANK (Multiple Contracts) Logistical & Organizational Services for Project Opening/Mid Term//Closing Events and etc.\n(Multiple Contracts)\n\n**Reference**\n\n**No.**\n\n**Market**\n**Approach**\n\n**Estimated**\n\n**Amount**\n**(in EUR) (*)**\n\nILBANK NCS 3 NCS RFQ National 370,000 EU Grant IBRD ILBANK NCS 4 NCS RFQ National 630,000 Loan ILBANK NCS 5 NCS RFQ National 125,000 EU Grant\n\n-\n\n-\n\n-\n\n(*) VAT included in Loan financing 10. **Advance Procurement.** Procurement Regulations Paragraphs 5.1 and 5.2 (Advance Contracting and Retroactive Financing) permits that the Borrower may wish to proceed with the procurement process before signing of the Legal Agreement. In such cases, if the eventual contracts are to be eligible for Bank financing the procurement procedures, including advertising, shall be consistent with Sections I, II and III of the Procurement Regulations which cover the _Bank’s Core Procurement Principles of economy, efficiency, transparency, fairness, fit-for purpose, value-for-money_ _and integrity._ With this understanding, ILBANK will initiate the selection of the PIU consultants and design review and supervision consultants’ selection upon publication of the General Procurement Notice in the UN Development Business online.\n\n11. **Procurement Methods and Standard Procurement Documents.** While comprehensive selection methods and use of Bank’s Standard Procurement Documents will be applicable, due to emergency response nature of the project, arrangements will be used in the project procurements. Higher thresholds will be used for the national streamlined procurement procedures and simple selection procedures will apply for the selection consultants as specified in the Page 83 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) approved procurement plan. Determined thresholds and selection methods will be reviewed by the Bank during the project supervisions and/or when the Bank decides and will be updated as appropriate.\n\n12. **National Procurement Procedures.** When approaching the national market, as agreed in the Procurement Plan, Turkey's national procurement procedures will be used by ILBANK in accordance with the provisions in paragraphs 3.21-3.23; 3.30; 3.32; 5.3; 5.4; 5.5 and 5.7(a) of the Procurement Regulations. In such procurements, bidding documents agreed by the Bank in Turkish language including mandatory provisions specified in the procurement plan will be used.\n\n13. **Procurement Risk Assessment.** The World Bank has conducted a procurement assessment for the project, with a focus on ILBANK in terms of: (i) procurement regulatory framework and management capability; (ii) integrity and oversight; (iii) procurement process and market readiness; and (iv) procurement complexity. The assessment concludes that: (a) applicable procurement policies and the regulatory system are designed broadly to meet Core Procurement Principles of value for money, economy, efficiency, effectiveness, integrity, transparency and fairness and accountability; (b) ILBANK has a clear system of accountability with clearly defined responsibilities and delegation of authority on who has control of procurement decisions; (c) there is a clear identified target market for all procurements; and (d) ILBANK/Municipalities effectively manages contracts to ensure delivery as per the contract conditions. The assessment is recorded in the Procurement Risk Assessment and Management System of the Bank.\n\n14. ILBANK will undertake the overall responsibility of the project implementation and coordination through its Project Management Unit (PMU) located under the International Relations Department. PMU was originally established in 2005 for the Bank financed MSP-I project and has been continuously operational throughout MSP-II, SCP-I, SCP-II, and SCP-II additional financing projects. Hence, PMU is experienced in Bank financed projects and familiar with World Bank procurement procedures and contract management. All the procurements under the proposed project will be conducted by ILBANK/PMU centrally. The technical requirements in the selection documents will be agreed by the municipalities/utilities. The beneficiary municipalities/utilities will attend the bid evaluation committees and sign the respective contracts.\n\n15. ILBANK PMU is linked to three separate units namely Contract Management Unit, Financial Management Unit and Technical Management Unit. The procurement management is under the responsibility of the Contract Management Unit. Considering fast track urgent response nature of the project, and heavy work load of existing PMU staff due to other Bank financed SCP-I, SCP-II and SCP-II additional financing projects, and also from projects of other IFIs, a dedicated procurement team will be established under the PMU with minimum 2 procurement experts who are familiar with the Bank procurement procedures. This dedicated procurement team (FRIT procurement team) will be supported by several specialized departments within ILBANK including Investment Coordination Department and Technical Management Team. The FRIT procurement team will be mainly responsible for the implementation and coordination of the procurement activities within the scope of the project. In case of need, Investment Coordination Department with vast experience in procurement under the public procurement procedures will bring additional capacity to the FRIT procurement team. When needed, PMU will be supported by Project Department responsible for review and approval of feasibility studies under the project. In addition, Infrastructure Implementation Department will provide support to the review of technical specifications; and several other administrative departments such as Accounting and Financial Affairs, IT, and Banking Services will be ready provide support when needed.\n\n16. The beneficiary municipalities/utilities have certain technical and procurement capacity to operate their regular services with varying staff capacity depends on the size of the city. For example, while there are 78 technical and Page 84 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) procurement staff exists in Osmaniye, similar staff number reaches to 554 in KOSKA (Konya Water and Wastewater Utility) including 433 technicians and 5 procurement experts. Some of the municipalities have experience to work with IFIs in their investment projects financing but in most of the cases experienced staff may not be available for the proposed project. Considering limited implementation duration of the project, the participating municipalities/utilities will establish dedicated project implementation units comprised of procurement and technical experts. These experts will be involved in the selection process of the design review and supervision consultants, works contractors, good suppliers and management of concluded contracts. The participating municipalities/utilities will hire experts experienced in the procurement and management of the contracts in the Bank financed projects, if such staff already not exist in their teams.\n\n17. Given the emergency nature of the project and limited implementation time; and taking into account the existing work load of ILBANK; and procurement and contract management capacity of beneficiary municipalities/utilities; the overall procurement risk for the project is assessed as “substantial”. The risk rating can be lowered to “Moderate” when the agreed actions in Table 4.2 below have been put in place. The assessment will be recorded in the Procurement Risk Assessment and Management System of the Bank.\n\n**Table 4.2: Identified Risks and Agreed Action Plan**\n**Action**\n**Identified Risk** **Mitigation Measure** **Responsible Party** **Time Frame**\n**No.**\n\nThroughout the ILBANK project procurement process.\n\nPage 85 of 94\n\n1.\n\n2.\n\nILBANK has been implementing three World Bank financed projects and the projects financed by other IFIs simultaneously. The teams in the PMU are overloaded. PMU may not be able to meet the procurement deadlines.\n\nProject implementation duration is relatively short and beneficiary municipalities has no WB procurement experience. If the procurement schedules are not met the contracts may not be completed on time. To accelerate the project implementation all the procurements will be conducted centrally by ILBANK.\nThis arrangement requires parallel implementation of procurement activities including establishment A dedicated PMU team established to implement the project activities and at least 2 experienced procurement specialists will be employed in this unit.\n\nILBANK PMU will be supported by the ILBANK’s Investment Coordination Department, as needed.\n\nILBANK TORs will be prepared by ILBANK and the positions will be advertised in advance before the project effectiveness.\n\nContracts will be signed within first month after the project effectiveness.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Action**\n**Identified Risk** **Mitigation Measure** **Responsible Party** **Time Frame**\n**No.**\n\nof various bid evaluation committees.\n\nBeneficiary municipalities/utilities will employ a staff knowledgeable in procurement and contract implementation.\n\nThis staff will be trained by PMU Procurement Specialists.\n\nILBANK to engage independent probity assurance providers in the contract negotiations.\n\nContracts will be signed within first month after the project effectiveness.\n\nSelection of independent probity assurance providers will be completed before inviting the consultants to submit their technical and financial proposals.\n\n3.\n\n4.\n\n5.\n\n6.\n\n7.\n\nBeneficiary municipalities/ utilities staff are unfamiliar with the World Bank Procurement Regulations and contract provisions. It may cause misinterpretation of procurement provisions and contract implementations.\n\nStakeholder perception on the integrity of the contract negotiation process in CQS selection.\n\nDifferentiation of procurement implementations among the implementation entities. It may create unnecessary questions from the procurement stakeholders.\n\nIncomplete environmental and social safeguard studies may delay commencement of the contract implementation.\n\nMisinterpretation of the Procurement Regulations and terms and conditions of the contracts. It may cause noncompliance and also time and cost overruns in the contract implementation.\n\nIf applicable, all safeguard studies will be completed before signing of the contracts.\n\nWork closely with World Bank Procurement Specialist.\n\nILBANK/ Municipalities/Utilities ILBANK Throughout the ILBANK/PMU Project.\n\nThroughout the ILBANK Project.\n\nDevelop a Project Before the Project ILBANK Operations Manual. effectiveness.\n\n18. **Procurement supervision frequency.** The World Bank will review the procurement arrangements performed by the implementing agencies, including contract packaging, applicable procedures, and the scheduling of the procurement processes, for their conformity with the Legal Agreement. Those procurements did not have ex-ante due diligence by the World Bank will be subject to ex-post due diligence on a sampling basis in accordance with the procedures set forth in Paragraph 4 of the Annex II to the Procurement Regulations. A post review of the procurement documents Page 86 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) will normally be undertaken annually during the World Bank’s supervision mission, or the World Bank may request to review any particular contract at any time. In such cases, the PMUs shall provide the World Bank the relevant documentation for its review.\n\n19. **Complaint review.** The procurement complaints other than covered under Annex III of the Procurement Regulations are to be handled by the ILBANK in accordance with the procedures agreed by the Bank and stipulated in the POM.\nImmediately upon received, the complaints will be recorded in the STEP complaint module by PMU. ILBANK will not proceed with the next stage/phase of the procurement process, including with awarding a contract without satisfactory resolution of the complaint(s).\n\n20. **Operational costs** will not be considered under procurement implementation which could be the incremental expenses, including office supplies, vehicles operation and maintenance cost, maintenance of equipment, communication costs, rental expenses, utilities expenses, consumables, transport and accommodation, per diem, cost for procurement advertisements, and salaries of locally contracted support staff.\n\nPage 87 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**ANNEX 5: Communications and Visibility Plan**\n\n1. A project Communications and Visibility Plan will be developed by the Project Management Unit (PMU) in\n\nİlBank for inclusion in the Project Operations Manual (POM), and the EU Delegation to Turkey will be consulted on it in advance, ahead of project launch. The following annex presents preliminary guidelines, principles, and indicative objectives/activities for communications efforts under the project.\n\n2. The final plan will be aligned with the European Union Communications and Visibility Manual for External Actions [60] as well as FRiT Facility Visibility Guidelines. [61] Communications and visibility activities under the project will be implemented in coordination with the EU Delegation in Turkey, [62] including visibility requirements. [63]\n\n**5.1 Initial Project Communications Objectives**\n\n3. The objectives of communications activities are to: help manage expectations and mitigate against local political and governance risks; facilitate outreach and engagement of refugees and host communities in project activities; and share results and disseminate project lessons learned with key audiences for broader impact. Activities will also seek to inform and communicate to project beneficiaries and project stakeholders, the EU’s financial contribution to support refugees and host communities in Turkey.\n\n4. **Indicative objectives and messages for each target group.** The Communications and Visibility activities set out in this Plan will target the following groups of beneficiaries:\n\n[60 http://ec.europa.eu/europeaid/work/visibility/documents/ communication_and_visibility_manual_en.pdf.](http://ec.europa.eu/europeaid/work/visibility/documents/%20communication_and_visibility_manual_en.pdf) 60 Communications and Visibility Manual for EU External Actions Projects funded under the EU Facility for Refugees in\n[TurkeyVisibilityGuidelines_May2017_FRIT_EN_20170605_Final.docx http://avrupa.info.tr/eu-funding-in-turkey/visibility-guidelines.html.](http://avrupa.info.tr/eu-funding-in-turkey/visibility-guidelines.html) 60 The EU Delegation will be informed about the events at least 10 days prior to allow for participation. Designs of all visibility materials, publications, promotional items, and videos will be submitted to the EU Delegation for review and approval before production.\n60 The EU-Turkey joint logo should be accompanied by the following text in English, Turkish, and Arabic (and any other language where needed).\n“This project is funded by the European Union”. Illustrations will be provided in the POM. The materials, items for beneficiary usage, and office supplies to be used by the partners and procured under EU funds should display the EU flag accompanied by the following text in English, Turkish, and Arabic (and any other language where needed): “Funded by the European Union.” Illustrations will be provided in the POM. The following disclaimer will be used in relevant materials: “This leaflet/brochure/video... has been produced with the financial support of the European Union. Its content is the sole responsibility of the Ministry of Industry and Technology and Turkish Development Agencies and may not reflect the views of the European Union.” Page 88 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Table 5.1 Indicative Objectives for Communications and Visibility Plan**\n**Target Group** **Indicative Objectives**\n**Overall population** **including both**\n**refugees and host**\n**community members** and solid waste facilities.\n**living in the 5** **provinces** decision-making mechanisms.\n\n**Overall population** - To raise awareness of the residing population including both refugees and host\n**including both** community members regularly on project progress and results, particularly on\n**refugees and host** interventions related to construction and rehabilitation works for water, sanitation\n**community members** and solid waste facilities.\n**living in the 5** - To promote participation to the public meetings to integrate communities into\n**provinces** decision-making mechanisms.\n\n- To make improvements visible for communities by acknowledging the work done\nand role of project partners in making it.\n\n- To inform communities well in advance about any type of temporary\nenvironmental impacts such as wastewater discharge, generation of dust and noise during construction and operation.\n**İlBank PIU’s,** - To launch a series of information meetings in the 5 provinces to engage PIU’s,\n**municipalities, SKI’s** municipalities, SKI’s and local stakeholders into dialogue on the project objectives\n**and local** and future activities.\n**stakeholders** - To inform and raise awareness of PIU’s municipalities, SKI’s and local stakeholders\n\n**İlBank PIU’s,** - To launch a series of information meetings in the 5 provinces to engage PIU’s,\n**municipalities, SKI’s** municipalities, SKI’s and local stakeholders into dialogue on the project objectives\n**and local** and future activities.\n**stakeholders** - To inform and raise awareness of PIU’s municipalities, SKI’s and local stakeholders\n\non the issues about sustainability, resource efficiency, climate change mainstreaming, gender and integrated approaches.\n\n- To update PIU’s, municipalities, SKI’s and local stakeholders about the project\nprogress and results regularly.\n**Media, national** - Increase awareness on the project’s progress and results; particularly promoting\n**authorities, and** project’s impact on:\n**communities/general** - Environment (decreased water loss, quality discharged water, closure of existing\n**public** dumpsites, increased energy efficiency, reduced non-revenue water...) and\n\n**Media, national** - Increase awareness on the project’s progress and results; particularly promoting\n**authorities, and** project’s impact on:\n**communities/general** - Environment (decreased water loss, quality discharged water, closure of existing\n**public** dumpsites, increased energy efficiency, reduced non-revenue water...) and\n\n- People’s everyday lives (improved access to safely managed water, improved\nservice delivery and related health) as a result of services provided to overall population (host population and refugees).\n\n**Indicative project implementation activities**\n\n_**(a)**_ _**Launch a series of information meetings in 5 provinces to engage PIU’s, municipalities, SKI’s and local**_ _**stakeholders into dialogue on the project objectives and roadmap.**_ 5. To increase the ownership among municipalities and build the bridges between project implementation units and management teams, it is important to organize a series of meetings in the beginning of project implementation. The meetings will set the ground for understanding the project objectives, agreeing collectively on a roadmap, addressing potential challenges on the way ahead. It will also become an effective platform to enable two-way communication where municipalities can raise operational bottlenecks during implementation. Close involvement of municipalities, sewerage utilities and other stakeholders to the entire process is key, particularly during strategic decision-making processes in which accurate background data and information is needed for effective project execution.\n\n**(b)** _**Raise awareness of the residing population regularly on project progress and results.**_\n\n6. The communities residing in selected provinces will benefit from the improved water, sanitation and solid Page 89 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) waste facilities at the end of the project. This will require a set of actions aiming at informing the residents at critical points where they will be aware on: (i) what happens in their neighborhood; (ii) why it is needed; (iii) when the construction work will start and how long it will take to complete it; and (iv) by whom it is led.\n\n7. Main project information and critical dates need to be shared in public information meetings and other online platforms (website and social media of municipalities, NGOs, governorate). In addition to that, construction parapets are functional and visible spaces to illustrate the planned work and inform community in the neighborhood about the expected results and start/end dates of operational work. Providing timely, relevant and life-enhancing information about the work carried out under the project would address some risks and underlying vulnerabilities. It would also avoid negative reactions rooting from ongoing construction and its temporary environmental impacts. Stressing the benefits (eg: “X amount of water will be saved with the rehabilitation of the pipelines.”) could be accompanied to the key project information content in the signboards surrounding the construction area.\n\n8. One way to address misinformation risks could be achieved by involving community leaders of affected groups and maintain a continuous dialogue with them via public information meetings on their questions and concerns. Updated FAQ sheets in print and visual format would be useful to reach target communities via social media, SMS, hand-outs and videos. It is also crucial to acknowledge project’s contribution once the operation work is completed in constructed or rehabilitated facilities via signboards and temporary outdoor ads. (eg: “With this water treatment plant, x amount of water was treated and provided as safe drinking water to X number of households in the municipality).\n\n_**(c)**_ _**Increase awareness on the project’s results; particularly promoting project’s impact on environment and**_ _**people’s everyday lives.**_ 9. Project opening and closing ceremonies, local information meetings, on-site ceremonies of completed works, workshops and trainings will be timely opportunities to inform public and local actors on the project results and progress. These events, as well as dissemination materials including posters, brochures, reports, training materials and all other project documents will be prepared in line with EU visibility guidelines. Any refurbishment, equipment and technology infrastructure will acknowledge EU’s financial support, providing an explanation on project’s contribution to water, wastewater and solid waste infrastructure in the provinces and EU’s financial support on construction sites such as new water reservoirs, pressure release chambers, rehabilitated pipelines, water reservoirs and pumping stations.\n\n10. News releases, event announcements, human-interest stories, audio-visual materials will be published on websites of municipalities and/or separate project website with clear acknowledgment of EU contributions.\nPress releases and success stories marking project achievements, updates and progress will be disseminated through local and national media outlets. Exclusive interviews and op-eds will be facilitated through the local and national TV/radio networks.\n\n11. For audio-visual materials, it would be possible to build a narrative around a group of people who have experienced a positive change that comes with the improved conditions in water facilities. Project’s impact in the areas of sanitation, provision of drinking water, waste water and solid waste management will be told through human stories. The stories could be coupled with before/after conditions of a project site (e.g: rehabilitated dumpsite) or improved access of a Turkish/refugee household to drinkable water etc.\n\nPage 90 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996) 12. It is key to have an online platform where all project information and audio-visual documents are shared and displayed for public and project stakeholders. This could be achieved either through launching a project website or creating project-dedicated webpages under each municipality website. Social media accounts of municipalities and their established connections (number of followers) will help reach target audience effectively and quickly.\n\n**Table 5.2 Indicative budget for Communications and Visibility activities**\n**Indicative** **Indicative Scope/Tools**\n**Activity**\n\n**Indicative** **Indicative Scope/Tools** **Indicative**\n**Activity** **Budget**\n\n**(Euro)**\n**Launch a series** - 5 provinces – ILBANK PIU’s, municipalities, SKI’s and local stakeholders 185,000\n**of information** - Local information meetings to facilitate information exchange ideas among local\n**campaigns in** stakeholders and municipalities.\n**the 5 provinces**\n\n185,000\n\n**Awareness-**\n**raising events**\n**for both host**\n**community and**\n**refugee**\n**population on**\n**project**\n**progress and**\n**results**\n\n**Increase**\n**awareness on**\n\n- 5 provinces – ILBANK PIU’s, municipalities, SKI’s and local stakeholders\n\n- Local information meetings to facilitate information exchange ideas among local\nstakeholders and municipalities.\n\nIndicative communication tools:\n\n- Information brochures, leaflets and posters calling stakeholders to take urgent\naction and featuring project objectives and interventions while raising awareness on the themes such as climate change, sustainability, water availability, current environmental constraints and project’s action points to address them. (social media, printed and online dissemination)\n\n- Infographic posters that will illustrate where/why/how the interventions take\nplace.\n\n- Dissemination of information materials in municipalities, SKI’s and local governing\nbodies.\n\n- Utilizing existing media channels of municipalities to provide information about the\nproject (Periodicals of ILBANK, Union of Municipalities of Turkey, Belediye TV)\n\n- Project promotional materials targeting local stakeholders and municipalities to\nincrease their ownership\n\n- 5 provinces – broad outreach to host community and refugee population\n\n- Organize a series of public information meetings that bring together local\ncommunity members to engage them in dialogue.\n\nIndicative communication tools:\n\n- Printed materials to be disseminated in public information meetings on project’s\ninterventions\n\n- Active use of social media channels of municipalities and their established\ncommunication networks to promote participation of communities to the meetings.\n\n- Clear infographics/FAQ sheets illustrating when, why and what will happen in the\nneighborhood.\n\n- Construction parapets providing information about ongoing work and clear\nmessages.\n\n- SMS messages and activation of existing WhatsApp groups to inform people about\nupcoming project activities.\n\n- Signboards and outdoor ads on-site and in streets, buses, metro stations once the\nrehabilitation and construction works are completed.\n\n- 5 provinces – ILBANK PIU’s, municipalities, SKI’s and local stakeholders 250,000\n\nPage 91 of 94 300,000", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**Indicative**\n**Activity**\n\n**Indicative** **Indicative Scope/Tools** **Indicative**\n**Activity** **Budget**\n\n**(Euro)**\n**the project’s** - Organization of opening and closing ceremonies and on-site completion ceremony\n**results** of a major work item.\n\n**the project’s** - Organization of opening and closing ceremonies and on-site completion ceremony\n**results** of a major work item.\n\nIndicative communication tools:\n\n- Press releases on project progress, announcements, and results. TV and radio\ninterviews of representatives from municipalities, community leaders through local and national media networks. Op-eds and interviews on sectoral periodicals.\n\n- Infographic videos describing project contribution to energy and water efficiency.\n\n- Human-interest stories (video) that narrate the change in people’s lives and\nhouseholds living around an area rehabilitated by solid waste management.\n\n- Photo stories that illustrate before/after conditions of the project sites with stories\nfrom people living/working in the neighborhood.\n\n- Visibility materials displayed in events, workshops and meetings (rollups, flags,\nbackdrops, banners)\n\n- Effective use of webpages under Municipality websites/ standalone project\nwebsites and social media channels of the municipalities.\n**TOTAL** **EUR 735,000**\n\nPage 92 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**ANNEX 6: Team List**\n\n1. Sanyu Lutalo, Senior Water Supply and Sanitation Specialist, Team Leader\n\n2. Canan Yildiz, Infrastructure Specialist, Co-Team Leader 3. Lisa Lui, Lead Counsel 4. Joanna Mclean Masic, Senior Urban Specialist, Co-Team Leader 5. Salih Bugra Erdurmus, Senior Procurement Specialist 6. Salih Kemal Kalyoncu, Senior Procurement Specialist 7. Ayse Seda Aroymak, Senior Financial Management Specialist 8. Arzu Uraz Yavas, Social Development Specialist 9. Esra Arikan, Senior Environmental Specialist 10. Gulana Hajiyeva, Senior Environmental Specialist 11. Christian Borja-Vega, Senior Economist 12. Ahmet Kindap, Urban Development Specialist 13. Toyoko Kodama, Water Supply and Sanitation Specialist 14. Felipe Vicente Lazaro, Senior Dam Specialist 15. Verena Schaidreiter, Junior Professional Officer 16. Ntombie Z. Siwale, Operations Analyst 17. Sylvie Ngo-Bodog, Senior Program Assistant 18. Ulker Karamullaoglu, Program Assistant 19. Turda Ozmen, Financial and Economic Consultant 20. Emre Tokcaer, Technical Consultant 21. Burcu Ergin, Social Safeguards Consultant 22. Merve Ayse Kocabas Yurtkuran, Environmental Consultant 23. Ibrahim Sirer, Procurement Consultant 24. Tomris Oksar, Procurement Consultant 25. Ayse Ozge Bayram, Communications Consultant 26. Turgrul Cem Icten, Environmental Consultant Page 93 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
-{"input": "**The World Bank**\nMunicipal Services Improvement Project in Refugee Affected Areas (P169996)\n\n**ANNEX 7: Project Map**\n\nPage 94 of 94", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source", "descriptive_data": "A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor", "vague_data": "A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source"}}, "source": "refugee_pads"}
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